CN113989285B - Belt deviation monitoring method, device and equipment based on image and storage medium - Google Patents
Belt deviation monitoring method, device and equipment based on image and storage medium Download PDFInfo
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Abstract
The invention discloses a belt deviation monitoring method, a belt deviation monitoring device, belt deviation monitoring equipment and a storage medium based on images, wherein the method comprises the following steps: selecting a plurality of monitoring images from a monitoring video stream of a coal conveying belt according to a time sequence, and denoising each monitoring image through a preset median filter to obtain a plurality of denoising monitoring images; carrying out standardization processing on each denoising monitoring image to obtain corresponding standard monitoring image data; and determining the deviation predicted value of the coal conveying belt according to the standard monitoring image data and the deviation prediction model, and sending out a coal conveying belt deviation warning when the deviation predicted value is greater than a preset threshold value. The problem that the deviation value of the coal conveying belt cannot be predicted in the coal conveying process in the prior art is solved, and deviation warning can be sent out before the coal conveying belt deviates so as to avoid safety accidents in advance.
Description
Technical Field
The invention relates to the technical field of coal conveying monitoring, in particular to a belt deviation monitoring method, device and equipment based on images and a storage medium.
Background
At present, in the coal conveying field, generally adopt the belt as the transportation carrier, but at coal conveying belt operation in-process, need detect it in real time or regularly, in order to prevent that coal conveying belt skew from leading to the incident, generally adopt artifical the mode of patrolling and examining and making a video recording control to detect coal conveying belt at present, but the subjectivity of artifical patrolling and examining is big and can not real-time detection, the control of making a video recording can realize real-time monitoring, but can only just send the early warning when coal conveying belt skew is great, easily cause the incident, how to predict the skew of coal conveying belt becomes the technical problem that awaits the opportune moment and solve with the emergence of prevention incident.
The above is only for the purpose of assisting understanding of the technical solution of the present invention, and does not represent an admission that the above is the prior art.
Disclosure of Invention
The invention mainly aims to provide a belt deviation monitoring method, a belt deviation monitoring device, belt deviation monitoring equipment and a storage medium based on images, and aims to solve the technical problem that the deviation of a coal conveying belt cannot be predicted in the coal conveying process in the prior art.
To achieve the above object, the present invention provides an image-based belt shift monitoring method, comprising the steps of:
selecting a plurality of monitoring images from a monitoring video stream of a coal conveying belt according to a time sequence, and denoising each monitoring image through a preset median filter to obtain a plurality of denoising monitoring images;
carrying out standardization processing on each denoising monitoring image to obtain corresponding standard monitoring image data;
and determining the deviation predicted value of the coal conveying belt according to the standard monitoring image data and the deviation prediction model, and sending out a coal conveying belt deviation warning when the deviation predicted value is greater than a preset threshold value.
Optionally, the normalizing each denoised monitoring image to obtain corresponding standard monitoring data includes:
carrying out gray level processing on each denoising monitoring image to obtain a gray level monitoring image;
carrying out contrast enhancement processing on each gray level monitoring image to obtain an enhanced monitoring image;
and acquiring a monitoring gray value and a corresponding coordinate position of each pixel point of the enhanced monitoring image, and storing the monitoring gray value and the corresponding coordinate position to a target memory to acquire standard monitoring image data.
Optionally, before the selecting a plurality of monitoring images from the monitoring video stream of the coal conveying belt according to the time sequence and denoising each monitoring image by using a preset median filter to obtain a plurality of denoised monitoring images, the method further includes:
acquiring a reference monitoring image of a coal conveying belt, and carrying out standardization processing on the reference monitoring image to obtain reference data;
determining a reference gray value and a corresponding coordinate position of each pixel point in the reference monitoring image according to the reference data;
determining a belt edge reference line of the reference monitoring image in a preset coordinate system according to the reference gray value and the corresponding coordinate position;
selecting a plurality of monitoring coordinates in the preset coordinate system, and determining a belt deviation mean value according to the belt edge reference line and the monitoring coordinates;
and determining an offset prediction function of the coal conveying belt through the belt offset mean value and a least square method to obtain an offset prediction model of the coal conveying belt.
Optionally, the determining, according to the reference gray value and the corresponding coordinate position, a belt edge reference line of the reference monitoring image in a preset coordinate system includes:
according to the coordinate position, the reference gray values of the adjacent positions are differentiated to obtain a gray difference value set;
when the gray difference value in the gray difference value set is larger than a preset gray value, adding the coordinate position corresponding to the gray difference value to a pre-selected characteristic coordinate position set;
mapping the preselected characteristic coordinate positions in the preselected characteristic coordinate position set to a preset coordinate system to obtain corresponding preselected characteristic points, and removing outliers in the preselected characteristic points to obtain characteristic points;
and determining a belt edge reference line of the reference monitoring image in a preset coordinate system according to the characteristic points.
