Monitoring data processing method, device and equipment based on industrial Internet of Things, and medium

Abstract

The invention discloses a monitoring data processing method, device and equipment based on an industrial internet of things, and a medium. The method comprises the following steps: acquiring an original image monitored by a factory, and determining a brightness value of each pixel point in the original image; determining a brightness evaluation index of each pixel point based on the brightness value of each pixel point by using a preset formula, and determining a pixel sequence based on the brightness evaluation index of each pixel point; starting from the first pixel point in the pixel sequence, sequentially taking each pixel point in the pixel sequence as a reference point, and carrying out multiple times of region division according to a preset strategy to obtain a plurality of regions; and calculating an atmospheric light value corresponding to each region, and processing the original image by using a dark channel prior algorithm based on the atmospheric light value of each region to obtain an enhanced image. According to the invention, the interference of the highlight area generated by the strong light source in the factory on the defogging enhancement of the industrial monitoring image can be reduced.

Description

Technical Field

[0001] The present application relates to the field of monitoring data processing, and in particular to a monitoring data processing method, device, equipment and medium based on industrial Internet of Things. Background Art

[0002] Since smoke and dust are easily generated during factory processing and production, the existing industrial monitoring system is seriously affected when monitoring equipment anomalies and personnel activities. Smoke not only blocks the line of sight of the surveillance camera, but also may cause image quality degradation, resulting in false alarms or missed alarms, thus affecting the timely identification of potential safety hazards. Therefore, in order to ensure that the monitoring system can operate accurately and effectively, it is necessary to enhance the defogging operation of the images collected by the monitoring.

[0003] The Dark Channel Prior algorithm is a technique used for image enhancement and dehazing. It uses the darkest pixel value in the image to estimate the concentration of fog, thereby restoring a clear image. However, when using the Dark Channel Prior algorithm to process factory monitoring images, due to the complex background in the factory monitoring images, the estimation of atmospheric light in the Dark Channel Prior algorithm will be biased, resulting in color distortion of the restored image.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a monitoring data processing method, device, equipment and medium based on the industrial Internet of Things, aiming to solve the technical problem that when using a dark channel prior algorithm to process factory monitoring images, due to the complex background in the factory monitoring images, the estimation of atmospheric light in the dark channel prior algorithm will be biased, resulting in distortion of the restored image color.

[0006] To achieve the above-mentioned purpose, the present application provides a monitoring data processing method based on the industrial Internet of Things, including: obtaining an original image of factory monitoring and determining the brightness value of each pixel in the original image; using a preset formula and based on the brightness value of each pixel to determine the brightness evaluation index of each pixel, and determining a pixel sequence based on the brightness evaluation index of each pixel, wherein each pixel in the pixel sequence is arranged in descending order according to the brightness evaluation index; starting from the first pixel in the pixel sequence, taking each pixel in the pixel sequence as a reference point in turn, performing multiple region divisions according to a preset strategy to obtain a plurality of regions, wherein after each region division is performed, the pixel sequence is updated to eliminate the pixels falling into the divided region, and the region division is performed again based on the updated sequence; the atmospheric light value of each region is calculated respectively, and the original image is processed based on the atmospheric light value of each region and using a dark channel prior algorithm to obtain an enhanced image.

[0007] Optionally, determining the brightness value of each pixel in the original image includes: converting the original image into an HSV image; and using the V channel value of each pixel in the HSV image as the brightness value of each pixel in the original image.

[0008] Optionally, the brightness evaluation index of each pixel point is determined by using a preset formula and based on the brightness value of each pixel point, including: determining the brightness distribution degree of each pixel point based on the difference between the brightness value of each pixel point and the brightness value of its adjacent pixel points; and taking the ratio of the brightness value of each pixel point to the brightness distribution degree as the brightness evaluation index of each pixel point.

[0009] Optionally, determining the brightness distribution degree of each pixel based on the difference between the brightness value of each pixel and the brightness value of its adjacent pixel includes: using the following formula (1) to respectively calculate the upper continuity, lower continuity, left continuity and right continuity of each pixel:

[0010]

[0011] Where: P i Indicates the continuity of the i-th pixel in the P direction, Indicates the distance between the i-th pixel and the x-th pixel in the P direction, V i Represents the brightness value of the i-th pixel, V i Px Indicates the brightness value of the x-th pixel in the P direction of the i-th pixel, X iP Represents the number of pixels in the P direction of the i-th pixel, where the P direction is any one of the upper, lower, left and right directions; the brightness distribution degree of each pixel is obtained by summing up the upper continuity, lower continuity, left continuity and right continuity of each pixel.

