CN116483132A

CN116483132A - Coal flow control system and method based on drive motor current control coordination

Info

Publication number: CN116483132A
Application number: CN202310195957.5A
Authority: CN
Inventors: 马进; 黄晨宇; 刘鹏飞; 季春; 戴冬冬; 凌峰; 王中山
Original assignee: Beijing Huaneng Xinrui Control Technology Co Ltd; Huaneng Taicang Power Generation Co Ltd
Current assignee: Beijing Huaneng Xinrui Control Technology Co Ltd; Huaneng Taicang Power Generation Co Ltd
Priority date: 2023-03-03
Filing date: 2023-03-03
Publication date: 2023-07-25

Abstract

The invention discloses a coal flow control system and a method based on drive motor current control coordination, which are based on an artificial intelligent monitoring technology of deep learning, so that the drive current of a drive motor is adaptively adjusted by utilizing a responsive logic association relation existing between high-dimensional implicit association mode characteristics of data discrete distribution in a local time window in coal flow data and current data of the drive motor, so that the coal flow can be kept stable and balanced.

Description

Coal flow control system and method based on drive motor current control coordination

Technical Field

The present application relates to the field of flow control technology, and more particularly, to a coal flow control system and method based on drive motor current control coordination.

Background

Before the material piling and taking equipment is unattended, the size of the coal taking flow is observed manually through eyes of an operator and controlled. After the unattended operation is realized, the eyes of the person are not used, and the eyes of the technology must keep up.

After the material piling and taking equipment is intelligent and unmanned, the automatic control technology of the coal flow is an important technology for intelligent operation of a bucket wheel machine, and an important technical aim is to control the stability and balance of the coal flow, namely, to keep the fluctuation of the coal flow within an acceptable range.

Accordingly, a coal flow control scheme is desired.

Disclosure of Invention

The present application has been made in order to solve the above technical problems. The embodiment of the application provides a coal flow control system and a coal flow control method based on drive motor current control coordination, which are based on an artificial intelligent monitoring technology of deep learning, so that the coal flow can be kept stable and balanced by capturing high-dimensional implicit association mode characteristics of data discrete distribution in a local time window in coal flow data and current data of a drive motor respectively and utilizing a responsive logic association relation existing between the coal flow data and the current data of the drive motor to carry out self-adaptive adjustment on the drive current of the drive motor.

Accordingly, according to one aspect of the present application, there is provided a coal flow control system based on drive motor current control synergy, comprising:

the sensor monitoring module is used for acquiring coal flow values at a plurality of preset time points in a preset time period and current values of a driving motor at the preset time points;

the time sequence vectorization module is used for arranging the coal flow values of the plurality of preset time points into a coal flow input vector according to a time dimension, and arranging the current values of the driving motors of the plurality of preset time points into a current input vector according to the time dimension;

The coal flow characteristic extraction module is used for obtaining a coal flow time sequence characteristic vector by using the first convolution neural network model of the one-dimensional convolution kernel from the coal flow input vector;

the current extraction module is used for obtaining a current time sequence feature vector through a second convolution neural network model using a one-dimensional convolution kernel;

a responsiveness estimation module for calculating responsiveness estimation of the coal flow time sequence feature vector relative to the current time sequence feature vector based on a Gaussian density map to obtain a classification feature matrix;

the feature distribution optimization module is used for carrying out feature local distribution optimization on the classification feature matrix to obtain an optimized classification feature vector; and

and a driving current control result for passing the optimized classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for representing that the current value of the driving motor at the current time point should be increased or decreased.

In the above-mentioned coal flow control system based on drive motor current control cooperation, the coal flow characteristic extraction module is further configured to: and respectively performing one-dimensional convolution kernel-based convolution processing and nonlinear activation processing on input data in forward transfer of layers by using each layer of the first convolution neural network model to output the coal flow time sequence feature vector by the last layer of the first convolution neural network model, wherein the input of the first layer of the first convolution neural network model is the coal flow input vector.

In the above-mentioned coal flow control system based on drive motor current control coordination, the current extraction module is further configured to: and respectively performing convolution processing and nonlinear activation processing based on a one-dimensional convolution kernel on input data in forward transfer of layers by using each layer of the second convolution neural network model to output the current time sequence feature vector by the last layer of the second convolution neural network model, wherein the input of the first layer of the second convolution neural network model is the current input vector.

In the above-mentioned coal flow control system based on drive motor current control coordination, the responsiveness estimation module includes: the Gaussian density map construction unit is used for constructing the Gaussian density map of the coal flow time sequence feature vector and the current time sequence feature vector to obtain a first Gaussian density map and a second Gaussian density map; a response unit, configured to calculate a response estimate of the first gaussian density map relative to the second gaussian density map according to the following formula, where the formula is:

wherein F is _a Representing the first Gaussian density map, F _b Representing the second Gaussian density map, F _c Representing the responsive gaussian density map,representing matrix multiplication; and the Gaussian discretization unit is used for performing Gaussian discretization on the responsive Gaussian density map to obtain the classification characteristic matrix.

