AI safety identification method and system for water conservancy project monitoring

Abstract

The present invention relates to the technical field of water conservancy projects, and specifically to a water conservancy project monitoring AI safety identification method and system, comprising the following steps: based on monitoring image data, using the YOLO algorithm to adjust image recognition parameters, matching the dam safety detection environment, adjusting the monitoring image processing process, optimizing the sensitivity of signal capture, and obtaining key signal optimization results. In the present invention, the safety monitoring capability of water conservancy projects is improved through image recognition and behavior analysis, and the image recognition parameters are finely adjusted so that the monitoring system can accurately identify potential threats in various environments, thereby improving the accuracy and efficiency of monitoring. Through real-time behavior tracking and pattern comparison, abnormal behaviors can be discovered and analyzed in a timely manner, and the occurrence of safety accidents can be effectively prevented. Combined with BIM model data, structural risks can be accurately assessed and warned, and the maintenance and management of water conservancy facilities can be optimized to ensure the safety of operations and the efficient use of resources.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and in particular to an AI safety identification method and system for water conservancy project monitoring. Background Art

[0002] The technical field of water conservancy engineering covers reservoirs, dams, river regulation, irrigation systems, drainage systems, flood control and soil and water conservation. Modern water conservancy engineering also includes water quality management, ecological protection and restoration, and strategies for responding to climate change. With the advancement of technology, water conservancy engineering is also constantly introducing new technologies such as intelligent monitoring systems, automated control and information management systems to improve the efficiency and safety of water conservancy facilities.

[0003] Among them, the AI ​​safety identification method for water conservancy project monitoring refers to the application of artificial intelligence technology in water conservancy projects, especially in monitoring and safety. By using AI algorithms to analyze the monitoring data of water conservancy facilities, potential security threats and abnormal situations can be identified in real time, thereby providing early warning and handling of problems, thereby significantly improving the operational safety of water conservancy facilities, reducing human errors, and optimizing maintenance and management processes. Its main purpose is to improve the automation and intelligence level of water conservancy project safety management and ensure the effective and sustainable use of water resources.

[0004] Existing technologies are not responsive enough in dynamic environments and have difficulty processing complex data inputs. For example, under changing climate and lighting conditions, recognition rates and response speeds drop significantly, leading to monitoring loopholes. For example, insufficient monitoring of tiny cracks and subtle water level changes increases the risk of emergencies in water conservancy facilities. Existing technologies also lack data integration and in-depth analysis capabilities, which limits the timeliness and accuracy of management decisions, leading to incorrect resource allocation and delayed implementation of response measures, thereby affecting the safe operation and efficiency of the overall water conservancy system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose an AI safety identification method and system for water conservancy project monitoring.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a water conservancy project monitoring AI safety identification method, comprising the following steps:

[0007] S1: Based on the monitoring image data, the YOLO algorithm is used to adjust the image recognition parameters, match the dam safety detection environment, adjust the monitoring image processing process, optimize the sensitivity of signal capture, and obtain key signal optimization results;

[0008] S2: Based on the optimization results of the key signals, the DeepSORT algorithm is used to identify and track the moving objects in the monitoring area, analyze the behavior patterns and compare them with known patterns, update the tracking parameters, and obtain the behavior trajectory refinement data;

[0009] S3: Based on the behavior trajectory, the data is refined, a detection threshold of abnormal behavior is set, deviations from target abnormal behavior patterns are monitored, real-time data is compared with abnormal patterns, and data synchronization is performed to obtain abnormal behavior warning records;

[0010] S4: Based on the abnormal behavior warning record and in combination with the BIM model data, the risk assessment model of the water conservancy project is adjusted to reflect the safety status of the water conservancy project management, and potential structural deviations are identified by comparing and analyzing the model data with the current monitoring data to obtain structural deviation warning indicators;

[0011] S5: Based on the structural deviation warning indicators, implement regular and temporary sluice monitoring inspections, adjust and calibrate inspection equipment, match difference monitoring requirements, compare with historical data, determine the urgency and priority of maintenance, and generate a monitoring maintenance strategy table;

[0012] S6: Based on the monitoring and maintenance strategy table, analyze the operating status of the water conservancy facilities in real time, adjust the monitoring strategy, optimize the detection accuracy and response speed, identify potential structural risk points, and generate a risk stability assessment log.

[0013] As a further solution of the present invention, the key signal optimization results include signal stability, false alarm rate, and detection distance; the behavior trajectory refinement data includes behavior duration, behavior frequency, and behavior pattern; the abnormal behavior warning record includes warning level, warning frequency, and warning response time; the structural deviation warning indicators include deviation type, deviation severity, and deviation detection accuracy; the monitoring maintenance strategy table includes maintenance cycle, maintenance type, and maintenance priority.

[0014] As a further solution of the present invention, based on the monitoring image data, the YOLO algorithm is used to adjust the image recognition parameters, match the dam safety detection environment, adjust the monitoring image processing process, optimize the sensitivity of signal capture, and obtain the key signal optimization results. The specific steps are:

[0015] S101: Based on the monitoring image data, the YOLO algorithm is used to adjust the color balance, adjust the contrast and brightness of the image, match the different external lighting conditions, and optimize the image clarity to highlight the potential key features in the image and obtain the ambient light adaptability configuration;

[0016] S102: Based on the ambient light adaptability configuration, optimize the image frame processing speed, adjust the signal and noise processing ratio, improve the image quality, refine the recognition accuracy of the target signal, and obtain image signal enhancement details;

[0017] S103: Based on the image signal enhancement details, perform detail enhancement processing on the image to capture every detail in the image, optimize monitoring accuracy and response speed, and obtain key signal optimization results.

