Safety monitoring system and method for gate for water conservancy and hydropower

Abstract

The invention belongs to the technical field of water conservancy monitoring, and discloses a water conservancy and hydropower gate safety monitoring system and method.The system comprises a support, a movable driving mechanism, a multi-stage electric push rod, an angle adjusting mechanism, a monitoring assembly, a gear shaft and a monitoring mechanism; a movable driving mechanism is arranged on the bracket; a sliding seat is slidably arranged on the bracket; the sliding seat is in transmission connection with a movable driving mechanism; two multi-stage electric push rods are arranged below the sliding seat; a rotating seat is fixedly arranged at the lower end of the multi-stage electric push rod; an angle adjusting mechanism is arranged on the rotating seat; a gear shaft is rotationally arranged on the rotating seat, and a monitoring assembly is detachably and fixedly arranged on the gear shaft; and a monitoring mechanism is fixedly arranged on the sliding seat. According to the invention, the gate can be comprehensively monitored, and intelligent data analysis can be carried out; the comprehensive evaluation module comprehensively analyzes and comprehensively evaluates the structure health condition and the operation stability of the gate from multiple dimensions; the risk prediction module predicts potential problems and risks in combination with multi-source data and historical data.

Description

Technical Field

[0001] This application relates to the technical field of water conservancy monitoring. More specifically, it relates to a safety monitoring system and method for gates used in water conservancy and hydropower projects. Background Art

[0002] In the field of water conservancy and hydropower, the gate system must be kept in a high-performance state to regulate the river water. To ensure that the system can be put into use at any time, the maintenance of equipment is inevitable. In the prior art, when a gate fails, corresponding measures are taken, but relatively large losses will still be caused, affecting the timeliness of maintenance.

[0003] The prior art document with the publication number CN115526515A provides a safety monitoring system for gates used in water conservancy and hydropower, including: a data acquisition module, arranged on the gate, for acquiring the operation data of the gate; a first determination module, for inputting the operation data into a pre-constructed regression model to determine the status information of the gate; a matching module, for matching the status information with the preset status information in a preset database, and determining whether the operation status of the gate is abnormal according to the matching result; an alarm module, for performing abnormal detection when the matching module determines that the operation status of the gate is abnormal, determining the abnormal device and sending an alarm prompt. Based on the operation data of the gate and the regression model, the status information of the gate is predicted, which is convenient for determining the status information of the gate in advance, and then judging whether the operation status of the gate is abnormal. It is convenient to reduce losses and improve the timeliness of maintenance. At the same time, the abnormal device in the gate system can be quickly located, which is convenient for quickly identifying the fault and taking corresponding measures.

[0004] Although the above prior art solution can achieve relevant beneficial effects through the structure of the prior art, there are still the following defects: 1. When monitoring water conservancy gates in the prior art, there are monitoring blind spots, resulting in the operating conditions of some areas being difficult to detect. For the underwater situation inside the gate, the prior art lacks effective monitoring means, so it is difficult to master the structural conditions and operating states of the underwater part. 2. The prior art lacks an effective multi-source data fusion method and cannot organically integrate various types of sensor data, image feature data, ultrasonic signal data, and temperature data, resulting in the analysis of the gate operating conditions being limited to a single data source and unable to comprehensively evaluate from multiple dimensions, and the evaluation results being incomplete and inaccurate. 3. It is difficult for the prior art to accurately predict future risks, lacks an effective early warning mechanism, and cannot provide strong support for maintenance decisions in advance.

[0005] In view of this, we propose a safety monitoring system and method for gates used in water conservancy and hydropower. Summary of the Invention

[0006] 1. Technical Problems to be Solved

[0007] The purpose of this application is to provide a gate safety monitoring system and method for water conservancy and hydropower, which solves the technical problems raised in the above-mentioned background technology and realizes the comprehensive monitoring of the gate and intelligent data analysis; the comprehensive evaluation module fuses various sensor data, image feature data, ultrasonic signal data and temperature data, comprehensively analyzes from multiple dimensions, and comprehensively evaluates the structural health status and operation stability of the gate; the risk prediction module combines multi-source data and historical data to predict potential problems and risks.

[0008] 2. Technical solution

[0009] The technical solution of this application provides a gate safety monitoring system for water conservancy and hydropower, including: a bracket, a moving drive mechanism, a sliding seat, a multi-stage electric push rod, an angle adjustment mechanism, a monitoring component, a gear shaft and a monitoring mechanism;

[0010] A moving drive mechanism is fixedly arranged on the bracket; a sliding seat is slidably arranged on the bracket; the sliding seat is in transmission connection with the moving drive mechanism, and the moving drive mechanism can drive the sliding seat to move; a laser rangefinder is fixedly arranged on the sliding seat.

[0011] Two multi-stage electric push rods are symmetrically and fixedly arranged below the sliding seat;

[0012] The lower end of the multi-stage electric push rod is fixedly provided with a rotating seat;

[0013] An angle adjustment mechanism is fixedly arranged on the rotating seat; a gear shaft is rotatably arranged on the rotating seat, and a monitoring component is detachably and fixedly arranged on the gear shaft; the gear shaft is in meshing transmission connection with the angle adjustment mechanism. A transparent waterproof cover is arranged outside the monitoring component;

[0014] A monitoring mechanism is fixedly arranged on the sliding seat, and the monitoring mechanism analyzes and evaluates the operation condition of the gate to detect abnormal conditions in time.

[0015] As an optional solution of the present invention, the monitoring mechanism includes:

[0016] Data collection module: Collect water area address data, water conservancy data and gate-related data (such as size, shape, and position), label the data, and use it as a reference sample;

[0017] Data acquisition module: Reasonably arrange the quantity and position of sensors (such as strain gauges, laser rangefinders, high-definition cameras, and acceleration sensors) according to the gate-related data;

[0018] Route planning module: Plan the optimal travel route of the monitoring component according to the gate-related data and monitoring requirements;

[0019] Image analysis module: Preprocess the collected images, including filtering for noise reduction, grayscale conversion, normalization, and image enhancement; extract features from the preprocessed images, including color, texture, and shape; analyze and identify the preprocessed images to promptly identify abnormal conditions during the operation of the gate.

[0020] Ultrasonic data analysis module: Analyze the data collected by ultrasonic sensors. By interpreting signal changes such as reflection and refraction when ultrasonic waves propagate inside the gate, determine whether there are defects in the internal structure of the gate, such as cavities and delaminations. When abnormal signals are detected and deviate from the normal standard range, abnormal conditions can be identified, providing a key basis for evaluating the integrity of the internal structure of the gate.

[0021] Infrared data analysis module: Deeply analyze the infrared data obtained by the infrared scanner. By observing the surface temperature distribution of the gate, identify abnormal temperature areas. An abnormal increase in temperature may be caused by factors such as increased water flow friction, stress concentration in the internal structure, or equipment failure. Once an abnormal temperature area is discovered, it is promptly judged as an abnormal condition, which helps to detect potential safety hazards in advance.

[0022] Comprehensive evaluation module: Integrate various types of sensor data from the data acquisition module, image feature data from the image analysis module, ultrasonic signal data from the ultrasonic data analysis module, and temperature data from the infrared data analysis module. Use complex algorithms and models to comprehensively analyze the operation of the gate from multiple dimensions, comprehensively evaluate the structural health status, operation stability, etc. of the gate, and give a comprehensive and accurate evaluation result of the gate operation status.

[0023] Risk prediction module: Integrate multi-source data, combine with historical data, and predict potential problems and risks. Use data mining and machine learning techniques to establish a risk prediction model. By learning the data characteristics before and after the occurrence of abnormal conditions in historical data, predict potential problems and risks that may occur in the future. For example, based on the change trend of the gate vibration data and corresponding environmental factors, operating conditions and other data over a period of time in the past, predict whether there may be a risk of structural damage caused by increased vibration in the future, and provide a basis for maintenance decisions in advance.

[0024] Alarm module: Includes an alarm. When abnormal conditions or potential risks are detected, an alarm is issued in a timely manner.

[0025] Control center: Network-connected to the data collection module, data acquisition module, image analysis module, ultrasonic data analysis module, infrared data analysis module, comprehensive evaluation module, risk prediction module, and alarm module.

[0026] The present invention provides a method for safety monitoring of a gate for water conservancy and hydropower, including the following steps:

[0027] S1. Fix and install the bracket above the water gate.

[0028] S2. Monitor the operation conditions on both sides of the gate through two monitoring components. The position of the monitoring components can be adjusted by the moving drive mechanism, the height can be adjusted by the multi-stage electric push rod, and the tilt angle can be adjusted by the angle adjustment mechanism. The multi-stage electric push rod on the inner side of the gate can drive the monitoring component to extend into the water to monitor the underwater conditions on the inner side of the gate.

[0029] S3. The data acquisition module reasonably arranges the quantity and positions of sensors (such as strain gauges, laser rangefinders, high-definition cameras, and acceleration sensors) according to the relevant data of the gate.

[0030] S4. The route planning module plans the optimal travel route of the monitoring components (including the settings of height, position, and tilt angle) according to the relevant data of the gate (such as size, shape, and position) and the monitoring requirements.

[0031] S5. The image analysis module preprocesses the collected images, extracts features from the preprocessed images, and the extracted features include color, texture, and shape. The preprocessed images are analyzed and recognized to promptly identify abnormal conditions in the operation of the gate.

[0032] S6. The ultrasonic data analysis module analyzes the data collected by the ultrasonic sensors, and judges whether there are defects in the internal structure of the gate by interpreting the signal changes such as reflection and refraction when the ultrasonic waves propagate inside the gate.

[0033] S7. The infrared data analysis module deeply analyzes the infrared data obtained by the infrared scanning head, and identifies the temperature abnormal areas by observing the surface temperature distribution of the gate.

[0034] S8. The comprehensive evaluation module fuses various sensor data from the data acquisition module, the image feature data from the image analysis module, the ultrasonic signal data from the ultrasonic data analysis module, and the temperature data from the infrared data analysis module, etc. Analyze the operation conditions of the gate from multiple dimensions, comprehensively evaluate the structural health status, operation stability, etc. of the gate, and give a comprehensive and accurate evaluation result of the operation state of the gate.

[0035] S9. The risk prediction module fuses multi-source data and predicts potential problems and risks in combination with historical data.

[0036] S10. When abnormal conditions or potential risks are detected, the alarm module issues an alarm in a timely manner.

[0037] 3. Beneficial effects

[0038] One or more technical solutions provided in the technical solution of this application have at least the following technical effects or advantages:

[0039] 1. The present invention can comprehensively monitor the gate in multiple dimensions; by installing a bracket above the water gate and equipping it with a monitoring component, using a mobile driving mechanism, a multi-stage electric push rod, and an angle adjustment mechanism, it is possible to conduct an all-round monitoring of both sides (including the underwater situation on the inner side) of the gate from multiple dimensions such as horizontal position, height, and tilt angle, ensuring no monitoring dead angle and comprehensively grasping the operating state of the gate.

[0040] 2. The data acquisition module reasonably arranges various sensors such as strain gauges, laser rangefinders, high-definition cameras, and acceleration sensors according to data such as the size and shape of the gate. Different types of sensors can obtain information on various aspects such as stress and strain, displacement, appearance, and vibration of the gate. Multiple data complement each other to achieve precise perception of the operating condition of the gate.

[0041] 3. Intelligent data analysis can be carried out; the image analysis module preprocesses and extracts features from the collected images, and can quickly identify abnormal situations in the operation of the gate; the ultrasonic data analysis module judges internal structure defects by interpreting changes in ultrasonic signals; the infrared data analysis module identifies abnormal areas based on temperature distribution. These intelligent analysis means can timely discover problems, provide a basis for quickly taking maintenance measures, reduce maintenance costs, and improve the pertinence and effectiveness of operation and maintenance.

[0042] 4. The comprehensive evaluation module fuses various sensor data, image feature data, ultrasonic signal data, and temperature data, comprehensively analyzes from multiple dimensions, comprehensively evaluates the structural health status and operating stability of the gate, gives an accurate evaluation result, and provides a comprehensive and reliable basis for decision-making.

[0043] 5. The risk prediction module combines multi-source data and historical data to predict potential problems and risks, and the alarm module issues an alarm in a timely manner when abnormal or potential risks are detected. Description of the Drawings

[0044] Figure 1 It is a schematic flowchart of a method for monitoring the safety of a water gate for water conservancy and hydropower disclosed in a preferred embodiment of this application;

[0045] Figure 2 It is a schematic structural diagram of a safety monitoring system for a water gate for water conservancy and hydropower disclosed in a preferred embodiment of this application;

[0046] Figure 3 It is a schematic structural diagram of a monitoring component of a safety monitoring system for a water gate for water conservancy and hydropower disclosed in a preferred embodiment of this application.

[0047] Reference signs: 1, support; 2, moving drive mechanism; 3, sliding seat; 4, multi-stage electric push rod; 5, angle adjustment mechanism; 6, monitoring component; 7, gear shaft; 8, rotating seat; 11, U-shaped plate; 12, trapezoidal frame; 13, sliding rod; 21, motor A; 22, lead screw; 31, connecting plate; 32, sliding sleeve; 33, threaded sleeve; 51, motor B; 52, gear; 61, fixing plate; 62, ultrasonic sensor; 63, infrared scanning head; 64, high-definition camera; 65, connecting rod; 66, rotating sleeve; 71, shaft rod. Specific embodiments

[0048] The present application will be further described in detail below in conjunction with the accompanying drawings of the specification.

[0049] Referring to Figure 1 and Figure 2 , an embodiment of the present application provides a gate safety monitoring system for water conservancy and hydropower, including: a support 1, a moving drive mechanism 2, a sliding seat 3, a multi-stage electric push rod 4, an angle adjustment mechanism 5, a monitoring component 6, a gear shaft 7 and a monitoring mechanism;

[0050] A moving drive mechanism 2 is fixedly arranged on the support 1; a sliding seat 3 is slidably arranged on the support 1; the sliding seat 3 is in transmission connection with the moving drive mechanism 2, and the moving drive mechanism 2 can drive the sliding seat 3 to move; a laser rangefinder is fixedly arranged on the sliding seat 3.

[0051] Two multi-stage electric push rods 4 are symmetrically and fixedly arranged below the sliding seat 3;

[0052] The lower end of the multi-stage electric push rod 4 is fixedly provided with a rotating seat 8;

[0053] An angle adjustment mechanism 5 is fixedly arranged on the rotating seat 8; a gear shaft 7 is rotatably arranged on the rotating seat 8, and a monitoring component 6 is detachably and fixedly arranged on the gear shaft 7; the gear shaft 7 and the angle adjustment mechanism 5 are in meshing transmission connection. A transparent waterproof cover is arranged outside the monitoring component 6, and the waterproof level can reach IP68;

[0054] A monitoring mechanism is fixedly arranged on the sliding seat 3, and the monitoring mechanism analyzes and evaluates the operation condition of the gate to timely detect abnormal conditions. A water quality monitor is arranged inside the gate to monitor the turbidity and salinity of the water.

[0055] In this technical solution, the bracket 1 is fixedly installed above the water gate; the operation conditions on both sides of the gate are monitored by two monitoring components 6. The position of the monitoring component 6 can be adjusted by driving it with the moving drive mechanism 2, the height can be adjusted by driving the monitoring component 6 with the multi-stage electric push rod 4; the tilt angle can be adjusted by driving the monitoring component 6 with the angle adjustment mechanism 5. When it is necessary to adjust the height of the monitoring component 6, the control system sends an instruction to the motor of the multi-stage electric push rod 4, and the motor rotates to drive the push rod to extend or retract, thereby driving the rotating seat 8 and the angle adjustment mechanism 5, the toothed shaft 7, the monitoring component 6, etc. installed on the rotating seat 8 to rise or fall as a whole. The multi-stage design enables the electric push rod to adapt to the adjustment requirements of different height ranges, has a large stroke range, and can flexibly meet the monitoring requirements of different height positions of the water gate. Whether it is to monitor the top or the bottom of the gate, the height adjustment can be easily achieved. The system uses two monitoring components 6 to synchronously monitor the inner and outer sides of the gate respectively. In this way, the operation condition information on both the inner and outer sides of the gate under the action of water flow can be comprehensively obtained, and the data on both sides can be compared and analyzed to more accurately judge the overall operation state of the gate.

[0056] Further, the bracket 1 includes a U-shaped plate 11, a trapezoidal frame 12 and a slide bar 13;

[0057] The number of the U-shaped plate 11, the trapezoidal frame 12 and the slide bar 13 is two each. Two slide bars 13 are detachably and fixedly arranged between the two trapezoidal frames 12;

[0058] The lower ends of the two trapezoidal frames 12 are fixedly provided with the U-shaped plate 11.

[0059] Further, the moving drive mechanism 2 includes a motor A21 and a lead screw 22;

[0060] The motor A21 is fixedly arranged on the trapezoidal frame 12 of the bracket 1; the lead screw 22 is rotatably arranged between the two trapezoidal frames 12; the output end of the motor A21 is coaxially and fixedly connected with the lead screw 22.

[0061] Further, the sliding seat 3 includes a connecting plate 31, a sliding sleeve 32 and a threaded sleeve 33;

[0062] The threaded sleeve 33 is symmetrically and fixedly provided with connecting plates 31 on both sides, and the other ends of the connecting plates 31 are fixedly provided with sliding sleeves 32;

[0063] The threaded sleeve 33 is in threaded fit connection with the lead screw 22; the sliding sleeve 32 is in sliding connection with the slide bar 13.

[0064] In this technical solution, when the motor A21 is started to drive the lead screw 22 to rotate, the lead screw 22 drives the threaded sleeve 33 to move, and the threaded sleeve 33 drives the multi-stage electric push rod 4, the angle adjustment mechanism 5 and the monitoring component 6 to move to adjust the position.

[0065] Refer toFigure 3 , the angle adjustment mechanism 5 includes a motor B51 and a gear 52;

[0066] The motor B51 is fixedly arranged on the turntable 8, and a gear 52 is coaxially and fixedly arranged at the output end of the motor B51; the gear 52 is in meshing transmission connection with the tooth shaft 7.

[0067] In this technical solution, starting the motor B51 drives the gear 52 to rotate, the gear 52 drives the tooth shaft 7 to rotate, and the tooth shaft 7 drives the monitoring component 6 to rotate to adjust the inclination angle.

[0068] Furthermore, the monitoring component 6 includes a fixing plate 61, an ultrasonic sensor 62, an infrared scanning head 63, a high-definition camera 64, a connecting rod 65 and a rotating sleeve 66;

[0069] The ultrasonic sensor 62, the infrared scanning head 63 and the high-definition camera 64 are fixedly arranged on the fixing plate 61;

[0070] An LED lamp, an inclinometer and a laser rangefinder are fixedly arranged on the fixing plate 61.

[0071] A connecting rod 65 is fixedly arranged on the fixing plate 61, and a rotating sleeve 66 is fixedly arranged at the other end of the connecting rod 65;

[0072] A shaft rod 71 is fixedly arranged on the tooth shaft 7, and the shaft rod 71 is detachably and fixedly connected to the rotating sleeve 66.

[0073] In this technical solution, the ultrasonic sensor 62 can be used to measure the thickness change of the internal structure of the gate or detect defects such as internal cavities; the infrared scanning head 63 can detect the temperature distribution on the surface of the gate, and by analyzing the temperature anomaly area, discover potential structural defects or abnormal conditions caused by factors such as water flow friction; the high-definition camera 64 can take surface images of the water conservancy gate, and clearly observe cracks, wear, corrosion, etc. on the surface of the gate through high-resolution images.

[0074] Furthermore, ultrasonic water level gauges and radar flow meters are fixedly arranged at both ends of the sliding seat 3 to monitor the water level difference and flow rate upstream and downstream of the gate in real time and calculate the water pressure load.

[0075] Furthermore, the monitoring mechanism includes:

[0076] Data collection module: Collect address data, water conservancy data, and gate-related data (such as size, shape, and location) of the water area, label the data, and use it as a reference sample; collect data from multiple aspects, including address data of the water area, covering information such as the geographical location of water conservancy facilities and the surrounding topography and landforms. These data help to understand the relationship between water conservancy projects and the surrounding environment from a macro level. Water conservancy data includes dynamic information such as water level changes, water flow velocity, and flow rate, which is crucial for evaluating the operating conditions of water conservancy facilities. At the same time, collect gate-related data, such as the size and shape of the gate, accurately record the length, width, height, and special structural design parameters, as well as the location data of the gate in the water conservancy facility, including installation coordinates and relative positions with upstream and downstream water flows. After collection, label these data in detail and organize them into a reference sample to provide basic data support for subsequent data analysis and evaluation.

[0077] Data acquisition module: According to the gate-related data (such as size and shape), reasonably arrange the quantity and location of sensors (such as strain gauges, laser rangefinders, high-definition cameras, and acceleration sensors); monitor the vibration frequency and amplitude through the acceleration sensor, and trigger an alarm when it exceeds 0.1mm.

[0078] Route planning module: Plan the optimal travel route of the monitoring component 6 according to the gate-related data (such as size, shape, and location) and monitoring requirements;

[0079] Image analysis module: Preprocess the collected images, including filtering, denoising, grayscale conversion, normalization, and image enhancement; extract features from the preprocessed images, and the extracted features include color, texture, and shape; analyze and identify the preprocessed images to promptly identify abnormal situations in the operation of the gate;

[0080] Ultrasonic data analysis module: Analyze the data collected by the ultrasonic sensor, and judge whether there are defects in the internal structure of the gate, such as cavities and delaminations, by interpreting signal changes such as reflection and refraction when ultrasonic waves propagate inside the gate. When abnormal signals are found and deviate from the normal standard range, abnormal situations can be identified, providing key evidence for evaluating the integrity of the internal structure of the gate.

[0081] Infrared data analysis module: Deeply analyze the infrared data obtained by the infrared scanner, and identify abnormal temperature areas by observing the surface temperature distribution of the gate. An abnormal increase in temperature may be caused by factors such as increased water flow friction, internal structural stress concentration, or equipment failure. Once an abnormal temperature area is found, it is immediately judged as an abnormal situation, which helps to detect potential safety hazards in advance.

[0082] Comprehensive Evaluation Module: It fuses various types of sensor data from the data acquisition module, image feature data from the image analysis module, ultrasonic signal data from the ultrasonic data analysis module, temperature data from the infrared data analysis module, and other multi-source data. By using complex algorithms and models, it comprehensively analyzes the operation status of the gate from multiple dimensions, comprehensively evaluates the structural health status, operation stability, etc. of the gate, and gives a comprehensive and accurate evaluation result of the gate operation status.

[0083] Risk Prediction Module: It fuses multi-source data and combines historical data to predict potential problems and risks. By using data mining and machine learning technologies, it establishes a risk prediction model. By learning the data characteristics before and after the occurrence of abnormal situations in historical data, it predicts potential problems and risks that may occur in the future. For example, according to the change trend of the gate vibration data and corresponding environmental factors, operating conditions and other data in the past period of time, it predicts whether there may be a risk of structural damage caused by increased vibration in the future, providing a basis for maintenance decisions in advance.

[0084] Alarm Module: It includes an alarm. When abnormal situations or potential risks are detected, it issues an alarm in a timely manner.

[0085] Control Center: It is network-connected to the data collection module, data acquisition module, image analysis module, ultrasonic data analysis module, infrared data analysis module, comprehensive evaluation module, risk prediction module, and alarm module.

[0086] Furthermore, the image analysis module preprocesses the collected images, extracts features from the preprocessed images, analyzes and identifies the preprocessed images, and promptly identifies abnormal situations in the gate operation; it includes the following steps:

[0087] 1. Image Preprocessing: It includes filtering and denoising, grayscale conversion, normalization, and image enhancement;

[0088] Filtering and Denoising: It uses algorithms such as Gaussian filtering and median filtering to remove noise interference in the images.

