CN117782217B - Paper processing detecting system based on artificial intelligence - Google Patents
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Abstract
The invention discloses a paper processing detection system based on artificial intelligence, and belongs to the technical field of paper manufacturing detection. The technical problems of low paper quality detection efficiency, poor accuracy and high wastewater treatment difficulty in the prior art are solved. The technical principle of the invention is as follows: collecting paper images by using a camera, collecting wastewater data in the papermaking process by using various sensors, analyzing the data by using artificial intelligence, and outputting the state and the characteristics of the collected data; optimizing the wastewater treatment process and parameters, outputting a wastewater treatment scheme and control signals, and adjusting parameters in the papermaking process; and displaying the detection and processing results, and sending out an alarm signal in case of abnormal conditions. The beneficial effects of the invention are as follows: the system can realize the rapid, accurate and automatic detection of the paper quality, and can effectively monitor and treat the waste water generated in the papermaking process, thereby improving the quality and efficiency of the paper and reducing the cost and environmental impact of papermaking.
Description
Technical Field
The invention relates to the field of paper manufacturing detection, in particular to a paper processing detection system based on artificial intelligence.
Background
Paper making is a process in which raw materials such as plant fibers are processed by chemical or mechanical methods to produce paper or paperboard. With the advancement of society and the development of technology, paper making technology is also continuously improved and innovated to meet the production of paper with different purposes and requirements. However, papermaking technology also faces problems and challenges, mainly including the following two aspects:
(1) The quality detection of paper is an important link in the papermaking process, and directly influences the performance and the application of the paper. The traditional paper quality detection method mainly depends on manual or simple instruments, has the defects of low efficiency, poor accuracy, large human error, incapability of feeding back in real time and the like, and is difficult to meet the requirements of high speed, high efficiency and high quality of the modern paper industry.
(2) A large amount of waste water is generated in the paper making process, and various harmful substances such as cellulose, lignin, pigment, heavy metals, organic matters and the like are contained in the waste water, so that the waste water can cause serious pollution and damage to the environment if the waste water is directly discharged without treatment. The traditional wastewater treatment method mainly depends on a manual or simple control system, has the defects of poor treatment effect, high treatment cost, difficult optimization of treatment parameters, incapability of real-time adjustment and the like, and is difficult to meet the requirements of energy conservation, environmental protection and low carbon in the modern papermaking industry.
(3) Along with the development of society, the national importance of environmental protection is increasing, the treatment of wastewater generated after processing is more and more strict, and enterprises also need to attach importance to wastewater treatment; with the development of economy, the requirements of people on life quality are improved, and the production of high-quality paper is necessary for paper-making enterprises, but the quality control of the paper is just about the processing cost, the pricing profit and the competitiveness of the production enterprises, and the unavoidable cost which affects the environmental protection expenditure is also driven to be great; in summary, only comprehensive consideration and management of paper quality control and wastewater environmental protection treatment are more comprehensively carried out, which is a more suitable development direction of the current paper-making enterprises.
Therefore, how to design and develop a device capable of detecting paper on the pipeline and monitoring and treating wastewater in real time is a technical problem to be solved at present.
Disclosure of Invention
Aiming at the problems, the invention provides an artificial intelligence-based paper processing detection system, which can be used for rapidly, accurately and automatically detecting the quality of paper and effectively monitoring and treating wastewater generated in the paper making process, so that the quality and efficiency of the paper are improved, and the paper making cost and the environmental influence are reduced.
The technical scheme adopted by the invention is as follows:
an artificial intelligence based paper processing detection system comprising:
The paper detection module comprises a camera and a detection artificial intelligent module, wherein the camera acquires paper images, the detection artificial intelligent module processes the paper images, extracts colors, gloss, flatness and defects of the paper and outputs the quality index of the paper;
the wastewater detection module comprises a sensor assembly and a support vector machine; the sensor assembly comprises a conductivity sensor, a pH sensor, a residual chlorine sensor, a turbidity sensor, an ORP sensor and a support vector machine; the conductivity sensor detects the conductivity of the wastewater; the pH sensor detects the pH value of the wastewater; the residual chlorine sensor detects the residual chlorine content of the wastewater; the turbidity sensor detects the turbidity of the wastewater; the ORP sensor detects the oxidation-reduction potential of the wastewater; the support vector machine processes the data of the wastewater and outputs the state and the characteristics of the wastewater;
The feedback processing module comprises a processing artificial intelligent module and a controller; the artificial intelligence module for treating the wastewater selects a wastewater treatment scheme and papermaking process parameters according to the data of the paper and the wastewater, outputs control signals of the wastewater treatment scheme and the papermaking process parameters, and the controller adjusts the water quantity, the temperature and the pressure in the papermaking process according to the control signals of the papermaking process parameters;
the man-machine interaction module comprises an input module, a display module and an alarm module; the input module collects input of a user; the display module displays the input result, the detection result of the paper detection module, the detection result of the wastewater detection module and the treatment result of the wastewater treatment module, and the alarm module sends an alarm signal to remind a user when detecting that the quality index of the paper is lower than a preset threshold value or the state or the characteristic of the wastewater exceeds a specified range;
The paper detection module, the wastewater treatment module and the man-machine interaction module are connected and communicated through data lines or wireless signals.
