Intelligent central feeding automatic control system

Abstract

The invention relates to the field of pneumatic conveying and feeding, in particular to an intelligent central feeding automatic control system. The system comprises an acquisition device, a central control system and a fan device, the acquisition device is connected with the central control system and is used for sending an acquired image of the conveying pipeline to the central control system; the central control system is connected with the fan device and is used for calculating the blocking degree of each area of the conveying pipeline according to the received images; and the intelligent central feeding automatic control system is controlled according to the blocking degree of each area. The problems that in the prior art, the material conveying efficiency is low, and fault misjudgment is likely to happen are effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of pneumatic material feeding, and more specifically, to an intelligent central material feeding automatic control system. Background Art

[0002] In the field of pneumatic conveying, the central feeding system is generally composed of a weighing system and a mixing and conveying system. The weighing system ensures that the materials are distributed according to the preset ratio. The mixing and conveying system is responsible for transporting the materials from the storage area to the production line, and usually includes components such as conveying pipelines, conveying fans, screw conveyors, and agitators. The traditional central feeding system implements fault alarms based on abnormal raw material feeding time, and stops the system operation for manual processing according to the degree of the fault. However, in most cases, the abnormal feeding time is just a short blockage somewhere in the feeding pipeline rather than a failure of the feeding system. In this case, it is not necessary to stop the system operation, and frequent shutdowns for manual processing will greatly reduce the feeding efficiency.

[0003] At present, the Chinese patent document with the authorization announcement number CN108636949B discloses a method and system for automatically clearing the blocked pipe of a pneumatic feeding system. The system measures the real-time wind speed of the feeding pipe and compares it with the preset wind speed to determine whether the feeding pipe is blocked. The above method indirectly determines the pipe blockage by wind speed, which is easily affected by the environment, cannot accurately and truly determine the blockage and is prone to errors.

[0004] Therefore, these traditional central feeding systems cannot accurately collect the material blockage conditions inside the feeding pipeline, and thus cannot reasonably adjust the feeding air pressure to solve some of the blockage problems by themselves. As a result, the feeding efficiency is low and fault misjudgment is prone to occur. Summary of the invention

[0005] In order to solve the problems of low feeding efficiency and easy misjudgment of faults in the prior art, the present invention provides the following solution.

[0006] The present invention provides an intelligent central material feeding automatic control system, comprising: a collection device, a central control system and a fan device; the collection device is connected to the central control system and is used to send the collected material conveying pipeline image to the central control system; the central control system is connected to the fan device and is used to calculate the blockage degree of each area of ​​the material conveying pipeline according to the received image; the intelligent central material feeding automatic control system is controlled according to the blockage degree of each area, and the specific method is: the material conveying pipeline is divided into n areas from the inlet to the outlet, when the blockage degree of the nth area is less than a first threshold, the wind pressure is predicted by a trained wind pressure prediction model to control the fan device; when the blockage degree of the nth area is greater than a second threshold, the fan device is shut down and an alarm message is sent; when the blockage degree of the nth area is greater than or equal to the first threshold and less than or equal to the second threshold, the predicted delivery time T is calculated, and when the actual delivery time is When , the machine stops and sends an alarm message, m is a preset constant, ,in, Indicates the number of areas divided from the inlet to the outlet of the conveying pipeline. It represents the blockage degree of the ith area, P represents the current wind pressure, and d is an empirical constant.

[0007] Beneficial effects: By collecting images of the inside of the feed pipeline, the images can be used as direct evidence to accurately and truly calculate the degree of blockage in the feed pipeline in real time. Based on the blockage degree and delivery time prediction, early warning and shutdown can be given, which reduces sudden downtime caused by blockage. When the blockage degree exceeds the standard, the system automatically shuts down and sends an alarm message to prevent equipment damage and production line stagnation, ensure production safety, and improve feed efficiency.

[0008] Preferably, calculating the blockage degree of each area of ​​the material conveying pipeline according to the received image includes: identifying the blockage area of ​​each area in the image according to the image, and calculating the blockage degree of each area according to the blockage area.

[0009] Beneficial effect: The blockage area can be determined more accurately through the image, thereby calculating the blockage degree.

