CN115759698B - Corrugated base paper production progress data monitoring method and system based on digital factory - Google Patents
Corrugated base paper production progress data monitoring method and system based on digital factory Download PDFInfo
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Abstract
According to the corrugated base paper production progress data monitoring method and system based on the digital factory, the comparison result of the production monitoring data to be processed after processing and the sample production monitoring data to be processed after processing is located in the configured comparison result interval, and the problem that the noise comparison result of the production monitoring data to be processed and the sample production monitoring data is unstable is solved. And projecting the processed sample production monitoring data and the processed production monitoring data to be processed, and then performing progress monitoring processing on the processed production monitoring data to be processed by using one or a plurality of projected sample production monitoring data to generate a target progress monitoring result corresponding to the processed production monitoring data to be processed. Therefore, the method and the device can accurately and reliably obtain the production monitoring data, ensure the normal operation of production equipment, and improve the production efficiency of the corrugated base paper.
Description
Technical Field
The application relates to the technical field of data monitoring, in particular to a corrugated base paper production progress data monitoring method and system based on a digital factory.
Background
The corrugated base paper is prepared by crushing, filtering, concentrating, pulping and other steps of waste paper materials such as corrugated waste paper; the prepared corrugated base paper can be used as a base paper source of cowhide boxes, kraft paper and the like. At present, with the development of data chemical plants, the production monitoring of corrugated base paper is increasingly intelligent. In a corrugated medium digitizing factory, when the data monitoring technology is combined with production, the data monitoring technology needs to monitor the production progress of the corrugated medium, so that the production quantity of the corrugated medium can be effectively ensured. However, in the process of monitoring, due to the fact that data analysis is inaccurate, accurate and reliable production monitoring data are difficult to ensure, and therefore production efficiency of corrugated base paper is affected.
Disclosure of Invention
In order to improve the technical problems in the related art, the application provides a corrugated base paper production progress data monitoring method and system based on a digital factory.
In a first aspect, a method for monitoring corrugated base paper production progress data based on a digital factory is provided, the method at least comprises: obtaining production monitoring data to be processed and one or a plurality of sample production monitoring data corresponding to the production monitoring data to be processed; performing data node debugging treatment on the production monitoring data to be treated and the sample production monitoring data one by one to obtain production monitoring data to be treated after treatment and sample production monitoring data to be treated after treatment; the comparison result of the production monitoring data which is processed and needs to be processed and the sample production monitoring data which is processed and is processed is positioned in a configured comparison result interval; projecting each processed sample production monitoring data and the processed production monitoring data to be processed to obtain one or a plurality of projected sample production monitoring data; and performing progress monitoring processing on the production monitoring data which is required to be processed and is finished to generate a target progress monitoring result corresponding to the production monitoring data which is required to be processed through the projected one or a plurality of sample production monitoring data.
In an independent embodiment, the step of performing data node debugging processing on the production monitoring data to be processed and the sample production monitoring data one by one to obtain production monitoring data to be processed and sample production monitoring data to be processed, which are processed, includes: based on the analysis data of the data analysis unit, compressing and decompressing the production monitoring data to be processed and the sample production monitoring data to obtain the production monitoring data to be processed after the processing is completed and the sample production monitoring data to be processed after the processing is completed; or, combining the analysis data of the data analysis unit, and carrying out data classification processing on the production monitoring data to be processed and the sample production monitoring data to obtain the production monitoring data to be processed after the processing and the sample production monitoring data to be processed; or, combining the analysis data of the data analysis unit, compressing and decompressing the production monitoring data to be processed and the sample production monitoring data and performing data classification processing to obtain the production monitoring data to be processed after the processing and the sample production monitoring data to be processed after the processing; the data analysis unit is used for collecting the production monitoring data to be processed and the sample production monitoring data.
In an independent embodiment, the compressing and decompressing process is performed on the production monitoring data to be processed and the sample production monitoring data based on the analysis data of the data analysis unit to obtain the production monitoring data to be processed after the processing is completed and the sample production monitoring data to be processed after the processing is completed, including: combining the data attribute vector indicated by the analysis data of the data analysis unit and the data classification quantity to determine compression and decompression information; and combining the compression and decompression information, compressing and decompressing the production monitoring data to be processed and the data node information of each data node in the sample production monitoring data one by one, and generating the production monitoring data to be processed after the processing and the sample production monitoring data after the processing are completed.
In an independent embodiment, the data classification processing is performed on the production monitoring data to be processed and the sample production monitoring data based on the analysis data of the data analysis unit, to obtain the production monitoring data to be processed after the processing and the sample production monitoring data to be processed after the processing, including: compressing and decompressing the comparison result indicated by the analysis data by combining the data attribute vector and the data classification number indicated by the analysis data of the data analysis unit to obtain comparison result information after switching; and combining the comparison result information after switching, carrying out data classification processing on the production monitoring data to be processed and the data node information of each data node in the sample production monitoring data one by one, and generating the production monitoring data to be processed after processing and the sample production monitoring data after processing.
In an independent embodiment, the projecting each processed sample production monitoring data with the processed production monitoring data to obtain one or several projected sample production monitoring data includes: performing compression processing on the sample production monitoring data after the processing and the production monitoring data which is required to be processed after the processing to generate a plurality of monitoring label tuples of different types; the monitoring tag binary group comprises a first monitoring tag corresponding to the processed sample production monitoring data and a second monitoring tag corresponding to the processed production monitoring data; determining a monitoring topic queue based on the first category of monitoring tag tuples and the configured first artificial intelligent training model; combining the monitoring subject queue, performing projection operation on a first monitoring tag in a second type of monitoring tag binary group, and generating a third monitoring tag; performing projection operation on the third monitoring tag by combining a second monitoring tag in the second type of monitoring tag binary group and a configured second artificial intelligent training model, and generating one or a plurality of projected sample production monitoring data corresponding to the processed sample production monitoring data; wherein the first species is smaller than the second species.
