An intelligent control system for the whole process of corrugated packaging production line

Abstract

An all-process intelligent control system for the whole-line production of corrugated packaging, which relates to the technical field of production process control, obtains the process information of the whole-line production of corrugated packaging, sets the monitoring points of corrugated packaging according to the process information, and obtains the production data of each point; monitors and controls the production data of each point in real time; divides the production monitoring indicators of each process subsequence into high-frequency abnormal production monitoring indicators or low-frequency abnormal production monitoring indicators; constructs a high-frequency index prediction model to obtain the first predicted production data; obtains the reference production monitoring indicators of the low-frequency abnormal production monitoring indicators; constructs a low-frequency index prediction model to obtain the second predicted production data; generates the third predicted production data according to the first predicted production data and the second predicted production data, and obtains the initial control item measures for the next collection cycle according to the third predicted production data, significantly improving the production efficiency and product quality of corrugated packaging.

Description

Technical Field

[0001] The present invention relates to the technical field of production process control, and specifically to a full-process intelligent control system for the whole-line production of corrugated packaging. Background Art

[0002] A Chinese patent with the publication number CN118789883A discloses a production process of corrugated paper packaging boxes, including the following steps: S1, raw material preparation; S2, designing and manufacturing a die cutter; S3, cutting; S4, waste removal: placing the cut corrugated cardboard into waste removal equipment, and then removing the excess corner materials according to the cutting lines formed by die cutting through the waste removal equipment to obtain semi-finished products and corner materials; S5, recycling; S6, box gluing.

[0003] A Chinese patent with the publication number CN118735192A discloses an intelligent recommendation method and system for a corrugated carton packaging production process, including obtaining order feature data of a target customer and inputting it into a multi-objective optimization model to obtain an initial recommendation plan, evaluating its first matching degree with the customer demand progress, adjusting the order priority if it does not meet the requirements, otherwise evaluating the second matching degree between raw material supply and customer demand, and matching an associated production process to the initial recommendation plan when the conditions are not met to generate a target recommendation plan; executing and configuring the initial parameters of the production process according to the target recommendation plan, obtaining the actual operation parameters of the production line in real time and drawing a change curve, predicting the production operation state and determining a parameter compensation factor, and updating the initial parameters of the production process.

[0004] As a packaging form widely used in the fields of logistics and consumer goods, the production process of corrugated packaging involves multiple complex links. In the existing whole-line production of corrugated packaging, production data is scattered in various departments and equipment, lacking a unified management platform, making it difficult to conduct comprehensive analysis, lacking data analysis tools, the value of production data not being fully utilized, lacking a data-based decision support system, and it being difficult for management to make scientific decisions. Summary of the Invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a full-process intelligent control system for the whole-line production of corrugated packaging, including a monitoring center, and the monitoring center is communicatively connected to a data acquisition module, a real-time monitoring module, a data analysis module, a high-frequency prediction module, a data processing module, a low-frequency prediction module, and a data warning module;

[0006] The data acquisition module is used to obtain the process information of the whole-line production of corrugated packaging, set the monitoring points of corrugated packaging according to the process information, and obtain the production data of each point;

[0007] The real-time monitoring module is used to conduct real-time monitoring and control of the production data of each point;

[0008] The data analysis module is used to classify the production monitoring indicators of each process subsequence into high-frequency abnormal production monitoring indicators or low-frequency abnormal production monitoring indicators;

[0009] The high-frequency prediction module is used to construct a high-frequency index prediction model and obtain the first predicted production data;

[0010] The data processing module is used to obtain the reference production monitoring indicators of the low-frequency abnormal production monitoring indicators;

[0011] The low-frequency prediction module is used to construct a low-frequency index prediction model and obtain the second predicted production data;

[0012] The data warning module is used to generate the third predicted production data according to the first predicted production data and the second predicted production data, and obtain the initial control item measures for the next collection cycle according to the third predicted production data.

