A hot melting control method and system for recycled glass mixed processing
By collecting data, analyzing mixed components, image acquisition and particle size feature recognition of the recovered glass, and combining the quality demand data to perform multi-level thermal melt control data matching and thermal melt cost evaluation, the problem of insufficient accuracy of thermal melt control in the recycling glass is solved, and more efficient and higher quality thermal melt treatment is achieved.
Patent Information
- Application Number
- CN202211529367.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-12-01
- Publication Date
- 2025-02-28
- Estimated Expiration
- 2042-12-01
AI Technical Summary
In the prior art, the thermal melt control accuracy of the recovered glass is insufficient, resulting in poor thermal melting treatment effect.
By collecting and recovering glass data, performing mixed component proportion analysis and pre-processing, using image acquisition equipment to collect broken glass images, perform particle size feature recognition and multi-level thermal melting control data matching, and conducting thermal melting cost evaluation based on quality demand data and processed component proportion data to generate the lowest cost multi-level thermal melting control data to control the thermal melting processing of glass.
It improves the accuracy and processing quality of thermal melt control of recycled glass, enhances the reliability, intelligence and rationality of thermal melt control, and reduces the cost of thermal melt treatment.
Smart Images
Figure CN115893811B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data processing, and in particular to a hot melting control method and system for mixed processing of recycled glass. Background Art
[0002] In our daily life, we often use various glass products such as glass windows, glass cups, glass doors, etc. Glass products are both beautiful and practical and are widely used. In the process of using glass products, a large amount of waste glass has accumulated. Glass is recyclable garbage, but unlike other recyclable garbage, glass cannot be incinerated or degraded in the soil by landfill. A large amount of accumulated waste glass will only pollute the soil and groundwater. After recycling and hot melting of waste glass, waste glass can be used as raw material for glass production, thus realizing the recycling of waste glass, which is of great significance.
[0003] In the prior art, there is a technical problem that the accuracy of hot-melt control of recycled glass is insufficient, which results in poor hot-melt treatment effect of recycled glass. Summary of the invention
[0004] The present application provides a hot melt control method and system for mixed processing of recycled glass, which solves the technical problem in the prior art that hot melt control accuracy of recycled glass is insufficient, thereby resulting in poor hot melt processing effect of recycled glass.
[0005] In view of the above problems, the present application provides a hot melt control method and system for recycled glass mixed processing.
[0006] In a first aspect, the present application provides a hot melt control method for mixed processing of recycled glass, wherein the method is applied to a hot melt control system for mixed processing of recycled glass, and the method includes: collecting recycled glass data, performing mixed component ratio analysis based on the recycled glass data, and generating processing component ratio data; pre-processing the recycled glass, and crushing the pre-processed recycled glass; acquiring images of the glass after crushing by the image acquisition device to obtain a glass image set; performing particle size feature recognition on the glass image set to generate particle size influencing parameters; obtaining quality requirement data, performing multi-level hot melt control data matching by the quality requirement data, the processing component ratio data set and the particle size influencing parameters to generate a multi-level hot melt control data matching set; 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 controlling the hot melt processing of the glass by the multi-level hot melt control data.
[0007] In a second aspect, the present application also provides a hot melt control system for mixed processing of recycled glass, wherein the system includes: a data acquisition and analysis module, the data acquisition and analysis module is used to acquire recycled glass data, perform mixed component ratio analysis based on the recycled glass data, and generate processing component ratio data; a crushing processing module, the crushing processing module is used to pre-process the recycled glass and crush the pre-processed recycled glass; a glass image acquisition module, the glass image acquisition module is used to acquire images of the glass after crushing through the image acquisition device, and obtain a glass image set; a particle size feature recognition module, the particle size feature recognition module The feature recognition module is used to identify the particle size features of the glass image set and generate particle size influencing parameters; the data matching module is used to obtain quality requirement data, and perform multi-level hot melt control data matching through the quality requirement data, the processing component ratio data set and the particle size influencing parameters to generate a multi-level hot melt control data matching set; the hot melt processing module is used to evaluate the hot melt cost of the multi-level hot melt control data matching set, obtain the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result, and control the hot melt processing of the glass through the multi-level hot melt control data.
