CN113674305A - Intelligent blank storage system applied to ceramic production and control method - Google Patents
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Abstract
The invention discloses an intelligent green brick storage system applied to ceramic production and a control method, belonging to the technical field of ceramic production, and comprising a front-end feedback module, a green brick storage module, a server and a storage module; the green body storage module is used for storing the ceramic green bodies, acquiring the storage environment of the green bodies, performing deep learning according to the acquired data, and adjusting the storage environment of the green bodies; selecting N blanks as observation samples to carry out real-time detection, processing detected unqualified data, and relearning unqualified data; the storage environment is adjusted according to the output result of the storage model, so that the adjustment of the storage environment of the green body is more intelligent, the quality of the green body is guaranteed, and the green body is prevented from being damaged due to long-time storage; the detected unqualified data are processed, the unqualified data are fed back to the storage model for relearning, relearning is carried out continuously in the production process, and the storage model is optimized.
Description
Technical Field
The invention belongs to the technical field of ceramic production, and particularly relates to an intelligent blank storage system applied to ceramic production and a control method.
Background
In the ceramic production process, a ceramic tile base plate is required to be used for supporting a ceramic tile blank, the ceramic tile base plate is placed in conveying equipment, the base plate and the ceramic tile blank are conveyed to a sintering furnace, then the ceramic tile blank is clamped into the sintering furnace by using a clamp, and the conveying device is circulating equipment, so that the number of the ceramic tile base plates placed in the circulating equipment is fixed, and when a new ceramic tile blank is required to be added into the conveying equipment together with the base plate, or the number of the base plates in the conveying device is required to be increased or reduced due to the failure of part of components, part of blanks are required to be stored firstly; therefore, the invention provides the intelligent blank storage system and the control method applied to ceramic production, and solves the storage problem of the blank.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides an intelligent blank storage system applied to ceramic production and a control method.
The purpose of the invention can be realized by the following technical scheme:
the intelligent green brick storage system applied to ceramic production comprises a front-end feedback module, a green brick storage module, a server and a storage module;
the front-end feedback module is used for planning and storing the ceramic body according to the acquired data;
the green body storage module is used for storing the ceramic green bodies, acquiring the storage environment of the green bodies, performing deep learning according to the acquired data, and adjusting the storage environment of the green bodies;
and selecting N blanks as observation samples to carry out real-time detection, processing detected unqualified data, and relearning unqualified data.
Further, the method for selecting N blanks as observation samples to carry out real-time detection comprises the following steps:
marking the selected blank as i, wherein i is 1, 2, … … and n, and n is a positive integer;
monitoring the selected blank in real time, acquiring the size of the blank in real time, comparing the size of the blank with the original size to obtain a size change value CHi(ii) a According to the formula
Obtaining the mean value of the dimensional change CHsAccording to the formula
Obtaining a size change stable value alpha;
detecting the surface of the blank in real time to obtain a crack value LF of the surface of the blanki＝b1×LKi+b2×LCiAccording to the formula
Obtaining a crack stability value beta;
according to the formula
Obtaining a sample value Qs(ii) a And judging whether the storage environment is qualified or not according to the sample value.
Further, the method for planning and storing the ceramic body by the front-end feedback module according to the acquired data comprises the following steps:
step SA 1: obtaining the blank processing limit of the subsequent step, marking the blank processing limit of the subsequent step as PTP, obtaining the blank number in the current subsequent step, marking the blank number in the current subsequent step as PTL, and obtaining the blank shortage PTM (packet transfer protocol) -PTP-PTL in the subsequent step;
step SA 2: and acquiring the number of produced blanks, acquiring the number of redundant blanks according to the number of the produced blanks, and transporting the redundant blanks to a blank storage module.
Further, acquiring a blank image and an equipment background image in the subsequent steps, carrying out image preprocessing on the blank image and the equipment background image, and respectively marking the image after the image preprocessing as a blank gray image and a background gray image;
establishing an image gray value three-dimensional coordinate system by taking the image center as an origin, inputting the image gray value into the coordinate system, and connecting adjacent gray value points of the same image by using a smooth curve to form a blank gray value curved surface and a background gray value curved surface.
