Ore pulp liquid level prediction method and system based on flotation froth dynamic characteristics and storage medium

Abstract

The invention provides an ore pulp liquid level prediction method and system based on flotation froth dynamic characteristics and a storage medium, and relates to the technical field of flotation process production detection.The method comprises the steps that under the working condition of a flotation machine, flotation froth images and ore pulp liquid levels are continuously collected; performing texture feature extraction on each flotation froth image to obtain texture features, and generating a texture distribution coefficient from the texture features associated with the ore pulp liquid level; using a Sobel operator to identify the marginal area of each flotation froth in the flotation froth image, and carrying out comprehensive analysis to generate a froth distribution index; analyzing two frames of adjacent flotation froth images to generate a flotation froth dynamic deviation degree; and constructing an ore pulp liquid level prediction model, and predicting the ore pulp liquid level by using the ore pulp liquid level prediction model. The technical problem that the ore pulp liquid level cannot be monitored in real time in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of flotation process production detection, and in particular to a method, system and storage medium for predicting slurry level based on dynamic characteristics of flotation foam. Background Art

[0002] Mineral flotation is a method of adding flotation reagents to the ore pulp under specific process conditions, filling it with air, and generating a large number of bubbles through stirring, and then recovering the foam containing useful minerals to achieve the goal of mineral processing. In the traditional flotation control process, the control of the ore pulp level usually relies on manual experience. Although this method can meet the needs to a certain extent, it is not accurate enough. Although the ore pulp level can also be accurately obtained through the level sensor, the deployment of the level sensor requires a lot of cost. In addition, since the flotation process involves complex chemical reactions, even if a lot of cost is paid to deploy the level sensor, it is extremely easy to be damaged. Therefore, in industrial production, level sensors are usually not deployed in general flotation machines. Due to the limitations of on-site conditions, there is no better way to solve the problem of real-time monitoring of the ore pulp level.

[0003] Flotation foam has the characteristics of large quantity, adhesion, mixing, and irregular shape. Although these foam characteristics are highly correlated with the slurry level, they are difficult to describe quantitatively. In addition, the foam characteristics are complex and diverse. It is difficult to determine which specific characteristics are sufficiently correlated with the slurry level. Moreover, these characteristics are not in a simple linear relationship with the slurry level. How to establish a connection between these foam characteristics and the slurry level is also a major technical difficulty.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a method and system for predicting slurry level based on dynamic characteristics of flotation foam, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for predicting slurry level based on dynamic characteristics of flotation foam, comprising the following specific steps: Step 1: During the operation of a flotation machine under known working conditions, continuously collect flotation foam images and corresponding slurry levels under different frames; the flotation machine working conditions include scraper speed, air intake and slurry concentration; Step 2: Use the gray-level co-occurrence matrix to extract the texture features of each flotation foam image to obtain the texture features, analyze the correlation between each texture feature and the slurry level, and generate the texture distribution coefficient according to the texture features associated with the slurry level; Step 3: Use the Sobel operator to identify the edge area of ​​each flotation bubble in the flotation bubble image, determine the flotation bubble area based on the edge area of ​​each flotation bubble, and perform a comprehensive analysis of the flotation bubble area and the edge area of ​​the flotation bubble to generate a foam distribution index; Step 4: Based on the texture distribution coefficient and the foam distribution index of the flotation foam image of the current frame and the previous frame, the flotation foam dynamic deviation degree of the flotation foam image of the current frame is calculated; Step 5: Take the flotation machine working conditions, flotation foam dynamic deviation, texture distribution coefficient and foam distribution index in the same frame as the model input, and the corresponding slurry level as the label, build and train the slurry level prediction model, obtain the model input at the current moment, and input it into the slurry level prediction model to predict the slurry level.