In an independent embodiment, the performing, by using the projected one or several sample production monitoring data, progress monitoring processing on the production monitoring data to be processed after the processing is completed, and generating a target progress monitoring result corresponding to the production monitoring data to be processed, includes: performing first progress monitoring processing on the production monitoring data which is required to be processed after the processing is completed, and obtaining a first transition monitoring tag corresponding to the production monitoring data which is required to be processed; and carrying out second progress monitoring processing on the first transition monitoring tag through the projected one or a plurality of sample production monitoring data, and generating a target progress monitoring result corresponding to the production monitoring data to be processed.
In an embodiment of the independent implementation, the number of the sample production monitoring data is a plurality of, and the second progress monitoring processing is performed on the first transition monitoring tag by using the projected plurality of sample production monitoring data, so as to generate a target progress monitoring result corresponding to the production monitoring data to be processed, where the target progress monitoring result includes: performing second progress monitoring processing on the first transition monitoring label by using projected 1 st sample production monitoring data to generate a 1 st second transition monitoring label; performing second progress monitoring processing on the m-1 th second transition monitoring label by using the projected m-th sample production monitoring data to generate an m-th second transition monitoring label; wherein m is 2, 3, …, n; and after the x-1 second transition monitoring label is processed by the second progress monitoring, determining the generated x second transition monitoring label as a target progress monitoring result corresponding to the production monitoring data to be processed.
In an independent embodiment, the second progress monitoring process is performed on the m-1 th second transition monitoring label by using the projected m-th sample production monitoring data, so as to generate an m-th second transition monitoring label, which includes: matching the projected m-th sample production monitoring data with the m-1-th second transition monitoring label to obtain a matched monitoring label; performing compression processing on the matched monitoring labels for at least one time to obtain a plurality of undetermined monitoring labels of different types; wherein, the data monitoring attributes included in the different types of pending monitoring tags are different; and carrying out various progress monitoring processing on the plurality of undetermined monitoring labels of different types to generate an mth second transition monitoring label.
In an independent embodiment, the performing a plurality of progress monitoring processes on the plurality of pending monitoring tags of different types to generate an mth second transition monitoring tag includes: progress monitoring treatment is carried out on each kind of to-be-monitored labels one by one, and cleaning monitoring labels corresponding to the to-be-monitored labels are generated; and splicing the cleaning monitoring labels corresponding to the undetermined monitoring labels of all kinds to generate the mth second transition monitoring label.
In an independent embodiment, the step of performing progress monitoring on each kind of pending monitoring label one by one to generate a cleaning monitoring label corresponding to the pending monitoring label includes: distributing a plurality of the pending monitoring tags according to the category priority sequence of each pending monitoring tag to obtain pending production monitoring distribution conditions, wherein the pending production monitoring distribution conditions comprise 1 st, 2 nd. Wherein x is a positive integer; performing progress monitoring treatment on the 1 st to-be-monitored label to generate a 1 st cleaning monitoring label corresponding to the 1 st to-be-monitored label; performing progress monitoring treatment on the y-th to-be-monitored label based on the y-1-th to-be-monitored label, and generating a y-th to-be-monitored label corresponding to the y-th to-be-monitored label, wherein y is a positive integer greater than 1 and less than x; and performing progress monitoring treatment on the x-th to-be-monitored label based on the x-1-th to-be-monitored label, and generating the x-th to-be-monitored label corresponding to the x-th to-be-monitored label.
In a second aspect, a corrugated medium production progress data monitoring system based on a digital factory is provided, comprising a processor and a memory in communication with each other, the processor being adapted to read a computer program from the memory and execute the computer program to implement the method described above.
According to the corrugated base paper production progress data monitoring method and system based on the digital factory, the production monitoring data to be processed and the data nodes of the sample production monitoring data are debugged to obtain the production monitoring data to be processed and the sample production monitoring data to be processed, so that the comparison result of the production monitoring data to be processed and the sample production monitoring data to be processed is located in the configured comparison result interval, and the problem that the noise comparison result of the production monitoring data to be processed and the sample production monitoring data is unstable is solved. And projecting the processed sample production monitoring data and the processed production monitoring data to be processed, and then performing progress monitoring processing on the processed production monitoring data to be processed by using one or a plurality of projected sample production monitoring data to generate a target progress monitoring result corresponding to the processed production monitoring data to be processed. Therefore, the production monitoring data can be accurately and reliably obtained, so that the normal operation of production equipment can be ensured, and the production efficiency of corrugated base paper is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a corrugated medium production progress data monitoring method based on a digital factory according to an embodiment of the present application.
Fig. 2 is a block diagram of a corrugated medium production progress data monitoring device based on a digital factory according to an embodiment of the present application.
Fig. 3 is a schematic diagram of a corrugated medium production progress data monitoring system based on a digital factory according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions described above, the following detailed description of the technical solutions of the present application is provided through the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limit the technical solutions of the present application, and the technical features of the embodiments and embodiments of the present application may be combined with each other without conflict.
Referring to fig. 1, a method for monitoring corrugated medium production progress data based on a digital factory is shown, which may include the following steps S101-S104.
S101, obtaining production monitoring data to be processed and one or a plurality of sample production monitoring data corresponding to the production monitoring data to be processed.
S102, carrying out data node debugging treatment on production monitoring data to be treated and sample production monitoring data one by one to obtain production monitoring data to be treated which is finished in treatment and sample production monitoring data which is finished in treatment; and the comparison result of the production monitoring data which is processed and needs to be processed and the sample production monitoring data which is processed is positioned in the configured comparison result interval.
S103, projecting each processed sample production monitoring data and the production monitoring data which is required to be processed after the processing is completed, and obtaining one or a plurality of projected sample production monitoring data.
S104, performing progress monitoring processing on the production monitoring data which is processed and needs to be processed by using one or a plurality of projected sample production monitoring data, and generating a target progress monitoring result corresponding to the production monitoring data which needs to be processed.
In the method, the production monitoring data to be processed and the sample production monitoring data to be processed are obtained by debugging the data nodes of the production monitoring data to be processed and the sample production monitoring data to be processed, the comparison result of the production monitoring data which is required to be processed and the sample production monitoring data which is required to be processed after being processed is positioned in the configured comparison result interval, so that the problem of unstable noise comparison result of the production monitoring data which is required to be processed and the sample production monitoring data is solved. And projecting the processed sample production monitoring data and the processed production monitoring data to be processed, and then performing progress monitoring processing on the processed production monitoring data to be processed by using one or a plurality of projected sample production monitoring data to generate a target progress monitoring result corresponding to the processed production monitoring data to be processed. Therefore, the production monitoring data can be accurately and reliably obtained, so that the normal operation of production equipment can be ensured, and the production efficiency of corrugated base paper is improved.
The following steps are further explained for S101-S104.
For S101: in practice, the production monitoring data and the sample production monitoring data to be processed can be a plurality of continuous monitoring tags collected by the monitoring tag collecting device.
For S102: it is found that the gain of the data analysis unit and the brightness of the monitoring tag are important factors for unstable comparison results of the monitoring tag. Based on the above-described studies, after obtaining production monitoring data to be processed and one or several sample production monitoring data, the present disclosure uses a comparison result stabilization technique to eliminate unstable noise comparison results caused by various factors in the sample production monitoring data and the production monitoring data to be processed, so that the comparison results of the production monitoring data to be processed and the sample production monitoring data to be processed, which are completed, are located within a configured comparison result interval.
Specifically, a comparison result stabilization technology can be used to debug the data nodes of the production monitoring data to be processed and the sample production monitoring data to obtain the production monitoring data to be processed and the sample production monitoring data to be processed, so that the comparison result of the production monitoring data to be processed and the monitoring label of the sample production monitoring data to be processed is stable.
In the embodiment of the disclosure, the data node debugging processing is performed on the production monitoring data to be processed and the sample production monitoring data one by one, so as to obtain the production monitoring data to be processed after the processing is completed and the sample production monitoring data to be processed after the processing is completed, which may include the following contents.
(a) Based on the analysis data of the data analysis unit, compressing and decompressing the production monitoring data to be processed and the sample production monitoring data to obtain the production monitoring data to be processed after the processing is completed and the sample production monitoring data to be processed.
(b) And carrying out data classification processing on the production monitoring data to be processed and the sample production monitoring data based on the analysis data of the data analysis unit to obtain the production monitoring data to be processed after the processing and the sample production monitoring data to be processed.
(c) Based on the analysis data of the data analysis unit, compressing and decompressing the production monitoring data to be processed and the sample production monitoring data and classifying the data to obtain the production monitoring data to be processed after the processing and the sample production monitoring data to be processed; the data analysis unit is used for collecting production monitoring data and sample production monitoring data which need to be processed.
For (a), in order to eliminate the instability of the comparison result caused by the gain of the data analysis unit, the production monitoring data to be processed and the sample production monitoring data may be compressed and decompressed based on the analysis data of the data analysis unit, so as to obtain the production monitoring data to be processed after the processing and the sample production monitoring data to be processed after the processing are completed. The analysis data are matched with the sensors of the data analysis unit, namely after the data analysis unit is determined, the corresponding analysis data of the data analysis unit can be obtained.
When the method is implemented, based on the analysis data of the data analysis unit, the production monitoring data to be processed and the sample production monitoring data are compressed and decompressed to obtain the production monitoring data to be processed after the processing is completed and the sample production monitoring data to be processed after the processing is completed, and the method specifically comprises the following steps of.
And step A1, determining compression and decompression information based on the data attribute vector indicated by the analysis data of the data analysis unit and the data classification quantity.
And A2, compressing and decompressing the production monitoring data to be processed and the data node information of each data node in the sample production monitoring data one by one based on the compression and decompression information to generate the production monitoring data to be processed and the sample production monitoring data to be processed after the processing is completed.
Here, compression and decompression information is determined based on the data attribute vector indicated by the analysis data of the data analysis unit and the number of data classifications; based on the compression and decompression information, compressing and decompressing data node information of each data node in the production monitoring data to be processed and the sample production monitoring data one by one to generate production monitoring data to be processed after processing and sample production monitoring data to be processed after processing.
Aiming at (b), considering that different brightness in the same monitoring label has a changed comparison result, in order to eliminate unstable comparison result caused by the brightness of the monitoring label, the analysis data of the data analysis unit is utilized to carry out data classification processing on the production monitoring data to be processed and the sample production monitoring data to be processed, so as to obtain the production monitoring data to be processed after the processing and the sample production monitoring data to be processed after the processing are completed.
When the method is implemented, based on the analysis data of the data analysis unit, the production monitoring data to be processed and the sample production monitoring data are subjected to data classification processing to obtain the production monitoring data to be processed after the processing is completed and the sample production monitoring data to be processed after the processing is completed, and the method specifically comprises the following steps of.
And step B1, based on the data attribute vector and the data classification quantity indicated by the analysis data of the data analysis unit, compressing and decompressing the comparison result indicated by the analysis data to obtain the comparison result information after switching.
And B2, carrying out data classification processing on the production monitoring data to be processed and the data node information of each data node in the sample production monitoring data one by one based on the comparison result information after switching, and generating the production monitoring data to be processed after processing and the sample production monitoring data after processing.
The comparison result indicated by the analysis data can be compressed and decompressed by using the data attribute vector and the data classification number indicated by the analysis data of the data analysis unit, so as to obtain the comparison result information after switching. And carrying out data classification processing on the production monitoring data to be processed and the data node information of each data node in the sample production monitoring data one by one based on the comparison result information after switching, and generating the production monitoring data to be processed after processing and the sample production monitoring data after processing.
And (c) compressing and decompressing the production monitoring data to be processed and the sample production monitoring data and performing data classification processing on the basis of the analysis data of the data analysis unit to obtain the production monitoring data to be processed and the sample production monitoring data to be processed which are processed. For example, the data node information of each data node in the production monitoring data and the sample production monitoring data to be processed may be compressed and decompressed according to the formula (1) by using the data attribute vector and the data classification number indicated by the analysis data of the data analysis unit, to generate the production monitoring data to be processed for the compression and decompression completion processing and the sample production monitoring data for the compression and decompression completion processing. And then using the data attribute vector and the data classification quantity indicated by the analysis data of the data analysis unit to perform data classification processing on the production monitoring data to be processed in the compression and decompression completion processing and the data node information of each data node in the sample production monitoring data to be processed in the compression and decompression completion processing one by one according to the formula (2), thereby obtaining the production monitoring data to be processed in the completion processing and the sample production monitoring data to be processed in the completion processing.
By the operation, the factors causing unstable comparison results of the production monitoring data to be processed and the sample production monitoring data, such as unstable comparison results caused by the data classification quantity or unstable comparison results caused by the brightness of the monitoring labels, are eliminated, so that the comparison results of the production monitoring data to be processed after the processing and the sample production monitoring data to be processed are stable, the follow-up progress monitoring processing of the production monitoring data to be processed is carried out on the basis of stable comparison results, the accuracy and the efficiency of the progress monitoring processing are effectively improved, and the production efficiency of corrugated base paper is improved.
For S103: after obtaining sample production monitoring data which is stable in comparison result and is processed and production monitoring data which is processed and needs to be processed are obtained, projecting the sample production monitoring data which is processed and the production monitoring data which is processed and needs to be processed, and obtaining projected sample production monitoring data; when the number of the processed sample production monitoring data is a plurality of, each processed sample production monitoring data needs to be projected with the processed production monitoring data which needs to be processed. For example, the first artificial intelligent training model can be used for realizing monitoring label projection, namely, convolution processing nodes and descriptors for extracting the binary groups of each node from processed sample production monitoring data and processed production monitoring data to be processed respectively; determining matched node doublets in the processed sample production monitoring data and the processed production monitoring data to be processed through matching the node descriptors; and determining a monitoring topic queue according to the matched node binary groups, and projecting the processed sample production monitoring data and the processed production monitoring data which are required to be processed according to the monitoring topic queue to obtain projected sample production monitoring data.
In an embodiment of the present disclosure, projecting each processed sample production monitoring data and the processed production monitoring data to be processed after the processing to obtain one or several projected sample production monitoring data may specifically include the following steps.
S1031, aiming at each processed sample production monitoring data, carrying out compression processing on the processed sample production monitoring data and the processed production monitoring data which need to be processed, and generating a plurality of monitoring label tuples of different types; the monitoring tag binary group comprises a first monitoring tag corresponding to sample production monitoring data which is processed and a second monitoring tag corresponding to production monitoring data which is processed and needs to be processed.
S1032, determining a monitoring topic queue based on the first category of monitoring tag tuples and the configured first artificial intelligence training model.
S1033, based on the monitoring subject queue, performing projection operation on the first monitoring tag in the second class of monitoring tag binary group to generate a third monitoring tag.
S1034, performing projection operation on the third monitoring tag based on the second monitoring tag in the second type of monitoring tag binary group and the configured second artificial intelligent training model, and generating one or a plurality of projected sample production monitoring data corresponding to the processed sample production monitoring data; wherein the first species is smaller than the second species.
And aiming at each processed sample production monitoring data, carrying out compression processing on the processed sample production monitoring data and the processed production monitoring data which need to be processed, and generating a plurality of monitoring label tuples of different types. The number of the monitoring tag tuples can be configured according to the needs, for example, the processed sample production monitoring data and the processed production monitoring data which need to be processed after the processing can be subjected to one-time compression processing to obtain a first monitoring tag tuple; and performing primary compression treatment on the obtained first monitoring tag binary group to obtain a second monitoring tag binary group and the like, wherein the type of the second monitoring tag binary group is smaller than that of the first monitoring tag binary group. The monitoring tag binary group comprises a first monitoring tag corresponding to sample production monitoring data which is processed and a second monitoring tag corresponding to production monitoring data which is processed and needs to be processed.
When the plurality of monitoring tag tuples comprise a first monitoring tag tuple of a first type and a second monitoring tag tuple of a second type, and the first type is smaller than the second type, determining a monitoring subject queue based on the second monitoring tag in the first monitoring tag tuple and the configured first artificial intelligent training model aiming at the first monitoring tag tuple. For example, the nodes of the first monitoring tag and the second monitoring tag in the first monitoring tag doublet can be extracted, descriptors corresponding to the node doublets are extracted, and then the matched node doublets in the first monitoring tag and the second monitoring tag are determined by matching the node descriptors; and determining a monitoring subject queue according to the matched node doublet.
And then, based on the monitoring subject queue, performing projection operation on the first monitoring tag in the second monitoring tag binary group to generate a third monitoring tag. And carrying out projection operation on the third monitoring tag based on the second monitoring tag in the second monitoring tag binary group and the configured second artificial intelligent training model, and generating projected sample production monitoring data corresponding to the processed sample production monitoring data.
For S104: when the method is implemented, the noise information can be determined by using one or a plurality of projected sample production monitoring data, and progress monitoring processing is carried out on the production monitoring data which is processed and needs to be processed by using the determined noise information to generate a cleaning monitoring label; and feeding back the cleaning monitoring label to obtain a target progress monitoring result corresponding to the production monitoring data to be processed.
In one possible implementation embodiment, the process monitoring process is performed on the production monitoring data to be processed after the processing is completed by using one or a plurality of projected sample production monitoring data, and a target progress monitoring result corresponding to the production monitoring data to be processed is generated.
S1041, performing first progress monitoring processing on the production monitoring data which is required to be processed after the processing is completed, and obtaining a first transition monitoring tag corresponding to the production monitoring data which is required to be processed.
S1042, performing a second progress monitoring process on the first transition monitoring tag by using the projected one or a plurality of sample production monitoring data, and generating a target progress monitoring result corresponding to the production monitoring data to be processed.
When the method is implemented, the strategy can be cleaned according to the configured sequence, and the projected one or a plurality of sample production monitoring data are utilized to carry out progress monitoring processing on the production monitoring data which are required to be processed after the processing is completed, so that a target progress monitoring result is generated. For example, the first progress monitoring process may be performed on the production monitoring data to be processed after the processing is completed, so as to obtain a first transition monitoring tag corresponding to the production monitoring data to be processed. And then sequentially utilizing one or a plurality of projected sample production monitoring data to carry out second progress monitoring processing on the first transition monitoring tag, and generating a target progress monitoring result corresponding to the production monitoring data to be processed.
When the number of the sample production monitoring data is one, the first transition monitoring label can be subjected to a second progress monitoring process by using the sample production monitoring data after projection, and a target progress monitoring result is generated. When the number of the sample production monitoring data is a plurality of, the first transition monitoring label can be sequentially subjected to second progress monitoring processing by using the sample production monitoring data after the plurality of projections, namely the number of times of the second progress monitoring processing is a plurality of times, so that a target progress monitoring result is generated.
When the number of the sample production monitoring data is a plurality of, performing second progress monitoring processing on the first transition monitoring tag by using the projected plurality of sample production monitoring data to generate a target progress monitoring result corresponding to the production monitoring data to be processed, which specifically comprises the following steps.
And a1, performing second progress monitoring processing on the first transition monitoring label by using the projected 1 st sample production monitoring data to generate a1 st second transition monitoring label.
A2, carrying out second progress monitoring treatment on the m-1 th second transition monitoring label by using the projected m-th sample production monitoring data to generate an m-th second transition monitoring label; wherein m is 2, 3, …, n.
And a3, after the x-1 second transition monitoring label is processed by the second progress monitoring, determining the generated x second transition monitoring label as a target progress monitoring result corresponding to the production monitoring data to be processed.
When the method is implemented, the projected 1 st sample production monitoring data can be utilized to carry out second progress monitoring processing on the first transition monitoring label, and a 1 st second transition monitoring label is generated; and carrying out second progress monitoring processing on the 1 st second transition monitoring label by using the projected 2 nd sample production monitoring data to generate the 2 nd second transition monitoring label. And so on, carrying out second progress monitoring treatment on the m-1 th second transition monitoring label by using the projected m-th sample production monitoring data to generate an m-th second transition monitoring label; wherein m is 2, 3, …, n; and after the second progress monitoring processing is finished on the x-1 second transition monitoring label by using the projected x sample production monitoring data, determining the generated x second transition monitoring label as a target progress monitoring result corresponding to the production monitoring data needing to be processed.
In the implementation, the projected sample production monitoring data can be further grouped to obtain a plurality of monitoring tag groups, and each monitoring tag group comprises one or a plurality of projected sample production monitoring data. And then sequentially utilizing a plurality of monitoring tag groups to carry out second progress monitoring processing on the first transition monitoring tag, and generating a target progress monitoring result corresponding to the production monitoring data to be processed.
And if the number of the monitoring tag groups is n, performing second progress monitoring processing on the first transition monitoring tag by using projected sample production monitoring data included in the first monitoring tag group to obtain a 1 st second transition monitoring tag. And carrying out second progress monitoring processing on the 1 st second transition monitoring label by using projected sample production monitoring data included in the 2 nd monitoring label group to generate the 2 nd second transition monitoring label. And so on, performing second progress monitoring processing on the x-1 second transition monitoring tag by using projected sample production monitoring data included in the x-th monitoring tag group to generate an x-th second transition monitoring tag; and determining the xth second transition monitoring label as a target progress monitoring result corresponding to the production monitoring data to be processed.
In step a2, the second progress monitoring process is performed on the m-1 th second transition monitoring label by using the projected m-th sample production monitoring data, so as to generate the m-th second transition monitoring label, which specifically may include the following steps.
And a21, matching the projected m-th sample production monitoring data with the m-1-th second transition monitoring label to obtain a matched monitoring label.
Step a22, performing compression processing on the matched monitoring labels for at least one time to obtain a plurality of undetermined monitoring labels of different types; wherein, the data monitoring attribute included in the different kinds of pending monitoring tags is different.
And a step a23 of performing multiple progress monitoring processing on a plurality of undetermined monitoring labels of different types to generate an mth second transition monitoring label.
And matching the projected m-th sample production monitoring data with the m-1-th second transition monitoring label to obtain a matched monitoring label.
And carrying out various progress monitoring processing on a plurality of undetermined monitoring labels of different types to generate an mth second transition monitoring label. For example, progress monitoring processing can be performed on each to-be-monitored tag, that is, noise with specific frequency included in the to-be-monitored tag is eliminated, and the cleaning monitoring tag is obtained. And splicing the cleaning monitoring labels corresponding to the to-be-determined monitoring labels one by one to generate an mth second transition monitoring label.
In step a23, a plurality of progress monitoring processes are performed on a plurality of undetermined monitoring tags of different types, and an mth second transition monitoring tag is generated, which may specifically include the following steps.
And a step a231, performing progress monitoring processing on each kind of undetermined monitoring label one by one, and generating a cleaning monitoring label corresponding to the undetermined monitoring label.
And a step a232 of splicing the cleaning monitoring labels corresponding to the undetermined monitoring labels of all kinds to generate an mth second transition monitoring label.
Here, progress monitoring processing can be performed on each kind of pending monitoring tags one by one, and cleaning monitoring tags corresponding to the pending monitoring tags are generated. For example, the cleaning nerve thread can be utilized to perform progress monitoring processing on each type of undetermined monitoring label, so as to generate a cleaning monitoring label corresponding to the undetermined monitoring label.
The progress monitoring treatment is carried out on the undetermined monitoring labels of all kinds one by one, so that noise matched with the undetermined monitoring label kinds can be effectively cleaned, and cleaning monitoring labels corresponding to the undetermined monitoring labels can be accurately generated; and then splicing the cleaning monitoring labels corresponding to the undetermined monitoring labels of all kinds to generate an m second transition monitoring label more accurately.
In a possible implementation embodiment, the progress monitoring process is performed on each kind of to-be-monitored tags one by one, and the cleaning monitoring tag corresponding to the to-be-monitored tag is generated, which specifically may include the following steps.
And b1, distributing a plurality of pending monitoring labels according to the category priority sequence of each pending monitoring label to obtain pending production monitoring distribution conditions, wherein the pending production monitoring distribution conditions comprise 1 st and 2 nd. Wherein x is a positive integer.
And b2, performing progress monitoring treatment on the 1 st undetermined monitoring label to generate a 1 st cleaning monitoring label corresponding to the 1 st undetermined monitoring label.
And b3, performing progress monitoring treatment on the y-th to-be-monitored label based on the y-1-th to-be-monitored label, and generating a y-th to-be-monitored label corresponding to the y-th to-be-monitored label, wherein y is a positive integer greater than 1 and less than x.
And b4, performing progress monitoring treatment on the x-th to-be-monitored label based on the x-1-th to-be-monitored label, and generating the x-th to-be-monitored label corresponding to the x-th to-be-monitored label.
Distributing a plurality of pending monitoring tags according to the category priority sequence of each pending monitoring tag to obtain pending production monitoring distribution conditions, wherein the pending production monitoring distribution conditions comprise 1 st, 2 nd. Wherein the type of the 1 st undetermined monitoring label is smaller than the type of the 2 nd undetermined monitoring label; the category of the y < th > pending monitoring tag is less than the y+1st pending monitoring tag.
And if n is 3, performing progress monitoring processing on the 1 st undetermined monitoring label to generate a 1 st cleaning monitoring label corresponding to the 1 st undetermined monitoring label. And performing progress monitoring treatment on the 2 nd undetermined monitoring label based on the 1 st cleaning monitoring label, and generating the 2 nd cleaning monitoring label corresponding to the 2 nd undetermined monitoring label. And by analogy, generating a 3 rd cleaning monitoring label corresponding to the 3 rd pending monitoring label.
An exemplary method for monitoring corrugated medium production progress data based on a digital factory is described, which includes the following steps.
S401: and obtaining production monitoring data to be processed and one or a plurality of sample production monitoring data corresponding to the production monitoring data to be processed.
S402: and carrying out data node debugging treatment on the production monitoring data to be treated and the sample production monitoring data one by one to obtain the production monitoring data to be treated which is finished in treatment and the sample production monitoring data which is finished in treatment.
S403: and projecting each processed sample production monitoring data and the processed production monitoring data to be processed to obtain one or a plurality of projected sample production monitoring data.
S404: and carrying out first progress monitoring treatment on the production monitoring data which is required to be treated and is treated by utilizing a plurality of cleaning threads a0, and obtaining a first transition monitoring label corresponding to the production monitoring data which is required to be treated.
S405: the sample production monitoring data after a plurality of projections are divided into x monitoring tag groups. Inputting the sample production monitoring data and the first transition monitoring label which are obtained by one or a plurality of projected samples in the 1 st monitoring label group into a plurality of cleaning threads a1 to obtain a1 st second transition monitoring label; inputting the sample production monitoring data and the 1 st second transition monitoring label which are included in the 2 nd monitoring label group and are projected into a plurality of cleaning threads a2 to obtain the 2 nd second transition monitoring label; similarly, an xth second transition monitoring tag can be obtained; and determining the x second transition monitoring label as a target progress monitoring result corresponding to the production monitoring data to be processed.
On the basis of the foregoing, please refer to fig. 2 in combination, there is provided a corrugated medium production progress data monitoring apparatus 200 based on a digital factory, which is applied to a corrugated medium production progress data monitoring cloud platform based on the digital factory, the apparatus comprising:
A data obtaining module 210, configured to obtain production monitoring data to be processed, and one or several sample production monitoring data corresponding to the production monitoring data to be processed;
the data debugging module 220 is configured to perform data node debugging processing on the production monitoring data to be processed and the sample production monitoring data one by one, so as to obtain production monitoring data to be processed after processing is completed and sample production monitoring data after processing is completed; the comparison result of the production monitoring data which is processed and needs to be processed and the sample production monitoring data which is processed and is processed is positioned in a configured comparison result interval;
the data projection module 230 is configured to project each processed sample production monitoring data and the processed production monitoring data that needs to be processed to obtain one or several projected sample production monitoring data;
and the result monitoring module 240 is configured to perform progress monitoring processing on the production monitoring data to be processed after the processing is completed according to the projected one or more sample production monitoring data, and generate a target progress monitoring result corresponding to the production monitoring data to be processed.
On the basis of the above, referring to fig. 3 in combination, there is shown a corrugated medium production progress data monitoring system 300 based on a digital factory, comprising a processor 310 and a memory 320 in communication with each other, the processor 310 being configured to read and execute a computer program from the memory 320 to implement the method described above.
On the basis of the above, there is also provided a computer readable storage medium on which a computer program stored which, when run, implements the above method.
In summary, based on the above scheme, the data nodes of the production monitoring data to be processed and the sample production monitoring data are debugged to obtain the production monitoring data to be processed after the processing and the sample production monitoring data to be processed, so that the comparison result of the production monitoring data to be processed after the processing and the sample production monitoring data to be processed after the processing is located in the configured comparison result interval, and the problem of unstable noise comparison result of the production monitoring data to be processed and the sample production monitoring data is solved. And projecting the processed sample production monitoring data and the processed production monitoring data to be processed, and then performing progress monitoring processing on the processed production monitoring data to be processed by using one or a plurality of projected sample production monitoring data to generate a target progress monitoring result corresponding to the processed production monitoring data to be processed. Therefore, the production monitoring data can be accurately and reliably obtained, so that the normal operation of production equipment can be ensured, and the production efficiency of corrugated base paper is improved.
It should be appreciated that the systems and modules thereof shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may then be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only with hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also with software, such as executed by various types of processors, and with a combination of the above hardware circuitry and software (e.g., firmware).
It should be noted that, the advantages that may be generated by different embodiments may be different, and in different embodiments, the advantages that may be generated may be any one or a combination of several of the above, or any other possible advantages that may be obtained.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations of the present application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this application, and are therefore within the spirit and scope of the exemplary embodiments of this application.
Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present application may be combined as suitable.
Furthermore, those skilled in the art will appreciate that the various aspects of the invention are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer storage medium may be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.
The computer program code necessary for operation of portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python, etc., a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, ruby and Groovy, or other programming languages, etc. The program code may execute entirely on the user's computer or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or the use of services such as software as a service (SaaS) in a cloud computing environment.
Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application and are not intended to limit the order in which the processes and methods of the application are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of this application. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present application may be considered in keeping with the teachings of the present application. Accordingly, embodiments of the present application are not limited to only the embodiments explicitly described and depicted herein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (2)
1. A corrugated medium production progress data monitoring method based on a digital factory, which is characterized by at least comprising the following steps:
obtaining production monitoring data to be processed and one or a plurality of sample production monitoring data corresponding to the production monitoring data to be processed;
performing data node debugging treatment on the production monitoring data to be treated and the sample production monitoring data one by one to obtain production monitoring data to be treated after treatment and sample production monitoring data to be treated after treatment; the comparison result of the production monitoring data which is processed and needs to be processed and the sample production monitoring data which is processed and is processed is positioned in a configured comparison result interval;
Projecting each processed sample production monitoring data and the processed production monitoring data to be processed to obtain one or a plurality of projected sample production monitoring data;
performing progress monitoring processing on the production monitoring data which is required to be processed and is finished by using the projected one or a plurality of sample production monitoring data, and generating a target progress monitoring result corresponding to the production monitoring data which is required to be processed and is finished;
and carrying out data node debugging treatment on the production monitoring data to be treated and the sample production monitoring data one by one to obtain the production monitoring data to be treated after the treatment and the sample production monitoring data to be treated after the treatment, wherein the method comprises the following steps:
based on the analysis data of the data analysis unit, compressing and decompressing the production monitoring data to be processed and the sample production monitoring data to obtain the production monitoring data to be processed after the processing is completed and the sample production monitoring data to be processed after the processing is completed;
or, combining the analysis data of the data analysis unit, and carrying out data classification processing on the production monitoring data to be processed and the sample production monitoring data to obtain the production monitoring data to be processed after the processing and the sample production monitoring data to be processed;
Or, combining the analysis data of the data analysis unit, compressing and decompressing the production monitoring data to be processed and the sample production monitoring data and performing data classification processing to obtain the production monitoring data to be processed after the processing and the sample production monitoring data to be processed after the processing; the data analysis unit is used for collecting the production monitoring data to be processed and the sample production monitoring data;
the data analysis unit-based analysis data compresses and decompresses the production monitoring data to be processed and the sample production monitoring data to obtain processed production monitoring data to be processed and processed sample production monitoring data, including:
combining the data attribute vector indicated by the analysis data of the data analysis unit and the data classification quantity to determine compression and decompression information;
the compression and decompression information is combined, the production monitoring data to be processed and the data node information of each data node in the sample production monitoring data are compressed and decompressed one by one, and production monitoring data to be processed after the processing is completed and sample production monitoring data to be processed are generated;
The data analysis unit-based analysis data performs data classification processing on the production monitoring data to be processed and the sample production monitoring data to obtain processed production monitoring data to be processed and processed sample production monitoring data, and the data analysis unit-based analysis data comprises:
compressing and decompressing the comparison result indicated by the analysis data by combining the data attribute vector and the data classification number indicated by the analysis data of the data analysis unit to obtain comparison result information after switching;
combining the comparison result information after switching, carrying out data classification processing on the production monitoring data to be processed and the data node information of each data node in the sample production monitoring data one by one, and generating production monitoring data to be processed after processing and sample production monitoring data to be processed after processing;
projecting each processed sample production monitoring data and the processed production monitoring data to be processed to obtain one or a plurality of projected sample production monitoring data, wherein the projecting comprises the following steps:
aiming at each processed sample production monitoring data, carrying out compression processing on the processed sample production monitoring data and the processed production monitoring data which are required to be processed, and generating a plurality of monitoring label tuples of different types; the monitoring tag doublet comprises a first monitoring tag corresponding to the processed sample production monitoring data and a second monitoring tag corresponding to the processed production monitoring data, wherein the first monitoring tag doublet is obtained by carrying out primary compression processing on the processed sample production monitoring data and the processed production monitoring data, and then carrying out primary compression processing on the obtained first monitoring tag doublet to obtain a second monitoring tag doublet, and the type of the second monitoring tag doublet is smaller than that of the first monitoring tag doublet;
Determining a monitoring topic queue based on a first type of monitoring tag tuples and a configured first artificial intelligent training model, wherein nodes of a first monitoring tag and a second monitoring tag in the first monitoring tag tuples are specifically extracted, descriptors corresponding to the node tuples are extracted, the matched node tuples in the first monitoring tag and the second monitoring tag are determined through matching the node descriptors, and the monitoring topic queue is determined according to the matched node tuples;
combining the monitoring subject queue, performing projection operation on a first monitoring tag in a second type of monitoring tag binary group, and generating a third monitoring tag; performing projection operation on the third monitoring tag by combining a second monitoring tag in the second type of monitoring tag binary group and a configured second artificial intelligent training model, and generating one or a plurality of projected sample production monitoring data corresponding to the processed sample production monitoring data; wherein the first species is smaller than the second species;
the step of performing progress monitoring processing on the production monitoring data to be processed after the completion of processing according to the projected one or a plurality of sample production monitoring data, and generating a target progress monitoring result corresponding to the production monitoring data to be processed after the completion of processing, including:
Performing first progress monitoring processing on the production monitoring data which is required to be processed after the processing is completed, and obtaining a first transition monitoring tag corresponding to the production monitoring data which is required to be processed;
performing second progress monitoring processing on the first transition monitoring tag through the projected one or a plurality of sample production monitoring data, and generating a target progress monitoring result corresponding to the production monitoring data which is required to be processed after the processing is completed;
the number of the sample production monitoring data is a plurality of, the second progress monitoring processing is performed on the first transition monitoring tag through the projected plurality of sample production monitoring data, and a target progress monitoring result corresponding to the production monitoring data which is required to be processed after the processing is completed is generated, and the method comprises the following steps:
performing second progress monitoring processing on the first transition monitoring label by using projected 1 st sample production monitoring data to generate a 1 st second transition monitoring label; performing second progress monitoring processing on the m-1 th second transition monitoring label by using the projected m-th sample production monitoring data to generate an m-th second transition monitoring label; wherein m is 2, 3, …, n; after the m-1 second transition monitoring label is processed by the second progress monitoring, determining the generated m second transition monitoring label as a target progress monitoring result corresponding to the production monitoring data to be processed;
The second progress monitoring processing is performed on the m-1 th second transition monitoring label by using the projected m-th sample production monitoring data, so as to generate an m-th second transition monitoring label, which comprises the following steps:
matching the projected m-th sample production monitoring data with the m-1-th second transition monitoring label to obtain a matched monitoring label;
performing compression processing on the matched monitoring labels for at least one time to obtain a plurality of undetermined monitoring labels of different types; wherein, the data monitoring attributes included in the different types of pending monitoring tags are different;
performing multiple progress monitoring treatment on the plurality of undetermined monitoring labels of different types to generate an mth second transition monitoring label;
and performing multiple progress monitoring processing on the plurality of undetermined monitoring tags of different types to generate an mth second transition monitoring tag, wherein the generating comprises the following steps of: progress monitoring treatment is carried out on each kind of to-be-monitored labels one by one, and cleaning monitoring labels corresponding to the to-be-monitored labels are generated; splicing the cleaning monitoring labels corresponding to the undetermined monitoring labels of all kinds to generate an mth second transition monitoring label;
the process monitoring processing is performed on each kind of undetermined monitoring label one by one, and the cleaning monitoring label corresponding to the undetermined monitoring label is generated, which comprises the following steps:
Distributing a plurality of the pending monitoring tags according to the category priority sequence of each pending monitoring tag to obtain pending production monitoring distribution conditions, wherein the pending production monitoring distribution conditions comprise 1 st, 2 nd, third and x th pending monitoring tags; wherein x is a positive integer; performing progress monitoring treatment on the 1 st to-be-monitored label to generate a 1 st cleaning monitoring label corresponding to the 1 st to-be-monitored label;
performing progress monitoring treatment on the y-th to-be-monitored label based on the y-1-th to-be-monitored label, and generating a y-th to-be-monitored label corresponding to the y-th to-be-monitored label, wherein y is a positive integer greater than 1 and less than x;
performing progress monitoring treatment on an x-th to-be-monitored label based on an x-1-th cleaning monitoring label, and generating an x-th cleaning monitoring label corresponding to the x-th to-be-monitored label;
when the number of the processed sample production monitoring data is a plurality of, each processed sample production monitoring data and the processed production monitoring data need to be processed are projected, the monitoring label projection is realized by using a first artificial intelligent training model, and the nodes are convolved and processed from the processed sample production monitoring data and the processed production monitoring data need to be processed and the descriptors corresponding to the node tuples are extracted; determining matched node doublets in the processed sample production monitoring data and the processed production monitoring data to be processed through matching the node descriptors; and determining a monitoring topic queue according to the matched node binary groups, and projecting the processed sample production monitoring data and the processed production monitoring data which are required to be processed according to the monitoring topic queue to obtain projected sample production monitoring data.
2. A corrugated medium production progress data monitoring system based on a digital factory, comprising a processor and a memory in communication with each other, the processor being adapted to read a computer program from the memory and execute it to implement the method of claim 1.
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