Fault detection method and device for tubular conveyor belt and electronic equipment

Abstract

The invention provides a fault detection method and device for a tubular conveyor belt and electronic equipment. The method comprises the following steps: acquiring multiple pieces of first radar point cloud data of the tubular conveyor belt at different visual angles; the multiple pieces of first radar point cloud data are converted into second radar point cloud data of a calibrated radar coordinate system, and the second radar coordinate systems corresponding to the multiple pieces of first radar point cloud data under different viewing angles are different; on the basis of the second radar point cloud data and a wavelet transform algorithm, determining package opening feature information corresponding to the tubular conveyor belt; fitting the second radar point cloud data to obtain a fitting curve; target contour information of the tubular conveyor belt is determined based on the fitting curve and the wrapping opening feature information; and comparing the target contour information with the preset contour information, and determining a detection result of the tubular conveyor belt. According to the technical scheme provided by the embodiment of the invention, the target contour information is generated through the laser radar, the detection result of the tubular conveyor belt can be quickly determined, and the detection efficiency is greatly improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of information processing, and in particular, relates to a method, an apparatus, and an electronic device for detecting faults of a tubular conveyor belt. Background Art

[0002] In industrial production, a conveyor belt is an important part of a conveyor, and is commonly used in industries such as cement, coking, metallurgy, chemical engineering, and iron and steel. A tubular conveyor belt is also one of the commonly used conveyor belts. Due to its structure, a tubular conveyor belt is prone to abnormal conditions such as twisting, tube expansion, and tube collapse. How to detect abnormal results of a tubular conveyor belt is a key problem in industrial production.

[0003] In the prior art, the commonly used method for detecting a tubular conveyor belt is to directly or indirectly use a contactless force device to detect the tubular conveyor belt. First, different physical devices and calibration parameters need to be formulated according to different sizes of the tubular conveyor belt. Moreover, a single contactless sensing detection method can only detect faults, but cannot classify faults. Not only is the detection efficiency low, but also the detection range is small and the accuracy is low. Summary of the Invention

[0004] Embodiments of the present disclosure provide a solution to solve the problems in the related art that faults cannot be classified, the detection efficiency is low, the detection range is small, and the accuracy is low.

[0005] In a first aspect, the present disclosure provides a method for detecting faults of a tubular conveyor belt, the method including:

[0006] Obtaining a plurality of first radar point cloud data of the tubular conveyor belt from different perspectives;

[0007] Converting the plurality of first radar point cloud data into second radar point cloud data in a calibrated radar coordinate system, where the second radar coordinate systems corresponding to the plurality of first radar point cloud data from different perspectives are different;

[0008] Based on the second radar point cloud data and a wavelet transform algorithm, determining port feature information corresponding to the tubular conveyor belt;

[0009] Fitting the second radar point cloud data to obtain a fitting curve;

[0010] Based on the fitting curve and the port feature information, determining target contour information of the tubular conveyor belt;

[0011] Comparing the target contour information with preset contour information to determine a detection result of the tubular conveyor belt.

[0012] In a second aspect, the present disclosure provides a device for detecting faults of a tubular conveyor belt, the device including:

[0013] An acquisition unit for acquiring a plurality of first lidar point cloud data of a tubular conveyor belt from different perspectives;

[0014] A conversion unit for converting the plurality of first lidar point cloud data into second lidar point cloud data calibrated in a lidar coordinate system, wherein the second lidar coordinate systems corresponding to the plurality of first lidar point cloud data from different perspectives are different;

[0015] A determination unit for determining the mouth feature information corresponding to the tubular conveyor belt based on the second lidar point cloud data and a wavelet transform algorithm;

[0016] A fitting unit for fitting the second lidar point cloud data to obtain a fitting curve;

[0017] The determination unit for determining the target contour information of the tubular conveyor belt based on the fitting curve and the mouth feature information;

[0018] The determination unit for comparing the target contour information with preset contour information to determine the detection result of the tubular conveyor belt.

[0019] In a third aspect, the present disclosure provides an electronic device, including:

[0020] A processor; and

[0021] A memory for storing executable instructions of the processor;

[0022] Wherein, the processor is configured to execute any method in the first aspect or possible implementation manners of the first aspect by executing the executable instructions.

