Belt running status detection method, device, electronic equipment and system

Abstract

This specification discloses a method, device, electronic device, and system for detecting the running status of a belt, which is applied to a belt conveyor. The method includes: determining a template frame and a comparison frame based on the image data of the belt; extracting stable feature points from the template frame and the comparison frame; constructing a transformation matrix between the template frame and the comparison frame based on the stable feature points; and determining the running status of the belt according to the transformation matrix. The technical solution of the present invention utilizes machine vision technology to break away from traditional contact detection and avoid the impact on detection accuracy caused by the sensor directly contacting the belt. The running status of the belt can be detected through a simple algorithm, which has the beneficial effects of simple and flexible deployment, high real-time performance, and high detection accuracy.

Description

Technical Field

[0001] This specification relates to the field of equipment detection technology, and in particular to a method, device, electronic equipment and system for detecting the running status of a belt of a belt conveyor. Background Art

[0002] Belt conveyors play a vital role in industrial and mining production and are crucial for material transportation in industries such as coal mining and metallurgy. To ensure the normal operation of conveying equipment, the operating status of the belts must be monitored.

[0003] At present, the status of belts is mainly monitored by setting physical sensors such as position sensors and speed sensors on the belts for contact detection. With the help of these physical sensors, parameters such as the distribution of materials carried by the belts, the position of the belts and the running speed of the belts are detected to determine the running status of the belts.

[0004] The drawback of existing detection methods is that sensors are easily damaged due to long-term contact with the belt, affecting the accuracy of detection results and system stability, thereby threatening production safety. Therefore, achieving stable and efficient status detection for belt conveyor belts is an urgent problem to be solved. Summary of the Invention

[0005] The present invention provides a belt status detection method, device, electronic equipment and system, which are applicable to belt conveyors and are used to solve the problem that sensors in contact detection methods are easily worn and damaged, resulting in reduced detection accuracy and stability.

[0006] To solve the above-mentioned technical problems, the present invention is achieved as follows:

[0007] In one aspect, the present invention provides a method for detecting a belt running state, which is applied to a belt conveyor, and the method comprises:

[0008] Determining a template frame and a comparison frame based on the image data of the belt;

[0009] Extracting stable feature points from the template frame and the comparison frame;

[0010] constructing a transformation matrix between the template frame and the comparison frame based on the stable feature points;

[0011] The running state of the belt is determined according to the transformation matrix.

[0012] In another aspect, the present invention provides a device for detecting a belt running state, which is applied to a belt conveyor, and comprises:

[0013] an image module configured to determine a template frame and a comparison frame based on image data of the belt;

[0014] an extraction module, configured to extract stable feature points based on the template frame and the comparison frame;

[0015] A construction module is configured to construct a transformation matrix between the template frame and the comparison frame based on the stable feature points;

[0016] The determination module is configured to determine the running state of the belt according to the transformation matrix.

[0017] On the other hand, the present invention also provides an electronic device for detecting the running state of a belt, comprising:

[0018] at least one processor; and,

[0019] a memory communicatively coupled to the at least one processor;

[0020] in,

[0021] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor, enabling the at least one processor to perform the belt running status detection method as described above.

[0022] On the other hand, the present invention further provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, enables the processor to implement the method for detecting the belt running state described in the present invention.

[0023] Another aspect of the present invention provides a belt operation status detection system, which is applied to a belt conveyor. The system includes the electronic device as described above, and an image acquisition device arranged along the conveyor belt; the electronic device processes the belt image captured by the image acquisition device to determine the operation status of the belt.

[0024] The technical solution of this invention utilizes machine vision technology to break away from traditional contact detection, preventing direct sensor contact with the belt. Even with long-term operation, this solution will not cause sensor wear, thereby minimizing the impact on detection accuracy. Based on the discovery that belts behave like rigid bodies, this solution detects belt operating conditions through simple algorithm optimization. This not only enables online, real-time processing of detection data, but also offers the advantages of simple and flexible deployment and high detection accuracy.

