CN115893811A - Hot melting control method and system for mixed processing of recycled glass - Google Patents
Hot melting control method and system for mixed processing of recycled glass Download PDFInfo
- Publication number
- CN115893811A CN115893811A CN202211529367.3A CN202211529367A CN115893811A CN 115893811 A CN115893811 A CN 115893811A CN 202211529367 A CN202211529367 A CN 202211529367A CN 115893811 A CN115893811 A CN 115893811A
- Authority
- CN
- China
- Prior art keywords
- data
- glass
- hot
- control
- granularity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000011521 glass Substances 0.000 title claims abstract description 220
- 238000002844 melting Methods 0.000 title claims abstract description 90
- 230000008018 melting Effects 0.000 title claims abstract description 57
- 238000012545 processing Methods 0.000 title claims abstract description 56
- 238000000034 method Methods 0.000 title claims abstract description 53
- 239000012943 hotmelt Substances 0.000 claims abstract description 172
- 238000011156 evaluation Methods 0.000 claims abstract description 46
- 238000005457 optimization Methods 0.000 claims description 54
- 239000002245 particle Substances 0.000 claims description 34
- 238000003908 quality control method Methods 0.000 claims description 12
- 238000012216 screening Methods 0.000 claims description 12
- 238000010128 melt processing Methods 0.000 claims description 10
- 238000011077 uniformity evaluation Methods 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 230000004927 fusion Effects 0.000 claims 1
- 230000000694 effects Effects 0.000 description 17
- 238000002156 mixing Methods 0.000 description 8
- 239000002699 waste material Substances 0.000 description 5
- 230000006978 adaptation Effects 0.000 description 3
- 238000004064 recycling Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 239000002689 soil Substances 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P40/00—Technologies relating to the processing of minerals
- Y02P40/50—Glass production, e.g. reusing waste heat during processing or shaping
Landscapes
- Processing Of Solid Wastes (AREA)
Abstract
The invention discloses a hot melt control method and a hot melt control system for mixed processing of recycled glass, and relates to the field of data processing, wherein the method comprises the following steps: performing mixed component ratio analysis on the collected and recovered glass data to generate processed component ratio data; pretreating the recovered glass, and crushing the pretreated recovered glass; collecting the glass image after crushing treatment by image collecting equipment to obtain a glass image set; generating a granularity influence parameter; generating a multi-level hot melt control data matching set through quality demand data, a processing component proportion data set and granularity influence parameters; and performing hot-melting cost evaluation to obtain multi-level hot-melting control data corresponding to the lowest cost in the hot-melting cost evaluation result, and performing hot-melting treatment on the glass according to the control. The hot melting control method and the hot melting control device have the advantages that the hot melting control accuracy of the recycled glass is improved, the hot melting treatment quality of the recycled glass is improved, and the like.
Description
Technical Field
The invention relates to the field of data processing, in particular to a hot melting control method and a hot melting control system for mixed processing of recycled glass.
Background
In daily life, people often use various glass products such as glass windows, glass cups, glass doors and the like. The glass product is beautiful and practical and has wide application. During the application of the glass product, a great amount of waste glass is accumulated. Glass is recyclable garbage, but different from other recyclable garbage, the glass cannot be burnt and cannot be buried in soil for degradation, and a large amount of accumulated waste glass only pollutes soil and underground water. After the waste glass is recycled and hot-melted, the waste glass becomes a raw material for producing glass, so that the waste glass is recycled, and the method has important significance.
In the prior art, the technical problem that the hot melting treatment effect of the recovered glass is poor due to the fact that the hot melting control accuracy of the recovered glass is not enough exists.
Disclosure of Invention
The application provides a hot melting control method and system for mixed processing of recycled glass. The technical problem of the prior art to retrieving glass's hot melt control accuracy not enough, and then cause the hot melt treatment effect not good of retrieving glass is solved.
In view of the foregoing, the present application provides a hot melt control method and system for recycled glass blending.
In a first aspect, the present application provides a hot melt control method for a recycled glass blending process, wherein the method is applied to a hot melt control system for a recycled glass blending process, the method comprising: collecting recycled glass data, and performing mixed component proportion analysis based on the recycled glass data to generate processing component proportion data; pretreating the recovered glass, and crushing the pretreated recovered glass; acquiring the glass image after the crushing treatment by the image acquisition equipment to obtain a glass image set; carrying out granularity characteristic identification on the glass image set to generate granularity influence parameters; acquiring quality demand data, and performing multi-level hot melt control data matching through the quality demand data, the processing component proportion data set and the granularity influence parameter to generate a multi-level hot melt control data matching set; and performing hot-melt cost evaluation on the multi-level hot-melt control data matching set to obtain multi-level hot-melt control data corresponding to the lowest cost in the hot-melt cost evaluation result, and performing hot-melt treatment on the glass by controlling the multi-level hot-melt control data.
