Dosage self-adaptive control method and device based on multi-mode identification

Abstract

The invention discloses a dosage self-adaptive control method and device based on multi-modal recognition, and belongs to the technical field of mode recognition and flotation dosing, and the method comprises the steps: carrying out the real-time recognition of a feeding coal quality image through a trained coal quality recognition model, judging a multi-modal working condition according to a recognition result, and then combining with a preset rule, and calculating the corresponding initial dosage. Key characteristics such as the tailing ash content, the tailing white foam amount and the tailing rough running condition are collected in real time, interval division and weighted calculation are conducted through a triangular membership fuzzy method, and the agent compensation amount is dynamically optimized. And timely transmitting a dosing amount decision value calculated based on the initial dosing amount and the comprehensive medicament compensation amount to an intelligent dosing station to complete closed-loop operation of dosing control. According to the method, the feeding coal quality and the tailing condition are comprehensively considered, accurate calculation of the dosage and real-time feedback of the control decision under the complex working condition are achieved, and stable operation of the flotation technology is guaranteed.

Description

Technical Field

[0001] The invention belongs to the technical field of pattern recognition and flotation dosing, and in particular relates to a dosing amount adaptive control method and device based on multi-modal recognition. Background Art

[0002] Flotation is a mineral separation technology that uses the differences in physical and chemical properties of mineral surfaces to separate useful minerals from useless minerals through reagents. Due to the widespread application of mechanized coal mining and heavy medium separation technology, the proportion of fine coal particles has increased significantly. As the demand for efficient utilization of fine coal slime resources increases, the importance of flotation technology has become increasingly prominent.

[0003] In the flotation process, the dosing process plays a decisive role in flotation efficiency and product quality. However, flotation dosing is affected by multi-modal conditions such as coal quality changes, foam characteristics, equipment status and operating parameters, making dosing control a key link and technical problem in the flotation process.

[0004] At present, flotation dosing in coal preparation plants mainly adopts two methods: manual dosing and intelligent dosing. The manual dosing method relies on experienced operators to make adjustments based on on-site observations, but due to perception bias, manual dosing has problems such as untimely response and difficulty in adapting to complex and changeable working conditions, resulting in large fluctuations in flotation efficiency and product quality. In recent years, with the improvement of industrial automation level, intelligent dosing technology has been widely studied and applied. The intelligent dosing method uses online detection data, combined with flotation models and control algorithms, to achieve automatic adjustment of reagents. However, the existing intelligent dosing methods are often designed only for a single working condition. When the working conditions change significantly, the intelligent dosing method lacks adaptability and is difficult to meet the control requirements under multi-modal conditions.

[0005] In summary, the existing technology cannot effectively solve the adaptability and stability problems of flotation dosing under multi-modal conditions. Therefore, exploring an intelligent dosing method that can effectively cope with multi-modal conditions is of great significance for improving flotation efficiency and product quality. Summary of the invention

[0006] Purpose of the invention: In order to solve the problems of insufficient adaptability and stability of existing dosing methods under multi-modal working conditions, the present invention provides a dosing amount adaptive control method based on multi-modal identification. The method uses a coal quality identification model to perform real-time analysis of the quality of the incoming coal, sets the initial dosing amount, combines the dynamic detection of key indicators such as tailings ash content, foam volume, and coarseness, calculates the comprehensive dosing compensation amount, combines the compensation result with the initial dosing amount through a control unit, generates a final dosing decision value, and realizes real-time dynamic adjustment of the dosing process. The method can effectively cope with the real-time changes of multi-modal working conditions such as coal quality, foam, and coarseness, overcomes the limitations of existing dosing technology under complex working conditions, and realizes stable control of flotation dosing.

[0007] Technical solution: A method for adaptively controlling dosage of a drug based on multimodal recognition of the present invention comprises the following steps:

[0008] Step 1: Set the initial dosage, build and train the coal quality recognition model, use the trained coal quality recognition model to perform real-time recognition of the feed video, determine the multimodal working conditions based on the recognition results, and combine them with the preset rules to output the initial dosage corresponding to different coal quality conditions;

[0009] Step 2, calculate the amount of drug addition compensation, first analyze and identify the tailings image through the tailings online ash meter, foam detection algorithm and coarseness detection algorithm, obtain the real-time tailings ash value, foam amount and coarseness, and then calculate the drug addition compensation based on ash, foam and coarseness respectively through the triangular membership fuzzy method, and then obtain the comprehensive drug addition compensation through weighted summation;

[0010] Step 3, dosing closed-loop control, first determine the dosing amount decision value according to the comprehensive dosing compensation amount and the initial dosing amount, then establish a communication connection with the intelligent dosing station, and perform dosing control according to the dosing amount decision value.