[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0009] By analyzing the proportion of mixed components in the recycled glass data, processing component ratio data is generated; pre-processing the recycled glass, and crushing the pre-processed recycled glass; collecting images of the crushed glass through the image acquisition device to obtain a glass image set; and identifying particle size characteristics to generate particle size influencing parameters; combining quality demand data and processing component ratio data sets to perform multi-level hot melt control data matching to generate a multi-level hot melt control data matching set; hot melt cost evaluation is performed on the multi-level hot melt control data matching set to obtain the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result, and the hot melt treatment of the glass is controlled by the multi-level hot melt control data. The technical effect of improving the accuracy of hot melt control of recycled glass and improving the quality of hot melt treatment of recycled glass is achieved; at the same time, the reliability, intelligence and rationality of hot melt control of recycled glass are improved; and the hot melt treatment cost of recycled glass is reduced. BRIEF DESCRIPTION OF THE DRAWINGS
[0010] Figure 1 A schematic flow chart of a hot melt control method for mixed processing of recycled glass in this application;
[0011] Figure 2This is a schematic flow chart of a hot-melt control method for mixed processing of recycled glass in the present application, in which the hot-melt treatment of glass is performed by correcting multi-level hot-melt control data;
[0012] Figure 3 A schematic flow chart of the adjustment of multi-level hot-melt control data without temperature control in a hot-melt control method for mixed processing of recycled glass in the present application;
[0013] Figure 4 This is a schematic structural diagram of a hot melt control system for recycled glass mixing processing in this application.
[0014] Explanation of the accompanying drawings: data acquisition and analysis module 11, crushing processing module 12, glass image acquisition module 13, particle size feature recognition module 14, data matching module 15, hot melt processing module 16. DETAILED DESCRIPTION
[0015] The present application provides a method and system for controlling the hot melt of recycled glass mixed processing. The method solves the technical problem that the hot melt control accuracy of recycled glass in the prior art is insufficient, which results in poor hot melt treatment effect of recycled glass. The method achieves the technical effect of improving the accuracy of hot melt control of recycled glass and the quality of hot melt treatment of recycled glass; at the same time, the reliability, intelligence and rationality of hot melt control of recycled glass are improved; and the hot melt treatment cost of recycled glass is reduced.
[0016] Embodiment 1
[0017] Please refer to the attached Figure 1 The present application provides a hot melt control method for recycled glass mixed processing, wherein the method is applied to a hot melt control system for recycled glass mixed processing, the system is communicatively connected with an image acquisition device and a temperature acquisition device, and the method specifically comprises the following steps:
[0018] Step S100: collecting recycled glass data, performing mixed component ratio analysis based on the recycled glass data, and generating processing component ratio data;
[0019] Specifically, the hot melt control system for mixed processing of recycled glass collects data on the recycled glass to obtain recycled glass data, and performs mixed component ratio analysis to obtain processed component ratio data. The recycled glass data includes parameter information such as the type, color, and quality of the recycled glass. The processed component ratio data includes parameter information such as the component composition, component content, and component ratio of the recycled glass. The technical effect of obtaining reliable processed component ratio data through recycled glass data and laying the foundation for subsequent hot melt processing of the recycled glass is achieved.