[0018] As a further solution of the present invention, based on the optimization results of the key signals, the DeepSORT algorithm is used to identify and track the moving objects in the monitoring area, analyze the behavior patterns and compare them with known patterns, update the tracking parameters, and obtain the behavior trajectory refinement data in the following steps:

[0019] S201: Based on the key signal optimization result, perform preliminary target recognition, adjust the recognition process, match the differentiated size and speed targets, and verify whether each moving object is marked to obtain the target marking accuracy;

[0020] S202: Based on the target marking accuracy, using the DeepSORT algorithm, tracking the movement trajectory of the object, analyzing the residence time and the movement path, comparing the behavior pattern in the known data, adjusting the behavior analysis parameters, and obtaining the behavior path analysis data;

[0021] S203: Based on the behavior path analysis data, update the target tracking parameters, optimize the continuity and reaction speed of tracking, track each target for a long time, and obtain behavior trajectory refinement data.

[0022] As a further solution of the present invention, the DeepSORT algorithm is based on the formula:

[0023]

[0024] Calculate the real-time position of a moving object, where For time The state estimation at is the state estimate of the previous time step, is the Kalman gain, is the observed current position, is the observation model, It is the regulatory system of environmental dynamic factors. For time Estimation of environmental noise, is the adjustment coefficient of the target dynamic factor, For the goal in time expected dynamic changes.

[0025] As a further solution of the present invention, based on the behavior trajectory, the data is refined, a detection threshold of abnormal behavior is set, the deviation from the target abnormal behavior pattern is monitored, the real-time data is compared with the abnormal pattern, and the data is synchronized to obtain the abnormal behavior warning record. Specifically, the steps are as follows:

[0026] S301: Based on the behavior trajectory refinement data, a dynamic threshold of abnormal behavior is set, the threshold sensitivity is optimized by analyzing the frequency and pattern of the behavior, and parameters are adjusted according to the abnormal characteristics to obtain abnormal activity perception settings;

[0027] S302: Based on the abnormal activity perception setting, the captured behavior is compared with the stored abnormal pattern in real time, the processing speed and analysis accuracy of the data stream are adjusted, and the abnormal behavior instant analysis results are obtained;

[0028] S303: Based on the abnormal behavior real-time analysis result, data integration is performed to synchronize abnormal behavior records of multiple monitoring nodes, and the consistency and integrity of the data are verified to obtain abnormal behavior warning records.

[0029] As a further solution of the present invention, based on the abnormal behavior warning record, combined with BIM model data, the risk assessment model of the water conservancy project is adjusted to reflect the safety status of water conservancy project management, and the potential structural deviation is identified by comparing and analyzing the model data with the current monitoring data. The steps of obtaining the structural deviation early warning indicator are specifically as follows:

[0030] S401: Based on the abnormal behavior warning record, combined with BIM model data, reflecting the current structural status and monitoring data, adjusting model parameters in real time, optimizing the response speed and accuracy of risk assessment, and obtaining risk response parameter configuration;

[0031] S402: Based on the risk response parameter configuration, identify data deviations through deep comparison analysis, adjust comparison parameters, optimize the accuracy of deviation identification, and synchronously update risk data, including the mined deviation points, to obtain data deviation identification results;

[0032] S403: Based on the data deviation identification result, the risk assessment model is updated, the associated data is synchronized, including the newly identified risk points, and the accuracy of the risk assessment and the data integrity are verified to obtain the structural deviation warning indicator.

[0033] As a further solution of the present invention, based on the structural deviation early warning indicators, regular and temporary sluice monitoring and inspection are implemented, inspection equipment is adjusted and calibrated, difference monitoring requirements are matched, and compared with historical data to determine the urgency and priority of maintenance. The steps of generating a monitoring and maintenance strategy table are specifically as follows:

[0034] S501: Based on the structural deviation warning indicator, set the inspection frequency, formulate inspection standards for different sluice environments and states, adjust the inspection equipment through real-time monitoring data, match the current monitoring needs, and generate a monitoring inspection parameter table;

[0035] S502: Based on the monitoring inspection parameter table, perform a calibration operation of the monitoring equipment, update the equipment operating parameters to match the environmental changes of the target sluice, analyze and compare the current and historical monitoring data, evaluate the equipment performance and monitoring effectiveness, and obtain a monitoring performance optimization record;

[0036] S503: Based on the monitoring performance optimization record, determine the urgency and priority of the maintenance activity, optimize the monitoring process according to the monitoring data and equipment performance, and generate a monitoring maintenance strategy table.

