CN113128126A - Modeling method of flotation dosing process based on generation of countermeasure network - Google Patents

Modeling method of flotation dosing process based on generation of countermeasure network Download PDF

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CN113128126A
CN113128126A CN202110455031.6A CN202110455031A CN113128126A CN 113128126 A CN113128126 A CN 113128126A CN 202110455031 A CN202110455031 A CN 202110455031A CN 113128126 A CN113128126 A CN 113128126A
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CN113128126B (en
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赵林
刘浪
李希
易嘉闻
邹尚
胡文静
吴健辉
张国云
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Hunan Institute of Science and Technology
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Abstract

The invention belongs to the technical field of flotation dosing, in particular to a modeling method of a flotation dosing process based on a generation countermeasure network, which fully utilizes the strong image generation capacity of the generation countermeasure network, calculates the characteristic difference before and after the foam image changes in the dosing process through an image discrimination network, and simultaneously models the incidence relation between the dosing quantity and the foam image by utilizing the mutual information maximization between the flotation dosing quantity and the foam image after the dosing prediction; in the model training process, the fighting game between the foam image prediction network and the foam image discrimination network is judged by using the dosed foam image, so that the performances of the two networks are improved, and the final prediction network can accurately predict the dosed foam image based on the initial foam image and the addition amount of the flotation reagent before and after dosing adjustment.

Description

Modeling method of flotation dosing process based on generation of countermeasure network
Technical Field
The invention belongs to the technical field of flotation dosing, and particularly relates to a modeling method of a flotation dosing process based on a generation countermeasure network.
Background
Mineral resources are important material bases for survival and development of human society, and the improvement of mineral recovery rate and comprehensive utilization rate is obvious in economic benefit. Mineral separation is an important link in mineral resource processing, and froth flotation is a mineral separation mode which is most widely applied.
The adjustment of the flotation dosing amount by the flotation operator is mainly dependent on the visual characteristics of the foam surface, such as foam size and color, which depends to a large extent on the operation experience; the manual medicine adding mode can cause mineral resource waste, increase environmental pollution and harm human health.
In the prior art, the traditional mode of judging the adding amount by depending on the experience of operators is limited to the operation experience, so that the automatic adding of the chemicals in the mineral flotation process has very important significance, but the relationship between the adding amount of the flotation chemicals and the foam image characteristics in the mineral flotation process is complex and the accurate modeling is difficult in the prior art.
Disclosure of Invention
In order to make up for the defects of the prior art and solve the problems that the relation between the addition amount of a flotation reagent and the characteristics of a froth image in the mineral flotation process is complex and accurate modeling is difficult, the invention provides a modeling method of a flotation dosing process based on generation of an antagonistic network.
The technical scheme adopted by the invention for solving the technical problems is as follows: a modeling method of a flotation dosing process based on a generation countermeasure network comprises the following steps:
s1: acquiring foam images before and after the addition amount of the flotation reagent is adjusted, and constructing a flotation dosing process data set by combining the adjustment record of the addition amount of the flotation reagent;
s2: establishing a flotation dosing network model based on a generated countermeasure network;
s3: inputting the flotation dosing process data into a flotation dosing network model, and training to obtain a parameter model of a flotation dosing network;
s4: inputting the initial foam image and the additive amount of the medicament before and after the medicament addition adjustment into a trained flotation medicament adding model, and predicting to obtain a foam image after medicament addition.