In a second aspect, the present application also provides a hot melt control system for a recycled glass blending process, wherein the system comprises: the data acquisition and analysis module is used for acquiring recycled glass data, and performing mixed component proportion analysis based on the recycled glass data to generate processed component proportion data; the crushing processing module is used for preprocessing the recovered glass and crushing the preprocessed recovered glass; the glass image acquisition module is used for acquiring glass images after crushing treatment through the image acquisition equipment to obtain a glass image set; the granularity characteristic identification module is used for carrying out granularity characteristic identification on the glass image set to generate granularity influence parameters; the data matching module is used for obtaining quality demand data, and performing multi-level hot melt control data matching through the quality demand data, the processing component proportion data set and the granularity influence parameter to generate a multi-level hot melt control data matching set; and the hot-melt processing module is used for carrying out hot-melt cost evaluation on the multi-level hot-melt control data matching set to obtain multi-level hot-melt control data corresponding to the lowest cost in a hot-melt cost evaluation result, and controlling the multi-level hot-melt control data to carry out hot-melt processing on the glass.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
performing mixed component ratio analysis on the recycled glass data to generate processing component ratio data; pretreating the recovered glass, and crushing the pretreated recovered glass; acquiring the glass image after the crushing treatment by the image acquisition equipment to obtain a glass image set; carrying out granularity characteristic identification on the particle to generate granularity influence parameters; performing multi-level hot melt control data matching by combining the quality demand data and the processing component proportion data set to generate a multi-level hot melt control data matching set; and performing hot-melt cost evaluation on the multi-level hot-melt control data matching set to obtain multi-level hot-melt control data corresponding to the lowest cost in the hot-melt cost evaluation result, and performing hot-melt treatment on the glass by controlling the multi-level hot-melt control data. The accuracy of hot melting control of the recycled glass is improved, and the hot melting treatment quality of the recycled glass is improved; meanwhile, the reliability, intelligence and rationality of the hot melting control of the recycled glass are improved; the hot melting treatment cost of the recycled glass is reduced.
Drawings
FIG. 1 is a schematic flow diagram of a hot melt control method for a hybrid process for recycling glass according to the present application;
FIG. 2 is a schematic flow chart of a hot-melt process for glass by modifying multi-level hot-melt control data in a hot-melt control method for recycled glass blending processing according to the present application;
FIG. 3 is a schematic flow chart illustrating the adjustment of multilevel thermal melting control data without temperature control in the thermal melting control method for hybrid processing of recycled glass according to the present application;
FIG. 4 is a schematic diagram of a hot melt control system for a recycled glass blending process according to the present application.
Description of reference numerals: the device comprises a data acquisition and analysis module 11, a crushing processing module 12, a glass image acquisition module 13, a granularity characteristic identification module 14, a data matching module 15 and a hot melting processing module 16.
Detailed Description
The application provides a hot melting control method and system for mixed processing of recycled glass. The technical problem of the prior art to retrieving glass's hot melt control accuracy not enough, and then cause the hot melt treatment effect not good of retrieving glass is solved. The accuracy of hot melting control of the recycled glass is improved, and the hot melting treatment quality of the recycled glass is improved; meanwhile, the reliability, intelligence and rationality of the hot melting control of the recycled glass are improved; the technical effect of reducing the hot melting treatment cost of the recycled glass.
Example one
Referring to fig. 1, the present application provides a hot melt control method for hybrid processing of recycled glass, wherein the method is applied to a hot melt control system for hybrid processing of recycled glass, the system is in communication connection with an image acquisition device and a temperature acquisition device, and the method specifically includes the following steps:
step S100: collecting recycled glass data, and performing mixed component proportion analysis based on the recycled glass data to generate processing component proportion data;
specifically, the hot melt control system for the mixed processing of the recycled glass acquires data of the recycled glass to obtain data of the recycled glass, and performs mixed component ratio analysis on the data to obtain proportion data of processing components. Wherein the recycled glass data comprises parameter information such as type, color, quality and the like of recycled glass. The processing component proportion data comprises parameter information such as component composition, component content, component proportion and the like of the recycled glass. The method achieves the technical effects of obtaining reliable processing component proportion data through the recycled glass data and laying a foundation for the subsequent hot melting treatment of the recycled glass.
Step S200: pretreating the recovered glass, and crushing the pretreated recovered glass;
step S300: acquiring the glass image after the crushing treatment by the image acquisition equipment to obtain a glass image set;
specifically, the method comprises the steps of preprocessing the recovered glass, crushing the preprocessed recovered glass, and collecting the crushed glass image by using image collection equipment to obtain a glass image set. The pretreatment comprises classification treatment, color selection treatment, impurity cleaning treatment and the like of the recycled glass. The crushing treatment includes crushing the pretreated recovered glass using a glass crusher or the like. The image acquisition equipment is in communication connection with the hot melt control system for the mixed processing of the recycled glass. The image capturing device may be any type of camera device or combination thereof capable of capturing the acquired image information. The glass image set comprises image data information of the recycled glass after the crushing treatment. The technical effects of preprocessing, crushing and image acquisition of the recycled glass, obtaining the glass image set of the recycled glass after crushing, and providing data support for subsequent granularity characteristic identification of the glass image set are achieved.
Step S400: carrying out granularity characteristic identification on the glass image set to generate granularity influence parameters;
further, step S400 of the present application further includes:
step S410: constructing an experimental granularity characteristic;
step S420: performing image feature matching identification on the glass image set based on the experimental granularity features, and generating size deviation evaluation data based on a feature matching identification result;
step S430: evaluating the uniformity of the granularity characteristics of the glass image set to generate evaluation data of the uniformity of the granularity;
step S440: and obtaining the particle size influence parameters according to the size deviation evaluation data and the particle size uniformity evaluation data.
Specifically, image feature matching recognition is carried out on the glass image set according to the experimental granularity features, a feature matching recognition result is obtained, and then size deviation evaluation data are determined. Further, the hot melting control system for the mixed processing of the recycled glass is used for evaluating the uniformity of the granularity characteristics of the glass image set to obtain evaluation data of the uniformity of the granularity, and the granularity influence parameters are determined by combining the evaluation data of the size deviation. Wherein the experimental particle size characteristics comprise particle size parameters such as particle size of the broken glass. The experimental granularity characteristic is determined by the self-adaptive setting of the hot melting control system for the mixed processing of the recycled glass according to the granularity requirement of the hot melting treatment of the recycled glass. The characteristic matching identification result comprises the matching condition of the glass image set to the experimental granularity characteristic. The size deviation evaluation data includes size difference information between the particle size of the glass and the experimental particle size characteristics in the characteristic matching recognition result. The particle size uniformity evaluation data includes particle uniformity parameter information for glass in a set of glass images. The granularity influence parameters comprise size deviation evaluation data and granularity uniformity evaluation data. The technical effects that accurate granularity influence parameters are obtained by carrying out experiment granularity characteristic identification and granularity characteristic uniformity evaluation on the glass image set, and the accuracy of follow-up multi-level hot melt control data matching is improved are achieved.
Further, step S400 of the present application further includes:
step S450: judging whether the granularity influence parameters meet an expected granularity control threshold value or not;
step S460: when the granularity influence parameter can not meet the expected granularity control threshold value, generating a granularity optimization instruction;
step S470: and continuing to perform crushing treatment on the recovered glass based on the granularity optimization instruction until the granularity of the recovered glass can meet the expected granularity control threshold, and stopping performing crushing treatment.
Specifically, whether the obtained particle size influence parameters meet an expected particle size control threshold value or not is judged, if the particle size influence parameters do not meet the expected particle size control threshold value, the hot-melting control system for the mixed processing of the recycled glass automatically obtains a particle size optimization instruction, the recycled glass is continuously crushed according to the particle size optimization instruction, and the crushing processing is finished until the particle size of the recycled glass after the crushing processing can meet the expected particle size control threshold value. Wherein the desired particle size control threshold comprises a desired particle size value for the recycled glass after the breaking process. The desired granularity control threshold may be determined by a custom setting. The granularity optimization instruction is instruction information used for representing that the granularity influence parameters do not meet the expected granularity control threshold value and needing to continue crushing treatment on the recycled glass. The technical effects that whether the particle size influence parameters meet the expected particle size control threshold value or not is judged, the particle size optimization instruction is adaptively generated, the recycled glass is reasonably crushed according to the particle size optimization instruction, the recycled glass after crushing meeting the expected particle size control threshold value is obtained, and the accuracy of hot melting control of the recycled glass is improved are achieved.
Step S500: acquiring quality demand data, and performing multi-level hot melt control data matching through the quality demand data, the processing component proportion data set and the granularity influence parameter to generate a multi-level hot melt control data matching set;
step S600: and performing hot-melt cost evaluation on the multi-level hot-melt control data matching set to obtain multi-level hot-melt control data corresponding to the lowest cost in the hot-melt cost evaluation result, and performing hot-melt treatment on the glass by controlling the multi-level hot-melt control data.