[0037] As a further solution of the present invention, based on the monitoring and maintenance strategy table, the operation status of the water conservancy facilities is analyzed in real time, the monitoring strategy is adjusted, the detection accuracy and response speed are optimized, the potential structural risk points are identified, and the steps of generating a risk stability assessment log are specifically as follows:

[0038] S601: Based on the monitoring and maintenance strategy table, the operation status of the water conservancy facilities is analyzed in real time, the monitoring parameters are adjusted, the detection accuracy and response speed are optimized, and the monitoring response configuration is optimized by comparing the monitoring data and the threshold value, and an optimized maintenance plan is generated;

[0039] S602: Based on the optimized maintenance plan, the monitoring detection accuracy and efficiency are adjusted, and the identification capability of potential risk points is refined through continuous parameter optimization to generate risk point identification results;

[0040] S603: Based on the risk point identification results, data integration and analysis are performed, risk information is updated, and the consistency of related information and whether it is updated in a timely manner are verified to generate a risk stability assessment log.

[0041] A water conservancy project monitoring AI safety identification system, the water conservancy project monitoring AI safety identification system is used to execute the above-mentioned water conservancy project monitoring AI safety identification method, the system includes:

[0042] The image optimization module uses the YOLO algorithm to adjust the processing parameters of the monitoring image based on the monitoring image data, optimize the signal capture sensitivity, match the dam safety detection environment, and generate key signal optimization results;

[0043] Based on the optimization results of the key signals, the behavior tracking module uses the DeepSORT algorithm to identify and track moving objects in the monitoring area, analyze behavior patterns and compare them with known patterns to obtain detailed behavior trajectory data;

[0044] The abnormal monitoring module refines the data based on the behavior trajectory, sets the detection threshold of abnormal behavior, monitors the deviation from the abnormal pattern, and compares the real-time data, synchronously processes the data, and obtains the abnormal behavior warning record;

[0045] The risk assessment module adjusts the risk assessment model based on the abnormal behavior warning record and in combination with the BIM model data, reflects the safety status of water conservancy project management, analyzes the potential deviation of the water conservancy structure, and generates structural deviation warning indicators;

[0046] Based on the structural deviation warning indicators, the maintenance and risk assessment module conducts regular and temporary monitoring facility inspections, adjusts and calibrates inspection equipment, compares with historical data, determines the urgency and priority of maintenance, optimizes detection accuracy and response speed, identifies potential structural risk points, and generates risk stability assessment logs.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, the safety monitoring capability of water conservancy projects is improved through image recognition and behavior analysis. The image recognition parameters are finely adjusted so that the monitoring system can accurately identify potential threats in various environments, thereby improving the accuracy and efficiency of monitoring. Through real-time behavior tracking and pattern comparison, abnormal behaviors can be discovered and analyzed in a timely manner, and the occurrence of safety accidents can be effectively prevented. Combined with BIM model data, structural risks can be accurately assessed and warned, and the maintenance and management of water conservancy facilities can be optimized to ensure the safety of operations and the efficient use of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] Embodiment 1

[0060] See also Figure 1 The present invention provides a technical solution: a water conservancy project monitoring AI safety identification method, comprising the following steps:

[0061] S1: Based on the monitoring image data, the YOLO algorithm is used to adjust the image recognition parameters, match the dam safety detection environment, adjust the monitoring image processing process, optimize the sensitivity of signal capture, and obtain key signal optimization results;

[0062] S2: Based on the optimization results of key signals, the DeepSORT algorithm is used to identify and track moving objects in the monitoring area, analyze behavior patterns and compare them with known patterns, update tracking parameters, and obtain behavior trajectory refinement data;

[0063] S3: Refine the data based on the behavior trajectory, set the detection threshold of abnormal behavior, monitor the deviation from the target abnormal behavior pattern, compare the real-time data with the abnormal pattern, and perform data synchronization to obtain the abnormal behavior warning record;

[0064] S4: Based on the abnormal behavior warning records and combined with BIM model data, the risk assessment model of the water conservancy project is adjusted to reflect the safety status of water conservancy project management. By comparing and analyzing the model data with the current monitoring data, potential structural deviations are identified and structural deviation warning indicators are obtained;

[0065] S5: Based on the structural deviation warning indicators, implement regular and temporary sluice monitoring inspections, adjust and calibrate inspection equipment, match difference monitoring requirements, compare with historical data, determine the urgency and priority of maintenance, and generate a monitoring maintenance strategy table;

[0066] S6: Based on the monitoring and maintenance strategy table, analyze the operating status of water conservancy facilities in real time, adjust the monitoring strategy, optimize the detection accuracy and response speed, identify potential structural risk points, and generate a risk stability assessment log.

[0067] The key signal optimization results include signal stability, false alarm rate, and detection distance. The behavior trajectory refinement data includes behavior duration, behavior frequency, and behavior pattern. The abnormal behavior warning records include warning level, warning frequency, and warning response time. The structural deviation warning indicators include deviation type, deviation severity, and deviation detection accuracy. The monitoring maintenance strategy table includes maintenance cycle, maintenance type, and maintenance priority.

[0068] See also Figure 2 Based on the monitoring image data, the YOLO algorithm is used to adjust the image recognition parameters, match the dam safety detection environment, adjust the monitoring image processing process, optimize the sensitivity of signal capture, and obtain the key signal optimization results. The specific steps are:

[0069] S101: Based on the monitoring image data, the YOLO algorithm is used to adjust the color balance, adjust the contrast and brightness of the image, match the different external lighting conditions, and optimize the image clarity to highlight the potential key features in the image and obtain the ambient light adaptability configuration;

[0070] When processing color balance based on monitoring image data, the color value of each pixel is adjusted, and the color adjustment coefficient is set as the initial value. For different external lighting conditions, the ambient lighting parameters are calculated, and the color adjustment coefficient is adjusted accordingly to ensure color consistency under changing lighting conditions. At the same time, the brightness adjustment parameter is calculated by analyzing the histogram of the image. This parameter is set based on the difference between the median and the average value of the image histogram. During the adjustment process, the contrast and brightness adjustments are dynamically adjusted according to changes in external lighting conditions to optimize image clarity and make key features more prominent.

