Dam crack monitoring and early warning system and method

Abstract

The invention discloses a dam crack monitoring and early warning system and method, and relates to the technical field of crack monitoring. Related data of a dam is collected through a data collection module, and then a data processing module is used for processing and calculating to obtain a comprehensive distance coefficient of the dam; a crack analysis module is used for comparing with the dam comprehensive distance coefficient in a threshold setting mode to determine whether the dam has cracks or not, then a horizontal proportion coefficient and a vertical proportion coefficient are set for comparison again to determine the type of the dam cracks, and crack related index data are collected through a crack quality judgment module; a crack comprehensive index is calculated to obtain a crack comprehensive index size coefficient, then a crack standard judgment result is obtained through output of a crack standard judgment model, image line matching is conducted through an image collecting and processing module to obtain a final crack image, and an early warning module is used for giving an alarm for the grade type of the image. The functions of detecting the grade type of the dam crack and performing early warning are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of crack monitoring, and in particular to a dam crack monitoring and early warning system and method. Background Art

[0002] In recent years, the economic losses caused by the collapse of river dams due to typhoons and heavy rains have become increasingly serious, and local safety inspection requirements for flood control dikes and seawalls have become increasingly stringent.

[0003] At present, most dam crack monitoring methods are combined with manual observation and manual inspection to ensure the safety of the dam. However, manual observation and manual inspection are affected by the dam area environment, weather, light, low observation accuracy, high labor intensity, and observation is greatly affected by the environment, resulting in low efficiency of dam crack monitoring. Summary of the invention

[0004] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a dam crack monitoring and early warning system and method, which can detect the grade type of dam cracks and issue an early warning.

[0005] In the first aspect, the purpose of the present invention can be achieved by the following technical solution: a dam crack monitoring and early warning system, comprising: Crack analysis module: used to set the comprehensive distance threshold of the dam, compare the comprehensive distance coefficient of the dam with the comprehensive distance threshold of the dam, and determine whether there are cracks in the dam according to the comparison result. If there are cracks, the horizontal scale coefficient and the vertical scale coefficient are set and then compared again. The crack type is determined according to the comparison result, and different types of crack signals are sent to the image acquisition and processing module, and the crack quality determination signal is sent to the crack quality determination module; The calculation process of the comprehensive distance coefficient of the dam is as follows: In the formula, Si is the horizontal distance of the dam, Ci is the vertical distance of the dam, Zi is the comprehensive distance coefficient of the dam, S0 is the preset standard horizontal coefficient, C0 is the preset standard vertical coefficient, k1 is the horizontal distance influence coefficient, k2 is the vertical distance influence coefficient, ln() is the logarithmic function with the natural constant e as the base, and t is the preset proportional coefficient; where i is the number of times the dam-related data is collected, and i=1, 2, 3,..., n, and n is the total number of times the dam-related data is collected; Crack quality determination module: used to collect crack-related index data, including crack size data and crack inclination, pre-process the crack-related index data to obtain processed crack-related index data, mark the processed crack size data, calculate the crack comprehensive index using the marked crack size data, obtain the crack comprehensive index size coefficient, input the crack comprehensive index size coefficient and the crack inclination into a pre-established crack standard discrimination model, output the crack standard discrimination result, and send the crack standard discrimination result to the image acquisition and processing module; Image acquisition and processing module: used to acquire images corresponding to different types of crack signals as the first image, and images corresponding to the crack standard discrimination results as the second image, match the first image with the second image, obtain the final crack image, and send the final crack image to the early warning module; Early warning module: used to issue voice alarm prompts according to the grade type of the final crack image.