Preferably, in S1, the flotation dosing process data set is composed of flotation dosing condition records, and each flotation dosing condition record specifically includes dosing amounts before and after adjustment of the addition amount of the chemical and a corresponding foam image; the chemical addition amount is adjusted at time t, the foam image at the initial time of adjusting the chemical addition amount collected by the flotation monitoring system at this time is recorded as x (t) (for simplicity, referred to as initial foam image), and the chemical addition amounts before and after the adjustment of the chemical addition operation are respectively recorded as u (t)-) And u (t)+) (ii) a The time lag exists due to the action of the flotation reagent, and the reagent addition amount u (t)+) The resulting post-medicated foam image under the action of (a) is denoted as x (t + τ) (referred to simply as post-medicated foam image), where τ is the delay time for the full onset of the agent; thus, the dosing process can be formalized as a record of the flotation dosing regime for a segment of the flotation dosing process
Figure BDA0003040231420000021
The flotation dosing process data set records C from a large number of flotation dosing working conditionsiI is 1,2, …, M.
Preferably, in S1, the flotation dosing process data set is composed of flotation dosing condition records, and each flotation dosing condition record specifically includes dosing amounts before and after adjustment of the addition amount of the chemical and a corresponding foam image; adjusting the addition amount of the reagent at the moment t, and adjusting the initial addition amount collected by the flotation monitoring system at the momentThe initial foam image is denoted by x (t) (for simplicity, referred to as the initial foam image), and the amounts of the added drug before and after adjustment of the drug addition operation are denoted by u (t)-) And u (t)+) (ii) a The time lag exists due to the action of the flotation reagent, and the reagent addition amount u (t)+) The resulting post-medicated foam image under the action of (a) is denoted as x (t + τ) (referred to simply as post-medicated foam image), where τ is the delay time for the full onset of the agent; thus, the dosing process can be formalized as a record of the flotation dosing regime for a segment of the flotation dosing process
Figure BDA0003040231420000022
The flotation dosing process data set records C from a large number of flotation dosing working conditionsiI is 1,2, …, M.
Preferably, in S2, the network model of the flotation dosing process is composed of a dosed foam image prediction network and a foam image discrimination network;
the foam image prediction network after dosing is of an Encoder-Decoder structure, namely divided into two parts of coding and decoding; in the encoding part, continuous 6 convolutions of 4 multiplied by 4 are adopted to carry out feature extraction on the input initial foam image, and Batch Normalization (BN) and LeakyReLU activation functions are adopted after convolution; for the input flotation dosage, the flotation dosage is converted into a matrix with (dosage) information through pretreatment, and then the matrix is fused with a characteristic diagram output by the coding part and then input into the decoding part; the decoding part adopts continuous 6 deconvolution of 4 multiplied by 4 to reduce the characteristic diagram of the embedded medicament amount information into a foam image after medicine adding with the same size as the input foam image, a BN layer and a ReLU activation function are adopted after a deconvolution layer, the last layer of deconvolution is still combined with a Tanh activation function, and image data is output;
the foam image discrimination network consists of two different full convolution neural networks and shares weight parameters of 6 convolution layers, and example Normalization (IN) and LeakyReLU activation functions are adopted after the convolution layers; the two full convolution neural networks are respectively used for acquiring the characteristic distribution of the image and maximally predicting the mutual information between the foam image and the (dosage); in the process of obtaining the image characteristic distribution, firstly, 6 convolutions of 4 multiplied by 4 are adopted to respectively carry out characteristic extraction on the real dosed foam image and the forecast dosed foam image, and then, the convolution with the step length of 1 and the kernel of 4 multiplied by 4 is used to output the characteristic distribution of the image and is used for measuring the distance between the characteristic distributions of the two foam images; in the process of maximizing mutual information, two full convolution neural networks share most convolution layer weights, so that the calculation amount of repeated feature extraction is reduced, the predicted foam image features extracted by the previous convolution layer are directly subjected to down-sampling by using 4 x 4 convolution, and then the mean value and the variance meeting Gaussian distribution are respectively output through two different 1 x 1 convolutions.
