CN117950324A - Intelligent plastic feeding control method and device - Google Patents
Intelligent plastic feeding control method and device Download PDFInfo
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Abstract
The invention discloses an intelligent plastic feeding control method and device. The intelligent plastic feeding control method comprises the following steps: configuring the optimal feeding quantity; analyzing a processing environment interference coefficient; intelligent automatic adjustment processing environment; analyzing a plastic feeding quality evaluation index; waste separation and qualified plastic feed acquisition. According to the invention, the temperature, the humidity, the atmospheric pressure and the volume and the density of the plastic material are analyzed during plastic material feeding processing, the covariance is used for evaluating the similarity between images, if the covariance of two images is a positive value, the pixel values of the two images tend to be increased or decreased simultaneously, so that certain similarity exists between the two images, the automatic classification of different products is realized, the differentiation of qualified plastic products and unqualified plastic products is facilitated, the effects of optimizing the processing process, controlling interference factors and improving the quality of the products are achieved, and the problem that the quality level of the plastic material feeding cannot be effectively improved in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of plastic feeding control, in particular to an intelligent plastic feeding control method and device.
Background
The feeding control technology and equipment are an extremely important technology and equipment in the intelligent plastic feeding control process, ensure the stable supply of plastic raw materials, improve the plastic feeding production efficiency and the product quality, reduce the waste and pollution of the plastic raw materials and realize the sustainable utilization of resources and environment.
The existing intelligent plastic feeding control method and device are used for carrying out data acquisition and monitoring on plastic auxiliary machines through the arrangement of a plurality of weighing devices, lack of detection on the volume, density and product quality of plastic feeding, improve the control precision of each technological parameter in the plastic feeding vulcanization process through introducing an exchange type Ethernet into industrial control, lack of consideration on plastic feeding processing environments such as temperature, humidity and atmospheric pressure, and therefore cannot effectively improve the quality level of plastic feeding.
For example, publication No.: the invention patent of CN107015534A discloses a control system for plastic molding, which comprises: a master control device; the weighing function machines are connected with the main control equipment through a first communication link; the plastic auxiliary machines are connected with the main control equipment through a second communication link; the main control equipment performs data acquisition and monitoring on each weighing function machine through a first communication link, and performs data acquisition and monitoring on each plastic auxiliary machine through a second communication link; the monitoring functions of the plastic auxiliary machine and the weighing function machine can be conveniently integrated into the main control equipment, so that the centralized monitoring of the system is realized.
For example, publication No.: the invention patent of CN101226400A discloses a method for controlling a precision plastic molding multi-machine control system, which comprises the following steps: the upper computer for central control of the multi-computer system is characterized in that each plastic molding device is connected to the annular industrial Ethernet through an intelligent Ethernet switching module, the data and control signal exchange of the whole annular industrial Ethernet is realized through a central Ethernet switch positioned in the annular industrial Ethernet, and the monitoring of the whole molding process is realized through configuration monitoring software positioned in the central upper computer.
However, in the process of implementing the technical scheme of the embodiment of the application, the application discovers that the above technology has at least the following technical problems:
In the prior art, data acquisition and monitoring are carried out on each weighing function machine, data acquisition and monitoring are carried out on each plastic auxiliary machine, a multi-machine control system is formed by an Ethernet link and a central control upper computer to monitor plastic feeding vulcanization process parameters, the weight factors of the plastic feeding itself, the factors of the plastic auxiliary machine and the vulcanization process parameters are singly considered, the consideration of the interference factors on the plastic feeding processing environment is ignored, meanwhile, the screening of waste materials and the extraction flow of qualified plastic feeding are lacked, and the problem that the quality level of the plastic feeding cannot be effectively improved exists.
Disclosure of Invention
The embodiment of the application solves the problem of unstable plastic feeding quality of the feeding control device in the prior art by providing the intelligent plastic feeding control method and the intelligent plastic feeding control device, and improves the stability of the plastic feeding quality of the intelligent plastic feeding control device.
The embodiment of the application provides an intelligent plastic feeding control method, which comprises the following steps:
Further, the processing environment parameters comprise temperature, humidity and atmospheric pressure during plastic feeding processing; the plastic feeding parameters comprise plastic feeding volume and plastic feeding density.