[0071] S102: Based on the ambient light adaptability configuration, the image frame processing speed is optimized, the signal and noise processing ratio is adjusted, the image quality is improved, the recognition accuracy of the target signal is refined, and the image signal enhancement details are obtained;

[0072] Based on the ambient light adaptability configuration, the optimization of image frame processing speed is implemented by adjusting the cache mechanism of the image processing pipeline. By calculating the difference between the current processing frame and the previous frame, the signal-to-noise processing ratio is defined. The initial ratio is set to the median value, and the processing ratio is dynamically adjusted according to the size of the difference value to reduce the impact of noise on image quality and improve the accuracy of signal processing. This can improve image quality and make the identification of target signals more accurate, thereby achieving the effect of image signal enhancement.

[0073] S103: Based on the image signal enhancement details, the image is enhanced in detail to capture every detail in the image, optimize the monitoring accuracy and response speed, and obtain the key signal optimization result;

[0074] On the basis of image signal enhancement, the image is further enhanced in detail. By capturing every detail in the image, the monitoring accuracy and response speed are optimized. This is achieved by analyzing the key signals in the image and optimizing the signal processing. The optimization measures include adjusting the color depth and contrast of the image, and using image post-processing technology to enhance the detail performance, ensuring that every important detail can be accurately captured and enhanced, which not only improves the overall quality of the image, but also ensures the accurate communication and rapid response of key information.

[0075] See also Figure 3 Based on the optimization results of key signals, the DeepSORT algorithm is used to identify and track moving objects in the monitoring area, analyze behavior patterns and compare known patterns, update tracking parameters, and obtain behavior trajectory refinement data in the following steps:

[0076] S201: Based on the key signal optimization results, perform preliminary target recognition, adjust the recognition process, match the differentiated size and speed targets, and verify whether each moving object is accurately marked to obtain the target marking accuracy;

[0077] When performing preliminary target recognition, the recognition process is adjusted based on the key signal optimization results to match targets of different sizes and moving speeds. This involves comparing the characteristics of each moving object with the pre-set target model to ensure that each target can be accurately identified and marked. By using image processing technology, the image resolution and dynamic range are adjusted to ensure that the recognition accuracy is maintained even in complex backgrounds or changing lighting conditions. After completing the steps, the accuracy of each marked object is verified through statistical analysis to obtain the accuracy index of the target marking.

[0078] S202: Based on the target marking accuracy, the DeepSORT algorithm is used to track the movement trajectory of the object, analyze the residence time and movement path, compare the behavior pattern in the known data, adjust the behavior analysis parameters, and obtain the behavior path analysis data;

[0079] According to the accuracy of target marking, the tracking of moving objects is deeply optimized. By using image sequence analysis technology, the movement trajectory of each object is carefully tracked, the object's residence time and movement path are analyzed, and compared with known behavioral data. The relevant parameters of the behavioral analysis are adjusted, including the time window size and movement speed threshold, to better capture behavioral patterns and path changes. Through adjustment, the sensitivity and accuracy of behavioral analysis are improved, and behavioral path analysis data is obtained.

[0080] DeepSORT algorithm, according to the formula:

[0081]

[0082] Calculate the real-time position of a moving object, where For time The state estimation at is the state estimate of the previous time step, is the Kalman gain, is the observed current position, is the observation model, It is the regulatory system of environmental dynamic factors. For time Estimation of environmental noise, is the adjustment coefficient of the target dynamic factor, For the goal in time Expected dynamic changes in

[0083] Execution process:

[0084] First, estimate the state from the previous time step Starting from, through the Kalman gain Correction of observation position With predicted position Next, we introduce the environmental noise estimation and target expectations change dynamically To enhance the adaptability of the model, by adjusting the coefficient and The influence of control factors is updated to , not only takes into account the actual observation deviation, but also takes into account the environment and target dynamics, thereby improving the accuracy and sensitivity of tracking, confirming and The specific values ​​are verified through data analysis and model verification to ensure optimal performance and achieve accurate tracking of moving objects.

[0085] S203: based on the behavior path analysis data, updating the target tracking parameters, optimizing the tracking continuity and reaction speed, tracking each target for a long time, and obtaining the behavior trajectory refinement data;

[0086] Based on the behavior path analysis data, the relevant parameters of target tracking are further updated, including the tracking time interval and response threshold. The parameter optimization aims to improve the continuity and response speed of tracking, ensure real-time response to the dynamic changes of each target, and refine the behavior trajectory data through long-term observation and data analysis of each target, including the use of image enhancement and signal processing technology to improve the performance of the tracking algorithm to handle fast-moving or difficult-to-identify targets in complex environments, thereby ensuring more accurate and detailed behavior trajectory data.