Preferably, in S2, the loss function of the dosed foam image prediction network consists of confrontation loss, reconstruction loss, content loss, and mutual information loss, and the loss function of the foam image discrimination network consists of confrontation loss and gradient penalty loss;
the total loss of the foam image prediction network after dosing is specifically expressed as:
Figure BDA0003040231420000031
wherein λ isg-adv,λrec,λcontent,λinfoWeight coefficients corresponding to the countermeasures loss, reconstruction loss, content loss and mutual information loss, respectively
Figure BDA0003040231420000032
Loss of reconstruction
Figure BDA0003040231420000033
Content loss
Figure BDA0003040231420000034
And mutual information loss
Figure BDA0003040231420000035
Respectively expressed as:
Figure BDA0003040231420000036
Figure BDA0003040231420000037
Figure BDA0003040231420000038
Figure BDA0003040231420000041
the overall loss of the image discrimination network is:
Figure BDA0003040231420000042
wherein λ isd-adv,λgpWeight coefficients corresponding to the penalty loss and the penalty loss in gradient, respectively, to combat the loss
Figure BDA0003040231420000043
And gradient penalty loss
Figure BDA0003040231420000044
Respectively expressed as:
Figure BDA0003040231420000045
Figure BDA0003040231420000046
wherein the content of the first and second substances,
Figure BDA0003040231420000047
sampling is performed for random interpolation between the foam image after adding the medicine and the prediction foam image.
Preferably, in S3, the training and updating of the flotation dosing process model based on generation of the antagonistic network is divided into three steps: firstly, fusing a high-dimensional characteristic image obtained by an initial foam image through a coding part of an image prediction network and a high-dimensional matrix with dosing amount information, then obtaining a predicted foam image through deconvolution operation of a decoding part, and calculating the countermeasure loss and reconstruction loss of the predicted foam image and the dosed foam image; secondly, inputting the predicted foam image and the medicated foam image into a coding part of an image prediction network again respectively, and obtaining content loss by calculating characteristic differences among the characteristic images after each convolution layer; thirdly, inputting the dosed foam image and the predicted foam image into an image discrimination network respectively to extract features, calculating the countermeasure loss and the gradient penalty loss after obtaining the image feature distribution, outputting the mean value and the variance related to the predicted image feature distribution in the process of maximizing mutual information, and finally calculating the mutual information loss between the foam image and the flotation dosing amount; the image prediction network performs back propagation and updates the network weight through the sum of the countermeasure loss, the reconstruction loss, the content loss and the mutual information loss, and the image discrimination network performs back propagation and updates the network weight through the sum of the countermeasure loss and the gradient penalty loss.
Preferably, in S4, a froth flotation monitoring system is used to collect a current froth image and a corresponding dosing amount, a planned adjustment dosing amount is given, and a predicted post-dosing froth image is obtained using a froth image prediction network; whether the predicted foam image after the medicine is added is an ideal foam image (flotation working condition) is further judged, and a basis can be provided for realizing optimal control of the medicine adding amount of the foam flotation based on machine vision.
The invention has the technical effects and advantages that:
the modeling method of the flotation dosing process based on the generation countermeasure network provided by the invention fully utilizes the strong image generation capacity of the generation countermeasure network, calculates the characteristic difference before and after the foam image changes in the dosing process through the image discrimination network, and simultaneously models the incidence relation between the dosing amount and the prediction image by utilizing the mutual information maximization between the flotation dosing amount and the prediction dosed foam image; in the model training process, the fighting game between the dosed foam image prediction network and the foam image prediction network is utilized to realize the improvement of the performances of the dosed foam image prediction network and the dosed foam image prediction network, and the final prediction network can realize the accurate prediction of the dosed foam image based on the initial foam image and the addition amount of the flotation reagent before and after dosing adjustment; the experimental verification and performance analysis are carried out by utilizing flotation dosing data and foam images acquired in an industrial field, and the results show that the flotation dosing process modeling method can effectively construct the relationship between the dosing amount and the foam images, and the characteristics of the dosed foam images obtained by models in different working conditions are consistent with those of the actual dosed foam images, so that the problems that the relationship between the addition amount of a flotation reagent and the characteristics of the foam images in the mineral flotation process is complex and the accurate modeling is difficult can be solved, the simulation of the continuous dosing amount regulation process in the flotation production field is realized, and the most key precondition is provided for the optimized control of the flotation dosing amount based on machine vision.
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The invention will be further explained with reference to the drawings.
FIG. 1 is a flow chart of a modeling method of the present invention;
FIG. 2 is a schematic diagram of an image prediction network in the present invention;
FIG. 3 is a schematic diagram of an image discrimination network in accordance with the present invention;
FIG. 4 is a schematic representation of a flotation dosing model of the present invention;
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
Referring to fig. 1-4, the modeling method of the flotation dosing process based on the generation of the countermeasure network according to the present invention includes the following steps:
s1: acquiring foam images before and after the addition amount of the flotation reagent is adjusted, and constructing a flotation dosing process data set by combining the adjustment record of the addition amount of the flotation reagent;
s2: establishing a flotation dosing network model based on a generated countermeasure network;
s3: inputting the flotation dosing process data into a flotation dosing network model, and training to obtain a parameter model of a flotation dosing network;
s4: inputting the initial foam image and the additive amount of the medicament before and after the medicament addition adjustment into a trained flotation medicament adding model, and predicting to obtain a foam image after medicament addition.
Preferably, in S1, the flotation dosing process data set is composed of flotation dosing condition records, and each flotation dosing condition record specifically includes dosing amounts before and after adjustment of the addition amount of the chemical and a corresponding foam image; the chemical addition amount is adjusted at time t, the foam image at the initial time of adjusting the chemical addition amount collected by the flotation monitoring system at this time is recorded as x (t) (for simplicity, referred to as initial foam image), and the chemical addition amounts before and after the adjustment of the chemical addition operation are respectively recorded as u (t)-) And u (t)+) (ii) a The time lag exists due to the action of the flotation reagent, and the reagent addition amount u (t)+) The resulting post-medicated foam image under the action of (a) is denoted as x (t + τ) (referred to simply as post-medicated foam image), where τ is the delay time for the full onset of the agent; thus, the dosing process can be formalized as a record of the flotation dosing regime for a segment of the flotation dosing process
Figure BDA0003040231420000061
The flotation dosing process data set records C from a large number of flotation dosing working conditionsiI is 1,2, …, M.
Preferably, in S1, the flotation dosing process data set is composed of flotation dosing condition records, and each flotation dosing condition record specifically includes dosing amounts before and after adjustment of the addition amount of the chemical and a corresponding foam image; the chemical addition amount is adjusted at time t, the foam image at the initial time of adjusting the chemical addition amount collected by the flotation monitoring system at this time is recorded as x (t) (for simplicity, referred to as initial foam image), and the chemical addition amounts before and after the adjustment of the chemical addition operation are respectively recorded as u (t)-) And u (t)+) (ii) a The time lag exists due to the action of the flotation reagent, and the reagent addition amount u (t)+) Regulation of the resulting dosing under the action ofThe post-foam image is denoted x (t + τ) (referred to simply as post-medicated foam image), where τ is the delay time for adequate onset of the agent; thus, the dosing process can be formalized as a record of the flotation dosing regime for a segment of the flotation dosing process
Figure BDA0003040231420000062
The flotation dosing process data set records C from a large number of flotation dosing working conditionsiI is 1,2, …, M.