Further, the method for configuring the optimal feed quantity comprises the following steps: setting a monitoring period, setting a plurality of monitoring time points in the monitoring period, and obtaining the plastic feeding demand and the current plastic feeding demand in each detection time point; summing and averaging the plastic feeding demand according to each monitoring time point to obtain historical average plastic feeding demand; constructing a model formula of the plastic feeding demand evaluation index, and calculating the plastic feeding demand evaluation index according to the model formula; the plastic feeding demand evaluation index model formula is as follows: In the above, the ratio of/> Evaluation index for the demand of plastic feed,/>For the current plastic feeding demand,/>For historical average plastic feed demand,/>A correction factor for the historical average plastic supply demand; when the plastic feeding demand is closer to the corrected historical plastic feeding average demand, the plastic feeding demand evaluation index is more than 1, which indicates that the current plastic feeding demand is closer to the optimal value, whereas if the current plastic feeding demand is further from the corrected historical plastic feeding average demand, the plastic feeding demand evaluation index is more than 0, which indicates that the current plastic feeding demand needs to be adjusted to achieve the optimal plastic feeding demand.
Further, the analysis method of the processing environment interference coefficient comprises the following steps: numbering for the period, and acquiring the temperature, humidity and atmospheric pressure of the plastic feeding materials collected by the sensor during processing every certain period; summing and averaging the temperature, the humidity and the atmospheric pressure collected by each period sensor, and calculating to obtain the average temperature, the average humidity and the average atmospheric pressure in a certain period; constructing a processing environment interference coefficient model formula, and calculating a plastic feeding processing environment interference coefficient according to the processing environment interference coefficient model formula; the processing environment interference coefficient model formula is as follows:
In the above, the ratio of/> For/>Environmental interference coefficient corresponding to period,/>、/>And/>Respectively expressed as the/>Temperature, humidity and barometric pressure collected by periodic sensors,/>、/>And/>Expressed as average temperature, average humidity and average atmospheric pressure over a period,/>, respectively、/>And/>Correction factors expressed as average temperature, average humidity and average atmospheric pressure, respectively,/>、/>And/>The weight of the temperature, the humidity and the atmospheric pressure are respectively expressed as the duty ratio; /(I)Number expressed as intelligentized plastic feed preparation cycle,/>，/>Expressed as the total number of intelligent plastic feed preparation cycles.
Further, the analysis method of the intelligent control algorithm comprises the following steps: the processing environment interference coefficient obtained through data processing and the controller design are utilized in the forward excitation process, and the signal input from the input layer is finally output from the output layer through the hidden layer, so that the intelligent automatic adjustment of the preparation parameters is realized; to simplify the formula, describing the forward excitation process in a matrix form, the environment parameter matrix from the input layer to the hidden layer is: In the above, the ratio of/> For processing environment data matrix,/>、And/>Respectively expressed as the/>Temperature, humidity and barometric pressure data collected by the periodic sensor during plastic feed processing; the environmental interference coefficient vector from the input layer to the hidden layer is: /(I)In the above, the ratio of/>The environmental interference coefficient vector is measured in all periods; the input vector of the hidden layer is: /(I); In the/>For/>Periodically measuring the calculated processing environment parameter vector; the excitation output of the hidden layer is: /(I)In the above, the ratio of/>For/>The periodic glue feeding control device is optimized by an intelligent control algorithm to obtain the vector of the plastic feeding processing environment parameters.
Further, the analysis method of the plastic feed quality evaluation index comprises the following steps: numbering the periods, and obtaining the volume and the density of plastic feeding in the period from a plastic feeding information database every certain period; acquiring the volume and the density of the plastic feeding material in real time through a sensor; constructing a model formula of the plastic feeding quality evaluation index, and calculating the plastic feeding quality evaluation index according to the model formula; the plastic feeding quality evaluation index model formula is as follows:
In the above, the ratio of/> For evaluating the quality of plastic feed, index,/>Representing the volume data of the plastic feed detected by the sensor in real time,/>Representing data of density of plastic feeding materials detected by a sensor in real time,/>Expressed as obtained from the plastic supply information databasePeriodic plastic feed volume data,/>Expressed as obtained from the plastic supply information databasePeriodic plastic feed density data,/>And/>The weight of the ratio of the plastic feeding volume data and the plastic feeding density dataNumber expressed as intelligentized plastic feed preparation cycle,/>，/>Expressed as the total number of intelligent plastic feed preparation cycles.
Further, the average gray scale estimation value formula of the plastic feeding structure sample image is as follows: In the above, the ratio of/> Average gray scale value expressed as sample image of sample plastic feed structure,/>Expressed as/>Sample Plastic feed preparation of sample images,/>，/>Representing the total number of sample images prepared for the plastic feed; the average gray level estimation value formula of the contrast qualified plastic feeding structure image is as follows: /(I)In the above, the ratio of/>Average gray scale value expressed as contrast qualified plastic feed structure image,/>Expressed as/>And comparing the qualified plastic feeding structure images.