The beneficial effects of the invention are as follows: the system realizes the rapid, accurate and automatic detection of the quality of paper, and can effectively monitor and treat the waste water generated in the papermaking process, thereby improving the quality and efficiency of the paper and reducing the cost and environmental impact of papermaking.
As a further improvement of the above scheme: the detection artificial intelligence module adopts a deep convolutional neural network and is used for extracting and classifying the characteristics of the paper image.
The beneficial effects of the invention are as follows: the deep convolutional neural network can accurately extract and classify the characteristics.
As a further improvement of the above scheme: the deep convolutional neural network has the structure that: the input layer receives the paper image, the convolution layer is used for carrying out convolution operation on the paper image, the pooling layer is used for downsampling output of the convolution layer, the full-connection layer is used for converting the output of the pooling layer into one-dimensional vectors, the output layer is used for generating a quality index of the paper according to the output of the full-connection layer, and the output of the convolution layer is thatWherein, O i,j,k is the output of the kth convolution kernel in the ith row and the jth column, W m,n,l,k is the weight of the kth convolution kernel in the ith row and the nth column of the ith layer, I i+m,j+n,l is the pixel value of the input image of the ith layer in the (i+m) row and the (j+n) th column, b k is the offset of the kth convolution kernel, M and N are the sizes of the convolution kernels, and L is the channel number of the input image.
The beneficial effects of the invention are as follows: by utilizing the artificial intelligence technology, the quality of the paper can be detected rapidly, accurately and automatically.
As a further improvement of the above scheme: the support vector machine adopts a kernel function to map the data of the wastewater from a low-dimensional space to a high-dimensional space, wherein the kernel function is as follows: k (X, y) =exp (- γ||x-y|| 2), X and y are data vectors of the wastewater, and γ is a parameter of a kernel function.
The beneficial effects of the invention are as follows: and analyzing the wastewater data by utilizing an artificial intelligence technology.
The invention also provides a paper processing detection method based on artificial intelligence, which comprises the following steps:
s1, acquiring a paper image, and shooting the paper in the processing process by using a camera to acquire image data of the paper in the processing process;
S2, processing the paper image, processing the image data by using a detection artificial intelligent module through a deep convolution neural network, extracting the color, luster, flatness and defects of the paper, and outputting the quality index of the paper;
S3, collecting wastewater data, and monitoring the wastewater in the papermaking process by using a conductivity sensor, a pH sensor, a residual chlorine sensor, a turbidity sensor and an ORP sensor, wherein the conductivity sensor detects the conductivity of the wastewater; the pH sensor detects the pH value of the wastewater; the residual chlorine sensor detects the residual chlorine content of the wastewater; the turbidity sensor detects the turbidity of the wastewater; the ORP sensor detects the oxidation-reduction potential of the wastewater; the support vector machine processes the data of the wastewater and outputs the state and the characteristics of the wastewater;
S4, wastewater data are processed, a support vector machine is used for processing radial basis functions of the wastewater data, components, concentration and pH value of the wastewater are extracted, and states and characteristics of the wastewater are output;
S5, selecting a wastewater treatment scheme and papermaking process parameters by utilizing a treatment artificial intelligent module according to the quality index of the paper, the state and the characteristics of the wastewater, and outputting control signals of the wastewater treatment scheme and the papermaking process parameters;
s6, regulating the papermaking process, and controlling a valve for controlling water quantity in the papermaking process, a heater for controlling temperature and a pressure gauge for controlling pressure by using a controller according to the papermaking process parameter control signal;
The beneficial effects of the invention are as follows: the method can realize the integration and collaborative optimization of paper detection and wastewater treatment, can flexibly adjust and control according to different paper types and wastewater conditions, improves the adaptability and reliability of the system, and can be convenient for users to use.
As a further improvement of the above technical scheme: the quality index of the paper is represented as Q (X) =α 1C(X)+α2G(X)+α3F(X)+α4 D (X), Q (X) is the quality index of the paper, C (X) is the color of the paper, G (X) is the gloss of the paper, F (X) is the flatness of the paper, D (X) is the defect of the paper, and α 1,α2,α3 and α 4 are weight coefficients of various indexes, and are determined according to an AHP hierarchical analysis method or historical data;
The beneficial effects of the invention are as follows: the quality of the paper is displayed in a mathematical mode, and the paper is more visual and scientific.
As a further improvement of the above technical scheme: the selection of wastewater treatment scheme and papermaking process parameters is processed by a processing artificial intelligent module, and the processing artificial intelligent module adopts a genetic algorithm for optimizing the wastewater treatment process and parameters.
The beneficial effects of the invention are as follows: and the optimal wastewater treatment scheme is selected according to the result by adopting a genetic algorithm, so that the intellectualization and optimization of wastewater treatment are realized.