[0006] In combination with the first aspect, in some implementations of the first aspect, the system further includes: a process in which the data processing module performs coordinate distance processing according to the dam-related data: Data acquisition module: used to collect dam-related data and send the dam-related data to the data processing module, wherein the dam-related data includes: a plurality of dam horizontal coordinates and a plurality of dam vertical coordinates; Data processing module: used to process coordinate distance according to dam-related data, obtain horizontal distance and vertical distance of dam, calculate comprehensive distance of dam based on horizontal distance and vertical distance of dam, obtain comprehensive distance coefficient of dam, and send comprehensive distance coefficient of dam to crack analysis module.

[0007] In combination with the first aspect, in some implementations of the first aspect, the system further includes: a process in which the data processing module of the data processing module performs coordinate distance processing according to the dam-related data: According to the horizontal coordinates of multiple dams in the dam-related data, the coordinates of the points with the largest distance between the two horizontal coordinates are determined, marked as two horizontal extreme value points, and the horizontal distance of the dam is determined according to the coordinates of the two horizontal extreme value points; According to the vertical coordinates of multiple dams in the dam-related data, the coordinates of the points with the largest vertical coordinate distance are determined, marked as two vertical extreme points, and the vertical distance of the dam is determined according to the coordinates of the two vertical extreme points.

[0008] In combination with the first aspect, in some implementations of the first aspect, the system further includes: the analysis process of the crack analysis module includes: Set the comprehensive distance threshold of the embankment Z0; If Zi≤Z0, it is determined that there are no cracks in the dam, and the crack analysis module sends a no-crack signal to the early warning module; If Zi>Z0, it is determined that there are cracks in the dam, and the type of cracks is further determined; Set the horizontal scale factor P1 and the vertical scale factor P2; wherein P2>P1>1; like , then the dam crack is determined to be a horizontal crack, and a horizontal crack signal is sent to the image acquisition and processing module. like , then the dam crack is determined to be a vertical crack, and a vertical crack signal is sent to the image acquisition and processing module. like , then the dam crack is determined to be a tortoise crack, and a tortoise crack signal is sent to the image acquisition and processing module; Furthermore, a crack quality determination signal is sent to the crack quality determination module.

[0009] In combination with the first aspect, in some implementations of the first aspect, the system further includes: a process in which the crack quality determination module performs identification processing on the processed crack size data: The crack length data is marked as Lj, the crack width data is marked as Dj, and the crack depth data is marked as Sj, where j is the number of crack size data collected, and j=1, 2, 3, ..., m, and m is the total number of crack size data collected.

[0010] In combination with the first aspect, in certain implementations of the first aspect, the system further includes: a crack comprehensive index calculation process of the crack quality determination module is as follows: Where Fj is the comprehensive index size coefficient of cracks, R1 is the influence coefficient of crack length, R2 is the influence coefficient of crack width, R3 is the influence coefficient of crack depth, and α is the preset correlation coefficient.

[0011] In combination with the first aspect, in certain implementations of the first aspect, the system further includes: a process in which the crack standard discrimination model of the crack quality determination module is trained based on an artificial intelligence model: Obtaining preset standard crack related indicators through a server, wherein the crack related indicators include a preset standard crack size and a preset standard crack inclination; The artificial intelligence model is trained through preset standard crack-related indicators to obtain and store a crack standard discrimination model.

[0012] In combination with the first aspect, in certain implementations of the first aspect, the system further includes: the crack standard identification result includes: first-level cracks, second-level cracks, and third-level cracks; The types of the final crack images include: first-level horizontal cracks, second-level horizontal cracks, third-level horizontal cracks, first-level vertical cracks, second-level vertical cracks, third-level vertical cracks, first-level tortoise cracks, second-level tortoise cracks, and third-level tortoise cracks.

[0013] In a second aspect, in order to achieve the above-mentioned purpose, the present invention discloses a dam crack monitoring and early warning method, the method comprising the following steps: Obtain dam-related data, including multiple horizontal dam coordinates and multiple vertical dam coordinates, perform coordinate distance processing on the dam-related data, and then perform dam comprehensive distance calculation to obtain the dam comprehensive distance coefficient; The calculation process of the comprehensive distance coefficient of the dam is as follows: In the formula, Si is the horizontal distance of the dam, Ci is the vertical distance of the dam, Zi is the comprehensive distance coefficient of the dam, S0 is the preset standard horizontal coefficient, C0 is the preset standard vertical coefficient, k1 is the horizontal distance influence coefficient, k2 is the vertical distance influence coefficient, ln() is the logarithmic function with the natural constant e as the base, and t is the preset proportional coefficient; where i is the number of times the dam-related data is collected, and i=1, 2, 3,..., n, and n is the total number of times the dam-related data is collected; A dam comprehensive distance threshold is set. If the dam comprehensive distance coefficient is less than or equal to the dam comprehensive distance threshold, it is determined that there is no crack. If the dam comprehensive distance coefficient is greater than the dam comprehensive distance threshold, it is determined that there is a crack. A horizontal scale factor and a vertical scale factor are set, and the product of the set coefficient and the embankment comprehensive distance threshold is compared with the embankment comprehensive distance coefficient, and the crack type is determined according to the comparison result, wherein the vertical scale factor is greater than the horizontal scale factor and is greater than one; Acquire crack-related index data and perform preprocessing to obtain processed crack-related index data, wherein the crack-related index data includes crack size data and crack inclination, and then use the crack size data to calculate the crack comprehensive index to obtain the crack comprehensive index size coefficient; The comprehensive crack index size coefficient and crack inclination are input into the pre-established crack standard discrimination model, and the crack standard discrimination result is output. The image of the crack standard discrimination result is matched with the images of different crack types to obtain the final crack image, and a voice alarm prompt is given according to the grade type of the final crack image.

[0014] Beneficial effects of the present invention: The present invention collects dam-related data through a data acquisition module, and then uses a data processing module to process and calculate to obtain a comprehensive distance coefficient of the dam, and then uses a crack analysis module to compare with the comprehensive distance coefficient of the dam by setting a threshold value to determine whether there are cracks in the dam, and then sets a horizontal scale coefficient and a vertical scale coefficient to perform another comparison to determine the type of dam cracks, and then uses a crack quality judgment module to collect crack-related index data, and performs crack comprehensive index calculation to obtain a crack comprehensive index size coefficient, and then uses a crack standard discrimination model output to obtain a crack standard discrimination result, and then uses an image acquisition and processing module to perform image row matching to obtain a final crack image, and uses an early warning module to give an alarm prompt for the grade type of the image, thereby realizing the function of being able to detect the grade type of dam cracks and provide an early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work. Figure 1 It is a schematic diagram of the system structure of the present invention; Figure 2 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: The following is an introduction to the relevant terms involved in the embodiments of the present application: ‌Dam is a building and structure for preventing water from flowing, which is mainly divided into two categories: earth-rock dam and concrete dam.‌

[0018] A dam is a structure used to prevent water from flowing. It is designed and built to prevent water from eroding the land and protect the surrounding area from flooding. There are two main types of dams: earth-rock dams and concrete dams. Earth-rock dams are usually built with earth or stone. Because the water pressure at the bottom is greater, the bottom is wider than the top. This type of dam is often built across a large river and uses common and cheap materials. Because the material is relatively loose, earth-rock dams can withstand the shaking of the foundation, but water will slowly seep into the dam, reducing its strength, so engineers will add a layer of waterproof clay on the surface of the dam or design some channels for water to flow away.

[0019] Horizontal cracks (‌ transverse cracks) are perpendicular or oblique to the dam axis. They often appear on the top of the dam and extend into the dam to a certain depth. In severe cases, they can develop to the dam slope, or even penetrate upstream and downstream to cause concentrated leakage, directly endangering the safety of the dam. The main reason for the generation of transverse cracks is the large uneven settlement of the foundation of adjacent dam sections.

[0020] Vertical cracks (longitudinal cracks) are parallel or nearly parallel to the axis of the dam. They often appear on the top of the dam or on the upper part of the dam slope. The cracks gradually extend vertically into the dam body. Longitudinal cracks are generally longer than transverse cracks. If not handled in time, rainwater intrusion will cause the dam to fall off the slope. The causes of longitudinal cracks include factors such as staged heightening, compaction quality and different filling materials.

[0021] Tortoise cracks are also called web cracks or chapped cracks. They are irregular cracks that are connected to form tortoise shell patterns, and the short side length is no more than 40cm. If there are parallel and dense cracks in the longitudinal direction of the road surface, although they have not formed a network, but the distance between them is no more than 30cm, they are all tortoise cracks.

[0022] like Figure 1 As shown, a dam crack monitoring and early warning system comprises: Data acquisition module, data processing module, crack analysis module, crack quality determination module, image acquisition and processing module and early warning module; The data acquisition module is used to collect dam-related data, and send the acquired dam-related data to the data processing module for processing, wherein the dam-related data includes: a plurality of dam horizontal coordinates and a plurality of dam vertical coordinates; It should be noted that the data acquisition module collects dam-related data in the following process: by establishing a plane rectangular coordinate system, with the horizontal direction being the X-axis and the vertical direction being the Y-axis, matching the plane rectangular coordinate system with the dam, and determining the coordinates of multiple points on the dam on the X-axis as multiple horizontal coordinates of the dam, and determining the coordinates of multiple points on the dam on the Y-axis as multiple vertical coordinates of the dam; After receiving the dam-related data sent by the data acquisition module, the data processing module performs data processing. Specifically, the processing process of the data processing module includes the following steps: According to the horizontal coordinates of multiple dams in the dam related data, the coordinates of the points with the largest distance between the two horizontal coordinates are determined, which are marked as two horizontal extreme value points. The horizontal distance of the dam is determined according to the coordinates of the two horizontal extreme value points; marked as Si; According to the vertical coordinates of multiple dams in the dam related data, the coordinates of the points with the largest vertical coordinates are determined, marked as two vertical extreme value points, and the vertical distance of the dam is determined according to the coordinates of the two vertical extreme value points; marked as Ci; Among them, i is the number of times the dam-related data are collected, and i=1, 2, 3, ..., n, and n is the total number of times the dam-related data are collected.

[0023] The obtained horizontal distance Si and vertical distance Ci of the dam are used to calculate the comprehensive distance of the dam, and the comprehensive distance coefficient of the dam is obtained. Specifically, the calculation process of the comprehensive distance of the dam in the data processing module is as follows: Wherein, Zi is the comprehensive distance coefficient of the embankment, S0 is the preset standard horizontal coefficient, C0 is the preset standard vertical coefficient, k1 is the horizontal distance influence coefficient, k2 is the vertical distance influence coefficient, ln() is the logarithmic function with the natural constant e as the base, and t is the preset proportional coefficient; Furthermore, in the specific implementation process, the preset standard horizontal coefficient and the preset standard vertical coefficient are obtained by multiple simulation calculations and data averages after the horizontal distance and vertical distance are obtained through daily collection; Among them, in this embodiment, the horizontal distance influence coefficient and the vertical distance influence coefficient are obtained by the application through daily acquisition of horizontal distance and vertical distance, and are calculated based on the comprehensive evaluation of external factors, including human factors, machine detection, and environmental factors, etc.; human factors are caused by human operation or improper scanning; the preset proportional coefficient is the proportional value of the comprehensive calculation obtained by multiple simulation calculations; The data processing module uses the calculated comprehensive distance coefficient Zi of the dam to send it to the crack analysis module; After receiving the dam comprehensive distance coefficient Zi sent by the data processing module, the crack analysis module performs crack analysis. Specifically, the analysis process of the crack analysis module includes the following steps: Set the comprehensive distance threshold of the embankment Z0; The dam comprehensive distance coefficient Zi is compared with the dam comprehensive distance threshold Z0, and the dam is determined to determine whether there are cracks and the type of cracks according to the comparison result, and different types of crack signals are sent to the image acquisition and processing module. The process is as follows: If Zi≤Z0, it is determined that there are no cracks in the dam, and the crack analysis module sends a no-crack signal to the early warning module; If Zi>Z0, it is determined that there are cracks in the dam, and the type of cracks needs to be further determined; Set the horizontal scale factor P1 and the vertical scale factor P2; wherein P2>P1>1; It should be noted that, in this embodiment, the horizontal proportional coefficient and the vertical proportional coefficient are obtained by performing multiple simulation calculations on the relevant data of horizontal cracks and vertical cracks according to the ratio to the set dam comprehensive distance threshold when the relevant data of horizontal cracks and vertical cracks are obtained daily; like , it means that the crack only exists in the horizontal direction at this time, and the dam crack is determined to be a horizontal crack. The crack analysis module sends a horizontal crack signal to the image acquisition and processing module, and sends a crack quality determination signal to the crack quality determination module; like , it means that the crack only exists in the vertical direction at this time, and the dam crack is determined to be a vertical crack. The crack analysis module sends a vertical crack signal to the image acquisition and processing module, and sends a crack quality determination signal to the crack quality determination module; like , it means that the cracks exist in multiple directions at this time, and the dam cracks are judged to be tortoise cracks. The crack analysis module sends tortoise crack signals to the image acquisition and processing module, and sends crack quality determination signals to the crack quality determination module; After receiving the crack quality determination signal, the crack quality determination module performs quality determination on the dam crack. Specifically, the process of quality determination by the crack quality determination module includes the following steps: Using a data collection center to collect fracture-related index data; wherein the fracture-related index data includes: fracture size data and fracture inclination, wherein the fracture size data includes: fracture length data, fracture width data, and fracture depth data; Among them, in this embodiment, the length of the crack is generally measured with a precision steel ruler. The width of the crack is generally detected using a reading magnifying glass or an electronic width gauge. For cracks with larger widths and lower precision requirements, the depth of the crack can be estimated by inserting a feeler gauge into the crack. For cracks with higher precision requirements, special instruments should be used for detection. In order to improve the accuracy of detection, this embodiment uses the ultrasonic method to measure the depth of the crack using the speed and attenuation characteristics of ultrasonic waves propagating in concrete. Preprocessing the crack-related index data to obtain processed crack-related index data; Wherein, the preprocessing mainly includes: Data cleaning, data integration, data transformation, data reduction, etc. These data processing technologies are used before data mining, which greatly improves the quality of data mining patterns and reduces the time required for actual mining.

[0024] In this embodiment, the crack-related indicator data is mainly cleaned. The data cleaning routine "cleans" the data by filling in missing values, smoothing noisy data, identifying or deleting outliers, and resolving inconsistencies. The main goals are to achieve the following: format standardization, abnormal data removal, error correction, and duplicate data removal.

[0025] The processed crack size data is labeled, wherein the crack length data is labeled as Lj, the crack width data is labeled as Dj, and the crack depth data is labeled as Sj, wherein j is the number of crack size data collected, and j=1, 2, 3, ..., m, and m is the total number of crack size data collected; The crack comprehensive index is calculated using the marked crack size data to obtain the crack comprehensive index size coefficient. The crack comprehensive index calculation process of the crack quality judgment module is as follows: Where, Fj is the size coefficient of the comprehensive crack index, R1 is the influence coefficient of the crack length, R2 is the influence coefficient of the crack width, R3 is the influence coefficient of the crack depth, and α is the preset correlation coefficient; Among them, in this embodiment, the crack length influence coefficient, the crack width influence coefficient and the crack depth influence coefficient are obtained by comprehensive evaluation and calculation based on the influence of external factors when the crack length data, crack width data and crack depth data are obtained daily by this application, including human factors, machine detection and environmental factors, etc.; human factors are caused by human operation or improper scanning; the preset correlation coefficient is a value related to the size during the comprehensive calculation obtained by multiple simulation calculations; The calculated fracture comprehensive index size coefficient and fracture inclination angle are input into a pre-established fracture standard discrimination model, and the fracture standard discrimination result is output; The crack standard identification results include cracks of different levels, including: first-level cracks, second-level cracks, and third-level cracks; Send the crack standard identification result to the image acquisition and processing module; Wherein, in this embodiment, the crack standard discrimination model is based on artificial intelligence model training; The process of training the crack standard discrimination model based on the artificial intelligence model: Obtaining preset standard crack related indicators through a server, wherein the crack related indicators include a preset standard crack size and a preset standard crack inclination; The artificial intelligence model is trained by using preset standard crack-related indicators to obtain and store a crack standard discrimination model; wherein the artificial intelligence model includes a deep convolutional neural network model and an RBF neural network model.