[0025] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 Schematic diagram of an application scenario of the belt running status detection method in an embodiment of the present invention;

[0028] Figure 2 Flowchart of a belt running status detection method according to an embodiment of the present invention;

[0029] Figure 3 is a basic flow chart of the SIFT algorithm in an embodiment of the present invention;

[0030] Figure 4 is a basic flow chart of the RANSAC algorithm in an embodiment of the present invention;

[0031] Figure 5 This is a flow chart of a belt running status detection method according to another embodiment of the present invention;

[0032] Figure 6 This is a structural block diagram of a belt running status detection device according to an embodiment of the present invention;

[0033] Figure 7 4 is a structural block diagram of a belt running status detection system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to help those skilled in the art better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0035] A belt conveyor, also known as a belt conveyor, is a friction-driven machine that continuously transports materials. It primarily consists of a frame, conveyor belt, rollers, drums, tensioning devices, and transmission devices. It forms a material conveying process along a specific conveyor line, from the initial feeding point to the final discharge point. The conveyor belt operates according to the principle of friction transmission and is suitable for transporting easily accessible powdered, granular, small, low-abrasive materials and bagged materials, such as coal, gravel, sand, cement, fertilizer, and grain. In mining production, belt conveyors are highly efficient and continuous transport equipment, offering advantages such as long conveying distances, large transport volumes, and continuous conveying. They are also reliable and easily automated and centrally controlled, making them crucial for material transportation in industries such as coal mining and metallurgy.

[0036] To ensure that the belt performs at its best during normal operation, it is crucial to accurately detect the belt's current operating status. Traditional belt operating status detection methods primarily use contact detection with mechanical sensors. The principle is to install physical sensors on the belt to monitor parameters such as the distribution of the material during operation, the position of the belt, and the operating speed. These sensors typically include position sensors, speed sensors, and other physical sensors. The working principle of traditional detection methods is relatively intuitive, but they rely on direct contact between the mechanical sensor and the belt or material. Over long-term operation, this can cause damage and wear to the sensor, thereby affecting detection accuracy and system stability.

[0037] After extensive research into conveyor belt operation data, the inventors discovered that the shape and size of large conveyor belts remain essentially unchanged during motion or after being subjected to force, exhibiting properties similar to those of a rigid body. Based on this discovery, the present invention proposes determining the belt's operating status by analyzing changes in the belt's image. Specifically, the method comprises: determining a template frame and a comparison frame based on the belt's image data; extracting stable feature points from the template frame and the comparison frame; constructing a transformation matrix between the template frame and the comparison frame based on the stable feature points; and determining the belt's operating status based on the transformation matrix. In practical implementation, the analysis can be completed using a simple algorithm.

[0038] Without loss of generality, this specification will take the belt conveyor in a coal mine as an example to illustrate the application scenario and specific implementation of the technical solution of the present invention. In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the drawings and specific implementation methods of the specification.

[0039] Figure 1 The following is a schematic diagram showing an application scenario of the belt running state detection method according to an embodiment of the present invention. Figure 1What is shown is only an example of an application scenario of an embodiment of the present invention to help those skilled in the art understand the technical content of the present invention, but it does not mean that the method for detecting the belt running status of an embodiment of the present invention cannot be applied to other scenarios.

[0040] like Figure 1 As shown, the scene includes a camera 110 , a detection server 120 , a display screen 130 , and a belt 140 to be detected.

[0041] In this scenario, the camera 110 is set along the conveying line of the belt 140 to be inspected, and the collected video data of the belt is uploaded to the inspection server 120. The inspection server 120 completes the analysis and inspection of the belt operation status based on this video data, and outputs the inspection results to the display screen 120 for the convenience of management personnel to view.

[0042] The following combination Figure 2 An embodiment of a belt running status detection method according to the present invention is described below.

[0043] Figure 2 The flowchart of the belt running status detection method according to an embodiment of the present invention is schematically shown.

[0044] like Figure 2 As shown, the method includes operations S210 to S240.

[0045] In operation S210, a template frame and a comparison frame are determined based on image data of the belt.

[0046] In operation S220 , stable feature points are extracted from the template frame and the comparison frame.

[0047] In operation S230 , a transformation matrix between the template frame and the comparison frame is constructed based on the stable feature points.

[0048] In operation S240, the running state of the belt is determined according to the transformation matrix.

