Auxiliary material packaging quality management method and system based on intelligent perception

Abstract

The invention discloses an auxiliary material packaging quality management method and system based on intelligent perception, and relates to the technical field of data processing, and the method comprises the steps: carrying out the perception equipment deployment of an auxiliary material packaging production line, collecting and obtaining an auxiliary material packaging perception data flow, carrying out the mapping preprocessing of the auxiliary material packaging perception data flow based on a data preprocessing branch channel, and carrying out the processing of the auxiliary material packaging quality. An auxiliary material package sensing feature data stream is obtained; a target auxiliary material packaging quality predictor is built based on the sensing parameter-packaging quality mapping relation, auxiliary material packaging sensing feature data flow is predicted, an auxiliary material packaging quality prediction result is output for parameter adjustment, a plurality of packaging control parameter sets are obtained, evaluation, feedback and optimization are carried out, and the packaging quality is improved. And determining a target packaging control parameter set to perform quality control on the auxiliary material packaging production line. The technical effects that comprehensive real-time monitoring and accurate quality control of the auxiliary material packaging process are achieved, the auxiliary material packaging quality management efficiency and accuracy are improved, and then the product packaging quality stability is ensured are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a quality management method and system for auxiliary material packaging based on intelligent perception. Background Art

[0002] In the pharmaceutical field, auxiliary materials are used in the production of pharmaceutical preparations, including synthetic phospholipids for liposomes, auxiliary materials for biological preparations, hyaluronic acid for medical aesthetics, etc. These auxiliary materials are widely used in high-end preparations, providing a strong guarantee for the quality and efficacy of drugs. As an important link in the product production process, the quality level of auxiliary material packaging is directly related to the overall quality of the product and is a key step to ensure product safety and maintain stable product performance. However, most of the existing quality inspections of auxiliary material packaging rely on manual inspection and sampling inspection, which have problems such as low detection efficiency, poor real-time performance, and low accuracy of packaging quality control. Summary of the Invention

[0003] This application provides a quality management method and system for auxiliary material packaging based on intelligent perception, solving the technical problems of low detection efficiency, poor real-time performance, and low accuracy of packaging quality control in the prior art, achieving the technical effect of comprehensively and real-time monitoring the auxiliary material packaging process and precisely controlling the quality through intelligent perception prediction of packaging and parameter optimization adjustment, improving the efficiency and accuracy of auxiliary material packaging quality management, and further ensuring the stability of product packaging quality.

[0004] In view of the above problems, the present invention provides a quality management method and system for auxiliary material packaging based on intelligent perception.

[0005] In a first aspect, the present application provides a quality management method for auxiliary material packaging based on intelligent perception. The method includes: S1: Obtain an auxiliary material packaging production line, conduct a perception demand analysis on the auxiliary material packaging production line, obtain a set of perception node demand parameters, and deploy perception devices based on the set of perception node demand parameters to obtain a set of packaging perception devices; S2: Perceive and monitor the auxiliary material packaging production process through the set of packaging perception devices, collect and obtain an auxiliary material packaging perception data stream, construct a data preprocessing branch channel according to the set of packaging perception devices, and perform mapping preprocessing on the auxiliary material packaging perception data stream based on the data preprocessing branch channel to obtain an auxiliary material packaging perception feature data stream; S3: Determine the mapping relationship between perception parameters and packaging quality, train and build a target auxiliary material packaging quality predictor based on the mapping relationship between perception parameters and packaging quality, and predict the auxiliary material packaging perception feature data stream through the target auxiliary material packaging quality predictor to output an auxiliary material packaging quality prediction result; S4: Based on the auxiliary material packaging quality prediction result, perform an adaptive adjustment of packaging parameters on the auxiliary material packaging production line to obtain multiple sets of packaging control parameters, evaluate and feedback optimize the multiple sets of packaging control parameters, determine a target set of packaging control parameters, and perform quality control on the auxiliary material packaging production line based on the target set of packaging control parameters.

[0006] In another aspect, the present application also provides a quality management system for auxiliary material packaging based on intelligent perception. The system includes: a perception device deployment module, configured to obtain an auxiliary material packaging production line, conduct a perception demand analysis on the auxiliary material packaging production line, obtain a set of perception node demand parameters, and deploy perception devices based on the set of perception node demand parameters to obtain a set of packaging perception devices; a data acquisition and processing module, configured to perceive and monitor the auxiliary material packaging production process through the set of packaging perception devices, collect and obtain an auxiliary material packaging perception data stream, construct a data preprocessing branch channel according to the set of packaging perception devices, and perform mapping preprocessing on the auxiliary material packaging perception data stream based on the data preprocessing branch channel to obtain an auxiliary material packaging perception feature data stream; a packaging quality prediction module, configured to determine the mapping relationship between perception parameters and packaging quality, train and build a target auxiliary material packaging quality predictor based on the mapping relationship between perception parameters and packaging quality, and predict the auxiliary material packaging perception feature data stream through the target auxiliary material packaging quality predictor to output an auxiliary material packaging quality prediction result; a packaging quality control module, configured to perform an adaptive adjustment of packaging parameters on the auxiliary material packaging production line based on the auxiliary material packaging quality prediction result to obtain multiple sets of packaging control parameters, evaluate and feedback optimize the multiple sets of packaging control parameters, determine a target set of packaging control parameters, and perform quality control on the auxiliary material packaging production line based on the target set of packaging control parameters.

[0007] In a third aspect, the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are connected through the bus. When the computer program is executed by the processor, it implements the steps in any one of the above-mentioned methods.

[0008] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps in any one of the above-mentioned methods.

[0009] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] By adopting the method of performing perception demand analysis on the auxiliary material packaging production line, obtaining a set of perception node demand parameters for deploying perception devices, then perceiving and monitoring the auxiliary material packaging production process through a set of packaging perception devices, collecting and obtaining the auxiliary material packaging perception data stream, performing mapping preprocessing on the auxiliary material packaging perception data stream based on the data preprocessing branch channel to obtain the auxiliary material packaging perception feature data stream; training and building a target auxiliary material packaging quality predictor based on the perception parameter-packaging quality mapping relationship, predicting the auxiliary material packaging perception feature data stream, and outputting the auxiliary material packaging quality prediction result, so as to adaptively adjust the packaging parameters of the auxiliary material packaging production line, obtain multiple sets of packaging control parameters, and then perform evaluation feedback optimization based on this to determine the target packaging control parameter set for quality control of the auxiliary material packaging production line. Furthermore, it achieves the technical effect of realizing comprehensive real-time monitoring and accurate quality control of the auxiliary material packaging process through packaging intelligent perception prediction and parameter optimization adjustment, improving the efficiency and accuracy of auxiliary material packaging quality management, and thus ensuring the stability of product packaging quality.

[0011] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the following specifically gives the specific implementation manners of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic flowchart of the method for auxiliary material packaging quality management based on intelligent perception of the present application;

[0013] Figure 2 It is a schematic flowchart of obtaining a set of packaging perception devices in the method for auxiliary material packaging quality management based on intelligent perception of the present application;

[0014] Figure 3 It is a schematic structural diagram of the auxiliary material packaging quality management system based on intelligent perception of the present application;

[0015] Figure 4 This is a schematic structural diagram of an exemplary electronic device of the present application.

[0016] Explanation of reference numerals in the drawings: Sensing device deployment module 11, data acquisition and processing module 12, packaging quality prediction module 13, packaging quality control module 14, bus 1110, processor 1120, transceiver 1130, bus interface 1140, memory 1150, operating system 1151, application program 1152, and user interface 1160. Detailed implementation manners

[0017] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, a device, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), and a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage media contains computer program code.

[0018] The above-mentioned computer-readable storage media can adopt any combination of one or more computer-readable storage media. The computer-readable storage media includes: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of the computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, compact disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, the computer-readable storage media can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or component.

[0019] In the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws.

[0020] The present application describes the provided method, device, and electronic device through flowcharts and / or block diagrams.

[0021] It should be understood that each block in the flowchart and / or block diagram, and the combination of each block in the flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, thereby producing a machine. These computer-readable program instructions are executed by a computer or other programmable data processing devices, generating a device that realizes the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0022] These computer-readable program instructions can also be stored in a computer-readable storage medium that can cause a computer or other programmable data processing apparatus to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product that includes functions / operations implemented to specify the functions / operations of the blocks in the flowchart and / or block diagram.

[0023] The computer-readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices, so that a series of operation steps are performed on the computer, other programmable data processing apparatus, or other devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus can provide a process for implementing the functions / operations specified by the blocks in the flowchart and / or block diagram.

[0024] The present application will be described below with reference to the accompanying drawings in the present application.

[0025] Embodiment 1

[0026] As Figure 1 shown, the present application provides a method for quality management of auxiliary material packaging based on intelligent perception. The method includes:

[0027] Step S1: Obtain the auxiliary material packaging production line, perform a perception demand analysis on the auxiliary material packaging production line, obtain a set of perception node demand parameters, and deploy perception devices based on the set of perception node demand parameters to obtain a set of packaging perception devices;

[0028] As Figure 2 shown, further, in obtaining the set of packaging perception devices in S1, the steps of the present application further include:

[0029] S11: Obtain perception device demand factors, where the perception device demand factors include device type, accuracy requirement, response speed, and perception threshold;

[0030] S12: Based on the perception device demand factors, perform perception factor evaluation on the set of perception node demand parameters in sequence according to the production line nodes to obtain a set of perception node device factor characteristic parameters;

[0031] S13: According to the set of perception node device factor characteristic parameters, perform coverage angle analysis on the functional structure information of the production line nodes respectively to determine a set of perception node device layout angles;

[0032] S14: Based on the set of characteristic parameters of the sensing node device factors and the set of deployment angles of the sensing node devices, determine the set of characteristic parameters of the sensing node device deployment, and perform sensing device deployment through the set of characteristic parameters of the sensing node device deployment to obtain the set of packaged sensing devices.

[0033] Specifically, to achieve precise analysis and control of the quality of auxiliary material packaging, it is necessary to perform real-time intelligent sensing on the auxiliary material packaging process. First, obtain the auxiliary material packaging production line through the auxiliary material production system. The auxiliary material packaging production line is a fully automatic production system integrating multiple packaging functions, including multiple key process nodes and node composition mechanisms, such as packaging process nodes like wrapping, filling, sealing, and labeling. Through the collaborative work of each functional node, rapid, accurate, and efficient packaging of auxiliary materials is achieved. Then, conduct a sensing requirement analysis on each process node in the auxiliary material packaging production line to obtain the corresponding set of sensing node requirement parameters. The set of sensing node requirement parameters includes the sensing state parameters that each process node needs to monitor, such as material flow rate, packaging speed, temperature, pressure, sealing status, etc.

[0034] Based on the set of sensing node requirement parameters, perform sensing device deployment. First, determine the sensing device requirement factors. The sensing device requirement factors are the required functional types of the sensing devices, including device types, i.e., sensor types and model specifications; accuracy requirements, i.e., the required sensor accuracy; response speed, i.e., the sensitivity of the sensor; and sensing threshold, i.e., the perceivable numerical range of the sensor. Based on the sensing device requirement factors, perform sensing factor evaluation on the set of sensing node requirement parameters in sequence according to the production line nodes to obtain the set of characteristic parameters of the sensing node device factors. The set of characteristic parameters of the sensing node device factors includes the sensor device requirement parameters corresponding to the sensing device requirement factors for each process node.

[0035] Then, based on the set of characteristic parameters of the sensing node device factors, perform coverage angle analysis on the functional structure information of the production line nodes respectively. Through the sensor types and model specifications of each process node, design the sensing angle for the packaging functional structure of this node to ensure that the sensor monitoring can comprehensively cover the corresponding sensing functions of this process node, thereby determining the set of deployment angles of the sensing node devices. Based on the set of characteristic parameters of the sensing node device factors and the set of deployment angles of the sensing node devices, determine the set of characteristic parameters of the sensing node device deployment. The set of characteristic parameters of the sensing node device deployment includes the set of characteristic parameters of the sensing node device factors and the set of deployment angles of the sensing devices for each process node. And perform sensing device deployment of the corresponding parameters through the set of characteristic parameters of the sensing node device deployment to obtain the set of packaged sensing devices for the comprehensive intelligent sensing of the auxiliary material packaging production line. Improve the accuracy and applicability of the sensing device deployment, and thus ensure the real-time sensing and monitoring of the auxiliary material packaging process.

[0036] Step S2: Sense and monitor the production process of auxiliary material packaging through the set of packaging sensing devices, collect and obtain the auxiliary material packaging sensing data stream, construct a data preprocessing branch channel according to the set of packaging sensing devices, and perform mapping preprocessing on the auxiliary material packaging sensing data stream based on the data preprocessing branch channel to obtain the auxiliary material packaging sensing feature data stream;

[0037] Furthermore, for obtaining the auxiliary material packaging sensing feature data stream in S2, the steps of this application further include:

[0038] S21: Classify and identify the auxiliary material packaging sensing data stream according to the structure type to obtain data structure type identification information, and match the data structure type identification information with the data preprocessing branch channel to obtain a data structure matching branch channel;

[0039] S22: Discriminate and divert the auxiliary material packaging sensing data stream based on the data structure type identification information, and map it with the data structure matching branch channel to obtain a branch channel sensing data stream;

[0040] S23: According to the data structure matching branch channel, load a data standardization program and a data feature extraction program, and perform data standardization processing on the branch channel sensing data stream based on the data standardization program to obtain a standard branch channel sensing data stream;

[0041] S24: Perform associated sensing feature extraction on the standard branch channel sensing data stream through the data feature extraction program to obtain the auxiliary material packaging sensing feature data stream.

[0042] Specifically, sense and monitor the production process of auxiliary material packaging through the set of packaging sensing devices, collect and obtain the auxiliary material packaging sensing data stream. The auxiliary material packaging sensing data stream includes various packaging status parameters and packaging status image information of each process node in the auxiliary material packaging process. Construct a data preprocessing branch channel according to the set of packaging sensing devices. The data preprocessing branch channel can be set according to the data acquisition structured format of the set of packaging sensing devices. For example, the branch channel settings include a structured data processing channel and an unstructured data processing channel such as an image. Perform mapping preprocessing on the auxiliary material packaging sensing data stream based on the data preprocessing branch channel. First, classify and identify the auxiliary material packaging sensing data stream according to the structure type to obtain corresponding data structure type identification information, and then match the data structure type identification information with the data preprocessing branch channel, and respectively match the data with the structured data processing channel and the unstructured data processing channel according to the structured type to obtain corresponding data structure matching branch channels.

[0043] Based on the data structure type identification information, the perception data stream of the auxiliary material packaging is discriminated and diverted. After the perception data stream is diverted according to the data structure type identification information, it is mapped to the matching data structure matching branch channel to obtain the branch channel perception data stream after diversion mapping. According to the data structure matching branch channel, a data standardization program is loaded, that is, the data standardization processing step. For example, for an unstructured channel, it includes filtering, enhancement, etc.; a data feature extraction program, that is, the associated feature extraction step of the perception data, which is extracted through the setting of the data quality association type. Based on the data standardization program, the corresponding standardization steps are performed on the branch channel perception data stream to obtain the processed standard branch channel perception data stream, ensuring data standardization and improving the data application quality. Then, through the data feature extraction program, the associated perception features are extracted from the standard branch channel perception data stream, and the perception feature information related to the auxiliary material packaging quality, such as material flow rate, packaging pressure, packaging color, weight and size, etc., is extracted to obtain the relevant auxiliary material packaging perception feature data stream. Realize the multi-channel mapping and synchronous processing of the perception data stream, improve the data processing efficiency, and ensure the accuracy of the packaging quality feature analysis.

[0044] Step S3: Determine the perception parameter-packaging quality mapping relationship, train and build a target auxiliary material packaging quality predictor based on the perception parameter-packaging quality mapping relationship, and predict the auxiliary material packaging perception feature data stream through the target auxiliary material packaging quality predictor to output the auxiliary material packaging quality prediction result;

[0045] Furthermore, in determining the perception parameter-packaging quality mapping relationship in S3, the steps of this application further include:

[0046] S31: Perform associated feature extraction on the auxiliary material packaging perception feature data stream to obtain a set of perception associated parameters, and use the set of perception associated parameters as parameter influence variables;

[0047] S32: Extract quantitative index based on the auxiliary material packaging quality evaluation standard to obtain a set of packaging quality evaluation parameters, and use the set of packaging quality evaluation parameters as target parameter variables;

[0048] S33: Perform associated auxiliary material packaging data collection based on the parameter influence variables and the target parameter variables to obtain an auxiliary material packaging quality data set;

[0049] S34: Calculate the correlation coefficient set of the parameter influence variables and the target parameter variables respectively according to the auxiliary material packaging quality data set, and determine the perception parameter-packaging quality mapping relationship according to the correlation coefficient set.

[0050] Furthermore, in building the auxiliary material packaging quality predictor in S3, the steps of this application further include:

[0051] S35: Classify and label the secondary material packaging quality data set to obtain a sample set of packaging quality perception features, and use a deep neural network structure to train the sample set of packaging quality perception features respectively to generate a set of packaging quality sub-predictors;

[0052] S36: Determine the key factor information of perception factors according to the perception parameter-packaging quality mapping relationship, and perform weighted fusion on the set of packaging quality sub-predictors based on the key factor information of perception factors to obtain an initial secondary material packaging quality predictor;

[0053] S37: Verify the accuracy of the initial secondary material packaging quality predictor through a validation set. When the accuracy verification result does not meet the standard, minimize the model loss function through the gradient descent algorithm to obtain model optimization parameters;

[0054] S38: Update the model parameter configuration of the initial secondary material packaging quality predictor based on the model optimization parameters to determine the target secondary material packaging quality predictor.

[0055] Specifically, to achieve accurate prediction of secondary material packaging quality, first extract the associated feature types of the secondary material packaging perception feature data stream to obtain a set of perception association parameters, and the set of perception association parameters includes material flow rate, packaging speed, pressure, etc., and use the set of perception association parameters as parameter influence variables that can affect secondary material packaging quality. At the same time, extract quantization indexes based on the secondary material packaging quality evaluation standard to obtain a set of packaging quality evaluation parameters, and the set of packaging quality evaluation parameters is used to evaluate the secondary material packaging quality, including appearance size and shape, compressive strength, sealing performance, etc., and use the set of packaging quality evaluation parameters as the target parameter variables of secondary material packaging quality.

[0056] Collect associated secondary material packaging data based on the parameter influence variables and the target parameter variables to obtain a secondary material packaging quality data set, and the secondary material packaging quality data set is the perception association feature parameter information and corresponding packaging quality data of historical secondary material packaging. The Pearson correlation coefficient method can be used to calculate the correlation coefficient set between the parameter influence variables and the target parameter variables respectively according to the secondary material packaging quality data set. The larger the absolute value of the correlation coefficient, the stronger the correlation between the parameter influence variable and the secondary material packaging quality. Determine the absolute value of the correlation coefficient set as the perception parameter-packaging quality mapping relationship to indicate the association strength between each perception parameter and the packaging quality, so as to quickly and accurately analyze the perception parameters that have a significant impact on the packaging quality.

[0057] To achieve intelligent perception and prediction of the quality of auxiliary material packaging, a target auxiliary material packaging quality predictor is trained and built based on the mapping relationship between the perception parameters and the packaging quality. The target auxiliary material packaging quality predictor is used to quickly predict and evaluate the packaging quality. The specific building process is as follows: First, the auxiliary material packaging quality data set is classified and identified through the set of perception correlation parameters. The data set is classified according to the types of perception correlation parameters to obtain the corresponding packaging quality perception feature sample set. Then, the deep neural network structure is used to train the packaging quality perception feature sample set respectively to generate the corresponding set of packaging quality sub-predictors. The set of packaging quality sub-predictors is used to predict and evaluate the quality of auxiliary material packaging based on each perception correlation parameter. Then, according to the mapping relationship between the perception parameters and the packaging quality, the correlation coefficients of each perception correlation parameter are determined, and they are used as the key factor information of the perception factors of each packaging quality sub-predictor respectively. Based on the key factor information of the perception factors, the set of packaging quality sub-predictors is weighted and fused. The larger the key factor of the predictor, the greater its voting right in the final auxiliary material packaging quality prediction model, and the initial auxiliary material packaging quality predictor after fusion is obtained, improving the comprehensiveness and accuracy of packaging quality prediction.

[0058] Furthermore, the accuracy of the initial auxiliary material packaging quality predictor is verified through the validation set. When the accuracy verification result does not meet the standard, it indicates that the current model prediction accuracy is insufficient and needs to be optimized. The loss function of the model is minimized by the gradient descent algorithm. Among them, the model loss function can preferably be the square loss function, and the model optimization parameters are obtained by minimizing the loss function. Based on the model optimization parameters, the model parameter configuration of the initial auxiliary material packaging quality predictor is updated to determine the optimized target auxiliary material packaging quality predictor. Through the target auxiliary material packaging quality predictor, the auxiliary material packaging perception feature data stream is predicted, and the auxiliary material packaging quality prediction result is output. The auxiliary material packaging quality prediction result includes the prediction and evaluation information corresponding to each packaging quality evaluation parameter. It realizes the intelligent perception and prediction of the auxiliary material packaging quality, improves the comprehensiveness and accuracy of the packaging quality prediction, and further improves the accuracy of subsequent packaging parameter optimization.

[0059] Step S4: Based on the auxiliary material packaging quality prediction result, the packaging parameters of the auxiliary material packaging production line are adaptively adjusted to obtain multiple packaging control parameter sets. The multiple packaging control parameter sets are evaluated, fed back, and optimized to determine the target packaging control parameter set, and the quality control of the auxiliary material packaging production line is carried out based on the target packaging control parameter set.

[0060] Furthermore, in step S4, when obtaining multiple packaging control parameter sets, the steps of this application also include:

[0061] S41: Determine the packaging quality benchmark deviation information according to the predicted result of the auxiliary material packaging quality, perform associated control parameter analysis based on the packaging quality benchmark deviation information, and obtain a set of packaging associated control parameters;

[0062] S42: Perform adaptive perturbation analysis based on the set of packaging associated control parameters to determine the value threshold of the packaging control parameters;

[0063] S43: Randomly obtain N packaging control parameters from the value threshold of the packaging control parameters, perform simulation calculations on the N packaging control parameters, and obtain N predicted packaging quality curves;

[0064] S44: Perform parameter screening based on the N predicted packaging quality curves, and inversely match to obtain the multiple sets of packaging control parameters.

[0065] Furthermore, for determining the target set of packaging control parameters in step S4, the steps of this application further include:

[0066] S45: Perform optimal control pre-packaging on the auxiliary material packaging production line based on the multiple sets of packaging control parameters to obtain auxiliary material packaging quality feedback parameters;

[0067] S46: If the auxiliary material packaging quality feedback parameters do not reach the preset packaging quality effect, obtain the variation direction of the control parameters, and set parameter variation rules according to the variation direction of the control parameters;

[0068] S47: Perform variation update on the multiple sets of packaging control parameters based on the parameter variation rules, and perform parameter analysis and optimization through the updated multiple sets of packaging control parameters to determine the target set of packaging control parameters.

[0069] Specifically, based on the predicted results of the auxiliary material packaging quality, the packaging parameters of the auxiliary material packaging production line are adaptively adjusted. First, the packaging quality benchmark deviation information from the auxiliary material packaging quality requirement standard is determined according to the predicted results of the auxiliary material packaging quality. Then, based on the packaging quality benchmark deviation information, the associated control parameter analysis is carried out to obtain the corresponding set of packaging associated control parameters. Exemplarily, the packaging control parameters associated with the sealing deviation include packaging speed, pressure, humidity, etc. Based on the set of packaging associated control parameters, the adaptive perturbation analysis is carried out to determine the value threshold of the packaging control parameters. The value threshold of the packaging control parameters is the optimized value range of each control parameter. N packaging control parameters are randomly obtained from the value threshold of the packaging control parameters, and the simulation calculation is carried out on the N packaging control parameters to obtain the corresponding N predicted packaging quality curves. Based on the N predicted packaging quality curves, parameter screening is carried out to obtain the set of packaging quality curves within the preset packaging quality threshold, and then multiple sets of packaging control parameters are obtained by reverse matching of the curve set for optimizing the control of the auxiliary material packaging production line.

[0070] To ensure the practical application of the auxiliary material packaging quality control, the evaluation and feedback optimization of the multiple sets of packaging control parameters are carried out. First, the packaging control parameters of the optimal quality curve are selected based on the multiple sets of packaging control parameters, and the control pre-packaging and quality inspection are carried out on the auxiliary material packaging production line with this to obtain the auxiliary material packaging quality feedback parameters. The auxiliary material packaging quality feedback parameters include appearance dimension and shape, compressive strength, packaging sealing performance, etc. If the auxiliary material packaging quality feedback parameters do not reach the preset packaging quality effect, it indicates that the current packaging control parameters need to be optimized. The unqualified packaging quality parameters are obtained through the auxiliary material packaging quality feedback parameters, and then the variation direction of the control parameters is analyzed and obtained. For example, increasing the packaging pressure, replacing the packaging material, etc. And the parameter variation rule is set according to the variation direction of the control parameters. The parameter variation rule is the basis for parameter change and update. Exemplarily, the packaging pressure parameter is variated and updated according to the normal distribution rule.

[0071] Based on the parameter variation rule, the multiple sets of packaging control parameters are variated and updated, and the parameter analysis and optimization are carried out through the updated and expanded multiple sets of packaging control parameters. The control parameter with the optimal predicted packaging quality among them is compared and determined as the target packaging control parameter set. And based on the target packaging control parameter set, the quality control of the auxiliary material packaging production line is carried out to ensure the real-time optimization and adjustment of the packaging control parameters, realize the comprehensive real-time monitoring and accurate quality control of the auxiliary material packaging process, improve the management efficiency and control accuracy of the auxiliary material packaging quality, and further ensure the stability of the product packaging quality.

[0072] In summary, the method and system for auxiliary material packaging quality management based on intelligent perception provided by this application have the following technical effects:

[0073] By adopting the method of perceiving the demand analysis of the auxiliary material packaging production line, obtaining the set of demand parameters of the perception nodes for deploying the perception devices, then perceiving and monitoring the auxiliary material packaging production process through the set of packaging perception devices, collecting and obtaining the auxiliary material packaging perception data stream, and performing mapping preprocessing on the auxiliary material packaging perception data stream based on the data preprocessing branch channel, the auxiliary material packaging perception feature data stream is obtained; training and building a target auxiliary material packaging quality predictor based on the perception parameter-packaging quality mapping relationship, predicting the auxiliary material packaging perception feature data stream, and outputting the auxiliary material packaging quality prediction result, so as to adaptively adjust the packaging parameters of the auxiliary material packaging production line, obtain multiple sets of packaging control parameters, and then perform evaluation feedback optimization based on this to determine the target packaging control parameter set for quality control of the auxiliary material packaging production line. Furthermore, through intelligent perception prediction and parameter optimization adjustment of packaging, the technical effect of realizing comprehensive real-time monitoring and accurate quality control of the auxiliary material packaging process is achieved, improving the efficiency and accuracy of auxiliary material packaging quality management, and further ensuring the stability of product packaging quality.

[0074] Embodiment 2

[0075] Based on the same inventive concept as the method for quality management of auxiliary material packaging based on intelligent perception in the foregoing embodiment, the present invention also provides a system for quality management of auxiliary material packaging based on intelligent perception, as Figure 3 shown, the system includes:

[0076] A perception device deployment module 11, configured to obtain an auxiliary material packaging production line, perform perception demand analysis on the auxiliary material packaging production line, obtain a set of demand parameters of perception nodes, and deploy perception devices based on the set of demand parameters of perception nodes to obtain a set of packaging perception devices;

[0077] A data acquisition and processing module 12, configured to perceive and monitor the auxiliary material packaging production process through the set of packaging perception devices, collect and obtain the auxiliary material packaging perception data stream, construct a data preprocessing branch channel according to the set of packaging perception devices, and perform mapping preprocessing on the auxiliary material packaging perception data stream based on the data preprocessing branch channel to obtain the auxiliary material packaging perception feature data stream;

[0078] A packaging quality prediction module 13, configured to determine the perception parameter-packaging quality mapping relationship, train and build a target auxiliary material packaging quality predictor based on the perception parameter-packaging quality mapping relationship, and predict the auxiliary material packaging perception feature data stream through the target auxiliary material packaging quality predictor to output the auxiliary material packaging quality prediction result;

[0079] The packaging quality control module 14 is used to adaptively adjust the packaging parameters of the auxiliary material packaging production line based on the predicted result of the auxiliary material packaging quality, obtain multiple sets of packaging control parameters, evaluate and feedback optimize the multiple sets of packaging control parameters, determine the target packaging control parameter set, and perform quality control on the auxiliary material packaging production line based on the target packaging control parameter set.

[0080] Furthermore, the system further includes:

[0081] The device requirement factor acquisition unit is used to acquire the perceived device requirement factors, and the perceived device requirement factors include device type, accuracy requirement, response speed, and perception threshold;

[0082] The perception factor evaluation unit is used to sequentially evaluate the perception factor of the set of perceived node requirement parameters according to the production line nodes based on the perceived device requirement factors, and obtain the set of perception node device factor characteristic parameters;

[0083] The coverage angle analysis unit is used to respectively perform coverage angle analysis on the functional structure information of the production line nodes according to the set of perception node device factor characteristic parameters, and determine the set of perception node device layout angles;

[0084] The perception device acquisition unit is used to determine the set of perception node device layout characteristic parameters based on the set of perception node device factor characteristic parameters and the set of perception node device layout angles, and deploy the perception devices through the set of perception node device layout characteristic parameters to obtain the set of packaging perception devices.

[0085] Furthermore, the system further includes:

[0086] The branch channel matching unit is used to classify and identify the auxiliary material packaging perception data stream according to the structure type, obtain the data structure type identification information, and match the data structure type identification information with the data preprocessing branch channel to obtain the data structure matching branch channel;

[0087] The data discrimination and diversion unit is used to discriminate and divert the auxiliary material packaging perception data stream based on the data structure type identification information, and map it with the data structure matching branch channel to obtain the branch channel perception data stream;

[0088] The standardization processing unit is used to load the data standardization program and the data feature extraction program according to the data structure matching branch channel, and perform data standardization processing on the branch channel perception data stream based on the data standardization program to obtain the standard branch channel perception data stream;

[0089] A perception feature extraction unit, configured to perform associated perception feature extraction on the standard branch channel perception data stream through the data feature extraction program to obtain the auxiliary material packaging perception feature data stream.

[0090] Further, the system further includes:

[0091] An associated feature extraction unit, configured to perform associated feature extraction on the auxiliary material packaging perception feature data stream to obtain a set of perception association parameters, and use the set of perception association parameters as parameter influence variables;

[0092] A quantization index extraction unit, configured to perform quantization index extraction based on the auxiliary material packaging quality evaluation standard to obtain a set of packaging quality evaluation parameters, and use the set of packaging quality evaluation parameters as target parameter variables;

[0093] A quality data set acquisition unit, configured to perform associated auxiliary material packaging data collection based on the parameter influence variables and the target parameter variables to obtain an auxiliary material packaging quality data set;

[0094] A mapping relationship determination unit, configured to calculate a set of correlation coefficients between the parameter influence variables and the target parameter variables respectively according to the auxiliary material packaging quality data set, and determine the perception parameter-packaging quality mapping relationship according to the set of correlation coefficients.

[0095] Further, the system further includes:

[0096] A sample set training unit, configured to perform classification identification on the auxiliary material packaging quality data set, obtain a set of packaging quality perception feature samples, and use a deep neural network structure to train the set of packaging quality perception feature samples respectively to generate a set of packaging quality sub-predictors;

[0097] A weighted fusion unit, configured to determine key factor information of perception factors according to the perception parameter-packaging quality mapping relationship, and perform weighted fusion on the set of packaging quality sub-predictors based on the key factor information of the perception factors to obtain an initial auxiliary material packaging quality predictor;

[0098] A model optimization parameter acquisition unit, configured to perform accuracy verification on the initial auxiliary material packaging quality predictor through a validation set, and when the accuracy verification result fails to meet the standard, perform minimization solution on the model loss function through a gradient descent algorithm to obtain model optimization parameters;

[0099] A parameter configuration update unit, configured to perform model parameter configuration update on the initial auxiliary material packaging quality predictor based on the model optimization parameters to determine the target auxiliary material packaging quality predictor.

[0100] Further, the system further includes:

[0101] A control parameter analysis unit, configured to determine packaging quality benchmark deviation information according to the predicted result of the auxiliary material packaging quality, perform associated control parameter analysis based on the packaging quality benchmark deviation information, and obtain a set of packaging associated control parameters;

[0102] An adaptability perturbation analysis unit, configured to perform adaptability perturbation analysis based on the set of packaging associated control parameters to determine the value threshold of the packaging control parameters;

[0103] A quality curve acquisition unit, configured to randomly obtain N packaging control parameters from the value threshold of the packaging control parameters, perform simulation calculations on the N packaging control parameters, and obtain N predicted packaging quality curves;

[0104] A control parameter screening unit, configured to perform parameter screening based on the N predicted packaging quality curves and obtain the multiple sets of packaging control parameters by reverse matching.

[0105] Further, the system further includes:

[0106] A feedback parameter acquisition unit, configured to perform optimal control pre-packaging on the auxiliary material packaging production line based on the multiple sets of packaging control parameters to obtain auxiliary material packaging quality feedback parameters;

[0107] A mutation rule setting unit, configured to, if the auxiliary material packaging quality feedback parameter does not reach the preset packaging quality effect, obtain the mutation direction of the control parameter, and set a parameter mutation rule according to the mutation direction of the control parameter;

[0108] A parameter analysis and optimization unit, configured to perform mutation update on the multiple sets of packaging control parameters based on the parameter mutation rule, and perform parameter analysis and optimization through the updated multiple sets of packaging control parameters to determine the target set of packaging control parameters.

[0109] The foregoing Figure 1 All the various change modes and specific examples of the intelligent perception-based auxiliary material packaging quality management method in Embodiment 1 are equally applicable to the intelligent perception-based auxiliary material packaging quality management system in this embodiment. Through the foregoing detailed description of the intelligent perception-based auxiliary material packaging quality management method, those skilled in the art can clearly know the implementation method of the intelligent perception-based auxiliary material packaging quality management system in this embodiment. Therefore, for the sake of brevity of the specification, it will not be elaborated herein.

[0110] In addition, the present application also provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected through the bus. When the computer program is executed by the processor, it implements each process of the method embodiment for controlling the output data, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0111] Exemplary electronic device

[0112] Specifically, referring to Figure 4 As shown, the present application also provides an electronic device, which includes a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.

[0113] In the present application, the electronic device further includes: a computer program stored in the memory 1150 and executable on the processor 1120. When the computer program is executed by the processor 1120, it implements each process of the method embodiment for controlling the output data.

[0114] The transceiver 1130 is configured to receive and send data under the control of the processor 1120.

[0115] In the present application, the bus architecture (represented by the bus 1110), the bus 1110 may include any number of interconnected buses and bridges. The bus 1110 connects various circuits including one or more processors represented by the processor 1120 and the memory represented by the memory 1150 together.

[0116] The bus 1110 represents one or more of any of several types of bus structures, including a memory bus and a memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any bus structure in various bus architectures. By way of example and not limitation, such architectures include: Industry Standard Architecture bus, Micro Channel Architecture bus, Extended bus, Video Electronics Standards Association, Peripheral Component Interconnect bus.

[0117] The processor 1120 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments may be completed by the integrated logic circuit in the hardware of the processor or instructions in software form. The above-mentioned processor includes: general-purpose processor, central processor, network processor, digital signal processor, application-specific integrated circuit, field programmable gate array, complex programmable logic device, programmable logic array, microcontroller unit or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in this application. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated on a single chip or located on multiple different chips.

[0118] The processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in combination with this application can be directly executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a readable storage medium well-known in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, registers, etc. The readable storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0119] The bus 1110 may also connect together various other circuits such as, for example, peripheral devices, voltage regulators, or power management circuits. The bus interface 1140 provides an interface between the bus 1110 and the transceiver 1130, which are all well-known in the art. Therefore, this application will not describe them further.

[0120] The transceiver 1130 may be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on the transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 is used to send the data processed by the processor 1120 to other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.

[0121] It should be understood that in this application, the memory 1150 may further include a memory remotely located relative to the processor 1120, and these remotely located memories can be connected to the server through a network. One or more parts of the above-mentioned network can be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the Internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless network, a Wi-Fi network, and a combination of two or more of the above networks. For example, the cellular telephone network and the wireless network can be a global mobile communication device, a code division multiple access device, a Worldwide Interoperability for Microwave Access device, a General Packet Radio Service device, a Wideband Code Division Multiple Access device, a Long Term Evolution device, an LTE Frequency Division Duplex device, an LTE Time Division Duplex device, an Advanced Long Term Evolution device, a Universal Mobile Telecommunications System device, an Enhanced Mobile Broadband device, a Massive Machine Type Communication device, an Ultra-Reliable Low Latency Communication device, etc.

[0122] It should be understood that the memory 1150 in this application can be a volatile memory or a non-volatile memory, or can include both a volatile memory and a non-volatile memory. Among them, the non-volatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.

[0123] The volatile memory includes: random access memory, which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in this application includes but is not limited to the above and any other suitable types of memory.

[0124] In this application, the memory 1150 stores the following elements of the operating system 1151 and the application program 1152: executable modules, data structures, or subsets thereof, or extended sets thereof.

[0125] Specifically, the operating system 1151 contains various device programs, such as: framework layer, core library layer, driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application program 1152 contains various application programs, such as: media player, browser, for implementing various application services. The program for implementing the method of this application can be included in the application program 1152. The application program 1152 includes: applets, objects, components, logics, data structures, and other computer device executable instructions for performing specific tasks or implementing specific abstract data types.

[0126] In addition, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements each process of the method embodiment for controlling the output data, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0127] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A quality management method for auxiliary material packaging based on intelligent perception, characterized in that, The method includes: S1: Obtain the auxiliary material packaging production line, conduct a perceived demand analysis on the auxiliary material packaging production line, obtain a set of perceived node demand parameters, and deploy sensing devices based on the set of perceived node demand parameters to obtain a set of packaging sensing devices; S2: Perceive and monitor the auxiliary material packaging production process through the set of packaging sensing devices, collect and obtain the auxiliary material packaging perceived data stream, construct a data preprocessing branch channel according to the set of packaging sensing devices, and perform mapping preprocessing on the auxiliary material packaging perceived data stream based on the data preprocessing branch channel to obtain the auxiliary material packaging perceived feature data stream; S3: Determine the mapping relationship between perceived parameters and packaging quality, train and build a target auxiliary material packaging quality predictor based on the mapping relationship between perceived parameters and packaging quality, and predict the auxiliary material packaging perceived feature data stream through the target auxiliary material packaging quality predictor to output the auxiliary material packaging quality prediction result; S4: Based on the auxiliary material packaging quality prediction result, perform adaptive adjustment of packaging parameters on the auxiliary material packaging production line to obtain multiple sets of packaging control parameters, evaluate and feedback optimize the multiple sets of packaging control parameters, determine the target packaging control parameter set, and perform quality control on the auxiliary material packaging production line based on the target packaging control parameter set.

2. The method according to claim 1, wherein Obtaining the set of packaging sensing devices in S1 includes: S11: Obtain the perceived device demand factors, where the perceived device demand factors include device type, accuracy requirement, response speed, and perceived threshold; S12: Based on the perceived device demand factors, perform perceived factor evaluation on the set of perceived node demand parameters in sequence according to the production line nodes to obtain a set of perceived node device factor characteristic parameters; S13: According to the set of perceived node device factor characteristic parameters, perform coverage angle analysis on the functional structure information of the production line nodes respectively to determine the set of perceived node device layout angles; S14: Based on the set of perceived node device factor characteristic parameters and the set of perceived node device layout angles, determine the set of perceived node device layout characteristic parameters, and deploy sensing devices through the set of perceived node device layout characteristic parameters to obtain the set of packaging sensing devices.

3. The method according to claim 2, wherein Obtaining the auxiliary material packaging perceived feature data stream in S2 includes: S21: Classify and label the auxiliary material packaging perceived data stream according to the structure type, obtain the data structure type identification information, and match the data structure type identification information with the data preprocessing branch channel to obtain the data structure matching branch channel; S22: Based on the data structure type identification information, perform discriminant splitting on the auxiliary material packaging perceived data stream, and map it with the data structure matching branch channel to obtain the branch channel perceived data stream; S23: According to the data structure matching branch channel, load the data standardization program and the data feature extraction program, and perform data standardization processing on the branch channel perceived data stream based on the data standardization program to obtain the standard branch channel perceived data stream; S24: Extract the correlation perception features from the standard branch channel perception data stream through the data feature extraction program to obtain the auxiliary material packaging perception feature data stream.

4. The method according to claim 1, wherein Determining the perception parameter-packaging quality mapping relationship in S3 includes: S31: Extract the correlation features from the auxiliary material packaging perception feature data stream to obtain a set of perception correlation parameters, and use the set of perception correlation parameters as parameter influence variables; S32: Extract the quantization index based on the auxiliary material packaging quality evaluation standard to obtain a set of packaging quality evaluation parameters, and use the set of packaging quality evaluation parameters as target parameter variables; S33: Collect the correlated auxiliary material packaging data based on the parameter influence variables and the target parameter variables to obtain the auxiliary material packaging quality data set; S34: Calculate the correlation coefficient set between the parameter influence variables and the target parameter variables respectively according to the auxiliary material packaging quality data set, and determine the perception parameter-packaging quality mapping relationship according to the correlation coefficient set.

5. The method according to claim 4, wherein Building the auxiliary material packaging quality predictor in S3 includes: S35: Classify and label the auxiliary material packaging quality data set to obtain a set of packaging quality perception feature samples, and use the deep neural network structure to train the set of packaging quality perception feature samples respectively to generate a set of packaging quality sub-predictors; S36: Determine the key factor information of the perception factors according to the perception parameter-packaging quality mapping relationship, and perform weighted fusion on the set of packaging quality sub-predictors based on the key factor information of the perception factors to obtain the initial auxiliary material packaging quality predictor; S37: Verify the accuracy of the initial auxiliary material packaging quality predictor through the validation set. When the accuracy verification result does not meet the standard, minimize the model loss function through the gradient descent algorithm to obtain the model optimization parameters; S38: Update the model parameter configuration of the initial auxiliary material packaging quality predictor based on the model optimization parameters to determine the target auxiliary material packaging quality predictor.

6. The method according to claim 1, characterized in that, Obtaining multiple sets of packaging control parameters in S4 includes: S41: Determine the packaging quality baseline deviation information according to the auxiliary material packaging quality prediction result, and analyze the correlated control parameters based on the packaging quality baseline deviation information to obtain a set of packaging correlated control parameters; S42: Perform adaptive perturbation analysis based on the set of packaging correlated control parameters to determine the value threshold of the packaging control parameters; S43: Randomly obtain N packaging control parameters from the value threshold of the packaging control parameters, and perform simulation calculations on the N packaging control parameters to obtain N predicted packaging quality curves; S44: Perform parameter screening based on the N predicted packaging quality curves, and obtain the multiple sets of packaging control parameters by reverse matching.

7. The method according to claim 1, characterized in that Determining the target packaging control parameter set in S4 includes: S45: Perform optimal control pre-packaging on the auxiliary material packaging production line based on the multiple sets of packaging control parameters to obtain the auxiliary material packaging quality feedback parameters; S46: If the quality feedback parameter of the auxiliary material packaging does not meet the preset packaging quality effect, obtain the variation direction of the control parameter, and set the parameter variation rule according to the variation direction of the control parameter; S47: Based on the parameter variation rule, perform variation update on the multiple packaging control parameter sets, and through the updated multiple packaging control parameter sets, perform parameter analysis and optimization to determine the target packaging control parameter set.

8. The intelligent perception-based auxiliary material packaging quality management system is characterized in that, A system for implementing the intelligent perception-based auxiliary material packaging quality management method according to any one of claims 1-7, the system comprising: A perception device deployment module, configured to obtain an auxiliary material packaging production line, perform perception requirement analysis on the auxiliary material packaging production line, obtain a set of perception node requirement parameters, and based on the set of perception node requirement parameters, perform perception device deployment to obtain a set of packaging perception devices; A data acquisition and processing module, configured to perform perception monitoring on the auxiliary material packaging production process through the set of packaging perception devices, collect and obtain an auxiliary material packaging perception data stream, construct a data preprocessing branch channel according to the set of packaging perception devices, and based on the data preprocessing branch channel, perform mapping preprocessing on the auxiliary material packaging perception data stream to obtain an auxiliary material packaging perception feature data stream; A packaging quality prediction module, configured to determine the mapping relationship between perception parameters and packaging quality, train and build a target auxiliary material packaging quality predictor based on the mapping relationship between perception parameters and packaging quality, and predict the auxiliary material packaging perception feature data stream through the target auxiliary material packaging quality predictor to output an auxiliary material packaging quality prediction result; A packaging quality control module, configured to perform adaptive adjustment of packaging parameters on the auxiliary material packaging production line based on the auxiliary material packaging quality prediction result, obtain multiple packaging control parameter sets, perform evaluation feedback and optimization on the multiple packaging control parameter sets, determine a target packaging control parameter set, and perform quality control on the auxiliary material packaging production line based on the target packaging control parameter set.

9. An electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected through the bus, and characterized in that, When the computer program is executed by the processor, it implements the steps in the intelligent perception-based auxiliary material packaging quality management method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the intelligent perception-based auxiliary material packaging quality management method according to any one of claims 1-7.

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