[0011] Furthermore, step 1 is specifically as follows: firstly, image data of coal on the feeding belt during normal operation of the coal preparation plant is collected, and then, according to the coal quality, the collected coal image data is divided into three coal quality categories: more lump coal, balanced coal and more fine coal, and different initial dosages are set for the three coal quality categories according to the experience of on-site workers, thus completing the data set construction;

[0012] The collected feed image data is used as input and the initial dosage of the corresponding coal quality category is used as a label to train the coal quality identification model, determine the reliable parameters of the coal quality identification model, and obtain a trained coal quality identification model.

[0013] Furthermore, in step 2, the foam amount detection algorithm includes the following process:

[0014] By using formula (1), the detection area image in the original image is extracted;

[0015] I ROI (x,y)=Crop(I(x,y),x0,y0,w,h)(1)

[0016] The input image I(x,y) is cropped by Crop(·) to limit the image to the rectangular range (x0,y0,w,h) to obtain the detection area image I ROI (x,y);

[0017] By formula (2), the obtained detection area image I ROI (x,y) converted to grayscale image Ig (x, y), and perform threshold segmentation to generate a binary image I b (x,y);

[0018]

[0019] In the formula, Respectively represent the red, green, and blue channel values ​​at the image pixel (x, y) of the detection area, 0.299, 0.587, and 0.114 represent the weights corresponding to the three channels, 1 represents the foam area, 0 represents the water surface area, and T represents the brightness threshold;

[0020] By formula (3), the binary image I b (x, y) is segmented into connected regions to obtain the foam region, and the total area A of the foam region is calculated by pixels. foam And the total area of ​​the detection area A ROI Based on the area values ​​of the two, the foam volume P is calculated. foam ;

[0021]

[0022] In the formula, R i represents the set of pixels contained in the i-th connected region, A i represents the area of ​​the i-th connected region, N represents the number of connected regions after segmentation, w represents the width of the detection area, and h represents the height of the detection area.

[0023] Furthermore, in step 2, the roughness detection algorithm includes the following process:

[0024] By using formula (1) and formula (2), the original image L(x, y) of the tailings running situation is extracted for detection area and converted into a grayscale image L g (x,y);

[0025] By using formula (4), the grayscale image L g (x, y) is Gaussian blurred to obtain the blurred image L b (x, y), and then the blurred image L b (x,y) is binarized using the adaptive threshold method to generate a binary image L t (x,y);

[0026]

[0027] Where G(i,j) is the Gaussian kernel, which indicates the influence of the pixel offset (i,j) on the center point, k is the radius of the Gaussian kernel, D(x,y) is the weighted average of the grayscale values ​​of the pixels in the local window, and C is the adjustment parameter used to refine the threshold.

[0028] By formula (5), the binary image L is obtained t (x, y) performs morphological operations to obtain the morphologically processed image L m (x, y), detect all connected contours, calculate the area of ​​each contour, set a threshold to exclude contours with too large an area, and the number of elements in the retained contour set is the number of detected roughness N d ;

[0029]

[0030] In the formula, MO(·) represents the morphological operation including corrosion and expansion, K is the structural element of the morphological operation, and C i represents the pixel set of the i-th connected contour, B i represents the area of ​​the i-th contour, B max represents the maximum area threshold, n represents the number of total contours, C f represents the set of retained contours, and |·| represents the operation of extracting the number of elements in the set.

[0031] Furthermore, in step 2, the triangular membership fuzzy method is used to calculate the dosage compensation amount based on ash content. Specifically, according to the detection result of the online ash content meter, the ash content value is fuzzy processed using the triangular membership function, the ash content value is divided into three fuzzy intervals, and the corresponding dosage compensation amount is calculated according to the membership. The specific method is as follows:

[0032] Too low interval: The membership degree of the gray value belonging to the "too low" fuzzy set is:

[0033]

[0034] Lower interval: The membership degree of the gray value belonging to the "lower" fuzzy set is:

[0035]

[0036] Normal interval: The membership degree of the gray value to the "normal" fuzzy set is:

[0037]