Further, the analysis method of the standard deviation of the sample image comprises the following steps: acquiring a plastic feeding structure image in real time through a sensor, comparing qualified plastic feeding structure images, measuring the similarity degree of the two images, and calculating the standard deviation of a sample image according to the similarity degree; the standard deviation formula of the contrast qualified plastic feeding structure image is as follows: In the above, the ratio of/> Expressed as standard deviation of the sample image,/>Expressed as a mathematical expectation of the sample image; the standard deviation formula of the sample image is as follows: In the above, the ratio of/> Expressed as standard deviation of the image of the contrast-qualified plastic feed structure,/>Expressed as a mathematical expectation of the sample image; the covariance formula of the sample plastic feeding structure sample image and the contrast qualified plastic feeding structure image is as follows: /(I)In the above, the ratio of/>Represented as the covariance of the sample plastic feed structure sample image and the contrast-qualified plastic feed structure image.
Further, the analysis method of the plastic feeding structure evaluation index comprises the following steps:
In the above, the ratio of/> Expressed as an evaluation index of the plastic feed structure,/>And/>Is constant.
The embodiment of the application provides an intelligent plastic feeding control device, which comprises an intelligent plastic feeding pre-estimating configuration module, an intelligent plastic feeding processing benefit analysis module, an intelligent plastic feeding equipment processor power consumption management module, an intelligent plastic feeding quality assessment module and an intelligent waste separation module; the intelligent plastic feeding pre-estimation configuration module is used for acquiring the historical demand and the current demand of feeding of each feeding device and configuring the optimal feeding quantity; the intelligent plastic feeding processing benefit analysis module is used for setting a monitoring period, periodically monitoring processing environment parameters and analyzing the processing environment interference coefficient; the intelligent plastic feeding equipment processor power consumption management module is used for intelligently and automatically adjusting the processing environment by adopting an intelligent processor scheduling strategy and an intelligent control algorithm; the intelligent plastic feeding quality evaluation module is used for acquiring historical plastic feeding parameters and real-time plastic feeding parameters and analyzing to obtain plastic feeding quality evaluation indexes; the intelligent waste separation module is used for capturing the prepared plastic feeding image in real time, extracting a plastic feeding structure sample image from the plastic feeding information database, analyzing the average gray level estimated value and the sample image standard deviation of the plastic feeding structure sample image, distinguishing processing waste and qualified plastic feeding, and separating the waste and acquiring the qualified plastic feeding.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. The parameters such as temperature, humidity and atmospheric pressure in the processing environment are periodically monitored, so that the processing environment interference coefficient is analyzed, the intelligent control algorithm is used for carrying out data processing, the intelligent automatic adjustment of the preparation parameters is realized, the plastic feeding processing environment is controlled in an optimal range, the stability and precision of plastic feeding and digging are practically ensured, the plastic feeding and digging are kept at the optimal level, the improvement of the plastic feeding quality level is realized, and the problem that the quality level of plastic feeding cannot be effectively improved in the prior art is effectively solved.
2. By acquiring historical plastic feeding parameters, analyzing a plastic feeding quality evaluation index, simultaneously capturing prepared plastic feeding images in real time, comparing the plastic feeding quality evaluation index with plastic feeding structure sample images in a plastic feeding information database, analyzing average gray scale evaluation values and sample image standard deviations of the plastic feeding structure sample images, distinguishing processing waste materials and qualified plastic feeding, separating waste materials and acquiring the qualified plastic feeding, thereby obtaining high-quality plastic feeding, and further improving the quality level of the plastic feeding.
3. Through obtaining the historical demand and the current demand of each feeding equipment conveying feed, use intelligent algorithm analysis historical demand and current demand to obtain plastic feed demand evaluation index, predict plastic feed device's best dispatch threshold to avoid plastic feed demand excessive or not enough, and then realized the improvement of plastic feed utilization efficiency.
Drawings
FIG. 1 is a flow chart of an intelligent plastic feeding control method according to an embodiment of the application;
fig. 2 is a block diagram of an intelligent plastic feeding control device according to an embodiment of the present application.
Detailed Description
According to the intelligent plastic feeding control method and device, the problem that the quality level of plastic feeding cannot be effectively improved in the prior art is solved, the processing environment interference coefficient is analyzed by periodically monitoring the temperature, humidity, atmospheric pressure and other parameters in the processing environment, the intelligent control algorithm is used for carrying out data processing, intelligent automatic adjustment of the preparation parameters is achieved, the plastic feeding processing environment is controlled in an optimal range, the stability and precision of plastic feeding digging are practically guaranteed, the plastic feeding quality level is kept at the optimal level, and further the problem that the quality level of plastic feeding cannot be effectively improved in the prior art is effectively solved.