As a further improvement of the above technical scheme: the genetic algorithm comprises the following steps:
s51 initialization: randomly generating a certain number of wastewater treatment schemes and papermaking process parameters as an initial population, wherein the wastewater treatment schemes and the papermaking process parameters are represented by binary codes, wherein the codes of one wastewater treatment scheme and papermaking process parameters are the binary numbers of the first position, the lengths of the codes are the requirements on the solution precision, and the size of the initial population and the probability of intersection and variation are set;
s52 evaluation: calculating fitness value f (X) =w 1Q(X)+w2E(X)-w3C(X)-w4 P (X) of each wastewater treatment scheme and papermaking process parameter according to quality and efficiency of paper and cost and environmental impact of papermaking, wherein f (X) is fitness value of one wastewater treatment scheme and papermaking process parameter, Q (X) is quality index of paper, E (X) is efficiency of paper, C (X) is cost of papermaking, P (X) is environmental impact of papermaking, ω 1 is weight coefficient of Q (X), Omega 2 is the weight coefficient of E (X), omega 3 is the weight coefficient of C (X), and omega 4 is the weight coefficient of P (X); Wherein E (X): efficiency of paper, the efficiency level of paper was evaluated according to the yield, qualification rate, speed of paper, E (X) =β 1Y(X)+β2R(X)+β3 S (X), where E (X) is the efficiency of paper, Y (X) is the yield of paper, R (X) is the qualification rate of paper, S (X) is the speed of paper, β 1 is the weight coefficient of Y (X), β 2 is the weight coefficient of R (X), Beta 3 is the weight coefficient of R (X); C (X): the cost of paper making is comprehensively calculated according to the raw materials, energy, labor and equipment cost of paper making, wherein C (X) =gamma 1M(X)+γ2E(X)+γ3L(X)+γ4 A (X), C (X) is the cost of paper making, M (X) is the raw materials cost of paper, E (X) is the energy cost of paper, L (X) is the labor cost of paper, A (X) is the equipment depreciation cost of paper, gamma 1 is the proportionality coefficient of M (X), gamma 2 is the proportionality coefficient of E (X), gamma 3 is the proportionality coefficient of L (X), gamma 4 is the proportionality coefficient of a (X); P (X): the environmental impact of paper making is comprehensively estimated according to the wastewater, waste gas and waste residue discharge of paper, wherein P (X) =delta 1W(X)+δ2G(X)+δ3 S (X) is the environmental impact of paper making, W (X) is the wastewater discharge amount of paper, G (X) is the waste gas discharge amount of paper, S (X) is the waste residue discharge amount of paper, delta 1 is the influence coefficient of W (X), Delta 2 is the influence coefficient of G (X), delta 3 is the influence coefficient of S (X);
S53, selecting: selecting a certain number of wastewater treatment schemes and papermaking process parameters as a next generation population by adopting a roulette method according to the fitness value; a certain number of chromosomes are selected from the initial population as parents of the next generation. The probability of selection is proportional to the fitness value, i.e., the higher the fitness value, the greater the probability that a chromosome is selected;
S54 cross: carrying out partial exchange on each two wastewater treatment schemes and papermaking process parameters with a certain probability to generate new wastewater treatment schemes and papermaking process parameters; pairing parent chromosomes according to the crossover probability, randomly selecting one or more crossover points on the chromosomes, exchanging chromosome fragments after the crossover points, and generating new chromosomes as offspring of the next generation;
S55 variation: randomly changing each wastewater treatment scheme and papermaking process parameters with a certain probability to generate new wastewater treatment scheme and papermaking process parameters; randomly modifying the sub-generation chromosome according to the mutation probability, namely randomly selecting one or more bits on the chromosome, turning over the chromosome, namely changing 0 into 1 and 1 into 0, and generating a new chromosome as a next generation child;
S56 updating: replacing the offspring chromosomes with the parent chromosomes to form a new population, and calculating the fitness value of each chromosome;
s57 ends: judging whether the preset iteration times or the fitness threshold value is reached, if so, outputting the optimal wastewater treatment scheme and papermaking process parameters, and if not, returning to the step S52.
The beneficial effects of the invention are as follows: and (3) mathematic of the selected wastewater scheme and parameters, and selecting the optimal wastewater treatment scheme to realize the optimization of wastewater treatment process and parameters.
Drawings
FIG. 1 is a schematic diagram of a paper processing inspection system;
FIG. 2 is a flow chart of the paper processing inspection system;
FIG. 3 is a workflow diagram for selecting a wastewater treatment scheme.
Detailed Description
In order that those skilled in the art will better understand the technical solutions, the following detailed description of the technical solutions is provided with reference to examples, which are exemplary and explanatory only and should not be construed as limiting the scope of the application in any way.
Example 1:
FIG. 1 is a schematic diagram of a system of the present invention, as shown, an artificial intelligence based paper processing detection system, comprising:
The paper detection module comprises a camera and a detection artificial intelligent module, acquires a paper image, processes the paper image, extracts indexes such as color, luster, flatness and defects of the paper, and outputs a quality index of the paper; the detection artificial intelligence module adopts a deep convolutional neural network and is used for extracting and classifying the characteristics of the paper image. The deep convolutional neural network has the structure that: the paper quality index generation device comprises an input layer, a convolution layer, a pooling layer, a full-connection layer and an output layer, wherein the input layer receives the paper image, the convolution layer is used for carrying out convolution operation on the paper image, the pooling layer is used for downsampling the output of the convolution layer, the full-connection layer is used for converting the output of the pooling layer into one-dimensional vectors, and the output layer is used for generating the paper quality index according to the output of the full-connection layer. The output of the convolution layer is Wherein, O i,j,k is the output of the kth convolution kernel in the ith row and the jth column, W m,n,l,k is the weight of the kth convolution kernel in the ith row and the nth column of the ith layer, I i+m,j+n,l is the pixel value of the input image of the ith layer in the (i+m) row and the (j+n) th column, b k is the offset of the kth convolution kernel, M and N are the sizes of the convolution kernels, and L is the channel number of the input image. Let the size of the input image be 28 x 3, i.e. both height and width are 28 pixels, and the number of channels is 3 (color image). The size of the convolution kernel is 5×5×3, i.e., the height and width are both 5 pixels, and the number of channels is the same as the input image. The number of convolution kernels is 16, i.e. the number of channels of the output image is 16. The stride is 1 and the convolution kernel is shifted one pixel at a time. The padding is 0 and no extension is made to the edges of the input image.