Specifically, the hot-melting treatment process of the recycled glass is hierarchically divided according to quality requirement data, a treatment component proportion data set and granularity influence parameters to obtain a multi-level hot-melting treatment process, and hot-melting control data such as hot-melting temperature and hot-melting time are matched for each level of the multi-level hot-melting treatment process to obtain a multi-level hot-melting control data matching set. Further, hot-melt cost evaluation is carried out on the multi-level hot-melt control data matching set to obtain a hot-melt cost evaluation result, and the hot-melt cost evaluation result is compared to determine the lowest cost. And further, determining multi-level hot-melt control data corresponding to the lowest cost in the hot-melt cost evaluation result based on the multi-level hot-melt control data matching set, and performing hot-melt processing on the recovered glass according to the multi-level hot-melt control data. Wherein the quality requirement data comprises a plurality of quality requirement information for performing hot-melt processing on the recycled glass. The multi-tier hot melt control data match set comprises a plurality of multi-tier hot melt control data. The multi-level hot melt control data comprises hot melt control parameter information such as a plurality of hot melt temperatures, a plurality of hot melt times and the like. And the hot melt cost evaluation result comprises hot melt cost information corresponding to each piece of multi-level hot melt control data in the multi-level hot melt control data matching set. The multi-level hot melt control data is multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result. The multilevel hot-melting control data with higher adaptation degree and lower hot-melting cost are obtained through multilevel hot-melting control data matching and hot-melting cost evaluation, so that the hot-melting treatment quality of the recovered glass is improved, and the technical effect of reducing the hot-melting treatment cost of the recovered glass is achieved.
Further, as shown in fig. 2, after step S600, the method further includes:
step S710: obtaining quality control fluctuation data of the multi-level hot melt control data matching set;
step S720: performing multi-level hot melt control data correction corresponding to the lowest cost in the hot melt cost evaluation result based on the quality control fluctuation data to obtain corrected multi-level hot melt control data;
step S730: and performing the hot melting treatment of the glass by correcting the multi-level hot melting control data.
Specifically, the hot-melt control system for the mixed processing of the recycled glass analyzes a multi-level hot-melt control data matching set to obtain quality control fluctuation data. Furthermore, after the obtained multi-level hot-melting control data is corrected according to the quality control fluctuation data, corrected multi-level hot-melting control data is obtained, and hot-melting treatment is carried out on the recovered glass according to the corrected multi-level hot-melting control data. The quality control fluctuation data comprises hot melt quality information corresponding to each piece of multi-level hot melt control data in a multi-level hot melt control data matching set. And the corrected multi-level hot melt control data comprises hot melt control parameter information obtained after hot melt temperature correction, hot melt time correction and the like are carried out on the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result according to the quality control fluctuation data. The technical effects that the multi-level hot-melting control data are adaptively corrected through the quality control fluctuation data, the corrected multi-level hot-melting control data with higher accuracy are obtained, and the accuracy of hot-melting treatment on the recycled glass is further improved are achieved.
Further, as shown in fig. 3, after step S600, the method further includes:
step S810: carrying out actual control temperature acquisition of hot melting treatment through the temperature acquisition equipment to obtain actual control temperature data;
step S820: carrying out temperature control influence analysis through the actual control temperature data and the multi-level hot melt control data to generate quality influence data;
step S830: judging whether the quality influence data can meet a preset quality range of the quality demand data;
step S840: and when the quality influence data can meet the preset quality range of the quality demand data, not adjusting the multi-level hot melt control data by temperature control.
Specifically, when the hot melting treatment is carried out on the recycled glass, the actual control temperature of the hot melting treatment of the recycled glass is collected by using a temperature collecting device, actual control temperature data is obtained, temperature control influence analysis is carried out by combining multi-level hot melting control data, and quality influence data is determined. And further, whether the quality influence data meet the preset quality range of the quality demand data or not is judged, and if the quality influence data meet the preset quality range of the quality demand data, the multi-level hot melt control data do not need to be adjusted. Wherein, the temperature acquisition equipment is in communication connection with the hot melt control system for the mixed processing of the recycled glass. The temperature acquisition equipment can be a temperature acquisition device, an intelligent temperature acquisition instrument and the like. The actual controlled temperature data includes a plurality of actual controlled temperatures of the recycled glass during the hot-melt process. The quality influence data comprises hot melt quality information of the recycled glass corresponding to the actual control temperature data. The predetermined quality range of the quality demand data comprises the hot melt quality information corresponding to the quality demand data. The technical effects that reliable quality influence data are obtained by analyzing the temperature control influence of actual control temperature data and multi-level hot melt control data, and whether the quality influence data can meet the preset quality range of quality demand data or not is accurately judged are achieved.