Optionally, the determining a predicted deviation value of the coal belt according to the standard monitoring image data and the deviation prediction model, and sending a warning of deviation of the coal belt when the predicted deviation value is greater than a preset threshold value includes:
determining pixel information of each pixel point according to each standard monitoring image data, and determining belt edge monitoring data corresponding to each monitoring image according to the pixel information;
inputting the belt edge monitoring data into an offset prediction model to obtain an offset prediction value output by the offset prediction model;
and when the deviation predicted value is larger than a preset threshold value, sending out a coal conveying belt deviation warning.
Optionally, the inputting the belt edge monitoring data into an offset prediction model to obtain an offset prediction value output by the offset prediction model includes:
inputting the belt edge monitoring data into an offset prediction model to generate a belt edge monitoring line corresponding to each monitoring image in the preset coordinate system;
selecting a plurality of monitoring points on each belt edge monitoring line according to the monitoring coordinates of the offset prediction model;
determining a belt deviation value between each monitoring point and the belt edge datum line according to the deviation prediction model, and determining a belt deviation mean value according to the belt deviation value;
and determining a current offset prediction function according to the offset mean value of each belt and the offset prediction model, and outputting the offset prediction value of the coal conveying belt through the current offset prediction function.
Optionally, after determining a predicted deviation value of the coal belt according to the standard monitoring image data and the deviation prediction model and sending a coal belt deviation warning when the predicted deviation value is greater than a preset threshold, the method further includes:
detecting whether a controller of the coal conveying belt receives a preset control signal or not;
when the controller does not receive the preset control signal within a first preset time length, sending a deceleration control signal to the controller;
and when the controller does not receive the preset control signal within a second preset time, sending a shutdown control signal to the controller.
Further, to achieve the above object, the present invention also proposes an image-based belt deviation monitoring apparatus, comprising:
the system comprises a denoising module, a monitoring module and a processing module, wherein the denoising module is used for selecting a plurality of monitoring images from a monitoring video stream of a coal conveying belt according to a time sequence, and denoising the monitoring images through a preset median filter to obtain a plurality of denoising monitoring images;
the processing module is used for carrying out standardized processing on each denoising monitoring image to obtain corresponding standard monitoring image data;
and the prediction module is used for determining the deviation prediction value of the coal conveying belt according to the standard monitoring image data and the deviation prediction model and sending out a coal conveying belt deviation warning when the deviation prediction value is greater than a preset threshold value.
Further, to achieve the above object, the present invention also proposes an image-based belt deviation monitoring apparatus, comprising: a memory, a processor and an image-based belt deviation monitoring program stored on the memory and executable on the processor, the image-based belt deviation monitoring program configured to implement the steps of the image-based belt deviation monitoring method as described above.
Furthermore, to achieve the above object, the present invention also proposes a storage medium having stored thereon an image-based belt deviation monitoring program which, when executed by a processor, implements the steps of the image-based belt deviation monitoring method as described above.
Selecting a plurality of monitoring images from a monitoring video stream of a coal conveying belt according to a time sequence, and denoising each monitoring image through a preset median filter to obtain a plurality of denoising monitoring images; carrying out standardization processing on each denoising monitoring image to obtain corresponding standard monitoring image data; and determining the deviation predicted value of the coal conveying belt according to the standard monitoring image data and the deviation prediction model, and sending out a coal conveying belt deviation warning when the deviation predicted value is greater than a preset threshold value. The method comprises the steps of denoising a monitoring image selected from a monitoring video stream through a preset median filter to obtain a denoising monitoring image, standardizing the denoising monitoring image to obtain standard monitoring image data, inputting the standard monitoring image data into an offset prediction model to obtain an offset prediction value, and sending a coal conveying belt offset warning when the offset prediction value is larger than a preset threshold value.
Drawings
FIG. 1 is a schematic diagram of an image-based belt deviation monitoring device for a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a method of image-based belt deviation monitoring in accordance with the present invention;
FIG. 3 is a schematic coordinate system diagram of a first embodiment of a method of image-based belt deviation monitoring according to the present invention;
FIG. 4 is a schematic flow chart diagram of a second embodiment of a method of image-based belt deviation monitoring in accordance with the present invention;
FIG. 5 is a schematic flow chart diagram of a third embodiment of a method of image-based belt deviation monitoring in accordance with the present invention;
FIG. 6 is a block diagram of a first embodiment of an image-based belt deviation monitoring apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an image-based belt deviation monitoring device of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the image-based belt deviation monitoring apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation of the image-based belt shift monitoring device, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and an image-based belt deviation monitoring program.
In the image-based belt deviation monitoring apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the image-based belt deviation monitoring apparatus of the present invention may be provided in an image-based belt deviation monitoring apparatus which calls an image-based belt deviation monitoring program stored in the memory 1005 through the processor 1001 and executes the image-based belt deviation monitoring method provided by the embodiment of the present invention.