In the above-mentioned coal flow control system based on drive motor current control coordination, the feature distribution optimizing module is further configured to: performing feature local distribution optimization on the classification feature matrix by using the following formula to obtain an optimized classification feature vector; wherein, the formula is:

wherein V is a classification feature vector obtained by expanding the classification feature matrix according to a row vector or a column vector, and II is V ₂ Representing the two norms of the classification feature vector,representing the square thereof, i.e. the inner product of the classification feature vector itself, v _i Is the ith eigenvalue of the classification eigenvector and v _i ' is the i-th eigenvalue of the optimized classification eigenvector, exp (·) represents the exponential operation of the vector, which represents the calculation of the natural exponential function value raised to the power of the eigenvalue at each position in the vector.

In the above-mentioned coal flow control system based on drive motor current control coordination, the drive current control result includes: the full-connection coding unit is used for carrying out full-connection coding on the optimized classification feature vector by using a full-connection layer of the classifier so as to obtain a coding classification feature vector; and the classification result generating unit is used for inputting the coding classification feature vector into a Softmax classification function of the classifier to obtain the classification result.

According to another aspect of the present application, there is also provided a coal flow control method based on drive motor current control synergy, including:

acquiring coal flow values at a plurality of preset time points in a preset time period and current values of a driving motor at the preset time points;

arranging the coal flow values of the plurality of preset time points into a coal flow input vector according to a time dimension, and arranging the current values of the driving motors of the plurality of preset time points into a current input vector according to the time dimension;

the coal flow input vector is subjected to a first convolution neural network model using a one-dimensional convolution kernel to obtain a coal flow time sequence feature vector;

the current input vector is processed through a second convolution neural network model using a one-dimensional convolution kernel to obtain a current time sequence feature vector;

calculating a responsiveness estimate of the coal flow time sequence feature vector relative to the current time sequence feature vector based on a Gaussian density map to obtain a classification feature matrix;

performing feature local distribution optimization on the classification feature matrix to obtain an optimized classification feature vector; and

and the optimized classification feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for representing that the current value of the driving motor at the current time point is increased or decreased.

In the above method for controlling coal flow based on the cooperation of current control of a driving motor, the step of obtaining the time sequence feature vector of the coal flow by using a first convolution neural network model of a one-dimensional convolution kernel, includes: and respectively performing one-dimensional convolution kernel-based convolution processing and nonlinear activation processing on input data in forward transfer of layers by using each layer of the first convolution neural network model to output the coal flow time sequence feature vector by the last layer of the first convolution neural network model, wherein the input of the first layer of the first convolution neural network model is the coal flow input vector.

In the above method for controlling coal flow based on drive motor current control coordination, the step of obtaining the current time sequence feature vector from the current input vector by using a second convolution neural network model of a one-dimensional convolution kernel includes: and respectively performing convolution processing and nonlinear activation processing based on a one-dimensional convolution kernel on input data in forward transfer of layers by using each layer of the second convolution neural network model to output the current time sequence feature vector by the last layer of the second convolution neural network model, wherein the input of the first layer of the second convolution neural network model is the current input vector.

In the above method for controlling coal flow based on drive motor current control coordination, the calculating the responsiveness estimation of the coal flow time sequence feature vector relative to the current time sequence feature vector based on the gaussian density map to obtain a classification feature matrix includes: constructing a Gaussian density map of the coal flow time sequence feature vector and the current time sequence feature vector to obtain a first Gaussian density map and a second Gaussian density map; calculating a responsiveness estimate of the first gaussian density map relative to the second gaussian density map to obtain a responsiveness gaussian density map with the following formula:

wherein F is _a Representing the first Gaussian density map, F _b Representing the second Gaussian density map, F _c Representing the responsive gaussian density map,representing matrix multiplication; and performing Gaussian discretization on the responsive Gaussian density map to obtain the classification feature matrix.

In the above method for controlling coal flow based on drive motor current control coordination, the performing feature local distribution optimization on the classification feature matrix to obtain an optimized classification feature vector includes: performing feature local distribution optimization on the classification feature matrix by using the following formula to obtain an optimized classification feature vector; wherein, the formula is:

In the above method for controlling coal flow based on the coordination of current control of the driving motor, the step of passing the optimized classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the current value of the driving motor at the current time point should be increased or decreased, and comprises the following steps: performing full-connection coding on the optimized classification feature vector by using a full-connection layer of the classifier to obtain a coding classification feature vector; and inputting the coding classification feature vector into a Softmax classification function of the classifier to obtain the classification result.

According to still another aspect of the present application, there is provided an electronic apparatus including: a processor; and a memory having stored therein computer program instructions that, when executed by the processor, cause the processor to perform a coal flow control method based on drive motor current control coordination as described above.

According to yet another aspect of the present application, there is provided a computer readable medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform a coal flow control method based on drive motor current control coordination as described above.

Compared with the prior art, the coal flow control system and the method based on the drive motor current control coordination are based on the artificial intelligent monitoring technology of deep learning, so that the coal flow can be kept stable and balanced by capturing high-dimensional implicit association mode characteristics of data discrete distribution in a local time window in the coal flow data and the current data of the drive motor respectively and utilizing the responsive logic association relation existing between the coal flow data and the current data of the drive motor to carry out self-adaptive adjustment on the drive current of the drive motor.

Drawings

The foregoing and other objects, features and advantages of the present application will become more apparent from the following more particular description of embodiments of the present application, as illustrated in the accompanying drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the application and not constitute a limitation to the application. In the drawings, like reference numerals generally refer to like parts or steps.

Fig. 1 is an application scenario diagram of a coal flow control system based on drive motor current control coordination according to an embodiment of the present application.

FIG. 2 is a block diagram of a coal flow control system based on drive motor current control coordination in accordance with an embodiment of the present application.

FIG. 3 is a schematic diagram of an architecture of a coal flow control system based on drive motor current control coordination in accordance with an embodiment of the present application.

FIG. 4 is a block diagram of a responsiveness estimation module in a coal flow control system based on drive motor current control coordination according to an embodiment of the present application.

Fig. 5 is a flow chart of a method of controlling coal flow based on drive motor current control coordination in accordance with an embodiment of the present application.

Fig. 6 is a block diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.

Summary of the application

As described above, the automatic control technique of the coal flow rate is an important technique for the intelligent operation of the bucket wheel machine, and an important technical object thereof is to control the stabilization and equalization of the coal flow rate, that is, to keep the fluctuation of the coal flow rate within an acceptable range. Accordingly, a coal flow control scheme is desired to enable the coal flow to remain stable and balanced.

Accordingly, in the technical scheme of the application, real-time coal flow data can be obtained through monitoring the coal flow, and then the current control value of the driving motor is adaptively adjusted through the obtained coal flow data, so that the stability and balance of the coal flow are maintained.

Specifically, the coal flow values at a plurality of predetermined time points within a predetermined period of time and the current values of the drive motor at the plurality of predetermined time points are acquired. Specifically, in the technical solution of the present application, the coal flow values at the plurality of predetermined time points may be obtained by an electronic belt scale or a laser scanner. Here, in the process of acquiring the coal flow value by using the laser scanner, the volume is calculated by using the mode of scanning the surface shape of the coal flow and adding the estimation of the density to convert the coal flow value into the flow, and the mode has the advantages of less error drift, relatively stability and less need of frequent correction.

Then, the coal flow rate values at the plurality of predetermined time points are arranged as a coal flow rate input vector in a time dimension, and the current values of the driving motors at the plurality of predetermined time points are arranged as a current input vector in the time dimension. That is, the time-series discrete distribution of the coal flow rate values and the time-series discrete distribution of the current values are structured as the structured coal flow rate input vector and the current input vector. And then, the coal flow input vector is processed through a first convolution neural network model using a one-dimensional convolution kernel to obtain a coal flow time sequence feature vector, and meanwhile, the current input vector is processed through a second convolution neural network model using the one-dimensional convolution kernel to obtain a current time sequence feature vector.

In other words, in the technical scheme of the application, the one-dimensional convolutional neural network model is used for carrying out one-dimensional convolutional encoding on the coal flow input vector and the current input vector so as to capture high-dimensional implicit association mode features of coal flow discrete distribution and current value discrete distribution in a local time window in the coal flow input vector and the current input vector, so that the coal flow time sequence feature vector and the current time sequence feature vector are obtained. In particular, the one-dimensional convolution kernels used by the first and second convolutional neural network models have a learnable neural network weight parameter that can be adaptively adjusted during training based on training purposes to meet training objectives.

Particularly, in the technical scheme of the application, the fluctuation of the coal flow caused by the driving current change of the driving motor, namely, in a high-dimensional logic space, the response logic association exists between the coal flow time sequence characteristic vector and the current time sequence characteristic vector, and if the logic relationship can be utilized, the accuracy of the driving current self-adaptive control of the driving motor can be obviously improved. In the technical scheme of the application, the responsiveness estimation of the coal flow time sequence feature vector relative to the current time sequence feature vector is calculated based on a Gaussian density map so as to obtain a classification feature matrix.

Specifically, firstly constructing a Gaussian density map of the coal flow time sequence feature vector and the current time sequence feature vector to obtain a first Gaussian density map and a second Gaussian density map, wherein the mean value vector of the first Gaussian density map is the coal flow time sequence feature vector, the value of each position in a covariance matrix of the first Gaussian density map is the variance between the feature values of two corresponding positions in the coal flow time sequence feature vector, the mean value vector of the second Gaussian density map is the current time sequence feature vector, and the value of each position in a covariance matrix of the second Gaussian density map is the variance between the feature values of two corresponding positions in the current time sequence feature vector. Here, the purpose of constructing the gaussian density map of the coal flow time series feature vector and the current time series feature vector is to perform feature level data enhancement based on posterior distribution on the coal flow time series feature vector and the current time series feature vector to improve the accuracy of feature representation and the accuracy of subsequent responsiveness estimation. Then, calculating a response estimation of the first Gaussian density map relative to the second Gaussian density map to obtain a response Gaussian density map, and obtaining the classification feature matrix by performing Gaussian discretization on the response Gaussian density map.