[0087] See also Figure 4 , based on the behavior trajectory, the data is refined, the detection threshold of abnormal behavior is set, the deviation from the target abnormal behavior pattern is monitored, the real-time data is compared with the abnormal pattern, and the data is synchronized. The specific steps to obtain the abnormal behavior warning record are:

[0088] S301: Refine the data based on the behavior trajectory, set the dynamic threshold of abnormal behavior, optimize the threshold sensitivity by analyzing the frequency and pattern of the behavior, adjust the parameters according to the abnormal characteristics, and obtain the abnormal activity perception setting;

[0089] Based on the refined data based on behavioral trajectories, dynamic thresholds for abnormal behaviors are set, which involves analyzing the frequency and patterns of behaviors to optimize the sensitivity of the thresholds. Through extensive analysis of historical behavioral data, the statistical range of normal behaviors and potential signals of abnormal behaviors are identified, and parameters are adjusted for abnormal features, including setting dynamically adjusted thresholds. The thresholds can be automatically adjusted based on real-time updates of behavioral data. In this way, any deviation from normal behavioral patterns can be perceived, thereby triggering the alarm mechanism in a timely manner.

[0090] S302: Based on the abnormal activity perception settings, the captured behavior is compared with the stored abnormal patterns in real time, the processing speed and analysis accuracy of the data stream are adjusted, and the abnormal behavior instant analysis results are obtained;

[0091] Based on the setting of abnormal activity perception, real-time behavior analysis is achieved, including comparing the current monitored behavior with the stored abnormal patterns, and timely adjusting the processing speed and analysis accuracy of the data stream to ensure a rapid response to abnormal situations. Through real-time data processing technology, the data stream analysis algorithm is optimized to improve the efficiency and accuracy of data processing. The frequency of data collection and the sensitivity of the analysis model are adjusted to match the needs of different monitoring scenarios, so that the system can identify and analyze abnormal behaviors in a short time.

[0092] S303: Based on the abnormal behavior instant analysis results, data integration is performed to synchronize abnormal behavior records of multiple monitoring nodes, and the consistency and integrity of the data are verified to obtain abnormal behavior warning records;

[0093] After obtaining the instant analysis results of abnormal behavior, data integration is carried out to synchronize the abnormal behavior records of multiple monitoring nodes to ensure the consistency and integrity of the data of each node in the monitoring system. By establishing a central database, the data of all monitoring nodes are summarized and compared here, and the data is verified to ensure that all records are accurate without duplication or omission. This not only improves the efficiency of data processing, but also enhances the security and reliability of the system. In this way, accurate abnormal behavior warning records can be effectively generated.

[0094] See also Figure 5 Based on the abnormal behavior warning records, combined with BIM model data, the risk assessment model of the water conservancy project is adjusted to reflect the safety status of water conservancy project management. By comparing and analyzing the model data with the current monitoring data, potential structural deviations are identified. The specific steps for obtaining structural deviation warning indicators are as follows:

[0095] S401: Based on the abnormal behavior warning records, combined with BIM model data, reflecting the current structural status and monitoring data, adjusting the model parameters in real time, optimizing the response speed and accuracy of risk assessment, and obtaining risk response parameter configuration;

[0096] Based on abnormal behavior warning records, combined with building information model (BIM) data, the current structural status and monitoring data are reflected in real time. By analyzing the correspondence between the structural parameters in the BIM model and the real-time monitoring data, the model parameters are adjusted to more accurately reflect the actual structural conditions, including dynamic adjustment of the parameters of the loads, stress points and potential vulnerable areas in the model, to ensure that risk assessment can respond to actual changes in a timely manner. In this way, not only the response speed of risk assessment is optimized, but also its accuracy is improved, thereby obtaining a risk response parameter configuration that is highly targeted and responsive.

[0097] S402: Based on the risk response parameter configuration, identify data deviations through in-depth comparative analysis, adjust the comparative parameters, optimize the accuracy of deviation identification, and synchronously update the risk data, including the mined deviation points, to obtain data deviation identification results;

[0098] Based on the risk response parameter configuration, data deviations are further identified through in-depth comparative analysis, including analyzing the differences between the data collected at each monitoring point and the model prediction values, adjusting the comparison parameters to optimize the accuracy of deviation identification, and refining the analysis parameters, adjusting the thresholds and sensitivities so that the system can more accurately identify structural or functional deviations. At the same time, risk data is updated synchronously, including newly discovered deviation points, to ensure that all relevant information is updated in a timely manner and accurately reflected.

[0099] S403: Based on the data deviation identification results, the risk assessment model is updated, the associated data is synchronized, including the newly identified risk points, and the accuracy of the risk assessment and the data integrity are verified to obtain the structural deviation warning indicators;

[0100] Based on the data deviation identification results, the risk assessment model is updated to ensure that the model reflects the latest monitoring data and risk points, including synchronizing associated data and integrating newly identified risk points into the existing assessment model, thereby enhancing the model's predictive ability and accuracy. Through this update, the accuracy of the risk assessment and data integrity are fully verified to ensure the reliability of the assessment results and provide structural deviation early warning indicators. The indicators can effectively guide actual structural maintenance and risk management measures.