An embodiment of the present invention provides an image-based belt deviation monitoring method, and referring to fig. 2, fig. 2 is a schematic flowchart of a first embodiment of the image-based belt deviation monitoring method according to the present invention.
In this embodiment, the image-based belt deviation monitoring method includes the following steps:
step S1: selecting a plurality of monitoring images from the monitoring video stream of the coal conveying belt according to the time sequence, and denoising each monitoring image through a preset median filter to obtain a plurality of denoising monitoring images.
It should be noted that the execution subject of the embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, an image-based belt deviation monitoring device, etc. capable of implementing the above functions. The present embodiment and the following embodiments will be described below by taking an image-based belt deviation monitoring apparatus as an example.
It can be understood that the monitoring video stream can be a monitoring video of the coal belt shot by the monitoring camera; the monitoring image can be an image selected from a monitoring video stream; selecting a plurality of monitoring images from the monitoring video stream of the coal conveying belt according to the time sequence can be selecting a plurality of frame images from the monitoring video stream as the monitoring images according to the time sequence; the preset median filter can remove noise in the monitored image to obtain a de-noising monitored image.
In the specific implementation, the image-based belt deviation monitoring equipment selects a plurality of monitoring images from the monitoring video stream according to the time sequence, and removes the noise in each monitoring image through a preset median filter to obtain the de-noising monitoring image.
Step S2: and carrying out standardization processing on each denoising monitoring image to obtain corresponding standard monitoring image data.
It should be understood that the normalization process may be a process of converting the denoised monitoring image into standard data, and the normalization process may filter out information that is not useful for the offset monitoring of the coal conveyor belt to improve the monitoring efficiency.
It can be understood that the normalization processing may be to perform image conversion on the de-noised monitoring image, and then perform pixel analysis on the converted image to obtain standard monitoring data.
Step S3: and determining the deviation predicted value of the coal conveying belt according to the standard monitoring image data and the deviation prediction model, and sending out a coal conveying belt deviation warning when the deviation predicted value is greater than a preset threshold value.
It is understood that the deviation prediction model can be a model for predicting the belt deviation value of the coal conveying belt in a future period of time according to historical standard monitoring image data; the preset threshold value can be a preset maximum value of the allowable deviation in the normal operation process of the coal conveying belt, and when the deviation value of the coal conveying belt is smaller than the preset threshold value, the normal operation of the coal conveying belt can be judged; and when the deviation value of the coal conveying belt is greater than the preset threshold value, judging that the coal conveying belt works abnormally, and sending a belt deviation warning.
In the concrete implementation, the image-based coal conveying monitoring equipment selects a plurality of monitoring images according to the sequence of image frames in a monitoring video stream of a coal conveying belt, performs noise reduction on the monitoring images through a preset median filter to obtain de-noised monitoring images, performs image conversion on each de-noised monitoring image, performs pixel analysis on the converted images to obtain a plurality of standard monitoring data, inputs the plurality of standard monitoring data into an offset prediction model to obtain a coal conveying belt offset prediction value, judges that the coal conveying belt has an offset fault when the offset prediction value is larger than the maximum value of allowed offset in the normal operation process, and outputs a belt offset warning.
Further, since whether the coal conveyor belt is shifted and the amount of shift can be determined according to the belt edge of the coal conveyor belt when monitoring the coal conveyor belt, in order to improve the efficiency of data processing, the step S2 includes: carrying out gray level processing on each denoising monitoring image to obtain a gray level monitoring image; carrying out contrast enhancement processing on each gray level monitoring image to obtain an enhanced monitoring image; and acquiring a monitoring gray value and a corresponding coordinate position of each pixel point of the enhanced monitoring image, and storing the monitoring gray value and the corresponding coordinate position into a target memory to acquire standard monitoring image data.
It can be understood that, when the coal conveying belt is subjected to offset monitoring based on the image, the offset monitoring is mainly realized by analyzing the belt edge of the coal conveying belt in the image, and the color in the image is irrelevant information, so that the gray level processing is performed on the monitored image; the gray level processing of the denoised image can be to analyze the pixels of the denoised image to obtain the RGB values of the pixels in the image, calculate the average values of the R value, the G value and the B value of the RGB values of the pixels, and replace the RGB values of the corresponding pixels with the average values to obtain the gray level monitoring image.
It should be understood that, when the coal conveying belt is subjected to offset monitoring based on the image, the key point is to determine the belt edge of the coal conveying belt so as to determine the belt offset, so that the gray level monitoring image can be subjected to contrast enhancement processing, the belt edge can be determined more easily, the belt edge in the image can be determined rapidly, and the gray level monitoring image can be subjected to contrast enhancement processing in a histogram equalization mode to obtain an enhanced monitoring image.