[0020] Step S200: pre-treating the recycled glass and crushing the pre-treated recycled glass;
[0021] Step S300: collecting images of the broken glass by the image acquisition device to obtain a glass image set;
[0022] Specifically, the recycled glass is first pre-processed, and then the pre-processed recycled glass is crushed, and the image acquisition device is used to collect the glass image after the crushing process to obtain a glass image set. The pre-processing includes classification processing, color selection processing, impurity cleaning processing, etc. of the recycled glass. The crushing process includes crushing the pre-processed recycled glass using equipment such as a glass crusher. The image acquisition device is communicatively connected to the hot melt control system of the mixed processing of recycled glass. The image acquisition device can be any type of camera device capable of collecting and acquiring image information in the prior art or a combination thereof. The glass image set includes image data information of the recycled glass after the crushing process. The recycled glass is pre-processed, crushed, and imaged to obtain a glass image set of the recycled glass after the crushing process, and the technical effect of providing data support for the subsequent granular feature recognition of the glass image set is achieved.
[0023] Step S400: performing particle size feature recognition on the glass image set to generate particle size influencing parameters;
[0024] Furthermore, step S400 of the present application also includes:
[0025] Step S410: constructing experimental granularity features;
[0026] Step S420: performing image feature matching and recognition of the glass image set based on the experimental particle size features, and generating size deviation evaluation data based on the feature matching and recognition results;
[0027] Step S430: performing particle size characteristic uniformity evaluation on the glass image set to generate particle size uniformity evaluation data;
[0028] Step S440: obtaining the particle size influencing parameter through the size deviation evaluation data and the particle size uniformity evaluation data.
[0029] Specifically, image feature matching and recognition are performed on the glass image set according to the experimental particle size feature to obtain a feature matching recognition result, and then the size deviation evaluation data is determined. Further, the glass image set is evaluated for particle size feature uniformity by the hot melt control system of the mixed processing of recycled glass to obtain particle size uniformity evaluation data, and the particle size influencing parameters are determined in combination with the size deviation evaluation data. Among them, the experimental particle size feature includes particle size parameters such as the particle size of the broken glass. The experimental particle size feature is determined by the hot melt control system of the mixed processing of recycled glass according to the particle size requirements of the hot melt processing of the recycled glass. The feature matching recognition result includes the matching of the glass image set to the experimental particle size feature. The size deviation evaluation data includes the size gap information between the particle size of the glass in the feature matching recognition result and the experimental particle size feature. The particle size uniformity evaluation data includes the particle uniformity parameter information of the glass in the glass image set. The particle size influencing parameters include size deviation evaluation data and particle size uniformity evaluation data. The technical effect of obtaining accurate particle size influencing parameters by experimentally identifying particle size features and evaluating particle size feature uniformity on a glass image set has been achieved, thereby improving the accuracy of subsequent multi-level hot melt control data matching.
[0030] Furthermore, step S400 of the present application also includes:
[0031] Step S450: determining whether the particle size influencing parameter meets the expected particle size control threshold;
[0032] Step S460: when the particle size influencing parameter cannot meet the expected particle size control threshold, a particle size optimization instruction is generated;
[0033] Step S470: continuing the crushing process of the recycled glass based on the particle size optimization instruction until the particle size of the recycled glass can meet the expected particle size control threshold, and then stopping the crushing process.
[0034] Specifically, it is judged whether the obtained particle size influencing parameters meet the expected particle size control threshold. If the particle size influencing parameters do not meet the expected particle size control threshold, the hot melt control system of the mixed processing of recycled glass automatically obtains the particle size optimization instruction, and continues to crush the recycled glass according to the particle size optimization instruction, until the particle size of the recycled glass after the crushing process can meet the expected particle size control threshold, the crushing process ends. Wherein, the expected particle size control threshold includes the expected particle size of the recycled glass after the crushing process. The expected particle size control threshold can be determined by custom settings. The particle size optimization instruction is an instruction information used to characterize that the particle size influencing parameters do not meet the expected particle size control threshold, and the recycled glass needs to continue to be crushed. The technical effect of judging whether the particle size influencing parameters meet the expected particle size control threshold, adaptively generating the particle size optimization instruction, and reasonably crushing the recycled glass according to the particle size optimization instruction, obtaining the recycled glass after the crushing process that meets the expected particle size control threshold, and then improving the accuracy of the hot melt control of the recycled glass is achieved.
[0035] Step S500: obtaining quality requirement data, performing multi-level hot melt control data matching through the quality requirement data, the processing component ratio data set and the particle size influencing parameter, and generating a multi-level hot melt control data matching set;
[0036] Step S600: perform hot melt cost evaluation on the multi-level hot melt control data matching set, obtain the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result, and control the hot melt processing of the glass through the multi-level hot melt control data.
[0037] Specifically, the hot melt treatment process of the recycled glass is hierarchically divided according to the quality requirement data, the processing component ratio data set, and the particle size influencing parameters to obtain a multi-level hot melt treatment process, and the hot melt control data such as hot melt temperature and hot melt time are matched for each level of the multi-level hot melt treatment process to obtain a multi-level hot melt control data matching set. Further, the hot melt cost evaluation result is obtained by evaluating the hot melt cost of the multi-level hot melt control data matching set, and the hot melt cost evaluation results are compared to determine the lowest cost. Then, based on the multi-level hot melt control data matching set, the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result is determined, and the recycled glass is hot melted according to the multi-level hot melt control data matching set. Among them, the quality requirement data includes multiple quality requirement information for hot melt treatment of recycled glass. The multi-level hot melt control data matching set includes multiple multi-level hot melt control data. The multiple multi-level hot melt control data includes multiple hot melt temperature, multiple hot melt time and other hot melt control parameter information. The hot melt cost evaluation result includes the hot melt cost information corresponding to each multi-level hot melt control data in the multi-level hot melt control data matching set. The multi-level hot melt control data is the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result. The multi-level hot melt control data with high adaptability and low hot melt cost is obtained through multi-level hot melt control data matching and hot melt cost evaluation, thereby improving the hot melt processing quality of recycled glass and reducing the hot melt processing cost of recycled glass.
[0038] Further, as attached Figure 2 As shown, after step S600, the present application further includes:
[0039] Step S710: obtaining quality control fluctuation data of the multi-level hot melt control data matching set;
[0040] Step S720: correcting the multi-level hot melt control data 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;
[0041] Step S730: performing heat-melting treatment on the glass by using the modified multi-level heat-melting control data.
[0042] Specifically, the hot melt control system for mixed processing of recycled glass analyzes the multi-level hot melt control data matching set to obtain quality control fluctuation data. Further, after the obtained multi-level hot melt control data is corrected according to the quality control fluctuation data, the corrected multi-level hot melt control data is obtained, and the recycled glass is hot melted according to the data. Wherein, the quality control fluctuation data includes the hot melt quality information corresponding to each multi-level hot melt control data in the multi-level hot melt control data matching set. The corrected multi-level hot melt control data includes the hot melt control parameter information obtained after the hot melt temperature correction, hot melt time correction, etc. of the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result are performed according to the quality control fluctuation data. The technical effect of adaptively correcting the multi-level hot melt control data through the quality control fluctuation data to obtain the corrected multi-level hot melt control data with higher accuracy is achieved, thereby further improving the accuracy of hot melt processing of recycled glass.
[0043] Further, as attached Figure 3 As shown, after step S600, the present application further includes:
[0044] Step S810: collecting the actual control temperature of the heat-melting process by the temperature collection device to obtain actual control temperature data;
[0045] Step S820: performing temperature control impact analysis based on the actual control temperature data and the multi-level hot melt control data to generate quality impact data;
[0046] Step S830: determining whether the quality impact data can meet a predetermined quality range of the quality requirement data;
[0047] Step S840: When the quality influencing data can satisfy the predetermined quality range of the quality requirement data, the temperature control of the multi-level hot melt control data is not adjusted.