[0101] See also Figure 6 ,Based on the structural deviation early warning indicators, implement regular and temporary sluice monitoring inspections, adjust and calibrate inspection equipment, match difference monitoring needs, compare with historical data, determine the urgency and priority of maintenance, and generate the monitoring maintenance strategy table as follows:

[0102] S501: Based on the structural deviation warning indicators, set the inspection frequency, formulate inspection standards for different sluice environments and states, adjust the inspection equipment through real-time monitoring data, match the current monitoring needs, and generate a monitoring inspection parameter table;

[0103] Based on the structural deviation warning indicators, the inspection frequency is set to match the differentiated sluice environment and status. According to the warning indicators, the key areas and structures that need frequent monitoring are identified, and specific inspection standards are formulated for the areas. The standards reflect the special needs and potential risks of the structures. Through real-time monitoring data, the settings and functions of the inspection equipment are adjusted to ensure that the equipment can match the current monitoring needs, including adjusting the equipment's sensitivity, resolution, response speed and other parameters, thereby generating a monitoring inspection parameter table.

[0104] S502: Based on the monitoring check parameter table, perform the calibration operation of the monitoring equipment, update the equipment operating parameters to match the environmental changes of the target sluice, analyze and compare the current and historical monitoring data, evaluate the equipment performance and monitoring effectiveness, and obtain the monitoring performance optimization record;

[0105] Based on the monitoring check parameter table, the calibration of the monitoring equipment is performed, including updating the equipment operating parameters to match the environmental changes of the target sluice. By making fine adjustments to the equipment, it is ensured that the equipment parameters match the current environmental conditions of the sluice. The calibration of the equipment is based on the analysis and comparison of current and historical monitoring data, which not only helps to evaluate the performance of the equipment, but also verifies the effectiveness of monitoring. Through the process, a monitoring performance optimization record is generated.

[0106] S503: Based on the monitoring performance optimization record, determine the urgency and priority of the maintenance activity, optimize the monitoring process according to the monitoring data and equipment performance, and generate a monitoring maintenance strategy table;

[0107] Based on the monitoring performance optimization records, determine the urgency and priority of maintenance activities, which involves analyzing monitoring data and equipment performance to optimize the monitoring process. According to the operating status of the equipment and the feedback of monitoring data, evaluate the maintenance needs of each monitoring point, set priorities and urgency, and analyze to help determine which equipment or areas require immediate maintenance and which can be carried out according to the regular plan. Finally, generate a monitoring maintenance strategy table that lists the schedule, required resources and expected goals of maintenance activities to ensure the long-term stability and efficient operation of the monitoring system.

[0108] See also Figure 7 ,Based on the monitoring and maintenance strategy table, the operation status of water conservancy facilities is analyzed in real time, the monitoring strategy is adjusted, the detection accuracy and response speed are optimized, potential structural risk points are identified, and the steps for generating risk stability assessment logs are as follows:

[0109] S601: Based on the monitoring and maintenance strategy table, the operation status of the water conservancy facilities is analyzed in real time, the monitoring parameters are adjusted, the detection accuracy and response speed are optimized, and the monitoring response configuration is optimized by comparing the monitoring data and thresholds to generate an optimized maintenance plan;

[0110] Based on the monitoring and maintenance strategy table, the operating status of the water conservancy facilities is analyzed in real time to ensure that each monitoring parameter can accurately reflect the current status of the facilities. By continuously monitoring the data input and output of the monitoring system, the monitoring parameters are adjusted to optimize the detection accuracy and response speed, including comparison with the threshold, and adjusting the response configuration according to the deviation between the actual operating data and the threshold. For example, if the monitored data continues to exceed the threshold, the system will automatically adjust the parameters to reduce the false alarm rate. Through optimization measures, an optimized maintenance plan is generated, which lists the improvement measures and implementation schedule in detail.

[0111] S602: Based on the optimized maintenance plan, adjust the monitoring and detection accuracy and efficiency, refine the ability to identify potential risk points through continuous parameter optimization, and generate risk point identification results;

[0112] According to the optimized maintenance plan, the accuracy and efficiency of monitoring and detection are further adjusted. Through continuous parameter optimization, the monitoring system's ability to identify potential risk points is refined, including enhancing the system's recognition of abnormal data patterns, such as by increasing data sampling frequency and adjusting data processing algorithms to improve recognition accuracy. Such continuous optimization helps identify and predict potential risk points, so that preventive measures can be taken before problems occur. Through refined measures, risk point identification results are generated, providing solid data support for subsequent risk management and response measures.

[0113] S603: Based on the risk point identification results, perform data integration and analysis, update risk information, verify the consistency of related information and whether it is updated in a timely manner, and generate a risk stability assessment log;

[0114] Based on the risk point identification results, data integration and analysis are carried out, the accuracy of risk information is updated and maintained, the collected data is summarized and organized, the consistency of data at each monitoring point is verified, and the timely update of data is checked. This integrated analysis not only improves the efficiency of data processing, but also ensures the consistency and real-time nature of information among relevant departments, and generates a risk stability assessment log, which records the stability status and historical changes of all key risk points.