It can be understood that the number of pixel points in each enhanced monitoring image is the same; the monitoring gray value can be the RGB value of the pixel point; the coordinate position can be the position of each pixel point in the enhanced monitoring image, referring to fig. 3, the length of the enhanced monitoring image can be used as an X axis, the width of the enhanced monitoring image can be used as a Y axis to construct a coordinate system, and the coordinate position can be the position of each pixel point in the coordinate system; and storing the monitoring gray value and the corresponding coordinate position into a target memory to obtain standard monitoring image data corresponding to each monitoring image.
Further, after the belt deviation warning is issued by the image-based belt deviation monitoring device in a real scene, the relevant staff may not take timely measures to cause a safety accident, in order to avoid the safety accident caused by the deviation of the coal conveying belt, after the step S3, the method further includes:
detecting whether a controller of the coal conveying belt receives a preset control signal or not; when the controller does not receive the preset control signal within a first preset time length, sending a deceleration control signal to the controller; and when the controller does not receive the preset control signal within a second preset time, sending a shutdown control signal to the controller.
It is understood that the Controller of the coal belt may be a Programmable Logic Controller (PLC) for controlling the operation of the coal belt; the preset control signal can be a deceleration control signal or a stop control signal; the image-based belt deviation monitoring device may detect a control signal received by the controller and identify a signal type of the control signal.
In the specific implementation, when the belt deviation monitoring equipment based on the image sends out a coal conveying belt deviation warning, the belt deviation monitoring equipment detects a control signal received by a PLC (programmable logic controller) and identifies the signal type of the control signal, judges whether the signal type is a deceleration control signal or a stop control signal, and sends out the deceleration control signal to the PLC if the PLC does not receive the deceleration control signal or the stop control signal within a first preset time period so as to reduce the running speed of the coal conveying belt to be below a safe speed; if the PLC does not receive the speed reduction control signal or the shutdown control signal within the second preset time, the PLC is sent a shutdown control signal to avoid safety accidents caused by belt deviation and improve the safety of the coal conveying process.
The method comprises the steps of selecting a plurality of monitoring images from a monitoring video stream of a coal conveying belt according to a time sequence, and denoising each monitoring image through a preset median filter to obtain a plurality of denoising monitoring images; carrying out standardization processing on each denoising monitoring image to obtain corresponding standard monitoring image data; and determining the deviation predicted value of the coal conveying belt according to the standard monitoring image data and the deviation prediction model, and sending out a coal conveying belt deviation warning when the deviation predicted value is greater than a preset threshold value. According to the method, the noise reduction is carried out on the monitoring image selected from the monitoring video stream through the preset median filter to obtain the noise reduction monitoring image, the noise reduction monitoring image is subjected to standardization processing to obtain the standard monitoring image data, the standard monitoring image data is input into the offset prediction model to obtain the offset prediction value, and when the offset prediction value is larger than the preset threshold value, the coal conveying belt offset warning is sent out.
Referring to fig. 4, fig. 4 is a schematic flow chart of a second embodiment of the image-based belt shift monitoring method of the present invention.
Based on the first embodiment, in this embodiment, before the step S1, the method further includes:
step S01: and acquiring a reference monitoring image of the coal conveying belt, and carrying out standardization processing on the reference monitoring image to obtain reference data.
It can be understood that the reference monitoring image can be a monitoring image of the coal conveying belt in the normal operation process of the coal conveying belt; the resolution of the reference monitoring image is the same as that of the monitoring image; the standard data obtained by standardizing the reference monitoring image can be obtained by sequentially carrying out gray processing, contrast enhancement processing and pixel analysis on the reference monitoring image.
Step S02: and determining the reference gray value and the corresponding coordinate position of each pixel point in the reference monitoring image according to the reference data.
It should be understood that the reference gray-scale value may be an RGB value of each pixel point, and the RGB value is used as the reference gray-scale value; the reference data comprises a reference gray value and a corresponding coordinate position of each pixel point of the reference monitoring image.
Step S03: and determining a belt edge reference line of the reference monitoring image in a preset coordinate system according to the reference gray value and the corresponding coordinate position.
It can be understood that, near the belt edge in the monitored image, the RGB value corresponding to the pixel point will jump; determining a coordinate position where the RGB value jumps according to the reference gray value and the corresponding coordinate position, and drawing a straight line in a preset coordinate system according to the coordinate position where the jumping occurs, wherein the straight line is a belt edge reference line; each pixel point in the preset coordinate system is a coordinate unit.
It should be understood that only the belt edge reference line of the reference monitoring image is reserved in the preset coordinate system, and the rest data are removed, so as to improve the efficiency of subsequent data processing.
Step S04: and selecting a plurality of monitoring coordinates in the preset coordinate system, and determining a belt deviation mean value according to the belt edge datum line and the monitoring coordinates.
It can be understood that the monitoring coordinate may be an abscissa selected in advance in a preset coordinate system; after the belt edge line of the monitoring image is input, a plurality of monitoring points can be determined on the belt edge line according to a plurality of monitoring coordinates, and the belt deviation mean value can be determined according to the distance from the monitoring points to the belt edge reference line.
In the specific implementation, a plurality of abscissa coordinates are selected in a preset coordinate system in advance, after the belt edge line of a monitoring image is input, a plurality of monitoring points are determined on the belt edge line according to the plurality of abscissa coordinates, the distances from the monitoring points to the belt edge reference line are calculated to obtain a plurality of monitoring distances, and the belt deviation mean value is determined according to the monitoring distances.
Step S05: and determining an offset prediction function of the coal conveying belt through the belt offset mean value and a least square method to obtain an offset prediction model of the coal conveying belt.
It should be understood that a plurality of belt deviation mean values can be obtained by inputting a plurality of belt edge lines according to a time sequence, and a deviation prediction function can be obtained by fitting a least square method to the plurality of belt deviation mean values, so that a deviation prediction model of the coal conveying belt is obtained; the offset prediction function may be a function between time and a mean of belt offset.
Further, in order to extract the belt edge from the reference monitoring image to improve the efficiency of subsequent data processing, the step S03 includes:
step S031: and according to the coordinate position, making difference on the reference gray value of the adjacent position to obtain a gray difference value set.
It can be understood that, the difference of the reference gray values of the adjacent positions according to the coordinate positions to obtain the gray difference set may be obtained by first obtaining a reference mean value of the reference gray values of each pixel point, and obtaining the gray difference set by subtracting the reference mean values of the adjacent positions according to the coordinate positions; in the gray difference value set, each gray difference value corresponds to a coordinate position, and if the reference gray values of the transversely adjacent positions are differed, the coordinate position with the smaller abscissa is taken as the coordinate position corresponding to the gray difference value; if the reference gray scale values of the vertical adjacent positions are differentiated, the coordinate position with the larger vertical coordinate is taken as the coordinate position of the gray scale difference value.
In a specific implementation, for example, the coordinate positions and the reference gray values of the two pixel points are respectively: pixel point A: coordinate position (2, 1), reference gray value (R =60, G =60, B = 60); and a pixel point B: coordinate position (3, 1), and reference gray scale value (R =80, G =80, B = 80), the reference mean value of pixel point a is 60, the reference mean value of pixel point B is 80, and the gray scale difference obtained after the difference is 20.
Step S032: and when the gray difference value in the gray difference value set is greater than a preset gray value, adding the coordinate position corresponding to the gray difference value to a preselected characteristic coordinate position set.
It can be understood that when the gray difference value is greater than the preset gray value, the coordinate position corresponding to the gray difference value is determined to be on the belt edge reference line, and then the coordinate position is added to the preselected feature coordinate position set as the preselected feature coordinate position.
Step S033: and mapping the preselected characteristic coordinate positions in the preselected characteristic coordinate position set to a preset coordinate system to obtain corresponding preselected characteristic points, and removing outliers in the preselected characteristic points to obtain characteristic points.
In the specific implementation, each preselected feature coordinate position is mapped to a preset coordinate system to obtain preselected feature points, outliers in the preselected feature points are removed, and the remaining points are feature points.
Step S034: and determining a belt edge reference line of the reference monitoring image in a preset coordinate system according to the characteristic points.
In a specific implementation, a straight line is drawn in a preset coordinate system according to the characteristic points, and the straight line is a belt edge reference line of the reference monitoring image.
The method comprises the steps of obtaining a reference monitoring image of a coal conveying belt, and carrying out standardization processing on the reference monitoring image to obtain reference data; determining a reference gray value and a corresponding coordinate position of each pixel point in the reference monitoring image according to the reference data; determining a belt edge reference line of the reference monitoring image in a preset coordinate system according to the reference gray value and the corresponding coordinate position; selecting a plurality of monitoring coordinates in the preset coordinate system, and determining a belt deviation mean value according to the belt edge reference line and the monitoring coordinates; and determining an offset prediction function of the coal conveying belt through the belt offset mean value and a least square method to obtain an offset prediction model of the coal conveying belt. According to the embodiment, the belt edge reference line in the extracted reference monitoring image is mapped to the preset coordinate system, the monitoring point is selected on the belt edge line of the input monitoring image through the preselected monitoring coordinate in the preset coordinate system, the belt deviation mean value is determined according to the distance between the monitoring point and the belt edge reference line, and the deviation prediction function is obtained through minimum and formation fitting according to a plurality of belt deviation mean values, so that the deviation prediction model is established, the data processing amount can be reduced, and the coal conveying belt deviation amount can be predicted.