[0010] Preferably, the calculation of the congestion degree of each area is specifically as follows: ,in, Indicates the degree of blockage in the xth area of ​​the conveying pipeline image collected at time t, represents normalization, represents the blocked area of ​​the xth region in the conveying pipeline image collected at time t, represents the blocked area of ​​the xth region in the conveying pipeline image collected at time t-1, Represents the total area of ​​the collected conveying pipeline image, Represents the time interval from time t-1 to time t.

[0011] Beneficial effects: By comparing the blockage areas at different time points, the changing trend of pipeline blockage can be dynamically evaluated. The calculation method takes the time interval into account, so that the system can reflect the changes in pipeline blockage in real time. Through normalization processing, it ensures that the system is consistent in sensitivity to blockages in different areas of the pipeline. By combining image processing, blockage area analysis and time factors, the blockage of the feed pipeline can be accurately calculated and monitored in real time.

[0012] Preferably, identifying the blocked area of ​​each region in the image based on the image includes: measuring the material thickness of each region in the image; presetting a thickness threshold, and the area of ​​each region in the image where the material thickness is greater than the thickness threshold is the blocked area.

[0013] Beneficial effects: This method can accurately identify the blockage area in the material conveying pipeline and calculate the blockage area by measuring the material thickness and comparing it with the preset thickness threshold.

[0014] Preferably, the identifying the blocked area of ​​each region in the image according to the image further comprises: A color data threshold is preset, the blocked area includes at least an area composed of a plurality of adjacent pixels, the plurality of adjacent pixels include at least three pixels, and the RGB data of any pixel exceeds the preset color data threshold.

[0015] Preferably, before measuring the material thickness of each area in the image, the method further includes: performing denoising on the image; adjusting the contrast and brightness of the image; and performing geometric correction on the image.

[0016] Beneficial effects: De-noising, adjusting contrast and brightness, and performing geometric correction can significantly improve image clarity and accuracy. These processes can reduce interference, highlight the edges of the material, and make the measurement of material thickness more accurate.

[0017] Preferably, the preset color data threshold, the blocked area includes at least an area composed of multiple adjacent pixels, the multiple adjacent pixels include at least three pixels, and the RGB data of any pixel exceeds the preset color data threshold, and also includes: denoising and enhancing the image; extracting the RGB data of all pixels in the image.

[0018] Beneficial effects: De-noising can improve image quality, enhancement can improve contrast and highlight key image features, and color space conversion can help separate the color and brightness of the image and improve the accuracy of color analysis.

[0019] Preferably, the sending of alarm information includes: issuing an alarm through the color of a fault warning light and / or the sound of a fault warning bell.

[0020] The beneficial effects of the present invention are as follows: by collecting images of the inside of the feed pipeline, the images can be used as direct evidence to accurately and truly calculate the degree of blockage of the feed pipeline in real time, and can predict the degree of blockage and delivery time, give early warning and shut down the machine, which reduces sudden downtime caused by blockage. When the degree of blockage exceeds the standard, the system automatically shuts down and sends an alarm message to prevent equipment damage and production line stagnation, ensure production safety, and improve feed efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a schematic diagram of an intelligent central feeding automatic control system provided by an embodiment of the present invention; Figure numerals: 1. Collection device; 2. Central control system; 3. Fan device; 4. Material. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] Figure 1 It is a schematic diagram of an intelligent central feeding automatic control system according to an embodiment of the present invention.

[0025] like Figure 1 As shown, the intelligent central feeding automatic control system provided by the embodiment of the present invention includes: a collection device 1, a central control system 2 and a fan device 3; The acquisition device 1 is connected to the central control system 2 and is used to send the acquired material delivery pipeline image to the central control system 2; The central control system 2 is connected to the fan device 3 and is used to calculate the blockage degree of each area of ​​the material conveying pipeline according to the received image; The intelligent central feeding automatic control system is controlled according to the blockage degree of each area.