[0007] Furthermore, the texture features include contrast, dissimilarity, homogeneity, energy and entropy. The specific formula used to calculate the correlation between each texture feature and the slurry level is: ; in, For the The correlation between the texture features and the slurry level, For the Frame flotation foam image The eigenvalues ​​of the texture features, is the total number of frames of flotation foam images collected, For collection The flotation foam image The average eigenvalue of texture features, For collection The average value of the pulp level of the flotation froth images, For the Frame flotation froth image of slurry level, is the flotation foam image frame index, and , is the index of the texture feature, and .

[0008] Furthermore, the texture feature association threshold is preset ,like , then the corresponding texture feature is judged to be associated with the slurry level, and the texture feature is defined as the associated texture feature. , then it is judged that the corresponding texture feature has no correlation with the slurry level; The specific formula for generating the texture distribution coefficient is: ; ; in, For the Texture distribution coefficient of frame flotation foam image, For the The weights of the key texture features, For the Frame flotation foam image Key texture feature values.

[0009] Furthermore, the Sobel operator is used to calculate the horizontal gradient value and the vertical gradient value of each pixel in the flotation foam image, and a first gradient amplitude data set including the gradient amplitude data of all pixel points is generated, and the error rate between the gradient amplitude of the current pixel point and the gradient amplitude of the adjacent pixel point is determined. If the error rate is less than the set error rate threshold, the pixel value of the current pixel point is retained, otherwise it is set to 0, and the part with the retained pixel value is defined as the edge area of ​​the flotation foam, and the closed area wrapped by the edge area is defined as the flotation foam part, and the set of all flotation foam positions is the flotation foam area.

[0010] Furthermore, when calculating the horizontal gradient value and the vertical gradient value of each pixel, the grayscale value of the pixel and the grayscale values ​​of its adjacent pixels are multiplied with the horizontal gradient template of the Sobel operator, and all the multiplication results are added to obtain the horizontal gradient value of the pixel, and the grayscale value of the pixel and the grayscale value of its adjacent pixels are multiplied with the vertical gradient template of the Sobel operator, and all the multiplication results are added to obtain the vertical gradient value of the pixel. The formula for calculating the horizontal gradient value and the vertical gradient value is: ; ; in, and Respectively Row, No. Column pixel horizontal gradient value and vertical gradient value; For the Row, No. The gray value of the pixel. , is the index of the pixel point of the flotation bubble image; The formula for generating the gradient amplitude of a pixel is: ; in, For the Row, No. The gradient magnitude of the pixel column.

[0011] The formula for generating the error rate between the gradient amplitude of the current pixel and the gradient amplitude of the adjacent pixel is: ; in, For the Row, No. The average gradient amplitude of all pixels adjacent to the column pixel, For the Row, No. The error rate of the pixel column.

[0012] Furthermore, the specific logic for generating the foam distribution index is as follows: count the number of pixels occupied by the flotation foam area, and divide the number of pixels occupied by the flotation foam area by the total number of pixels in the flotation foam image to obtain the flotation foam proportion; count the total number of flotation foams in each flotation foam image, and for each flotation foam, make the minimum circumscribed circle of the flotation foam, starting from the overlap position of the flotation foam and the minimum circumscribed circle, and go clockwise every Calculate the distance between the flotation foam and the minimum circumscribed circle on the same radius of the flotation foam, record it as the deviation distance, calculate the variance of the deviation distance of the same flotation foam, calculate the area ratio of the flotation foam to the corresponding minimum circumscribed circle as the area similarity, generate a shape regularity index based on the variance of the deviation distance and the area similarity, and calculate the average value of the shape regularity index of each flotation foam in the same flotation foam image; divide the flotation foam image into M blocks, count the number of flotation foams in each block, calculate the variance of the number of flotation foams in the same flotation foam image, record it as the uniformity of foam distribution; generate a foam distribution index based on the total number of flotation foams, the proportion of flotation foams, the average value of the shape regularity index and the uniformity of foam distribution; The specific formula for calculating the shape regularity index is: ; actually, is the shape regularity index, is the area similarity, is the variance of the deviation distance; The specific formula upon which the Foam Distribution Index is generated is: ; in, is the foam distribution index, is the total flotation foam quantity, is the flotation foam ratio, is the average value of the shape regularity index, is the uniformity of foam distribution; Furthermore, the specific formula for calculating the dynamic deviation of flotation foam is: ; in, For the The flotation foam dynamic deviation of the frame flotation foam image, For the Texture distribution coefficient of frame flotation foam image, For the Texture distribution coefficient of frame flotation foam image, For the Foam distribution index of frame flotation foam image; For the Foam distribution index of frame flotation foam images.

[0013] The present invention further provides a slurry level system based on the dynamic characteristics of flotation foam, the system is used to implement the slurry level prediction method based on the dynamic characteristics of flotation foam, specifically comprising: A data acquisition module is used to continuously acquire flotation foam images and corresponding slurry levels in different frames during the operation of a flotation machine under known working conditions; the flotation machine working conditions include scraper speed, air intake and slurry concentration; The texture analysis module uses the gray-level co-occurrence matrix to extract the texture features of each flotation foam image to obtain the texture features, analyzes the correlation between each texture feature and the slurry level, and generates the texture distribution coefficient according to the texture features associated with the slurry level; A foam analysis module is used to identify the edge area of ​​each flotation foam in the flotation foam image using the Sobel operator, determine the flotation foam area based on the edge area of ​​each flotation foam, and perform a comprehensive analysis on the flotation foam area and the edge area of ​​the flotation foam to generate a foam distribution index; A dynamic analysis module, for calculating the flotation foam dynamic deviation of the flotation foam image in the current frame based on the texture distribution coefficient and the foam distribution index of the flotation foam image in the current frame and the previous frame; The final prediction module is used to build and train a slurry level prediction model with the flotation machine working conditions, flotation foam dynamic deviation, texture distribution coefficient and foam distribution index in the same frame as model input and the corresponding slurry level as a label, obtain the model input at the current moment, and input it into the slurry level prediction model to predict the slurry level.

[0014] The present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program executable by a processor, and when the computer program is executed by the processor, the method for predicting the slurry level based on the dynamic characteristics of flotation foam can be implemented.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention quantitatively describes many features of flotation foam through image processing technology, which not only overcomes the problem that foam morphology is diverse and difficult to describe quantitatively, but also provides a solid data foundation for subsequent feature correlation analysis. By calculating the correlation coefficient between each texture feature and the slurry level, the key features that are highly correlated with the slurry level are effectively screened out. It ensures that the extracted and analyzed flotation foam features have sufficient correlation with the slurry level, providing a scientific theoretical basis for the prediction of flotation foam.

[0016] The present invention not only takes into account the static image characteristics of flotation foam, but also successfully establishes a complex relationship model between foam characteristics and slurry level through the analysis and quantification of adjacent frame images and the dynamic characteristics of flotation foam through a deep learning model, thereby greatly improving the accuracy and robustness of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0018] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] Example: See also Figure 1 , the present invention provides a technical solution: A method for predicting slurry level based on dynamic characteristics of flotation foam, comprising the following specific steps: Step 1: During the operation of a flotation machine under known working conditions, continuously collect flotation foam images and corresponding slurry levels under different frames; the flotation machine working conditions include scraper speed, air intake and slurry concentration; The slurry level is obtained by an ultrasonic level sensor.

[0022] Step 2: Use the gray-level co-occurrence matrix to extract the texture features of each flotation foam image to obtain the texture features, analyze the correlation between each texture feature and the slurry level, and generate the texture distribution coefficient according to the texture features associated with the slurry level; The texture features include contrast, dissimilarity, homogeneity, energy and entropy. The flotation foam image is grayed to obtain the grayscale co-occurrence matrix of the flotation foam image. The grayscale co-occurrence matrix of the flotation foam image is expressed as follows: ; in, is the gray-level co-occurrence matrix, The gray level in the flotation foam image is The pixels and gray levels are The number of times adjacent pixels appear in pairs, and are grayscale, and , .