[0023] The technical solution provided by the present disclosure acquires a plurality of first lidar point cloud data of a tubular conveyor belt from different perspectives; converts the plurality of first lidar point cloud data into second lidar point cloud data calibrated in a lidar coordinate system, wherein the second lidar coordinate systems corresponding to the plurality of first lidar point cloud data from different perspectives are different; determines the mouth feature information corresponding to the tubular conveyor belt based on the second lidar point cloud data and a wavelet transform algorithm; fits the second lidar point cloud data to obtain a fitting curve; determines the target contour information of the tubular conveyor belt based on the fitting curve and the mouth feature information; compares the target contour information with preset contour information to determine the detection result of the tubular conveyor belt. The technical solutions provided by the embodiments of the present disclosure acquire a plurality of first lidar point cloud data of a tubular conveyor belt from different perspectives through a lidar, and then determine the contour information of the tubular conveyor belt at the current moment, compare it with the preset contour information, and determine the detection result of the tubular conveyor belt. Through comparison, it can be determined whether there is an abnormality in the tubular conveyor belt, greatly improving the detection efficiency. Description of the Drawings

[0024] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or related technologies, the following will briefly introduce the accompanying drawings required for the description of the embodiments or related technologies. Obviously, the accompanying drawings in the following description are some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings. In the accompanying drawings:

[0025] Figure 1 It is a schematic flowchart of a fault detection method for a tubular conveyor belt provided by an embodiment of the present disclosure;

[0026] Figure 2 It is a schematic installation diagram of a lidar provided by an embodiment of the present disclosure;

[0027] Figure 3 It is a schematic diagram of the usage range of a lidar provided by an embodiment of the present disclosure;

[0028] Figure 4 It is a schematic diagram of signal wavelet decomposition provided by an embodiment of the present disclosure;

[0029] Figure 5 It is a schematic diagram of the normal contour of a tubular conveyor belt provided by an embodiment of the present disclosure;

[0030] Figure 6 It is a schematic diagram of the abnormal contour of a tubular conveyor belt provided by an embodiment of the present disclosure;

[0031] Figure 7 It is a schematic structural diagram of a fault detection device for a tubular conveyor belt provided by an embodiment of the present disclosure;

[0032] Figure 8 It is a schematic installation diagram of a tubular conveyor belt provided by an embodiment of the present disclosure;

[0033] Figure 9 It is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. Detailed Embodiments

[0034] The following will describe the embodiments of the present disclosure in detail. The examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and should not be construed as a limitation of the present disclosure.

[0035] In the description, claims, and drawings of the embodiments of the present disclosure, terms such as "first" and "second" are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0036] The fault detection method of the tubular conveyor belt provided by the embodiments of the present disclosure can run on a terminal device or a server. Among them, the terminal device can be a local terminal device, including wearable devices such as VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality). Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0037] In industrial production, the conveyor belt is an important part of the conveyor, and is commonly used in industries such as cement, coking, metallurgy, chemical industry, and steel. The tubular conveyor belt is also one of the commonly used conveyor belts. Due to its structure, the tubular conveyor belt is prone to abnormal conditions such as twisting, tube expansion, and tube collapse. How to detect the abnormal results of the tubular conveyor belt is a key issue in industrial production.

[0038] In the prior art, the commonly used detection method for tubular conveyor belts is to directly or indirectly use a contactless force device to detect the tubular conveyor belt. First, different physical devices and calibration parameters need to be formulated according to the tubular conveyor belts of different sizes. Moreover, a single contact sensing detection method can only detect faults, but cannot classify faults. Not only is the detection efficiency low, but the detection range is small and the accuracy is low.

[0039] The following uses specific embodiments to elaborate in detail on the technical solutions of the present disclosure and how the technical solutions of the present disclosure solve the above technical problems. These several specific embodiments below can be combined with each other. For the same or similar concepts or processes, they may not be repeated in some embodiments. The following will describe the embodiments of the present disclosure with reference to the drawings.

[0040] Figure 1Schematic flowchart of a fault detection method for a tubular conveyor belt provided by an exemplary embodiment of the present disclosure. This method can be applied to a device with data processing functions. Taking lidar as an example of the application of this method, this solution at least includes the following steps S101 - S106:

[0041] S101. Obtain a plurality of first radar point cloud data of the tubular conveyor belt from different perspectives.

[0042] In some embodiments, the method is applicable to a radar device, and a plurality of lidars for obtaining a plurality of first radar point cloud data of the tubular conveyor belt from different perspectives are configured on the radar device;

[0043] Among them, the lidar corresponds to the first radar point cloud data one by one.

[0044] In some embodiments, a plurality of lidars are arranged on the radar device, and the plurality of lidars are located at different positions of the tubular conveyor belt. Specifically, the number of lidars is preferably 4, and they are respectively arranged at four positions of the tubular conveyor belt.

[0045] For example, as shown in combination with Figure 2 and Figure 3 , around the tubular conveyor belt 10, four lidars are arranged, namely lidar 11, lidar 12, lidar 13, and lidar 14. The laser ranges of the four lidars only need to cover the entire tubular conveyor belt, and the arrangement positions of the four lidars can be specifically set according to specific situations.

[0046] In some embodiments, obtaining a plurality of first radar point cloud data of the tubular conveyor belt from different perspectives includes steps S11 - S12:

[0047] S11. Obtain a plurality of original radar point cloud data of the tubular conveyor belt from different perspectives.

[0048] In some embodiments, as shown in combination with Figure 2 and Figure 3 , different perspectives refer to different lidars. For example, taking lidar 11, lidar 12, lidar 13, and lidar 14 as examples, the obtained plurality of original radar point cloud data are respectively the radar point cloud data obtained by lidar 11, the radar point cloud data obtained by lidar 12, the radar point cloud data obtained by lidar 13, and the radar point cloud data obtained by lidar 14.

[0049] S12. Preprocess the plurality of original lightning point cloud data to obtain the plurality of first radar point cloud data.

[0050] To ensure the accuracy of the radar point cloud data obtained by the lidar, it is necessary to preprocess the obtained radar point cloud data in order to obtain accurate radar point cloud data. Therefore, it is necessary to preprocess the obtained radar point cloud data. Among them, the preprocessing at least includes: low-pass filtering and windowing correlation.

[0051] In this embodiment, the multiple first radar point cloud data refers to the preprocessed radar point cloud data within the current frame.

[0052] Specifically, low-pass filtering and windowing correlation are performed on the multiple original radar point cloud data within the current frame to obtain radar point cloud data with a confidence level that meets the filtering requirements. Specifically, it can be performed through the following formula:

[0053] P m,n = ap m,n +(1 - α)p m,n-1 ;

[0054]

[0055] Among them, P m,n is the first radar point cloud data within the current frame, p m,n is the original radar point cloud data within the current frame, α is the filtering coefficient, m is the number of frames, n is the azimuth direction, OC is the windowing confidence level, N is the windowing length, and β is the confidence threshold.

[0056] In this embodiment, as shown in combination with Figure 2 and Figure 3 , using the above formula, the radar point cloud data obtained by four lidars can be processed to obtain four first radar point cloud data corresponding to each lidar.

[0057] S102. Convert the multiple first radar point cloud data into second radar point cloud data in the calibrated radar coordinate system.

[0058] Among them, the second radar coordinate systems corresponding to the multiple first radar point cloud data under different perspectives are different.

[0059] In some embodiments, the calibrated radar coordinate system refers to the world coordinate system where one of the multiple lidars is located. Specifically, as shown in combination with Figure 2 , if the world coordinate system corresponding to lidar 11 is used as the calibrated radar coordinate system, then the radar point cloud data obtained by lidar 12, lidar 13, and lidar 14 needs to be converted into the world coordinate system corresponding to lidar 11.

[0060] In some embodiments, converting the multiple first radar point cloud data into second radar point cloud data in the calibrated radar coordinate system includes steps S21 - S22:

[0061] S21. Determine the second radar coordinate system corresponding to each first radar point cloud data, the translation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system, and the rotation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system.

[0062] In some embodiments, determining the second radar coordinate system corresponding to each first radar point cloud data, the translation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system, and the rotation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system includes: determining the second radar coordinate system corresponding to each first radar point cloud data, and using one of the multiple second radar coordinate systems as the calibrated radar coordinate system, and then determining the translation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system, and the rotation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system.

[0063] S22. Based on the multiple first radar point cloud data, the second radar coordinate system corresponding to each first radar point cloud data, the translation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system, and the rotation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system, determine the second radar point cloud data.

[0064] In some embodiments, to determine the second radar point cloud data, it can be determined by the following formula:

[0065] Q m,n = R * P m,n + T;

[0066] where Q m,n is the second radar point cloud data, T is the translation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system, and R is the translation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system.