From these parameters, we can calculate the size of the output image as:
OC＝FN＝16
Wherein, O H,OW,OC represents the height, width and channel number of the output image, I H,IW,IC represents the height, width and channel number of the input image, F H,FW,FC represents the height, width and channel number of the convolution kernel, F N represents the number of convolution kernels, P represents the padding, and S represents the stride.
Therefore, the size of the output image is 24×24×16. In order to calculate the value of each pixel of the output image, a convolution operation needs to be performed on the input image and the convolution kernel. For the pixels of the ith row and jth column of the kth channel of the output image, the 5×5 region of the ith row and jth column of the ith channel of the input image and the 5×5 region of the ith channel of the kth convolution kernel are multiplied by elements, then summed, plus the offset term of the kth convolution kernel. This process can be expressed by the following formula:
Where O i,j,k is the pixel value of the ith row and jth column of the kth channel of the output image, W m,n,l,k is the weight value of the mth row and nth column of the ith channel of the kth convolution kernel, I i+m,j+n,l is the pixel value of the ith+mth row and jth+nth column of the ith channel of the input image, and b k is the offset term of the kth convolution kernel. To calculate this value, the 5 x 5 region of the upper left corner of the first channel of the input image and the 5 x 5 region of the first channel of the first convolution kernel are element-wise multiplied and then summed together with the offset term of the first convolution kernel. The sum of the products of the corresponding regions of the second and third channels of the input image and the corresponding regions of the second and third channels of the first convolution kernel are calculated and then added to obtain the value of O 0,0,0. To calculate the values of the other pixels of the output image, we need to slide the convolution kernel over the input image, one pixel at a time, repeating the convolution operation described above. To calculate the values of the other channels of the output image, we need to repeat the convolution operation described above using a different convolution kernel.
The wastewater detection module comprises a sensor assembly and a support vector machine; the sensor assembly comprises a conductivity sensor, a pH sensor, a residual chlorine sensor, a turbidity sensor, an ORP sensor and a support vector machine; the conductivity sensor detects the conductivity of the wastewater; the pH sensor detects the pH value of the wastewater; the residual chlorine sensor detects the residual chlorine content of the wastewater; the turbidity sensor detects the turbidity of the wastewater; the ORP sensor detects the oxidation-reduction potential of the wastewater; the support vector machine processes the data of the wastewater and outputs the state and the characteristics of the wastewater; the support vector machine adopts a kernel function to map the data of the wastewater from a low-dimensional space to a high-dimensional space, wherein the kernel function is as follows: k (X, y) =exp (- γ||x-y|| 2), X and y are data vectors of the wastewater, and γ is a parameter of a kernel function. Assume that the data for wastewater is two-dimensional, i.e., x= (x 1,x2),y＝(y1,y2), where x 1 and y 1 represent the conductivity of the wastewater and x 2 and y 2 represent the pH of the wastewater. Assuming that the parameter γ=0.5 of the kernel function, the value of the kernel function is:
K(x,y)＝exp(-0.5[(x1-y1)2+(x2-y2)2])
The value of this kernel function represents the similarity of the two wastewater data, with a larger value representing more similar and a smaller value representing less similar. For example, if there are two wastewater data x= (1, 7) and y= (2, 8), the value of the kernel function is:
K(x,y)＝exp(-0.5[(1-2)2+(7-8)2])≈0.6065
this means that the two wastewater data are relatively similar. If there are two other wastewater data x= (1, 7) and z= (5, 3), the value of the kernel function is:
K(x,z)＝exp(-0.5[(1-5)2+(7-3)2])≈0.0183
this means that the two wastewater data are not very similar.
A kernel matrix is constructed using the values of the kernel function, which is a symmetric positive definite matrix representing the similarity between wastewater data. For example, if there are four wastewater data x 1＝(1,7),x2＝(2,8),x3＝(5,3),x4 = (6, 4), the kernel matrix is:
And (3) calculating:
And using a core matrix, and using SVM to classify or regress the wastewater data.
The feedback processing module comprises a processing artificial intelligent module and a controller; the artificial intelligence module for treating the wastewater selects a wastewater treatment scheme and papermaking process parameters according to the data of the paper and the wastewater, outputs control signals of the wastewater treatment scheme and the papermaking process parameters, and the controller adjusts the water quantity, the temperature and the pressure in the papermaking process according to the control signals of the papermaking process parameters;
The man-machine interaction module comprises an input module, a display module and an alarm module, and displays the input result according to the input of a user, automatically displays the detection of the paper detection module, the results of the wastewater detection module and the wastewater treatment module, and sends an alarm signal to remind the user when detecting that the paper quality index is lower than a preset threshold value or the state or the characteristic of the wastewater exceeds a specified range;
The paper detection module, the wastewater treatment module and the man-machine interaction module are connected and communicated through data lines or wireless signals.
Example 2:
FIG. 2 is a flow chart of the present system, and FIG. 3 is a flow chart of the process of selecting a wastewater treatment scheme, as shown, an artificial intelligence based paper inspection method comprising the steps of:
S1, acquiring a paper image, and shooting the paper by using a camera to acquire image data of the paper;
S2, processing the paper image, processing the image data by using a detection artificial intelligent module through a deep convolution neural network, extracting the color, luster, flatness and defects of the paper, and outputting the quality index of the paper;
S3, collecting wastewater data, and monitoring the wastewater in the papermaking process by using a conductivity sensor, a pH sensor, a residual chlorine sensor, a turbidity sensor and an ORP sensor to obtain the conductivity, the pH value, the residual chlorine content, the turbidity and the oxidation-reduction potential of the wastewater;
S4, wastewater data are processed, a support vector machine is used for processing radial basis functions of the wastewater data, components, concentration and pH value of the wastewater are extracted, and states and characteristics of the wastewater are output;
S5, selecting a wastewater treatment scheme and papermaking process parameters by utilizing a treatment artificial intelligent module according to the quality index of the paper, the state and the characteristics of the wastewater, and outputting control signals of the wastewater treatment scheme and the papermaking process parameters;
S6, regulating the papermaking process, and controlling a valve for controlling water quantity, a heater for controlling temperature and a pressure gauge for controlling pressure in the papermaking process by using a controller according to the control signal;
And S7, displaying a detection result, namely displaying the quality index of the paper, the state and the characteristic of the wastewater, the wastewater treatment scheme and the input result of a user by using a display module, and sending out an alarm signal to remind the user when the quality index of the paper is detected to be lower than a preset threshold value or the state or the characteristic of the wastewater exceeds a specified range.
Wherein, S5 selects a wastewater treatment scheme specifically as follows:
S51 initialization: randomly generating a certain amount of wastewater treatment schemes and papermaking process parameters as an initial population, wherein the wastewater treatment schemes and the papermaking process parameters are expressed as x= (x 1,x2,...,xn) by binary codes, x is the code of one wastewater treatment scheme and papermaking process parameter, x i is the binary number of the ith bit, and n is the length of the code; the requirements for the accuracy of the solution are set, the size of the initial population is set, and the probability of crossover and mutation is set. Assuming that the wastewater treatment schemes are four, namely A, B, C, D, can be represented by two-bit binary numbers, A00, B01, C10 and D11, assuming that the wastewater treatment parameters are two, namely p and q, the values of the wastewater treatment parameters are respectively [0,1], and can be represented by fifteen-bit binary numbers, p:0.000000000000000q:0.000000000000000, one wastewater treatment scheme and papermaking process parameters are encoded as follows: x= (X1, X2,., xn) where X is the code of one wastewater treatment scheme and papermaking process parameters, xi is the binary number of the ith bit, n is the length of the code, n=2+15+15=32.
If a wastewater treatment scheme and papermaking process parameters are: scheme B, p=0.5, q=0.75, then its code is:
X＝(0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0)
S52 evaluation: calculating the fitness value f (X) =w 1Q(X)+w2E(X)-w3C(X)-w4 P (X) of each wastewater treatment scheme and papermaking process parameter according to the quality and efficiency of the paper and the cost and environmental impact of papermaking, wherein f (X) is the fitness value of one wastewater treatment scheme and papermaking process parameter, Q (X) is the quality index of the paper, E (X) is the efficiency of the paper, C (X) is the cost of papermaking, P (X) is the environmental impact of papermaking, and w 1,w2,w3 and w 4 are weight coefficients of each item;
Wherein Q (X) =α 1C(X)+α2G(X)+α3F(X)+α4 D (X), Q (X) is a quality index of the paper, C (X) is a color of the paper, G (X) is a gloss of the paper, F (X) is a flatness of the paper, D (X) is a defect of the paper, and α 1,α2,α3 and α 4 are weight coefficients of the respective indexes, which are determined according to an AHP hierarchical analysis method or history data; assuming that the weight coefficient of each index is α 1＝0.2,α2＝0.3,α3＝0.4,α4 =0.1, a paper is set to have a color of 8, a gloss of 7, a flatness of 9, and a defect of 8, Q (X) =8.1.
E (X): the efficiency of the paper can be evaluated according to the yield, qualification rate and speed of the paper, wherein E (X) =beta 1Y(X)+β2R(X)+β3 S (X), E (X) is the efficiency of the paper, Y (X) is the yield of the paper, R (X) is the qualification rate of the paper, S (X) is the speed of the paper, beta 1,β2 and beta 3 are weight coefficients of various indexes, and the efficiency is determined according to an AHP (advanced high-performance) hierarchical analysis method or historical data; assuming that the weight coefficient of each index is β 1＝0.4,β2＝0.3,β3 =0.3, the yield is 10, the yield is 0.9, and the speed is 12, the weight coefficient is determined according to the formula E (X) =β 1Y(X)+β2R(X)+β3 S (X), and E (X) =9.9.