Further, step S830 of the present application further includes:
step S831: when the quality influence data cannot meet the preset quality range of the quality demand data, obtaining a quality estimation difference value according to the quality influence data and the quality demand data;
specifically, when judging whether the quality influence data meet the preset quality range of the quality demand data, if the quality influence data do not meet the preset quality range of the quality demand data, the quality influence data and the quality demand data are analyzed by the hot melt control system for the mixed processing of the recycled glass, and then a quality estimation difference value is obtained. And the quality estimation difference comprises quality difference information between the quality influence data and the quality demand data. The technical effects of obtaining the quality estimation difference value and optimizing and adjusting the tamping foundation for the subsequent multi-level hot melt control data are achieved.
Step S832: matching temperature control optimization data through the quality pre-estimation difference;
further, step S832 of the present application further includes:
step S8321: performing stage-by-stage temperature estimation optimization based on the quality estimation difference value to generate a temperature optimization control scheme set;
step S8322: and performing cost screening on the temperature control optimization scheme set, and obtaining the temperature control optimization data based on a cost screening result.
Step S833: and optimizing and adjusting the multi-level hot melt control data through the temperature control optimization data.
Specifically, the obtained quality estimation difference is subjected to stage-by-stage temperature estimation optimization by the hot melt control system for the mixed processing of the recycled glass, so that a temperature optimization control scheme set is obtained. Furthermore, cost screening is carried out on the temperature control optimization scheme set to obtain a cost screening result, temperature control optimization data are further determined, and optimization adjustment is carried out on the multi-level hot melt control data according to the temperature control optimization data. The temperature optimization control scheme set comprises a plurality of optimized hot-melting temperature information for carrying out stage-by-stage temperature estimation optimization on hot-melting treatment of the recycled glass according to the quality estimation difference value. And the cost screening result comprises the lowest cost information corresponding to the temperature control optimization scheme set. The temperature control optimization data comprises optimized hot-melt temperature information corresponding to the cost screening result. The technical effects of obtaining temperature control optimization data with higher accuracy and stronger adaptation degree through the quality pre-estimation difference, the temperature optimization control scheme set and the cost screening result, and performing optimization adjustment on the multi-level hot-melt control data according to the temperature control optimization data so as to improve the accuracy of hot-melt control of the recycled glass are achieved.
In summary, the hot melting control method for the mixed processing of the recycled glass provided by the application has the following technical effects:
1. performing mixed component ratio analysis on the recovered glass data to generate processing component ratio data; pretreating the recovered glass, and crushing the pretreated recovered glass; acquiring the glass image after the crushing treatment by the image acquisition equipment to obtain a glass image set; carrying out granularity characteristic identification on the particle to generate granularity influence parameters; performing multi-level hot melt control data matching by combining the quality demand data and the processing component proportion data set to generate a multi-level hot melt control data matching set; and performing hot-melt cost evaluation on the multi-level hot-melt control data matching set to obtain multi-level hot-melt control data corresponding to the lowest cost in the hot-melt cost evaluation result, and performing hot-melt treatment on the glass by controlling the multi-level hot-melt control data. The accuracy of hot melting control of the recycled glass is improved, and the hot melting treatment quality of the recycled glass is improved; meanwhile, the reliability, intelligence and rationality of the hot melting control of the recycled glass are improved; the hot melting treatment cost of the recycled glass is reduced.
2. Through experiment granularity characteristic identification and granularity characteristic uniformity evaluation on the glass image set, accurate granularity influence parameters are obtained, and the accuracy of multi-level hot melt control data matching is further improved.
3. Whether the particle size influence parameters meet the expected particle size control threshold is judged, a particle size optimization instruction is adaptively generated, the recycled glass is reasonably crushed according to the particle size optimization instruction, the recycled glass after crushing treatment meeting the expected particle size control threshold is obtained, and the accuracy of hot melting control of the recycled glass is improved.
4. The multi-level hot-melting control data with high adaptability and low hot-melting cost are obtained through multi-level hot-melting control data matching and hot-melting cost evaluation, so that the hot-melting treatment quality of the recovered glass is improved, and the hot-melting treatment cost of the recovered glass is reduced.
5. When the quality influence data does not meet the preset quality range of the quality demand data, based on the quality estimation difference, the temperature control optimization data with higher accuracy and stronger adaptation degree is obtained by carrying out stage-by-stage temperature estimation optimization and cost screening, and the multi-stage hot-melt control data is optimized and adjusted according to the temperature control optimization data, so that the accuracy of hot-melt control of the recovered glass is improved.
Example two
Based on the same concept as the hot melting control method for the mixed processing of the recycled glass in the previous embodiment, the present invention further provides a hot melting control system for the mixed processing of the recycled glass, referring to fig. 4, where the system includes:
the data acquisition and analysis module 11 is used for acquiring recycled glass data, and performing mixed component proportion analysis based on the recycled glass data to generate processed component proportion data;
the crushing treatment module 12 is used for preprocessing the recovered glass and crushing the preprocessed recovered glass;
the glass image acquisition module 13 is used for acquiring the glass image after the crushing treatment by the image acquisition equipment to obtain a glass image set;
the granularity characteristic identification module 14 is used for carrying out granularity characteristic identification on the glass image set to generate granularity influence parameters;
the data matching module 15 is configured to obtain quality demand data, perform multi-level hot melt control data matching according to the quality demand data, the processing component proportion data set, and the granularity impact parameter, and generate a multi-level hot melt control data matching set;
and the hot-melt processing module 16 is configured to perform hot-melt cost evaluation on the multi-level hot-melt control data matching set, obtain multi-level hot-melt control data corresponding to the lowest cost in a hot-melt cost evaluation result, and perform hot-melt processing on the glass by controlling the multi-level hot-melt control data.
Further, the system further comprises:
a granularity characteristic determination module for constructing experimental granularity characteristics;
the size deviation evaluation data determination module is used for carrying out image feature matching identification on the glass image set based on the experimental granularity features and generating size deviation evaluation data based on a feature matching identification result;
the granularity uniformity evaluation data determination module is used for evaluating the granularity characteristic uniformity of the glass image set to generate granularity uniformity evaluation data;
and the granularity influence parameter determining module is used for obtaining the granularity influence parameters through the size deviation evaluation data and the granularity uniformity evaluation data.
Further, the system further comprises:
a quality control fluctuation data determination module for obtaining quality control fluctuation data of the multi-level hot melt control data matching set;
a modified multi-level hot melt control data determination module, configured to modify multi-level hot melt control data corresponding to a lowest cost in the hot melt cost evaluation result based on the quality control fluctuation data, to obtain modified multi-level hot melt control data;
and the correction hot melting processing module is used for carrying out hot melting processing on the glass through correcting the multi-level hot melting control data.
Further, the system further comprises:
the actual control temperature data determining module is used for acquiring the actual control temperature of the hot melting treatment through the temperature acquisition equipment to obtain actual control temperature data;
the temperature control influence analysis module is used for carrying out temperature control influence analysis through the actual control temperature data and the multi-level hot melt control data to generate quality influence data;
the first judgment module is used for judging whether the quality influence data can meet the preset quality range of the quality demand data;
a first execution module, configured to not perform adjustment of temperature control on the multi-level hot melt control data when the quality-affecting data can meet a predetermined quality range of the quality-requirement data.
Further, the system further comprises:
the quality estimation difference determining module is used for obtaining a quality estimation difference according to the quality influence data and the quality demand data when the quality influence data cannot meet the preset quality range of the quality demand data;
the temperature control optimization data determination module is used for matching the temperature control optimization data through the quality estimation difference;
and the optimization adjusting module is used for carrying out optimization adjustment on the multi-level hot melt control data through the temperature control optimization data.
Further, the system further comprises:
the temperature optimization control scheme set determining module is used for performing stage-by-stage temperature estimation optimization based on the quality estimation difference value to generate a temperature optimization control scheme set;
and the temperature control optimization data acquisition module is used for carrying out cost screening on the temperature control optimization scheme set and acquiring the temperature control optimization data based on a cost screening result.
Further, the system further comprises:
a second determining module, configured to determine whether the granularity-affecting parameter meets an expected granularity control threshold;
a granularity optimization instruction determination module, configured to generate a granularity optimization instruction when the granularity impact parameter cannot meet the expected granularity control threshold;
and the second execution module is used for continuing the crushing treatment of the recovered glass based on the granularity optimization instruction until the granularity of the recovered glass can meet the expected granularity control threshold value, and stopping the crushing treatment.
The application provides a hot melting control method for recycling glass mixing processing, wherein the method is applied to a hot melting control system for recycling glass mixing processing, and the method comprises the following steps: performing mixed component ratio analysis on the recycled glass data to generate processing component ratio data; pretreating the recovered glass, and crushing the pretreated recovered glass; acquiring the glass image after the crushing treatment by the image acquisition equipment to obtain a glass image set; carrying out granularity characteristic identification on the particle to generate granularity influence parameters; performing multi-level hot melt control data matching by combining the quality demand data and the processing component proportion data set to generate a multi-level hot melt control data matching set; and performing hot-melt cost evaluation on the multi-level hot-melt control data matching set to obtain multi-level hot-melt control data corresponding to the lowest cost in the hot-melt cost evaluation result, and performing hot-melt processing on the glass by controlling the multi-level hot-melt control data. The technical problem that the hot melting control accuracy of the recycled glass is not enough in the prior art, and therefore the hot melting treatment effect of the recycled glass is not good is solved. The accuracy of hot melting control of the recycled glass is improved, and the hot melting treatment quality of the recycled glass is improved; meanwhile, the reliability, intelligence and rationality of the hot melting control of the recycled glass are improved; the technical effect of reducing the hot melting treatment cost of the recycled glass.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The specification and drawings are merely illustrative of the present application, and it is intended that the present invention cover modifications and variations of this invention provided they come within the scope of the invention and their equivalents.
Claims (8)
1. A hot melt control method for mixed processing of recycled glass is applied to a hot melt control system, the hot melt control system is in communication connection with an image acquisition device, and the method comprises the following steps:
collecting recycled glass data, and performing mixed component proportion analysis based on the recycled glass data to generate processing component proportion data;
pretreating the recovered glass, and crushing the pretreated recovered glass;
acquiring the glass image after the crushing treatment by the image acquisition equipment to obtain a glass image set;
identifying the granularity characteristic of the glass image set to generate granularity influence parameters;
acquiring quality demand data, and performing multi-level hot melt control data matching through the quality demand data, the processing component proportion data set and the granularity influence parameter to generate a multi-level hot melt control data matching set;
and performing hot-melt cost evaluation on the multi-level hot-melt control data matching set to obtain multi-level hot-melt control data corresponding to the lowest cost in the hot-melt cost evaluation result, and performing hot-melt processing on the glass by controlling the multi-level hot-melt control data.
2. The method of claim 1, wherein the method further comprises:
constructing an experimental granularity characteristic;
performing image feature matching identification on the glass image set based on the experimental granularity features, and generating size deviation evaluation data based on a feature matching identification result;
evaluating the uniformity of the granularity characteristics of the glass image set to generate evaluation data of the uniformity of the granularity;
and obtaining the particle size influence parameter according to the size deviation evaluation data and the particle size uniformity evaluation data.
3. The method of claim 1, wherein the method further comprises:
obtaining quality control fluctuation data of the multi-level hot melt control data matching set;
performing multi-level hot melt control data correction corresponding to the lowest cost in the hot melt cost evaluation result based on the quality control fluctuation data to obtain corrected multi-level hot melt control data;
and performing the hot melting treatment of the glass by correcting the multi-level hot melting control data.
4. The method of claim 1, wherein the hot melt control system is communicatively coupled to a temperature acquisition device, the method further comprising:
acquiring actual control temperature of the hot melting treatment through the temperature acquisition equipment to obtain actual control temperature data;
carrying out temperature control influence analysis through the actual control temperature data and the multi-level hot melt control data to generate quality influence data;
judging whether the quality influence data can meet a preset quality range of the quality demand data;
and when the quality influence data can meet the preset quality range of the quality demand data, not adjusting the multi-level hot melt control data by temperature control.
5. The method of claim 4, wherein the method further comprises:
when the quality influence data cannot meet the preset quality range of the quality demand data, obtaining a quality estimation difference value according to the quality influence data and the quality demand data;
matching temperature control optimization data through the quality pre-estimation difference;
and optimizing and adjusting the multi-level hot melt control data through the temperature control optimization data.
6. The method of claim 5, wherein the method further comprises:
performing stage-by-stage temperature estimation optimization based on the quality estimation difference value to generate a temperature optimization control scheme set;
and performing cost screening on the temperature control optimization scheme set, and acquiring the temperature control optimization data based on a cost screening result.
7. The method of claim 1, wherein the method further comprises:
judging whether the granularity influence parameters meet an expected granularity control threshold value or not;
when the granularity influencing parameter can not meet the expected granularity control threshold value, generating a granularity optimization instruction;
and continuing to perform crushing treatment on the recovered glass based on the granularity optimization instruction until the granularity of the recovered glass can meet the expected granularity control threshold, and stopping performing crushing treatment.
8. A thermal fusion control system for hybrid processing of recycled glass, the system communicatively coupled to an image capture device, the system comprising:
the data acquisition and analysis module is used for acquiring recovered glass data, and performing mixed component proportion analysis based on the recovered glass data to generate processing component proportion data;
the crushing processing module is used for preprocessing the recovered glass and crushing the preprocessed recovered glass;
the glass image acquisition module is used for acquiring glass images after crushing treatment through the image acquisition equipment to obtain a glass image set;
the granularity characteristic identification module is used for carrying out granularity characteristic identification on the glass image set to generate granularity influence parameters;
the data matching module is used for obtaining quality demand data, and performing multi-level hot melt control data matching through the quality demand data, the processing component proportion data set and the granularity influence parameter to generate a multi-level hot melt control data matching set;
and the hot-melt processing module is used for carrying out hot-melt cost evaluation on the multi-level hot-melt control data matching set to obtain multi-level hot-melt control data corresponding to the lowest cost in a hot-melt cost evaluation result, and controlling the multi-level hot-melt control data to carry out hot-melt processing on the glass.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211529367.3A CN115893811A (en) | 2022-12-01 | 2022-12-01 | Hot melting control method and system for mixed processing of recycled glass |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211529367.3A CN115893811A (en) | 2022-12-01 | 2022-12-01 | Hot melting control method and system for mixed processing of recycled glass |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115893811A true CN115893811A (en) | 2023-04-04 |
Family
ID=86472667
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211529367.3A Pending CN115893811A (en) | 2022-12-01 | 2022-12-01 | Hot melting control method and system for mixed processing of recycled glass |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115893811A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0372781A (en) * | 1989-05-10 | 1991-03-27 | Canon Inc | Picture processor |
| CN102070294A (en) * | 2009-11-25 | 2011-05-25 | 淄博宜臣轻工制品有限公司 | Glass hot melting and bonding processing technology |
| US20110141265A1 (en) * | 2009-12-10 | 2011-06-16 | Mark Edwin Holtkamp | System and Method for Monitoring Hot Glass Containers to Enhance Their Quality and Control the Forming Process |
-
2022
- 2022-12-01 CN CN202211529367.3A patent/CN115893811A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0372781A (en) * | 1989-05-10 | 1991-03-27 | Canon Inc | Picture processor |
| CN102070294A (en) * | 2009-11-25 | 2011-05-25 | 淄博宜臣轻工制品有限公司 | Glass hot melting and bonding processing technology |
| US20110141265A1 (en) * | 2009-12-10 | 2011-06-16 | Mark Edwin Holtkamp | System and Method for Monitoring Hot Glass Containers to Enhance Their Quality and Control the Forming Process |
Non-Patent Citations (1)
| Title |
|---|
| 刘泛函;王华;徐建新;: "气体射流不稳定性的建模和实验研究", 化工进展, no. 11, 5 November 2016 (2016-11-05) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Svyrydov et al. | Improving efficienty of image recognition process: Approach and case study | |
| CN110263494B (en) | Alumina production operation optimization system and method based on cloud edge cooperation | |
| CN115202310B (en) | Automatic processing method and system for electric automobile motor fastener | |
| CN112036755A (en) | Supervision method and system for building engineering quality detection | |
| CN111813618A (en) | Data anomaly detection method, device, equipment and storage medium | |
| CN116597239B (en) | Processing method and system for recycled steel pipe for building | |
| CN111676365A (en) | LSTM-based method for predicting annealing temperature set value of transition steel coil | |
| CN115893811A (en) | Hot melting control method and system for mixed processing of recycled glass | |
| CN114997608A (en) | Production efficiency assessment method and system based on industrial chain data analysis | |
| CN116690823A (en) | Intelligent preparation method and system of modified PBT material | |
| JP4225807B2 (en) | Road surface condition determination method and apparatus | |
| CN116152709A (en) | Intelligent grading pretreatment method and system for decoration garbage | |
| US20230015295A1 (en) | Object recognition apparatus and method based on environment matching | |
| CN111081286A (en) | Video editing system for artificial intelligence teaching | |
| CN112598618B (en) | Image recognition technology-based ore feeding amount prediction method for mill | |
| CN115049646A (en) | Intelligent control method for drilling robot equipment for coal mine | |
| CN112651339A (en) | Silkworm pupa gender feature identification system and identification method based on image processing | |
| CN113392151A (en) | System, method, device and storage medium for realizing data mining | |
| JP2002008186A (en) | Vehicle type identification device | |
| CN117132828B (en) | Automatic classification method and system for solid waste in copper pipe machining process | |
| CN112101331B (en) | Security video fusion scheduling method and device | |
| CN117193223B (en) | Plastic product production control system | |
| CN115861451A (en) | Multifunctional image processing method and system based on machine vision | |
| CN114522791B (en) | Crushed stone size screening system and method | |
| CN112347975B (en) | Visual converged communication scheduling system and implementation method thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |