CN116429790B - Wooden packing box production line intelligent management and control system based on data analysis - Google Patents
Wooden packing box production line intelligent management and control system based on data analysis Download PDFInfo
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- 238000012856 packing Methods 0.000 title claims abstract description 55
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 22
- 238000007405 data analysis Methods 0.000 title claims abstract description 14
- 238000004806 packaging method and process Methods 0.000 claims abstract description 80
- 238000000034 method Methods 0.000 claims abstract description 75
- 238000001514 detection method Methods 0.000 claims abstract description 32
- 238000004458 analytical method Methods 0.000 claims abstract description 28
- 239000002023 wood Substances 0.000 claims abstract description 14
- 230000005540 biological transmission Effects 0.000 claims abstract description 9
- 238000004026 adhesive bonding Methods 0.000 claims description 13
- 230000002159 abnormal effect Effects 0.000 claims description 11
- 239000003292 glue Substances 0.000 claims description 10
- 238000007789 sealing Methods 0.000 claims description 10
- 238000009423 ventilation Methods 0.000 claims description 10
- 239000011248 coating agent Substances 0.000 claims description 8
- 238000000576 coating method Methods 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 4
- 230000000149 penetrating effect Effects 0.000 claims description 4
- 230000000977 initiatory effect Effects 0.000 claims description 3
- 230000007547 defect Effects 0.000 description 6
- 230000000694 effects Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
- G01M3/26—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors
- G01M3/32—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for containers, e.g. radiators
- G01M3/3281—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for containers, e.g. radiators removably mounted in a test cell
- G01M3/329—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for containers, e.g. radiators removably mounted in a test cell for verifying the internal pressure of closed containers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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Abstract
The invention relates to the technical field of production line management and control, and particularly discloses an intelligent management and control system of a wood packing box production line based on data analysis, which comprises the following components: the light transmission detection component is arranged after the production line pre-fixing process and before the final fixing process and comprises a light source generating device, an image acquisition device and an analysis end; in the process of detecting by the light transmission detection component: the light source generating device is used for emitting light with a preset wavelength to the packaging box; the image acquisition module is used for acquiring an image of the detection surface of the packaging box; the analysis end is used for carrying out feature recognition on the image acquired by the image acquisition module, judging the pre-fixed state of the packaging box according to the result of the feature recognition, and determining whether the state of the packaging box is normal according to the judging result; if the working parameters are normal, the working parameters of the final fixing procedure are adjusted; otherwise, reworking the packaging box.
Description
Technical Field
The invention relates to the technical field of production line management and control, in particular to an intelligent management and control system of a wood packing box production line based on data analysis.
Background
The wooden packing box is a packing container commonly used in the process of packing and transporting products, has light weight and high strength, and can have better protection effect on the acquisition of the package; along with the rapid development of intelligent manufacturing technology, the production process of the wooden packaging box is mainly finished through an automatic production line, and the integral structure of the wooden packaging box is further prepared and finished through assembling stacked boards; in the process of wood board fixing, due to the difference of materials of different parts of wood, the gap between the wood boards is difficult to meet the requirement in the fixing control process, so that the operation can be completed through two fixing procedures, a detection station is arranged between the two fixing procedures, and the adjustment process of parameters of a second fixing procedure is further realized.
In the prior art, defects and unqualified gaps existing on the surface of a wooden packing box can be identified and judged through an image identification technology, further subsequent procedures such as adjustment, reworking and scrapping are facilitated, however, compared with uniformity of surface features of plastic and metal profiles, accuracy of image identification can be affected by the surface grain features of wood, and further larger errors exist in detection results relative to actual conditions.
Disclosure of Invention
The invention aims to provide an intelligent management and control system of a wood packaging box production line based on data analysis, which solves the following technical problems:
how to ensure the fixed quality of the wooden packing box in the production process of the wooden packing box.
The aim of the invention can be achieved by the following technical scheme:
an intelligent management and control system of a wood packaging box production line based on data analysis, the system comprising:
the light transmission detection component is arranged after the production line pre-fixing process and before the final fixing process and comprises a light source generating device, an image acquisition device and an analysis end;
in the process of detecting by the light transmission detection component:
the light source generating device is used for emitting light with a preset wavelength to the packaging box;
the image acquisition module is used for acquiring an image of the detection surface of the packaging box;
the analysis end is used for carrying out feature recognition on the image acquired by the image acquisition module, judging the pre-fixed state of the packaging box according to the result of the feature recognition, and determining whether the state of the packaging box is normal according to the judging result;
if the working parameters are normal, the working parameters of the final fixing procedure are adjusted;
otherwise, reworking the packaging box.
In one embodiment, the feature identification process includes:
s1, identifying an outline area of a wooden packing box in a picture based on AI;
s2, determining a corresponding color identification interval according to a preset wavelength, and extracting the outline of the outline area according to the color identification interval to obtain a risk position area;
and S3, acquiring shape features of the risk location area as a feature recognition result.
In one embodiment, the judging process of whether the package box is abnormal is as follows:
acquiring a connecting gap area and a non-gap area of a packing box;
judging whether a risk position area appears in the non-gap area:
if the special case appears, reworking the packing case;
otherwise, judging whether a risk position area appears in the connection gap area or not:
if not, not adjusting the working parameters of the final fixing procedure;
otherwise, dividing the connecting gap area into a linear area and an angular area;
by the formulaCalculating to obtain a risk value AN;
wherein ,the maximum value of the straight line area gap in the risk position area relative to the normal line of the outer contour of the packing box;area of the risk location area in the corner area; />Is a fixed reference value; />The total projection length of the slits of the linear region and the angular region on the outer contour of the packing box is; />Is the total area of the risk location area; q is an adjustment coefficient; c is the total circumference of the risk location area;
comparing the risk value AN with the early warning values W1 and W2:
if AN is more than or equal to W2, reworking the packaging box;
if W1 is less than or equal to AN and less than W2, the working parameters of the final fixing procedure are adjusted;
if AN < W1, the working parameters of the final fixing procedure are not adjusted.
In one embodiment, the process of adjusting the operating parameters of the final fixing process includes:
dividing the connecting gap area according to the fixing sites of the packaging box in advance;
by the formulaCalculating to obtain a reference acting force F (k) of a kth region;
wherein ,for reference force, ++>For presetting the function of the comparison table, < >>For correction factor +.>S (k) is the area of the kth risk position area and L (k) is the projection length of the kth risk position area on the outer contour of the packing box;
the kth region is reinforced in accordance with the reference force F (k).
In one embodiment, the system further comprises an air tightness detection component;
the air tightness detection assembly comprises a cover body, an air breather and a pressure sensor, wherein the air breather and the pressure sensor are arranged on the cover body in a penetrating way, and a sealing ring matched with the packaging box is further arranged on the cover body;
the process of the operation of the air tightness detection assembly comprises the following steps:
the cover body is arranged on the unsealed packing box, and the sealing ring is arranged in a matched manner with the packing box;
ventilating according to a preset strategy through a ventilating device, and detecting through a pressure sensor to obtain a real-time pressure change curve;
and the analysis end judges the airtight state of the packing box according to the real-time pressure change curve.
In one embodiment, the process of determining the airtight status includes:
by the formulaCalculating to obtain an air tightness state value At;
wherein ,is the ventilation initiation time point; />Is the end of ventilation time point; at->M-2 time points are obtained at fixed intervals in the period, i=1, 2, …, m; />The corresponding pressure value of the ith time point on the real-time pressure change curve is obtained;the corresponding pressure value of the ith time point on the standard pressure change curve is obtained; />The influence degree of the ith time point;
comparing At with a critical value:
if At is not less than the critical value, judging that the airtight state is abnormal.
In an embodiment, the analysis end is further configured to perform feature analysis according to the real-time pressure change curve state and a preset standard pressure change curve state, and determine a specific cause affecting the airtight state according to the comparison result.
In one embodiment, the process of performing the feature analysis by the analysis end includes:
by the formulaCalculating to obtain a difference change rate k (t) at the time t;
wherein ,t∈[，/>]；
Comparing K (t) with a preset interval [ K1, K2 ]:
if K (t) epsilon [ K1, K2], judging that the gluing of the packaging box is omitted;
if K (t) is E (K2, infinity), judging that the gluing of the packaging box is not firm.
In one embodiment, when there is a missing problem of the glue on the package:
by the formulaCalculating to obtain a size range of the missing glue coating;
and Ex is a piecewise function, and the size interval of the corresponding missing gluing range is determined according to the interval where the numerical value is located.