[0023] ; ; ; ; ; ; in, After normalization , is the correction number, in order to avoid , its value is very small, and can be 0.0001, 0.001 and other decimals, and there is no restriction here. The contrast of the degree co-occurrence matrix, The difference of gray-level co-occurrence matrix, is the homogeneity of the gray-level co-occurrence matrix; is the energy of the gray-level co-occurrence matrix, is the entropy of the gray-level co-occurrence matrix, is the gray-level co-occurrence matrix OK Elements of a column; The specific formula used to calculate the correlation between each texture feature and the slurry level is: ; in, For the The correlation between the texture features and the slurry level, For the Frame flotation foam image The eigenvalues ​​of the texture features, is the total number of frames of flotation foam images collected, For collection The flotation foam image The average eigenvalue of texture features, For collection The average value of the pulp level of the flotation froth images, For the Frame flotation froth image of slurry level, is the flotation foam image frame index, and , is the index of the texture feature, and .

[0024] Preset texture feature association threshold ,like , then the corresponding texture feature is judged to be associated with the slurry level, and the texture feature is defined as the associated texture feature. , then it is judged that the corresponding texture feature has no correlation with the slurry level; The specific formula for generating the texture distribution coefficient is: ; ; in, For the Texture distribution coefficient of frame flotation foam image, For the The weights of the key texture features, For the Frame flotation foam image Key texture feature values.

[0025] The texture distribution coefficient reflects the comprehensive distribution of texture of flotation foam image. Flotation foam image plays an important role in analyzing slurry level. The texture distribution of flotation foam is an important property of flotation foam image and also plays an important role in analyzing slurry level.

[0026] Step 3: Use the Sobel operator to identify the edge area of ​​each flotation bubble in the flotation bubble image, determine the flotation bubble area based on the edge area of ​​each flotation bubble, and perform a comprehensive analysis of the flotation bubble area and the edge area of ​​the flotation bubble to generate a foam distribution index; The Sobel operator is used to calculate the horizontal gradient value and the vertical gradient value of each pixel in the flotation foam image, and a first gradient amplitude data set including the gradient amplitude data of all pixel points is generated, and the error rate between the gradient amplitude of the current pixel point and the gradient amplitude of the adjacent pixel point is determined. If the error rate is less than the set error rate threshold, the pixel value of the current pixel point is retained, otherwise it is set to 0, and the part with the retained pixel value is defined as the edge area of ​​the flotation foam, and the closed area wrapped by the edge area is defined as the flotation foam part, and the set of all flotation foam positions is the flotation foam area.

[0027] When calculating the horizontal gradient value and vertical gradient value of each pixel, the grayscale value of the pixel and the grayscale values ​​of its adjacent pixels are multiplied with the horizontal gradient template of the Sobel operator, and all the results of the products are added to obtain the horizontal gradient value of the pixel. The grayscale value of the pixel and the grayscale value of its adjacent pixels are multiplied with the vertical gradient template of the Sobel operator, and all the results of the products are added to obtain the vertical gradient value of the pixel. The formula for calculating the horizontal gradient value and the vertical gradient value is: ; ; in, and Respectively Row, No. Column pixel horizontal gradient value and vertical gradient value; For the Row, No. The gray value of the pixel. , is the index of the pixel point of the flotation bubble image; The formula for generating the gradient amplitude of a pixel is: ; in, For the Row, No. The gradient magnitude of the pixel column.

[0028] The formula for generating the error rate between the gradient amplitude of the current pixel and the gradient amplitude of the adjacent pixel is: ; in, For the Row, No. The average gradient amplitude of all pixels adjacent to the column pixel, For the Row, No. The error rate of the pixel column.

[0029] The specific logic for generating the foam distribution index is as follows: count the number of pixels occupied by the flotation foam area, and divide the number of pixels occupied by the flotation foam area by the total number of pixels in the flotation foam image to get the flotation foam proportion; count the total number of flotation foams in each flotation foam image, and for each flotation foam, make the minimum circumscribed circle of the flotation foam, starting from the overlap position of the flotation foam and the minimum circumscribed circle, and go clockwise every Calculate the distance between the flotation foam and the minimum circumscribed circle on the same radius of the flotation foam, recorded as the deviation distance. On the same radius, the radius intersects with the boundary of the flotation foam and the minimum circumscribed circle of the flotation foam at the same time, and the deviation distance is the distance between the two intersections. Calculate the variance of the deviation distance of the same flotation foam, calculate the area ratio of the flotation foam and the corresponding minimum circumscribed circle as the area similarity, generate the shape regularity index according to the variance of the deviation distance and the area similarity, and calculate the average value of the shape regularity index of each flotation foam in the same flotation foam image; divide the flotation foam image into M blocks, count the number of flotation foams in each block, calculate the variance of the number of flotation foams in the same flotation foam image, and record it as the uniformity of foam distribution; generate the foam distribution index according to the total number of flotation foams, the proportion of flotation foams, the average value of the shape regularity index and the uniformity of foam distribution; the specific formula for calculating the shape regularity index is: ; actually, is the shape regularity index, is the area similarity, is the variance of the deviation distance; The shape regularity index reflects the regularity of the shape of the flotation foam. The larger its value, the more regular the shape of the flotation foam. The area similarity reflects the proximity of the flotation foam to the area of ​​the minimum circumscribed circle. The larger its value, the closer the flotation foam area is to the minimum circumscribed circle. The variance of the deviation distance reflects the proximity of the flotation foam contour to the minimum circumscribed circle. The smaller its value, the closer the flotation foam contour is to the minimum circumscribed circle. The circle is the ideal shape of the flotation foam. The closer its area and contour are to its minimum circumscribed circle, the more regular the flotation foam shape is. The specific formula upon which the Foam Distribution Index is generated is: ; in, is the foam distribution index, is the total flotation foam quantity, is the flotation foam ratio, is the average value of the shape regularity index, is the uniformity of foam distribution; It reflects the average size of each flotation bubble. The larger the value, the larger the average flotation bubble. The average value of the shape regularity index reflects the comprehensive shape regularity of the flotation bubble in the flotation bubble image. The larger the value, the more regular the shape of the flotation bubble is. The foam distribution uniformity reflects the uniform distribution of the flotation bubble in the flotation bubble image. The smaller the value, the more uniform the distribution of the flotation bubble in the flotation bubble image. The foam distribution index reflects the distribution of the flotation bubble. The larger the value, the larger the flotation bubble is, and the more regular the shape and distribution of the flotation bubble are. Generally speaking, the lower the slurry level, the thicker the flotation foam layer is, the less likely it is to produce large bubbles, and the shape and distribution of the bubbles tend to be more disordered.

[0030] Step 4: Based on the texture distribution coefficient and the foam distribution index of the flotation foam image of the current frame and the previous frame, the flotation foam dynamic deviation degree of the flotation foam image of the current frame is calculated; in, For the The flotation foam dynamic deviation of the frame flotation foam image, For the Texture distribution coefficient of frame flotation foam image, For the Texture distribution coefficient of frame flotation foam image, For the Foam distribution index of frame flotation foam image; For the Foam distribution index of frame flotation foam images.

[0031] The flotation foam dynamic deviation reflects the dynamic changes of texture distribution and comprehensive foam distribution of two adjacent flotation foam images. The larger the value, the greater the dynamic change. Usually, under certain flotation machine working conditions, the dynamic change of a certain slurry level will fluctuate within a certain range. The generation of flotation foam dynamic deviation can provide an important basis for the prediction of slurry level. Reflects the texture distribution deviation of two adjacent flotation foam images. The larger the value, the greater the texture distribution deviation of two adjacent flotation foam images. The greater the foam distribution deviation of two adjacent flotation foam images, the greater the value thereof, and the greater the foam distribution deviation of two adjacent flotation foam images.

[0032] Step 5: Take the flotation machine working conditions, flotation foam dynamic deviation, texture distribution coefficient and foam distribution index in the same frame as the model input, and the corresponding slurry level as the label, build and train the slurry level prediction model, obtain the model input at the current moment, and input it into the slurry level prediction model to predict the slurry level.

[0033] The slurry level prediction model adopts a feedforward neural network, takes the slurry level as a label, and uses the flotation machine working conditions, flotation foam dynamic deviation, texture distribution coefficient and foam distribution index in the same frame to train and optimize the slurry level prediction model; specifically, it includes: input layer, hidden layer, output layer and activation function. The input layer is responsible for receiving the flotation machine working conditions, flotation foam dynamic deviation, texture distribution coefficient and foam distribution index in the same frame; the hidden layer is used to process the flotation machine working conditions, flotation foam dynamic deviation, texture distribution coefficient and foam distribution index in the same frame; it consists of multiple layers, each layer contains multiple time nodes, and the time node of each hidden layer is connected to the previous layer through weights. It is used to perform feature abstraction and nonlinear transformation on the working conditions of the flotation machine, the dynamic deviation of the flotation foam, the texture distribution coefficient and the foam distribution index under the same input frame; by using the ReLu activation function, the nonlinear relationship is introduced so that the model can fit the complex feature relationship; an independent neuron is set in the output layer, which is responsible for converting the local and high-level feature representations extracted by the hidden layer for outputting the slurry level; the root mean square error loss function is adopted; the input data is calculated once through the network to obtain the output result, the loss function is calculated according to the predicted value and the true value, the gradient of the loss function for each weight and bias is calculated by the chain rule, and the weights and biases of the network are updated using the gradient descent algorithm to minimize the loss function.

[0034] See also Figure 2 The present invention further provides a slurry level system based on the dynamic characteristics of flotation foam, the system is used to implement the slurry level prediction method based on the dynamic characteristics of flotation foam, specifically comprising: A data acquisition module is used to continuously acquire flotation foam images and corresponding slurry levels in different frames during the operation of a flotation machine under known working conditions; the flotation machine working conditions include scraper speed, air intake and slurry concentration; The texture analysis module uses the gray-level co-occurrence matrix to extract the texture features of each flotation foam image to obtain the texture features, analyzes the correlation between each texture feature and the slurry level, and generates the texture distribution coefficient according to the texture features associated with the slurry level; A foam analysis module is used to identify the edge area of ​​each flotation foam in the flotation foam image using the Sobel operator, determine the flotation foam area based on the edge area of ​​each flotation foam, and perform a comprehensive analysis on the flotation foam area and the edge area of ​​the flotation foam to generate a foam distribution index; A dynamic analysis module, for calculating the flotation foam dynamic deviation of the flotation foam image in the current frame based on the texture distribution coefficient and the foam distribution index of the flotation foam image in the current frame and the previous frame; The final prediction module is used to build and train a slurry level prediction model with the flotation machine working conditions, flotation foam dynamic deviation, texture distribution coefficient and foam distribution index in the same frame as model input and the corresponding slurry level as a label, obtain the model input at the current moment, and input it into the slurry level prediction model to predict the slurry level.

[0035] The present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program executable by a processor, and when the computer program is executed by the processor, the method for predicting the slurry level based on the dynamic characteristics of flotation foam can be implemented.

[0036] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0037] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0038] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0039] The above is only a specific implementation method of the present application, but the protection scope of the present application is not limited thereto. Any technical personnel familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A method for predicting slurry level based on dynamic characteristics of flotation foam, characterized in that: The specific steps include: Step 1: During the operation of a flotation machine under known working conditions, continuously collect flotation foam images and corresponding slurry levels under different frames; the flotation machine working conditions include scraper speed, air intake and slurry concentration; Step 2: Use the gray-level co-occurrence matrix to extract the texture features of each flotation foam image to obtain the texture features, analyze the correlation between each texture feature and the slurry level, and generate the texture distribution coefficient according to the texture features associated with the slurry level; Step 3: Use the Sobel operator to identify the edge area of ​​each flotation bubble in the flotation bubble image, determine the flotation bubble area based on the edge area of ​​each flotation bubble, and perform a comprehensive analysis of the flotation bubble area and the edge area of ​​the flotation bubble to generate a foam distribution index; Step 4: Based on the texture distribution coefficient and the foam distribution index of the flotation foam image of the current frame and the previous frame, the flotation foam dynamic deviation degree of the flotation foam image of the current frame is calculated; Step 5: Take the flotation machine working conditions, flotation foam dynamic deviation, texture distribution coefficient and foam distribution index in the same frame as the model input, and the corresponding slurry level as the label, build and train the slurry level prediction model, obtain the model input at the current moment, and input it into the slurry level prediction model to predict the slurry level.

2. A method for predicting slurry level based on dynamic characteristics of flotation foam according to claim 1, characterized in that: The texture features include contrast, dissimilarity, homogeneity, energy and entropy, The specific formula used to calculate the correlation between each texture feature and the slurry level is: ； in, For the The correlation between the texture features and the slurry level, For the Frame flotation foam image The eigenvalues ​​of the texture features, is the total number of frames of flotation foam images collected, For collection The flotation foam image The average eigenvalue of texture features, For collection The average value of the pulp level of the flotation froth images, For the Frame flotation froth image of slurry level, is the flotation foam image frame index, and , is the index of the texture feature, and .

3. A method for predicting slurry level based on dynamic characteristics of flotation foam according to claim 2, characterized in that: The specific logic for judging whether the texture feature is consistent with the slurry level is: Preset texture feature association threshold ,like , then the corresponding texture feature is judged to be associated with the slurry level, and the texture feature is defined as the associated texture feature. , then it is judged that the corresponding texture feature has no correlation with the slurry level; The specific formula for generating the texture distribution coefficient is: ； ； in, For the Texture distribution coefficient of frame flotation foam image, For the The weights of the key texture features, For the Frame flotation foam image Key texture feature values.

4. The method for predicting slurry level based on dynamic characteristics of flotation foam according to claim 1, characterized in that: The Sobel operator is used to calculate the horizontal gradient value and the vertical gradient value of each pixel in the flotation foam image, and a first gradient amplitude data set including the gradient amplitude data of all pixel points is generated, and the error rate between the gradient amplitude of the current pixel point and the gradient amplitude of the adjacent pixel point is determined. If the error rate is less than the set error rate threshold, the pixel value of the current pixel point is retained, otherwise it is set to 0, and the portion with the retained pixel value is defined as the edge area of ​​the flotation foam, and the closed area wrapped by the edge area is defined as the flotation foam, and the set of all flotation foam positions is the flotation foam area.

5. The method for predicting slurry level based on dynamic characteristics of flotation foam according to claim 4, characterized in that: When calculating the horizontal gradient value and vertical gradient value of each pixel, the grayscale value of the pixel and the grayscale values ​​of its adjacent pixels are multiplied with the horizontal gradient template of the Sobel operator, and all the results of the products are added to obtain the horizontal gradient value of the pixel. The grayscale value of the pixel and the grayscale value of its adjacent pixels are multiplied with the vertical gradient template of the Sobel operator, and all the results of the products are added to obtain the vertical gradient value of the pixel. The formula for calculating the horizontal gradient value and the vertical gradient value is: ； ； in, and Respectively Row, No. Column pixel horizontal gradient value and vertical gradient value; For the Row, No. The gray value of the pixel. , is the index of the pixel point of the flotation bubble image; The formula for generating the gradient amplitude of a pixel is: ； in, For the Row, No. The gradient magnitude of the column pixel; The formula for generating the error rate between the gradient amplitude of the current pixel and the gradient amplitude of the adjacent pixel is: ； in, For the Row, No. The average gradient amplitude of all pixels adjacent to the column pixel, For the Row, No. The error rate of the pixel column.

6. A method for predicting slurry level based on dynamic characteristics of flotation foam according to claim 5, characterized in that: The specific logic for generating the foam distribution index is as follows: the number of pixels occupied by the flotation foam area is counted, and the number of pixels occupied by the flotation foam area is divided by the total number of pixels of the flotation foam image to obtain the flotation foam proportion; Count the total number of flotation bubbles in each flotation bubble image. For each flotation bubble, make the minimum circumscribed circle of the flotation bubble. Start from the overlap position of the flotation bubble and the minimum circumscribed circle, and go clockwise every Calculate the distance between the flotation foam and the minimum circumscribed circle on the same radius of the flotation foam, record it as the deviation distance, calculate the variance of the deviation distance of the same flotation foam, calculate the area ratio of the flotation foam and the corresponding minimum circumscribed circle as the area similarity, generate the shape regularity index according to the variance of the deviation distance and the area similarity, calculate the average value of the shape regularity index of each flotation foam in the same flotation foam image; divide the flotation foam image into M blocks, count the number of flotation foams in each block, calculate the variance of the number of flotation foams in the same flotation foam image, record it as the uniformity of foam distribution; generate the foam distribution index according to the total number of flotation foams, the proportion of flotation foams, the average value of the shape regularity index and the uniformity of foam distribution; The specific formula for calculating the shape regularity index is: ； actually, is the shape regularity index, is the area similarity, is the variance of the deviation distance; The specific formula upon which the Foam Distribution Index is generated is: ； in, is the foam distribution index, is the total flotation foam quantity, is the flotation foam ratio, is the average value of the shape regularity index, It is the uniformity of foam distribution.

7. The method for predicting slurry level based on dynamic characteristics of flotation foam according to claim 1, characterized in that: The specific formula for calculating the dynamic deviation of flotation foam is: ； in, For the The flotation foam dynamic deviation of the frame flotation foam image, For the Texture distribution coefficient of frame flotation foam image, For the Texture distribution coefficient of frame flotation foam image, For the Foam distribution index of frame flotation foam image; For the Foam distribution index of frame flotation foam images.

8. A slurry level system based on the dynamic characteristics of flotation foam, characterized by: The system is used to implement the method for predicting slurry level based on dynamic characteristics of flotation foam according to any one of claims 1 to 7, and specifically comprises: A data acquisition module is used to continuously acquire flotation foam images and corresponding slurry levels in different frames during the operation of a flotation machine under known working conditions; the flotation machine working conditions include scraper speed, air intake and slurry concentration; The texture analysis module uses the gray-level co-occurrence matrix to extract the texture features of each flotation foam image to obtain the texture features, analyzes the correlation between each texture feature and the slurry level, and generates the texture distribution coefficient according to the texture features associated with the slurry level; A foam analysis module is used to identify the edge area of ​​each flotation foam in the flotation foam image using the Sobel operator, determine the flotation foam area based on the edge area of ​​each flotation foam, and perform a comprehensive analysis on the flotation foam area and the edge area of ​​the flotation foam to generate a foam distribution index; A dynamic analysis module, for calculating the flotation foam dynamic deviation of the flotation foam image in the current frame based on the texture distribution coefficient and the foam distribution index of the flotation foam image in the current frame and the previous frame; The final prediction module is used to build and train a slurry level prediction model with the flotation machine working conditions, flotation foam dynamic deviation, texture distribution coefficient and foam distribution index in the same frame as model input and the corresponding slurry level as a label, obtain the model input at the current moment, and input it into the slurry level prediction model to predict the slurry level.

9. A computer-readable storage medium, characterized in that: The storage medium internally stores a computer program that can be executed by a processor, and when the computer program is executed by the processor, the method for predicting the slurry level based on the dynamic characteristics of flotation foam described in any one of claims 1 to 7 can be implemented.

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