Further, marking the overlapped part between the blank gray value curved surface and the background gray value curved surface, obtaining the boundary outline of the marked part, setting a judgment model, inputting the marked boundary outline and the blank outline into the judgment model, obtaining a judgment result, counting the number of the blank outlines, and marking the blank outlines as the number of marks.
Further, eliminating the marked part in the grey value curved surface of the blank, marking the eliminated grey value curved surface of the blank as a background-free image, extracting the residual image contour in the background-free image, inputting the image contour and the blank contour into a judgment model, obtaining a judgment result, counting the number of the blank contours, and marking the blank contours as the number of divisions.
Further, the number of divisions is added to the number of marks to obtain the number of blanks.
The intelligent storage control method applied to ceramic production comprises the following specific steps:
the method comprises the following steps: planning and storing the ceramic body according to the acquired data;
step two: and curing the stored green bodies.
Compared with the prior art, the invention has the beneficial effects that: deep learning is carried out according to the obtained data, the model subjected to the deep learning is marked as a storage model, and the storage environment is adjusted according to the output result of the storage model, so that the adjustment of the storage environment of the green body is more intelligent, the quality of the green body is guaranteed, and the green body is prevented from being damaged due to long-time storage; processing the detected unqualified data, feeding the unqualified data back to the storage model for relearning, and continuously relearning in the production process to optimize the storage model; through the setting of front end feedback module, the body after can be reasonable plans, sends unnecessary body to storing up the base module and stores, avoids leading to the body to damage because of emergency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the intelligent green brick storage system applied to ceramic production comprises a front-end feedback module, a green brick storage module, a server and a storage module;
the front-end feedback module is used for planning and storing the ceramic body according to the acquired data, and the specific method comprises the following steps:
step SA 1: acquiring blank processing limit of the subsequent step, wherein the subsequent step is the next step after a blank is processed in a production line, the blank processing limit of the subsequent step is marked as PTP, the blank number in the current subsequent step is acquired, the blank number in the current subsequent step is marked as PTL, and blank shortage PTM in the subsequent step is acquired as PTP-PTL;
step SA 2: acquiring the number of produced blanks, acquiring the number of redundant blanks according to the number of the produced blanks, and transporting the redundant blanks to a blank storage module;
through the setting of front end feedback module, the body after can be reasonable planning, send unnecessary body to storing up the base module and store, avoid leading to the body to damage because of emergency, for example: subsequent equipment is damaged;
the method for acquiring the number of blanks in the current subsequent step in the step SA1 includes:
acquiring a blank image and an equipment background image in the subsequent steps, wherein the blank image is a panoramic image containing a blank, and the equipment background image is an image shot at the same angle with the blank image and is an image without the blank; carrying out image preprocessing on the green body image and the equipment background image, and respectively marking the image after the image preprocessing as a green body gray image and a background gray image, wherein the image preprocessing comprises image segmentation, image denoising, image enhancement and gray conversion;
establishing an image gray value three-dimensional coordinate system by taking the image center as an origin, inputting the image gray value into the coordinate system, and connecting adjacent gray value points of the same image by using a smooth curve to form a blank gray value curved surface and a background gray value curved surface;
marking the overlapped part between the blank gray value curved surface and the background gray value curved surface, acquiring the boundary outline of the marked part, setting a judgment model, inputting the marked boundary outline and the blank outline into the judgment model to acquire a judgment result, counting the number of the blank outlines and marking the blank outlines as the number of marks, wherein the judgment result is that whether the boundary outline is the blank outline or not;
removing the marked part in the grey value curved surface of the blank, marking the removed grey value curved surface of the blank as a background-free image, extracting the residual image contour in the background-free image, inputting the image contour and the blank contour into a judgment model to obtain a judgment result, counting the number of the blank contours, marking the blank contours as the number of partitions, and adding the number of the partitions and the number of the marks to obtain the number of the blanks;
the method for setting the judgment model comprises the following steps: acquiring judgment historical data, wherein the judgment historical data comprises a boundary contour and a blank contour; setting a judgment result for judging the historical data; judging whether the boundary contour is a blank contour or not; constructing an artificial intelligence model; the artificial intelligence model comprises an error reverse propagation neural network, an RBF neural network and a deep convolution neural network; dividing the judgment historical data and the corresponding state labels into a training set, a test set and a check set according to a set proportion; the set proportion comprises 2: 1: 1. 3: 2: 1 and 3: 1: 1; training, testing and verifying the artificial intelligent model through a training set, a testing set and a verifying set; marking the trained artificial intelligence model as a judgment model;
the storage module is used for storing ceramic blanks, and the specific method comprises the following steps:
obtaining stored blank species data, wherein the blank species data comprise data such as model, size and material, obtaining a storage environment corresponding to a blank from a storage module and the Internet according to the blank species data, performing deep learning according to the obtained data, namely learning by using a neural network model, marking the model subjected to the deep learning as a storage model, adjusting the storage environment according to an output result of the storage model, and obtaining the output result of the storage model as the storage environment corresponding to the blank;
randomly selecting N blanks from the stored blanks, wherein N is a preset value, taking the selected blanks as observation samples to perform real-time detection, processing detected unqualified data, namely, collating the detection data into training data of a storage model, and feeding unqualified data back to the storage model to perform relearning;
deep learning is carried out according to the obtained data, the model subjected to the deep learning is marked as a storage model, and the storage environment is adjusted according to the output result of the storage model, so that the adjustment of the storage environment of the green body is more intelligent, the quality of the green body is guaranteed, and the green body is prevented from being damaged due to long-time storage; processing the detected unqualified data, feeding the unqualified data back to the storage model for relearning, and continuously relearning in the production process to optimize the storage model;
the method for detecting the selected blank body as the observation sample in real time comprises the following steps:
marking the selected blank as i, wherein i is 1, 2, … … and n, and n is a positive integer;
monitoring the selected blank in real time, acquiring the size of the blank in real time, comparing the size of the blank with the original size to obtain a size change value CHi(ii) a According to the formula
Obtaining the mean value of the dimensional change CHsAccording to the formula
Obtaining a size change stable value alpha;
detecting the surface of the blank in real time to obtain a crack value LF of the surface of the blanki＝b1×LKi+b2×LCiWherein b is1And b2All are proportionality coefficients, LK is the crack width, and LC is the crack length; according to the formula
Obtaining a crack stability value beta;
according to the formula
Obtaining a sample value Qs(ii) a According to sample value QsJudging whether the storage environment is qualified or not; the judgment can be made according to the actual production setting threshold value X1.
The intelligent storage control method applied to ceramic production comprises the following specific steps:
the method comprises the following steps: planning and storing the ceramic body according to the acquired data;
step SA 1: obtaining the blank processing limit of the subsequent step, marking the blank processing limit of the subsequent step as PTP, obtaining the blank number in the current subsequent step, marking the blank number in the current subsequent step as PTL, and obtaining the blank shortage PTM (packet transfer protocol) -PTP-PTL in the subsequent step;
step SA 2: acquiring the number of produced blanks, acquiring the number of redundant blanks according to the number of the produced blanks, and transporting the redundant blanks to a blank storage module;
the method for acquiring the number of blanks in the current subsequent step in the step SA1 includes:
acquiring a blank image and an equipment background image in the subsequent steps, carrying out image preprocessing on the blank image and the equipment background image, and respectively marking the image after the image preprocessing as a blank gray image and a background gray image;
establishing an image gray value three-dimensional coordinate system by taking the image center as an origin, inputting the image gray value into the coordinate system, and connecting adjacent gray value points of the same image by using a smooth curve to form a blank gray value curved surface and a background gray value curved surface;
marking the overlapped part between the blank gray value curved surface and the background gray value curved surface, acquiring the boundary outline of the marked part, setting a judgment model, inputting the marked boundary outline and the blank outline into the judgment model, acquiring a judgment result, counting the number of the blank outlines, and marking the blank outlines as the number of marks;
removing the marked part in the grey value curved surface of the blank, marking the removed grey value curved surface of the blank as a background-free image, extracting the residual image contour in the background-free image, inputting the image contour and the blank contour into a judgment model to obtain a judgment result, counting the number of the blank contours, marking the blank contours as the number of partitions, and adding the number of the partitions and the number of the marks to obtain the number of the blanks;
step two: curing the stored green body;
obtaining stored blank species data, obtaining a storage environment corresponding to the blank from a storage module and the Internet according to the blank species data, performing deep learning according to the obtained data, marking a model subjected to the deep learning as a storage model, and adjusting the storage environment according to an output result of the storage model;
randomly selecting N blanks from the stored blanks, taking the selected blanks as observation samples to perform real-time detection, processing detected unqualified data, and feeding the unqualified data back to the storage model to perform relearning;
the method for detecting the selected blank body as the observation sample in real time comprises the following steps:
marking the selected blank as i, wherein i is 1, 2, … … and n, and n is a positive integer;
monitoring the selected blank in real time, acquiring the size of the blank in real time, comparing the size of the blank with the original size to obtain a size change value CHi(ii) a According to the formula
Obtaining the mean value of the dimensional change CHsAccording to the formula
Obtaining a size change stable value alpha;
detecting the surface of the blank in real time to obtain a crack value LF of the surface of the blanki＝b1×LKi+b2×LCiWherein b is1And b2All are proportionality coefficients, LK is the crack width, and LC is the crack length; according to the formula
Obtaining a crack stability value beta;
according to the formula
Obtaining a sample value Qs(ii) a And judging whether the storage environment is qualified or not according to the sample value.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows: planning and storing the ceramic body according to the acquired data; obtaining the blank processing limit of the subsequent step, marking the blank processing limit of the subsequent step as PTP, obtaining the blank number in the current subsequent step, marking the blank number in the current subsequent step as PTL, and obtaining the blank shortage PTM (packet transfer protocol) -PTP-PTL in the subsequent step; acquiring the number of produced blanks, acquiring the number of redundant blanks according to the number of the produced blanks, and transporting the redundant blanks to a blank storage module; acquiring a blank image and an equipment background image in the subsequent steps, carrying out image preprocessing on the blank image and the equipment background image, and respectively marking the image after the image preprocessing as a blank gray image and a background gray image; establishing an image gray value three-dimensional coordinate system by taking the image center as an origin, inputting the image gray value into the coordinate system, and connecting adjacent gray value points of the same image by using a smooth curve to form a blank gray value curved surface and a background gray value curved surface; marking the overlapped part between the blank gray value curved surface and the background gray value curved surface, acquiring the boundary outline of the marked part, setting a judgment model, inputting the marked boundary outline and the blank outline into the judgment model, acquiring a judgment result, counting the number of the blank outlines, and marking the blank outlines as the number of marks; removing the marked part in the grey value curved surface of the blank, marking the removed grey value curved surface of the blank as a background-free image, extracting the residual image contour in the background-free image, inputting the image contour and the blank contour into a judgment model to obtain a judgment result, counting the number of the blank contours, marking the blank contours as the number of partitions, and adding the number of the partitions and the number of the marks to obtain the number of the blanks;
curing the stored green body; obtaining stored blank species data, obtaining a storage environment corresponding to the blank from a storage module and the Internet according to the blank species data, performing deep learning according to the obtained data, marking a model subjected to the deep learning as a storage model, and adjusting the storage environment according to an output result of the storage model; randomly selecting N blanks from the stored blanks, taking the selected blanks as observation samples to perform real-time detection, processing detected unqualified data, and feeding the unqualified data back to the storage model to perform relearning; marking the selected blank as i, monitoring the selected blank in real time, acquiring the size of the blank in real time, comparing the size of the blank with the original size to obtain a size change value CHi(ii) a According to the formula
Obtaining the mean value of the dimensional change CHsAccording to the formula
Obtaining a size change stable value alpha; to the surface of the blankReal-time detection is carried out to obtain the crack value LF of the surface of the blanki＝b1×LKi+b2×LCiWherein b is1And b2All are proportionality coefficients, LK is the crack width, and LC is the crack length; according to the formula
Obtaining a crack stability value beta; according to the formula
Obtaining a sample value Qs(ii) a And judging whether the storage environment is qualified or not according to the sample value.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and there may be other divisions when the actual implementation is performed; the modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of the embodiment.
It will also be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above examples are only intended to illustrate the technical process of the present invention and not to limit the same, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical process of the present invention without departing from the spirit and scope of the technical process of the present invention.
Claims (8)
1. The intelligent green brick storage system is applied to ceramic production and is characterized by comprising a front-end feedback module, a green brick storage module, a server and a storage module;
the front-end feedback module is used for planning and storing the ceramic body according to the acquired data;
the green body storage module is used for storing the ceramic green bodies, acquiring the storage environment of the green bodies, performing deep learning according to the acquired data, and adjusting the storage environment of the green bodies;
and selecting N blanks as observation samples to carry out real-time detection, processing detected unqualified data, and relearning unqualified data.
2. The intelligent blank storage system applied to ceramic production as claimed in claim 1, wherein the method for selecting N blanks as observation samples for real-time detection comprises:
marking the selected blank as i, acquiring the size of the blank in real time, comparing the size of the blank with the original size to obtain a size change value CHi(ii) a According to the formula
Obtaining the mean value of the dimensional change CHsAccording to the formula
Obtaining a size change stable value alpha;
detecting the surface of the blank in real time to obtain a crack width value LKiAnd crack length value CKi(ii) a Obtaining the crack value LF of the surface of the blanki＝b1×LKi+b2×LCiAccording to the formula
Obtaining a crack stability value beta;
according to the formula
Obtaining a sample value Qs(ii) a According to sample value QsAnd judging whether the storage environment is qualified or not.
3. The intelligent storage system applied to ceramic production as claimed in claim 1, wherein the method for planning and storing the ceramic body by the front-end feedback module according to the acquired data comprises:
step SA 1: obtaining the blank processing limit of the subsequent step, marking the blank processing limit of the subsequent step as PTP, obtaining the blank number in the current subsequent step, marking the blank number in the current subsequent step as PTL, and obtaining the blank shortage PTM (packet transfer protocol) -PTP-PTL in the subsequent step;
step SA 2: and acquiring the number of produced blanks, acquiring the number of redundant blanks according to the number of the produced blanks, and transporting the redundant blanks to a blank storage module.
4. The intelligent green brick storage system applied to ceramic production according to claim 3, wherein a green brick image and an equipment background image in the subsequent steps are obtained, the green brick image and the equipment background image are subjected to image preprocessing, and the images after the image preprocessing are respectively marked as a green brick gray image and a background gray image;
establishing an image gray value three-dimensional coordinate system by taking the image center as an origin, inputting the image gray value into the coordinate system, and connecting adjacent gray value points of the same image by using a smooth curve to form a blank gray value curved surface and a background gray value curved surface.
5. The intelligent green body storage system applied to ceramic production as claimed in claim 4, wherein the overlapping portion between the green body gray value curved surface and the background gray value curved surface is marked, the boundary contour of the marked portion is obtained, a judgment model is set, the marked boundary contour and the green body contour are input into the judgment model, a judgment result is obtained, the number of the green body contours is counted, and the green body contours are marked as the number of marks.
6. The intelligent green body storage system applied to ceramic production as claimed in claim 5, wherein the marked part in the green body gray value curved surface is removed, the removed green body gray value curved surface is marked as a background-free image, the remaining image contour in the background-free image is extracted, the image contour and the green body contour are input into a judgment model, a judgment result is obtained, the number of the green body contours is counted, and the green body contours are marked as the number of divisions.
7. The intelligent blank storage system applied to ceramic production according to claim 6, wherein the number of blanks is obtained by adding the number of divisions to the number of marks.
8. The intelligent storage blank system control method based on any one of claims 1-7, characterized in that the specific method comprises the following steps:
the method comprises the following steps: planning and storing the ceramic body according to the acquired data;
step two: and curing the stored green bodies.
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