[0089] Grayscale Conversion: It converts the color image into a grayscale image to simplify the subsequent processing process. Through grayscale conversion, the three-dimensional color image is converted into a one-dimensional grayscale image, reducing the data processing volume while retaining the main structure and texture information of the image.

[0090] Normalization: It performs normalization processing on the grayscale image, mapping the image pixel values to a specific range.

[0091] Image Enhancement: It uses technologies such as histogram equalization and contrast-limited adaptive histogram equalization (CLAHE) to enhance the contrast and details of the image.

[0092] 2. Feature extraction: including color feature extraction, texture feature extraction, and shape feature extraction;

[0093] Shape feature extraction: Use edge detection algorithms such as the Canny algorithm to extract the edges of objects in the image. Accurately find the edge contours of objects in the image. For the gate image, the gate contour, weld edges, etc. can be clearly detected. If the edges are discontinuous, distorted, or new edges appear, it may be a sign of deformation or cracks in the gate structure. Based on the edge detection results, extract the contours of objects in the image. By calculating geometric parameters such as the perimeter, area, and aspect ratio of the contours, describe the shape features of the objects. For example, under normal circumstances, the perimeter and area of the gate contour are within a certain range. If the perimeter becomes longer or the area changes abnormally, it may indicate that the gate has deformed. The moment features of the contours, such as central moments and invariant moments, can also be used to more accurately describe and analyze the contour shape to determine whether the shape is normal.

[0094] Hough transform: Use the Hough transform to detect geometric shapes such as lines and circles in the image. In the gate image, the border lines of the gate, the circles of the bolt holes, etc. can be detected by the Hough transform. If the detected lines or circles show position offsets, quantity changes, or shape distortions, it may imply abnormal situations such as displacement and damage of the gate components.

[0095] 3. Model construction: Adopt the support vector machine (SVM) model. Collect a large number of gate images in the normal state, extract their color, texture, and shape features, and organize these feature data into a database. Using these feature data as training samples, establish a feature model of the gate image in the normal state. This model can define the range and distribution rules of normal color, texture, and shape features.

[0096] 4. Real-time feature comparison: Compare the color, texture, and shape features extracted from the current monitoring image with the established normal model. For color features, calculate the similarity between the color histogram of the current image and the color histogram of the normal model. If the similarity is lower than the set threshold, judge that the color is abnormal. For texture features, compare the differences between GLCM parameters, wavelet coefficients, or LBP feature vectors and the normal model. If it exceeds the allowable range, determine that the texture is abnormal. For shape features, compare the contour geometric parameters, moment features, and the results of the Hough transform with the normal model. If significant deviations occur, determine that the shape is abnormal.

[0097] 5. Abnormal type judgment: Based on the abnormal conditions of color, texture, and shape features, comprehensively judge the abnormal type of the gate operation. If the color is abnormal and the texture shows wear characteristics, it may be surface corrosion or wear of the gate; if the shape is deformed and crack features appear at the edge, it can be judged as structural deformation or cracking of the gate. At the same time, combined with the data of other monitoring modules, such as the association between the abnormal temperature area and the abnormal color and texture areas in the image, further accurately judge the abnormal situation, send out an alarm in time and provide detailed abnormal information, providing strong support for the maintenance decision-making of management personnel.

[0098] Further, the ultrasonic data analysis module analyzes the data collected by the ultrasonic sensors, and judges whether there are defects in the internal structure of the gate, such as cavities, delaminations, etc., by interpreting the signal changes such as reflection and refraction when the ultrasonic waves propagate inside the gate. The steps include:

[0099] 1. Data acquisition and collation: Collect the data of ultrasonic sensors; the sensors emit high-frequency ultrasonic pulses into the gate interior and receive the reflected signals. Store the collected original ultrasonic data in a data storage device in a specific format. During storage, conduct preliminary screening to eliminate the data points with obvious abnormalities such as sensor failures and external electromagnetic interferences.

[0100] 2. Signal preprocessing: including denoising processing and signal enhancement;

[0101] Denoising processing: Use filtering algorithms, such as low-pass filtering, band-pass filtering, etc., to remove the noise in the ultrasonic signals. Low-pass filtering can filter out high-frequency noise, which may come from electrical equipment interference, etc.; band-pass filtering can retain the signals within a specific frequency range and remove the useless signals in other frequency bands.

[0102] Signal enhancement: Adopt signal enhancement techniques, such as time gain compensation (TGC). Since the ultrasonic waves will attenuate when propagating in the medium, the reflected signals from the parts farther away from the sensor are weaker. The TGC technique compensates the gain of the signals at different depths according to the signal propagation time, making the intensities of the reflected signals received from different depths relatively uniform and improving the overall quality of the signals.

[0103] 3. Reflection and refraction signal analysis:

[0104] 3.1. Reflection signal interpretation: Analyze the reflection signals of ultrasonic waves at the interfaces of different media. Under normal circumstances, the internal material of the gate is uniform, and the reflection signals show certain patterns. When encountering a cavity, the ultrasonic waves enter the air from the metal medium, resulting in a sudden change in acoustic impedance and generating a strong reflection signal. If there is stratification, that is, the interface between different material layers, it will also cause reflection. By comparing the characteristics of the reflection signals in the normal state, such as the amplitude, phase, arrival time, etc. of the reflection waves, to determine whether abnormal reflections occur. For example, the amplitude of the normal reflection wave fluctuates within a certain range. If the amplitude of a certain reflection wave suddenly increases several times, it may indicate an abnormal interface here, such as a cavity or severe stratification.

[0105] 3.2. Refraction signal analysis: Pay attention to the refraction phenomenon of ultrasonic waves during propagation. When ultrasonic waves encounter internal structural changes, such as regions with non-uniform materials, the propagation direction will change, resulting in changes in the characteristics of the refraction signals. By analyzing information such as the angle and propagation path of the refraction waves, the uniformity of the internal structure of the gate is judged. For example, if the angle of the refraction wave deviates from the angle of the normal propagation path beyond the allowable range, it may mean that there are material changes or defects in this area.

[0106] 4. Feature extraction:

[0107] 4.1. Amplitude feature extraction: Extract the amplitude information of the reflection waves and refraction waves, and calculate statistical features such as the maximum amplitude, average amplitude, and standard deviation of the amplitude. Large-amplitude reflection waves are often related to larger defects, and the standard deviation of the amplitude can reflect the degree of signal fluctuation. Abnormal fluctuations may imply an increase in the complexity of the internal structure and the existence of potential defects.

[0108] 4.2. Time feature extraction: Measure the propagation time of the reflection waves and refraction waves, and calculate the transit time, delay time, etc. of the signals. Changes in the transit time can reflect the depth of the defect, and abnormal delay times may indicate local structural changes. For example, if the transit time of a certain reflection wave is longer than normal, it means that the corresponding reflection interface may be located deeper. Combining with other features, it can be speculated that there may be cavities or loose areas.

[0109] When entering water for monitoring, calculate the defect depth according to the following formula:

[0110] H = 0.5vt eff t tr ; v eff =(d w v w +d m v m ) / (d w +d m ); v w = 1402.3 + 5.03△T depth (d);

[0111] △T depth (d) = T0e -bd ; where t tr is the transit time of the signal, obtained by measuring the time difference between the transmission and reception of the ultrasonic signal. It reflects the total time spent by the ultrasonic wave on the propagation path, and its change plays a crucial role in judging the depth of internal defects in the gate. h is the depth of the defect, that is, the depth from the possible defect location inside the gate detected by ultrasonic testing to the sensor or a certain reference surface. v eff is the equivalent velocity considering the propagation paths of water and gate material. v w is the propagation velocity of ultrasonic wave in water, and v m is the propagation velocity of ultrasonic wave in gate material. d w is the distance that the ultrasonic wave propagates in water. This distance is related to factors such as the depth of water and the position of the sensor relative to the gate, and is one of the important parameters for calculating the equivalent velocity. △T depth (d) is the water temperature at the underwater depth d. T0 represents the water surface temperature, which is the water temperature when the depth d = 0, and can usually be obtained through actual measurement, and is a known parameter in the formula. e is the natural constant, approximately equal to 2.71828, and b is a positive constant, whose value depends on the specific characteristics of the water body, such as the thermal conductivity, specific heat capacity, light conditions, and water flow conditions of the water body, etc. The larger the value of b, the faster the water temperature drops with depth; the smaller the value of b, the slower the water temperature drops with depth. It is a key parameter describing the rate of change of water temperature with depth and needs to be determined through experimental measurement and data analysis of a specific water body. d represents the water depth. d m is the distance that the ultrasonic wave propagates in the gate material.

