Crack information identification system, method and device, storage medium, processor and program product

Abstract

The invention discloses a crack information identification system, method and device, a storage medium, a processor and a program product. The method comprises the following steps: an optical fiber sensor module used for controlling a distributed fan-shaped optical fiber sensor and monitoring a coal mine dam body by using an optical signal to obtain optical fiber signal data of the coal mine dam body, the optical fiber signal data being used for representing the state of a crack in the coal mine dam body; the data transmission module is used for transmitting the optical fiber signal data to a data processing system; and the data processing system is used for performing feature extraction on the optical fiber signal data to obtain local feature data, and identifying the local feature data to obtain damage information of the crack. The technical problem of low accuracy of crack monitoring is solved.

Description

Technical Field

[0001] The present application relates to the field of coal mine flaw detection, and in particular, to a crack information recognition system, method, device, storage medium, processor and program product. Background Art

[0002] At present, the monitoring methods for cracks are usually completed using ultrasonic infrared technology, eddy current method and piezoelectric impedance technology. However, the above methods all require external excitation, the test results are easily disturbed by the external environment, and it is difficult to achieve long-term online monitoring. Therefore, there is a technical problem of low accuracy of crack monitoring.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a crack information recognition system, method, device, storage medium, processor and program product to at least solve the technical problem of low accuracy of crack monitoring.

[0005] According to one aspect of an embodiment of the present application, a crack information identification system is provided, which may include: an optical fiber sensor module, used to control a distributed fan-shaped optical fiber sensor, use an optical signal to monitor a coal mine dam body, and obtain optical fiber signal data of the coal mine dam body, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam body; a data transmission module, used to transmit the optical fiber signal data to a data processing system; a data processing system, used to extract features from the optical fiber signal data to obtain local feature data, and identify the local feature data to obtain damage information of the crack.

[0006] Optionally, the optical fiber sensor module includes: a trigger front end for generating an optical signal and coupling the optical signal into a distributed fan-shaped optical fiber sensor; and a distributed fan-shaped optical fiber sensor for collecting optical fiber signal data through the optical signal.

[0007] Optionally, the data processing system includes: a crack analysis calculation module, which is used to use a convolutional neural network to extract features from optical fiber signal data to obtain local feature data, and identify the local feature data to obtain damage information; and a data display unit, which is used to display damage information.

[0008] Optionally, the data processing system includes: a data storage unit for storing historical optical fiber signal data, and comparing the historical optical fiber signal data with the optical fiber signal data to obtain a comparison result, wherein the comparison result is used to determine the change of the crack.

[0009] According to another aspect of an embodiment of the present application, a method for identifying crack information is also provided, which may include: obtaining a distributed fan-shaped optical fiber sensor, monitoring a coal mine dam using an optical signal, and obtaining optical fiber signal data of the coal mine dam, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam; performing feature extraction on the optical fiber signal data to obtain local feature data; identifying the local feature data to obtain damage information of the crack; and displaying the damage information.

[0010] Optionally, the method may further include: acquiring historical optical fiber signal data of the coal mine dam body; comparing the optical fiber signal data with the historical optical fiber signal data to obtain a comparison result, wherein the comparison result is used to determine the change of the crack.

[0011] According to another aspect of an embodiment of the present application, a device for identifying crack information is also provided, and the device may include: an acquisition unit, used to acquire distributed fan-shaped optical fiber sensors, use optical signals to monitor the coal mine dam body, and obtain optical fiber signal data of the coal mine dam body, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam body; an extraction unit, used to extract features from the optical fiber signal data to obtain local feature data; an identification unit, used to identify the local feature data to obtain damage information of the crack; and a display unit, used to display the damage information.

[0012] According to another aspect of an embodiment of the present application, a computer-readable storage medium is further provided, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the crack information identification method of the embodiment of the present application.

[0013] According to another aspect of an embodiment of the present application, a processor is further provided, the processor being used to run a program, wherein the program, when run by the processor, executes the method for identifying crack information of an embodiment of the present application.

[0014] According to another aspect of an embodiment of the present application, a computer program product is further provided. The computer program product includes computer instructions, wherein the computer instructions, when executed by a processor, implement the method for identifying crack information of an embodiment of the present application.

[0015] In an embodiment of the present application, a distributed fan-shaped optical fiber sensor is controlled by an optical fiber sensor module, and the coal mine dam is monitored by using an optical signal to obtain optical fiber signal data of the coal mine dam, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam; the optical fiber signal data is transmitted to a data processing system through a data transmission module; the optical fiber signal data is feature extracted through the data processing system to obtain local feature data, and the local feature data is identified to obtain damage information of the crack, thereby solving the technical problem of low accuracy of crack monitoring and achieving the technical effect of improving the accuracy of crack monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 is a schematic diagram of a crack information identification system according to an embodiment of the present application;

[0018] Figure 2 is a schematic diagram of a distributed optical fiber deployment path according to an embodiment of the present application;

[0019] Figure 3 is a schematic diagram of a one-dimensional convolutional neural network structure according to an embodiment of the present application;

[0020] Figure 4 is a flow chart of a method for identifying crack information according to an embodiment of the present application;

[0021] Figure 5 is a schematic diagram of a crack information identification device according to an embodiment of the present application;

[0022] Figure 6 is a structural block diagram of a computer terminal according to an embodiment of the present application;

[0023] Figure 7 It is a block diagram of an electronic device according to a method for identifying crack information in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] According to an embodiment of the present application, an embodiment of a crack information identification system is provided. Figure 1 is a schematic diagram of a crack information recognition system according to an embodiment of the present application, such as Figure 1 As shown, the system may include:

[0027] The optical fiber sensor module 102 is used to control the distributed fan-shaped optical fiber sensor, monitor the coal mine dam body using optical signals, and obtain optical fiber signal data of the coal mine dam body, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam body.

[0028] In this embodiment, the above-mentioned optical fiber sensor module 102 may include a distributed fan-shaped optical fiber sensor, which can be used to collect optical fiber signal data of a coal mine dam using a distributed fan-shaped optical fiber sensor. The above-mentioned optical fiber signal data can be used to characterize the state of cracks in the coal mine dam, can be used to characterize stress and temperature changes, and can be optical fiber monitoring data, for example, can be one-dimensional optical fiber signal data, can be image data. It should be noted that this is only an example, and there is no specific limitation on the type of optical fiber signal data. The above-mentioned coal mine dam can be a coal mine underground reservoir dam.

[0029] Optionally, the optical signal may be a trigger light, a pump light or an excitation light, which may be used to stimulate specific effects in the optical fiber so that the optical fiber sensor can measure physical quantities such as strain and temperature, and may be generated by a laser.

[0030] Optionally, at the bend of the optical fiber, the trigger light will generate bending loss due to the geometric deformation of the optical fiber, causing part of the light to overflow from the fiber core and interact with the material on the surface of the optical fiber or in the surrounding environment (e.g., diamond color center). This interaction will excite the material to produce fluorescence or other forms of scattered light, and these optical signals contain information about strain or temperature, so that the distributed fan-shaped optical fiber sensor can collect optical fiber signal data.

[0031] For example, in the monitoring of the dam of the underground reservoir in a coal mine, the fiber optic sensor can capture the following data: at a specific fiber position, the strain signal increased from the initial 0.002 to 0.005, indicating that a new crack may have appeared at that location or the expansion of an existing crack. The temperature signal rose from 20 degrees Celsius (℃) to 22℃ during the rise in the reservoir water level. This may be because the increase in water temperature caused the temperature of the dam to change. Further analysis is needed to determine whether this temperature change has an impact on the safety of the dam.

[0032] Optionally, the optical fiber signal data can be sent to the data processing system 106 through the data transmission module 104, where it can be analyzed by a deep learning algorithm to locate, quantify and track cracks. By continuously monitoring this data, the system can promptly detect potential problems with the dam body and provide important information for maintenance and management.

[0033] As an optional implementation, the above-mentioned optical fiber sensor module may also include: a trigger front end for generating an optical signal and coupling the optical signal into a distributed fan-shaped optical fiber sensor; and a distributed fan-shaped optical fiber sensor for collecting optical fiber signal data through the optical signal.