[0026] exist Figure 1 In the embodiment shown, the acquisition device 1 is installed above the inside of the pipeline. In other embodiments, the acquisition device 1 can also be installed on the side. If the material conveying pipeline is not closed, the acquisition device 1 can also be installed next to the material conveying pipeline, as long as the image acquisition range of the acquisition device 1 can cover the entire range of the material 4. In addition, the acquisition device 1 can include a variety of devices such as a camera, an ultrasonic sensor or a laser sensor. The central control system 2 is connected to the acquisition device 1 on one side and to the fan device 3 on the other side. Generally, it includes at least an image receiving unit, a processing unit, a calculation unit and an information sending unit. The image receiving unit is used to receive the image sent from the acquisition device 1. The processing unit is used to process the image to make the image clearer and less error-prone, which is convenient for subsequent calculations. The calculation unit is used to calculate the blockage degree of each area according to the processed image, and determine the control information of the fan device 3 according to the blockage degree of each area, including whether the material conveying air pressure needs to be adjusted and how to adjust it, whether a shutdown alarm is needed, and the information sending unit is used to send the control information of the fan device 3 to the fan device 3. The fan device 3 is installed on one side of the material conveying pipeline, and generally includes two types: a blowing pressure fan and a suction pressure fan. The suction pressure fan generates negative pressure in the conveying pipeline to suck the material 4 from the feed port into the conveying system. The blowing pressure fan generates positive pressure to blow the material 4 from one location to the destination. The material 4 is pushed forward through the pipeline with the help of the airflow generated by the fan.

[0027] In some embodiments, calculating the degree of blockage of each area of ​​the material conveying pipeline according to the received image includes: identifying the blockage area of ​​each area in the image according to the image, and calculating the degree of blockage of each area according to the blockage area.

[0028] Generally, the blockage of the material conveying pipeline is generally manifested in the thickening of the material 4 or the darkening of the color of the material 4. Therefore, the present invention proposes two methods for calculating the blockage area of ​​each area in the image.

[0029] The first method comprises: measuring the thickness of the material 4 in each area of ​​the image; and presetting a thickness threshold, wherein the area in each area of ​​the image where the thickness of the material 4 is greater than the thickness threshold is the blockage area.

[0030] In some embodiments, before measuring the thickness of the material 4 in each area of ​​the image, the method further includes: performing denoising on the image; adjusting the contrast and brightness of the image; and performing geometric correction on the image.

[0031] For example, first, a denoising method such as Gaussian filtering or bilateral filtering can be used to remove image noise introduced by uneven light or jitter of the acquisition device 1; after removing the noise, the contrast and brightness of the image are adjusted to make the boundary of the material 4 more prominent. For example, if the original image is relatively dark, after adjustment, the material 4 appears brighter white, which forms a clear contrast with the dark inner wall of the pipe, which is convenient for measuring the thickness of the material 4; since the installation angle of the camera may cause perspective distortion, the real shape of the material 4 appears deformed in the image, so the image needs to be geometrically corrected. For example, the shape of the pipe in the image can be restored to a standard state through perspective transformation to ensure that the proportion is accurate when measuring the thickness of the material 4; after the image is denoised, the brightness contrast is adjusted, and the geometric correction is performed, edge detection can be performed to determine the boundary of the material 4, for example, a multi-level edge detection algorithm or a Sobel operator is used to extract the outline of the material 4 and the edge of the inner wall of the pipe, which is convenient for measuring the thickness of the material 4 in the pipe; after edge detection, the boundary of the material 4 is clear, and the next step is to convert the pixel distance in the image into the actual physical thickness according to the actual distance between the camera and the material 4 and the calibration information. After calculating the thickness of material 4, the areas where the thickness exceeds the thickness threshold can be marked according to the preset thickness threshold, and the areas of these areas can be calculated using image processing technology to obtain the blockage area. For example, the pixels exceeding the thickness threshold can be marked and the actual physical area corresponding to these pixels can be calculated.

[0032] In the above method, the acquisition device 1 uses a camera. In other embodiments, the acquisition device 1 can be a laser sensor or an ultrasonic sensor, and the method of measuring thickness is somewhat different from the above method. If a laser sensor is used, there are generally two methods to measure the thickness of the material 4 in each area of ​​the conveying pipeline: First, install a laser sensor above the inside of the conveying pipeline to measure the reflection time or displacement of the laser emitted from the laser sensor to the target object, thereby calculating the distance, and the thickness of the material 4 is obtained by calculating the distance difference between the inner wall of the pipeline and the surface of the material 4; Second, if the pipeline is transparent or translucent, two laser sensors can be installed on the upper and lower sides of the outside of the conveying pipeline, one for emitting lasers and the other for receiving light passing through the material 4, and the thickness of the material 4 is calculated according to the attenuation of the light or the arrival time difference. If an ultrasonic sensor is used, there are generally two methods for measuring the thickness of material 4 in each area of ​​the conveying pipeline: 1. Reflection method, an ultrasonic sensor is installed above the inside of the conveying pipeline. The ultrasonic sensor emits sound waves. When the sound waves encounter the surface of material 4, reflection occurs. The ultrasonic sensor receives the reflected waves and calculates the time difference of the round-trip sound waves to obtain the distance to the surface of material 4; 2. Transmission method, two ultrasonic sensors are placed on the upper and lower sides of the pipeline, one as the transmitting end and the other as the receiving end. When the sound wave passes through material 4, the ultrasonic sensor can calculate the thickness of material 4 based on the time difference of the sound wave passing through material 4.

[0033] The second method for calculating the blocked area of ​​each area includes: presetting a color data threshold, the blocked area includes at least an area composed of multiple adjacent pixels, the multiple adjacent pixels include at least three pixels, and the RGB data of any pixel exceeds the preset color data threshold.

[0034] In some embodiments, the preset color data threshold, the blocked area includes at least an area composed of multiple adjacent pixels, the multiple adjacent pixels include at least three pixels, and the RGB data of any pixel exceeds the preset color data threshold, and also includes: denoising and enhancing the image; extracting the RGB data of all pixels in the image.

[0035] For example, in a pipeline for conveying white powder, under normal circumstances, the material 4 in the pipeline flows evenly, and the color remains light gray (mainly the color of the inner wall of the pipeline). The accumulation of material 4 is rarely seen in the image. If a blockage occurs, the white powder begins to accumulate, and a large area of ​​white area will appear in the image. Therefore, the RGB data of the color data threshold can be preset as: 180<R<220, 180<G<220, 180<B<220. Extract the RGB data of each pixel in the image from the collected image. If the RGB data of a certain pixel exceeds the preset color data threshold, it is considered that the material 4 has accumulated at this location. The accumulation of material 4 at a single pixel is generally not considered to be a blockage. Therefore, in the embodiment of the present invention, it is required that the area composed of at least three adjacent pixels is the blockage area, and the area sum of multiple blockage areas in the image is the blockage area. In addition, the pixel size in the image may not correspond to the actual physical area 1:1, so it is also necessary to calculate the actual physical area corresponding to these pixels through a conversion formula. It should be noted that calculating the actual physical area based on the pixel belongs to a known technology and will not be repeated here.

[0036] After obtaining the blocked area of ​​each region, in some embodiments, the blocked degree of each region may be calculated, specifically: ,in, Indicates the degree of blockage in the xth area of ​​the conveying pipeline image collected at time t, represents normalization, represents the blocked area of ​​the xth region in the conveying pipeline image collected at time t, represents the blocked area of ​​the xth region in the conveying pipeline image collected at time t-1, Represents the total area of ​​the collected conveying pipeline image, Represents the time interval from time t-1 to time t.

[0037] The intelligent central feeding automatic control system is controlled according to the blockage degree of each area. The specific method is: The material delivery pipeline is divided into n areas from the inlet to the outlet. When the blockage degree of the nth area is less than the first threshold, the trained wind pressure prediction model is used to predict the wind pressure and control the fan device. The wind pressure prediction model is generally a model built based on a neural network, with the input being the blockage degree of the nth area and the output being the predicted wind pressure. When the blockage degree of the nth area is greater than the second threshold, the machine is shut down and an alarm message is sent. When the blockage degree of the nth area is greater than or equal to the first threshold and less than or equal to the second threshold, the predicted delivery time T is calculated. When the actual delivery time is When the alarm is sent, the machine stops and an alarm message is sent. m is a preset constant, which is usually set to 1.15. ,in, Indicates the number of areas divided from the inlet to the outlet of the conveying pipeline. represents the degree of blockage in the i-th area, P represents the current wind pressure, and d is an empirical constant. The d represents the time for unit length transportation under an ideal condition without blockage, which can be understood as the time it takes for the material conveying pipeline to pass through the length of an area under an ideal condition without blockage.

