Dam safety monitoring and evaluation method and system

Abstract

The invention relates to the technical field of water conservancy and hydropower, in particular to a dam safety monitoring and evaluating method and system.The method mainly comprises the steps that a current image of a dam is obtained and recognized, an image recognition result is output, and a dam safety evaluation result is obtained through an image analysis model and a dam safety evaluation model; according to the scheme, the current condition of the dam is evaluated, emergency danger, general danger, normal or possible danger signals are output, a worker can judge according to the signals and make corresponding treatment, and under certain conditions, the current general danger signals can be further promoted to the emergency danger signals, so that the safety of the dam is improved. And the early warning state can be output more accurately. By means of the method, manual real-time monitoring can be avoided, the burden of workers can be relieved, the working efficiency can be improved, meanwhile, errors of manual judgment can be avoided through judgment of the model, and the judgment accuracy can be improved as much as possible.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy and hydropower, and in particular to a dam safety monitoring and evaluation method and system. Background Art

[0002] A dam is an important water conservancy project facility, mainly used for various purposes such as water storage, power generation, flood control and irrigation. The construction technology of modern dams has undergone long-term development and progress, involving many fields such as civil engineering, structural engineering and environmental science. The types of dams mainly include gravity dams, arch dams and earth dams. Different types of dams are suitable for different terrains and hydrological conditions. Gravity dams resist water pressure through their own gravity, while arch dams use arch structures to convert water pressure into compressive stress on the mountain, which is suitable for construction in narrow areas such as canyons.

[0003] At present, the detection methods of dams mainly include physical detection, sensor monitoring and drone inspection. First of all, physical detection is a traditional means of dam safety monitoring, including visual inspection, ultrasonic detection and infrared imaging. Engineers evaluate the structural safety of the dam by regularly checking the cracks, leakage and settlement of the dam. Secondly, sensor monitoring technology has gradually become popular, and various sensors are used to monitor the operating status of the dam in real time. Common sensors include displacement sensors, inclinometers, pressure sensors and temperature sensors. These instruments can collect important data in real time and timely discover potential safety hazards through data analysis. In addition, drone inspection technology has also received more and more attention. Drones are equipped with high-definition cameras and lidars, which can take comprehensive and rapid aerial photos of dams, obtain high-resolution images and terrain data, and thus realize the evaluation of dam bodies.

[0004] These detection methods can effectively improve the scientificity and accuracy of dam safety management and ensure the long-term safety and stability of dams. In the future, with the development of technology, artificial intelligence and big data analysis may further improve the efficiency and accuracy of dam monitoring, thereby better preventing and responding to safety risks caused by natural disasters or human factors.

[0005] However, most of the problems with dam safety inspections are carried out through manual intervention, such as patrol inspections, or real-time image observation and monitoring in a monitoring room and using drones. The work efficiency is low and real-time manual inspection and observation are always required. Summary of the invention

[0006] The purpose of the present invention is to provide a dam safety monitoring and evaluation method and system to solve the above-mentioned problems in the prior art.

[0007] The embodiments of the present invention are implemented by the following technical solutions:

[0008] In a first aspect, the present invention provides a dam safety monitoring and evaluation method, comprising:

[0009] Obtain the current image of the dam, identify the current image, output the image recognition result, and determine whether cracks or seepage areas appear from the image recognition result;

[0010] Establish an image analysis model, if cracks or water seepage areas appear, obtain basic data of the cracks or water seepage areas, output a first evaluation value through the image analysis model, set a first evaluation value threshold, and output an emergency danger or general danger signal by comparing the first evaluation value threshold with the first evaluation value;

[0011] Establish a dam safety assessment model. If no cracks or seepage areas are found, obtain basic data of the current dam, output a second assessment value through the dam safety assessment model, set a second assessment value threshold, and output a normal or potentially dangerous signal by comparing the second assessment value threshold with the second assessment value.

[0012] If the output signal is a general danger, the first evaluation value is obtained, and the third evaluation value is output through the dam safety evaluation model. By comparing the second evaluation value threshold with the third evaluation value, a signal is output to maintain the current signal or to upgrade it to an emergency danger.