After the classification feature matrix is obtained, the classification feature matrix is passed through a classifier to obtain a classification result, and the current value of the driving motor for representing the current time point of the classification result is increased and reduced. That is, the class probability label to which the classification feature matrix belongs is determined using the classifier, wherein the class probability label includes that the current value of the driving motor at the current time point should be increased (first label) and that the current value of the driving motor at the current time point should be decreased (second label). It should be noted that the class probability tag of the classifier is a driving current control policy tag of a driving motor, and thus, after the classification result is obtained, the driving current of the driving motor can be adaptively adjusted based on the classification result so that the coal flow can be kept stable and balanced.

Particularly, in the technical scheme of the application, when the classification feature matrix is obtained by calculating the response estimation of the coal flow time sequence feature vector relative to the current time sequence feature vector by using a Gaussian density chart, special abnormal local distribution is introduced into the classification feature matrix due to the randomness in the Gaussian discretization process, which causes poor dependence of the classification feature matrix on a single classification result when the classification feature matrix is classified by a classifier, and influences the accuracy of the classification result.

Therefore, the hilbert probability spatialization of the vector-normalized classification feature vector obtained after the expansion of the classification feature matrix is specifically expressed as:

v is the classification feature vector, |V| | ₂ Representing the two norms of the classification feature vector,representing the square thereof, i.e. the inner product of the classification feature vector itself, v _i Is the ith eigenvalue of the classification eigenvector V, and V _i 'is the ith eigenvalue of the optimized classification eigenvector V'.

Here, the hilbert probability spatialization of the vector assignment carries out probabilistic interpretation of the classification feature vector V in the hilbert space defining the vector inner product through assignment of the classification feature vector V itself, and reduces hidden disturbance of class expression of special local distribution of the classification feature vector V to class expression of the whole hilbert space topology, thereby improving robustness of classification regression of feature distribution of the classification feature vector V to a predetermined classification probability, and improving long-range dependence of feature distribution of the classification feature vector V across classifiers by means of establishment of a metric-induced probability space structure. Therefore, the optimized classification feature vector V' is classified directly through the classifier, the dependence of the classification result when the classification feature matrix is classified through the classifier is improved, and the accuracy of the classification result is improved.

Based on this, the application provides a coal flow control system based on driving motor current control cooperation, it includes: the sensor monitoring module is used for acquiring coal flow values at a plurality of preset time points in a preset time period and current values of a driving motor at the preset time points; the time sequence vectorization module is used for arranging the coal flow values of the plurality of preset time points into a coal flow input vector according to a time dimension, and arranging the current values of the driving motors of the plurality of preset time points into a current input vector according to the time dimension; the coal flow characteristic extraction module is used for obtaining a coal flow time sequence characteristic vector by using the first convolution neural network model of the one-dimensional convolution kernel from the coal flow input vector; the current extraction module is used for obtaining a current time sequence feature vector through a second convolution neural network model using a one-dimensional convolution kernel; a responsiveness estimation module for calculating responsiveness estimation of the coal flow time sequence feature vector relative to the current time sequence feature vector based on a Gaussian density map to obtain a classification feature matrix; the feature distribution optimization module is used for carrying out feature local distribution optimization on the classification feature matrix to obtain an optimized classification feature vector; and a driving current control result for passing the optimized classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for indicating that the current value of the driving motor at the current time point should be increased or decreased.

Fig. 1 is an application scenario diagram of a coal flow control system based on drive motor current control coordination according to an embodiment of the present application. As shown in fig. 1, in this application scenario, first, the coal flow values at a plurality of predetermined time points within a predetermined period of time acquired by a laser scanner (e.g., sc as illustrated in fig. 1) and the current values of a drive motor (e.g., D as illustrated in fig. 1) for the plurality of predetermined periods of time acquired by a current monitor (e.g., M as illustrated in fig. 1) are acquired. Further, the coal flow values at a plurality of predetermined time points in the predetermined period and the current values of the drive motor at the plurality of predetermined time points are input to a server (e.g., S as illustrated in fig. 1) in which a drive motor current control cooperative coal flow control algorithm is deployed, wherein the server is capable of processing the coal flow values at the plurality of predetermined time points in the predetermined period and the current values of the drive motor at the plurality of predetermined time points based on the drive motor current control cooperative coal flow control algorithm to obtain a classification result indicating that the current value of the drive motor at the current time point should be increased or decreased.

Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.

Exemplary System

FIG. 2 is a block diagram of a coal flow control system based on drive motor current control coordination in accordance with an embodiment of the present application. As shown in fig. 2, a coal flow control system 100 based on drive motor current control coordination according to an embodiment of the present application includes: a sensor monitoring module 110 for acquiring the coal flow values at a plurality of predetermined time points in a predetermined period of time and the current values of the driving motor at the plurality of predetermined time points; a time sequence vectorization module 120, configured to arrange the coal flow values at the plurality of predetermined time points into a coal flow input vector according to a time dimension, and arrange the current values of the driving motors at the plurality of predetermined time points into a current input vector according to the time dimension; a coal flow feature extraction module 130, configured to obtain a coal flow time sequence feature vector by using the first convolutional neural network model of the one-dimensional convolutional kernel with the coal flow input vector; a current extraction module 140, configured to obtain a current timing feature vector by using the current input vector through a second convolutional neural network model of the one-dimensional convolutional kernel; a responsiveness estimation module 150 for calculating a responsiveness estimate of the coal flow time series feature vector relative to the current time series feature vector based on a gaussian density map to obtain a classification feature matrix; the feature distribution optimization module 160 is configured to perform feature local distribution optimization on the classification feature matrix to obtain an optimized classification feature vector; and a driving current control result 170 for passing the optimized classification feature vector through a classifier to obtain a classification result indicating whether the current value of the driving motor at the present time point should be increased or decreased.

FIG. 3 is a schematic diagram of an architecture of a coal flow control system based on drive motor current control coordination in accordance with an embodiment of the present application. As shown in fig. 3, first, coal flow values at a plurality of predetermined time points in a predetermined period of time and current values of a driving motor at the plurality of predetermined time points are acquired; then, arranging the coal flow values of the plurality of preset time points into a coal flow input vector according to a time dimension, and arranging the current values of the driving motors of the plurality of preset time points into a current input vector according to the time dimension; then, the coal flow input vector is processed through a first convolution neural network model using a one-dimensional convolution kernel to obtain a coal flow time sequence feature vector, and meanwhile, the current input vector is processed through a second convolution neural network model using the one-dimensional convolution kernel to obtain a current time sequence feature vector; then, calculating a responsiveness estimate of the coal flow time sequence feature vector relative to the current time sequence feature vector based on a Gaussian density map to obtain a classification feature matrix; then, carrying out feature local distribution optimization on the classification feature matrix to obtain an optimized classification feature vector; finally, the optimized classification feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for representing that the current value of the driving motor at the current time point should be increased or decreased.

In the above-described coal flow control system 100 based on the cooperation of the driving motor current control, the sensor monitoring module 110 is configured to obtain the coal flow values at a plurality of predetermined time points in a predetermined period of time and the driving motor current values at the plurality of predetermined time points. Specifically, in the technical solution of the present application, the coal flow values at the plurality of predetermined time points may be obtained by an electronic belt scale or a laser scanner. Here, in the process of acquiring the coal flow value by using the laser scanner, the volume is calculated by using the mode of scanning the surface shape of the coal flow and adding the estimation of the density to convert the coal flow value into the flow, and the mode has the advantages of less error drift, relatively stability and less need of frequent correction.

In the above-mentioned coal flow control system 100 based on the coordination of the current control of the driving motor, the timing vectorization module 120 is configured to arrange the coal flow values at the plurality of predetermined time points into a coal flow input vector according to a time dimension, and arrange the current values of the driving motor at the plurality of predetermined time points into a current input vector according to the time dimension. That is, the time-series discrete distribution of the coal flow rate values and the time-series discrete distribution of the current values are structured as the structured coal flow rate input vector and the current input vector.

In the above-mentioned coal flow control system 100 based on the coordination of the current control of the driving motor, the coal flow characteristic extraction module 130 is configured to obtain the coal flow time sequence characteristic vector from the coal flow input vector by using a first convolutional neural network model of a one-dimensional convolutional kernel. In other words, in the technical scheme of the application, the one-dimensional convolutional neural network model is used for carrying out one-dimensional convolutional encoding on the coal flow input vector so as to capture the high-dimensional implicit association mode characteristics of the discrete distribution of the coal flow in the local time window in the coal flow input vector, and the coal flow time sequence characteristic vector is obtained. In particular, the one-dimensional convolution kernel used by the first convolution neural network model has a learnable neural network weight parameter that can be adaptively adjusted to meet a training target based on a training purpose during training.

Specifically, in the embodiment of the present application, the coal flow characteristic extraction module 130 is further configured to: and respectively performing one-dimensional convolution kernel-based convolution processing and nonlinear activation processing on input data in forward transfer of layers by using each layer of the first convolution neural network model to output the coal flow time sequence feature vector by the last layer of the first convolution neural network model, wherein the input of the first layer of the first convolution neural network model is the coal flow input vector.

In the above-mentioned coal flow control system 100 based on the coordination of the current control of the driving motor, the current extraction module 140 is configured to obtain the current timing feature vector by using the second convolutional neural network model of the one-dimensional convolutional kernel. In the technical scheme of the application, a one-dimensional convolution neural network model is used for carrying out one-dimensional convolution coding on the current input vector so as to capture high-dimensional implicit association mode characteristics of current value discrete distribution in a local time window in the current input vector, and the current time sequence characteristic vector is obtained. In particular, the one-dimensional convolution kernel used by the second convolution neural network model has a learnable neural network weight parameter that can be adaptively adjusted to meet a training target based on a training purpose during training.

Specifically, in the embodiment of the present application, the current extraction module 140 is further configured to: and respectively performing convolution processing and nonlinear activation processing based on a one-dimensional convolution kernel on input data in forward transfer of layers by using each layer of the second convolution neural network model to output the current time sequence feature vector by the last layer of the second convolution neural network model, wherein the input of the first layer of the second convolution neural network model is the current input vector.

In the above-mentioned coal flow control system 100 based on the drive motor current control coordination, the responsiveness estimation module 150 is configured to calculate a responsiveness estimation of the coal flow time series feature vector relative to the current time series feature vector based on a gaussian density map to obtain a classification feature matrix. Particularly, in the technical scheme of the application, the fluctuation of the coal flow caused by the driving current change of the driving motor, namely, in a high-dimensional logic space, the response logic association exists between the coal flow time sequence characteristic vector and the current time sequence characteristic vector, and if the logic relationship can be utilized, the accuracy of the driving current self-adaptive control of the driving motor can be obviously improved. In the technical scheme of the application, the responsiveness estimation of the coal flow time sequence feature vector relative to the current time sequence feature vector is calculated based on a Gaussian density map so as to obtain a classification feature matrix.

FIG. 4 is a block diagram of a responsiveness estimation module in a coal flow control system based on drive motor current control coordination according to an embodiment of the present application. As shown in fig. 4, the responsiveness estimation module 150 includes: a gaussian density map construction unit 151 for constructing a gaussian density map of the coal flow time series feature vector and the current time series feature vector to obtain a first gaussian density map and a second gaussian density map; a response unit 152, configured to calculate a response estimate of the first gaussian density map relative to the second gaussian density map according to the following formula, where the formula is:

wherein F is _a Representing the first Gaussian density map, F _b Representing the second Gaussian density map, F _c Representing the responsive gaussian density map,representing matrix multiplication; and a gaussian discretization unit 153, configured to perform gaussian discretization on the responsive gaussian density map to obtain the classification feature matrix.

In the above-mentioned coal flow control system 100 based on the driving motor current control coordination, the feature distribution optimization module 160 is configured to perform feature local distribution optimization on the classification feature matrix to obtain an optimized classification feature vector. Particularly, in the technical scheme of the application, when the classification feature matrix is obtained by calculating the response estimation of the coal flow time sequence feature vector relative to the current time sequence feature vector by using a Gaussian density chart, special abnormal local distribution is introduced into the classification feature matrix due to the randomness in the Gaussian discretization process, which causes poor dependence of the classification feature matrix on a single classification result when the classification feature matrix is classified by a classifier, and influences the accuracy of the classification result.

Here, the hilbert probability spatialization of the vector assignment carries out probabilistic interpretation of the classification feature vector V in the hilbert space defining the vector inner product through assignment of the classification feature vector V itself, and reduces hidden disturbance of class expression of special local distribution of the classification feature vector V to class expression of the whole hilbert space topology, thereby improving robustness of classification regression of feature distribution of the classification feature vector V to a predetermined classification probability, and improving long-range dependence of feature distribution of the classification feature vector V across classifiers by means of establishment of a metric-induced probability space structure. Therefore, the dependence of the classification feature matrix on classification results when the classification is carried out through the classifier is improved, and the accuracy of the classification results is improved.

In the above-described coal flow control system 100 based on the driving motor current control coordination, the driving current control result 170 is used to pass the optimized classification feature vector through a classifier to obtain a classification result, where the classification result is used to indicate that the current value of the driving motor at the current time point should be increased or should be decreased. That is, the class probability label to which the classification feature matrix belongs is determined using the classifier, wherein the class probability label includes that the current value of the driving motor at the current time point should be increased (first label) and that the current value of the driving motor at the current time point should be decreased (second label). It should be noted that the class probability tag of the classifier is a driving current control policy tag of a driving motor, and thus, after the classification result is obtained, the driving current of the driving motor can be adaptively adjusted based on the classification result so that the coal flow can be kept stable and balanced.

Specifically, in the embodiment of the present application, the driving current control result 170 includes: the full-connection coding unit is used for carrying out full-connection coding on the optimized classification feature vector by using a full-connection layer of the classifier so as to obtain a coding classification feature vector; and the classification result generating unit is used for inputting the coding classification feature vector into a Softmax classification function of the classifier to obtain the classification result.

In summary, the driving motor current control collaboration-based coal flow control system 100 according to the embodiments of the present application is illustrated, which is based on an artificial intelligence monitoring technology of deep learning, so as to capture the coal flow data and the high-dimensional implicit correlation pattern characteristics of the discrete data distribution in the local time window in the current data of the driving motor, and utilize the responsive logic correlation relationship existing between the two to adaptively adjust the driving current of the driving motor, so that the coal flow can be kept stable and balanced.

As described above, the coal flow control system 100 based on the driving motor current control coordination according to the embodiment of the present application may be implemented in various terminal devices, such as a server or the like for the coal flow control based on the driving motor current control coordination. In one example, the drive motor current control coordination-based coal flow control system 100 according to embodiments of the present application may be integrated into the terminal device as a software module and/or hardware module. For example, the coal flow control system 100 based on the drive motor current control coordination may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the coal flow control system 100 based on the coordination of the drive motor current control may also be one of the numerous hardware modules of the terminal equipment.

Alternatively, in another example, the drive motor current control-based collaborative coal flow control system 100 and the terminal device may be separate devices, and the drive motor current control-based collaborative coal flow control system 100 may be connected to the terminal device through a wired and/or wireless network and transmit interactive information in a agreed data format.

Exemplary method

Fig. 5 is a flow chart of a method of controlling coal flow based on drive motor current control coordination in accordance with an embodiment of the present application. As shown in fig. 5, a coal flow control method based on drive motor current control coordination according to an embodiment of the present application includes: s110, acquiring coal flow values at a plurality of preset time points in a preset time period and current values of a driving motor at the preset time points; s120, arranging the coal flow values of the plurality of preset time points into a coal flow input vector according to a time dimension, and arranging the current values of the driving motors of the plurality of preset time points into a current input vector according to the time dimension; s130, the coal flow input vector is processed through a first convolution neural network model using a one-dimensional convolution kernel to obtain a coal flow time sequence feature vector; s140, the current input vector is processed through a second convolution neural network model using a one-dimensional convolution kernel to obtain a current time sequence feature vector; s150, calculating the response estimation of the coal flow time sequence feature vector relative to the current time sequence feature vector based on a Gaussian density map to obtain a classification feature matrix; s160, performing feature local distribution optimization on the classification feature matrix to obtain an optimized classification feature vector; and S170, passing the optimized classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for indicating that the current value of the driving motor at the current time point should be increased or decreased.

In one example, in the above method for controlling coal flow based on drive motor current control coordination, the step of obtaining the coal flow time sequence feature vector by using a first convolution neural network model of a one-dimensional convolution kernel by using the coal flow input vector includes: and respectively performing one-dimensional convolution kernel-based convolution processing and nonlinear activation processing on input data in forward transfer of layers by using each layer of the first convolution neural network model to output the coal flow time sequence feature vector by the last layer of the first convolution neural network model, wherein the input of the first layer of the first convolution neural network model is the coal flow input vector.

In one example, in the above method for controlling coal flow based on drive motor current control coordination, the step of obtaining the current timing feature vector by using a second convolution neural network model of a one-dimensional convolution kernel includes: and respectively performing convolution processing and nonlinear activation processing based on a one-dimensional convolution kernel on input data in forward transfer of layers by using each layer of the second convolution neural network model to output the current time sequence feature vector by the last layer of the second convolution neural network model, wherein the input of the first layer of the second convolution neural network model is the current input vector.

In one example, in the above method for controlling coal flow based on drive motor current control coordination, the calculating a response estimate of the coal flow time series feature vector relative to the current time series feature vector based on a gaussian density map to obtain a classification feature matrix includes: constructing a Gaussian density map of the coal flow time sequence feature vector and the current time sequence feature vector to obtain a first Gaussian density map and a second Gaussian density map; calculating a responsiveness estimate of the first gaussian density map relative to the second gaussian density map to obtain a responsiveness gaussian density map with the following formula:

In one example, in the above method for controlling coal flow based on drive motor current control coordination, the performing feature local distribution optimization on the classification feature matrix to obtain an optimized classification feature vector includes: performing feature local distribution optimization on the classification feature matrix by using the following formula to obtain an optimized classification feature vector; wherein, the formula is:

In one example, in the above method for controlling coal flow based on drive motor current control coordination, the step of passing the optimized classification feature vector through a classifier to obtain a classification result, where the classification result is used to indicate that the current value of the drive motor at the current time point should be increased or decreased includes: performing full-connection coding on the optimized classification feature vector by using a full-connection layer of the classifier to obtain a coding classification feature vector; and inputting the coding classification feature vector into a Softmax classification function of the classifier to obtain the classification result.

In summary, the method for controlling the coal flow based on the current control coordination of the driving motor is explained, which is based on an artificial intelligent monitoring technology of deep learning, so that the driving current of the driving motor is adaptively adjusted by capturing the coal flow data and the high-dimensional implicit association mode characteristics of the discrete data distribution in a local time window in the current data of the driving motor respectively and utilizing the responsive logic association relation existing between the coal flow data and the current data of the driving motor, so that the coal flow can be kept stable and balanced.

Exemplary electronic device

Next, an electronic device according to an embodiment of the present application is described with reference to fig. 6. Fig. 6 is a block diagram of an electronic device according to an embodiment of the present application. As shown in fig. 6, the electronic device 10 includes one or more processors 11 and a memory 12.

The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

Memory 12 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by the processor 11 to perform the functions of the drive motor current control based coordinated coal flow control method of the various embodiments of the present application described above and/or other desired functions. Various contents such as a coal flow rate value and a current value of a driving motor may also be stored in the computer-readable storage medium.

In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).

The input means 13 may comprise, for example, a keyboard, a mouse, etc.

The output device 14 may output various information including the classification result and the like to the outside. The output means 14 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.

Of course, only some of the components of the electronic device 10 that are relevant to the present application are shown in fig. 6 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.

Exemplary computer program product and computer readable storage Medium

In addition to the methods and apparatus described above, embodiments of the present application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform steps in the functions of the coal flow control method based on drive motor current control synergy described in the "exemplary methods" section of the present application.

The computer program product may write program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.

Furthermore, embodiments of the present application may also be a computer-readable storage medium, having stored thereon computer program instructions, which when executed by a processor, cause the processor to perform steps in the functions of the coal flow control method based on drive motor current control synergy described in the above-described "exemplary methods" section of the present application.

The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The basic principles of the present application have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not limiting, and these advantages, benefits, effects, etc. are not to be considered as necessarily possessed by the various embodiments of the present application. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the application is not intended to be limited to the details disclosed herein as such.

The block diagrams of the devices, apparatuses, devices, systems referred to in this application are only illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.

It is also noted that in the apparatus, devices and methods of the present application, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent to the present application.

The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.

Claims

1. A coal flow control system based on drive motor current control coordination, comprising:

2. The drive motor current control synergy based coal flow control system of claim 1, wherein the coal flow feature extraction module is further configured to:

and respectively performing one-dimensional convolution kernel-based convolution processing and nonlinear activation processing on input data in forward transfer of layers by using each layer of the first convolution neural network model to output the coal flow time sequence feature vector by the last layer of the first convolution neural network model, wherein the input of the first layer of the first convolution neural network model is the coal flow input vector.

3. The drive motor current control synergy based coal flow control system of claim 2, wherein the current extraction module is further configured to:

and respectively performing convolution processing and nonlinear activation processing based on a one-dimensional convolution kernel on input data in forward transfer of layers by using each layer of the second convolution neural network model to output the current time sequence feature vector by the last layer of the second convolution neural network model, wherein the input of the first layer of the second convolution neural network model is the current input vector.

4. The drive motor current control coordination-based coal flow control system of claim 3, wherein the responsiveness estimation module comprises:

the Gaussian density map construction unit is used for constructing the Gaussian density map of the coal flow time sequence feature vector and the current time sequence feature vector to obtain a first Gaussian density map and a second Gaussian density map;

a response unit, configured to calculate a response estimate of the first gaussian density map relative to the second gaussian density map according to the following formula, where the formula is:

wherein F is _a Representing the first Gaussian density map, F _b Representing the second Gaussian density map, F _c Representing the responsive gaussian density map,representing matrix multiplication; and

and the Gaussian discretization unit is used for performing Gaussian discretization on the responsive Gaussian density map to obtain the classification characteristic matrix.

5. The drive motor current control synergy based coal flow control system of claim 4, wherein the profile optimization module is further configured to:

performing feature local distribution optimization on the classification feature matrix by using the following formula to obtain an optimized classification feature vector;

Wherein, the formula is:

6. The drive motor current control coordination-based coal flow control system of claim 5, wherein the drive current control results comprise:

the full-connection coding unit is used for carrying out full-connection coding on the optimized classification feature vector by using a full-connection layer of the classifier so as to obtain a coding classification feature vector; and

and the classification result generating unit is used for inputting the coding classification feature vector into a Softmax classification function of the classifier to obtain the classification result.

7. The coal flow control method based on the cooperation of the current control of the driving motor is characterized by comprising the following steps of:

8. The method for controlling coal flow based on the coordination of current control of a driving motor according to claim 7, wherein the step of obtaining the coal flow time sequence feature vector by using a first convolution neural network model of a one-dimensional convolution kernel from the coal flow input vector comprises the following steps:

9. The method for controlling coal flow based on cooperative current control of a driving motor according to claim 8, wherein the step of obtaining the current timing eigenvector by using a second convolutional neural network model of a one-dimensional convolutional kernel comprises:

10. The method for controlling coal flow based on drive motor current control coordination according to claim 9, wherein the performing feature local distribution optimization on the classification feature matrix to obtain an optimized classification feature vector comprises:

wherein, the formula is: