CN116228028B - Application performance evaluation method and system for plastic bags - Google Patents
Application performance evaluation method and system for plastic bags Download PDFInfo
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Abstract
The invention provides an application performance evaluation method and system for a plastic case, which relate to the technical field of data analysis, and are characterized in that preset performance parameter information of an application performance index of the plastic case is extracted, according to the preset performance parameter information, performance parameter information to be measured is determined, production process correlation analysis is carried out to obtain relevant production process information, a production process is monitored, a production monitoring data source is obtained, data preprocessing is carried out, target monitoring data are extracted and input into an evaluation model, a performance evaluation result is obtained, the technical problems that the application performance evaluation method for the plastic case in the prior art is more conventional, the accuracy of the evaluation result is insufficient due to insufficient pertinence and depth of the performance evaluation, the information coverage of the analysis process is insufficient, the analysis is carried out on the basis of the measurement result, the production process correlation influence analysis is carried out on the performance to be measured, modeling evaluation is carried out on the collected and the correlation process node monitoring data, the evaluation analysis is directly carried out on the basis of the production level, and the accurate evaluation of the application performance of the plastic case is realized.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to an application performance evaluation method and system for a plastic case.
Background
Due to the characteristics of light material, good environmental protection performance, convenience in transportation and transfer and the like, the plastic case has wider applicability, for example, before plastic case production is completed and market release is carried out, quality detection is required to be strictly carried out, and the plastic case can meet application performance requirements. At present, quality detection of plastic bags is mainly carried out by determining multidimensional detection indexes; the application record data is mostly adopted for large data analysis and evaluation, and the large data analysis and evaluation are combined for application performance evaluation. The current application performance evaluation method also has certain disadvantages, and the final application performance evaluation result is influenced due to the influence of the dominant factors and the insufficient rigor of the evaluation method.
In the prior art, the application performance evaluation method for the plastic bags is more conventional, and the accuracy of the evaluation result is insufficient due to insufficient pertinence and depth of performance evaluation and insufficient information coverage in the analysis process.
Disclosure of Invention
The application performance evaluation method and system are used for solving the technical problems that in the prior art, the application performance evaluation method for the plastic bag is more conventional, and the accuracy of an evaluation result is insufficient due to insufficient pertinence and depth of performance evaluation and insufficient information coverage in an analysis process.
In view of the above problems, the present application provides a method and a system for evaluating application performance of a plastic case.
In a first aspect, the present application provides a method for evaluating application performance of a plastic bag, the method comprising:
extracting preset performance parameter information, wherein the preset performance parameter information is used for representing application performance indexes of the plastic bags;
determining performance parameter information to be detected according to the preset performance parameter information, and carrying out production flow correlation analysis to obtain related production flow information, wherein the related production flow information comprises production equipment, workpiece parameters and flow nodes;
monitoring the production process based on the production equipment, the parameters of the machined parts and the process nodes to obtain a production monitoring data source;
performing data preprocessing on the production monitoring data source, and extracting target monitoring data;
and inputting the target monitoring data into an evaluation model to obtain a performance evaluation result.
In a second aspect, the present application provides an application performance evaluation system for a plastic bag, the system comprising:
the information extraction module is used for extracting preset performance parameter information, and the preset performance parameter information is used for representing application performance indexes of the plastic bags;
the correlation analysis module is used for determining performance parameter information to be detected according to the preset performance parameter information, carrying out production process correlation analysis and obtaining related production process information, wherein the related production process information comprises production equipment, workpiece parameters and process nodes;
the production monitoring module is used for monitoring the production process based on the production equipment, the parameters of the machined parts and the flow nodes to obtain a production monitoring data source;
the data preprocessing module is used for preprocessing the data of the production monitoring data source and extracting target monitoring data;
and the model evaluation module is used for inputting the target monitoring data into an evaluation model to obtain a performance evaluation result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the application performance evaluation method for the plastic luggage, preset performance parameter information representing application performance indexes of the plastic luggage is extracted, performance parameter information to be measured is determined according to the preset performance parameter information, production flow correlation analysis is carried out, relevant production flow information is obtained, the production flow information comprises production equipment, workpiece parameters and flow nodes, production process monitoring is further carried out, and a production monitoring data source is obtained; the production monitoring data source is subjected to data preprocessing, target monitoring data are extracted and input into an evaluation model, a performance evaluation result is obtained, the technical problem that the accuracy of the evaluation result is insufficient due to the fact that the application performance evaluation method for the plastic bags is more conventional and the pertinence and depth of performance evaluation are insufficient and the information coverage area in the analysis process is insufficient in the prior art is solved, the production process association influence analysis is conducted on the performance to be tested, the association process node monitoring data are collected for modeling evaluation, the evaluation analysis is directly conducted on the basis of the production layer, and the accurate evaluation of the application performance of the plastic bags is achieved.
Drawings
Fig. 1 is a schematic flow chart of an application performance evaluation method of a plastic case;
fig. 2 is a schematic diagram of a target monitoring data acquisition flow in the application performance evaluation method of a plastic case;
fig. 3 is a schematic diagram of a preset performance parameter information obtaining process in the application performance evaluation method of a plastic case;
fig. 4 is a schematic structural diagram of an application performance evaluation system for a plastic bag according to the present application.
Reference numerals illustrate: the system comprises an information extraction module 11, a correlation analysis module 12, a production monitoring module 13, a data preprocessing module 14 and a model evaluation module 15.
Detailed Description
According to the application performance evaluation method and system for the plastic bags, preset performance parameter information representing application performance indexes of the plastic bags is extracted, according to the preset performance parameter information, performance parameter information to be measured is determined, production flow correlation analysis is conducted to obtain relevant production flow information, a production process is monitored, a production monitoring data source is obtained, data preprocessing is conducted, target monitoring data are extracted and input into an evaluation model, and a performance evaluation result is obtained.