The technical scheme in the embodiment of the application aims to solve the problem that the quality level of plastic feeding cannot be effectively improved, and the overall thought is as follows:
The method comprises the steps of analyzing processing environment parameters obtained through periodic monitoring to obtain a processing environment interference coefficient, performing data processing through an intelligent control algorithm to realize intelligent automatic adjustment of preparation parameters, controlling the processing environment of plastic feeding in an optimal range, acquiring historical plastic feeding parameters, analyzing a plastic feeding quality evaluation index, simultaneously capturing prepared plastic feeding images in real time, comparing the obtained plastic feeding images with plastic feeding structure sample images in a plastic feeding information database, analyzing average gray scale evaluation values and sample image standard deviations of the plastic feeding structure sample images, distinguishing processing waste materials and qualified plastic feeding, separating waste materials and acquiring qualified plastic feeding, thereby obtaining high-quality plastic feeding, obtaining historical demand and current demand of feeding by each feeding device, analyzing the historical demand and the current demand by using the intelligent algorithm to obtain a plastic feeding demand evaluation index, predicting an optimal scheduling threshold of plastic feeding, and effectively improving the quality level of the plastic feeding device.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
As shown in fig. 1, a flowchart of an intelligent plastic feeding control method according to an embodiment of the present application is provided, and the method is applied to an intelligent plastic feeding control device, and includes the following steps: optimal feed quantity is configured: acquiring the historical demand and the current demand of the conveying and feeding of each feeding device, and configuring the optimal feeding quantity; analyzing the interference coefficient of the processing environment: setting a monitoring period, periodically monitoring processing environment parameters, and analyzing a processing environment interference coefficient; intelligent automatic adjusting processing environment: an intelligent processor scheduling strategy is adopted, and the processing environment is intelligently and automatically adjusted through an intelligent control algorithm; analyzing the quality evaluation index of plastic feeding: acquiring historical plastic feeding parameters and real-time plastic feeding parameters, and analyzing to obtain a plastic feeding quality evaluation index; waste separation and qualified plastic feed acquisition: and capturing the prepared plastic feeding image in real time, extracting a plastic feeding structure sample image from a plastic feeding information database, analyzing the average gray level estimated value of the plastic feeding structure sample image and the standard deviation of the sample image, distinguishing processing waste materials and qualified plastic feeding, and separating the waste materials and acquiring the qualified plastic feeding.
Further, the processing environment parameters include temperature, humidity and atmospheric pressure during plastic feeding processing; the plastic feeding parameters include plastic feeding volume and plastic feeding density.
In this embodiment, the processing environment parameters include, but are not limited to, temperature, humidity and atmospheric pressure during plastic feed processing, and the multi-dimension factor takes into account the processing environment; the plastic feed parameters include, but are not limited to, plastic feed volume and plastic feed density, and the plastic feed itself is comprehensively analyzed for physical properties such as quality, smoothness, etc.
Further, the method for configuring the optimal feeding quantity comprises the following steps: setting a monitoring period, setting a plurality of monitoring time points in the monitoring period, and obtaining the plastic feeding demand and the current plastic feeding demand in each detection time point; summing and averaging the plastic feeding demand according to each monitoring time point to obtain historical average plastic feeding demand; constructing a model formula of the plastic feeding demand evaluation index, and calculating the plastic feeding demand evaluation index according to the model formula; the plastic feeding demand evaluation index model formula is as follows: In the above, the ratio of/> Evaluation index for the demand of plastic feed,/>For the current plastic feeding demand,/>For historical average plastic feed demand,/>A correction factor for the historical average plastic supply demand; when the plastic feeding demand is closer to the corrected historical plastic feeding average demand, the plastic feeding demand evaluation index is more than 1, which indicates that the current plastic feeding demand is closer to the optimal value, whereas if the current plastic feeding demand is further from the corrected historical plastic feeding average demand, the plastic feeding demand evaluation index is more than 0, which indicates that the current plastic feeding demand needs to be adjusted to achieve the optimal plastic feeding demand.
In this embodiment, the demand of certain plastic feed products may be affected by seasonal variations. For example, there may be a lower demand for material in summer and a higher demand for material in winter. Therefore, the correction factor of the historical average plastic feeding demand is used for correcting the historical average feeding demand according to the change of seasonal demand, so that the accuracy and reliability of data are ensured.