[0112] 4.3. Frequency Feature Extraction: Perform spectral analysis on the ultrasonic signal to extract the frequency components of the signal, such as the center frequency, frequency bandwidth, etc. Internal defects may cause the ultrasonic wave to interact with the defects, resulting in a change in the signal frequency. For example, when there are micro-cracks, the ultrasonic wave scatters at the cracks, increasing the high-frequency components of the signal. By comparing the frequency characteristics of normal and abnormal signals, it helps to detect such subtle defects. The frequency feature extraction is carried out according to the following formula:

[0113] X(a,b) = [1 / sqt(|a|)]∑ ∞ t＝-∞{x(t)Ψ[(t - b) / a]}; where x(t) represents the original time-domain ultrasonic signal, that is, the signal received by the ultrasonic sensor varying with time t, which contains various features regarding the internal structure information of the gate and is the basic data for subsequent analysis. X(a, b) is the frequency-domain signal obtained after discrete wavelet transform. a is the scale parameter that controls the stretching and shrinking of the wavelet function Ψ. When a increases, the wavelet function is stretched, corresponding to the analysis of the low-frequency part of the signal, which can capture the overall trend and long-term features of the signal; when a decreases, the wavelet function is compressed for analyzing the high-frequency part of the signal, which can highlight the details and mutation information of the signal. For example, when detecting fine cracks inside the gate, a smaller a value helps to amplify the changes in the high-frequency features of the signal at the crack. b is the translation parameter that determines the position of the wavelet function on the time axis. By changing b, the signal can be analyzed at different time positions, that is, sliding the wavelet function along the time axis to observe the features of the signal at each moment, so as to locate the time point when abnormal features appear in the signal. Ψ(t) is the wavelet basis function, which is the core of discrete wavelet transform. Different types of wavelet basis functions have different characteristics. Selecting an appropriate wavelet basis function is crucial for accurately extracting the features of ultrasonic signals. Different wavelet basis functions will have different effects on the transformation results, and it needs to be determined according to the characteristics of the ultrasonic signal and the analysis purpose.

[0114] 5. Abnormality judgment: Collect ultrasonic data of a large number of normal gates, determine the range of various characteristic parameters of ultrasonic signals under normal conditions, such as the reflection wave amplitude range, transit time range, frequency range, etc., and construct a normal standard model. Compare the characteristic parameters extracted from the currently monitored ultrasonic data with the normal standard range. If one or more characteristic parameters deviate from the normal range by more than the set threshold, the system determines it as an abnormal situation.

[0115] 6. Report generation: Once an abnormality is identified, the system automatically generates a detailed abnormality report. The report content includes the location where the abnormality occurred (estimated based on the sensor layout and signal propagation path), speculation on the type of abnormality (such as possible voids, delaminations, or cracks, etc.), and assessment of the degree of abnormality (according to the degree of deviation of the characteristic parameters from the normal range). At the same time, attach relevant ultrasonic signal diagrams to visually display the differences between abnormal signals and normal signals, providing a key basis for subsequent maintenance personnel to evaluate the integrity of the internal structure of the gate.

[0116] Furthermore, the infrared data analysis module conducts in-depth analysis on the infrared data obtained by the infrared scanning head, and identifies temperature abnormal areas by observing the surface temperature distribution of the gate. It includes the following steps:

[0117] 1. Data preprocessing: Perform filtering, calibration, and spatial interpolation processing on the collected infrared data;

[0118] Filter the collected original infrared data to remove outliers and fluctuations caused by factors such as equipment noise and environmental interference. Common filtering algorithms such as mean filtering and median filtering can be used, and an appropriate filtering window size can be selected according to the actual situation.

[0119] Calibrate the infrared radiation intensity data and convert it into the actual temperature value. This requires conversion calculations based on the calibration parameters of the infrared scanning head and the known temperature-radiation intensity relationship.

[0120] Perform spatial interpolation on the data. For missing data points caused by reasons such as scanning intervals during the scanning process, interpolation algorithms (such as linear interpolation and spline interpolation) are used to supplement them to obtain continuous and complete temperature distribution data on the gate surface.

[0121] 2. When entering water monitoring, correct the infrared data and perform correction according to the following formula:

[0122] T 正 =hc / {λkln[2hc 2 / (I 水 C(t,s)λ 5 )+1]}+△T depth (d);

[0123] △T depth (d)=T0e -bd ; In the formula, T 正 is the finally obtained corrected temperature value. h is the Planck constant, h = 6.62607015×10 -34 J·s, which is a fundamental constant in quantum mechanics and plays a key role in describing the relationships such as the energy and frequency of microscopic particles. c is the speed of light in a vacuum, which is an important constant in physics, and the propagation speed of light in a vacuum is constant. λ is the wavelength of infrared radiation. k is the Boltzmann constant, k = 1.380649×10 -23 J / K, which relates temperature to the energy of microscopic particles and is an important constant in statistical physics. I 水 is the original infrared radiation intensity collected in water, which reflects the energy magnitude of the infrared radiation received by the infrared scanner in water. C(t,s) is a correction coefficient determined according to the water quality parameters turbidity t and salinity s, which is a dimensionless value. This coefficient is established through experiments and is used to correct the change in infrared radiation intensity caused by the attenuation of water to infrared radiation. △T depth(d) is the water temperature at an underwater depth d, in Kelvin (K) or degrees Celsius. It takes into account the effect of the change in water temperature with increasing depth on the infrared data, and its specific value can be determined through experiments or empirical formulas. T0 represents the water surface temperature, which is the water temperature at a depth d = 0 and can usually be obtained through actual measurement. It is a known parameter in the formula. e is the natural constant, approximately equal to 2.71828, and b is a positive constant whose value depends on the specific characteristics of the water body, such as the thermal conductivity, specific heat capacity, lighting conditions, and water flow conditions of the water body, etc. The larger the value of b, the faster the water temperature drops with increasing depth; the smaller the value of b, the slower the water temperature drops with increasing depth. It is a key parameter describing the rate of change of water temperature with depth and needs to be determined through experimental measurements and data analysis of a specific water body. d represents the water depth.

[0124] 3. Temperature distribution visualization: Generate a visualization image of the temperature distribution on the gate surface based on the processed data. It can be displayed using two-dimensional or three-dimensional graphics, representing temperature values in different colors or heights for intuitive observation of the temperature distribution.

[0125] Mark the key structural parts of the gate (such as support points, joints, etc.) on the visualization image for convenient reference in subsequent analysis.

[0126] 4. Identification of abnormal areas: Set a judgment threshold for temperature anomalies. This threshold can be determined comprehensively based on the normal operating temperature range of the gate, historical data, and industry standards, etc. For example, when the temperature in a certain area exceeds a certain percentage of the normal average temperature, it is determined as a temperature abnormal area.

[0127] Traverse the entire temperature distribution data and compare the temperature value of each data point with the set threshold. When it is found that the temperature value in a certain area (which can be defined as an area composed of several consecutive data points) exceeds the threshold, mark this area as a temperature abnormal area.

[0128] For the marked temperature abnormal areas, record detailed information such as their position information (coordinate range), the degree of temperature anomaly (the difference between the highest temperature value and the normal temperature, etc.), and the area size of this area.

[0129] 5. Judgment of abnormal situations: For the identified temperature abnormal areas, combined with information such as the working state and operation history of the gate, further analyze the possible reasons for the abnormal increase in temperature. For example, if the temperature anomaly occurs during a period of large water flow, it may be related to increased water flow friction; if there are records of equipment maintenance or structural adjustment in the recent period, it may be related to internal structural stress concentration or equipment failure.