[0013] Further, the process in which the data acquisition module acquires the process information of the corrugated packaging whole line, sets the corrugated packaging monitoring points according to the process information, and acquires the production data of each point includes:

[0014] Extract the process unit characteristics of each corrugated packaging production equipment from the process information, and divide the corrugated packaging whole line production process into several process subsequences according to the process unit characteristics;

[0015] Set corrugated packaging monitoring points in each process subsequence, and use data retrieval to obtain the production monitoring indicators of each corrugated packaging monitoring point according to the functional characteristics in the process unit characteristics of the corresponding process subsequence;

[0016] The corrugated packaging monitoring points obtain production data in real time according to the production monitoring indicators, mark the collection time, and set the collection cycle.

[0017] Further, the process in which the real-time monitoring module performs real-time monitoring and control on the production data of each point includes:

[0018] Obtain the production data of each corrugated packaging monitoring point, extract various types of production monitoring indicators in the production data, preset the threshold intervals of various types of production monitoring indicators in each corrugated packaging monitoring point, compare the various types of production monitoring indicators in the production data with the corresponding threshold intervals. If there is a production monitoring indicator that is not within the corresponding threshold interval, mark the production monitoring indicator as an abnormal production monitoring indicator, and generate an abnormal alarm signal for the process subsequence based on the abnormal production monitoring indicator. The monitoring center arranges relevant personnel to detect and analyze the process subsequence according to the abnormal alarm signal, obtains the corresponding control item measures, pre-constructs a control database, and stores the production data and the control item measures in the control database after matching them.

[0019] Further, the process by which the data analysis module classifies the production monitoring indicators of each process subsequence into high-frequency abnormal production monitoring indicators or low-frequency abnormal production monitoring indicators includes:

[0020] Obtain all the historical collection cycles in which each process subsequence generates an abnormal alarm signal, mark the historical collection cycles as abnormal historical collection cycles, extract each abnormal production monitoring indicator in the abnormal historical collection cycles for statistical analysis, obtain the occurrence times of each abnormal production monitoring indicator, preset an occurrence times threshold, mark the abnormal production monitoring indicators with occurrence times greater than the occurrence times threshold as high-frequency abnormal production monitoring indicators, and mark the abnormal production monitoring indicators with occurrence times less than or equal to the occurrence times threshold as low-frequency abnormal production monitoring indicators.

[0021] Further, the process by which the high-frequency prediction module constructs a high-frequency indicator prediction model and obtains the first predicted production data includes:

[0022] Obtain the production data within the historical collection cycles to which each high-frequency abnormal production monitoring indicator of each process subsequence belongs, use the production data as a training set and a test set, construct a high-frequency indicator prediction model based on deep learning, input the training set into the high-frequency indicator prediction model for training until the loss function is trained stably, save the model parameters, test the high-frequency indicator prediction model through the test set until it meets the preset requirements, and output the high-frequency indicator prediction model;

[0023] According to the high-frequency indicator prediction model, output the predicted production data for the next collection cycle of each process subsequence, and mark the predicted production data as the first predicted production data.

[0024] Further, the process by which the data processing module obtains the reference production monitoring indicators of the low-frequency abnormal production monitoring indicators includes:

[0025] Obtain the historical collection periods to which the various low-frequency abnormal production monitoring indicators of each process subsequence belong, extract the high-frequency abnormal production monitoring indicators within the historical collection periods, perform statistical analysis on the high-frequency abnormal production monitoring indicators within the historical collection periods, and obtain the cumulative number of times that each high-frequency abnormal production monitoring indicator and each low-frequency abnormal production monitoring indicator appear in the same historical collection period;

[0026] Mark the high-frequency abnormal production monitoring indicator with the highest cumulative number of times of appearing in the same historical collection period as the low-frequency abnormal production monitoring indicator as the reference production monitoring indicator of the low-frequency abnormal production monitoring indicator.