C (X): the cost of paper making is comprehensively calculated according to the raw materials, energy, labor and equipment costs of paper, wherein C (X) =gamma 1M(X)+γ2E(X)+γ3L(X)+γ4 A (X) is the cost of paper making, M (X) is the raw materials cost of paper, E (X) is the energy cost of paper, L (X) is the labor cost of paper, A (X) is the equipment depreciation cost of paper, gamma 1,γ2,γ3 and gamma 4 are the proportionality coefficients of the costs, and according to an AHP hierarchical analysis method or historical data, the cost score of various paper can be calculated according to the formula C (X) =gamma 1M(X)+γ2E(X)+γ3L(X)+γ4 A (X) assuming that the raw materials are 100, the energy is 80, the labor is 60, the equipment is 40 and the proportionality coefficient of the costs is gamma 1＝0.5,γ2＝0.2,γ3＝0.2,γ4 =0.1, and the cost score of various paper is: c (X) =90.
P (X): the environmental impact of paper making is comprehensively evaluated according to the wastewater, waste gas and waste residue emission of paper, wherein the environmental pollution degree P (X) =delta 1W(X)+δ2G(X)+δ3 S (X) of paper making is comprehensively evaluated, P (X) is the environmental impact of paper making, W (X) is the wastewater emission amount of paper, G (X) is the waste gas emission amount of paper, S (X) is the waste residue emission amount of paper, delta 1,δ2 and delta 3 are the influence coefficients of the emissions, and the environmental impact is determined according to an AHP analytic hierarchy process or historical data. Assuming that a paper waste water is 50, an exhaust gas is 40, a waste residue is 30, and an influence coefficient of each discharge is δ 1＝0.4,δ2＝0.3,δ3 =0.3, environmental influence scores of various papers can be calculated according to the formula P (X) =δ 1W(X)+δ2G(X)+δ3 S (X): p (X) =38.
From the database, w 1,w2,w3 and w 4 can be determined, and f (x) is calculated, with higher fitness values indicating better wastewater treatment schemes and papermaking process parameters.
S53, selecting: selecting a certain number of wastewater treatment schemes and papermaking process parameters as a next generation population by adopting a roulette method according to the fitness value; a certain number of chromosomes are selected from the initial population as parents of the next generation. The probability of selection is proportional to the fitness value, i.e., the higher the fitness value the greater the probability that a chromosome is selected.
S54 cross: carrying out partial exchange on each two wastewater treatment schemes and papermaking process parameters with a certain probability to generate new wastewater treatment schemes and papermaking process parameters; pairing parent chromosomes according to the crossover probability, randomly selecting one or more crossover points on the chromosomes, and exchanging chromosome fragments after the crossover points to generate new chromosomes as offspring of the next generation.
S55 variation: randomly changing each wastewater treatment scheme and papermaking process parameters with a certain probability to generate new wastewater treatment scheme and papermaking process parameters; according to the mutation probability, the sub-generation chromosomes are randomly modified, namely one or more bits are randomly selected on the chromosomes, and are turned over, namely 0 is changed into 1,1 is changed into 0, so that new chromosomes are generated and serve as the offspring of the next generation.
S56 updating: and replacing the offspring chromosomes with the parent chromosomes to form a new population, and calculating the fitness value of each chromosome.
S57 ends: judging whether the preset iteration times or the fitness threshold value is reached, if so, outputting the optimal wastewater treatment scheme and papermaking process parameters, and if not, returning to the step S52. The number of iterations in this embodiment is 100.
The invention has the specific working principle that: the method comprises the steps of collecting image data of paper by using a camera, inputting the image data into a detection artificial intelligent module, extracting and classifying the image data by using a deep convolutional neural network, and outputting the quality index of the paper according to indexes such as color, luster, flatness and defects of the paper. The method comprises the steps of monitoring wastewater in the papermaking process by using a conductivity sensor, a pH sensor, a residual chlorine sensor, a turbidity sensor and an ORP sensor, obtaining data such as conductivity, pH value, residual chlorine content, turbidity, oxidation-reduction potential and the like of the wastewater, inputting the data into a support vector machine, mapping the data from a low-dimensional space to a high-dimensional space by using a radial basis function by a wastewater detection module, searching an optimal classification hyperplane in the high-dimensional space, and outputting the state and characteristics of the wastewater according to indexes such as components, concentration, pH value and the like of the wastewater. The wastewater treatment module selects wastewater treatment process and parameters according to output data of the paper detection module and the wastewater detection module, optimizes the wastewater treatment process and parameters by adopting a genetic algorithm, calculates the fitness value of each wastewater treatment scheme and papermaking process parameter according to the quality and efficiency of paper and the cost and environmental influence of papermaking, selects the scheme and parameter with the highest fitness value, outputs the wastewater treatment scheme and control signals, and then utilizes a controller to control equipment such as a valve, a heater, a pressure gauge, a motor and the like according to the control signals, adjusts the water quantity, the temperature, the pressure and the speed in the papermaking process, and realizes the effective treatment and recycling of wastewater. The man-machine interaction module receives input of a user by using the input module, displays the input result of the user by using the display module and outputs the result of the paper detection module, the waste water detection module and the waste water treatment module, and sends an alarm signal to remind the user when detecting that the quality index of the paper is lower than a preset threshold value or the state or the characteristic of the waste water exceeds a specified range by using the alarm module, wherein the module and other modules are connected and communicated by a data line or a wireless signal to realize the man-machine interaction function.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The principles and embodiments of the present application are described herein by applying specific examples, and the above examples are only used to help understand the method and core idea of the present application. The foregoing is merely illustrative of the preferred embodiments of this application, and it is noted that there is objectively no limit to the specific structure disclosed herein, since numerous modifications, adaptations and variations can be made by those skilled in the art without departing from the principles of the application, and the above-described features can be combined in any suitable manner; such modifications, variations and combinations, or the direct application of the concepts and aspects of the application in other applications without modification, are intended to be within the scope of the present application.