[0049] According to an embodiment of the present invention, during operation S210, an industrial smart camera or camera installed on a coal and mineral material transportation production line can be placed at a station to be inspected to collect image data of the conveyor belt's operation. The belt's image data comprises a continuous temporal image sequence captured by the camera, from which two frames can be extracted for comparison during inspection. In some embodiments, the template frame and comparison frame are adjacent sampling frames, and the sampling interval can be freely set, such as a 1-second interval. To ensure the accuracy of the detection results, the temporal granularity between adjacent frames should not be too large, otherwise the impact of lighting may be negligible. It is generally controlled to around 1 second. To maintain continuous detection, template frames and comparison frames can be sampled from the continuous temporal image sequence of the belt captured by the camera based on a preset sampling rate. During algorithm initialization, the first frame is processed as the template frame, and the next sampled frame is processed as the comparison frame. Each round of calculation uses two adjacent frames, and the calculation can be continued sequentially, allowing real-time detection of changes in the belt's operating status. Regarding the sampling rate, a sampling rate of 1 frame / second generally meets detection requirements. Increasing the sampling rate increases the computational burden, but the detection results are more robust to lighting. In environments with minimal lighting changes, the sampling rate can be increased to reduce the computational load.

[0050] According to an embodiment of the present invention, when performing operation S220, a SIFT (Scale Invariant Feature Transform) algorithm may be used to extract stable feature points from the template frame and the comparison frame. The feature points extracted by the SIFT algorithm are scale-invariant and rotation-invariant, performing well in complex scenes and with large changes in lighting and shadows, and exhibiting excellent robustness.

[0051] The basic process of SIFT algorithm is as follows Figure 3 shown.

[0052] a. Scale-space extremum detection: Gaussian filtering is applied to the input image to generate image pyramids of different scales. At each level of the pyramid, local extremum points, i.e., candidate locations of key points, are detected.

[0053] b. Keypoint localization: At each scale, the differential image is used to locate the precise position of the keypoint. The keypoint is accurately located by fitting the local image around the keypoint.

[0054] c. Direction Assignment: Assign a main direction to each keypoint to make it rotation invariant. The main direction is determined based on the direction histogram of local gradients.

[0055] d. Keypoint description: Create a descriptor in the neighborhood around the keypoint. The descriptor is a vector that describes the appearance of the keypoint. The descriptor includes the gradient histogram of the area around the keypoint.

[0056] The feature points extracted by the SIFT algorithm are more robust and can be applied to complex production scenarios.

[0057] The SIFT algorithm can be used to extract features only from the belt area in the template frame and the sampling frame. Specifically, the belt area is manually delineated in the image data sent back by the current camera, and then features are extracted only from the belt area in the adjacent frames obtained by sampling.

[0058] According to an embodiment of the present invention, when implementing operation S230, based on the extracted stable feature points, the RANSAC (Random Sample Consensus) algorithm is used to construct a homography matrix, which is used as the transformation matrix between the template frame and the comparison frame. RANSAC is a robust estimation algorithm for fitting mathematical models. The core of the algorithm is to select data points by random sampling, fit the model, and filter out data points that meet the model through internal point testing, thereby estimating robust model parameters. In operation S230, in addition to using the RANSAC algorithm to obtain the homography matrix between adjacent sampling frames, algorithms such as LMS (Least Median of Squares) and MSAC (M-estimator Sample Consensus) can also be used to obtain the transformation matrix.

[0059] The basic process of the RANSAC algorithm is as follows Figure 4 shown.

[0060] a. Initialization: Randomly select a certain number of data points from the data set as initial samples. In the embodiment of the present invention, the data set is the extracted stable feature points.

[0061] b. Model fitting: Use the initial sample to fit the model and obtain the model parameters.

[0062] c. Inlier test: bring other data points into the model and determine whether they are consistent with the model based on a predefined threshold, and mark the consistent points as inliers.

[0063] d. Model evaluation: Count the number of inliers as the performance indicator of the model.

[0064] Repeat the above steps until a satisfactory confidence level is reached. Since RANSAC has fewer parameters (only 6), compared to conventional neural network models, it does not require a large amount of data training and can quickly iterate to obtain model parameters. Its calculation speed is very fast, which can ensure the real-time performance of online detection.

[0065] According to an embodiment of the present invention, when performing operation S240, the condition number of the transformation matrix can be calculated; if the condition number is close to 1, it is determined that the belt is in a stationary state; if the condition number falls within a preset range, it is determined that the belt is in a running state. The condition number of the transformation matrix can be calculated as follows:

[0066] Perform SVD decomposition on the transformation matrix H output by RANSAC to obtain the eigenvalue matrix of H;

[0067] Take out the maximum value max and minimum value min in the eigenvalue matrix of H, and the condition number cond = max / min.