[0115] See also Figure 8 , Water conservancy project monitoring AI safety identification system, the system includes:

[0116] The image optimization module uses the YOLO algorithm to adjust the processing parameters of the monitoring image based on the monitoring image data, optimize the signal capture sensitivity, match the dam safety detection environment, and generate key signal optimization results;

[0117] The behavior tracking module uses the DeepSORT algorithm based on the key signal optimization results to identify and track moving objects in the monitoring area, analyze behavior patterns and compare them with known patterns to obtain detailed behavior trajectory data;

[0118] The anomaly monitoring module refines data based on behavior trajectories, sets detection thresholds for abnormal behaviors, monitors deviations from abnormal patterns, and compares and synchronously processes data to obtain abnormal behavior warning records;

[0119] The risk assessment module adjusts the risk assessment model based on abnormal behavior warning records and BIM model data to reflect the safety status of water conservancy project management, analyze the potential deviations of water conservancy structures, and generate structural deviation warning indicators;

[0120] The maintenance and risk assessment module conducts regular and temporary monitoring facility inspections based on structural deviation warning indicators, adjusts and calibrates inspection equipment, compares with historical data, determines the urgency and priority of maintenance, optimizes detection accuracy and response speed, identifies potential structural risk points, and generates risk stability assessment logs.

[0121] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The AI ​​safety identification method for water conservancy project monitoring is characterized by: The following steps are involved: Based on the monitoring image data, the YOLO algorithm is used to adjust the image recognition parameters, match the dam safety detection environment, adjust the monitoring image processing process, optimize the sensitivity of signal capture, and obtain key signal optimization results; Based on the optimization results of the key signals, the DeepSORT algorithm is used to identify and track the moving objects in the monitoring area, analyze the behavior patterns and compare them with known patterns, update the tracking parameters, and obtain the behavior trajectory refinement data; Based on the behavior trajectory, the data is refined, a detection threshold of abnormal behavior is set, deviations from target abnormal behavior patterns are monitored, real-time data is compared with abnormal patterns, and data synchronization is performed to obtain abnormal behavior warning records; Based on the abnormal behavior warning records, combined with BIM model data, the risk assessment model of the water conservancy project is adjusted to reflect the safety status of water conservancy project management, and potential structural deviations are identified by comparing and analyzing the model data with the current monitoring data to obtain structural deviation warning indicators; Based on the structural deviation warning indicators, implement regular and temporary sluice monitoring inspections, adjust and calibrate inspection equipment, match difference monitoring requirements, compare with historical data, determine maintenance urgency and priority, and generate a monitoring maintenance strategy table; Based on the monitoring and maintenance strategy table, the operating status of the water conservancy facilities is analyzed in real time, the monitoring strategy is adjusted, the detection accuracy and response speed are optimized, potential structural risk points are identified, and a risk stability assessment log is generated.

2. The water conservancy project monitoring AI safety identification method according to claim 1 is characterized in that: The key signal optimization results include signal stability, false alarm rate, and detection distance; the behavior trajectory refinement data includes behavior duration, behavior frequency, and behavior pattern; the abnormal behavior warning record includes warning level, warning frequency, and warning response time; the structural deviation warning indicators include deviation type, deviation severity, and deviation detection accuracy; the monitoring maintenance strategy table includes maintenance cycle, maintenance type, and maintenance priority.

3. The water conservancy project monitoring AI safety identification method according to claim 1 is characterized in that: Based on the monitoring image data, the YOLO algorithm is used to adjust the image recognition parameters, match the dam safety detection environment, adjust the monitoring image processing process, optimize the sensitivity of signal capture, and obtain the key signal optimization results. The specific steps are as follows: Based on the monitoring image data, the YOLO algorithm is used to adjust the color balance, adjust the contrast and brightness of the image, match the different external lighting conditions, optimize the image clarity, highlight the potential key features in the image, and obtain the ambient light adaptability configuration; Based on the ambient light adaptability configuration, the image frame processing speed is optimized, the signal and noise processing ratio is adjusted, the image quality is improved, the recognition accuracy of the target signal is refined, and the image signal enhancement details are obtained; Based on the image signal enhancement details, the image is subjected to detail enhancement processing to capture every detail in the image, optimize monitoring accuracy and response speed, and obtain key signal optimization results.

4. The water conservancy project monitoring AI safety identification method according to claim 1 is characterized in that: Based on the optimization results of the key signals, the DeepSORT algorithm is used to identify and track the moving objects in the monitoring area, analyze the behavior patterns and compare them with known patterns, update the tracking parameters, and obtain the behavior trajectory refinement data in the following steps: Based on the key signal optimization results, perform preliminary target recognition, adjust the recognition process, match the differentiated size and speed targets, and verify whether each moving object is marked to obtain the target marking accuracy; Based on the target marking accuracy, the DeepSORT algorithm is used to track the movement trajectory of the object, analyze the residence time and movement path, compare the behavior pattern in the known data, adjust the behavior analysis parameters, and obtain the behavior path analysis data; Based on the behavior path analysis data, the target tracking parameters are updated, the continuity and reaction speed of tracking are optimized, each target is tracked for a long time, and the behavior trajectory refinement data is obtained.

5. The water conservancy project monitoring AI safety identification method according to claim 4 is characterized in that: The DeepSORT algorithm, according to the formula: Calculate the real-time position of a moving object, where For time The state estimation at is the state estimate of the previous time step, is the Kalman gain, is the observed current position, is the observation model, It is the regulatory system of environmental dynamic factors. For time Estimation of environmental noise, is the adjustment coefficient of the target dynamic factor, For the goal in time expected dynamic changes.