Further, the analysis method of the interference coefficient of the processing environment comprises the following steps: numbering for the period, and acquiring the temperature, humidity and atmospheric pressure of the plastic feeding materials collected by the sensor during processing every certain period; summing and averaging the temperature, the humidity and the atmospheric pressure collected by each period sensor, and calculating to obtain the average temperature, the average humidity and the average atmospheric pressure in a certain period; constructing a processing environment interference coefficient model formula, and calculating a plastic feeding processing environment interference coefficient according to the processing environment interference coefficient model formula; the processing environment interference coefficient model formula is as follows: In the above, the ratio of/> For/>Environmental interference coefficient corresponding to period,/>、/>And/>Respectively expressed as the/>Temperature, humidity and barometric pressure collected by periodic sensors,/>、/>And/>Expressed as average temperature, average humidity and average atmospheric pressure over a period,/>, respectively、/>And/>Correction factors expressed as average temperature, average humidity and average atmospheric pressure, respectively,/>、/>And/>The weight of the temperature, the humidity and the atmospheric pressure are respectively expressed as the duty ratio; /(I)Number expressed as intelligentized plastic feed preparation cycle,/>，/>Expressed as the total number of intelligent plastic feed preparation cycles.
In the present embodiment, due to the functionThe function value of (2) is always greater than 0, so the environmental interference coefficient is always greater than 0, and if the temperature, the humidity and the atmospheric pressure deviate from the corrected average temperature, the corrected average humidity and the corrected average atmospheric pressure, the environmental interference coefficient is greater. For example,/>，Wherein/>The parameter values in the internal are obviously more/>The parameter values in the system are far away from the corrected average temperature, average humidity and average atmospheric pressure, so/>The value of (3) is obviously greater than/>Is large.
Further, the analysis method of the intelligent control algorithm comprises the following steps: the processing environment interference coefficient obtained through data processing and the controller design are utilized in the forward excitation process, and the signal input from the input layer is finally output from the output layer through the hidden layer, so that the intelligent automatic adjustment of the preparation parameters is realized; to simplify the formula, describing the forward excitation process in a matrix form, the environment parameter matrix from the input layer to the hidden layer is: In the above, the ratio of/> For processing environment data matrix,/>、/>And/>Respectively expressed as the/>Temperature, humidity and barometric pressure data collected by the periodic sensor during plastic feed processing; the environmental interference coefficient vector from the input layer to the hidden layer is: /(I)In the above, the ratio of/>The environmental interference coefficient vector is measured in all periods; the input vector of the hidden layer is: /(I); In the/>For/>Periodically measuring the calculated processing environment parameter vector; the excitation output of the hidden layer is: /(I)In the above, the ratio of/>For/>The periodic glue feeding control device is optimized by an intelligent control algorithm to obtain the vector of the plastic feeding processing environment parameters.
In the present embodiment, it is assumed that the input layer-to-hidden layer environment parameter matrix isThe environmental interference coefficient vector from the input layer to the hidden layer is/>The input vector of the hidden layer is/>The excitation output of the hidden layer is: /(I)The vector is the plastic feeding processing environment parameter optimized by the intelligent control algorithm.
Further, the analysis method of the plastic feeding quality evaluation index comprises the following steps: numbering the periods, and obtaining the volume and the density of plastic feeding in the period from a plastic feeding information database every certain period; acquiring the volume and the density of the plastic feeding material in real time through a sensor; constructing a model formula of the plastic feeding quality evaluation index, and calculating the plastic feeding quality evaluation index according to the model formula; the plastic feed quality evaluation index model formula is: In the above, the ratio of/> For evaluating the quality of plastic feed, index,/>Representing the volume data of the plastic feed detected by the sensor in real time,/>Representing data of density of plastic feeding materials detected by a sensor in real time,/>Expressed as obtained from the plastic supply information databasePeriodic plastic feed volume data,/>Expressed as obtained from the plastic supply information databasePeriodic plastic feed density data,/>And/>The weight of the ratio of the plastic feeding volume data and the plastic feeding density dataNumber expressed as intelligentized plastic feed preparation cycle,/>，/>Expressed as the total number of intelligent plastic feed preparation cycles.
The relative weight of the plastic feed volume data and the plastic feed density data in this example are evaluated by analysis of the plastic product characteristics and application requirements to determine the relative importance of volume and density in the overall feed.
Further, the average gray scale estimation value formula of the plastic feeding structure sample image is as follows: In the above, the ratio of/> Average gray scale value expressed as sample image of sample plastic feed structure,/>Expressed as/>Sample Plastic feed preparation of sample images,/>，/>Representing the total number of sample images prepared for the plastic feed; the average gray level estimated value formula of the contrast qualified plastic feeding structure image is as follows: /(I)In the above, the ratio of/>Average gray scale value expressed as contrast qualified plastic feed structure image,/>Expressed as/>And comparing the qualified plastic feeding structure images.
In this embodiment, the gray scale value ranges from 0 to 255,0 represents pure black, 255 represents pure white, and the middle number represents a transition color between black and white. The smaller the gray value, the darker the image color, the larger the gray value, and the whiter the image color. For example, the gray scale estimate for the sample image of the plastic feed structure comprises 13, 27, 65, 158, then the average gray scale estimate for the sample image of the plastic feed structureRepresenting the image darker in color, and assuming the gray scale estimates for the image of the qualifying plastic feed structure are 177, 183, 210, 225, the average gray scale estimate for the image of the qualifying plastic feed structureIndicating that the color of the contrast-acceptable plastic feed image is greyish white compared to the sample image.
Further, the analysis method of the standard deviation of the sample image comprises the following steps: acquiring a plastic feeding structure image in real time through a sensor, comparing qualified plastic feeding structure images, measuring the similarity degree of the two images, and calculating the standard deviation of a sample image according to the similarity degree; the standard deviation formula of the contrast qualified plastic feeding structure image is as follows: In the above, the ratio of/> Expressed as standard deviation of the sample image,/>Expressed as a mathematical expectation of the sample image; the standard deviation formula of the sample image is: In the above, the ratio of/> Expressed as standard deviation of the image of the contrast-qualified plastic feed structure,/>Expressed as a mathematical expectation of the sample image; the covariance formula of the sample plastic feeding structure sample image and the contrast qualified plastic feeding structure image is as follows: /(I)In the above, the ratio of/>Represented as the covariance of the sample plastic feed structure sample image and the contrast-qualified plastic feed structure image.
In this embodiment, the standard deviation is a common indicator of the degree of dispersion of the metric data. Standard deviation of sample imageStandard deviation/>, of comparative acceptable plastic feed structure imagesThe standard deviation of the images is used as an estimated value for comparison so as to evaluate the difference degree of the texture or detail information of the images. Covariance is used to evaluate the degree of similarity between images. If the covariance of the two images is positive, meaning that their pixel values tend to increase or decrease simultaneously, indicating that some similarity exists between them; whereas if the covariance is negative, it means that their pixel values tend to change inversely with each other, indicating that they differ significantly.
Further, the analysis method of the evaluation index of the plastic feeding structure comprises the following steps:
In the above, the ratio of/> Expressed as an evaluation index of the plastic feeding structure,And/>Is constant.
In the present embodiment of the present invention, in the present embodiment,The algorithm is mainly used for detecting the similarity of two images with the same size, and the constant/>And/>To avoid instability problems when the denominator is 0,/>The value range is [0,1], and the larger the value is, the smaller the difference between the sample image and the qualified image is, namely, the better the image quality is.
As shown in fig. 2, a block diagram of an intelligent plastic feeding control device according to an embodiment of the present application includes: the intelligent plastic feeding prediction configuration module, the intelligent plastic feeding processing benefit analysis module, the intelligent plastic feeding equipment processor power consumption management module, the intelligent plastic feeding quality assessment module and the intelligent waste separation module; the intelligent plastic feeding pre-estimation configuration module is used for acquiring the historical demand and the current demand of feeding of each feeding device and configuring the optimal feeding quantity; the intelligent plastic feeding processing benefit analysis module is used for setting a monitoring period, periodically monitoring processing environment parameters and analyzing the processing environment interference coefficient; the intelligent plastic feeding equipment processor power consumption management module is used for intelligently and automatically adjusting the processing environment by adopting an intelligent processor scheduling strategy and an intelligent control algorithm; the intelligent plastic feeding quality evaluation module is used for acquiring historical plastic feeding parameters and real-time plastic feeding parameters and analyzing to obtain plastic feeding quality evaluation indexes; the intelligent waste separation module is used for capturing the prepared plastic feeding image in real time, extracting a plastic feeding structure sample image from the plastic feeding information database, analyzing the average gray scale estimated value and the standard deviation of the sample image of the plastic feeding structure sample image, distinguishing processing waste and qualified plastic feeding, and separating the waste and acquiring the qualified plastic feeding.
The technical scheme provided by the embodiment of the application at least has the following technical effects or advantages: relative to publication No.: in the embodiment of the application, the volume and the density of the plastic material are obtained in real time through a sensor, a plastic material quality evaluation index model formula is constructed, and meanwhile, the average gray scale evaluation value of a sample image of a plastic material structure and the standard deviation of the sample image are calculated, so that the plastic material structure evaluation index is obtained, the physical characteristics of the plastic material are comprehensively considered, the structural similarity of the plastic material is compared and analyzed, and the quality level of the plastic material is improved; relative to publication No.: according to the method for the precise plastic molding multi-machine control system disclosed by CN101226400A, in the embodiment of the application, a processing environment interference coefficient model is constructed by acquiring the temperature, the humidity and the atmospheric pressure during processing of the plastic feed collected by a sensor, and the processing environment interference coefficient obtained by data processing and a controller design are utilized in a forward excitation process, and finally the signal is output from an input layer through an hidden layer, so that the intelligent automatic adjustment of preparation parameters is realized, and the quality level of the plastic feed is effectively improved.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. An intelligent plastic feeding control method is characterized by comprising the following steps:
Acquiring the historical demand and the current demand of the conveying and feeding of each feeding device, and configuring the optimal feeding quantity;
Setting a monitoring period, periodically monitoring processing environment parameters, and analyzing a processing environment interference coefficient;
an intelligent processor scheduling strategy is adopted, and the processing environment is intelligently and automatically adjusted through an intelligent control algorithm;
Acquiring historical plastic feeding parameters and real-time plastic feeding parameters, and analyzing to obtain a plastic feeding quality evaluation index;
And capturing the prepared plastic feeding image in real time, extracting a plastic feeding structure sample image from a plastic feeding information database, analyzing the average gray level estimated value of the plastic feeding structure sample image and the standard deviation of the sample image, distinguishing processing waste materials and qualified plastic feeding, and separating the waste materials and acquiring the qualified plastic feeding.
2. The intelligent plastic feeding control method according to claim 1, wherein: the processing environment parameters comprise temperature, humidity and atmospheric pressure during plastic feeding processing;
the plastic feeding parameters comprise plastic feeding volume and plastic feeding density.
3. The intelligent plastic feeding control method according to claim 2, wherein the method for configuring the optimal feeding amount is as follows:
setting a monitoring period, setting a plurality of monitoring time points in the monitoring period, and obtaining the plastic feeding demand and the current plastic feeding demand in each detection time point;
summing and averaging according to the plastic feeding demand of each monitoring time point to obtain historical average plastic feeding demand;
constructing a model formula of the plastic feeding demand evaluation index, and calculating the plastic feeding demand evaluation index according to the model formula;
the plastic feeding demand evaluation index model formula is as follows: ，
in the method, in the process of the invention, Evaluation index for the demand of plastic feed,/>For the current plastic feeding demand,/>For historical average plastic feed demand,/>A correction factor for the historical average plastic supply demand;
When the plastic feeding demand is closer to the corrected historical plastic feeding average demand, the plastic feeding demand evaluation index is more than 1, which indicates that the current plastic feeding demand is closer to the optimal value, whereas if the current plastic feeding demand is further from the corrected historical plastic feeding average demand, the plastic feeding demand evaluation index is more than 0, which indicates that the current plastic feeding demand needs to be adjusted to achieve the optimal plastic feeding demand.
4. The intelligent plastic feeding control method according to claim 2, wherein the analysis method of the processing environment interference coefficient is as follows:
numbering for the period, and acquiring the temperature, humidity and atmospheric pressure of the plastic feeding materials collected by the sensor during processing every certain period;
Summing and averaging the temperature, the humidity and the atmospheric pressure collected by each period sensor, and calculating to obtain the average temperature, the average humidity and the average atmospheric pressure in a certain period;
constructing a processing environment interference coefficient model formula, and calculating a plastic feeding processing environment interference coefficient according to the processing environment interference coefficient model formula;
The processing environment interference coefficient model formula is as follows:
,
in the method, in the process of the invention, For/>Environmental interference coefficient corresponding to period,/>、/>And/>Respectively expressed as the/>Temperature, humidity and barometric pressure collected by periodic sensors,/>、/>And/>Expressed as average temperature, average humidity and average atmospheric pressure over a period,/>, respectively、/>And/>Correction factors expressed as average temperature, average humidity and average atmospheric pressure, respectively,/>、And/>The weight of the temperature, the humidity and the atmospheric pressure are respectively expressed as the duty ratio; /(I)Number expressed as intelligentized plastic feed preparation cycle,/>，/>Expressed as the total number of intelligent plastic feed preparation cycles.
5. The intelligent plastic feeding control method according to claim 4, wherein the analysis method of the intelligent control algorithm is as follows:
The processing environment interference coefficient obtained through data processing and the controller design are utilized in the forward excitation process, and the signal input from the input layer is finally output from the output layer through the hidden layer, so that the intelligent automatic adjustment of the preparation parameters is realized;
To simplify the formula, describing the forward excitation process in a matrix form, the environment parameter matrix from the input layer to the hidden layer is:
，
in the method, in the process of the invention, For processing environment data matrix,/>、/>And/>Respectively expressed as the/>Temperature, humidity and barometric pressure data collected by the periodic sensor during plastic feed processing;
the environmental interference coefficient vector from the input layer to the hidden layer is: ，
in the method, in the process of the invention, The environmental interference coefficient vector is measured in all periods;
the input vector of the hidden layer is: ；
in the method, in the process of the invention, For/>Periodically measuring the calculated processing environment parameter vector;
the excitation output of the hidden layer is: ，
in the method, in the process of the invention, For/>The periodic glue feeding control device is optimized by an intelligent control algorithm to obtain the vector of the plastic feeding processing environment parameters.
6. The intelligent plastic feeding control method according to claim 2, wherein the analysis method of the plastic feeding quality evaluation index is as follows:
Numbering the periods, and obtaining the volume and the density of plastic feeding in the period from a plastic feeding information database every certain period;
Acquiring the volume and the density of the plastic feeding material in real time through a sensor;
Constructing a model formula of the plastic feeding quality evaluation index, and calculating the plastic feeding quality evaluation index according to the model formula;
the plastic feeding quality evaluation index model formula is as follows:
，
in the method, in the process of the invention, For evaluating the quality of plastic feed, index,/>Representing the volume data of the plastic feed detected by the sensor in real time,/>Representing data of density of plastic feeding materials detected by a sensor in real time,/>Expressed as obtained from the plastic supply information databasePeriodic plastic feed volume data,/>Expressed as obtained from the plastic supply information databasePeriodic plastic feed density data,/>And/>The weight of the ratio of the plastic feeding volume data and the plastic feeding density dataNumber expressed as intelligentized plastic feed preparation cycle,/>，/>Expressed as the total number of intelligent plastic feed preparation cycles.
7. The intelligent plastic feeding control method according to claim 1, wherein the average gray scale estimation value formula of the plastic feeding structure sample image is:，
in the method, in the process of the invention, Average gray scale value expressed as sample image of sample plastic feed structure,/>Expressed as/>Sample Plastic feed preparation of sample images,/>，/>Representing the total number of sample images prepared for the plastic feed;
the average gray level estimation value formula of the contrast qualified plastic feeding structure image is as follows: ，
in the method, in the process of the invention, Average gray scale value expressed as contrast qualified plastic feed structure image,/>Expressed as/>And comparing the qualified plastic feeding structure images.
8. The intelligent plastic feeding control method according to claim 7, wherein the analysis method of the standard deviation of the sample image is as follows:
Acquiring a plastic feeding structure image in real time through a sensor, comparing qualified plastic feeding structure images, measuring the similarity degree of the two images, and calculating the standard deviation of a sample image according to the similarity degree;
The standard deviation formula of the contrast qualified plastic feeding structure image is as follows: ，
in the method, in the process of the invention, Expressed as standard deviation of the sample image,/>Expressed as a mathematical expectation of the sample image;
the standard deviation formula of the sample image is as follows: ，
in the method, in the process of the invention, Expressed as standard deviation of the image of the contrast-qualified plastic feed structure,/>Expressed as a mathematical expectation of the sample image;
The covariance formula of the sample plastic feeding structure sample image and the contrast qualified plastic feeding structure image is as follows:
，
in the method, in the process of the invention, Represented as the covariance of the sample plastic feed structure sample image and the contrast-qualified plastic feed structure image.
9. The intelligent plastic feeding control method according to claim 8, wherein the analysis method of the plastic feeding structure evaluation index is as follows:，
in the method, in the process of the invention, Expressed as an evaluation index of the plastic feed structure,/>And/>Is constant.
10. An intelligent plastic feed control device, characterized by comprising: the intelligent plastic feeding prediction configuration module, the intelligent plastic feeding processing benefit analysis module, the intelligent plastic feeding equipment processor power consumption management module, the intelligent plastic feeding quality assessment module and the intelligent waste separation module;
the intelligent plastic feeding pre-estimation configuration module is used for acquiring the historical demand and the current demand of feeding of each feeding device and configuring the optimal feeding quantity;
The intelligent plastic feeding processing benefit analysis module is used for setting a monitoring period, periodically monitoring processing environment parameters and analyzing the processing environment interference coefficient;
The intelligent plastic feeding equipment processor power consumption management module is used for intelligently and automatically adjusting the processing environment by adopting an intelligent processor scheduling strategy and an intelligent control algorithm;
The intelligent plastic feeding quality evaluation module is used for acquiring historical plastic feeding parameters and real-time plastic feeding parameters and analyzing to obtain plastic feeding quality evaluation indexes;
The intelligent waste separation module is used for capturing the prepared plastic feeding image in real time, extracting a plastic feeding structure sample image from the plastic feeding information database, analyzing the average gray level estimated value and the sample image standard deviation of the plastic feeding structure sample image, distinguishing processing waste and qualified plastic feeding, and separating the waste and acquiring the qualified plastic feeding.
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