Claims (6)
1. The paper processing detection method based on artificial intelligence is characterized by comprising the following steps of:
s1, acquiring a paper image, and shooting the paper in the processing process by using a camera to acquire image data of the paper in the processing process;
S2, processing the paper image, processing the image data by using a detection artificial intelligent module through a deep convolution neural network, extracting the color, luster, flatness and defects of the paper, and outputting the quality index of the paper;
S3, collecting wastewater data, and monitoring the wastewater in the papermaking process by using a conductivity sensor, a pH sensor, a residual chlorine sensor, a turbidity sensor and an ORP sensor, wherein the conductivity sensor detects the conductivity of the wastewater; the pH sensor detects the pH value of the wastewater; the residual chlorine sensor detects the residual chlorine content of the wastewater; the turbidity sensor detects the turbidity of the wastewater; the ORP sensor detects the oxidation-reduction potential of the wastewater;
S4, wastewater data are processed, a support vector machine is used for processing radial basis functions of the wastewater data, components, concentration and pH value of the wastewater are extracted, and states and characteristics of the wastewater are output;
S5, selecting a wastewater treatment scheme and papermaking process parameters by utilizing a treatment artificial intelligent module according to the quality index of the paper, the state and the characteristics of the wastewater, and outputting control signals of the wastewater treatment scheme and the papermaking process parameters;
s6, regulating the papermaking process, and controlling a valve for controlling water quantity in the papermaking process, a heater for controlling temperature and a pressure gauge for controlling pressure by using a controller according to the papermaking process parameter control signal;
S7, displaying a detection result, namely displaying the quality index of the paper, the state and the characteristic of the wastewater, the wastewater treatment scheme and the input result of a user by using a display module, and sending an alarm signal to remind the user when the quality index of the paper is detected to be lower than a preset threshold value or the state or the characteristic of the wastewater exceeds a specified range;
the method comprises the steps that the selected wastewater treatment scheme and papermaking process parameters are treated by a treatment artificial intelligent module, and the treatment artificial intelligent module adopts a genetic algorithm and is used for optimizing the wastewater treatment scheme and papermaking process parameters;
The genetic algorithm comprises the following steps:
S51 initialization: randomly generating a certain number of wastewater treatment schemes and parameters as an initial population, wherein the wastewater treatment schemes and parameters are expressed by binary codes as x= (x 1,x2,…,xn), wherein x is the code of one wastewater treatment scheme and parameter, x i is the binary number of the ith bit, n is the length of the code, the requirement on the solution precision is set, and the size of the initial population and the probability of intersection and variation are set;
S52 evaluation: calculating fitness value f (X) =w 1Q(x)+w2E(x)-w3C(x)-w4 P (X) of each wastewater treatment scheme and parameter according to the quality and efficiency of the paper and the cost and environmental impact of papermaking, wherein f (X) is the fitness value of one wastewater treatment scheme and parameter, Q (X) is the quality index of the paper, E (X) is the efficiency of the paper, C (X) is the cost of papermaking, P (X) is the environmental impact of papermaking, ω 1 is the weight coefficient of Q (X), Omega 2 is the weight coefficient of E (X), omega 3 is the weight coefficient of C (X), and omega 4 is the weight coefficient of P (X); Wherein E (X): efficiency of paper, the efficiency level of paper was evaluated according to the yield, qualification rate, speed of paper, E (X) =β 1Y(X)+β2R(X)+β3 S (X), where E (X) is the efficiency of paper, Y (X) is the yield of paper, R (X) is the qualification rate of paper, S (X) is the speed of paper, β 1 is the weight coefficient of Y (X), β 2 is the weight coefficient of R (X), Beta 3 is the weight coefficient of R (X); C (X): the cost of paper making is comprehensively calculated according to the raw materials, energy, labor and equipment cost of paper making, wherein C (X) =gamma 1M(X)+γ2E(X)+γ3L(X)+γ4 A (X), C (X) is the cost of paper making, M (X) is the raw materials cost of paper, E (X) is the energy cost of paper, L (X) is the labor cost of paper, A (X) is the equipment depreciation cost of paper, gamma 1 is the proportionality coefficient of M (X), gamma 2 is the proportionality coefficient of E (X), gamma 3 is the proportionality coefficient of L (X), gamma 4 is the proportionality coefficient of a (X); P (X): the environmental impact of paper making is comprehensively estimated according to the wastewater, waste gas and waste residue discharge of paper, wherein P (X) =delta 1W(X)+δ2G(X)+δ3 S (X) is the environmental impact of paper making, W (X) is the wastewater discharge amount of paper, G (X) is the waste gas discharge amount of paper, S (X) is the waste residue discharge amount of paper, delta 1 is the influence coefficient of W (X), Delta 2 is the influence coefficient of G (X), delta 3 is the influence coefficient of S (X);
S53, selecting: selecting a certain number of wastewater treatment schemes and parameters as a next generation population by adopting a roulette method according to the fitness value; selecting a certain number of chromosomes from the initial population as the father of the next generation; the probability of selection is proportional to the fitness value, i.e., the higher the fitness value, the greater the probability that a chromosome is selected;
S54 cross: carrying out partial exchange on each two wastewater treatment schemes and parameters with a certain probability to generate new wastewater treatment schemes and parameters; pairing parent chromosomes according to the crossover probability, randomly selecting one or more crossover points on the chromosomes, exchanging chromosome fragments after the crossover points, and generating new chromosomes as offspring of the next generation;
S55 variation: carrying out random change on each wastewater treatment scheme and parameters according to a certain probability to generate new wastewater treatment schemes and parameters; randomly modifying the sub-generation chromosome according to the mutation probability, namely randomly selecting one or more bits on the chromosome, turning over the chromosome, namely changing 0 into 1 and 1 into 0, and generating a new chromosome as a next generation child;
S56 updating: replacing the offspring chromosomes with the parent chromosomes to form a new population, and calculating the fitness value of each chromosome;
S57 ends: judging whether the preset iteration times or the fitness threshold value is reached, if so, outputting the optimal wastewater treatment scheme and parameters, and if not, returning to the step S52.