[0068] If the belt is not moving, the transformation matrix H is almost the same as the unit diagonal matrix, and cond approaches 1. If the belt is in operation, the transformation matrix H is significantly different from the unit diagonal matrix, and cond will not be close to 1. When |cond-1|>τ, the belt can be determined to be in operation, where τ is a preset threshold, for example, τ=0.3. τ can be set according to the changes in lighting conditions and the size of the belt area. For example, if the lighting changes dramatically, τ needs to be set larger to avoid misjudging the changes in light and shadow as belt movement. Since the condition number is calculated from the maximum and minimum values ​​of the eigenvalues ​​of the homography matrix, it is more stable than the original data (such as pixels or matrices) and can be used as a basis for judgment.

[0069] According to the technical solution of the embodiment of the present invention, the almost rigid body characteristics of the belt and machine vision technology are utilized, and the running status of the belt is determined by comparing the sampled images and using the condition number of the homography matrix of adjacent images. The applied algorithm is simple, highly real-time, and has a high detection accuracy.

[0070] Figure 5 The flowchart of another embodiment of the belt running status detection method according to the present invention is schematically shown.

[0071] In order to maintain continuous monitoring of the belt state, after the current adjacent frames are processed and the running state of the belt is determined, subsequent operations S250 and S260 may be continued.

[0072] like Figure 5 As shown, this method Figure 2 The method of the embodiment shown may further include operation S250 and operation S260.

[0073] In operation S250, if the belt is determined to be in motion, the previous comparison frame is used as the new template frame, and the next round of calculations is performed after the new comparison frame is obtained. After the belt is determined to be in motion, the template frame and sampling frame need to be updated, and detection can be continued by sliding backward. The previous comparison frame can be used as the template frame for the next round, and the next adjacent frame from the image sequence can be used as the comparison frame. This ensures that the next round of calculations will remain accurate and effective.

[0074] In operation S260, if the belt is determined to be stationary, the original template frame is kept unchanged and the next round of calculation is performed after obtaining a new comparison frame. After the belt is determined to be stationary, only the comparison frame needs to be updated, which can also reduce the amount of data to be detected.

[0075] After the detection of the current adjacent frame is completed, the sampling can be slid backward in the image sequence to continue the detection according to the above operations S250 and S260, or the adjacent frame can be sampled and performed again after a period of time. The former has better robustness, and the latter has a smaller system load, which can be set according to actual needs.

[0076] Based on the same inventive concept, this specification also provides a belt running state detection device 600, which can be used in a belt conveyor. Figure 6 The detection device 600 will be described.

[0077] Figure 6 The block diagram of the detection device 600 according to the embodiment of the present invention is schematically shown. The detection device 600 can be implemented as part or all of an electronic device through software, hardware, or a combination of both.

[0078] like Figure 6 As shown, the detection device 600 includes an image module 610, an extraction module 620, a construction module 630, and a determination module 640. The detection device 600 can execute the various methods described above.

[0079] An image module 610 is configured to determine a template frame and a comparison frame based on the image data of the belt;

[0080] An extraction module 620 is configured to extract stable feature points based on the template frame and the comparison frame;

[0081] A construction module 630 is configured to construct a transformation matrix between the template frame and the comparison frame based on the stable feature points;

[0082] The determination module 640 is configured to determine the running state of the belt according to the transformation matrix.

[0083] According to the belt running status detection device of the embodiment of the present invention, the almost rigid body characteristics of the belt are utilized, and the running status of the belt is determined by comparing the sampled images and using the condition number of the homography matrix of adjacent images. The deployment is flexible, and the application algorithm is simple and efficient, which can meet the requirements of real-time and accurate detection of the belt running status.

[0084] This specification also provides an electronic device 710 for detecting a belt running state, the electronic device 710 including at least one processor; and

[0085] a memory communicatively coupled to the at least one processor;

[0086] in,

[0087] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor, enabling the at least one processor to execute any belt running status detection method as described above.

[0088] Based on the same inventive concept, this specification also provides a belt running status detection system 700, which can be applied to a belt conveyor. Figure 7 A block diagram of a detection system 700 for implementing an embodiment of the present invention is schematically shown.

[0089] like Figure 7 As shown, the detection system 700 includes the aforementioned electronic device 710 and an image acquisition device 720 disposed along the conveyor belt. The electronic device 710 processes the belt image acquired by the image acquisition device 720 to determine the running status of the belt.

[0090] This specification also provides a computer-readable storage medium. This computer-readable storage medium can be included in the electronic device or computer system described in the above embodiments, or it can be a standalone computer-readable storage medium not incorporated into the device. The computer-readable storage medium stores executable instructions that, when executed by a processor, cause the processor to implement the belt operating status detection method described in this specification.

[0091] The foregoing description describes specific embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences between the other embodiments. In particular, the device, electronic device, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0093] The apparatus, electronic device, and non-volatile computer storage medium provided in the embodiments of this specification correspond to the method. Therefore, the apparatus, electronic device, and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, electronic device, and non-volatile computer storage medium will not be repeated here.

[0094] In the 1990s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements today can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD through their own programming, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly done using "logic compiler" software. This is similar to the software compiler used when developing programs. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There is not just one HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages ​​and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0095] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to implementing the controller in a purely computer-readable program code format, the controller can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules that implement the method and structures within the hardware component.

[0096] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0097] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0098] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data optimization device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data optimization device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data optimization device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data optimization device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide for implementing the process described in the flow. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0102] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0103] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0104] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0106] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0107] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0108] The foregoing is merely an embodiment of the present invention and is not intended to limit the present application. For those skilled in the art, various modifications and variations may be made to the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for detecting the running state of a belt, applied to a belt conveyor, characterized in that: The method comprises: Determining a template frame and a comparison frame based on the image data of the belt; Extracting stable feature points from the template frame and the comparison frame includes: extracting stable feature points based on the template frame and the comparison frame using a scale-invariant feature transformation algorithm; constructing a transformation matrix between the template frame and the comparison frame based on the stable feature points; Determining the running state of the belt according to the transformation matrix includes: calculating the condition number cond of the transformation matrix; if cond approaches 1, determining that the belt is not moving; and determining that the belt is in a running state when |cond - 1| >τ, where τ is a preset threshold value set according to changes in the lighting conditions of the belt, and the condition number cond is the ratio of the maximum value to the minimum value of the eigenvalue of the transformation matrix.

2. The method according to claim 1, characterized in that The step of determining the template frame and the comparison frame based on the image data of the belt includes: Based on a preset sampling rate, template frames and comparison frames are sampled from the continuous time image sequence of the belt.

3. The method according to claim 1, characterized in that The step of constructing a transformation matrix between the template frame and the comparison frame based on the stable feature points includes: Based on the stable feature points, a random sampling consistency algorithm is used to construct a homography matrix as a transformation matrix between the template frame and the comparison frame.

4. The method according to claim 1, wherein The method further comprises: If it is determined that the belt is in a running state, the comparison frame is used as a new template frame, and the next round of calculation is performed after the new comparison frame is obtained.

5. The method according to claim 1, wherein The method further comprises: If it is determined that the belt is in a stationary state, the template frame is kept unchanged, and the next round of calculation is performed after a new comparison frame is obtained.

6. A belt running status detection device, applied to a belt conveyor, characterized in that: The device comprises: an image module configured to determine a template frame and a comparison frame based on image data of the belt; An extraction module is configured to extract stable feature points based on the template frame and the comparison frame, including: extracting stable feature points based on the template frame and the comparison frame using a scale-invariant feature transformation algorithm; A construction module is configured to construct a transformation matrix between the template frame and the comparison frame based on the stable feature points; The determination module is configured to determine the running state of the belt according to the transformation matrix, including: calculating the condition number cond of the transformation matrix; if cond approaches 1, determining that the belt is not moving; when |cond - 1| >τ, determining that the belt is in a running state; τ is a preset threshold value set according to changes in the lighting conditions of the belt; the condition number cond is the ratio of the maximum value to the minimum value of the eigenvalue of the transformation matrix.

7. An electronic device for detecting the running status of a belt, comprising: at least one processor; as well as, a memory communicatively coupled to the at least one processor; in, The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method according to claims 1 to 5.

8. A belt running status detection system, applied to a belt conveyor, characterized in that: The system includes the electronic device as described in claim 7 and an image acquisition device arranged along the conveyor belt; the electronic device processes the belt image acquired by the image acquisition device to determine the running status of the belt.

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