[0067] Among them, R and T can be obtained by the following formula:

[0068]

[0069] where E(R, T) is the mean square error of the objective function, k is the number of pairs of point clouds to be registered, w i is the weight of each pair of point clouds to be registered, q m,n is the point cloud to be registered, and q′ m,n is the reference point cloud.

[0070] In some embodiments, the point cloud to be registered refers to the first radar point cloud data.

[0071] In some embodiments, the reference point cloud refers to the reference point cloud data in the calibrated radar coordinate system.

[0072] In some embodiments, the number of point cloud pairs to be matched refers to the number of point clouds to be matched.

[0073] In some embodiments, the objective function refers to the contour model function corresponding to the tubular conveyor belt.

[0074] S103. Based on the second radar point cloud data and the wavelet transform algorithm, determine the mouth feature information corresponding to the tubular conveyor belt.

[0075] In some embodiments, the mouth feature information refers to the lightning point cloud data at the mouth position of the tubular conveyor belt.

[0076] Specifically, the transformation formula of wavelet transform is as follows:

[0077]

[0078] Among them, WT(a,τ) is the mouth feature information, a is the dilation scale of the wavelet function, f(t) is the second radar point cloud data of the current input frame, ψ is the wavelet basis function, and τ is the translation scale of the wavelet function.

[0079] In some embodiments, as Figure 4 shown, the specific process of signal wavelet decomposition is as follows: S represents the second radar point cloud data. After one wavelet transform, the high-frequency signal d1 and the low-frequency signal a1 are obtained. Then, the low-frequency signal a1 is subjected to wavelet transform to obtain the high-frequency signal d2 and the low-frequency signal a2 of the low-frequency signal a1. The wavelet transform of the low-frequency signal is repeated until the required mouth feature information is obtained.

[0080] S104. Fit the second radar point cloud data to obtain a fitting curve.

[0081] In some embodiments, fitting the second radar point cloud data to obtain a fitting curve includes: based on the piecewise least squares algorithm, fitting the second radar point cloud data to obtain a fitting curve.

[0082] Specifically, establish the model function of the tubular conveyor belt contour, and use the piecewise least squares algorithm and the second radar point cloud data to solve the model function to obtain the fitting result, that is, the coordinates corresponding to the fitting curve.

[0083] S105. Based on the fitting curve and the mouth feature information, determine the target contour information of the tubular conveyor belt.

[0084] In some embodiments, by combining the bag mouth feature information obtained in steps S103 and S104 and the fitting curve, and fusing the two, the target contour information of the complete tubular conveyor belt can be obtained. Specifically, reference can be made to Figure 5 .

[0085] S106. Compare the target contour information with the preset contour information to determine the detection result of the tubular conveyor belt.

[0086] In some embodiments, the detection result includes at least one of the following: the tubular conveyor belt is normal, the tubular conveyor belt is reverse-wrapped, the tubular conveyor belt is twisted, the tubular conveyor belt is expanded, and the tubular conveyor belt is collapsed.

[0087] In some embodiments, the preset contour information includes: normal contour information, reverse-wrapped contour information, twisted contour information, expanded contour information, and collapsed contour information.

[0088] Specifically, in combination with Figure 6 As shown, each abnormal state of the tubular conveyor belt corresponds to a preset contour information. If the target contour information is the same as the normal contour information, the tubular conveyor belt is normal. If the target contour information is the same as the reverse-wrapped contour information, the tubular conveyor belt is reverse-wrapped.

[0089] In some embodiments, after comparing the target contour information with the preset contour information to determine the detection result of the tubular conveyor belt, it further includes: when the detection result is not that the tubular conveyor belt is normal, an early warning message is sent. In this way, relevant personnel can be notified in a timely manner, which is beneficial to ensuring the safety of the tubular conveyor belt.

[0090] To better understand the embodiments of the present solution, in combination with Figures 2 to 7 As shown, the process of the fault detection method of the tubular conveyor belt in the present solution is further explained as follows: Four lidars are installed on the mounting bracket and are respectively located in four directions of the tubular conveyor belt. During the normal operation of the tubular conveyor belt, the four lidars continuously obtain a plurality of first lidar point cloud data of the tubular conveyor belt. Taking the plurality of first lidar point cloud data in the current frame as an example, then the world coordinate system corresponding to one of the lidars is used as the calibration lidar coordinate system, and then the first lidar point cloud data obtained by the remaining three lidars is converted into the second lidar point cloud data in the calibration lidar coordinate system. The target contour information of the tubular conveyor belt is determined through the wavelet transform algorithm and the bag mouth feature information. Finally, the detection result of the tubular conveyor belt is determined by comparing the target contour information with the preset contour information. By adopting this method, the efficiency of detecting the abnormal state of the tubular conveyor belt can be greatly improved, and the cost can be saved.

[0091] The technical solution provided by the present disclosure is to obtain multiple first radar point cloud data of the tubular conveyor belt from different perspectives; convert the multiple first radar point cloud data into second radar point cloud data calibrated in the radar coordinate system, where the second radar coordinate systems corresponding to the multiple first radar point cloud data from different perspectives are different; determine the mouthpiece feature information corresponding to the tubular conveyor belt based on the second radar point cloud data and the wavelet transform algorithm; fit the second radar point cloud data to obtain a fitting curve; determine the target contour information of the tubular conveyor belt based on the fitting curve and the mouthpiece feature information; compare the target contour information with the preset contour information to determine the detection result of the tubular conveyor belt. The technical solutions provided by the embodiments of the present disclosure use lidar to obtain multiple first radar point cloud data of the tubular conveyor belt from different perspectives, and then determine the contour information of the tubular conveyor belt at the current moment, compare it with the preset contour information, and determine the detection result of the tubular conveyor belt. Through comparison, it can be determined whether there is an abnormality in the tubular conveyor belt, greatly improving the detection efficiency.

[0092] Figure 8 FIG. 4 is a schematic structural diagram of a fault detection device for a tubular conveyor belt provided by an exemplary embodiment of the present disclosure;

[0093] Wherein, the device includes: an acquisition unit 201, a conversion unit 202, a determination unit 203, and a fitting unit 204;

[0094] The acquisition unit 201 is configured to acquire multiple first radar point cloud data of the tubular conveyor belt from different perspectives;

[0095] The conversion unit 202 is configured to convert the multiple first radar point cloud data into second radar point cloud data calibrated in the radar coordinate system, where the second radar coordinate systems corresponding to the multiple first radar point cloud data from different perspectives are different;

[0096] The determination unit 203 is configured to determine the mouthpiece feature information corresponding to the tubular conveyor belt based on the second radar point cloud data and the wavelet transform algorithm;

[0097] The fitting unit 204 is configured to fit the second radar point cloud data to obtain a fitting curve;

[0098] The determination unit 203 is configured to determine the target contour information of the tubular conveyor belt based on the fitting curve and the mouthpiece feature information;

[0099] The determination unit 203 is configured to compare the target contour information with the preset contour information to determine the detection result of the tubular conveyor belt.

[0100] In some embodiments, the device is used to obtain a plurality of first radar point cloud data of the tubular conveyor belt from different perspectives. Specifically, the device is configured to:

[0101] Obtain a plurality of original radar point cloud data of the tubular conveyor belt from different perspectives;

[0102] Preprocess the plurality of original lightning point cloud data to obtain the plurality of first radar point cloud data;

[0103] Wherein, the preprocessing at least includes: low-pass filtering and windowed correlation.

[0104] In some embodiments, the device is used to convert the plurality of first radar point cloud data into second radar point cloud data in a calibrated radar coordinate system. Specifically, the device is configured to:

[0105] Determine the second radar coordinate system corresponding to each first radar point cloud data and the translation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system;

[0106] Based on the plurality of first radar point cloud data, the second radar coordinate system corresponding to each first radar point cloud data, and the translation matrix between the second radar coordinate system corresponding to each first radar point cloud data and the calibrated radar coordinate system, determine the second radar point cloud data.

[0107] In some embodiments, the device is used to fit the second radar point cloud data to obtain a fitting curve. Specifically, the device is configured to:

[0108] Based on the piecewise least squares algorithm, fit the second radar point cloud data to obtain a fitting curve.

[0109] In some embodiments, the detection result at least includes one of the following: the tubular conveyor belt is normal, the tubular conveyor belt is reverse-wrapped, the tubular conveyor belt is twisted, the tubular conveyor belt is expanded, the tubular conveyor belt is collapsed.

[0110] In some embodiments, the preset contour information includes: normal contour information, reverse-wrapped contour information, twisted contour information, expanded contour information, collapsed contour information.

[0111] In some embodiments, after the device is used to compare the target contour information with the preset contour information to determine the detection result of the tubular conveyor belt, specifically, the device is configured to: when the detection result is not that the tubular conveyor belt is normal, send a warning message.

[0112] In some embodiments, the device is applicable to a radar device, and a plurality of lidars configured to obtain a plurality of first radar point cloud data of the tubular conveyor belt from different perspectives are arranged on the radar device;

[0113] Among them, the lidar corresponds one-to-one with the first radar point cloud data.

[0114] In some embodiments, a plurality of lidars are arranged on the radar device, and the plurality of lidars are located at different orientations of the tubular conveyor belt.

[0115] The technical solution provided by the present disclosure obtains a plurality of first radar point cloud data of the tubular conveyor belt from different perspectives; converts the plurality of first radar point cloud data into second radar point cloud data calibrated in the radar coordinate system, wherein the second radar coordinate systems corresponding to the plurality of first radar point cloud data from different perspectives are different; determines the mouth feature information corresponding to the tubular conveyor belt based on the second radar point cloud data and the wavelet transform algorithm; fits the second radar point cloud data to obtain a fitting curve; determines the target contour information of the tubular conveyor belt based on the fitting curve and the mouth feature information; compares the target contour information with the preset contour information to determine the detection result of the tubular conveyor belt. The technical solutions provided by the embodiments of the present disclosure obtain a plurality of first radar point cloud data of the tubular conveyor belt from different perspectives through lidar, and then determine the contour information of the tubular conveyor belt at the current moment, compare it with the preset contour information, and determine the detection result of the tubular conveyor belt. Through comparison, it can be determined whether there is an abnormality in the tubular conveyor belt, greatly improving the detection efficiency.

[0116] It should be understood that the device embodiments and the method embodiments can correspond to each other, and similar descriptions can refer to the method embodiments. To avoid repetition, it will not be elaborated here. Specifically, the device can execute the above method embodiments, and the foregoing and other operations and / or functions of each module in the device respectively correspond to the corresponding processes in each method in the above method embodiments. For the sake of brevity, it will not be elaborated here.

[0117] In the above, the device of the embodiments of the present disclosure has been described from the perspective of functional modules in combination with the drawings. It should be understood that the functional module can be implemented in the form of hardware, can also be implemented by instructions in the form of software, and can also be implemented by a combination of hardware and software modules. Specifically, the steps of the method embodiments in the present disclosure can be completed by the integrated logic circuit in the hardware in the processor and / or instructions in the form of software. The steps of the method disclosed in combination with the embodiments of the present disclosure can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps in the above method embodiments.

[0118] Figure 9 It is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. The electronic device may include:

[0119] A memory 301 and a processor 302. The memory 301 is used to store a computer program and transmit the program code to the processor 302. In other words, the processor 302 can call and run the computer program from the memory 301 to implement the method in the embodiment of the present disclosure.

[0120] For example, the processor 302 can be used to execute the above method embodiment according to the instructions in the computer program.

[0121] In some embodiments of the present disclosure, the processor 302 may include but is not limited to:

[0122] A general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on.

[0123] In some embodiments of the present disclosure, the memory 301 includes but is not limited to:

[0124] Volatile memory and / or non-volatile memory. Among them, the non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synch Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0125] In some embodiments of the present disclosure, the computer program may be divided into one or more modules, which are stored in the memory 301 and executed by the processor 302 to complete the method provided by the present disclosure. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0126] As Figure 9 shown, the electronic device may further include:

[0127] A transceiver 303, which may be connected to the processor 302 or the memory 301.

[0128] Among them, the processor 302 may control the transceiver 303 to communicate with other devices. Specifically, it may send information or data to other devices, or receive information or data sent by other devices. The transceiver 303 may include a transmitter and a receiver. The transceiver 303 may further include an antenna, and the number of antennas may be one or more.

[0129] It should be understood that the components in the electronic device are connected through a bus system. Among them, the bus system includes not only a data bus, but also a power bus, a control bus, and a status signal bus.

[0130] The present disclosure also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a computer, the computer can execute the methods in the above method embodiments. Or rather, the embodiments of the present disclosure also provide a computer program product containing instructions. When the instructions are executed by a computer, the computer executes the methods in the above method embodiments.

[0131] When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present disclosure are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, a computer, a server, or a data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or a data center that integrates one or more available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0132] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0133] In several embodiments provided by the present disclosure, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or modules can be in electrical, mechanical, or other forms.

[0134] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. For example, in each embodiment of the present disclosure, the various functional modules can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0135] The above is only the specific implementation manner of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present disclosure can easily think of changes or substitutions, which should all be covered by the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A fault detection method for a tubular conveyor belt, characterized in that, The method includes: Obtaining a plurality of first lidar point cloud data of the tubular conveyor belt from different perspectives; Converting the plurality of first lidar point cloud data into second lidar point cloud data in a calibrated lidar coordinate system, where the second lidar coordinate systems corresponding to the plurality of first lidar point cloud data from different perspectives are different; Determining the mouth wrapping feature information corresponding to the tubular conveyor belt based on the second lidar point cloud data and the wavelet transform algorithm; Fitting the second lidar point cloud data to obtain a fitted curve; Determining the target contour information of the tubular conveyor belt based on the fitted curve and the mouth wrapping feature information; Comparing the target contour information with the preset contour information to determine the detection result of the tubular conveyor belt.

2. The method according to claim 1, characterized in that, Obtaining a plurality of first lidar point cloud data of the tubular conveyor belt from different perspectives includes: Obtaining a plurality of original lidar point cloud data of the tubular conveyor belt from different perspectives; Preprocessing the plurality of original lightning point cloud data to obtain the plurality of first lidar point cloud data; Wherein, the preprocessing at least includes: low-pass filtering and windowing correlation.

3. The method according to claim 1, wherein Converting the plurality of first lidar point cloud data into second lidar point cloud data in a calibrated lidar coordinate system includes: Determining the second lidar coordinate system corresponding to each first lidar point cloud data, the translation matrix between the second lidar coordinate system corresponding to each first lidar point cloud data and the calibrated lidar coordinate system, and the rotation matrix between the second lidar coordinate system corresponding to each first lidar point cloud data and the calibrated lidar coordinate system; Determining the second lidar point cloud data based on the plurality of first lidar point cloud data, the second lidar coordinate system corresponding to each first lidar point cloud data, the translation matrix between the second lidar coordinate system corresponding to each first lidar point cloud data and the calibrated lidar coordinate system, and the rotation matrix between the second lidar coordinate system corresponding to each first lidar point cloud data and the calibrated lidar coordinate system.

4. The method according to claim 1, wherein Fitting the second lidar point cloud data to obtain a fitted curve includes: Based on the piecewise least squares algorithm, fitting the second lidar point cloud data to obtain a fitted curve.

5. The method according to claim 1, characterized in that The detection result at least includes one of the following: the tubular conveyor belt is normal, the tubular conveyor belt is reverse wrapped, the tubular conveyor belt is twisted, the tubular conveyor belt is expanded, the tubular conveyor belt is collapsed; The preset contour information includes: normal contour information, reverse wrapping contour information, twisting contour information, expanding contour information, collapsing contour information.

6. The method according to claim 1, wherein After comparing the target contour information with the preset contour information to determine the detection result of the tubular conveyor belt, it further includes: when the detection result is not that the tubular conveyor belt is normal, sending out a warning message.

7. The method according to any one of claims 1-6, characterized in that, The method is applicable to a radar device, and a plurality of lidars for obtaining a plurality of first lidar point cloud data of the tubular conveyor belt from different perspectives are configured on the radar device; Wherein, the lidar corresponds to the first lidar point cloud data one by one.

8. The method according to claim 7, wherein A plurality of lidars are arranged on the radar device, and the plurality of lidars are located at different positions of the tubular conveyor belt.

9. A detection device for a tubular conveyor belt, characterized in that, The device includes: An acquisition unit for acquiring a plurality of first lidar point cloud data of the tubular conveyor belt from different perspectives; A conversion unit for converting the multiple first radar point cloud data into second radar point cloud data in a calibrated radar coordinate system, where the second radar coordinate systems corresponding to the multiple first radar point cloud data from different perspectives are different; A determination unit for determining the bag mouth feature information corresponding to the tubular conveyor belt based on the second radar point cloud data and the wavelet transform algorithm; A fitting unit for fitting the second radar point cloud data to obtain a fitting curve; The determination unit for determining the target contour information of the tubular conveyor belt based on the fitting curve and the bag mouth feature information; The determination unit for comparing the target contour information with the preset contour information to determine the detection result of the tubular conveyor belt.

10. An electronic device, characterized in that, Comprising: A processor; And A memory for storing the executable instructions of the processor; Wherein, the processor is configured to execute the method according to any one of claims 1-9 by executing the executable instructions.

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