[0012] Optionally, the process of single area division includes: selecting a reference point; calculating the brightness difference between the reference point and the pixel points adjacent to it to obtain a first growth point, wherein the first growth point is the pixel point with the maximum brightness difference with the reference point among each adjacent growth point, and the reference point is the parent node of the first growth point; taking the first growth point as a child growth point, calculating the brightness difference of each pixel point adjacent to it and after excluding the parent node to obtain a second growth point, wherein the second growth point is the pixel point with the maximum brightness difference with the child growth point among each adjacent growth point after excluding the parent node; repeating the growth process of the child growth point until, among the obtained growth points, the number of growth points with brightness values ​​greater than a preset brightness mean is the same as the number of growth points with brightness values ​​less than the brightness mean.

[0013] Optionally, the preset brightness mean is the brightness mean of all pixels in the original image.

[0014] Optionally, respectively calculating the atmospheric light value of each area includes: for any area, calculating the dark channel value of each pixel in the area, and determining a dark channel image of the area based on the dark channel value of each pixel; determining a number of candidate pixels of the area based on the dark channel image of the area; and determining the atmospheric light value of the area based on the number of candidate pixels.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a monitoring data processing device based on the industrial Internet of Things, including: an image acquisition module, used to acquire the original image of factory monitoring and determine the brightness value of each pixel in the original image; a pixel sequence acquisition module, used to determine the brightness evaluation index of each pixel based on the brightness value of each pixel using a preset formula, and determine the pixel sequence based on the brightness evaluation index of each pixel, wherein the pixels in the pixel sequence are arranged in descending order according to the brightness evaluation index; a region division module, used to start from the first pixel in the pixel sequence, and sequentially use the pixels in the pixel sequence as reference points to perform multiple region divisions according to a preset strategy to obtain a number of regions, wherein after each region division is performed, the pixel sequence is updated to eliminate the pixels falling into the divided region, and the region division is performed again based on the updated sequence; an image processing enhancement module, used to calculate the atmospheric light value of each region respectively, and process the original image based on the atmospheric light value of each region and using a dark channel prior algorithm to obtain an enhanced image.

[0016] The present application also provides a monitoring data processing device based on the industrial Internet of Things, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned monitoring data processing method based on the industrial Internet of Things.

[0017] The present application also provides a computer-readable storage medium, comprising: a computer program stored therein, and when the computer program is executed by a processor, the above-mentioned monitoring data processing method based on industrial Internet of Things is implemented.

[0018] The present application proposes a monitoring data processing method, device, equipment and medium based on the industrial Internet of Things. The original factory monitoring image is divided based on the brightness value and brightness distribution of the pixel points in the factory monitoring image, and the several small areas obtained by the division include both highlight areas and other areas, which solves the problem of color distortion of the restored image obtained by the highlight area interfering with the atmospheric light estimation of the entire image. The highlight area and other areas are combined in the small areas obtained by the division of the present application, so that more details are retained when the image is enhanced and restored, which is more in line with the actual lighting scene. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flowchart of a monitoring data processing method based on the industrial Internet of Things according to an embodiment of the present application;

[0020] Figure 2 It is a structural block diagram of a monitoring data processing device based on industrial Internet of Things according to an implementation mode of the present application;

[0021] Figure 3 It is a schematic diagram of the structure of a monitoring data processing device based on the industrial Internet of Things according to an embodiment of the present application.

[0022] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0024] The dark channel prior algorithm assumes that the atmospheric light is relatively uniform. However, in a factory environment, there are many strong light sources (such as bright light generated by welding and cutting), which is reflected in the appearance of some highlight areas in the factory monitoring images. The highlight areas will make it impossible to accurately estimate the atmospheric light when processing the factory monitoring images using the dark channel prior algorithm, and the final enhanced restored image will be distorted in color.

[0025] In order to solve the above problems, the present application provides a monitoring data processing method, device, equipment and medium based on industrial Internet of Things. The present application scheme is introduced in detail below.

[0026] Figure 1 This is a flowchart of a monitoring data processing method based on the industrial Internet of Things according to an embodiment of the present application, referring to Figure 1 , the monitoring data processing method based on industrial Internet of Things may include the following steps:

[0027] S1, obtaining the original image of the factory monitoring and determining the brightness value of each pixel in the original image.

[0028] In the specific implementation process, factory monitoring images are acquired frame by frame, wherein the original images may contain a variety of information such as equipment status, personnel activity records, environmental data, and production progress.

[0029] In one embodiment, in step S1, determining the brightness value of each pixel in the original image may specifically include:

[0030] S11, converting the original image into an HSV image;

[0031] S12, using the V channel value of each pixel in the HSV image as the brightness value of each pixel in the original image.

[0032] It should be noted that during welding and cutting, the light generated usually has high brightness but may be a combination of multiple colors. Compared with RGB images, selecting the V channel in HSV can ignore the influence of color and focus on brightness, thereby improving the recognition accuracy of bright light. Using a single channel can simplify calculations and increase processing speed.

[0033] S2, using a preset formula and based on the brightness value of each pixel point to determine a brightness evaluation index of each pixel point, and determining a pixel sequence based on the brightness evaluation index of each pixel point, wherein the pixels in the pixel sequence are arranged in descending order according to the brightness evaluation index.

[0034] S3, starting from the first pixel in the pixel sequence, performing multiple region divisions according to a preset strategy using each pixel in the pixel sequence as a reference point to obtain a plurality of regions, wherein after each region division is performed, the pixel sequence is updated to eliminate the pixels falling into the divided region, and region division is performed again based on the updated sequence.

[0035] In this exemplary embodiment, a dark channel a priori algorithm is used to enhance the surveillance image. The specific steps of the dark channel a priori algorithm are as follows:

[0036] First, the dark channel map of each pixel is calculated from the input image, usually by selecting the minimum color value of each pixel in a small window;

[0037] Next, the atmospheric illumination is estimated using the dark channel map, usually by selecting the brightest pixel value in the dark channel;

[0038] Then, the transmittance is calculated by introducing the relationship between the atmospheric light and the dark channel, and smoothing is performed to remove noise;

[0039] Finally, the original image, transmittance, and atmospheric light are combined to restore the image to reduce haze or other blurring effects, thereby improving image clarity and visibility.

[0040] Among them, when highlights appear in the input image, the atmospheric light estimation will be overestimated, thereby introducing excessive brightness when restoring the image, resulting in overall image color distortion and detail loss, reducing the quality and authenticity of the restored image.

[0041] Based on this, in an embodiment of the present application, a brightness evaluation index of each pixel is first determined. The brightness evaluation index can be used to characterize the possibility of each pixel falling into a highlight area, and then a pixel sequence is determined based on the brightness evaluation index of each pixel. Starting from the first pixel in the pixel sequence, multiple region divisions are performed according to a preset strategy using each pixel in the pixel sequence as a reference point to obtain several regions, so that the highlight area is divided into other adjacent regions. In this way, after the region division, the region where the highlight area is located includes both part of the highlight area and part of other regions, that is, the highlight part and the normal part are included in the same region, so that the brightness change of the region can be smoothed, the normal lighting environment can be better simulated, and the obtained atmospheric value is more accurate.

[0042] In one embodiment, in step S2, using a preset formula and based on the brightness value of each pixel point to determine the brightness evaluation index of each pixel point may specifically include:

[0043] S21, determining the brightness distribution degree of each pixel based on the difference between the brightness value of each pixel and the brightness value of its adjacent pixels;

[0044] S22. Taking the ratio of the brightness value of each pixel point to the brightness distribution degree as the brightness evaluation index of each pixel point.

[0045] Among them, the brightness distribution degree can characterize the difference between the brightness values ​​of a pixel and its neighboring pixels. For example, when a pixel falls in a high brightness area, the brightness values ​​of the pixel and its neighboring pixels are both large, and the difference between the brightness values ​​of the pixel and its neighboring pixels is small, that is, the consistency is large; when a pixel is a noise point, the brightness value of the pixel and its neighboring pixels will be different, and the difference between the brightness values ​​of the pixel and its neighboring pixels is large, that is, the consistency is low. Therefore, the brightness evaluation index can characterize the possibility of each pixel being a pixel in the high brightness area.

[0046] In a specific implementation process, taking the i-th pixel point as an example, the following formula (1) can be used to calculate the upper continuity, lower continuity, left continuity and right continuity of the i-th pixel point respectively:

[0047]

[0048] Where: P i Indicates the continuity of the i-th pixel in the P direction, Indicates the distance between the i-th pixel and the x-th pixel in the P direction, V i Represents the brightness value of the i-th pixel, V i Px Indicates the brightness value of the x-th pixel in the P direction of the i-th pixel, X iPRepresents the number of pixels in the P direction of the i-th pixel, where the P direction is any one of the upper side, lower side, left side and right side.

[0049] Specifically, the upper continuity of the i-th pixel can be calculated using the following formula (1-1):

[0050]

[0051] In the formula, γ i Indicates the upper continuity of the i-th pixel, Represents the distance between the i-th pixel and the u-th pixel above it, V i Represents the brightness value of the i-th pixel, V i u Indicates the brightness value of the u-th pixel above the i-th pixel, U i Indicates the number of pixels above the i-th pixel.

[0052] Furthermore, the bottom continuity of the i-th pixel can be calculated using the following formula (1-2):

[0053]

[0054] In the formula, λ i Indicates the bottom continuity of the i-th pixel, Represents the distance between the i-th pixel and the b-th pixel below it, V i Represents the brightness value of the i-th pixel, V i b represents the brightness value of the bth pixel below the i-th pixel, B i Indicates the number of pixels below the i-th pixel.

[0055] Furthermore, the left continuity of the i-th pixel can be calculated using the following formula (1-3):

[0056]

[0057] In the formula, δ i Indicates the left continuity of the i-th pixel, Represents the distance between the ith pixel and the lth pixel on its left, V i Represents the brightness value of the i-th pixel, V i l Indicates the brightness value of the lth pixel to the left of the ith pixel, L i Indicates the number of pixels to the left of the i-th pixel.

[0058] Furthermore, the right continuity of the i-th pixel can be calculated using the following formula (1-4):

[0059]

[0060] In the formula, τ i Indicates the right continuity of the i-th pixel, Represents the distance between the i-th pixel and the r-th pixel to its right, V i Represents the brightness value of the i-th pixel, V i r Represents the brightness value of the rth pixel to the right of the i-th pixel, R i Indicates the number of pixels to the right of the i-th pixel.

[0061] Furthermore, the brightness distribution degree of each pixel point is obtained by summing up the upper continuity, the lower continuity, the left continuity and the right continuity of each pixel point.

[0062] Furthermore, the ratio of the brightness value of each pixel point to the brightness distribution degree is used as the brightness evaluation index of each pixel point.

[0063] It should be noted that the ratio of the brightness value of each pixel and the brightness distribution degree is used as the brightness evaluation index of each pixel. When the brightness value of a pixel is large and the brightness distribution degree is small, it means that the pixel is more likely to fall into the brightness area.

[0064] Further, each pixel is arranged in descending order according to the brightness evaluation index to form a pixel sequence, wherein each element in the pixel sequence is a pixel position. It should be noted that the original image can be arranged from left to right and from top to bottom using a Z-shaped arrangement to represent the position of each pixel, so that the position of each pixel can be represented by one-dimensional data.

[0065] In a specific implementation process, if there are two or more pixels with the same brightness evaluation index, the pixels with the same brightness evaluation index are arranged in the pixel sequence in ascending order according to the pixel position.

[0066] Further, starting from the first pixel in the pixel sequence, the pixel points in the pixel sequence are used as reference points and the region division is performed multiple times according to a preset strategy to obtain a plurality of regions. It should be noted that the regions obtained by the division do not overlap with each other.

[0067] It should be noted that since the pixels in the pixel sequence are arranged from large to small according to the brightness evaluation index, the first pixel in the pixel sequence can represent the highlight area in the original image of the factory monitoring. In this way, the region division starting from the first pixel in the pixel sequence can ensure that the highlight area is first divided into multiple sub-regions, and the sub-regions are merged with other surrounding regions, that is, the first several small regions obtained include not only the highlight area but also the normal area.

[0068] In one embodiment, in step S3, the process of single area division may specifically include:

[0069] S31, select a reference point;

[0070] S32, calculating the brightness difference between the reference point and the pixel points adjacent to the reference point to obtain a first growth point, wherein the first growth point is a pixel point having the maximum brightness difference between each adjacent growth point and the reference point, and the reference point is a parent node of the first growth point;

[0071] S33, taking the first growth point as a sub-growth point, calculating the brightness difference of each pixel point adjacent to the first growth point and after excluding the parent node to obtain a second growth point, wherein the second growth point is a pixel point having the maximum brightness difference with the sub-growth point among each adjacent growth point after excluding the parent node;

[0072] S34. Repeat the growth process of the sub-growing points until the number of the growing points whose brightness values ​​are greater than the preset brightness mean is the same as the number of the growing points whose brightness values ​​are less than the brightness mean.

[0073] In a specific implementation process, the preset brightness mean is the brightness mean of all pixels in the original image.

[0074] In the above step S32, growth is performed with the selected reference point as the starting point to obtain multiple growth points, wherein the growth point with the maximum brightness difference with the reference point among the multiple growth points is the first growth point, and the multiple growth points are eight-neighborhood pixel points of the reference point.

[0075] In the above step S33, growth is performed with the first growth point as the starting point to obtain multiple growth points, wherein, after excluding the parent node of the first growth point, i.e., the reference point, the growth point among the multiple growth points that has the maximum brightness difference with the first growth point is the second growth point, and the multiple growth points are the seven-neighborhood pixel points of the first growth point after excluding the parent node of the first growth point.

[0076] S4, respectively calculating the atmospheric light value of each region, and processing the original image based on the atmospheric light value of each region and using a dark channel priori algorithm to obtain an enhanced image.

[0077] The enhanced image is the original image of the factory monitoring after dehazing and enhancement.

[0078] It should be noted that when there are bright light areas in factory monitoring due to equipment welding, cutting, etc., by dividing the factory monitoring image into regions, the interference of the highlight area on the atmospheric light estimation of the entire image can be effectively reduced. And when dividing the factory monitoring image into regions, if only the highlight area is considered, that is, the highlight area is separated from other areas when dividing the region, the lighting relationship between the highlight area and other areas will be ignored when calculating the atmospheric light, which will lead to the loss or distortion of details in other areas during the image restoration process. Processing the highlight area separately from other areas will also cause the transition of the highlight area near the edge where the highlight area is divided from other areas to affect the atmospheric light estimation of other areas, which will lead to blurred edges or unnatural transitions between the highlight area and other areas in the restored image.

[0079] Based on this, the embodiment of the present application is based on the prior condition that the illumination in normal scenes is usually gradual rather than sudden. Through region division, it is equivalent to cutting the highlight region and recombining it with the non-highlight region of the neighborhood to obtain several small regions. In other words, each of the several small regions obtained includes both highlight pixels and non-highlight pixels, thereby effectively reducing the interference of the highlight region on the atmospheric light estimation of the entire image. And combining the highlight pixels with other non-highlight pixels in the small region helps to retain more details when restoring the image, which is more in line with the actual illumination scene.

[0080] In one embodiment, in step S4, respectively calculating the atmospheric light value corresponding to each area may specifically include:

[0081] S41, for any area, calculating the dark channel value of each pixel point in the area, and determining a dark channel image of the area based on the dark channel value of each pixel point;

[0082] S42, determining a number of candidate pixel points in the region based on the dark channel image of the region;

[0083] S43. Determine the atmospheric light value of the area based on a number of candidate pixel points.

[0084] It should be noted that the calculation of the atmospheric light value of each area can be performed according to the general steps of the dark channel prior algorithm. It can be understood that the dark channel prior algorithm usually estimates the atmospheric light of the entire image, and this embodiment divides the image into several areas, that is, the dark channel prior algorithm is used to estimate the atmospheric light of each area separately.

[0085] Furthermore, the original image is processed based on the atmospheric light value of each region and using a dark channel prior algorithm to obtain an enhanced image.

[0086] Based on the above embodiments, Figure 2 FIG. 1 is a structural block diagram of a monitoring data processing device based on the industrial Internet of Things according to an embodiment of the present application, such as Figure 2 As shown, the monitoring data processing device based on the industrial Internet of Things may include: an image acquisition module 210, a pixel sequence acquisition module 220, a region division module 230, and an image processing enhancement module 240, wherein:

[0087] The image acquisition module 210 is used to obtain the original image of the factory monitoring and determine the brightness value of each pixel in the original image;

[0088] The pixel sequence acquisition module 220 is used to determine the brightness evaluation index of each pixel point based on the brightness value of each pixel point by using a preset formula, and determine the pixel sequence based on the brightness evaluation index of each pixel point, wherein the pixels in the pixel sequence are arranged in descending order according to the brightness evaluation index;

[0089] The region division module 230 is used to start from the first pixel in the pixel sequence, and sequentially use each pixel in the pixel sequence as a reference point to perform multiple region divisions according to a preset strategy to obtain a plurality of regions, wherein after each region division is performed, the pixel sequence is updated to remove the pixels falling into the divided region, and the region division is performed again based on the updated sequence;

[0090] The image processing enhancement module 240 is used to calculate the atmospheric light value of each region respectively, and process the original image based on the atmospheric light value of each region and using a dark channel priori algorithm to obtain an enhanced image.

[0091] In an exemplary embodiment, the image acquisition module 210 may also convert the original image into an HSV image; and use the V channel value of each pixel point in the HSV image as the brightness value of each pixel point in the original image.

[0092] In an exemplary embodiment, the pixel sequence acquisition module 220 can also be used to determine the brightness evaluation index of each pixel point based on the brightness value of each pixel point using a preset formula, including: determining the brightness distribution degree of each pixel point based on the difference between the brightness value of each pixel point and the brightness value of its adjacent pixel points; and using the ratio of the brightness value of each pixel point to the brightness distribution degree as the brightness evaluation index of each pixel point.

[0093] In an exemplary embodiment, the pixel sequence acquisition module 220 can use the following formula (1) to respectively

[0094]

[0095] Where: P i Indicates the continuity of the i-th pixel in the P direction, Indicates the distance between the i-th pixel and the x-th pixel in the P direction, V i Represents the brightness value of the i-th pixel, V i Px Indicates the brightness value of the x-th pixel in the P direction of the i-th pixel, X iP Represents the number of pixels in the P direction of the i-th pixel point, where the P direction is any one of the upper, lower, left and right directions; the pixel sequence acquisition module 220 can also be used to accumulate the upper continuity, lower continuity, left continuity and right continuity of each pixel point to obtain the brightness distribution degree of each pixel point.

[0096] In an exemplary embodiment, the area division module 230 can also be used to select a reference point; calculate the brightness difference between the reference point and the pixel points adjacent to it to obtain a first growth point, wherein the first growth point is the pixel point with the maximum brightness difference with the reference point among each adjacent growth point, and the reference point is the parent node of the first growth point; take the first growth point as a child growth point, calculate the brightness difference of each pixel point adjacent to it and after excluding the parent node to obtain a second growth point, wherein the second growth point is the pixel point with the maximum brightness difference with the child growth point among each adjacent growth point after excluding the parent node; repeat the growth process of the child growth point until, among the obtained growth points, the number of growth points with brightness values ​​greater than a preset brightness mean is the same as the number of growth points with brightness values ​​less than the brightness mean.

[0097] In an exemplary embodiment, the preset brightness mean value in the region division module 230 is the brightness mean value of all pixels in the original image.

[0098] In an exemplary embodiment, the image processing enhancement module 240 can also be used to calculate the dark channel value of each pixel in any area, determine the dark channel image of the area based on the dark channel value of each pixel; determine a number of candidate pixels in the area based on the dark channel image of the area; and determine the atmospheric light value of the area based on the number of candidate pixels.

[0099] Those skilled in the art should understand that the division of the various modules in the embodiment is merely a division of logical functions, and in actual application, all or part of them can be integrated into one or more actual carriers, and these modules can all be implemented in the form of software called through a processing unit, or all in the form of hardware, or in the form of a combination of software and hardware. It should be noted that each module in a monitoring data processing device based on the industrial Internet of Things in this embodiment corresponds one-to-one to each step in a monitoring data processing method based on the industrial Internet of Things in the aforementioned embodiment. Therefore, the specific implementation method of this embodiment can refer to the implementation method of the aforementioned monitoring data processing method based on the industrial Internet of Things, which will not be repeated here.

[0100] Based on the above embodiments, Figure 3 FIG. 1 is a schematic diagram of a monitoring data processing device based on the industrial Internet of Things according to an embodiment of the present application, such as Figure 3 As shown, the electronic device may include: a processor 310 , a communications interface 320 , a memory 330 and a communications bus 340 , wherein the processor 310 , the communications interface 320 , and the memory 330 communicate with each other via the communications bus 340 . The processor 310 can call the logic instructions in the memory 330 to execute a monitoring data processing method based on the industrial Internet of Things, the method comprising: obtaining an original image of factory monitoring and determining the brightness value of each pixel in the original image; using a preset formula and based on the brightness value of each pixel to determine the brightness evaluation index of each pixel, and determining a pixel sequence based on the brightness evaluation index of each pixel, wherein each pixel in the pixel sequence is arranged in descending order according to the brightness evaluation index; starting from the first pixel in the pixel sequence, taking each pixel in the pixel sequence as a reference point in turn, performing multiple region divisions according to a preset strategy to obtain a plurality of regions, wherein after each region division is performed, the pixel sequence is updated to eliminate the pixels falling into the divided region, and the region division is performed again based on the updated sequence; respectively calculating the atmospheric light value of each region, and processing the original image based on the atmospheric light value of each region and using a dark channel prior algorithm to obtain an enhanced image.

[0101] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0102] On the basis of the above embodiments, on the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the monitoring data processing method based on the industrial Internet of Things provided by the above methods, the method including: obtaining an original image of factory monitoring and determining the brightness value of each pixel in the original image; using a preset formula and based on the brightness value of each pixel to determine a brightness evaluation index of each pixel, and determining a pixel sequence based on the brightness evaluation index of each pixel, wherein each pixel in the pixel sequence is arranged in descending order according to the brightness evaluation index; starting from the first pixel in the pixel sequence, taking each pixel in the pixel sequence as a reference point in turn according to a preset strategy, performing multiple region divisions to obtain a plurality of regions, wherein after each region division is performed, the pixel sequence is updated to eliminate the pixels falling into the divided region, and the region division is performed again based on the updated sequence; respectively calculating the atmospheric light value of each region, and processing the original image based on the atmospheric light value of each region and using a dark channel prior algorithm to obtain an enhanced image.

[0103] On the basis of the above embodiments, on another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the monitoring data processing method based on the industrial Internet of Things provided by the above methods, the method comprising: obtaining an original image of factory monitoring and determining the brightness value of each pixel in the original image; determining a brightness evaluation index of each pixel based on the brightness value of each pixel using a preset formula, and determining a pixel sequence based on the brightness evaluation index of each pixel, wherein each pixel in the pixel sequence is arranged in descending order according to the brightness evaluation index; starting from the first pixel in the pixel sequence, taking each pixel in the pixel sequence as a reference point in turn, performing multiple region divisions according to a preset strategy to obtain a plurality of regions, wherein after each region division is performed, the pixel sequence is updated to eliminate the pixels falling into the divided region, and the region division is performed again based on the updated sequence; respectively calculating the atmospheric light value of each region, processing the original image based on the atmospheric light value of each region and using a dark channel prior algorithm to obtain an enhanced image.

[0104] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A monitoring data processing method based on industrial Internet of Things, characterized in that: include: Obtaining the original image of the factory monitoring and determining the brightness value of each pixel in the original image; Determine a brightness evaluation index of each pixel point based on the brightness value of each pixel point using a preset formula, and determine a pixel sequence based on the brightness evaluation index of each pixel point, wherein the pixels in the pixel sequence are arranged in descending order according to the brightness evaluation index; Starting from the first pixel in the pixel sequence, the pixel sequences are sequentially used as reference points to perform multiple region divisions according to a preset strategy to obtain a plurality of regions, wherein the pixel sequence is updated after each region division to remove the pixel points falling into the divided region, and the region division is performed again based on the updated sequence; The atmospheric light value of each area is calculated respectively, and the original image is processed based on the atmospheric light value of each area and the dark channel prior algorithm to obtain an enhanced image.

2. The monitoring data processing method based on industrial Internet of Things according to claim 1, characterized in that: Determining the brightness value of each pixel in the original image includes: Convert the original image to HSV image; The V channel value of each pixel in the HSV image is used as the brightness value of each pixel in the original image.

3. The monitoring data processing method based on industrial Internet of Things according to claim 1, characterized in that: The method of determining the brightness evaluation index of each pixel point based on the brightness value of each pixel point by using a preset formula includes: Determine the brightness distribution of each pixel based on the brightness value of each pixel and the brightness value difference of its adjacent pixels; The ratio of the brightness value of each pixel to the brightness distribution degree is used as the brightness evaluation index of each pixel.

4. The monitoring data processing method based on industrial Internet of Things according to claim 3 is characterized in that: The determining of the brightness distribution degree of each pixel based on the difference between the brightness value of each pixel and the brightness value of its adjacent pixel includes: The upper continuity, lower continuity, left continuity and right continuity of each pixel are calculated using the following formula (1): Where: P i Indicates the continuity of the i-th pixel in the P direction, Indicates the distance between the i-th pixel and the x-th pixel in the P direction, V i Represents the brightness value of the i-th pixel, V i Px Indicates the brightness value of the x-th pixel in the P direction of the i-th pixel, X iP represents the number of pixels in the P direction of the i-th pixel, where the P direction is any one of the upper side, lower side, left side and right side; The brightness distribution degree of each pixel point is obtained by summing up the upper continuity, lower continuity, left continuity and right continuity of each pixel point.

5. The monitoring data processing method based on industrial Internet of Things according to claim 3 is characterized in that: The process of single-region division includes: Select a reference point; Calculating the brightness difference between the reference point and the pixel points adjacent to the reference point to obtain a first growth point, wherein the first growth point is a pixel point having the maximum brightness difference with the reference point among the adjacent growth points, and the reference point is a parent node of the first growth point; Taking the first growth point as a sub-growth point, calculating the brightness difference of each pixel point adjacent to the first growth point and after removing the parent node to obtain a second growth point, wherein the second growth point is a pixel point having the maximum brightness difference with the sub-growth point among the adjacent growth points after removing the parent node; The growth process of the sub-growing points is repeatedly executed until, among the obtained growing points, the number of the growing points having brightness values ​​greater than the preset brightness average is the same as the number of the growing points having brightness values ​​less than the brightness average.

6. The monitoring data processing method based on industrial Internet of Things according to claim 5, characterized in that: The preset brightness mean is the brightness mean of all pixels in the original image.

7. The monitoring data processing method based on industrial Internet of Things according to claim 5, characterized in that: The calculating of the atmospheric light value of each area respectively includes: For any area, calculating the dark channel value of each pixel in the area, and determining a dark channel image of the area based on the dark channel value of each pixel; Determine a number of candidate pixel points in the area based on a dark channel image of the area; The atmospheric light value of the area is determined based on a number of candidate pixel points.

8. A monitoring data processing device based on industrial Internet of Things, characterized in that: The monitoring data processing device based on the industrial Internet of Things includes: An image acquisition module is used to obtain the original image of the factory monitoring and determine the brightness value of each pixel in the original image; A pixel sequence acquisition module, used to determine a brightness evaluation index of each pixel point based on the brightness value of each pixel point using a preset formula, and determine a pixel sequence based on the brightness evaluation index of each pixel point, wherein the pixels in the pixel sequence are arranged in descending order according to the brightness evaluation index; A region division module, which is used to start from the first pixel in the pixel sequence and sequentially use each pixel in the pixel sequence as a reference point to perform multiple region divisions according to a preset strategy to obtain a plurality of regions, wherein after each region division is performed, the pixel sequence is updated to remove the pixels falling into the divided region, and the region division is performed again based on the updated sequence; The image processing enhancement module is used to calculate the atmospheric light value of each area respectively, and process the original image based on the atmospheric light value of each area and using the dark channel prior algorithm to obtain an enhanced image.

9. A monitoring data processing device based on industrial Internet of Things, characterized in that: The monitoring data processing equipment based on the industrial Internet of Things includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the monitoring data processing method based on the industrial Internet of Things as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the monitoring data processing method based on the industrial Internet of Things as described in any one of claims 1 to 7 is implemented.

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