[0048] Specifically, when the recycled glass is subjected to heat melting treatment, the actual control temperature of the heat melting treatment of the recycled glass is collected by using a temperature collection device to obtain the actual control temperature data, and the temperature control impact analysis is performed in combination with the multi-level heat melting control data to determine the quality impact data. Further, it is judged whether the quality impact data meets the predetermined quality range of the quality requirement data. If the quality impact data meets the predetermined quality range of the quality requirement data, there is no need to adjust the multi-level heat melting control data. Wherein, the temperature collection device is connected to the heat melting control system of the mixed processing of recycled glass in communication. The temperature collection device can be a temperature collector, an intelligent temperature collector, etc. The actual control temperature data includes a plurality of actual control temperatures of the recycled glass during the heat melting treatment process. The quality impact data includes the heat melting quality information of the recycled glass corresponding to the actual control temperature data. The predetermined quality range of the quality requirement data includes the heat melting quality information corresponding to the quality requirement data. The technical effect of obtaining reliable quality impact data by performing temperature control impact analysis on the actual control temperature data and the multi-level heat melting control data, and then accurately judging whether the quality impact data can meet the predetermined quality range of the quality requirement data is achieved.
[0049] Furthermore, step S830 of the present application also includes:
[0050] Step S831: when the quality influencing data cannot meet the predetermined quality range of the quality requirement data, obtaining a quality estimation difference value according to the quality influencing data and the quality requirement data;
[0051] Specifically, when judging whether the quality-influencing data meets the predetermined quality range of the quality-requirement data, if the quality-influencing data does not meet the predetermined quality range of the quality-requirement data, the hot melt control system for recycled glass mixed processing analyzes the quality-influencing data and the quality-requirement data to obtain a quality-estimated difference. The quality-estimated difference includes the quality-estimated difference information between the quality-influencing data and the quality-requirement data. The technical effect of obtaining the quality-estimated difference and laying a solid foundation for the subsequent optimization and adjustment of the multi-level hot melt control data is achieved.
[0052] Step S832: Matching temperature control optimization data through the quality estimation difference;
[0053] Furthermore, step S832 of the present application also includes:
[0054] Step S8321: performing step-by-step temperature estimation optimization based on the quality estimation difference to generate a set of temperature optimization control schemes;
[0055] Step S8322: Perform cost screening on the temperature control optimization solution set, and obtain the temperature control optimization data based on the cost screening result.
[0056] Step S833: Optimizing and adjusting the multi-level hot melt control data through the temperature control optimization data.
[0057] Specifically, the hot melt control system for mixed processing of recycled glass performs step-by-step temperature estimation optimization on the obtained quality estimation difference to obtain a set of temperature optimization control schemes. Further, by performing cost screening on the set of temperature control optimization schemes, a cost screening result is obtained, and then the temperature control optimization data is determined, and the multi-level hot melt control data is optimized and adjusted accordingly. Among them, the set of temperature optimization control schemes includes a plurality of optimized hot melt temperature information for performing step-by-step temperature estimation optimization on the hot melt treatment of recycled glass according to the quality estimation difference. The cost screening result includes the minimum cost information corresponding to the set of temperature control optimization schemes. The temperature control optimization data includes the optimized hot melt temperature information corresponding to the cost screening result. The technical effect of obtaining temperature control optimization data with high accuracy and strong adaptability through the quality estimation difference, the set of temperature optimization control schemes, and the cost screening result, and optimizing and adjusting the multi-level hot melt control data according to the data, thereby improving the accuracy of hot melt control of recycled glass, is achieved.
[0058] In summary, the hot melt control method for mixed processing of recycled glass provided in this application has the following technical effects:
[0059] 1. Generate processing component ratio data by analyzing the proportion of mixed components of recycled glass data; pre-process recycled glass and crush the pre-processed recycled glass; collect images of the crushed glass by the image acquisition device to obtain a glass image set; and identify particle size characteristics to generate particle size influencing parameters; perform multi-level hot melt control data matching in combination with quality requirement data and processing component ratio data set to generate a multi-level hot melt control data matching set; perform hot melt cost evaluation on the multi-level hot melt control data matching set to obtain the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result, and control the hot melt treatment of glass by the multi-level hot melt control data. The technical effect of improving the accuracy of hot melt control of recycled glass and improving the quality of hot melt treatment of recycled glass is achieved; at the same time, the reliability, intelligence and rationality of hot melt control of recycled glass are improved; and the hot melt treatment cost of recycled glass is reduced.