The invention has the beneficial effects that:
(1) According to the invention, the light source generating device is used for emitting light with a preset wavelength to the packaging box, the preset wavelength is used for selecting a wavelength with larger distinction degree according to the color of the box body, then the image acquisition module is used for acquiring the image of the detection surface of the packaging box, and the analysis end is used for carrying out characteristic recognition on the image, so that the defect position and the gap position of the packaging box can be accurately acquired, and whether the state of the packaging box is normal or not is judged.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a logic block diagram of an intelligent management and control system of a wood packaging box production line based on data analysis;
FIG. 2 is a flow chart of a process of feature identification in the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, in one embodiment, an intelligent management and control system of a wooden packing box production line based on data analysis is provided, the system comprises a light transmission detection assembly, and the light transmission detection assembly comprises a light source generating device, an image acquisition device and an analysis end; the device is arranged after the product line pre-fixing process and before the final fixing process, can detect products after the pre-fixing process, and correspondingly adjusts parameters of the final fixing process according to detection results; the specific monitoring step comprises the steps that firstly, a light source generating device is used for emitting light with a preset wavelength to a packaging box; the light source generating device can be realized by a uniform light source with adjustable wavelength, and then an image of a detection surface of the packaging box is acquired by the image acquisition module; the image acquired by the image acquisition module is subjected to feature recognition through an analysis end, the result of the feature recognition obviously shows the fixed state of the packaging box after the pre-fixing procedure, then the pre-fixing state of the packaging box is judged according to the result of the feature recognition, and whether the state of the packaging box is normal or not is determined according to the judgment result; if normal, obviously explaining that the product of the pre-fixing procedure is abnormal and larger, so the working parameters of the final fixing procedure are adjusted; if the packing box is abnormal, the problem that the packing box is abnormal and possibly damaged is solved, so that the packing box is reworked.
Through the technical scheme, the detection process of the state of the wooden packing box and the adjustment of the working parameters of the subsequent final fixing procedure are realized through the light transmission detection assembly, so that the judgment of the state of the wooden packing box is realized; specifically, the light source generating device is used for emitting light with a preset wavelength to the packaging box, the preset wavelength is used for selecting a wavelength with larger distinction degree according to the color of the box body, the process is carried out in an environment with weaker ambient light, so that the difference between the transmitted corresponding light and the ambient state is obvious, the image of the detection surface of the packaging box is collected through the image collecting module, the analysis end is used for carrying out feature recognition on the image, further, the defect position and the gap position of the packaging box can be accurately obtained, further, whether the state of the packaging box is normal is determined through the obtained result, the working parameters of the final fixing procedure are correspondingly adjusted in normal time, and reworking processing is directly carried out in abnormal time.
As an implementation manner of the present invention, referring to fig. 2, the present embodiment provides a feature recognition process, which mainly includes S1, recognizing a contour area of a wooden packing box in a picture based on AI; s2, determining a corresponding color identification interval according to a preset wavelength, and extracting the outline of the outline area according to the color identification interval to obtain a risk position area; and S3, acquiring shape features of the risk location area as a feature recognition result.
Through the technical scheme, in the characteristic recognition process, the embodiment recognizes the packaging box based on the AI recognition technology, wherein the AI recognition technology can be obtained by performing convolutional neural training on a plurality of groups of wood packaging box images, and is not limited herein, so that the outline area of the packaging box is recognized and obtained; then determining a corresponding color identification interval according to the wavelength, wherein the color identification interval is determined according to the color interval after the color of the packaging box and the wavelength are overlapped, for example, the wavelength is controlled to be in a range of 550-400 nm (blue light), and the color of the wooden packaging box is yellow, so that the color interval is identified to be in a range of 550-400 nm (blue light) and the color at the overlapped edge of the color interval and the color interval; therefore, contour extraction is carried out according to the color recognition interval, and further a risk position area can be accurately obtained; and then, according to the shape characteristics of the risk position area, the shape characteristics are taken as the characteristic recognition result, and the state of the packing box can be determined according to the shape characteristics.
As one embodiment of the present invention, the judging process of whether the package box is abnormal is: acquiring a connecting gap area and a non-gap area of a packing box; judging whether a risk position area appears in the non-gap area: if the defect problem of the packing box is shown, and the packing box is not repaired or is not repaired completely, reworking the packing box; otherwise, judging whether a risk position area appears in the connection gap area or not: if not, not adjusting the working parameters of the final fixing procedure; otherwise, dividing the connecting gap area into a linear area and an angular area;
by the formulaCalculating to obtain a risk value AN; wherein (1)>The maximum value of the straight line area gap in the risk position area relative to the normal line of the outer contour of the packing box; />Area of the risk location area in the corner area; />Is a fixed reference value; />The total projection length of the slits of the linear region and the angular region on the outer contour of the packing box is;is a risk locationThe total area of the region; q is an adjustment coefficient; c is the total circumference of the risk location area;
comparing the risk value AN with the early warning values W1 and W2: if AN is more than or equal to W2, reworking the packaging box; if W1 is less than or equal to AN and less than W2, the working parameters of the final fixing procedure are adjusted; if AN < W1, the working parameters of the final fixing procedure are not adjusted.
Through the above technical scheme, the embodiment provides a specific method for judging the abnormal state of the packaging box, specifically, firstly, the connection gap area and the non-gap area of the packaging box are obtained according to the identified packaging box image, the obtaining process sets a predetermined dividing ratio according to the structure of the packaging box, and then the dividing ratio is obtained after the dividing ratio is divided, which is not described in detail herein;
judging whether a risk position area appears in the non-gap area, and obviously, if the risk position area appears in the non-gap area, indicating that the packing box has defect problem and is not repaired or is not completely repaired, and carrying out rework; if not, analyzing the risk position area existing in the connection gap area, dividing the connection gap area into a straight line area and an angle area, dividing the connection gap area into the connection gap area and the non-gap area, and then passing through a formulaObtaining a risk value AN to judge the characteristics of the region of the risk position, wherein the formula of the risk value AN is according to +.>Obtained after simplification, wherein->A width average value state of the risk location area; />A width extremum of the risk location area that is a straight line area; />Critical shape of corner regionA state; />The characteristic state of the risk location area; the adjustment coefficient q mainly plays a role in adjusting a numerical range and is obtained by fitting according to test data; therefore, through the risk value AN, early warning can be carried out through the numerical value when defects appear in the corner region, gaps are large, and the risk position region is irregular, further the subsequent processing process is determined through comparing the risk value AN with the early warning values W1 and W2, if AN is more than or equal to W2, the obvious abnormality appears, and the packaging box is reworked; if W1 is less than or equal to AN and less than W2, the problem is that the initial fixing is not completely caused, so that the packaging box is fixed again after the working parameters of the final fixing procedure are adjusted; if AN is smaller than W1, the packaging box meets the requirement, so that the working parameters of the final fixing procedure are not adjusted.
It should be noted that, the early warning values W1 and W2 are obtained by fitting data in a critical state.
As one embodiment of the present invention, the operation parameter adjustment process of the final fixing step includes: dividing the connecting gap area according to the fixing sites of the packaging box in advance; by the formulaCalculating to obtain a reference acting force F (k) of a kth region;
wherein ,for reference force, ++>For presetting the function of the comparison table, < >>For correction factor +.>For the maximum value of the k-th region gap in the risk position region relative to the package outline normal, S (k) is the area of the k-th risk position region, and L (k) is the k-th risk position regionThe projection length of each risk position area on the outer contour of the packing box;
the kth region is reinforced in accordance with the reference force F (k).
Through the technical scheme, the embodiment provides a method for adjusting the working parameters on the basis of judging that the final fixed working parameters need to be adjusted, and the method comprises the following steps ofCalculating a reference force F (k) for the kth region, wherein +>Is the reference force and the initial fixation is carried out according to the parameters>Obtaining the adjustment quantity of acting force according to the comparison table and the correction coefficient according to the interval where the value is located for presetting the comparison table functionFor adjusting->Opposite->The numerical range of the box body is obtained after substitution according to empirical data, so that the obtained reference acting force F (k) can be suitable for the actual condition of the box body, the fixing effect is further ensured, and meanwhile, the damage to the packaging box caused by overlarge acting force is avoided.
As an embodiment of the present invention, the system further comprises an air tightness detection component; the air tightness detection assembly comprises a cover body, an air breather and a pressure sensor, wherein the air breather and the pressure sensor are arranged on the cover body in a penetrating way, and a sealing ring matched with the packaging box is further arranged on the cover body;
the process of the operation of the air tightness detection assembly comprises the following steps:
the cover body is arranged on the unsealed packing box, and the sealing ring is arranged in a matched manner with the packing box; ventilating according to a preset strategy through a ventilating device, and detecting through a pressure sensor to obtain a real-time pressure change curve; and the analysis end judges the airtight state of the packing box according to the real-time pressure change curve.
Because part of the packaging boxes need to have a dampproof effect, glue coating is needed to be carried out on the ventilation gaps after the final fixing of the packaging boxes is finished, so that the packaging boxes have a better dampproof effect, meanwhile, after the glue coating and drying process is finished, the air tightness of the packaging boxes needs to be judged, and because the size of the wooden packaging boxes is large, the air tightness state of the boxes is judged through the arrangement of the air tightness detection assembly, and in particular, the air tightness detection assembly comprises a cover body, an air breather and a pressure sensor, the air breather and the pressure sensor are all arranged on the cover body in a penetrating manner, a sealing ring matched with the packaging boxes is further arranged on the cover body, and in the detection process, the cover body is arranged on the unsealed packaging boxes, and the sealing ring is matched with the packaging boxes; ventilating according to a preset strategy through a ventilating device, and detecting through a pressure sensor to obtain a real-time pressure change curve; the real-time pressure change curve state is judged through the analysis end, if the difference between the real-time pressure change curve and the standard change curve of the packing box in the state of conforming to the tightness is smaller, the tightness state of the packing box is conforming to the requirement, if the tightness state of the packing box is reversed, the problem of air leakage of the packing box is solved, and meanwhile, the state of specific air leakage can be judged according to the change condition of the real-time pressure change curve, namely whether the problem of poor tightness exists can be judged.
It should be noted that, the preset strategy refers to that the pressure is linearly adjusted to the pressure setting value in a preset period, the pressure setting value does not exceed the bearing range of the packaging box, and specific parameter settings can be selected according to the performance of the ventilation device, which is not limited herein.
As one embodiment of the present invention, the process of judging the airtight state includes: by the formulaCalculating to obtain gasA confidentiality status value At; wherein (1)>Is the ventilation initiation time point; />Is the end of ventilation time point; at->M-2 time points are obtained at fixed intervals in the period, i=1, 2, …, m; />The corresponding pressure value of the ith time point on the real-time pressure change curve is obtained; />The corresponding pressure value of the ith time point on the standard pressure change curve is obtained; />The influence degree of the ith time point;
comparing At with a critical value: if At is more than or equal to the critical value, the deviation state of the real-time pressure change curve and the standard pressure change curve is larger, and the air tightness state is judged to be abnormal.
Through the above technical scheme, the embodiment provides a specific process of judging the airtight state through a formulaCalculating to obtain air tightness state value At for judgment, wherein +.>The degree of influence at the ith time point is obtained by testing the change characteristics in the standard state, so that whether the sealing abnormality problem occurs can be judged through the calculation process of the air tightness state value.
It should be noted that, the critical value is obtained after the test data of the plurality of groups of sealing normal states are brought in; at the same time, the method comprises the steps of,is the data in the ideal state, so there is no +.>Greater than or equal to->Conditions of (2); in addition, the airtight state detection mainly judges whether an obvious ventilation point exists or not, and the influence caused by ventilation due to a small gap is limited and is not considered here.
As an implementation mode of the invention, the analysis end is also used for carrying out characteristic analysis according to the real-time pressure change curve state and the preset standard pressure change curve state, and judging the specific reason influencing the air tightness state according to the comparison result.
The process of the analysis end for characteristic analysis comprises the following steps:
by the formulaCalculating to obtain a difference change rate k (t) at the time t;
wherein ,t∈[，/>]；
Comparing K (t) with a preset interval [ K1, K2 ]: if K (t) epsilon [ K1, K2], judging that the gluing of the packaging box is omitted; if K (t) is E (K2, infinity), judging that the gluing of the packaging box is not firm.
When the problem of glue spreading omission of the packaging box exists: by the formulaCalculating to obtain a size range of the missing glue coating; and Ex is a piecewise function, and the size interval of the corresponding missing gluing range is determined according to the interval where the numerical value is located.
Through the technical scheme, the embodiment provides a judgment shadowThe method for responding to the specific reason of the airtight state is used for facilitating the subsequent reworking process, and specifically, the characteristic analysis is carried out according to the real-time pressure change curve state and the preset standard pressure change curve state, and the characteristic analysis is carried out according to the formulaCalculating to obtain a difference change rate k (t) at the time t; if the packing box has the problem of glue coating omission, the packing box is +.>In a slowly increasing state, the range of K (t) is therefore limited, so by associating K (t) with a preset interval [ K1, K2]]Comparing, presetting intervals [ K1, K2]]Obtained from a fit of the data in the critical state, so when K (t) ∈ [ K1, K2]]Judging that the problem is that the gluing of the packaging box is omitted, and when K (t) is E (K2, infinity), indicating that the difference change rate K (t) is obviously increased, namely +_>The large value change is generated, so that the occurrence of the air outlet increase and the +.>The larger change of the numerical value, and then when K (t) is E (K2, infinity), the problem that the gluing of the packaging box is not firm is judged.
In addition, when the problem of missing the gluing of the packaging box exists, the embodiment also uses the formulaCalculating to obtain the size range of the missing glue coating range, and further providing relevant parameter data during the processing, wherein the segmentation function Ex determines different segments according to the corresponding values of the test data of the opening sizes of different cross sections, so that the segmentation function Ex is used for determining the different segments according to +.>The size of the opening corresponding to the segment is determined by the segment where the glue coating is located.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (6)
1. Wooden packing box product line intelligence management and control system based on data analysis, its characterized in that, the system includes:
the light transmission detection component is arranged after the production line pre-fixing process and before the final fixing process and comprises a light source generating device, an image acquisition device and an analysis end;
in the process of detecting by the light transmission detection component:
the light source generating device is used for emitting light with a preset wavelength to the packaging box;
the image acquisition device is used for acquiring images of the detection surface of the packaging box;
the analysis end is used for carrying out feature recognition on the image acquired by the image acquisition device, judging the pre-fixing state of the packaging box according to the result of the feature recognition, and determining whether the state of the packaging box is normal according to the judging result;
if the working parameters are normal, the working parameters of the final fixing procedure are adjusted;
otherwise, reworking the packaging box;
the process of feature identification comprises the following steps:
s1, identifying an outline area of a wooden packing box in a picture based on AI;
s2, determining a corresponding color identification interval according to a preset wavelength, and extracting the outline of the outline area according to the color identification interval to obtain a risk position area;
s3, acquiring shape features of the risk position area as a feature recognition result;
the judging process of whether the state of the packaging box is abnormal is as follows:
acquiring a connecting gap area and a non-gap area of a packing box;
judging whether a risk position area appears in the non-gap area:
if the special case appears, reworking the packing case;
otherwise, judging whether a risk position area appears in the connection gap area or not:
if not, not adjusting the working parameters of the final fixing procedure;
otherwise, dividing the connecting gap area into a linear area and an angular area;
by the formulaCalculating to obtain a risk value AN;
wherein ,the maximum value of the straight line area gap in the risk position area relative to the normal line of the outer contour of the packing box; />Area of the risk location area in the corner area; />Is a fixed reference value; />The total projection length of the slits of the linear region and the angular region on the outer contour of the packing box is; />Is the total area of the risk location area; q is an adjustment coefficient; c is the total circumference of the risk location area;
comparing the risk value AN with the early warning values W1 and W2:
if AN is more than or equal to W2, reworking the packaging box;
if W1 is less than or equal to AN and less than W2, the working parameters of the final fixing procedure are adjusted;
if AN is less than W1, the working parameters of the final fixing procedure are not adjusted;
the working parameter adjusting process of the final fixing procedure comprises the following steps:
dividing the connecting gap area according to the fixing sites of the packaging box in advance;
by the formulaCalculating to obtain a reference acting force F (k) of a kth region;
wherein ,for reference force, ++>For presetting the function of the comparison table, < >>For correction factor +.>S (k) is the area of the kth risk position area and L (k) is the projection length of the kth risk position area on the outer contour of the packing box;
the kth region is reinforced in accordance with the reference force F (k).
2. The intelligent management and control system of a wood packaging box production line based on data analysis according to claim 1, wherein the system further comprises an air tightness detection component;
the air tightness detection assembly comprises a cover body, an air breather and a pressure sensor, wherein the air breather and the pressure sensor are arranged on the cover body in a penetrating way, and a sealing ring matched with the packaging box is further arranged on the cover body;
the process of the operation of the air tightness detection assembly comprises the following steps:
the cover body is arranged on the unsealed packing box, and the sealing ring is arranged in a matched manner with the packing box;
ventilating according to a preset strategy through a ventilating device, and detecting through a pressure sensor to obtain a real-time pressure change curve;
and the analysis end judges the airtight state of the packing box according to the real-time pressure change curve.
3. The intelligent management and control system of a wooden packing box production line based on data analysis according to claim 2, wherein the process of judging the air tightness state comprises the following steps:
by the formulaCalculating to obtain an air tightness state value At;
wherein ,is the ventilation initiation time point; />Is the end of ventilation time point; at->M-2 time points are obtained at fixed intervals in the period, i=1, 2, …, m; />The corresponding pressure value of the ith time point on the real-time pressure change curve is obtained; />The corresponding pressure value of the ith time point on the standard pressure change curve is obtained; />The influence degree of the ith time point;
comparing At with a critical value:
if At is not less than the critical value, judging that the airtight state is abnormal.
4. The intelligent management and control system of the wood packaging box production line based on data analysis according to claim 3, wherein the analysis end is further used for performing feature analysis according to the real-time pressure change curve state and the preset standard pressure change curve state, and judging specific reasons affecting the air tightness state according to the comparison result.
5. The intelligent management and control system of a wood packaging box production line based on data analysis according to claim 4, wherein the process of performing feature analysis by the analysis end comprises:
by the formulaCalculating to obtain a difference change rate k (t) at the time t;
wherein ,；
comparing K (t) with a preset interval [ K1, K2 ]:
if K (t) epsilon [ K1, K2], judging that the gluing of the packaging box is omitted;
if K (t) is E (K2, infinity), judging that the gluing of the packaging box is not firm.
6. The intelligent management and control system of a wood packaging box production line based on data analysis according to claim 5, wherein when a packaging box gluing omission problem exists:
by the formulaCalculating to obtain a size range of the missing glue coating;
and Ex is a piecewise function, and the size interval of the corresponding missing gluing range is determined according to the interval where the numerical value is located.
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Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5960104A (en) * | 1996-08-16 | 1999-09-28 | Virginia Polytechnic & State University | Defect detection system for lumber |
| US6122065A (en) * | 1996-08-12 | 2000-09-19 | Centre De Recherche Industrielle Du Quebec | Apparatus and method for detecting surface defects |
| DE102008029952A1 (en) * | 2008-06-26 | 2009-12-31 | Wincor Nixdorf International Gmbh | Method and device for detecting transport containers |
| JP2010151587A (en) * | 2008-12-25 | 2010-07-08 | Asahi Breweries Ltd | Visual inspection device of container |
| DE102013013079A1 (en) * | 2013-08-06 | 2015-02-12 | Interseroh Dienstleistungs Gmbh | Method and device for identifying packaging |
| CN107179324A (en) * | 2017-05-17 | 2017-09-19 | 珠海格力电器股份有限公司 | Method, device and system for detecting product package |
| CN108430876A (en) * | 2015-12-28 | 2018-08-21 | 尤妮佳股份有限公司 | Method and apparatus for the packaging product for checking absorbent commodity |
| CN111523611A (en) * | 2020-05-07 | 2020-08-11 | 北京智机科技有限公司 | Glue detection method |
| KR102161315B1 (en) * | 2019-11-08 | 2020-10-05 | 주식회사 지팬스 | Automatic identification monitoring system of container |
| CN111985466A (en) * | 2020-08-19 | 2020-11-24 | 上海海事大学 | A kind of container dangerous goods sign identification method |
| CN112222010A (en) * | 2020-09-30 | 2021-01-15 | 龙岩烟草工业有限责任公司 | Packing box conveying system |
| CN112561098A (en) * | 2021-02-19 | 2021-03-26 | 浙江大胜达包装股份有限公司 | Automatic recycling system and method based on express paper packaging box image analysis |
| KR102358948B1 (en) * | 2021-08-18 | 2022-02-08 | (주)썬컴퍼니 | Automated system for manufacturing paper boxes for packaging based on ai |
| CN114577810A (en) * | 2022-02-09 | 2022-06-03 | 南京港龙潭集装箱有限公司 | A kind of container intelligent crossing inspection system and method |
| CN115201208A (en) * | 2022-08-04 | 2022-10-18 | 扬州安睿自动化科技有限公司 | Online flaw detection method and device for plane packet |
| KR102470422B1 (en) * | 2021-11-26 | 2022-11-25 | 호전실업 주식회사 | Method of automatically detecting sewing stitch based on CNN feature map and system for the same |
| CN115456652A (en) * | 2022-09-29 | 2022-12-09 | 广东格林精密部件股份有限公司 | Tracing method for defective products of precision injection molding parts based on artificial intelligence |
| CN218369251U (en) * | 2022-11-02 | 2023-01-24 | 山东力乐新材料研究院有限公司 | Height-adjustable wood plastic box |
| CN115690807A (en) * | 2022-10-18 | 2023-02-03 | 青岛杰瑞工控技术有限公司 | Box number identification method based on OCR technology |
| WO2023019508A1 (en) * | 2021-08-19 | 2023-02-23 | 宁德时代新能源科技股份有限公司 | Method for detecting glue application area, electronic device and computer-readable storage medium |
| CN116110053A (en) * | 2023-04-13 | 2023-05-12 | 济宁能源发展集团有限公司 | Container surface information detection method based on image recognition |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA3123632A1 (en) * | 2018-12-21 | 2020-06-25 | Canscan Softwares And Technologies Inc. | Automated inspection system and associated method for assessing the condition of shipping containers |
-
2023
- 2023-06-14 CN CN202310698483.6A patent/CN116429790B/en active Active
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6122065A (en) * | 1996-08-12 | 2000-09-19 | Centre De Recherche Industrielle Du Quebec | Apparatus and method for detecting surface defects |
| US5960104A (en) * | 1996-08-16 | 1999-09-28 | Virginia Polytechnic & State University | Defect detection system for lumber |
| DE102008029952A1 (en) * | 2008-06-26 | 2009-12-31 | Wincor Nixdorf International Gmbh | Method and device for detecting transport containers |
| JP2010151587A (en) * | 2008-12-25 | 2010-07-08 | Asahi Breweries Ltd | Visual inspection device of container |
| DE102013013079A1 (en) * | 2013-08-06 | 2015-02-12 | Interseroh Dienstleistungs Gmbh | Method and device for identifying packaging |
| CN108430876A (en) * | 2015-12-28 | 2018-08-21 | 尤妮佳股份有限公司 | Method and apparatus for the packaging product for checking absorbent commodity |
| CN107179324A (en) * | 2017-05-17 | 2017-09-19 | 珠海格力电器股份有限公司 | Method, device and system for detecting product package |
| KR102161315B1 (en) * | 2019-11-08 | 2020-10-05 | 주식회사 지팬스 | Automatic identification monitoring system of container |
| CN111523611A (en) * | 2020-05-07 | 2020-08-11 | 北京智机科技有限公司 | Glue detection method |
| CN111985466A (en) * | 2020-08-19 | 2020-11-24 | 上海海事大学 | A kind of container dangerous goods sign identification method |
| CN112222010A (en) * | 2020-09-30 | 2021-01-15 | 龙岩烟草工业有限责任公司 | Packing box conveying system |
| CN112561098A (en) * | 2021-02-19 | 2021-03-26 | 浙江大胜达包装股份有限公司 | Automatic recycling system and method based on express paper packaging box image analysis |
| KR102358948B1 (en) * | 2021-08-18 | 2022-02-08 | (주)썬컴퍼니 | Automated system for manufacturing paper boxes for packaging based on ai |
| WO2023019508A1 (en) * | 2021-08-19 | 2023-02-23 | 宁德时代新能源科技股份有限公司 | Method for detecting glue application area, electronic device and computer-readable storage medium |
| KR102470422B1 (en) * | 2021-11-26 | 2022-11-25 | 호전실업 주식회사 | Method of automatically detecting sewing stitch based on CNN feature map and system for the same |
| CN114577810A (en) * | 2022-02-09 | 2022-06-03 | 南京港龙潭集装箱有限公司 | A kind of container intelligent crossing inspection system and method |
| CN115201208A (en) * | 2022-08-04 | 2022-10-18 | 扬州安睿自动化科技有限公司 | Online flaw detection method and device for plane packet |
| CN115456652A (en) * | 2022-09-29 | 2022-12-09 | 广东格林精密部件股份有限公司 | Tracing method for defective products of precision injection molding parts based on artificial intelligence |
| CN115690807A (en) * | 2022-10-18 | 2023-02-03 | 青岛杰瑞工控技术有限公司 | Box number identification method based on OCR technology |
| CN218369251U (en) * | 2022-11-02 | 2023-01-24 | 山东力乐新材料研究院有限公司 | Height-adjustable wood plastic box |
| CN116110053A (en) * | 2023-04-13 | 2023-05-12 | 济宁能源发展集团有限公司 | Container surface information detection method based on image recognition |
Non-Patent Citations (1)
| Title |
|---|
| 木质板材表面缺陷自动检测技术研究;马娟娟;金永;;林业机械与木工设备(09);28-30 * |
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