[0038] Among them, x 灰 represents the gray value of real-time detection, a1, a2, a3 are the upper bound, median and lower bound of the triangular fuzzy based on gray value, respectively, M 灰1 , M 灰2 , M 灰3 It indicates the benchmark dosage compensation amount corresponding to the too low, low and normal ash content ranges set by experts;

[0039] According to the maximum membership principle, the membership interval of the current ash value is determined, and the ash-based dosing in the corresponding interval is obtained. Further, in step 2, the triangular membership fuzzy method calculates the foam-based dosing compensation amount, specifically: according to the foam detection result, the triangular membership function is used to fuzzy process the foam amount, the foam amount is divided into three fuzzy intervals, and the corresponding dosing compensation amount is calculated according to the membership. The specific method is as follows:

[0040] Excessive interval: The degree of membership of the foam quantity to the "excessive" fuzzy set is:

[0041]

[0042] More interval: The degree of membership of the foam quantity to the "more" fuzzy set is:

[0043]

[0044] Normal interval: The degree of membership of the foam volume to the "normal" fuzzy set is:

[0045]

[0046] Among them, x 泡 represents the amount of foam detected in real time, b1, b2, b3 are the upper bound, median and lower bound of the triangular fuzzy based on foam, respectively, M 泡1 , M 泡2 , M 泡3 It indicates the benchmark dosage compensation amount corresponding to the excessive, high and normal foam volume ranges set by experts;

[0047] According to the maximum membership principle, determine the membership interval of the current foam volume and obtain the foam-based dosing compensation amount M in the corresponding interval. 泡 .

[0048] Furthermore, in step 2, the triangular membership fuzzy method calculates the dosage compensation amount based on roughing, specifically: according to the roughing detection result, the roughing amount is fuzzy processed by using the triangular membership function, the roughing amount is divided into three fuzzy intervals, and the corresponding dosage compensation amount is calculated according to the membership. The specific method is as follows:

[0049] Too many intervals: The membership degree of the running rough quantity belonging to the "too many" fuzzy set is:

[0050]

[0051] More interval: The membership degree of the running rough quantity belonging to the "more" fuzzy set is:

[0052]

[0053] Normal interval: The degree of membership of the running rough quantity to the "normal" fuzzy set is:

[0054]

[0055] Among them, x 粗 represents the roughness of real-time detection, c1, c2, c3 are the upper bound, median and lower bound of the triangular fuzzy based on roughness, M 粗1 , M 粗2 , M 粗3 It indicates the benchmark dosing compensation amount corresponding to the excessive, more and normal running rough ranges set by experts;

[0056] According to the maximum membership principle, determine the membership interval of the current roughing amount, and obtain the roughing-based dosing compensation amount M in the corresponding interval. 粗 .

[0057] Furthermore, in step 2, the comprehensive drug addition compensation amount is calculated by the following formula:

[0058] M 综 =ω1·M 灰 +ω2·M 泡 +ω3·M 粗 (15)

[0059] Where M 灰 、M 泡 、M 粗 They represent the compensation amount of dosing based on ash, foam and coarseness calculated in the preset interval, ω1, ω2, ω3 represent the weights of the compensation amount of dosing based on ash, foam and coarseness, M 综 Indicates the comprehensive dosing compensation amount, the value can be negative.

[0060] Furthermore, step 3 is specifically as follows: in the dosing control process, the initial dosing amount M is firstly calculated by formula (16). 初 and comprehensive dosing compensation amount M 综 The dosage decision value M is obtained by superposition. 决 . Then, a communication connection is established with the intelligent dosing station through PLC, and the dosing decision value is converted into an actual dosing control signal to control the amount of reagent added during the flotation process in real time;

[0061] M 决 =M 初 +M 综 (16)

[0062] The present invention also discloses a drug dosage adaptive control device based on multi-modal recognition, the device comprising an initial drug dosage setting unit, a drug dosage compensation unit and a drug dosage control unit;

[0063] The initial dosing setting unit uses the trained coal quality recognition model to perform real-time recognition of the feed video, determines the multimodal working conditions based on the recognition results, and combines them with preset rules to output the initial dosing amounts corresponding to different coal quality conditions. By matching the real-time recognition results with the rules, the initial dosing amount is dynamically adjusted under variable feed conditions;