[0026] After receiving different types of crack signals sent by the crack analysis module and the crack standard identification results sent by the crack quality determination module, the image acquisition and processing module acquires images corresponding to different types of crack signals as first images, and images corresponding to the crack standard identification results as second images, matches the first image with the second image, and obtains a final crack image, wherein the types of the final crack image include: first-level horizontal cracks, second-level horizontal cracks, third-level horizontal cracks, first-level vertical cracks, second-level vertical cracks, third-level vertical cracks, first-level tortoise cracks, second-level tortoise cracks, and third-level tortoise cracks; and sends the final crack image to the early warning module; After receiving the final crack image sent by the image acquisition and processing module, the early warning module issues a voice alarm prompt according to the grade type of the final crack image.

[0027] Specifically, the present invention is further described below by way of embodiments: By designing a mobile platform with multiple degrees of freedom (such as horizontal movement, vertical lifting, rotation, etc.), the detection equipment can be moved flexibly on the surface of the dam and penetrate into hard-to-reach areas for detection.

[0028] The data acquisition module and the crack data determination module include multiple sensor modules, each of which has an independent data processing unit that can analyze and collect and preliminarily determine crack information in real time.

[0029] By designing a telescopic detection arm: (it is necessary to consider how the detection arm can detect the depth and width) equipped with a telescopic detection arm, the length and angle can be automatically adjusted according to the depth and position of the crack, ensuring that the sensor can accurately contact and detect the depth and width of the crack. Optical fibers or wires can be integrated inside the detection arm for transmitting data and power. This enables the collection and detection of the depth and width of the crack; Specifically, an automated warning and marking device can be designed to automatically trigger a sound alarm when a crack is detected. At the same time, a marking spraying mechanism can be provided to automatically spray marking liquids of different colors according to the size of the crack for subsequent processing.

[0030] Embodiment 2: A dam crack monitoring and early warning method, the method comprising the following steps: S101: obtaining dam-related data, including multiple dam horizontal coordinates and multiple dam vertical coordinates, performing coordinate distance processing on the dam-related data, and then performing dam comprehensive distance calculation to obtain a dam comprehensive distance coefficient; S102: setting a dam comprehensive distance threshold, if the dam comprehensive distance coefficient is less than or equal to the dam comprehensive distance threshold, it is determined that there is no crack, if the dam comprehensive distance coefficient is greater than the dam comprehensive distance threshold, it is determined that there is a crack; S103: setting a horizontal scale factor and a vertical scale factor, comparing the product of the set coefficient and the embankment comprehensive distance threshold with the embankment comprehensive distance coefficient, and determining the crack type according to the comparison result, wherein the vertical scale factor is greater than the horizontal scale factor and is greater than one; S104: Acquire crack-related index data and perform preprocessing to obtain processed crack-related index data, wherein the crack-related index data includes crack size data and crack inclination, and then use the crack size data to calculate the crack comprehensive index to obtain the crack comprehensive index size coefficient; S105: Input the comprehensive index size coefficient of the crack and the crack inclination into a pre-established crack standard discrimination model, output a crack standard discrimination result, match the image of the crack standard discrimination result with images of different crack types to obtain a final crack image, and issue a voice alarm prompt according to the grade type of the final crack image.

[0031] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0032] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by the processor to execute the above method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0033] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0034] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0035] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure may have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure to be protected.

Claims

1. A dam crack monitoring and early warning system, characterized in that: include: Crack analysis module: used to set the comprehensive distance threshold of the dam, compare the comprehensive distance coefficient of the dam with the comprehensive distance threshold of the dam, and determine whether there are cracks in the dam according to the comparison result. If there are cracks, the horizontal scale coefficient and the vertical scale coefficient are set and then compared again. The crack type is determined according to the comparison result, and different types of crack signals are sent to the image acquisition and processing module, and the crack quality determination signal is sent to the crack quality determination module; The calculation process of the comprehensive distance coefficient of the dam is as follows: In the formula, Si is the horizontal distance of the dam, Ci is the vertical distance of the dam, Zi is the comprehensive distance coefficient of the dam, S0 is the preset standard horizontal coefficient, C0 is the preset standard vertical coefficient, k1 is the horizontal distance influence coefficient, k2 is the vertical distance influence coefficient, ln() is the logarithmic function with the natural constant e as the base, and t is the preset proportional coefficient; where i is the number of times the dam-related data is collected, and i=1, 2, 3,..., n, and n is the total number of times the dam-related data is collected; Crack quality determination module: used to collect crack-related index data, including crack size data and crack inclination, pre-process the crack-related index data to obtain processed crack-related index data, mark the processed crack size data, calculate the crack comprehensive index using the marked crack size data, obtain the crack comprehensive index size coefficient, input the crack comprehensive index size coefficient and the crack inclination into a pre-established crack standard discrimination model, output the crack standard discrimination result, and send the crack standard discrimination result to the image acquisition and processing module; Image acquisition and processing module: used to acquire images corresponding to different types of crack signals as the first image, and images corresponding to the crack standard discrimination results as the second image, match the first image with the second image, obtain the final crack image, and send the final crack image to the early warning module; Early warning module: used to issue voice alarm prompts according to the grade type of the final crack image.

2. A dam crack monitoring and early warning system according to claim 1, characterized in that: Also includes: Data acquisition module: used to collect dam-related data and send the dam-related data to the data processing module, wherein the dam-related data includes: a plurality of dam horizontal coordinates and a plurality of dam vertical coordinates; Data processing module: used to process coordinate distance according to dam-related data, obtain horizontal distance and vertical distance of dam, calculate comprehensive distance of dam based on horizontal distance and vertical distance of dam, obtain comprehensive distance coefficient of dam, and send comprehensive distance coefficient of dam to crack analysis module.

3. A dam crack monitoring and early warning system according to claim 2, characterized in that: The data processing module of the data processing module performs coordinate distance processing according to the dam related data: According to the horizontal coordinates of multiple dams in the dam-related data, the coordinates of the points with the largest distance between the two horizontal coordinates are determined, marked as two horizontal extreme value points, and the horizontal distance of the dam is determined according to the coordinates of the two horizontal extreme value points; According to the vertical coordinates of multiple dams in the dam-related data, the coordinates of the points with the largest vertical coordinate distance are determined, marked as two vertical extreme points, and the vertical distance of the dam is determined according to the coordinates of the two vertical extreme points.

4. A dam crack monitoring and early warning system according to claim 1, characterized in that: The analysis process of the crack analysis module includes: Set the comprehensive distance threshold of the embankment Z0; If Zi≤Z0, it is determined that there are no cracks in the dam, and the crack analysis module sends a no-crack signal to the early warning module; If Zi>Z0, it is determined that there are cracks in the dam, and the type of cracks is further determined; Set the horizontal scale factor P1 and the vertical scale factor P2; wherein P2>P1>1; like , then the dam crack is determined to be a horizontal crack, and a horizontal crack signal is sent to the image acquisition and processing module. like , then the dam crack is determined to be a vertical crack, and a vertical crack signal is sent to the image acquisition and processing module. like , then the dam crack is determined to be a tortoise crack, and a tortoise crack signal is sent to the image acquisition and processing module; Furthermore, a crack quality determination signal is sent to the crack quality determination module.

5. A dam crack monitoring and early warning system according to claim 1, characterized in that: The process of the crack quality determination module marking the processed crack size data: The crack length data is marked as Lj, the crack width data is marked as Dj, and the crack depth data is marked as Sj, where j is the number of crack size data collected, and j=1, 2, 3, ..., m, and m is the total number of crack size data collected.

6. A dam crack monitoring and early warning system according to claim 5, characterized in that: The calculation process of the crack comprehensive index of the crack quality judgment module is as follows: Where Fj is the comprehensive index size coefficient of cracks, R1 is the influence coefficient of crack length, R2 is the influence coefficient of crack width, R3 is the influence coefficient of crack depth, and α is the preset correlation coefficient.

7. A dam crack monitoring and early warning system according to claim 6, characterized in that: The crack standard discrimination model of the crack quality judgment module is based on the process of artificial intelligence model training: Obtaining preset standard crack related indicators through a server, wherein the crack related indicators include a preset standard crack size and a preset standard crack inclination; The artificial intelligence model is trained through preset standard crack-related indicators to obtain and store a crack standard discrimination model.

8. A dam crack monitoring and early warning system according to claim 1, characterized in that: The crack standard identification results include: first-level cracks, second-level cracks and third-level cracks; The types of the final crack images include: first-level horizontal cracks, second-level horizontal cracks, third-level horizontal cracks, first-level vertical cracks, second-level vertical cracks, third-level vertical cracks, first-level tortoise cracks, second-level tortoise cracks, and third-level tortoise cracks.

9. A dam crack monitoring and early warning method, characterized in that: The method comprises the following steps: Obtain dam-related data, including multiple horizontal dam coordinates and multiple vertical dam coordinates, perform coordinate distance processing on the dam-related data, and then perform dam comprehensive distance calculation to obtain the dam comprehensive distance coefficient; The calculation process of the comprehensive distance coefficient of the dam is as follows: In the formula, Si is the horizontal distance of the dam, Ci is the vertical distance of the dam, Zi is the comprehensive distance coefficient of the dam, S0 is the preset standard horizontal coefficient, C0 is the preset standard vertical coefficient, k1 is the horizontal distance influence coefficient, k2 is the vertical distance influence coefficient, ln() is the logarithmic function with the natural constant e as the base, and t is the preset proportional coefficient; where i is the number of times the dam-related data is collected, and i=1, 2, 3,..., n, and n is the total number of times the dam-related data is collected; A dam comprehensive distance threshold is set. If the dam comprehensive distance coefficient is less than or equal to the dam comprehensive distance threshold, it is determined that there is no crack. If the dam comprehensive distance coefficient is greater than the dam comprehensive distance threshold, it is determined that there is a crack. A horizontal scale factor and a vertical scale factor are set, and the product of the set coefficient and the embankment comprehensive distance threshold is compared with the embankment comprehensive distance coefficient, and the crack type is determined according to the comparison result, wherein the vertical scale factor is greater than the horizontal scale factor and is greater than one; Acquire crack-related index data and perform preprocessing to obtain processed crack-related index data, wherein the crack-related index data includes crack size data and crack inclination, and then use the crack size data to calculate the crack comprehensive index to obtain the crack comprehensive index size coefficient; The comprehensive crack index size coefficient and crack inclination are input into the pre-established crack standard discrimination model, and the crack standard discrimination result is output. The image of the crack standard discrimination result is matched with the images of different crack types to obtain the final crack image, and a voice alarm prompt is given according to the grade type of the final crack image.

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