[0130] Based on the analysis results, determine whether it is an abnormal situation that needs attention. If it is determined to be an abnormal situation, issue a warning signal in a timely manner. The warning signal can be sent to relevant management and maintenance personnel through means such as sound, light, text message, or system notification. Record the detailed information of the abnormal situation, including the occurrence time, description of the abnormal area, analysis of possible causes, etc., for subsequent tracking and handling.

[0131] 6. Data storage: Store data such as the temperature data, abnormal area information, and abnormal situation judgment results obtained from each scan analysis into the database to establish a historical data record. Regularly analyze the historical data to observe the temperature change trend, the frequency and pattern of abnormal situations, etc. Through long-term data analysis, the threshold and analysis model for temperature anomaly judgment can be further optimized to improve the early warning ability for potential safety hazards.

[0132] Furthermore, the comprehensive evaluation module comprehensively evaluates the structural health status and operation stability of the gate, including the following steps:

[0133] 1. Data collection and integration: Collect various sensor data from the data acquisition module. These sensors may include pressure sensors, displacement sensors, etc., to obtain data such as pressure and displacement; obtain image feature data from the image analysis module, such as information on cracks and deformations on the surface of the gate; obtain ultrasonic signal data from the ultrasonic data analysis module for detecting internal structural defects of the gate; obtain temperature data from the infrared data analysis module to discover potential abnormal heating points. Conduct a preliminary check on the collected multi-source data to remove obvious incorrect data or outliers. Uniformly convert data in different formats and standards so that they can be effectively fused in subsequent processing.

[0134] 2. Data preprocessing: Perform data preprocessing, including data cleaning and data normalization;

[0135] 3. Feature extraction: Extract representative features from the preprocessed data. For image data, features such as texture, shape, and edges can be extracted; for ultrasonic signal data, features such as amplitude, frequency, and transit time can be extracted; for temperature data, features such as temperature change rate and temperature distribution characteristics can be extracted. These features can more accurately reflect the operating conditions of the gate. Among the numerous extracted features, there may be some redundant or irrelevant features. Use feature selection methods, such as correlation analysis, principal component analysis (PCA), etc., to screen out the key features that have a greater impact on the evaluation of the gate's operating state, reduce the data dimension, and improve the efficiency and accuracy of subsequent analysis.

[0136] 4. Data fusion: Select a data fusion method that combines feature-level fusion and convolutional neural network (CNN) to fuse multi-source data; then process time series data through a recurrent neural network RNN.

[0137] 5. Comprehensive analysis and evaluation: Conduct a comprehensive analysis of the fused data from multiple dimensions. Combining structural mechanics knowledge, analyze the structural stress and strain conditions of the gate, and evaluate its structural health status; from a dynamic perspective, analyze the operating stability of the gate, such as vibration conditions, displacement changes, etc.; environmental factors such as temperature and humidity affecting the operation of the gate can also be considered.

[0138] S 综合 = Σ k1 i＝1 w i Σ t l＝1 β t-l f il ; Σ k1 i＝1 w i = 1; where S 综合 is the comprehensive evaluation score. K1 represents the number of selected key features. These key features are selected from numerous data features and have important impacts on the evaluation of the gate operating state. For example, they can be the amplitude, frequency, and transit time of the ultrasonic signal mentioned above, the texture, shape, and edge features of the image data, the temperature change rate of the temperature data, etc. w i is the weight of the i-th key feature. The weight w i can be determined using methods such as expert experience and historical data. t represents the current time point. l represents the sequence number of the time point in the time series, and its value range is from 1 to t. β is the time decay coefficient, which is a constant between 0 and 1. f il represents the data value of the i-th key feature at the l-th time point.

[0139] 6. Build an evaluation model: Based on the results of the multi-dimensional analysis, build an evaluation model for the gate operating state. Expert experience and historical data can be used to determine the influence weights of different features and indicators on the gate operating state, and calculate the comprehensive evaluation score through methods such as weighted summation.

[0140] 7. Output of evaluation results: Give a comprehensive and accurate evaluation result of the gate operating state according to the comprehensive evaluation score calculated by the evaluation model. The evaluation results can be presented in a graded manner, such as normal, slightly abnormal, moderately abnormal, severely abnormal, etc. At the same time, a detailed evaluation report can be attached to explain the evaluation basis and potential problems.

[0141] Furthermore, the risk prediction module fuses multi-source data and combines historical data to predict potential problems and risks. It includes the following steps:

[0142] 1. Data Integration: Collect real-time data from various sensors, such as pressure sensor data, displacement sensor data, vibration sensor data, as well as environmental factor data like temperature and humidity, and operating condition data such as the number of times the gate is opened and the opening duration. Extract the above-mentioned various types of data from the database over a past period to form a historical data set. Integrate the real-time data with the historical data to form a complete data set for analysis.

[0143] 2. Data Preprocessing: Clean the data, including data cleaning and data normalization;

[0144] 3. Feature Engineering:

[0145] 3.1 Feature Extraction: Extract trend features and correlation features;

[0146] Trend Feature: For time series data, such as vibration data, calculate its change trend. It can be calculated by the change of data at adjacent time points or by using the moving average method to calculate the average change rate over a period of time. Calculate the vibration trend feature according to the following formula:

[0147] △V u (t) = uV(t + 1) - (1 - u)V(t); where △V u (t) represents the difference result of the vibration data at time t. u is the weight parameter, and its value range is 0 < u < 1. V(t) is the value of the vibration data in the time series at time t, reflecting the actual situation of the vibration at that moment, such as the amplitude and frequency of the vibration after quantization. V(t + 1) is the value of the vibration data in the time series at time t + 1, representing the vibration state at the next moment. Correlation Feature: Calculate the correlation between different data features. For example, judge the degree of association between vibration data and temperature data.

[0148] 3.2 Feature Selection: Use principal component analysis (PCA) for feature selection. PCA can convert the original numerous features into a smaller number of new features that contain key information through specific calculations, reducing the data dimension.

[0149] 4. Build a risk prediction model: Select a multi-layer perceptron (MLP) model. Divide the preprocessed and feature-engineered data into a training set and a test set. Determine the structure of the neural network. The number of neurons in the input layer is equal to the number of features, and the number of neurons in the hidden layer is determined by experience or experiments. Input the sample data. After being processed by the hidden layer, the output result is obtained at the output layer. Activation functions are used in the processing of the hidden layer and the output layer to make the data undergo non-linear changes. Adopt the cross-entropy loss function. According to the true labels of the training samples and the predicted output of the model, calculate the degree of difference between the two. Calculate the influence degree of the loss function on the model parameters (weights and biases) through the backpropagation algorithm, and use the gradient descent method to adjust these parameters to make the model prediction result closer to the true label. Evaluate the trained model with the test set, and use indicators such as accuracy, recall rate, and F1 value to measure the performance of the model.

[0150] 5. Risk prediction: Input the data that is collected in real time and has undergone preprocessing and feature engineering into the trained risk prediction model. The model outputs the probability value of whether potential problems and risks may occur in the future. Based on the predicted probability value, set a risk threshold. When the predicted probability value exceeds this threshold, it is determined that risks may occur in the future, providing a basis for maintenance decisions in advance, such as suggesting to arrange early maintenance, adjust the operating conditions, etc.

[0151] The present invention provides a safety monitoring method for a gate used in water conservancy and hydropower, including the following steps:

[0152] S1. Fix and install the bracket 1 above the water conservancy gate;

[0153] S2. Monitor the operating conditions on both the inner and outer sides of the gate through two monitoring components 6. The position of the monitoring component 6 can be adjusted by driving the moving driving mechanism 2, and the height of the monitoring component 6 can be adjusted by driving the multi-stage electric push rod 4; the tilt angle of the monitoring component 6 can be adjusted by driving the angle adjustment mechanism 5. The multi-stage electric push rod 4 on the inner side of the gate can drive the monitoring component 6 to extend into the water to monitor the underwater conditions on the inner side of the gate;

[0154] S3. The data acquisition module reasonably arranges the quantity and positions of sensors (such as strain gauges, laser rangefinders, high-definition cameras, and acceleration sensors, etc.) according to the relevant data of the gate (such as size and shape, etc.);

[0155] S4. The route planning module plans the optimal travel route of the monitoring component 6 (including the settings of height, position, and tilt angle) according to the relevant data of the gate (such as size, shape, and position, etc.) and the monitoring requirements;

[0156] S5. The image analysis module preprocesses the collected images, extracts features from the preprocessed images, and the extracted features include color, texture, and shape; analyzes and identifies the preprocessed images to promptly identify abnormal conditions in the operation of the gate.

[0157] S6. The ultrasonic data analysis module analyzes the data collected by the ultrasonic sensors, and determines whether there are defects in the internal structure of the gate, such as cavities, delaminations, etc., by interpreting signal changes such as reflection and refraction when ultrasonic waves propagate inside the gate.

[0158] S7. The infrared data analysis module deeply analyzes the infrared data obtained by the infrared scanner, and identifies abnormal temperature regions by observing the surface temperature distribution of the gate.

[0159] S8. The comprehensive evaluation module fuses various types of sensor data from the data acquisition module, image feature data from the image analysis module, ultrasonic signal data from the ultrasonic data analysis module, and temperature data from the infrared data analysis module, etc. Comprehensively analyzes the operation of the gate from multiple dimensions, comprehensively evaluates the structural health status, operation stability, etc. of the gate, and gives a comprehensive and accurate evaluation result of the gate operation status.

[0160] S9. The risk prediction module fuses multi-source data, combines historical data, and predicts potential problems and risks.

[0161] S10. When abnormal conditions or potential risks are detected, the alarm module promptly issues an alarm.

[0162] The working principle of a gate safety monitoring system for water conservancy and hydropower of the present invention is as follows: The bracket 1 is fixedly installed above the water gate; the operation conditions on both the inner and outer sides of the gate are monitored by two monitoring components 6. The position of the monitoring component 6 can be adjusted by driving the moving driving mechanism 2, and the height of the monitoring component 6 can be adjusted by driving the multi-stage electric push rod 4; the tilt angle of the monitoring component 6 can be adjusted by driving the angle adjustment mechanism 5. The multi-stage electric push rod 4 on the inner side of the gate can drive the monitoring component 6 to extend into the water to monitor the underwater conditions on the inner side of the gate; the data acquisition module reasonably arranges the quantity and positions of sensors (such as strain gauges, laser rangefinders, high-definition cameras, and acceleration sensors, etc.) according to the relevant data of the gate (size, shape, etc.); the route planning module plans the optimal travel route of the monitoring component 6 (including the settings of height, position, and tilt angle) according to the relevant data of the gate (size, shape, position, etc.) and the monitoring requirements; the image analysis module preprocesses the collected images, extracts features from the preprocessed images, and the extracted features include color, texture, and shape; the preprocessed images are analyzed and recognized to timely identify abnormal conditions in the operation of the gate; the ultrasonic data analysis module analyzes the data collected by the ultrasonic sensor, and judges whether there are defects in the internal structure of the gate, such as cavities, delaminations, etc. by interpreting signal changes such as reflection and refraction when ultrasonic waves propagate inside the gate; the infrared data analysis module deeply analyzes the infrared data obtained by the infrared scanner, and identifies the temperature abnormal area by observing the surface temperature distribution of the gate. The comprehensive evaluation module fuses multi-source data such as various sensor data from the data acquisition module, image feature data from the image analysis module, ultrasonic signal data from the ultrasonic data analysis module, and temperature data from the infrared data analysis module. The operation conditions of the gate are comprehensively analyzed from multiple dimensions, and the structural health status, operation stability, etc. of the gate are comprehensively evaluated to give a comprehensive and accurate evaluation result of the gate operation status. The risk prediction module fuses multi-source data, combines historical data, and predicts potential problems and risks. When abnormal conditions or potential risks are detected, the alarm module issues an alarm in a timely manner.

[0163] The present invention can comprehensively monitor the gate in multiple dimensions. By installing a bracket above the water conservancy gate and equipping it with a monitoring component, using a mobile driving mechanism, a multi-stage electric push rod, and an angle adjustment mechanism, it can comprehensively monitor both sides of the gate from multiple dimensions such as horizontal position, height, and tilt angle, ensuring no monitoring dead angle and comprehensively grasping the operating state of the gate. The data acquisition module reasonably arranges various sensors such as strain gauges, laser rangefinders, high-definition cameras, and acceleration sensors according to data such as the size and shape of the gate. Different types of sensors can obtain information on various aspects such as stress and strain, displacement, appearance, and vibration of the gate. The multiple data complement each other to achieve precise perception of the operating condition of the gate. Intelligent data analysis can be carried out. The image analysis module preprocesses and extracts features from the collected images, and can quickly identify abnormal situations in the operation of the gate. The ultrasonic data analysis module judges internal structure defects by interpreting changes in ultrasonic signals. The infrared data analysis module identifies abnormal areas based on temperature distribution. These intelligent analysis means can timely discover problems, provide a basis for quickly taking maintenance measures, reduce maintenance costs, and improve the pertinence and effectiveness of operation and maintenance. The comprehensive evaluation module fuses various sensor data, image feature data, ultrasonic signal data, and temperature data, comprehensively analyzes from multiple dimensions, comprehensively evaluates the structural health status and operating stability of the gate, gives an accurate evaluation result, and provides a comprehensive and reliable basis for decision-making. The risk prediction module combines multi-source data and historical data to predict potential problems and risks, and the alarm module issues an alarm in a timely manner when abnormal or potential risks are detected. This early warning mechanism enables operation and maintenance personnel to take measures in advance to prevent accidents, ensure the safe and stable operation of the water conservancy gate, and avoid possible major losses.

[0164] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for safety monitoring of gates used in water conservancy and hydropower projects, characterized in that, It includes the following steps: S1. Fix and install the bracket above the water gate. S2. Two monitoring components monitor the operating conditions on both sides of the gate; the moving drive mechanism drives the monitoring components to adjust the position, the multi-stage electric push rod drives the monitoring components to adjust the height; the angle adjustment mechanism drives the monitoring components to adjust the tilt angle. S3. The data acquisition module reasonably arranges the quantity and positions of the sensors. S4. The route planning module plans the optimal travel route of the monitoring components. S5. The image analysis module preprocesses the images, extracts features and analyzes and identifies them, and promptly identifies abnormal conditions in the operation of the gate. S6. The ultrasonic data analysis module analyzes the ultrasonic data to judge whether there are defects in the internal structure of the gate. S7. The infrared data analysis module deeply analyzes the infrared data, observes the surface temperature distribution of the gate, and identifies the temperature abnormal areas. S8. The comprehensive evaluation module fuses multi-source data and comprehensively evaluates the structural health status of the gate. S9. The risk prediction module fuses multi-source data and combines historical data to predict potential problems and risks. S10. When abnormal conditions or potential risks are detected, the alarm module issues an alarm in a timely manner.

2. The method for safety monitoring of the gate for water conservancy and hydropower according to claim 1, characterized in that: Step S6 includes the following steps: S61. Data acquisition and collation: Collect ultrasonic sensor data and screen and collate it. S62. Signal preprocessing: including noise reduction processing and signal enhancement. S63. Reflection and refraction signal analysis: By comparing the reflection signal characteristics in the normal state, judge whether abnormal reflections occur; analyze the refraction phenomenon of ultrasonic waves during propagation; judge the uniformity of the internal structure of the gate. S64. Feature extraction S65. Abnormality judgment: Determine the range of various characteristic parameters of the ultrasonic signal under normal conditions, compare the characteristic parameters extracted from the currently monitored ultrasonic data with the normal standard range, and judge the abnormal conditions. S66. Report generation: Once an abnormality is identified, generate a detailed abnormality report.

3. The safety monitoring method for the water conservancy and hydropower gate according to claim 2, characterized in that: Step S64 includes the following steps: S64.

1. Amplitude feature extraction: Extract the amplitude information of the reflected wave and the refracted wave, and calculate the maximum amplitude, average amplitude, and standard deviation of the amplitude. S64.

2. Time feature extraction: Measure the propagation time of the reflected wave and the refracted wave, and calculate the transit time and delay time of the signal. S64.

3. Frequency feature extraction: Conduct spectral analysis on the ultrasonic signal, extract the frequency components of the signal, and perform frequency feature extraction according to the following formula: X(a,b) = [1 / sqt(|a|)]∑ ∞ t＝-∞ {x(t)Ψ[(t - b) / a]}; where x(t) represents the original time-domain ultrasonic signal; X(a,b) is the frequency-domain signal obtained after discrete wavelet transform; a is the scale parameter; b is the translation parameter; Ψ(t) is the wavelet basis function.

4. The safety monitoring method for the water conservancy and hydropower gate according to claim 1, characterized in that: Step S7 includes the following steps: S71. Data preprocessing: Filter, calibrate, and perform spatial interpolation on the collected infrared data. S72. When entering the water for monitoring, perform correction processing on the infrared data, and perform correction according to the following formula: T 正 = hc / {λk ln[2hc 2 / (I 水 C(t, s)λ 5 ) + 1]} + ΔT depth (d); △T depth (d) = T0e -bd ; where, T 正 is the finally obtained corrected temperature value; h is Planck's constant; c is the speed of light in vacuum; λ is the wavelength of infrared radiation; k is Boltzmann's constant; I 水 is the original infrared radiation intensity collected in water; C(t, s) is the correction coefficient determined according to the water quality parameters turbidity t and salinity s; △T depth (d) is the water temperature at the underwater depth d; T0 is the representative water surface temperature; e is the natural constant; b is a positive constant; d represents the water depth; S73. Temperature distribution visualization: Generate a visualization image of the surface temperature distribution of the gate based on the processed data; mark the key structural parts of the gate on the visualization image. S74. Abnormal area identification: Set the judgment threshold for temperature abnormality, traverse the entire temperature distribution data, compare the temperature value of each data point with the set threshold, and identify the temperature abnormal areas. S75, abnormal situation determination: for the identified abnormal temperature area, analyze the possible causes of the abnormal temperature increase, and determine whether it is an abnormal situation based on the analysis results; S76. Data storage: The temperature data, abnormal area information, and abnormal situation judgment result data obtained by scanning and analysis are stored in a database.

5. The gate safety monitoring method for water conservancy and hydropower according to claim 1, characterized in that: Step S8 includes the following steps: S81. Data collection and integration: Collect various sensor data from the data acquisition module, obtain image feature data from the image analysis module, obtain ultrasound signal data from the ultrasound data analysis module, obtain temperature data from the infrared data analysis module, and check and convert the collected multi-source data into different formats. S82. Data preprocessing: preprocess the data, including data cleaning and data normalization; S83, Feature extraction: Extract representative features from preprocessed data; S84, Data Fusion: Select a data fusion method that combines feature layer fusion and convolutional neural network (CNN) to fuse multi-source data; then use recurrent neural network (RNN) to process time series data; S85. Comprehensive analysis and evaluation: comprehensively analyze the fused data from multiple dimensions; conduct comprehensive evaluation according to the following formula: S 综合 = Σ k1 i＝1 w i Σ t l＝1 β t-l f il ; Σ k1 i＝1 w i = 1; In the formula, S 综合 is the comprehensive evaluation score; K1 represents the number of selected key features; w i is the weight of the i-th key feature; t represents the current time point; l represents the serial number of the time point in the time series; β is the time decay coefficient; f il represents the data value of the i-th key feature at the l-th time point; S86. Construct an evaluation model: Construct a gate operation status evaluation model based on the results of the multi-dimensional analysis; S87. Evaluation result output: Based on the comprehensive evaluation score calculated by the evaluation model, a comprehensive and accurate evaluation result of the gate operation status is given.

6. The safety monitoring method for the gate used in water conservancy and hydropower according to claim 1, characterized in that: Step S9 includes the following steps: S91. Data integration: Collect data from multiple sources, extract various types of data from the database over a period of time to form a historical data set; integrate real-time data with historical data; S92. Data preprocessing: Clean the data, including data cleaning and data normalization; S93. Feature Engineering: Extract trend features and correlation features; use principal component analysis (PCA) for feature selection; S94. Build a risk prediction model: Select a multi-layer perceptron (MLP) model and divide the preprocessed and feature-engineered data into a training set and a test set. Use the cross-entropy loss function to calculate the difference between the true labels of the training samples and the model's predicted output. Use the test set to evaluate the trained model. S95. Risk prediction: Input the data collected in real time and after preprocessing and feature engineering into the trained risk prediction model to predict potential risks.

7. The safety monitoring method for the gate used in water conservancy and hydropower according to claim 1, characterized in that: The bracket includes a U-shaped plate, a ladder frame and a sliding rod; The number of U-shaped plates, ladder frames and slide bars is two, and two slide bars are detachably fixed between the two ladder frames; the lower ends of the two ladder frames are fixed with U-shaped plates; The mobile driving mechanism includes a motor A and a screw rod; The motor A is fixedly arranged on the ladder frame of the bracket; the screw rod is rotatably arranged between the two ladder frames; the output end of the motor A is coaxially fixedly connected to the screw rod.

8. The method for safety monitoring of the gate for water conservancy and hydropower according to claim 7, characterized in that: The angle adjustment mechanism includes a motor B and gears; Motor B is fixedly mounted on the swivel seat, and a gear is coaxially fixedly mounted on the output end of motor B; the gear is meshed with the gear shaft for transmission connection; The monitoring component includes a fixing plate, an ultrasonic sensor, an infrared scanning head, a high-definition camera, a connecting rod, and a rotating sleeve; the ultrasonic sensor, the infrared scanning head, and the high-definition camera are fixedly arranged on the fixing plate; a connecting rod is fixedly arranged on the fixing plate, and a rotating sleeve is fixedly arranged at the other end of the connecting rod; a shaft rod is fixedly arranged on the gear shaft, and the shaft rod is detachably and fixedly connected to the rotating sleeve.

9. The safety monitoring method for the gate used in water conservancy and hydropower according to claim 1, characterized in that: The monitoring mechanism includes: Data collection module: Collect water area address data, water conservancy data, and gate-related data, and label the data as reference samples. Data acquisition module: Reasonably arrange the quantity and position of sensors according to the gate-related data. Route planning module: Plan the optimal travel route of the monitoring component according to the gate-related data and monitoring requirements. Image analysis module: Preprocess, extract features, and analyze and identify the collected images to promptly identify abnormal conditions in the operation of the gate. Ultrasonic data analysis module: Analyze the data collected by the ultrasonic sensor to judge whether there are defects in the internal structure of the gate. Infrared data analysis module: Deeply analyze the infrared data to identify areas with abnormal temperatures. Comprehensive evaluation module: Integrate multi-source data to comprehensively evaluate the structural health status and operation stability of the gate. Risk prediction module: Integrate multi-source data, combine with historical data, and predict potential problems and risks. Alarm module: When abnormal conditions or potential risks are detected, promptly issue an alarm. Control center: Network-connected to the data collection module, data acquisition module, image analysis module, ultrasonic data analysis module, infrared data analysis module, comprehensive evaluation module, risk prediction module, and alarm module.

10. A gate safety monitoring system for water conservancy and hydropower, comprising: A bracket, a mobile driving mechanism, a sliding seat, a multi-stage electric push rod, an angle adjustment mechanism, a monitoring component, a gear shaft, and a monitoring mechanism; characterized in that: A mobile driving mechanism is fixedly arranged on the bracket; a sliding seat is slidably arranged on the bracket; the sliding seat is in transmission connection with the mobile driving mechanism, and the mobile driving mechanism can drive the sliding seat to move; two multi-stage electric push rods are symmetrically and fixedly arranged below the sliding seat; a rotating seat is fixedly arranged at the lower end of the multi-stage electric push rod. An angle adjustment mechanism is fixedly arranged on the rotating seat; a gear shaft is rotatably arranged on the rotating seat, and a monitoring component is detachably and fixedly arranged on the gear shaft; the gear shaft is in meshing transmission connection with the angle adjustment mechanism; a transparent waterproof cover is arranged outside the monitoring component. A monitoring mechanism is fixedly arranged on the sliding seat, and the monitoring mechanism analyzes and evaluates the operation condition of the gate to promptly detect abnormal conditions.

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