Preferably, in S2, the network model of the flotation dosing process is composed of a dosed foam image prediction network and a foam image discrimination network;
the foam image prediction network after dosing is of an Encoder-Decoder structure, namely divided into two parts of coding and decoding; in the encoding part, continuous 6 convolutions of 4 multiplied by 4 are adopted to carry out feature extraction on the input initial foam image, and Batch Normalization (BN) and LeakyReLU activation functions are adopted after convolution; for the input flotation dosage, the flotation dosage is converted into a matrix with (dosage) information through pretreatment, and then the matrix is fused with a characteristic diagram output by the coding part and then input into the decoding part; the decoding part adopts continuous 6 deconvolution of 4 multiplied by 4 to reduce the characteristic diagram of the embedded medicament amount information into a foam image after medicine adding with the same size as the input foam image, a BN layer and a ReLU activation function are adopted after a deconvolution layer, the last layer of deconvolution is still combined with a Tanh activation function, and image data is output;
the foam image discrimination network consists of two different full convolution neural networks and shares weight parameters of 6 convolution layers, and example Normalization (IN) and LeakyReLU activation functions are adopted after the convolution layers; the two full convolution neural networks are respectively used for acquiring the characteristic distribution of the image and maximally predicting the mutual information between the foam image and the (dosage); in the process of obtaining the image characteristic distribution, firstly, 6 convolutions of 4 multiplied by 4 are adopted to respectively carry out characteristic extraction on the real dosed foam image and the forecast dosed foam image, and then, the convolution with the step length of 1 and the kernel of 4 multiplied by 4 is used to output the characteristic distribution of the image and is used for measuring the distance between the characteristic distributions of the two foam images; in the process of maximizing mutual information, two full convolution neural networks share most convolution layer weights, so that the calculation amount of repeated feature extraction is reduced, the predicted foam image features extracted by the previous convolution layer are directly subjected to down-sampling by using 4 x 4 convolution, and then the mean value and the variance meeting Gaussian distribution are respectively output through two different 1 x 1 convolutions.
Preferably, in S2, the loss function of the dosed foam image prediction network consists of confrontation loss, reconstruction loss, content loss, and mutual information loss, and the loss function of the foam image discrimination network consists of confrontation loss and gradient penalty loss;
the total loss of the foam image prediction network after dosing is specifically expressed as:
Figure BDA0003040231420000071
wherein λ isg-adv,λrec,λcontent,λinfoWeight coefficients corresponding to the countermeasures loss, reconstruction loss, content loss and mutual information loss, respectively
Figure BDA0003040231420000072
Loss of reconstruction
Figure BDA0003040231420000073
Content loss
Figure BDA0003040231420000074
And mutual information loss
Figure BDA0003040231420000075
Respectively expressed as:
Figure BDA0003040231420000076
Figure BDA0003040231420000077
Figure BDA0003040231420000078
Figure BDA0003040231420000081
the overall loss of the image discrimination network is:
Figure BDA0003040231420000082
wherein λ isd-adv,λgpWeight coefficients corresponding to the penalty loss and the penalty loss in gradient, respectively, to combat the loss
Figure BDA0003040231420000083
And gradient penalty loss
Figure BDA0003040231420000084
Respectively expressed as:
Figure BDA0003040231420000085
Figure BDA0003040231420000086
wherein the content of the first and second substances,
Figure BDA0003040231420000087
sampling is performed for random interpolation between the foam image after adding the medicine and the prediction foam image.
Preferably, in S3, the training and updating of the flotation dosing process model based on generation of the antagonistic network is divided into three steps: firstly, fusing a high-dimensional characteristic image obtained by an initial foam image through a coding part of an image prediction network and a high-dimensional matrix with dosing amount information, then obtaining a predicted foam image through deconvolution operation of a decoding part, and calculating the countermeasure loss and reconstruction loss of the predicted foam image and the dosed foam image; secondly, inputting the predicted foam image and the medicated foam image into a coding part of an image prediction network again respectively, and obtaining content loss by calculating characteristic differences among the characteristic images after each convolution layer; thirdly, inputting the dosed foam image and the predicted foam image into an image discrimination network respectively to extract features, calculating the countermeasure loss and the gradient penalty loss after obtaining the image feature distribution, outputting the mean value and the variance related to the predicted image feature distribution in the process of maximizing mutual information, and finally calculating the mutual information loss between the foam image and the flotation dosing amount; the image prediction network performs back propagation and updates the network weight through the sum of the countermeasure loss, the reconstruction loss, the content loss and the mutual information loss, and the image discrimination network performs back propagation and updates the network weight through the sum of the countermeasure loss and the gradient penalty loss.
As an embodiment of the present invention, in S4, a froth flotation monitoring system is used to collect a current froth image and a corresponding dosing amount, a planned adjustment dosing amount is given, and a predicted post-dosing froth image is obtained by using a froth image prediction network; whether the predicted foam image after the medicine is added is an ideal foam image (flotation working condition) is further judged, and a basis can be provided for realizing optimal control of the medicine adding amount of the foam flotation based on machine vision.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (6)

1. A modeling method of a flotation dosing process based on a generation countermeasure network is characterized by comprising the following steps: the modeling method comprises the following steps:
s1: acquiring foam images before and after the addition amount of the flotation reagent is adjusted, and constructing a flotation dosing process data set by combining the adjustment record of the addition amount of the flotation reagent;
s2: establishing a flotation dosing network model based on a generated countermeasure network;
s3: inputting the flotation dosing process data into a flotation dosing network model, and training to obtain a parameter model of a flotation dosing network;
s4: inputting the initial foam image and the additive amount of the medicament before and after the medicament addition adjustment into a trained flotation medicament adding model, and predicting to obtain a foam image after medicament addition.
2. The modeling method of the flotation dosing process based on generation of the countermeasure network according to claim 1, characterized in that: in the S1, the flotation dosing process data set consists of flotation dosing working condition records, and each flotation dosing working condition record specifically comprises dosing amount before and after the addition amount of the medicament is adjusted and a corresponding foam image; the chemical addition amount is adjusted at time t, the foam image at the initial time of adjusting the chemical addition amount collected by the flotation monitoring system at this time is recorded as x (t) (for simplicity, referred to as initial foam image), and the chemical addition amounts before and after the adjustment of the chemical addition operation are respectively recorded as u (t)-) And u (t)+) (ii) a The time lag exists due to the action of the flotation reagent, and the reagent addition amount u (t)+) The resulting post-medicated foam image under the action of (a) is denoted as x (t + τ) (referred to simply as post-medicated foam image), where τ is the delay time for the full onset of the agent; thus, the dosing process can be formalized as a record of the flotation dosing regime for a segment of the flotation dosing process
Figure FDA0003040231410000011
The flotation dosing process data set records C from a large number of flotation dosing working conditionsiI is 1,2, …, M.
3. The modeling method of the flotation dosing process based on generation of the countermeasure network according to claim 2, characterized in that: in the S2, the flotation dosing process network model is composed of a dosed foam image prediction network and a foam image discrimination network;
the foam image prediction network after dosing is of an Encoder-Decoder structure, namely divided into two parts of coding and decoding; in the encoding part, continuous 6 convolutions of 4 multiplied by 4 are adopted to carry out feature extraction on the input initial foam image, and Batch Normalization (BN) and LeakyReLU activation functions are adopted after convolution; for the input flotation dosage, the flotation dosage is converted into a matrix with (dosage) information through pretreatment, and then the matrix is fused with a characteristic diagram output by the coding part and then input into the decoding part; the decoding part adopts continuous 6 deconvolution of 4 multiplied by 4 to reduce the characteristic diagram of the embedded medicament amount information into a foam image after medicine adding with the same size as the input foam image, a BN layer and a ReLU activation function are adopted after a deconvolution layer, the last layer of deconvolution is still combined with a Tanh activation function, and image data is output;
the foam image discrimination network consists of two different full convolution neural networks and shares weight parameters of 6 convolution layers, and example Normalization (IN) and LeakyReLU activation functions are adopted after the convolution layers; the two full convolution neural networks are respectively used for acquiring the characteristic distribution of the image and maximally predicting the mutual information between the foam image and the (dosage); in the process of obtaining the image characteristic distribution, firstly, 6 convolutions of 4 multiplied by 4 are adopted to respectively carry out characteristic extraction on the real dosed foam image and the forecast dosed foam image, and then, the convolution with the step length of 1 and the kernel of 4 multiplied by 4 is used to output the characteristic distribution of the image and is used for measuring the distance between the characteristic distributions of the two foam images; in the process of maximizing mutual information, two full convolution neural networks share most convolution layer weights, so that the calculation amount of repeated feature extraction is reduced, the predicted foam image features extracted by the previous convolution layer are directly subjected to down-sampling by using 4 x 4 convolution, and then the mean value and the variance meeting Gaussian distribution are respectively output through two different 1 x 1 convolutions.