[0027] Further, the process of the low-frequency prediction module constructing a low-frequency index prediction model and obtaining the second predicted production data includes:

[0028] Obtain the production data within the historical collection period to which the reference production monitoring indicator of the low-frequency abnormal production monitoring indicator belongs, use the production data as training data, construct a large-sample model based on deep learning, train the large-sample model with the training data, obtain the trained large-sample model, and obtain the model parameters in the large-sample model;

[0029] Construct a low-frequency index prediction model based on deep learning, use the model parameters in the large-sample model as the initial model parameters of the low-frequency index prediction model, obtain the production data within the historical collection period to which the low-frequency abnormal production monitoring indicator belongs, use the production data as the training data of the low-frequency index prediction model to train the low-frequency index prediction model, and output the trained low-frequency index prediction model;

[0030] According to the low-frequency index prediction model, output the predicted production data for the next collection period of each process subsequence, and mark the predicted production data as the second predicted production data.

[0031] Further, the process of the data warning module generating the third predicted production data based on the first predicted production data and the second predicted production data includes:

[0032] For each type of production monitoring indicator in the first predicted production data for the next collection period, obtain the production monitoring indicators that were marked as low-frequency abnormal production monitoring indicators in the historical collection period among each type of production monitoring indicator, and remove the production monitoring indicators from the first predicted production data;

[0033] For each type of production monitoring indicator in the second predicted production data for the next collection period, obtain the production monitoring indicators that were marked as high-frequency abnormal production monitoring indicators in the historical collection period among each type of production monitoring indicator, and remove the production monitoring indicators from the second predicted production data;

[0034] Subsequently, the first estimated production data and the second estimated production data are fused to generate the third estimated production data, and the estimated numerical time series of various types of production monitoring indicators in the next collection period is output according to the third estimated production data.

[0035] Further, the process by which the data warning module obtains the initial control item measures for the next collection period according to the third estimated production data includes:

[0036] Compare the estimated numerical time series of various types of production monitoring indicators in the next collection period with the corresponding threshold intervals. If there is an estimated numerical time series corresponding to a production monitoring indicator that is not within the corresponding threshold interval, input the estimated numerical time series of various types of production monitoring indicators into the control database for data retrieval, obtain the production data with a similarity meeting the preset standard in the control database, obtain the control item measures matching the production data, and use the control item measures as the initial control item measures for the next collection period.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows. Of course, the following are the detailed beneficial effects of this full-process intelligent control system in the corrugated packaging whole-line production:

[0038] 1. By monitoring the data of each production point in real time, the system can quickly detect and solve abnormal situations in the production process, reduce downtime and production delays, thereby significantly improving production efficiency. Through high-frequency and low-frequency prediction models, the system can predict potential equipment failures in advance for preventive maintenance, avoid production interruptions caused by equipment failures, and further improve production efficiency. The system can detect the operating status of equipment in real time, promptly discover and handle potential safety hazards, reduce the occurrence of safety accidents, and through the data warning module, the system can issue an alarm before a dangerous situation occurs to remind the operator to take corresponding measures to ensure production safety.

[0039] 2. Since it is difficult to obtain training data for low-frequency abnormal production monitoring indicators (such as abnormal bonding speed and abnormal cutting speed), it is difficult to train an efficient and accurate prediction model. However, high-frequency abnormal production monitoring indicators (such as abnormal corrugated paper temperature and abnormal folding accuracy, etc.) have sufficient training data sets and can effectively learn an efficient and accurate prediction model. Therefore, the present invention first uses high-frequency abnormal production monitoring indicators to train a large-sample model, and then by studying the cumulative number of times that each high-frequency abnormal production monitoring indicator and each low-frequency abnormal production monitoring indicator appear in the same historical collection period, uses the model parameters of the large-sample model as the initial model parameters of the low-frequency indicator prediction model, and continues to train the low-frequency indicator prediction model on the training set of the low-frequency indicator prediction model, thereby constructing an efficient and accurate low-frequency indicator prediction model and avoiding the disadvantages of insufficient samples and information.

[0040] Through these detailed beneficial effects, the application of the full-process intelligent control system in the whole-line production of corrugated packaging not only improves production efficiency and product quality, but also reduces production costs, enhances production flexibility and safety, bringing all-round improvement to the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of a full-process intelligent control system for the whole-line production of corrugated packaging according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0043] As Figure 1 shown, a full-process intelligent control system for the whole-line production of corrugated packaging includes a monitoring center, and the monitoring center is communicatively connected with a data acquisition module, a real-time monitoring module, a data analysis module, a high-frequency prediction module, a data processing module, a low-frequency prediction module, and a data warning module;

[0044] The data acquisition module is used to obtain the process information of the whole-line production of corrugated packaging, set the monitoring points for corrugated packaging according to the process information, and obtain the production data of each point;

[0045] The real-time monitoring module is used to perform real-time monitoring and control on the production data of each point;

[0046] The data analysis module is used to divide the production monitoring indicators of each process subsequence into high-frequency abnormal production monitoring indicators or low-frequency abnormal production monitoring indicators;

[0047] The high-frequency prediction module is used to construct a high-frequency index prediction model and obtain the first predicted production data;

[0048] The data processing module is used to obtain the reference production monitoring indicators of the low-frequency abnormal production monitoring indicators;

[0049] The low-frequency prediction module is used to construct a low-frequency index prediction model and obtain the second predicted production data;

[0050] The data warning module is used to generate the third predicted production data according to the first predicted production data and the second predicted production data, and obtain the initial control item measures for the next acquisition cycle according to the third predicted production data.

[0051] It should be further noted that in the specific implementation process, the process of the data acquisition module obtaining the process information of the whole corrugated packaging production line, setting the corrugated packaging monitoring points according to the process information, and obtaining the production data of each point includes:

[0052] Extracting the process unit characteristics of each corrugated packaging production equipment from the process information, and dividing the whole corrugated packaging production process into several process subsequences according to the process unit characteristics;

[0053] Setting corrugated packaging monitoring points in each process subsequence, and obtaining the production monitoring indicators of each corrugated packaging monitoring point by using data retrieval according to the functional characteristics in the process unit characteristics of the corresponding process subsequence;

[0054] The corrugated packaging monitoring points obtain production data in real time according to the production monitoring indicators, mark the collection time, and set the collection period.

[0055] It should be further noted that in the specific implementation process, the process of the real-time monitoring module performing real-time monitoring and control on the production data of each point includes:

[0056] Obtaining the production data of each corrugated packaging monitoring point, extracting the numerical time series corresponding to each type of production monitoring indicator in the production data, presetting the threshold interval of each type of production monitoring indicator in each corrugated packaging monitoring point, comparing the numerical time series corresponding to each type of production monitoring indicator in the production data with the corresponding threshold interval. If there is a numerical time series corresponding to a production monitoring indicator that is not within the corresponding threshold interval, then mark the production monitoring indicator as an abnormal production monitoring indicator, generate an abnormal alarm signal for the process subsequence according to the abnormal production monitoring indicator, the monitoring center arranges relevant personnel to detect and analyze the process subsequence according to the abnormal alarm signal, obtains the corresponding control measures, and the control measures include adjusting equipment parameters, production line operation parameters, etc. A control database is pre-constructed, and the production data is matched with the control measures and stored in the control database.

[0057] It should be further noted that in the specific implementation process, the process of the data analysis module dividing the production monitoring indicators of each process subsequence into high-frequency abnormal production monitoring indicators or low-frequency abnormal production monitoring indicators includes:

[0058] Obtain all historical collection cycles of each process subsequence that generate abnormal alarm signals, mark the historical collection cycles as abnormal historical collection cycles, extract each abnormal production monitoring index in the abnormal historical collection cycles for statistical analysis, obtain the occurrence times of each abnormal production monitoring index, preset an occurrence times threshold, mark the abnormal production monitoring indexes with occurrence times greater than the occurrence times threshold as high-frequency abnormal production monitoring indexes, and mark the abnormal production monitoring indexes with occurrence times less than or equal to the occurrence times threshold as low-frequency abnormal production monitoring indexes.

[0059] It should be further noted that in the specific implementation process, the process of the high-frequency prediction module constructing a high-frequency index prediction model and obtaining the first predicted production data includes:

[0060] Obtain the production data within the historical collection cycles to which each high-frequency abnormal production monitoring index of each process subsequence belongs, use the production data as the training set and the test set, construct a high-frequency index prediction model based on deep learning, input the training set into the high-frequency index prediction model for training until the loss function is trained stably, save the model parameters, test the high-frequency index prediction model through the test set until it meets the preset requirements, and output the high-frequency index prediction model;

[0061] According to the high-frequency index prediction model, output the predicted production data of the next collection cycle of each process subsequence, and mark the predicted production data as the first predicted production data.

[0062] It should be further noted that in the specific implementation process, the process of the data processing module obtaining the reference production monitoring index of the low-frequency abnormal production monitoring index includes:

[0063] Obtain the historical collection cycles to which each low-frequency abnormal production monitoring index of each process subsequence belongs, extract the high-frequency abnormal production monitoring indexes within the historical collection cycles, conduct statistical analysis on the high-frequency abnormal production monitoring indexes within the historical collection cycles, and obtain the cumulative times of each high-frequency abnormal production monitoring index and each low-frequency abnormal production monitoring index appearing in the same historical collection cycle;

[0064] Mark the high-frequency abnormal production monitoring index with the highest cumulative times of appearing in the same historical collection cycle as the low-frequency abnormal production monitoring index as the reference production monitoring index of the low-frequency abnormal production monitoring index.

[0065] It should be further noted that in the specific implementation process, the process of the low-frequency prediction module constructing a low-frequency index prediction model and obtaining the second predicted production data includes:

[0066] Obtain the production data within the historical collection period to which the reference production monitoring indicators for low-frequency abnormal production monitoring indicators belong, use the production data as training data, construct a large-sample model based on deep learning, train the large-sample model with the training data, obtain the trained large-sample model, and obtain the model parameters in the large-sample model;

[0067] Construct a low-frequency indicator prediction model based on deep learning, use the model parameters in the large-sample model as the initial model parameters of the low-frequency indicator prediction model, obtain the production data within the historical collection period to which the low-frequency abnormal production monitoring indicators belong, use the production data as the training data of the low-frequency indicator prediction model to train the low-frequency indicator prediction model, and output the trained low-frequency indicator prediction model;

[0068] According to the low-frequency indicator prediction model, output the predicted production data for the next collection period of each process subsequence, and mark the predicted production data as the second predicted production data.

[0069] Since it is difficult to obtain the training data for low-frequency abnormal production monitoring indicators (such as abnormal bonding speed, abnormal cutting speed), it is difficult to train an efficient and accurate prediction model. However, high-frequency abnormal production monitoring indicators (such as abnormal corrugated paper temperature, abnormal folding accuracy, etc.) have sufficient training data sets and can effectively learn an efficient and accurate prediction model. Therefore, the present invention first uses high-frequency abnormal production monitoring indicators to train a large-sample model, and then by studying the cumulative number of times each high-frequency abnormal production monitoring indicator and each low-frequency abnormal production monitoring indicator appear in the same historical collection period, uses the model parameters of the large-sample model as the initial model parameters of the low-frequency indicator prediction model, and continues to train the low-frequency indicator prediction model on the training set of the low-frequency indicator prediction model, thereby constructing an efficient and accurate low-frequency indicator prediction model and avoiding the disadvantages of insufficient samples and information.

[0070] It should be further noted that in the specific implementation process, the process by which the data warning module generates the third predicted production data based on the first predicted production data and the second predicted production data includes:

[0071] For each type of production monitoring indicator in the first predicted production data for the next collection period, obtain the production monitoring indicators that are marked as low-frequency abnormal production monitoring indicators in the historical collection period among each type of production monitoring indicator, and remove the production monitoring indicators from the first predicted production data;

[0072] For each type of production monitoring indicator in the second predicted production data for the next collection period, obtain the production monitoring indicators that are marked as high-frequency abnormal production monitoring indicators in the historical collection period among each type of production monitoring indicator, and remove the production monitoring indicators from the second predicted production data;

[0073] Subsequently, the first estimated production data and the second estimated production data are fused to generate the third estimated production data, and the estimated numerical time series of various types of production monitoring indicators in the next collection period are output according to the third estimated production data.

[0074] It should be further noted that in the specific implementation process, the process by which the data warning module obtains the initial control item measures for the next collection period according to the third estimated production data includes:

[0075] Compare the estimated numerical time series of various types of production monitoring indicators in the next collection period with the corresponding threshold intervals. If there is an estimated numerical time series corresponding to a production monitoring indicator that is not within the corresponding threshold interval, input the estimated numerical time series of various types of production monitoring indicators into the control database for data retrieval, obtain the production data with a similarity meeting the preset standard in the control database, obtain the control item measures matching the production data, and use the control item measures as the initial control item measures for the next collection period.

[0076] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A full-process intelligent control system for the production of corrugated packaging lines, characterized in that: It includes a monitoring center, which is communicatively connected with a data acquisition module, a real-time monitoring module, a data analysis module, a high-frequency estimation module, a data processing module, a low-frequency estimation module and a data early warning module; The data acquisition module is used to obtain process information of the entire corrugated packaging production line, set corrugated packaging monitoring points according to the process information, and obtain production data of each point; The real-time monitoring module is used to monitor and control the production data of each point in real time; The data analysis module is used to divide the production monitoring indicators of each process subsequence into high-frequency abnormal production monitoring indicators or low-frequency abnormal production monitoring indicators; The high-frequency estimation module is used to construct a high-frequency indicator estimation model to obtain first estimated production data; The data processing module is used to obtain reference production monitoring indicators of low-frequency abnormal production monitoring indicators; The low-frequency estimation module is used to construct a low-frequency index estimation model to obtain second estimated production data; The data early warning module is used to generate third estimated production data according to the first estimated production data and the second estimated production data, and obtain initial control item measures for the next collection cycle according to the third estimated production data; The process of the data processing module obtaining the reference production monitoring index of the low-frequency abnormal production monitoring index includes: Obtain the historical collection period to which each low-frequency abnormal production monitoring indicator of each process subsequence belongs, extract the high-frequency abnormal production monitoring indicator within the historical collection period, perform statistical analysis on the high-frequency abnormal production monitoring indicator within the historical collection period, and obtain the cumulative number of times each high-frequency abnormal production monitoring indicator and each low-frequency abnormal production monitoring indicator appear in the same historical collection period; The high-frequency abnormal production monitoring indicator that appears the highest cumulative number of times in the same historical collection period as the low-frequency abnormal production monitoring indicator shall be marked as the reference production monitoring indicator of the low-frequency abnormal production monitoring indicator.

2. The full-process intelligent control system for the production of corrugated packaging lines according to claim 1 is characterized in that: The data acquisition module obtains the process information of the whole production line of corrugated packaging, sets the corrugated packaging monitoring points according to the process information, and obtains the production data of each point, including: Extracting process unit characteristics of each corrugated packaging production equipment from the process information, and dividing the entire corrugated packaging production line into a number of process sub-sequences according to the process unit characteristics; Set up corrugated packaging monitoring points in each process subsequence, and use data retrieval to obtain production monitoring indicators of each corrugated packaging monitoring point according to the functional characteristics in the process unit characteristics of the corresponding process subsequence; The corrugated packaging monitoring point acquires production data in real time according to the production monitoring index, marks the acquisition time, and sets the acquisition cycle.

3. The full-process intelligent control system for the production of corrugated packaging lines according to claim 2 is characterized in that: The process of real-time monitoring and controlling the production data of each point by the real-time monitoring module includes: Acquire the production data of each corrugated packaging monitoring point, extract various types of production monitoring indicators in the production data, preset the threshold range of each type of production monitoring indicators in each corrugated packaging monitoring point, compare each type of production monitoring indicator in the production data with the corresponding threshold range, if there is a production monitoring indicator that is not in the corresponding threshold range, then mark the production monitoring indicator as an abnormal production monitoring indicator, and generate an abnormal alarm signal of the process subsequence according to the abnormal production monitoring indicator. The monitoring center arranges relevant personnel to detect and analyze the process subsequence according to the abnormal alarm signal, obtains the corresponding control item measures, pre-builds a control database, and matches the production data with the control item measures and stores them in the control database.

4. The full-process intelligent control system for the production of a corrugated packaging line according to claim 3 is characterized in that: The process of the data analysis module dividing the production monitoring indicators of each process subsequence into high-frequency abnormal production monitoring indicators or low-frequency abnormal production monitoring indicators includes: Obtain all historical collection cycles in which each process subsequence generates an abnormal alarm signal, mark the historical collection cycle as an abnormal historical collection cycle, extract each abnormal production monitoring indicator in the abnormal historical collection cycle for statistical analysis, obtain the number of occurrences of each abnormal production monitoring indicator, preset an occurrence threshold, mark the abnormal production monitoring indicator whose occurrence number is greater than the occurrence threshold as a high-frequency abnormal production monitoring indicator, and mark the abnormal production monitoring indicator whose occurrence number is less than or equal to the occurrence threshold as a low-frequency abnormal production monitoring indicator.

5. The full-process intelligent control system for the production of a corrugated packaging line according to claim 4 is characterized in that: The high-frequency estimation module constructs a high-frequency index estimation model, and the process of obtaining the first estimated production data includes: Obtain the production data within the historical collection period to which each high-frequency abnormal production monitoring indicator of each process subsequence belongs, use the production data as a training set and a test set, build a high-frequency indicator prediction model based on deep learning, input the training set into the high-frequency indicator prediction model for training until the loss function training is stable, save the model parameters, test the high-frequency indicator prediction model through the test set until it meets the preset requirements, and output the high-frequency indicator prediction model; The estimated production data of the next acquisition cycle of each process subsequence is output according to the high-frequency indicator estimation model, and the estimated production data is marked as the first estimated production data.

6. The full-process intelligent control system for the production of a corrugated packaging line according to claim 5 is characterized in that: The low-frequency estimation module constructs a low-frequency index estimation model, and the process of obtaining the second estimated production data includes: Obtain production data within a historical collection period to which a reference production monitoring indicator of a low-frequency abnormal production monitoring indicator belongs, use the production data as training data, build a large sample model based on deep learning, train the large sample model through the training data, obtain the trained large sample model, and obtain model parameters in the large sample model; A low-frequency indicator prediction model is constructed based on deep learning, and the model parameters in the large sample model are used as the initial model parameters of the low-frequency indicator prediction model. The production data within the historical collection period to which the low-frequency abnormal production monitoring indicator belongs is obtained, and the production data is used as the training data of the low-frequency indicator prediction model to train the low-frequency indicator prediction model, and the trained low-frequency indicator prediction model is output; Output the estimated production data of the next acquisition cycle of each process subsequence according to the low-frequency indicator estimation model, and mark the estimated production data as second estimated production data.

7. The full-process intelligent control system for the production of a corrugated packaging line according to claim 6 is characterized in that: The process of the data early warning module generating the third estimated production data according to the first estimated production data and the second estimated production data includes: For each type of production monitoring indicator in the first estimated production data in the next collection cycle, obtain the production monitoring indicator marked as a low-frequency abnormal production monitoring indicator in the historical collection cycle among the various types of production monitoring indicators, and remove the production monitoring indicator from the first estimated production data; For each type of production monitoring indicator in the second estimated production data in the next collection cycle, obtain the production monitoring indicator marked as a high-frequency abnormal production monitoring indicator in the historical collection cycle among the various types of production monitoring indicators, and remove the production monitoring indicator from the second estimated production data; Subsequently, the first estimated production data and the second estimated production data are merged to generate third estimated production data, and the estimated numerical time series of various types of production monitoring indicators in the next collection cycle are output based on the third estimated production data.

8. The full-process intelligent control system for the production of a corrugated packaging line according to claim 7 is characterized in that: The process of the data early warning module obtaining the initial control item measures for the next collection cycle according to the third estimated production data includes: Compare the estimated numerical time series sequence of each type of production monitoring indicator in the next acquisition cycle with the corresponding threshold interval. If there is a production monitoring indicator whose corresponding estimated numerical time series sequence is not within the corresponding threshold interval, input the estimated numerical time series sequence of each type of production monitoring indicator into the control database for data retrieval, obtain production data whose similarity meets the preset standard in the control database, obtain control item measures that match the production data, and use the control item measures as the initial control item measures for the next acquisition cycle.

Images