[0038] In addition, in some embodiments, the sending of alarm information includes: alarming through the color of a fault warning light and / or the sound of a fault warning bell. This method can quickly and timely remind maintenance personnel to come for maintenance.

[0039] An intelligent central material feeding automatic control system provided by an embodiment of the present invention can collect images of the inside of the material feeding pipeline. The images can be used as direct evidence to accurately and truly calculate the degree of blockage of the material feeding pipeline in real time. It can give early warning and shut down the machine based on the prediction of the degree of blockage and the conveying time, thereby reducing sudden downtime caused by blockage. When the degree of blockage exceeds the standard, the system automatically shuts down and sends an alarm message to prevent equipment damage and production line stagnation, ensure production safety, and improve material feeding efficiency.

[0040] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0041] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. An intelligent central feeding automatic control system, characterized in that: include: Collection device, central control system and fan device; The acquisition device is connected to the central control system and is used to send the acquired material delivery pipeline image to the central control system; The central control system is connected to the fan device and is used to calculate the blockage degree of each area of ​​the material conveying pipeline according to the received image; The intelligent central feeding automatic control system is controlled according to the blockage degree of each area. The specific method is: The material delivery pipeline is divided into n areas from the inlet to the outlet. When the blockage degree of the nth area is less than the first threshold, the trained wind pressure prediction model is used to predict the wind pressure and control the fan device; When the congestion level of the nth block area is greater than the second threshold, the machine is shut down and an alarm message is sent; when the congestion level of the nth block area is greater than or equal to the first threshold and less than or equal to the second threshold, the predicted delivery time T is calculated. When the actual delivery time When , the machine stops and sends an alarm message, m is a preset constant, ,in, Indicates the number of areas divided from the inlet to the outlet of the conveying pipeline. It represents the blockage degree of the ith area, P represents the current wind pressure, and d is an empirical constant.

2. The intelligent central feeding automatic control system according to claim 1 is characterized in that: The step of calculating the blockage degree of each area of ​​the conveying pipeline according to the received image includes: The blocked area of ​​each region in the image is identified according to the image, and the blocked degree of each region is calculated according to the blocked area.

3. The intelligent central feeding automatic control system according to claim 2 is characterized in that: The calculation of the congestion degree of each area is specifically as follows: ,in, Indicates the degree of blockage in the xth area of ​​the conveying pipeline image collected at time t, represents normalization, represents the blocked area of ​​the xth region in the conveying pipeline image collected at time t, represents the blocked area of ​​the xth region in the conveying pipeline image collected at time t-1, Represents the total area of ​​the collected conveying pipeline image, Represents the time interval from time t-1 to time t.

4. The intelligent central feeding automatic control system according to claim 2 is characterized in that: The step of identifying the blocked area of ​​each region in the image according to the image comprises: Measure the material thickness of each area in the image; A thickness threshold is preset, and the area in each region of the image where the material thickness is greater than the thickness threshold is the blockage area.

5. The intelligent central feeding automatic control system according to claim 2 is characterized in that: The step of identifying the blocked area of ​​each region in the image according to the image further comprises: A color data threshold is preset, the blocked area includes at least an area composed of a plurality of adjacent pixels, the plurality of adjacent pixels include at least three pixels, and the RGB data of any pixel exceeds the preset color data threshold.

6. The intelligent central feeding automatic control system according to claim 4 is characterized in that: Before measuring the material thickness of each area in the image, the method further includes: Performing denoising on the image; adjusting contrast and brightness of the image; The image is geometrically corrected.

7. The intelligent central feeding automatic control system according to claim 5 is characterized in that: The preset color data threshold, the blocked area at least includes an area consisting of a plurality of adjacent pixels, the plurality of adjacent pixels include at least three pixels, and the RGB data of any pixel exceeds the preset color data threshold, and further includes: De-noise and enhance the image; Extract the RGB data of all pixels in the image.

8. The intelligent central feeding automatic control system according to claim 1 is characterized in that: The sending of alarm information comprises: The alarm is issued through the color of the fault warning light and / or the sound of the fault warning bell.

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