[0064] The dosing compensation unit first analyzes and identifies the tailings image through the tailings online ash meter, foam detection algorithm and coarseness detection algorithm to obtain the real-time tailings ash value, foam amount and coarseness, and then calculates the dosing compensation amount based on ash, foam and coarseness through the triangular membership fuzzy method, and then obtains the comprehensive dosing compensation amount through weighted summation; and dynamically adjusts the dosing compensation amount by comprehensively analyzing various key tailings parameters;

[0065] The dosing control unit first determines the dosing amount decision value by combining the comprehensive dosing compensation amount with the initial dosing amount, then establishes a communication connection with the intelligent dosing station through the PLC, converts the dosing amount decision value into an actual dosing control signal, and controls the amount of reagent added during the flotation process in real time; through closed-loop control, real-time optimization of the dosing amount is achieved.

[0066] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0067] (1) The present invention provides a flotation intelligent dosing control method under complex working conditions. The method uses an image recognition model, an online ash analyzer and a detection algorithm to perform real-time detection of the feed coal quality, tailings ash content, tailings foam content and tailings coarseness, and based on multi-modal working condition modeling, achieves accurate adaptation to complex working conditions. Through an adaptive dosing strategy, the initial dosing amount is dynamically adjusted, and combined with a triangular membership fuzzy control method, the dosing compensation amount based on ash content, foam and coarseness is comprehensively calculated to optimize the dosing decision.

[0068] (2) The present invention adopts a PLC-intelligent dosing station closed-loop control system to convert the calculated dosing amount into an actual dosing control signal to achieve full-process automated control. Compared with the traditional dosing method, this method can not only achieve adaptive, accurate and stable dosing optimization under multi-modal complex working conditions and improve flotation recovery rate, but also reduce reagent consumption and manual intervention through closed-loop control and intelligent feedback mechanism, thereby ensuring the stability and economy of the flotation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a schematic diagram of the process of the present invention.

[0070] Figure 2 Flowchart for training and testing coal quality identification model.

[0071] Figure 3 This is a diagram of the foam amount detection process.

[0072] Figure 4 This is a diagram of the rough inspection process. DETAILED DESCRIPTION

[0073] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0074] The present invention provides a flotation intelligent dosing control method under complex working conditions, the flow chart is as follows Figure 1 As shown, it includes the following units:

[0075] The initial dosing setting unit uses the trained coal quality recognition model to perform real-time recognition of the feeding video, and then determines the multi-modal working conditions based on the recognition results and combines them with the preset rules to output the initial dosing amount corresponding to different coal quality conditions. By matching the real-time recognition results with the rules, the initial dosing amount can be dynamically adjusted under variable feeding conditions to improve the adaptability of the dosing process.

[0076] The dosing compensation unit first analyzes and identifies the tailings image through the tailings online ash meter, foam detection algorithm and coarse run detection algorithm to obtain the real-time tailings ash value, foam and coarse run, and then calculates the dosing compensation based on ash, foam and coarse run through the triangle membership fuzzy method, and then obtains the comprehensive dosing compensation through weighted summation. Through comprehensive analysis of various key tailings parameters, the dosing compensation amount is dynamically adjusted to improve the stability of flotation dosing.

[0077] The dosing control unit first determines the dosing amount decision value by combining the comprehensive dosing compensation amount with the initial dosing amount, then establishes a communication connection with the intelligent dosing station through the PLC, converts the dosing amount decision value into an actual dosing control signal, and controls the amount of reagent added during the flotation process in real time. Through closed-loop control, real-time optimization of the dosing amount is achieved, effectively coping with dynamic changes under complex working conditions.

[0078] In the initial dosing setting unit, the training and testing process of the trained coal quality identification model is as follows: Figure 2 As shown:

[0079] Step A1: real-time collection of input coal quality image data;

[0080] Step A2: performing cropping, denoising and normalization preprocessing on the collected input coal quality image data;

[0081] Step A3: according to manual experience, the pre-processed image data is divided into three categories: more lump coal, balanced between the two, and more fine coal, and then different initial dosages are calibrated for the three categories;

[0082] Step A4: Use the preprocessed image as the input of the coal quality recognition model and the manually calibrated image category as the output to perform model training;

[0083] Step A5: The trained coal quality recognition model performs real-time recognition on the pre-processed input coal quality image, matches the manually calibrated initial dosage according to the judgment category, and obtains the final initial dosage value M 初 .

[0084] The coal quality identification model can combine manual experience and real-time feeding conditions to output the appropriate initial dosage in a timely manner.

[0085] In the dosing compensation unit, the dosing compensation amount based on ash content is calculated by the triangular membership fuzzy method: according to the detection result of the online ash content meter, the ash content value is fuzzy processed by the triangular membership function, the ash content value is divided into three fuzzy intervals, and the corresponding dosing compensation amount is calculated according to the membership. The specific method is as follows:

[0086] Too low interval: The membership degree of the gray value belonging to the "too low" fuzzy set is:

[0087]

[0088] Lower interval: The membership degree of the gray value belonging to the "lower" fuzzy set is:

[0089]

[0090] Normal interval: The membership degree of the gray value to the "normal" fuzzy set is:

[0091]

[0092] Among them, x 灰 represents the gray value of real-time detection, a1, a2, a3 are the upper bound, median and lower bound of the triangular fuzzy based on gray value, respectively, M 灰1 , M 灰2 , M 灰3 It indicates the benchmark dosage compensation amount corresponding to the too low, low and normal ash content ranges set by experts.

[0093] According to the maximum membership principle, determine the membership interval of the current ash value and calculate the dosage compensation amount M based on the ash content. 灰 .

[0094] In the dosing compensation unit, the foam amount detection process is as follows Figure 3 As shown, according to the detection algorithm, the process of calculating the foam-based dosing compensation amount using the triangular membership fuzzy method includes the following steps:

[0095] Step B1: Extract the detection area image in the original image through formula (1) to improve the detection accuracy.

[0096] I ROI(x,y)=Crop(I(x,y),x0,y0,w,h)(1)

[0097] The input image I(x,y) is cropped by Crop(·) to limit the image to the rectangular range (x0,y0,w,h) to obtain the detection area image I ROI (x,y).

[0098] Step B2: Using formula (2), the obtained detection area image I ROI (x,y) converted to grayscale image I g (x, y), and perform threshold segmentation to generate a binary image I b (x,y).

[0099]

[0100] In the formula, They represent the red, green, and blue channel values ​​at the image pixel (x, y) in the detection area, respectively. 0.299, 0.587, and 0.114 represent the weights corresponding to the three channels, respectively. 1 represents the foam area, 0 represents the water surface area, and T represents the brightness threshold.

[0101] Step B3: Using formula (3), the binary image I is obtained b (x, y) is segmented into connected regions to obtain the foam region, and the total area A of the foam region is calculated by pixels. foam And the total detection area A ROI Based on the area values ​​of the two, the foam volume P is calculated. foam .

[0102]

[0103] In the formula, R i represents the set of pixels contained in the i-th connected region, A i represents the area of ​​the i-th connected region, N represents the number of connected regions after segmentation, w represents the width of the detection area, and h represents the height of the detection area.

[0104] Step B4: Based on the detected foam volume P foam , combined with the triangle membership fuzzy method to calculate the foam-based dosing compensation amount, the process is as follows:

[0105] Excessive interval: The degree of membership of the foam quantity to the "excessive" fuzzy set is:

[0106]

[0107] More interval: The degree of membership of the foam quantity to the "more" fuzzy set is:

[0108]

[0109] Normal interval: The degree of membership of the foam volume to the "normal" fuzzy set is:

[0110]

[0111] Among them, x 泡 represents the amount of foam detected in real time, b1, b2, b3 are the upper bound, median and lower bound of the triangular fuzzy based on foam, respectively, M 泡1 , M 泡2 , M 泡3 It indicates the benchmark dosage compensation amount corresponding to the excessive, heavy and normal foam volume ranges set by experts.

[0112] According to the maximum membership principle, determine the current foam amount membership interval, and obtain the calculation of the corresponding interval based on the foam dosing compensation amount M 泡 .

[0113] In the dosing compensation unit, the rough running amount detection process is as follows Figure 4 As shown, according to the detection algorithm, the process of calculating the dosing compensation amount based on running rough includes the following steps:

[0114] Step C1: By using formula (1) and formula (2), the original image L(x, y) of the tailings running situation is extracted for detection area and converted into a grayscale image L g (x,y).

[0115] Step C2: Use formula (4) to obtain the grayscale image L g (x, y) is Gaussian blurred to obtain the blurred image L b (x, y), and then the blurred image L b (x,y) is binarized using the adaptive threshold method to generate a binary image L t (x,y).

[0116]

[0117] Where G(i,j) is the Gaussian kernel, which indicates the influence of the pixel offset (i,j) on the center point, k is the radius of the Gaussian kernel, D(x,y) is the weighted average of the grayscale values ​​of the pixels in the local window, and C is the adjustment parameter used to refine the threshold.

[0118] Step C3: Using formula (5), obtain the binary image L t (x, y) performs morphological operations to obtain the morphologically processed image L m(x, y), detect all connected contours, calculate the area of ​​each contour, set a threshold to exclude contours with too large an area, and the number of elements in the retained contour set is the number of detected roughness N d .

[0119]

[0120] In the formula, MO(·) represents the morphological operation including corrosion and expansion, K is the structural element of the morphological operation, and C i represents the pixel set of the i-th connected contour, B i represents the area of ​​the i-th contour, B max represents the maximum area threshold, n represents the number of total contours, C f represents the set of retained contours, and |·| represents the operation of extracting the number of elements in the set.

[0121] Step C4: Based on the detected running roughness N d , combined with the triangle membership fuzzy method to calculate the foam-based dosing compensation amount, the process is as follows:

[0122] Too many intervals: The membership degree of the running rough quantity belonging to the "too many" fuzzy set is:

[0123]

[0124] More interval: The membership degree of the running rough quantity belonging to the "more" fuzzy set is:

[0125]

[0126] Normal interval: The degree of membership of the running rough quantity to the "normal" fuzzy set is:

[0127]

[0128] Among them, x 粗 represents the roughness of real-time detection, c1, c2, c3 are the upper bound, median and lower bound of the triangular fuzzy based on roughness, M 粗1 , M 粗2 , M 粗3 It indicates the benchmark dosing compensation amount corresponding to the excessive, more and normal running amount ranges set by experts.

[0129] According to the maximum membership principle, determine the membership interval of the current roughing amount and obtain the corresponding roughing-based dosing compensation amount M 粗 .

[0130] In the drug addition compensation unit, the comprehensive drug addition compensation amount is calculated by the following formula:

[0131] M 综 =ω1·M灰 +ω2·M 泡 +ω3·M 粗 (15)

[0132] Where M 灰 、M 泡 、M 粗 They represent the compensation amount of dosing based on ash, foam and coarseness calculated in the preset interval, ω1, ω2, ω3 represent the weights of the compensation amount of dosing based on ash, foam and coarseness, M 综 Indicates the comprehensive dosing compensation amount, which can be a negative number. This weighted calculation can flexibly adjust the weights of the dosing compensation amount based on ash content, foam and coarseness under different working conditions to achieve coordinated optimization of multiple factors.

[0133] In the dosing control unit, the initial dosing amount M is first calculated by formula (16). 初 and comprehensive dosing compensation amount M 综 The dosage decision value M is obtained by superposition. 决 Then, a communication connection is established with the intelligent dosing station through PLC, and the dosing decision value is converted into an actual dosing control signal to control the dosing amount in real time during the flotation process.

[0134] M 决 =M 初 +M 综 (16)

[0135] Through the above process, the intelligent dosing control of coal flotation under complex working conditions is completed.

Claims

1. A method for adaptive control of dosage based on multimodal recognition, characterized in that: The following steps are involved: Step 1: Set the initial dosage, build and train the coal quality recognition model, use the trained coal quality recognition model to perform real-time recognition of the feed video, determine the multimodal working conditions based on the recognition results, and combine them with the preset rules to output the initial dosage corresponding to different coal quality conditions; Step 2, calculate the amount of drug addition compensation, first analyze and identify the tailings image through the tailings online ash meter, foam detection algorithm and coarseness detection algorithm, obtain the real-time tailings ash value, foam amount and coarseness, and then calculate the drug addition compensation based on ash, foam and coarseness respectively through the triangular membership fuzzy method, and then obtain the comprehensive drug addition compensation through weighted summation; Step 3, dosing closed-loop control, first determine the dosing amount decision value according to the comprehensive dosing compensation amount and the initial dosing amount, then establish a communication connection with the intelligent dosing station, and perform dosing control according to the dosing amount decision value.

2. The method for adaptive control of dosage based on multimodal recognition according to claim 1, characterized in that: Step 1 is as follows: first collect the image data of coal on the feeding belt during normal operation of the coal preparation plant, and then divide the collected coal image data into three coal quality categories according to the coal quality: more lump coal, balanced coal and more fine coal. Different initial dosages are set for the three coal quality categories according to the experience of on-site workers to complete the data set construction; The collected feed image data is used as input and the initial dosage of the corresponding coal quality category is used as a label to train the coal quality identification model, determine the reliable parameters of the coal quality identification model, and obtain a trained coal quality identification model.

3. The method for adaptive control of dosage based on multimodal recognition according to claim 1, characterized in that: In step 2, the foam amount detection algorithm includes the following process: By using formula (1), the detection area image in the original image is extracted; I ROI (x,y)=Crop(I(x,y),x0,y0,w,h) (1) The input image I(x,y) is cropped by Crop(·) to limit the image to the rectangular range (x0,y0,w,h) to obtain the detection area image I ROI (x,y); By formula (2), the obtained detection area image I ROI (x,y) converted to grayscale image I g (x, y), and perform threshold segmentation to generate a binary image I b (x,y); In the formula, Respectively represent the red, green, and blue channel values ​​at the image pixel (x, y) of the detection area, 0.299, 0.587, and 0.114 represent the weights corresponding to the three channels, 1 represents the foam area, 0 represents the water surface area, and T represents the brightness threshold; By formula (3), the binary image I b (x, y) is segmented into connected regions to obtain the foam region, and the total area A of the foam region is calculated by pixels. foam And the total area of ​​the detection area A ROI Based on the area values ​​of the two, the foam volume P is calculated. foam ; In the formula, R i represents the set of pixels contained in the i-th connected region, A i represents the area of ​​the i-th connected region, N represents the number of connected regions after segmentation, w represents the width of the detection area, and h represents the height of the detection area.

4. The method for adaptive control of dosage based on multimodal recognition according to claim 3 is characterized in that: In step 2, the rough running detection algorithm includes the following process: By using formula (1) and formula (2), the original image L(x, y) of the tailings running situation is extracted for detection area and converted into a grayscale image L g (x,y); By using formula (4), the grayscale image L g (x, y) is Gaussian blurred to obtain the blurred image L b (x, y), and then the blurred image L b (x,y) is binarized using the adaptive threshold method to generate a binary image L t (x,y); Where G(i,j) is the Gaussian kernel, which indicates the influence of the pixel offset (i,j) on the center point, k is the radius of the Gaussian kernel, D(x,y) is the weighted average of the grayscale values ​​of the pixels in the local window, and C is the adjustment parameter used to refine the threshold. By formula (5), the binary image L is obtained t (x, y) performs morphological operations to obtain the morphologically processed image L m (x, y), detect all connected contours, calculate the area of ​​each contour, set a threshold to exclude contours with too large an area, and the number of elements in the retained contour set is the number of detected roughness N d ; In the formula, MO(·) represents the morphological operation including corrosion and expansion, K is the structural element of the morphological operation, and C i represents the pixel set of the i-th connected contour, B i represents the area of ​​the i-th contour, B max represents the maximum area threshold, n represents the number of total contours, C f represents the set of retained contours, and |·| represents the operation of extracting the number of elements in the set.

5. The method for adaptive control of dosage based on multimodal recognition according to claim 1, characterized in that: In step 2, the triangular membership fuzzy method is used to calculate the dosage compensation amount based on ash content. Specifically, according to the detection result of the online ash content meter, the ash content value is fuzzy processed using the triangular membership function, the ash content value is divided into three fuzzy intervals, and the corresponding dosage compensation amount is calculated according to the membership. The specific method is as follows: Too low interval: The membership degree of the ash value to the "too low" fuzzy set is: Lower interval: The membership degree of the gray value to the "lower" fuzzy set is: Normal interval: The membership degree of the gray value to the "normal" fuzzy set is: Among them, x 灰 represents the gray value of real-time detection, a1, a2, a3 are the upper bound, median and lower bound of the triangular fuzzy based on gray value, respectively, M 灰1 , M 灰2 , M 灰3 It indicates the benchmark dosage compensation amount corresponding to the too low, low and normal ash content ranges set by experts; According to the maximum membership principle, determine the membership interval of the current ash value and obtain the ash-based dosing compensation amount M in the corresponding interval. 灰 .

6. The method for adaptive control of dosage based on multimodal recognition according to claim 1, characterized in that: In step 2, the triangular membership fuzzy method calculates the dosing compensation amount based on foam, specifically: according to the foam detection result, the foam amount is fuzzy processed using the triangular membership function, the foam amount is divided into three fuzzy intervals, and the corresponding dosing compensation amount is calculated according to the membership. The specific method is as follows: Excessive interval: The degree of membership of the foam quantity to the "excessive" fuzzy set is: More interval: The degree of membership of the foam quantity to the "more" fuzzy set is: Normal interval: The degree of membership of the foam volume to the "normal" fuzzy set is: Among them, x 泡 represents the amount of foam detected in real time, b1, b2, b3 are the upper bound, median and lower bound of the triangular fuzzy based on foam, respectively, M 泡1 , M 泡2 , M 泡3 It indicates the benchmark dosage compensation amount corresponding to the excessive, high and normal foam volume ranges set by experts; According to the maximum membership principle, determine the membership interval of the current foam volume and obtain the foam-based dosing compensation amount M in the corresponding interval. 泡 .

7. The method for adaptive control of dosage based on multimodal recognition according to claim 1, characterized in that: In step 2, the triangular membership fuzzy method calculates the dosage compensation amount based on roughing, specifically: according to the roughing detection result, the roughing amount is fuzzy processed by using the triangular membership function, the roughing amount is divided into three fuzzy intervals, and the corresponding dosage compensation amount is calculated according to the membership. The specific method is as follows: Too many intervals: The membership degree of the rough running quantity belonging to the "too many" fuzzy set is: More interval: The membership degree of the running rough quantity belonging to the "more" fuzzy set is: Normal interval: The degree of membership of the running rough quantity to the "normal" fuzzy set is: Among them, x 粗 represents the roughness of real-time detection, c1, c2, c3 are the upper bound, median and lower bound of the triangular fuzzy based on roughness, M 粗1 , M 粗2 , M 粗3 It indicates the benchmark dosing compensation amount corresponding to the excessive, more and normal running rough ranges set by experts; According to the maximum membership principle, determine the membership interval of the current roughing amount, and obtain the roughing-based dosing compensation amount M in the corresponding interval. 粗 .

8. The method for adaptive control of dosage based on multimodal recognition according to claim 1, characterized in that: In step 2, the comprehensive drug addition compensation amount is calculated by the following formula: M 综 =ω1·M 灰 +ω2·M 泡 +ω3·M 粗 (15) Where M 灰 、M 泡 、M 粗 They represent the compensation amount of dosing based on ash, foam and coarseness calculated in the preset interval, ω1, ω2, ω3 represent the weights of the compensation amount of dosing based on ash, foam and coarseness, M 综 Indicates the comprehensive dosing compensation amount, the value can be negative.

9. The method for adaptive control of dosage based on multimodal recognition according to claim 1, characterized in that: Step 3 is as follows: In the dosing control process, the initial dosing amount M is firstly calculated by formula (16). 初 and comprehensive dosing compensation amount M 综 The dosage decision value M is obtained by superposition. 决 , and then establish a communication connection with the intelligent dosing station through PLC, convert the dosing amount decision value into an actual dosing control signal, and control the amount of reagent added in the flotation process in real time; M 决 =M 初 +M 综 (16)。 10. A dosing amount adaptive control device based on multi-modal recognition, used to implement the method according to claim 1, characterized in that: The device comprises an initial dosing setting unit, a dosing compensation unit and a dosing control unit; The initial dosing setting unit uses the trained coal quality recognition model to perform real-time recognition of the feed video, determines the multimodal working conditions based on the recognition results, and combines them with preset rules to output the initial dosing amounts corresponding to different coal quality conditions. By matching the real-time recognition results with the rules, the initial dosing amount is dynamically adjusted under variable feed conditions; The dosing compensation unit first analyzes and identifies the tailings image through the tailings online ash meter, foam detection algorithm and coarseness detection algorithm to obtain the real-time tailings ash value, foam amount and coarseness, and then calculates the dosing compensation amount based on ash, foam and coarseness through the triangular membership fuzzy method, and then obtains the comprehensive dosing compensation amount through weighted summation; and dynamically adjusts the dosing compensation amount by comprehensively analyzing various key tailings parameters; The dosing control unit first determines the dosing amount decision value by combining the comprehensive dosing compensation amount with the initial dosing amount, then establishes a communication connection with the intelligent dosing station through the PLC, converts the dosing amount decision value into an actual dosing control signal, and controls the amount of reagent added during the flotation process in real time; through closed-loop control, real-time optimization of the dosing amount is achieved.

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