Referring to fig. 5, fig. 5 is a schematic flow chart of a third embodiment of the image-based belt shift monitoring method of the present invention.
Based on the foregoing embodiments, in this embodiment, the step S3 includes:
step S31: and determining pixel information of each pixel point according to the standard monitoring image data, and determining belt edge monitoring data corresponding to each monitoring image according to the pixel information.
It can be understood that the pixel information includes the coordinate position and the monitoring gray value of the pixel point; determining the belt edge monitoring data corresponding to each monitoring image according to the pixel information may be to calculate a monitoring gray difference value of adjacent positions according to the coordinate position and the monitoring gray value of each pixel point, and when the monitoring gray difference value is greater than a preset gray value, obtaining the coordinate position corresponding to the monitoring gray difference value to obtain the belt edge monitoring data.
Step S32: inputting the belt edge monitoring data into an offset prediction model to obtain an offset prediction value output by the offset prediction model.
In specific implementation, after the belt edge monitoring data, namely the coordinate position, is input into the deviation prediction model, the deviation prediction model inputs the deviation prediction value.
Step S33: and when the deviation predicted value is larger than a preset threshold value, sending out a coal conveying belt deviation warning.
Further, in order to improve the accuracy of the coal belt deviation prediction, the step S32 includes: inputting the belt edge monitoring data into an offset prediction model to generate a belt edge monitoring line corresponding to each monitoring image in the preset coordinate system; selecting a plurality of monitoring points on each belt edge monitoring line according to the monitoring coordinates of the offset prediction model; determining a belt deviation value between each monitoring point and the belt edge datum line according to the deviation prediction model, and determining a belt deviation mean value according to the belt deviation value; and determining a current offset prediction function according to the offset mean value of each belt and the offset prediction model, and outputting the offset prediction value of the coal conveying belt through the current prediction function.
In the concrete implementation, belt edge monitoring data, namely coordinate positions, are input into an offset prediction model, the offset prediction model draws straight lines, namely belt edge monitoring lines, in a preset coordinate system according to the coordinate positions, a plurality of monitoring points are selected on the belt edge monitoring lines through the monitoring coordinates of the offset prediction model, the distance between each monitoring point and a belt edge reference line is calculated, each specific mean value is calculated to obtain a belt offset mean value, a corresponding offset prediction function is determined through the offset prediction model and the belt offset mean values, and the offset prediction function outputs an offset prediction value of the coal conveying belt.
The embodiment determines the pixel information of each pixel point according to the standard monitoring data, and determines the belt edge monitoring data corresponding to each monitoring image according to the pixel information; inputting the belt edge monitoring data into an offset prediction model to obtain an offset prediction value output by the offset prediction model; and when the deviation predicted value is larger than a preset threshold value, sending out a coal conveying belt deviation warning. In the embodiment, the belt edge monitoring data is input into the deviation prediction model to obtain the deviation prediction value, and the deviation warning is sent out when the deviation prediction value is larger than the preset threshold value, so that the deviation of the coal conveying belt can be predicted in advance to prevent safety accidents.
Furthermore, an embodiment of the present invention further provides a storage medium, on which an image-based belt deviation monitoring program is stored, which when executed by a processor implements the steps of the image-based belt deviation monitoring method as described above.
Referring to fig. 6, fig. 6 is a block diagram illustrating a first embodiment of the image-based belt deviation monitoring apparatus according to the present invention.
As shown in fig. 6, an image-based belt shift monitoring apparatus according to an embodiment of the present invention includes: a noise reduction module 10, a processing module 20 and a prediction module 30.
The denoising module 10 is used for selecting a plurality of monitoring images from the monitoring video stream of the coal conveying belt according to a time sequence, and denoising each monitoring image through a preset median filter to obtain a plurality of denoising monitoring images;
the processing module 20 is used for carrying out standardization processing on each denoising monitoring image to obtain corresponding standard monitoring image data;
and the prediction module 30 is used for determining the deviation prediction value of the coal conveying belt according to the standard monitoring image data and the deviation prediction model, and sending out a coal conveying belt deviation warning when the deviation prediction value is greater than a preset threshold value.
The method comprises the steps of selecting a plurality of monitoring images from a monitoring video stream of a coal conveying belt according to a time sequence, and denoising each monitoring image through a preset median filter to obtain a plurality of denoising monitoring images; carrying out standardization processing on each denoising monitoring image to obtain corresponding standard monitoring image data; and determining the deviation predicted value of the coal conveying belt according to the standard monitoring image data and the deviation prediction model, and sending out a coal conveying belt deviation warning when the deviation predicted value is greater than a preset threshold value. According to the method, the noise reduction is carried out on the monitoring image selected from the monitoring video stream through the preset median filter to obtain the noise reduction monitoring image, the noise reduction monitoring image is subjected to standardization processing to obtain the standard monitoring image data, the standard monitoring image data is input into the offset prediction model to obtain the offset prediction value, and when the offset prediction value is larger than the preset threshold value, the coal conveying belt offset warning is sent out.