6. The water conservancy project monitoring AI safety identification method according to claim 1 is characterized in that: Based on the behavior trajectory, the data is refined, a detection threshold of abnormal behavior is set, deviations from the target abnormal behavior pattern are monitored, real-time data is compared with the abnormal pattern, and data synchronization is performed to obtain abnormal behavior warning records. Specifically, the steps are as follows: Based on the behavior trajectory refinement data, a dynamic threshold for abnormal behavior is set, the threshold sensitivity is optimized by analyzing the frequency and pattern of the behavior, and the parameters are adjusted according to the abnormal characteristics to obtain abnormal activity perception settings; Based on the abnormal activity perception setting, the captured behavior is compared with the stored abnormal pattern in real time, the processing speed and analysis accuracy of the data stream are adjusted, and the abnormal behavior instant analysis results are obtained; Based on the abnormal behavior instant analysis results, data integration is performed, abnormal behavior records of multiple monitoring nodes are synchronized, and the consistency and integrity of the data are verified to obtain abnormal behavior warning records.

7. The water conservancy project monitoring AI safety identification method according to claim 1 is characterized in that: Based on the abnormal behavior warning records, combined with BIM model data, the risk assessment model of the water conservancy project is adjusted to reflect the safety status of water conservancy project management. By comparing and analyzing the model data with the current monitoring data, potential structural deviations are identified. The specific steps for obtaining structural deviation warning indicators are as follows: Based on the abnormal behavior warning record, combined with BIM model data, reflecting the current structural status and monitoring data, adjusting model parameters in real time, optimizing the response speed and accuracy of risk assessment, and obtaining risk response parameter configuration; Based on the risk response parameter configuration, identify data deviations through in-depth comparative analysis, adjust comparative parameters, optimize the accuracy of deviation identification, and synchronously update risk data, including the mined deviation points, to obtain data deviation identification results; Based on the data deviation identification results, the risk assessment model is updated, the associated data is synchronized, including the newly identified risk points, and the accuracy of the risk assessment and the data integrity are verified to obtain structural deviation warning indicators.

8. The water conservancy project monitoring AI safety identification method according to claim 1 is characterized in that: Based on the structural deviation warning indicators, regular and temporary sluice monitoring and inspection are implemented, inspection equipment is adjusted and calibrated, difference monitoring requirements are matched, and compared with historical data to determine the urgency and priority of maintenance. The specific steps of generating a monitoring and maintenance strategy table are as follows: Based on the structural deviation warning indicators, set the inspection frequency, formulate inspection standards for different sluice environments and states, adjust the inspection equipment through real-time monitoring data, match the current monitoring needs, and generate a monitoring inspection parameter table; Based on the monitoring check parameter table, perform a calibration operation of the monitoring equipment, update the equipment operating parameters to match the environmental changes of the target sluice, analyze and compare current and historical monitoring data, evaluate equipment performance and monitoring effectiveness, and obtain a monitoring performance optimization record; Based on the monitoring performance optimization record, the urgency and priority of the maintenance activity are determined, the monitoring process is optimized according to the monitoring data and equipment performance, and a monitoring maintenance strategy table is generated.

9. The water conservancy project monitoring AI safety identification method according to claim 1 is characterized in that: Based on the monitoring and maintenance strategy table, the operation status of water conservancy facilities is analyzed in real time, the monitoring strategy is adjusted, the detection accuracy and response speed are optimized, potential structural risk points are identified, and the steps of generating a risk stability assessment log are as follows: Based on the monitoring and maintenance strategy table, the operating status of the water conservancy facilities is analyzed in real time, the monitoring parameters are adjusted, the detection accuracy and response speed are optimized, and the monitoring response configuration is optimized by comparing the monitoring data and thresholds to generate an optimized maintenance plan; Based on the optimized maintenance plan, the monitoring and detection accuracy and efficiency are adjusted, and through continuous parameter optimization, the ability to identify potential risk points is refined to generate risk point identification results; Based on the risk point identification results, data integration and analysis are performed, risk information is updated, and the consistency and timely update of related information are verified to generate a risk stability assessment log.

10. Water conservancy project monitoring AI safety identification system, characterized by: According to the water conservancy project monitoring AI safety identification method according to any one of claims 1 to 9, the system comprises: The image optimization module uses the YOLO algorithm to adjust the processing parameters of the monitoring image based on the monitoring image data, optimize the signal capture sensitivity, match the dam safety detection environment, and generate key signal optimization results; Based on the optimization results of the key signals, the behavior tracking module uses the DeepSORT algorithm to identify and track moving objects in the monitoring area, analyze behavior patterns and compare them with known patterns to obtain detailed behavior trajectory data; The abnormal monitoring module refines the data based on the behavior trajectory, sets the detection threshold of abnormal behavior, monitors the deviation from the abnormal pattern, and compares the real-time data, synchronously processes the data, and obtains the abnormal behavior warning record; The risk assessment module adjusts the risk assessment model based on the abnormal behavior warning record and in combination with the BIM model data, reflects the safety status of water conservancy project management, analyzes the potential deviation of the water conservancy structure, and generates structural deviation warning indicators; Based on the structural deviation warning indicators, the maintenance and risk assessment module conducts regular and temporary monitoring facility inspections, adjusts and calibrates inspection equipment, compares with historical data, determines the urgency and priority of maintenance, optimizes detection accuracy and response speed, identifies potential structural risk points, and generates risk stability assessment logs.

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