2. The method for detecting paper processing based on artificial intelligence according to claim 1, wherein the quality index of the paper is represented by Q (X) =α 1C(X)+α2G(X)+α3F(X)+α4 D (X), Q (X) is the quality index of the paper, C (X) is the color of the paper, G (X) is the gloss of the paper, F (X) is the flatness of the paper, D (X) is the defect of the paper, α 1 is the weight coefficient of C (X), α 2 is the weight coefficient of G (X), α 3 is the weight coefficient of F (X), and α 4 is the weight coefficient of D (X).
3. The paper processing detection system of an artificial intelligence based paper processing detection method according to claim 1 or 2, comprising:
The paper detection module comprises a camera and a detection artificial intelligent module, wherein the camera acquires paper images, the detection artificial intelligent module processes the paper images, extracts colors, gloss, flatness and defects of the paper and outputs the quality index of the paper;
the wastewater detection module comprises a sensor assembly and a support vector machine; the sensor assembly comprises a conductivity sensor, a pH sensor, a residual chlorine sensor, a turbidity sensor, an ORP sensor and a support vector machine; the conductivity sensor detects the conductivity of the wastewater; the pH sensor detects the pH value of the wastewater; the residual chlorine sensor detects the residual chlorine content of the wastewater; the turbidity sensor detects the turbidity of the wastewater; the ORP sensor detects the oxidation-reduction potential of the wastewater; the support vector machine processes the data of the wastewater and outputs the state and the characteristics of the wastewater;
the feedback processing module comprises a processing artificial intelligent module and a controller; the artificial intelligence module for treating the wastewater selects a wastewater treatment scheme and papermaking process parameters according to the data of the paper and the wastewater, and outputs control signals of the wastewater treatment scheme and the papermaking process parameters; the controller adjusts the water quantity, temperature and pressure in the papermaking process according to the papermaking process parameter control signal;
The man-machine interaction module comprises an input module, a display module and an alarm module; the input module collects input of a user; the display module displays the input result, the detection result of the paper detection module, the detection result of the wastewater detection module and the treatment result of the wastewater treatment module; the alarm module sends an alarm signal to remind a user when detecting that the quality index of the paper is lower than a preset threshold value or the state or the characteristic of the wastewater exceeds a specified range;
The paper detection module, the wastewater treatment module and the man-machine interaction module are connected and communicated through data lines or wireless signals.
4. The artificial intelligence based paper processing detection system of claim 3, wherein the detection artificial intelligence module employs a deep convolutional neural network for feature extraction and classification of the paper images.
5. The artificial intelligence based paper machine check system of claim 4, wherein the deep convolutional neural network is structured as: the input layer receives the paper image, the convolution layer is used for carrying out convolution operation on the paper image, the pooling layer is used for downsampling output of the convolution layer, the full-connection layer is used for converting the output of the pooling layer into one-dimensional vectors, the output layer is used for generating a quality index of the paper according to the output of the full-connection layer, and the output of the convolution layer is thatWherein, O i,j,k is the output of the kth convolution kernel in the ith row and the jth column, W m,n,l,k is the weight of the kth convolution kernel in the ith row and the nth column of the ith layer, I i+m,j+n,l is the pixel value of the input image of the ith layer in the (i+m) row and the (j+n) th column, b k is the offset of the kth convolution kernel, M and N are the sizes of the convolution kernels, and L is the channel number of the input image.
6. The artificial intelligence based paper processing inspection system of claim 3 wherein the support vector machine uses a kernel function to map the data of the wastewater from a low dimensional space to a high dimensional space, wherein the kernel function is: k (x, y) =exp (- γ||x-y|| 2), x and y are data vectors of the wastewater, and gamma is a parameter of a kernel function.
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