4. The modeling method of the flotation dosing process based on generation of the countermeasure network according to claim 3, characterized in that: in the step S2, the loss function of the foam image prediction network after dosing consists of confrontation loss, reconstruction loss, content loss and mutual information loss, and the loss function of the foam image discrimination network consists of confrontation loss and gradient penalty loss;
the total loss of the foam image prediction network after dosing is specifically expressed as:
Figure FDA0003040231410000021
wherein λ isg-adv,λrec,λcontent,λinfoWeight coefficients corresponding to the countermeasures loss, reconstruction loss, content loss and mutual information loss, respectively
Figure FDA0003040231410000026
Loss of reconstruction
Figure FDA0003040231410000027
Content loss
Figure FDA0003040231410000028
And mutual information loss
Figure FDA0003040231410000029
Respectively expressed as:
Figure FDA0003040231410000022
Figure FDA0003040231410000023
Figure FDA0003040231410000024
Figure FDA0003040231410000025
the overall loss of the image discrimination network is:
Figure FDA0003040231410000031
wherein λ isd-adv,λgpWeight coefficients corresponding to the penalty loss and the penalty loss in gradient, respectively, to combat the loss
Figure FDA0003040231410000035
And gradient penalty loss
Figure FDA0003040231410000036
Respectively expressed as:
Figure FDA0003040231410000032
Figure FDA0003040231410000033
wherein the content of the first and second substances,
Figure FDA0003040231410000034
sampling is performed for random interpolation between the foam image after adding the medicine and the prediction foam image.
5. The modeling method of the flotation dosing process based on generation of the countermeasure network according to claim 4, characterized in that: in the step S3, the training and updating of the flotation dosing process model based on the generation of the countermeasure network are divided into three steps: firstly, fusing a high-dimensional characteristic image obtained by an initial foam image through a coding part of an image prediction network and a high-dimensional matrix with dosing amount information, then obtaining a predicted foam image through deconvolution operation of a decoding part, and calculating the countermeasure loss and reconstruction loss of the predicted foam image and the dosed foam image; secondly, inputting the predicted foam image and the medicated foam image into a coding part of an image prediction network again respectively, and obtaining content loss by calculating characteristic differences among the characteristic images after each convolution layer; thirdly, inputting the dosed foam image and the predicted foam image into an image discrimination network respectively to extract features, calculating the countermeasure loss and the gradient penalty loss after obtaining the image feature distribution, outputting the mean value and the variance related to the predicted image feature distribution in the process of maximizing mutual information, and finally calculating the mutual information loss between the foam image and the flotation dosing amount; the image prediction network performs back propagation and updates the network weight through the sum of the countermeasure loss, the reconstruction loss, the content loss and the mutual information loss, and the image discrimination network performs back propagation and updates the network weight through the sum of the countermeasure loss and the gradient penalty loss.
6. The modeling method of the flotation dosing process based on generation of the countermeasure network according to claim 5, characterized in that: in the step S4, a froth flotation monitoring system is used for collecting a current froth image and a corresponding medicine adding amount, a planned adjustment medicine adding amount is given, and a predicted post-medicine adding froth image is obtained by using a froth image prediction network; whether the predicted foam image after the medicine is added is an ideal foam image (flotation working condition) is further judged, and a basis can be provided for realizing optimal control of the medicine adding amount of the foam flotation based on machine vision.
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