[0060] 2. By conducting experimental particle size feature recognition and particle size feature uniformity evaluation on the glass image set, accurate particle size influencing parameters can be obtained, thereby improving the accuracy of multi-level hot melt control data matching.
[0061] 3. By judging whether the particle size influencing parameters meet the expected particle size control threshold, adaptively generate particle size optimization instructions, and reasonably crush the recycled glass according to the particle size optimization instructions to obtain recycled glass after crushing that meets the expected particle size control threshold, thereby improving the accuracy of hot melt control of the recycled glass.
[0062] 4. Through multi-level hot melt control data matching and hot melt cost evaluation, multi-level hot melt control data with high adaptability and low hot melt cost is obtained, thereby improving the hot melt processing quality of recycled glass and reducing the hot melt processing cost of recycled glass.
[0063] 5. When the quality influencing data does not meet the predetermined quality range of the quality requirement data, based on the quality estimation difference, through step-by-step temperature estimation optimization and cost screening, the temperature control optimization data with higher accuracy and stronger adaptability is obtained, and the multi-level hot melt control data is optimized and adjusted accordingly, so as to improve the accuracy of hot melt control of recycled glass.
[0064] Embodiment 2
[0065] Based on the hot melt control method for mixed processing of recycled glass in the above embodiment, the present invention also provides a hot melt control system for mixed processing of recycled glass, see the attached Figure 4 , the system comprising:
[0066] A data collection and analysis module 11, the data collection and analysis module 11 is used to collect recycled glass data, perform mixed component ratio analysis based on the recycled glass data, and generate processing component ratio data;
[0067] A crushing processing module 12, wherein the crushing processing module 12 is used to pre-process the recycled glass and crush the pre-processed recycled glass;
[0068] A glass image acquisition module 13, wherein the glass image acquisition module 13 is used to acquire images of the broken glass through the image acquisition device to obtain a glass image set;
[0069] A particle size feature recognition module 14, wherein the particle size feature recognition module 14 is used to perform particle size feature recognition on the glass image set and generate particle size influencing parameters;
[0070] A data matching module 15, the data matching module 15 is used to obtain quality requirement data, perform multi-level hot melt control data matching through the quality requirement data, the processing component ratio data set and the particle size influencing parameter, and generate a multi-level hot melt control data matching set;
[0071] The hot melt processing module 16 is used to evaluate the hot melt cost of the multi-level hot melt control data matching set, obtain the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result, and control the hot melt processing of the glass through the multi-level hot melt control data.
[0072] Furthermore, the system also includes:
[0073] A particle size feature determination module, wherein the particle size feature determination module is used to construct an experimental particle size feature;
[0074] A size deviation evaluation data determination module, the size deviation evaluation data determination module is used to perform image feature matching and recognition of the glass image set based on the experimental particle size feature, and generate size deviation evaluation data based on the feature matching and recognition result;
[0075] a particle size uniformity evaluation data determination module, the particle size uniformity evaluation data determination module being used to perform particle size feature uniformity evaluation on the glass image set to generate particle size uniformity evaluation data;
[0076] A particle size influence parameter determination module is used to obtain the particle size influence parameter through the size deviation evaluation data and the particle size uniformity evaluation data.
[0077] Furthermore, the system also includes:
[0078] A quality control fluctuation data determination module, the quality control fluctuation data determination module is used to obtain the quality control fluctuation data of the multi-level hot melt control data matching set;
[0079] a modified multi-level hot melt control data determination module, the modified multi-level hot melt control data determination module being used to modify the multi-level hot melt control data corresponding to the 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;
[0080] A modified heat-melt processing module is used to perform heat-melt processing on the glass by using the modified multi-level heat-melt control data.
[0081] Furthermore, the system also includes:
[0082] An actual control temperature data determination module, the actual control temperature data determination module is used to collect the actual control temperature of the heat melting process through the temperature collection device to obtain the actual control temperature data;
[0083] A temperature control impact analysis module, the temperature control impact analysis module is used to perform temperature control impact analysis through the actual control temperature data and the multi-level hot melt control data to generate quality impact data;
[0084] A first judgment module, the first judgment module is used to judge whether the quality impact data can meet the predetermined quality range of the quality requirement data;
[0085] The first execution module is used for not adjusting the multi-level hot melt control data by temperature control when the quality impact data can meet the predetermined quality range of the quality requirement data.
[0086] Furthermore, the system also includes:
[0087] A quality estimation difference determination module, wherein the quality estimation difference determination module is used to obtain a quality estimation difference according to the quality estimation data and the quality requirement data when the quality estimation difference data cannot meet a predetermined quality range of the quality requirement data;
[0088] a temperature control optimization data determination module, the temperature control optimization data determination module being used to match the temperature control optimization data through the quality estimation difference;
[0089] An optimization and adjustment module, wherein the optimization and adjustment module is used to optimize and adjust the multi-level hot melt control data through the temperature control optimization data.
[0090] Furthermore, the system also includes:
[0091] A temperature optimization control scheme set determination module, the temperature optimization control scheme set determination module is used to perform step-by-step temperature estimation optimization based on the quality estimation difference to generate a temperature optimization control scheme set;
[0092] A temperature control optimization data acquisition module is used to perform cost screening on the temperature control optimization solution set and obtain the temperature control optimization data based on the cost screening result.
[0093] Furthermore, the system also includes:
[0094] A second judgment module, the second judgment module is used to judge whether the particle size influencing parameter meets the expected particle size control threshold;
[0095] A granularity optimization instruction determination module, wherein the granularity optimization instruction determination module is used to generate a granularity optimization instruction when the granularity influencing parameter cannot meet the expected granularity control threshold;
[0096] The second execution module is used to continue the crushing process of the recycled glass based on the particle size optimization instruction until the particle size of the recycled glass can meet the expected particle size control threshold, and then stop the crushing process.
[0097] The present application provides a hot melt control method for mixed processing of recycled glass, wherein the method is applied to a hot melt control system for mixed processing of recycled glass, and the method comprises: generating processing component ratio data by analyzing the mixed component ratio of recycled glass data; pre-processing the recycled glass, and crushing the pre-processed recycled glass; acquiring glass images after crushing through the image acquisition device to obtain a glass image set; and performing particle size feature recognition to generate particle size influencing parameters; performing multi-level hot melt control data matching in combination with quality requirement data and processing component ratio data set to generate a multi-level hot melt control data matching set; 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 controlling the hot melt treatment of glass through the multi-level hot melt control data. The method solves the technical problem that the accuracy of hot melt control for recycled glass in the prior art is insufficient, which results in poor hot melt treatment effect of recycled glass. The method achieves the technical effect of improving the accuracy of hot melt control of recycled glass and improving the hot melt treatment quality of recycled glass; at the same time, improving the reliability, intelligence and rationality of hot melt control of recycled glass; and reducing the hot melt treatment cost of recycled glass.
[0098] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0099] This specification and the drawings are merely exemplary illustrations of the present application. If modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, the present invention is intended to include these modifications and variations.
Claims
1. A method for controlling hot melting of recycled glass mixed processing, characterized in that: The method is applied to a hot melt control system, the hot melt control system is communicatively connected with an image acquisition device, and the method comprises: Collecting recycled glass data, performing mixed component ratio analysis based on the recycled glass data, and generating processing component ratio data; Pre-treating the recycled glass and crushing the pre-treated recycled glass; The image acquisition device is used to acquire images of the broken glass to obtain a glass image set; Performing particle size feature recognition on the glass image set to generate particle size influencing parameters; Obtaining quality requirement data, performing multi-level hot melt control data matching through the quality requirement data, the processing component ratio data and the particle size influencing parameters, and generating a multi-level hot melt control data matching set; Perform hot melt cost evaluation on the multi-level hot melt control data matching set to obtain the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result, and control the hot melt processing of the glass through the multi-level hot melt control data.
2. The method according to claim 1, characterized in that The method further comprises: Construct experimental granular features; Performing image feature matching and recognition of the glass image set based on the experimental particle size features, and generating size deviation evaluation data based on the feature matching and recognition results; Performing particle size characteristic uniformity evaluation on the glass image set to generate particle size uniformity evaluation data; The particle size influencing parameter is obtained through the size deviation evaluation data and the particle size uniformity evaluation data.
3. The method according to claim 1, characterized in that The method further comprises: Obtaining quality control fluctuation data of the multi-level hot melt control data matching set; Based on the quality control fluctuation data, the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result is corrected to obtain the corrected multi-level hot melt control data; The glass is heat-melted by means of the modified multi-level heat-melt control data.
4. The method according to claim 1, characterized in that The hot melt control system is communicatively connected with the temperature acquisition device, and the method further comprises: The actual control temperature of the heat melting process is collected by the temperature collection device to obtain actual control temperature data; Perform temperature control impact analysis through the actual control temperature data and the multi-level hot melt control data to generate quality impact data; Determining whether the quality impact data can meet a predetermined quality range of the quality requirement data; When the quality influencing data can satisfy the predetermined quality range of the quality requirement data, the adjustment of the multi-level hot melt control data by temperature control is not performed.
5. The method according to claim 4, characterized in that The method further comprises: When the quality impact data cannot meet the predetermined quality range of the quality requirement data, obtaining a quality estimation difference value according to the quality impact data and the quality requirement data; Matching temperature control optimization data by the mass estimation difference; The multi-level hot melt control data is optimized and adjusted through the temperature control optimization data.
6. The method according to claim 5, characterized in that The method further comprises: Performing step-by-step temperature estimation optimization based on the quality estimation difference to generate a set of temperature optimization control schemes; Cost screening is performed on the temperature control optimization solution set, and the temperature control optimization data is obtained based on the cost screening result.
7. The method according to claim 1, characterized in that The method further comprises: Determining whether the particle size influencing parameter meets the expected particle size control threshold; When the particle size influencing parameter cannot meet the expected particle size control threshold, a particle size optimization instruction is generated; The crushing process of the recycled glass is continued based on the particle size optimization instruction until the particle size of the recycled glass can meet the expected particle size control threshold, and then the crushing process is stopped.
8. A hot melt control system for recycled glass mixing processing, characterized in that: The system is connected in communication with the image acquisition device, and the system comprises: A data collection and analysis module, which is used to collect recycled glass data, perform mixed component ratio analysis based on the recycled glass data, and generate processed component ratio data; A crushing processing module, wherein the crushing processing module is used to pre-process the recycled glass and crush the pre-processed recycled glass; A glass image acquisition module, the glass image acquisition module is used to acquire images of the broken glass through the image acquisition device to obtain a glass image set; A particle size feature recognition module, the particle size feature recognition module is used to perform particle size feature recognition on the glass image set and generate particle size influencing parameters; A data matching module, the data matching module is used to obtain quality requirement data, perform multi-level hot melt control data matching through the quality requirement data, the processing component ratio data and the particle size influencing parameter, and generate a multi-level hot melt control data matching set; A hot melt processing module is used to evaluate the hot melt cost of the multi-level hot melt control data matching set, obtain the multi-level hot melt control data corresponding to the lowest cost in the hot melt cost evaluation result, and control the hot melt processing of the glass through the multi-level hot melt control data.
Citation Information
Patent Citations
Glass hot melting and bonding processing technology
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Picture processor
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