[0013] Preferably, the identifying the current image includes:

[0014] Establish a CNN-based image recognition neural network model, obtain historical images of the dam with cracks and seepage areas, train the image recognition neural network model, and output the best image recognition neural network model;

[0015] Through the current image of the dam, identify and extract the characteristic image of cracks or seepage areas;

[0016] Acquire a feature image with cracks and gaps, mark the starting point and end point of each crack and gap, and determine whether there are multiple end points in the same crack and gap;

[0017] If there are two or more end points, the characteristic image with cracks and gaps is output;

[0018] If there is only one end point, the output is a feature image with cracks and gaps to be confirmed, and the feature image with cracks and gaps to be confirmed is recognized and judged again.

[0019] Preferably, the re-identification and judgment of the characteristic image with cracks and gaps to be confirmed includes:

[0020] Acquire the characteristic image with cracks and gaps to be confirmed, establish a rectangular coordinate system, connect the marking points marked by the starting point and the end point, and obtain a comparison line segment;

[0021] Determine whether the comparison line segment and the extracted crack gap overlap, if so, determine it as a non-crack gap and delete the feature image;

[0022] If they do not overlap, the feature image with cracks and gaps is output.

[0023] Preferably, the determining whether the comparison line segment and the extracted crack gap overlap comprises:

[0024] The crack gap is divided into several equal standard segments, and the starting points and end points of the several equal standard segments are marked to obtain the slopes of the several standard segments. By comparing the slopes of the standard segments with the slopes of the comparison line segments, the number of standard segments whose slopes are inconsistent with the comparison line segments is obtained;

[0025] A similarity threshold is set and a similarity model is established. The similarity model includes:

[0026]

[0027] Where S is the similarity value, S1 is the similarity threshold, N is the number of standard segments, and n is the number of standard segments whose slopes are inconsistent with those of the comparison segment.

[0028] Preferably, it also includes:

[0029] Acquire a feature image with water seepage area, extract the primary water seepage area, and set the interval time for acquiring the secondary image;

[0030] After the interval time is reached, a secondary image is acquired, and the secondary water seepage area in the extracted image is identified;

[0031] Compare the secondary water seepage area with the primary water seepage area to determine whether the secondary water seepage area is smaller than the primary water seepage area. If it is not smaller than the primary water seepage area, output a feature image with the water seepage area.

[0032] If it is smaller than the primary water seepage area, the feature image with the water seepage area will not be output.

[0033] Preferably, the establishing of the image analysis model comprises:

[0034]

[0035] Where E1 is the first evaluation value, f is the number of cracks, g is the number of seepage areas, s is the area of ​​the seepage area, and s i is the area of ​​the i-th seepage area, c j is the value of the width of the jth crack.

[0036] Preferably, the first evaluation value threshold is:

[0037]

[0038] Where, E1 ′ is the first evaluation value threshold, s a is the value of the dam slope area, l is the value of the dam length;

[0039] If the first evaluation value exceeds the first evaluation value threshold, the output is urgent danger; if the first evaluation value does not exceed the first evaluation value threshold, the output is general danger.

[0040] Preferably, the establishing of the dam safety assessment model comprises:

[0041]

[0042] Where E2 is the second evaluation value, l s is the value of water level, Q v is the current temperature value, B is the current noise value, T is the vibration intensity value, when the first evaluation value is obtained, E1 takes the original value, when the first evaluation value is not obtained, E1 is 0.

[0043] Preferably, the second evaluation value threshold is:

[0044]

[0045] Where, E2 ′ is the second evaluation value threshold;

[0046] When the second evaluation value is greater than the second evaluation value threshold, a potentially dangerous signal is output; when the second evaluation value is not greater than the second evaluation value threshold, a normal signal is output;

[0047] When the third evaluation value is greater than the second evaluation value threshold, a signal is outputted to upgrade to an emergency danger level, and when the third evaluation value is not greater than the second evaluation value threshold, a signal is outputted to maintain the current signal.

[0048] In a second aspect, the present invention further provides a dam safety monitoring and evaluation system, comprising:

[0049] The image acquisition module is configured to acquire the current image of the dam, recognize the current image, output the image recognition result, and determine whether cracks or seepage areas appear from the image recognition result;

[0050] The first analysis module is configured to establish an image analysis model, and if cracks or water seepage areas appear, obtain basic data of the cracks or water seepage areas, output a first evaluation value through the image analysis model, and set a first evaluation value threshold, and output an urgent danger or general danger signal by comparing the first evaluation value threshold with the first evaluation value;

[0051] The second analysis module is configured to establish a dam safety evaluation model. If no cracks or seepage areas appear, basic data of the current dam is obtained, a second evaluation value is output through the dam safety evaluation model, and a second evaluation value threshold is set. By comparing the second evaluation value threshold with the second evaluation value, a normal or possibly dangerous signal is output;

[0052] The third analysis module is configured to obtain the first evaluation value if the output signal is a general danger, output the third evaluation value through the dam safety evaluation model, and output a signal to maintain the current signal or to upgrade to an emergency danger by comparing the second evaluation value threshold with the third evaluation value;

[0053] The main control module is connected with the image acquisition module, the first analysis module, the second analysis module and the third analysis module, and is used to execute the above-mentioned dam safety monitoring and evaluation method.

[0054] The technical solution of the embodiment of the present invention has at least the following advantages and beneficial effects:

[0055] The method provided by the present invention mainly includes: obtaining the current image of the dam, identifying the current image, outputting the image recognition result, evaluating the current situation of the dam through the image analysis model and the dam safety evaluation model, and outputting emergency danger, general danger, normal or possible danger signals. The staff can make judgments through the above signals and make corresponding disposals. Under certain conditions, this scheme can further upgrade the current general danger signal to an emergency danger signal, and achieve a more accurate output of the early warning status. Through the above method of the present invention, manual real-time monitoring can be avoided to a certain extent, the burden of the staff can be reduced, and work efficiency can be improved. At the same time, the judgment of the model can also avoid the error of manual judgment and improve the accuracy of judgment as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0057] Figure 1It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0059] The terms "first, second" and the like in the specification and claims of this application and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The named or numbered process steps can be changed in the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0060] The modules or submodules described independently may be physically separated or not: they may be implemented by software or hardware, and some modules or submodules may be implemented by software, and the processor may call the software to implement the functions of these modules or submodules, and other modules or submodules may be implemented by hardware, such as by hardware circuits. In addition, some or all of the modules may be selected according to actual needs to achieve the purpose of the present application.

[0061] Please refer to Figure 1 The present invention provides a dam safety monitoring and evaluation method, comprising:

[0062] S101: obtaining a current image of the dam, identifying the current image, outputting an image recognition result, and determining whether cracks or seepage areas appear from the image recognition result;

[0063] When a dam is in danger, cracks often appear in the dam, or water seepage occurs in some areas. In this embodiment, the image can be captured by a surveillance camera or a drone.

[0064] S102: Establishing an image analysis model, if cracks or water seepage areas appear, obtaining basic data of the cracks or water seepage areas, outputting a first evaluation value through the image analysis model, and setting a first evaluation value threshold, and outputting an emergency danger or general danger signal through comparison and judgment between the first evaluation value threshold and the first evaluation value;

[0065] In this embodiment, the emergency danger is more serious than the general danger. Accordingly, the emergency danger signal needs to remind the staff to take corresponding countermeasures immediately, and the general danger signal can remind the staff to increase the frequency of manual inspection.

[0066] S103: Establish a dam safety assessment model. If no cracks or seepage areas are found, obtain basic data of the current dam, output a second assessment value through the dam safety assessment model, set a second assessment value threshold, and output a normal or possibly dangerous signal by comparing the second assessment value threshold with the second assessment value.

[0067] In this embodiment, the urgency of a potentially dangerous signal is lower than that of a general danger. A potentially dangerous signal can correspondingly remind the staff to pay attention to the working status of the dam. If it is a normal signal, normal work can be carried out.

[0068] S104: If the output signal is a general danger, a first evaluation value is obtained, a third evaluation value is output through a dam safety evaluation model, and a signal is output to maintain the current signal or to upgrade to an emergency danger by comparing the second evaluation value threshold with the third evaluation value.

[0069] In this embodiment, if the result output by the image analysis model is a general danger, and after re-evaluation by the dam safety assessment model, it exceeds the set threshold, the current signal should be upgraded to an emergency danger.

[0070] In an exemplary embodiment of the present invention, recognizing the current image includes:

[0071] S201: Establish a CNN-based image recognition neural network model, obtain historical images of the dam with cracks and seepage areas, train the image recognition neural network model, and output the best image recognition neural network model;

[0072] In this example, the image recognition neural network model can adopt a conventional image recognition model, and after learning the historical images, it can identify and extract features, which will not be described in detail in this embodiment.

[0073] S202: using the current image of the dam, identifying and extracting characteristic images of cracks or seepage areas;

[0074] S203: Acquire a characteristic image with cracks and gaps, mark the starting point and end point of each crack and gap, and determine whether there are multiple end points in the same crack and gap;

[0075] S204: If there are two or more end points, output the feature image with the crack gap;

[0076] S205: If there is only one end point, the feature image with cracks and gaps to be confirmed is output, and the feature image with cracks and gaps to be confirmed is recognized and judged again.

[0077] Since the extracted images of some suspected cracks may not be real cracks but may be regular linear grooves and other structures on the dam, they are distinguished by the method provided in this embodiment.

[0078] The grooves that may interfere with the crack identification in the dam are generally regular and are a straight line with a starting point and an end point. Therefore, in this embodiment, the crack gaps with multiple end points are identified as real crack gaps.

[0079] Specifically, the re-identification and judgment of the confirmed feature image with cracks and gaps includes:

[0080] Obtain a feature image with cracks and gaps to be confirmed, establish a rectangular coordinate system, connect the marking points marked by the starting point and the end point, and obtain a comparison line segment; determine whether the comparison line segment coincides with the extracted cracks and gaps, if so, determine it as a non-crack gap and delete the feature image; if not, output the feature image with cracks and gaps.

[0081] In this embodiment, the grooves that cause interference are generally composed of straight line segments, and there will be no irregular lines with a certain degree of distortion such as cracks or gaps.

[0082] Specifically, judging whether the comparison line segment and the extracted crack gap coincide with each other includes:

[0083] The crack gap is divided into several equal standard segments, and the starting points and end points of the several equal standard segments are marked to obtain the slopes of the several standard segments. By comparing the slopes of the standard segments with the slopes of the comparison line segments, the number of standard segments whose slopes are inconsistent with the comparison line segments is obtained;

[0084] A similarity threshold is set and a similarity model is established. The similarity model includes:

[0085]

[0086] Where S is the similarity value, S1 is the similarity threshold, N is the number of standard segments, and n is the number of standard segments whose slopes are inconsistent with those of the comparison segment.

[0087] An exemplary embodiment of the present invention further includes:

[0088] Acquire a feature image with water seepage area, extract the primary water seepage area, and set the interval time for acquiring the secondary image;

[0089] After the interval time is reached, a secondary image is acquired, and the secondary water seepage area in the extracted image is identified;

[0090] Compare the secondary water seepage area with the primary water seepage area to determine whether the secondary water seepage area is smaller than the primary water seepage area. If it is not smaller than the primary water seepage area, output a feature image with the water seepage area.

[0091] If it is smaller than the primary water seepage area, the feature image with the water seepage area will not be output.

[0092] Similarly, in this embodiment, some misjudged water seepage areas need to be eliminated. If the initially judged water seepage area is gradually decreasing, it can be judged as a non-water seepage area. In addition, this judgment step may not be used when it rains, and accurate judgment may not be possible at this time.

[0093] In an exemplary embodiment of the present invention, establishing an image analysis model comprises:

[0094]

[0095] Where E1 is the first evaluation value, f is the number of cracks, g is the number of seepage areas, s is the area of ​​the seepage area, and s i is the area of ​​the i-th seepage area, c j is the value of the width of the jth crack.

[0096] In an exemplary embodiment of the present invention, the first evaluation value threshold is:

[0097]

[0098] Where, E1 ′ is the first evaluation value threshold, s a is the value of the dam slope area, l is the value of the dam length;

[0099] If the first evaluation value exceeds the first evaluation value threshold, the output is urgent danger; if the first evaluation value does not exceed the first evaluation value threshold, the output is general danger.

[0100] In an exemplary embodiment of the present invention, the establishment of a dam safety assessment model comprises:

[0101]

[0102] Where E2 is the second evaluation value, l s is the value of water level, Q v is the current temperature value, B is the current noise value, T is the vibration intensity value, when the first evaluation value is obtained, E1 takes the original value, when the first evaluation value is not obtained, E1 is 0.

[0103] In an exemplary embodiment of the present invention, the second evaluation value threshold is:

[0104]

[0105] Where, E2 ′ is the second evaluation value threshold;

[0106] When the second evaluation value is greater than the second evaluation value threshold, a potentially dangerous signal is output; when the second evaluation value is not greater than the second evaluation value threshold, a normal signal is output;

[0107] When the third evaluation value is greater than the second evaluation value threshold, a signal is outputted to upgrade to an emergency danger level, and when the third evaluation value is not greater than the second evaluation value threshold, a signal is outputted to maintain the current signal.

[0108] It should be noted that the first evaluation value threshold and the second evaluation value threshold in this implementation are reference settings of this scheme, and different thresholds can be set according to different situations. For example, after collecting data on historical dams when problems are about to occur, specific data can be obtained through the model provided by the present invention, which can be used as the setting of the first evaluation value threshold and the second evaluation value threshold.

[0109] In addition, the present invention also provides a dam safety monitoring and evaluation system, comprising:

[0110] The image acquisition module is configured to acquire the current image of the dam, recognize the current image, output the image recognition result, and determine whether cracks or seepage areas appear from the image recognition result;

[0111] The first analysis module is configured to establish an image analysis model, and if cracks or water seepage areas appear, obtain basic data of the cracks or water seepage areas, output a first evaluation value through the image analysis model, and set a first evaluation value threshold, and output an urgent danger or general danger signal by comparing the first evaluation value threshold with the first evaluation value;

[0112] The second analysis module is configured to establish a dam safety evaluation model. If no cracks or seepage areas appear, basic data of the current dam is obtained, a second evaluation value is output through the dam safety evaluation model, and a second evaluation value threshold is set. By comparing the second evaluation value threshold with the second evaluation value, a normal or possibly dangerous signal is output;

[0113] The third analysis module is configured to obtain the first evaluation value if the output signal is a general danger, output the third evaluation value through the dam safety evaluation model, and output a signal to maintain the current signal or to upgrade to an emergency danger by comparing the second evaluation value threshold with the third evaluation value;

[0114] A main control module is connected to the image acquisition module, the first analysis module, the second analysis module and the third analysis module, and is used to execute the above-mentioned dam safety monitoring and evaluation method.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0117] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dam safety monitoring and evaluation method, characterized in that: include: Obtain the current image of the dam, identify the current image, output the image recognition result, and determine whether cracks or seepage areas appear from the image recognition result; Establish an image analysis model, if cracks or water seepage areas appear, obtain basic data of the cracks or water seepage areas, output a first evaluation value through the image analysis model, set a first evaluation value threshold, and output an emergency danger or general danger signal by comparing the first evaluation value threshold with the first evaluation value; Establish a dam safety assessment model. If no cracks or seepage areas are found, obtain basic data of the current dam, output a second assessment value through the dam safety assessment model, set a second assessment value threshold, and output a normal or potentially dangerous signal by comparing the second assessment value threshold with the second assessment value. If the output signal is a general danger, the first evaluation value is obtained, and the third evaluation value is output through the dam safety evaluation model. By comparing the second evaluation value threshold with the third evaluation value, a signal is output to maintain the current signal or to upgrade it to an emergency danger.

2. A dam safety monitoring and evaluation method according to claim 1, characterized in that: The identifying of the current image comprises: Establish a CNN-based image recognition neural network model, obtain historical images of the dam with cracks and seepage areas, train the image recognition neural network model, and output the best image recognition neural network model; Through the current image of the dam, identify and extract the characteristic image of cracks or seepage areas; Acquire a feature image with cracks and gaps, mark the starting point and end point of each crack and gap, and determine whether there are multiple end points in the same crack and gap; If there are two or more end points, the characteristic image with cracks and gaps is output; If there is only one end point, the output is a feature image with cracks and gaps to be confirmed, and the feature image with cracks and gaps to be confirmed is recognized and judged again.

3. A dam safety monitoring and evaluation method according to claim 2, characterized in that: The re-identification and judgment of the feature image with cracks and gaps to be confirmed includes: Acquire the characteristic image with cracks and gaps to be confirmed, establish a rectangular coordinate system, connect the marking points marked by the starting point and the end point, and obtain a comparison line segment; Determine whether the comparison line segment and the extracted crack gap overlap, if so, determine it as a non-crack gap and delete the feature image; If they do not overlap, the feature image with cracks and gaps is output.

4. A dam safety monitoring and evaluation method according to claim 3, characterized in that: The determining whether the comparison line segment and the extracted crack gap overlap includes: The crack gap is divided into several equal standard segments, and the starting points and end points of the several equal standard segments are marked to obtain the slopes of the several standard segments. By comparing the slopes of the standard segments with the slopes of the comparison line segments, the number of standard segments whose slopes are inconsistent with the comparison line segments is obtained; A similarity threshold is set and a similarity model is established. The similarity model includes: Where S is the similarity value, S1 is the similarity threshold, N is the number of standard segments, and n is the number of standard segments whose slopes are inconsistent with those of the comparison segment.

5. A dam safety monitoring and evaluation method according to claim 2, characterized in that: Also includes: Acquire a feature image with water seepage area, extract the primary water seepage area, and set the interval time for acquiring the secondary image; After the interval time is reached, a secondary image is acquired, and the secondary water seepage area in the extracted image is identified; Compare the secondary water seepage area with the primary water seepage area to determine whether the secondary water seepage area is smaller than the primary water seepage area. If it is not smaller than the primary water seepage area, output a feature image with the water seepage area. If it is smaller than the primary water seepage area, the feature image with the water seepage area will not be output.

6. A dam safety monitoring and evaluation method according to claim 1, characterized in that: The image analysis model is established comprising: Where E1 is the first evaluation value, f is the number of cracks, g is the number of seepage areas, s is the area of ​​the seepage area, and s i is the area of ​​the i-th seepage area, c j is the value of the width of the jth crack.

7. A dam safety monitoring and evaluation method according to claim 6, characterized in that: The first evaluation value threshold is: Where, E1 ′ is the first evaluation value threshold, s a is the value of the dam slope area, l is the value of the dam length; If the first evaluation value exceeds the first evaluation value threshold, the output is urgent danger; if the first evaluation value does not exceed the first evaluation value threshold, the output is general danger.

8. A dam safety monitoring and evaluation method according to claim 7, characterized in that: The establishment of the dam safety assessment model comprises: Where E2 is the second evaluation value, l s is the value of water level, Q v is the current temperature value, B is the current noise value, T is the vibration intensity value, when the first evaluation value is obtained, E1 takes the original value, when the first evaluation value is not obtained, E1 is 0.

9. A dam safety monitoring and evaluation method according to claim 8, characterized in that: The second evaluation value threshold is: Where, E2 ′ is the second evaluation value threshold; When the second evaluation value is greater than the second evaluation value threshold, a potentially dangerous signal is output; when the second evaluation value is not greater than the second evaluation value threshold, a normal signal is output; When the third evaluation value is greater than the second evaluation value threshold, a signal is outputted to upgrade to an emergency danger level, and when the third evaluation value is not greater than the second evaluation value threshold, a signal is outputted to maintain the current signal.

10. A dam safety monitoring and evaluation system, characterized in that: include: The image acquisition module is configured to acquire the current image of the dam, recognize the current image, output the image recognition result, and determine whether cracks or seepage areas appear from the image recognition result; The first analysis module is configured to establish an image analysis model, and if cracks or water seepage areas appear, obtain basic data of the cracks or water seepage areas, output a first evaluation value through the image analysis model, and set a first evaluation value threshold, and output an urgent danger or general danger signal by comparing the first evaluation value threshold with the first evaluation value; The second analysis module is configured to establish a dam safety evaluation model. If no cracks or seepage areas appear, basic data of the current dam is obtained, a second evaluation value is output through the dam safety evaluation model, and a second evaluation value threshold is set. By comparing the second evaluation value threshold with the second evaluation value, a normal or possibly dangerous signal is output; The third analysis module is configured to obtain the first evaluation value if the output signal is a general danger, output the third evaluation value through the dam safety evaluation model, and output a signal to maintain the current signal or to upgrade to an emergency danger by comparing the second evaluation value threshold with the third evaluation value; A main control module is connected to the image acquisition module, the first analysis module, the second analysis module and the third analysis module, and is used to execute a dam safety monitoring and evaluation method as described in any one of claims 1-9.

Images