[0034] In this embodiment, the optical fiber sensor module may adopt a distributed fan-shaped optical fiber sensor, which is covered and pasted on the surface of the object to be measured (for example, a coal mine dam).

[0035] Optionally, the fiber optic sensor module can use distributed fan-shaped fiber optic sensors as sensing sensitive elements and transmission signal media, and adopt advanced distributed fiber optic sensing technology (such as OTDR technology and OFDR technology) to detect changes in temperature and strain at different positions along the optical fiber to obtain fiber optic signal data.

[0036] Optionally, the optical fiber sensor module may include a trigger front end, a sensing optical fiber, and a processing terminal. The trigger front end includes a trigger light unit, which is used to generate trigger light and couple the trigger light into the sensing optical fiber of the distributed fan-shaped optical fiber sensor. The sensing optical fiber includes a plurality of bending portions, the bending portions are bare optical fibers, and the surface of the bare optical fiber may be provided with diamond color centers. After the trigger light enters the sensing optical fiber, bending loss occurs at the bending portion. The bending loss causes the trigger light to overflow from the fiber core and excite the diamond color center. The diamond color center generates stress fluorescence under the action of the trigger light and surrounding environmental factors.

[0037] Figure 2 is a schematic diagram of a distributed optical fiber deployment path according to an embodiment of the present application, such as Figure 2As shown, the distributed fan-shaped optical fiber sensor may include an optical fiber head, an optical fiber tail, and an optical fiber. The distributed fan-shaped optical fiber sensor may be clamped by a clamping area.

[0038] Optionally, the above-mentioned processing terminal may include several groups of photodetectors, and a photodetector is arranged near each curved portion. The photodetector can be used to receive the stress fluorescence generated by the diamond color center and transmit the fluorescence information to the terminal processing equipment for analysis and processing; a filtering structure is arranged between adjacent curved portions.

[0039] In this embodiment, the distributed fan-shaped optical fiber sensor can collect optical fiber signal data by utilizing optical signals.

[0040] The data transmission module 104 is used to transmit the optical fiber signal data to the data processing system.

[0041] In this embodiment, the data transmission module 104 may transmit the optical fiber signal data to the data processing system for processing via the optical fiber data transmission network.

[0042] Optionally, the optical fiber sensor module can transmit the optical fiber monitoring data (ie, optical fiber signal data) to the data processing system through the data transmission module.

[0043] The data processing system is used to extract features from the optical fiber signal data to obtain local feature data, and identify the local feature data to obtain damage information of the crack. The damage information may include data such as the location, shape, and increased width or depth of the crack. It should be noted that this is only an example and does not specifically limit the type of damage information.

[0044] As an optional implementation, the data processing system may include: a crack analysis calculation module, which is used to use a convolutional neural network to extract features from optical fiber signal data to obtain local feature data, and identify the local feature data to obtain damage information; and a data display unit, which is used to display damage information.

[0045] In this embodiment, the data processing system may include a crack analysis calculation module, a data display unit, and a data storage unit. The crack analysis calculation module may use a deep learning method to analyze and process the optical fiber signal data detected by the optical fiber sensor module to determine whether cracks appear in the object being measured (i.e., the coal mine dam), thereby improving the accuracy of crack resolution.

[0046] Optionally, the above-mentioned deep learning method can adopt a convolutional neural network model algorithm, which can compress a two-dimensional image and reduce one of its dimensions, so that the image can be regarded as a one-dimensional data structure. Conversely, the one-dimensional data sequence can also be regarded as a picture with a height of 1 pixel.

[0047] Optionally, the optical fiber sensor module acquires optical fiber signal data, and uses the data transmission module to transmit the acquired optical fiber signal data to a data processing system. The data processing system may use a deep learning model, such as a convolutional neural network (CNN), to extract features from the optical fiber signal data.

[0048] For example, the above convolutional neural network can use a one-dimensional convolution layer to process one-dimensional scattering spectra or time series signal data. Each convolution kernel will scan along the signal axis to capture local pattern features. Using the pooling layer, the size of the feature map is reduced to extract the most significant features while maintaining the spatial hierarchy to improve the model's ability to resist overfitting. Using the fully connected layer: Integrate the features extracted by the convolution layer and the pooling layer to perform more complex feature combinations to identify patterns such as cracks. Through the above neural network layer, the data processing system can process the optical fiber signal data to obtain the damage information of the crack. Based on the damage information, the growth of the crack and the impact of the crack on the coal mine dam can be determined.

[0049] Optionally, the above-mentioned crack analysis calculation module (e.g., LetNet-5) can be an efficient convolutional neural network for handwritten character recognition, which can be composed of two convolutional layers, two pooling layers and three fully connected layers, and the output data can be processed by a mathematical function (e.g., SoftMax).

[0050] Optionally, when designing the network structure, three fully connected layers and one SoftMax layer can be selected, and an equal number of convolutional layers and pooling layers can be used. The number of convolutional layers can be searched and optimized around 2 (1 to 5). A network structure with two convolutional layers can be selected. In order to make the network more stable, network structures such as normalization layer (Batchnorm, referred to as BN), activation layer (Rectified linear un its, referred to as ReLU), and loss layer (Droput) can be added. Finally, a one-dimensional CNN structure model containing 3 blocks was determined.

[0051] In this embodiment, Figure 3 is a schematic diagram of a one-dimensional convolutional neural network structure according to an embodiment of the present application, such as Figure 3As shown, the input sequence (which may be optical fiber signal data) may be placed in block 1 301 (Block1) for processing. Block1 may include five functional layers, for example, functional layer 3011 (Conv_1), functional layer 3012 (BN_1), functional layer 3013 (ReLU_1), functional layer 3014 (Droput_1) and functional layer 3015 (MaxPoo l_1). Among them, the input size of Block1, that is, the input size of Conv_1, may be 40×1. Conv_1 may be a convolution layer, and may have 32 convolution kernels, each of which may be composed of a column vector containing 5 elements. Table 1 is a detailed configuration table of the network structure. As shown in Table 1, the step size of the sliding window of the convolution kernel in Block1 in the convolution operation may be 1 (i.e., one data point). Conv_1 may generate 32 feature maps, each of which may contain a column vector of 36 elements. The size of the Conv_1 output characteristic map in Table 1 is 36×32×1. BN_1, ReLU_1 and Droput_1 have no effect on the output size. After downsampling by MaxPool_1, the output size of Block1 is 35×32×1 and is further passed to Blocks2.

[0052] Optionally, the above-mentioned block two 302 may include: a functional layer 3021 (Conv_2), a functional layer 3022 (BN_2), a functional layer 3023 (ReLU_2), a functional layer 3024 (Droput_2) and a functional layer 3025 (MaxPoo l_2).

[0053] Optionally, block three 303 (B l ock3) can be a fully connected classification layer, which can be composed of 3 fully connected layers, for example, a fully connected layer 3031 (FC_1), a fully connected layer 3032 (FC_2) and a fully connected layer 3033 (FC_3), and can also include a function layer 3034 (SoftMax) and an output layer 3035 (Class Output). Since the final identification is the three situations of structural health status and damage form, the data size of the last fully connected layer data can be 3.

[0054] Table 1 Detailed configuration of network structure

[0055]

[0056] It should be noted that the numbers in the table are for illustration only and are not specifically limited here.

[0057] Optionally, the BN may be a normalization layer, which may be used to normalize the layer input of each training mini-batch, may be used to reduce the absolute difference between data, and may speed up the training process.

[0058] Optionally, the above-mentioned ReLU can be an activation layer, and the ReLU activation function is similar to the sigmoid activation function in traditional artificial neural networks. Unlike the sigmoid activation function, ReLU can set all negative inputs to zero, which can be used to avoid the occurrence of gradient instability, reduce the network calculation cost, and speed up the training process.

[0059] Optionally, in order to avoid overfitting of the training network due to small training data, the network structure can also introduce a Droput layer. The Droput layer can use a special discarding mechanism, where certain elements in the input feature map are deleted with a certain probability, thereby making the model more generalizable and avoiding overfitting.

[0060] In this embodiment, the data display unit may be a touch-sensitive light emitting diode (LED) display screen, and the identification result, such as loss information, may be displayed in the digital display unit.

[0061] Optionally, the data monitored and processed by the system is displayed, and the system-related settings and display parameters can be adjusted through the touch LED display.

[0062] As an optional implementation, the data processing system may further include: a data storage unit for storing historical optical fiber signal data, and comparing the historical optical fiber signal data with the optical fiber signal data to obtain a comparison result, wherein the comparison result is used to determine the change of the crack.

[0063] In this embodiment, the above-mentioned data storage unit can adopt a flash memory (flash) on-chip storage unit, and the system can store the collected crack data (i.e., optical fiber signal data), and in subsequent monitoring, it can retrieve previous monitoring data (i.e., historical optical fiber signal data) and compare the two monitoring data to determine whether the crack has increased.

[0064] Optionally, the data storage unit can be used to store historical optical fiber signal data, which can be signal data collected at historical moments. The historical optical fiber signal data can be compared with the currently collected optical fiber signal data to obtain a comparison result. The comparison result can be used to determine the change of cracks in the coal mine dam body.

[0065] In this embodiment, the above crack information identification system can be a sector-type fan-shaped optical fiber dynamic crack monitoring system for the dam of a coal mine underground reservoir, which can include an optical fiber sensor module, a data transmission module, and a data processing system. Among them, the above optical fiber sensor module can transmit the optical fiber monitoring data to the data processing system through the data transmission module. The above data processing system can be used to process the optical fiber monitoring data and can be used to evaluate the degree of damage of the crack.

[0066] Optionally, the strain distribution measured by distributed sensors can be used to accurately locate, quantify, track and visualize cracks on the surface and inside the structure during loading, avoiding complex signal analysis to interpret the sensor data.

[0067] At present, as a new type of measurement technology, fiber optic sensors have many excellent properties compared with traditional sensors, such as small size, high temperature resistance, and anti-electromagnetic interference. Distributed optical fibers have the characteristics of high density and continuous measurement, which are very suitable for embedding in composite materials to monitor the structural state. Therefore, the distributed optical fiber can be embedded between the thermal insulation material and the matrix to monitor the debonding damage of the composite laminated structure without affecting the interlayer bonding performance, so as to verify the feasibility of distributed optical fiber sensors in damage monitoring.

[0068] Traditional crack monitoring methods, such as ultrasonic infrared technology, eddy current method and piezoelectric impedance technology, all require external excitation, the test results are easily disturbed by the external environment, and it is difficult to achieve long-term online monitoring. Therefore, there is a technical problem of low accuracy of crack monitoring.

[0069] In order to solve the above problems, in this embodiment, a distributed fan-shaped optical fiber sensor is used as a dynamic crack monitoring device for underground reservoir dams in coal mines. No external excitation is required, the test results are not easily disturbed by the external environment, and long-term online monitoring can be achieved. The strain distribution measured by the distributed sensor can be used to accurately locate, quantify, track and visualize cracks on the surface and inside of the structure during loading, avoiding complex signal analysis to interpret sensor data.

[0070] It should be noted that, in addition to monitoring cracks in coal mine dams, the above embodiments can also be used for crack development and leakage monitoring of dams, concrete structures, etc. in water conservancy, civil engineering, construction, and mining industries. It should be understood that the above detailed description of the technical solution of the present application with the help of the preferred embodiments is illustrative and not restrictive. A person of ordinary skill in the art can modify the technical solutions described in each embodiment based on reading the specification of this application, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

[0071] According to an embodiment of the present application, an embodiment of a method for identifying crack information is also provided. The steps shown in the flowchart of the accompanying drawing can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0072] Figure 4 is a flow chart of a method for identifying crack information according to an embodiment of the present application, such as Figure 4 As shown, the method may include the following steps:

[0073] Step S402, obtaining a distributed fan-shaped optical fiber sensor, monitoring the coal mine dam body using an optical signal, and obtaining optical fiber signal data of the coal mine dam body, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam body.

[0074] Step S404: extract features from the optical fiber signal data to obtain local feature data.

[0075] In the technical solution provided in the above step S404 of the present application, a convolutional neural network can be used to extract features from the optical fiber signal data to obtain local feature data. The above local feature data can be used to characterize the growth of the crack, for example, it can be used to characterize the growth shape, length, width and other information of the crack. It should be noted that this is only an example, and there is no specific limitation on the type of local feature data.

[0076] Optionally, in the segmented fan-shaped optical fiber dynamic crack monitoring system for the coal mine underground reservoir dam, the local feature data can refer to the key information directly related to the crack state of the dam body extracted from the original signal captured by the optical fiber sensor (i.e., the optical fiber signal data), which can be used to reflect the specific properties of the crack, which is crucial for accurately identifying and locating the crack.

[0077] In this embodiment, a convolutional neural network model in deep learning can be used. By training the model to identify and learn the local feature data of the crack, automatic and intelligent monitoring of the crack can be achieved, the complexity and subjectivity of manual analysis can be reduced, and the accuracy and efficiency of monitoring can be improved. Ultimately, these local feature data can be converted into damage information of the crack.

[0078] Step S406: identify the local feature data to obtain crack damage information.

[0079] In the technical solution provided in the above step S406 of the present application, the data processing system receives the optical fiber signal data from the optical fiber sensor module, and can pre-process and feature select the optical fiber signal data, so that the local feature information related to the crack state can be extracted. Then, the above local feature data can be identified and classified by a deep learning model (such as a convolutional neural network) to determine the existence, location and severity of the crack, and obtain the damage information of the crack. The above damage information can be presented to the operator through a data display unit, and at the same time, the data (e.g., damage information, optical fiber signal data) can be stored for subsequent analysis and comparison.

[0080] In this embodiment, historical damage information can also be stored, and the historical damage information can be compared with the currently acquired damage information to determine the change of the crack. For example, the data storage unit can be used to store the collected crack data (which can be damage information) using the flash on-chip storage unit, and in subsequent monitoring, the previous historical damage data can be retrieved and the conditions of the two cracks can be compared to determine whether the crack has increased.

[0081] Step S408: display damage information.

[0082] In the technical solution provided in the above step S408 of the present application, the damage information can be displayed on the display screen so that the user can intuitively see the changes in the cracks.

[0083] As an optional implementation, the method may also include: acquiring historical fiber optic signal data of the coal mine dam; comparing the fiber optic signal data with the historical fiber optic signal data to obtain a comparison result, wherein the comparison result is used to determine changes in cracks.

[0084] Through the above-mentioned steps S402 and S408 of the present application, a distributed fan-shaped optical fiber sensor is obtained, and the coal mine dam is monitored using optical signals to obtain optical fiber signal data of the coal mine dam, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam; feature extraction is performed on the optical fiber signal data to obtain local feature data; the local feature data is identified to obtain damage information of the crack; and the damage information is displayed, thereby solving the technical problem of low accuracy of crack monitoring and achieving the technical effect of improving the accuracy of crack monitoring.

[0085] According to an embodiment of the present application, a device for identifying crack information is also provided. It should be noted that the device for identifying crack information of this embodiment can be used to execute the method for identifying crack information of embodiment 1 of the present application.

[0086] Figure 5 is a schematic diagram of a crack information identification device according to an embodiment of the present application. Figure 5As shown, the crack information identification device 50 may include: an acquisition unit 502 , an extraction unit 504 , an identification unit 506 and a display unit 508 .

[0087] The acquisition unit 502 is used to acquire the distributed fan-shaped optical fiber sensor, and use the optical signal to monitor the coal mine dam to obtain the optical fiber signal data of the coal mine dam, wherein the optical fiber signal data is used to characterize the state of the cracks in the coal mine dam.

[0088] The extraction unit 504 is used to extract features from the optical fiber signal data to obtain local feature data.

[0089] The identification unit 506 is used to identify the local feature data to obtain the damage information of the crack.

[0090] The display unit 508 is used to display damage information.

[0091] The crack information recognition device of this embodiment obtains distributed fan-shaped optical fiber sensors through an acquisition unit, and uses optical signals to monitor the coal mine dam body to obtain optical fiber signal data of the coal mine dam body, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam body; through the extraction unit, feature extraction is performed on the optical fiber signal data to obtain local feature data; through the recognition unit, the local feature data is recognized to obtain damage information of the crack; and through the display unit, the damage information is displayed, thereby solving the technical problem of low accuracy of crack monitoring and achieving the technical effect of improving the accuracy of crack monitoring.

[0092] The embodiment of the present application may provide a computer terminal, which may be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal may also be replaced by a terminal device such as a mobile terminal.

[0093] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0094] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the method for identifying crack information: obtaining a distributed fan-shaped optical fiber sensor, using an optical signal to monitor the coal mine dam body, and obtaining optical fiber signal data of the coal mine dam body, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam body; extracting features from the optical fiber signal data to obtain local feature data; identifying the local feature data to obtain damage information of the crack; and displaying the damage information.

[0095] Optionally, Figure 6 is a structural block diagram of a computer terminal according to an embodiment of the present application, such as Figure 6As shown, the computer terminal 608 may include: one or more (only one is shown in the figure) processors 602 , a memory 604 , and a transmission device 606 .

[0096] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the crack information identification method and device in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned crack information identification method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the computer terminal 608 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0097] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: obtain the distributed fan-shaped optical fiber sensor, use the optical signal to monitor the coal mine dam, and obtain the optical fiber signal data of the coal mine dam, wherein the optical fiber signal data is used to characterize the state of the cracks in the coal mine dam; extract the features of the optical fiber signal data to obtain local feature data; identify the local feature data to obtain the damage information of the crack; and display the damage information.

[0098] It can be understood by those skilled in the art that Figure 6 The structure shown is for illustration only, and the computer terminal 608 may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, referred to as MID), a PAD, or other terminal devices. Figure 6 The structure of the computer terminal 608 is not limited. For example, the computer terminal 608 may also include Figure 6 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 6 Different configurations shown.

[0099] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0100] According to an embodiment of the present application, a computer-readable storage medium is further provided, the computer-readable storage medium including a stored program, wherein the program executes the method for identifying crack information in the above embodiment.

[0101] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the mobile terminals in a mobile terminal group.

[0102] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining a distributed fan-shaped optical fiber sensor, monitoring the coal mine dam using an optical signal, and obtaining optical fiber signal data of the coal mine dam, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam; performing feature extraction on the optical fiber signal data to obtain local feature data; identifying the local feature data to obtain damage information of the crack; and displaying the damage information.

[0103] Optionally, the computer-readable storage medium may also execute program code of the following steps: obtaining historical optical fiber signal data of the coal mine dam; comparing the optical fiber signal data with the historical optical fiber signal data to obtain a comparison result, wherein the comparison result is used to determine the change of the crack.

[0104] In this embodiment, a distributed fan-shaped optical fiber sensor is obtained, and the coal mine dam is monitored using an optical signal to obtain optical fiber signal data of the coal mine dam, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam; feature extraction is performed on the optical fiber signal data to obtain local feature data; the local feature data is identified to obtain damage information of the crack; and the damage information is displayed, thereby solving the technical problem of low accuracy of crack monitoring and achieving the technical effect of improving the accuracy of crack monitoring.

[0105] According to an embodiment of the present application, a processor is also provided, which is used to run a program, wherein the method for identifying crack information in the above embodiment is executed when the program is run by the processor.

[0106] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0107] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the method for identifying crack information: obtaining a distributed fan-shaped optical fiber sensor, using an optical signal to monitor the coal mine dam body, and obtaining optical fiber signal data of the coal mine dam body, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam body; extracting features from the optical fiber signal data to obtain local feature data; identifying the local feature data to obtain damage information of the crack; and displaying the damage information.

[0108] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the crack information identification method and device in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned crack information identification method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0109] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: obtain the distributed fan-shaped optical fiber sensor, use the optical signal to monitor the coal mine dam, and obtain the optical fiber signal data of the coal mine dam, wherein the optical fiber signal data is used to characterize the state of the cracks in the coal mine dam; extract the features of the optical fiber signal data to obtain local feature data; identify the local feature data to obtain the damage information of the crack; and display the damage information.

[0110] Optionally, the processor may also execute program code of the following steps: comparing the optical fiber signal data with historical optical fiber signal data to obtain a comparison result, wherein the comparison result is used to determine the change of the crack.

[0111] By adopting the embodiment of the present application, a distributed fan-shaped optical fiber sensor is obtained, and the coal mine dam is monitored using an optical signal to obtain optical fiber signal data of the coal mine dam, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam; feature extraction is performed on the optical fiber signal data to obtain local feature data; the local feature data is identified to obtain damage information of the crack; and the damage information is displayed, thereby solving the technical problem of low accuracy of crack monitoring and achieving the technical effect of improving the accuracy of crack monitoring.

[0112] According to an embodiment of the present application, a computer program product is further provided. The computer program product includes computer instructions, wherein when the computer instructions are executed by a processor, the method for identifying crack information in the above embodiment is implemented.

[0113] An embodiment of the present application may provide an electronic device, which may include a memory and a processor.

[0114] Figure 7 It is a block diagram of an electronic device for a method for identifying crack information according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0115] like Figure 7 As shown, the device 70 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 to a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 70 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0116] A number of components in the device 70 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 70 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0117] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as a verification method for data. For example, in some embodiments, the verification method for data may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 70 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the data verification method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the data verification method in any other appropriate manner (e.g., by means of firmware).

[0118] According to an embodiment of the present application, a method for identifying crack information is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0119] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor that may be a dedicated or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0120] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0121] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0122] To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or an LCD (liquid crystal display, monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a path ball) through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0123] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a Local Area Network (LAN), a Wide Area Network (WAN), and the Internet.

[0124] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0125] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0126] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0127] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0128] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0129] In addition, each functional unit in each embodiment of the present application 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.

[0130] 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. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0131] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A crack information recognition system, characterized in that: include: The optical fiber sensor module is used to control the distributed fan-shaped optical fiber sensor, monitor the coal mine dam body by using the optical signal, and obtain the optical fiber signal data of the coal mine dam body, wherein the optical fiber signal data is used to characterize the state of the cracks in the coal mine dam body; A data transmission module, used for transmitting the optical fiber signal data to a data processing system; The data processing system is used to extract features from the optical fiber signal data to obtain local feature data, and to identify the local feature data to obtain damage information of the crack.

2. The system according to claim 1, characterized in that The optical fiber sensor module comprises: A trigger front end, used for generating the optical signal and coupling the optical signal into the distributed fan-shaped optical fiber sensor; The distributed fan-shaped optical fiber sensor is used to collect the optical fiber signal data through the optical signal.

3. The system according to claim 1, characterized in that The data processing system comprises: A crack analysis calculation module, used to use a convolutional neural network to perform feature extraction on the optical fiber signal data to obtain the local feature data, and to identify the local feature data to obtain the damage information; A data display unit is used to display the damage information.

4. The system according to claim 1, characterized in that The data processing system comprises: The data storage unit is used to store historical optical fiber signal data, and compare the historical optical fiber signal data with the optical fiber signal data to obtain a comparison result, wherein the comparison result is used to determine the change of the crack.

5. A method for identifying cracks, characterized in that: include: Obtaining a distributed fan-shaped optical fiber sensor, and using an optical signal to monitor a coal mine dam, to obtain optical fiber signal data of the coal mine dam, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam; Extracting features from the optical fiber signal data to obtain local feature data; Identifying the local feature data to obtain damage information of the crack; The damage information is displayed.

6. The method according to claim 5, characterized in that The method further comprises: Acquiring historical optical fiber signal data of the coal mine dam; The optical fiber signal data and the historical optical fiber signal data are compared to obtain a comparison result, wherein the comparison result is used to determine the change of the crack.

7. A crack identification device, characterized in that: include: An acquisition unit is used to acquire distributed fan-shaped optical fiber sensors, and use optical signals to monitor the coal mine dam to obtain optical fiber signal data of the coal mine dam, wherein the optical fiber signal data is used to characterize the state of cracks in the coal mine dam; An extraction unit, used for performing feature extraction on the optical fiber signal data to obtain local feature data; An identification unit, used to identify the local feature data to obtain damage information of the crack; A display unit is used to display the damage information.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 5 and 6.

9. A processor, characterized in that: The processor is used to run a program, wherein the program executes the method according to any one of claims 5 and 6 when run by the processor.

10. A computer program product, characterized in that The method comprises computer instructions, which implement the method described in any one of claims 5 and 6 when executed by a processor.

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