A second embodiment of the image-based belt deviation monitoring apparatus of the present invention is presented based on the first embodiment of the image-based belt deviation monitoring apparatus of the present invention described above.
In this embodiment, the processing module 20 is further configured to perform gray level processing on each denoising monitor image to obtain a gray level monitor image; carrying out contrast enhancement processing on each gray level monitoring image to obtain an enhanced monitoring image; and acquiring a monitoring gray value and a corresponding coordinate position of each pixel point of the enhanced monitoring image, and storing the monitoring gray value and the corresponding coordinate position to a target memory to acquire standard monitoring image data.
The noise reduction module 10 is further configured to obtain a reference monitoring image of the coal conveyor belt, and perform standardization processing on the reference monitoring image to obtain reference data; determining a reference gray value and a corresponding coordinate position of each pixel point in the reference monitoring image according to the reference data; determining a belt edge reference line of the reference monitoring image in a preset coordinate system according to the reference gray value and the corresponding coordinate position; selecting a plurality of monitoring coordinates in the preset coordinate system, and determining a belt deviation mean value according to the belt edge reference line and the monitoring coordinates; and determining an offset prediction function of the coal conveying belt through the belt offset mean value and a least square method to obtain an offset prediction model of the coal conveying belt.
The noise reduction module 10 is further configured to perform a difference on the reference gray values of the adjacent positions according to the coordinate position to obtain a gray difference value set; when the gray difference value in the gray difference value set is larger than a preset gray value, adding the coordinate position corresponding to the gray difference value to a pre-selected characteristic coordinate position set; mapping the preselected characteristic coordinate positions in the preselected characteristic coordinate position set to a preset coordinate system to obtain corresponding preselected characteristic points, and removing outliers in the preselected characteristic points to obtain characteristic points; and determining a belt edge reference line of the reference monitoring image in a preset coordinate system according to the characteristic points.
The prediction module 30 is further configured to determine pixel information of each pixel according to each standard monitoring image data, and determine belt edge monitoring data corresponding to each monitoring image according to the pixel information; inputting the belt edge monitoring data into an offset prediction model to obtain an offset prediction value output by the offset prediction model; and when the deviation predicted value is larger than a preset threshold value, sending out a coal conveying belt deviation warning.
The prediction module 30 is further configured to input the belt edge monitoring data into an offset prediction model, so as to generate a belt edge monitoring line corresponding to each monitored image in the preset coordinate system; selecting a plurality of monitoring points on each belt edge monitoring line according to the monitoring coordinates of the offset prediction model; determining a belt deviation value between each monitoring point and the belt edge datum line according to the deviation prediction model, and determining a belt deviation mean value according to the belt deviation value; and determining a current offset prediction function according to the offset mean value of each belt and the offset prediction model, and outputting the offset prediction value of the coal conveying belt through the current offset prediction function.
The prediction module 30 is further configured to detect whether a controller of the coal conveyor belt receives a preset control signal; when the controller does not receive the preset control signal within a first preset time length, sending a deceleration control signal to the controller; and when the controller does not receive the preset control signal within a second preset time, sending a shutdown control signal to the controller.
Other embodiments or specific implementation manners of the image-based belt deviation monitoring device of the present invention may refer to the above method embodiments, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., a rom/ram, a magnetic disk, an optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. An image-based belt runout monitoring method, the method comprising:
selecting a plurality of monitoring images from a monitoring video stream of a coal conveying belt according to a time sequence, and denoising each monitoring image through a preset median filter to obtain a plurality of denoising monitoring images;
carrying out standardization processing on each denoising monitoring image to obtain corresponding standard monitoring image data;
determining an offset prediction value of the coal conveying belt according to the standard monitoring image data and the offset prediction model, and sending out a coal conveying belt offset warning when the offset prediction value is greater than a preset threshold value;
before selecting a plurality of monitoring images from the monitoring video stream of the coal conveying belt according to the time sequence and denoising each monitoring image through a preset median filter to obtain a plurality of denoising monitoring images, the method further comprises:
acquiring a reference monitoring image of a coal conveying belt, and carrying out standardization processing on the reference monitoring image to obtain reference data;
determining a reference gray value and a corresponding coordinate position of each pixel point in the reference monitoring image according to the reference data;
determining a belt edge reference line of the reference monitoring image in a preset coordinate system according to the reference gray value and the corresponding coordinate position;
selecting a plurality of monitoring coordinates in the preset coordinate system, and determining a belt deviation mean value according to the belt edge reference line and the monitoring coordinates;
and determining an offset prediction function of the coal conveying belt through the belt offset mean value and a least square method to obtain an offset prediction model of the coal conveying belt.
2. The method of claim 1, wherein the normalizing each denoised monitor image to obtain corresponding standard monitor data comprises:
carrying out gray level processing on each denoising monitoring image to obtain a gray level monitoring image;
carrying out contrast enhancement processing on each gray level monitoring image to obtain an enhanced monitoring image;
and acquiring a monitoring gray value and a corresponding coordinate position of each pixel point of the enhanced monitoring image, and storing the monitoring gray value and the corresponding coordinate position to a target memory to acquire standard monitoring image data.
3. The method of claim 1, wherein said determining a belt edge reference line of said reference monitor image in a preset coordinate system based on said reference gray value and corresponding coordinate position comprises:
according to the coordinate position, the reference gray values of the adjacent positions are differentiated to obtain a gray difference value set;
when the gray difference value in the gray difference value set is larger than a preset gray value, adding the coordinate position corresponding to the gray difference value to a pre-selected characteristic coordinate position set;
mapping the preselected characteristic coordinate positions in the preselected characteristic coordinate position set to a preset coordinate system to obtain corresponding preselected characteristic points, and removing outliers in the preselected characteristic points to obtain characteristic points;
and determining a belt edge reference line of the reference monitoring image in a preset coordinate system according to the characteristic points.
4. The method of claim 3, wherein determining a deviation prediction value for the coal belt based on the standard monitoring image data and the deviation prediction model, and issuing a coal belt deviation warning when the deviation prediction value is greater than a preset threshold value comprises:
determining pixel information of each pixel point according to each standard monitoring image data, and determining belt edge monitoring data corresponding to each monitoring image according to the pixel information;
inputting the belt edge monitoring data into an offset prediction model to obtain an offset prediction value output by the offset prediction model;
and when the deviation predicted value is larger than a preset threshold value, sending out a coal conveying belt deviation warning.
5. The method of claim 4, wherein said inputting said belt edge monitoring data into an offset prediction model to obtain an offset prediction value output by said offset prediction model comprises:
inputting the belt edge monitoring data into an offset prediction model to generate a belt edge monitoring line corresponding to each monitoring image in the preset coordinate system;
selecting a plurality of monitoring points on each belt edge monitoring line according to the monitoring coordinates of the offset prediction model;
determining a belt deviation value between each monitoring point and the belt edge datum line according to the deviation prediction model, and determining a belt deviation mean value according to the belt deviation value;
and determining a current offset prediction function according to the offset mean value of each belt and the offset prediction model, and outputting the offset prediction value of the coal conveying belt through the current offset prediction function.
6. The method of any one of claims 1-5, wherein the method further comprises, after determining a predicted deviation value for the coal belt based on the standard monitored image data and the deviation prediction model and issuing a coal belt deviation warning when the predicted deviation value is greater than a predetermined threshold value:
detecting whether a controller of the coal conveying belt receives a preset control signal or not;
when the controller does not receive the preset control signal within a first preset time length, sending a deceleration control signal to the controller;
and when the controller does not receive the preset control signal within a second preset time, sending a shutdown control signal to the controller.
7. An image-based belt deviation monitoring apparatus, the apparatus comprising:
the noise reduction module is used for selecting a plurality of monitoring images from a monitoring video stream of the coal conveying belt according to a time sequence, and reducing noise of each monitoring image through a preset median filter to obtain a plurality of noise reduction monitoring images;
the processing module is used for carrying out standardization processing on each denoising monitoring image to obtain corresponding standard monitoring image data;
the prediction module is used for determining an offset prediction value of the coal conveying belt according to the standard monitoring image data and the offset prediction model and sending out a coal conveying belt offset warning when the offset prediction value is greater than a preset threshold value;
the noise reduction module is also used for acquiring a reference monitoring image of the coal conveying belt and carrying out standardization processing on the reference monitoring image to acquire reference data; determining a reference gray value and a corresponding coordinate position of each pixel point in the reference monitoring image according to the reference data; determining a belt edge reference line of the reference monitoring image in a preset coordinate system according to the reference gray value and the corresponding coordinate position; selecting a plurality of monitoring coordinates in the preset coordinate system, and determining a belt deviation mean value according to the belt edge reference line and the monitoring coordinates; and determining an offset prediction function of the coal conveying belt through the belt offset mean value and a least square method to obtain an offset prediction model of the coal conveying belt.
8. An image-based belt deviation monitoring apparatus, the apparatus comprising: a memory, a processor and an image-based belt deviation monitoring program stored on the memory and executable on the processor, the image-based belt deviation monitoring program configured to implement the steps of the image-based belt deviation monitoring method of any one of claims 1 to 6.
9. A storage medium having stored thereon an image-based belt deviation monitoring program which, when executed by a processor, implements the steps of the image-based belt deviation monitoring method of any one of claims 1 to 6.
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