Example 1
As shown in fig. 1, the present application provides a method for evaluating application performance of a plastic case, the method comprising:
step S100: extracting preset performance parameter information, wherein the preset performance parameter information is used for representing application performance indexes of the plastic bags;
specifically, the plastic bags are wider and wider in applicability due to the characteristics of light materials, good environmental protection performance, convenience in transportation and transfer and the like, for example, quality detection is required to be strictly performed before plastic bags are produced and put in the market, and the plastic bags can meet application performance requirements. According to the application performance evaluation method for the plastic luggage, performance parameter information to be detected and evaluated is determined based on performance requirements, process node matching is conducted, production monitoring is conducted aiming at performance relevance process nodes, and monitoring data sources are obtained to conduct performance evaluation. Specifically, based on the application requirement of the plastic case, a plurality of performance indexes to be met in the application process are determined and used as the preset performance parameter information, the preset performance parameter information comprises a plurality of application performance indexes, and the embodiment of the application monitors and evaluates each application performance index in the preset performance parameter information in a targeted manner, so that the analysis depth is ensured, and the accuracy and the comprehensiveness of an analysis result are improved.
Further, as shown in fig. 2, the extracting preset performance parameter information, step S100 of the present application further includes:
step S110: obtaining order demands of plastic bags;
step S120: carrying out multidimensional parameter decomposition on order demands, wherein the multidimensional parameter decomposition comprises application scene information, material information and structure information;
step S130: based on the application scene information, the material information and the structure information, performance requirement analysis is carried out to obtain a plastic case performance parameter set;
step S140: and clustering the plastic case performance parameter set to obtain the preset performance parameter information.
Further, the step S140 of clustering the plastic case performance parameter set to obtain the preset performance parameter information further includes:
step S141: obtaining the performance defect information of the plastic case through big data;
step S142: determining target performance parameters based on the performance defect information, and taking the target performance parameters as a clustering center;
step S143: based on each clustering center, the plastic case performance parameter sets are clustered, and target performance parameters with the clustering quantity reaching the preset requirement are used as the preset performance parameter information.
Specifically, order information of the plastic bags of the current production batch is called, and the order requirements are extracted. Determining an application environment of the plastic case based on the order requirement, and determining environmental characteristics, such as a high-temperature environment, a high-pressure environment and the like, as the application scene information; determining preparation materials of the plastic bags, wherein the preparation materials comprise preparation raw materials and auxiliary additive materials which are used as the material information; and determining the basic structural characteristics of the plastic case, and acquiring the basic structural characteristics based on a production drawing as the structural information. Based on the application scene information, the material information and the structure information, performance requirement analysis is carried out, and the performance requirement meeting the normal putting applicable specification is determined, for example, when the material is applied to a high-temperature scene, the requirement on temperature tolerance performance is high, and the melting point needs to be improved; if the anti-stress material is applied to article transmission, the anti-stress performance needs to be ensured. And determining performance requirements, such as hardness, temperature resistance, compression resistance and the like, which need to be met in the application process, and integrally generating the plastic case performance parameter set. And further carrying out clustering treatment on the plastic case performance parameter set, and taking the clustering quantity in the clustering result as the preset performance parameter information, wherein the clustering quantity meets the preset requirement.
Specifically, research and statistics are performed based on big data, a plurality of pieces of detection record information are obtained, performance defect extraction is performed to obtain the performance defect information of the plastic case, for example, the compression resistance is weaker, and the material is easy to age, poor in heat resistance, insufficient in strength, insufficient in hardness and the like. And determining performance defect influence association based on the performance defect information, and determining the target performance parameter. For example, the compressive property is weak, which may be caused by insufficient strength, insufficient hardness, or the like, and the target performance parameter is taken as the clustering center. Based on the clustering centers, the plastic case performance parameter sets are clustered to generate a plurality of clustering results, for example, the compressive property, strength, hardness and the like are used as one clustering result, and the strength, the hardness and the like are the associated influence performance of the compressive property. The preset requirements are further set, the preset requirements are the set clustering quantity defined by screening the clustering results, for example, 5, the rest clustering results are ignored, and the target performance parameters meeting the preset requirements are used as the preset performance parameter information. By performing performance screening, it is determined that a plurality of detection performances exist which evaluate necessity, the degree of influence being high.
Step S200: determining performance parameter information to be detected according to the preset performance parameter information, and carrying out production flow correlation analysis to obtain related production flow information, wherein the related production flow information comprises production equipment, workpiece parameters and flow nodes;
specifically, the preset performance parameter information includes a plurality of performance parameter information, and parameter information to be subjected to performance detection is extracted as the performance parameter information based on the preset performance parameter information. Further, the production process flow of the plastic case is obtained, all flow nodes are traversed to conduct correlation analysis, whether performance influence exists or not is judged, the performance influence comprises direct influence and indirect influence, for example, the judgment can be directly carried out based on the process node execution step and the node production effect, and the flow nodes with the performance influence correlation are extracted. Further, extracting specific process information of the process node, including the production equipment and the workpiece parameters, wherein the workpiece parameters are processing parameters including workpiece performance parameters before and after processing, mapping and corresponding the process node, the production equipment and the workpiece parameters, and generating the related production process information. And determining a subsequent processing detection range based on the related production flow information, and providing a basis for subsequent production detection.
Step S300: monitoring the production process based on the production equipment, the parameters of the machined parts and the process nodes to obtain a production monitoring data source;
further, based on the production equipment, the workpiece parameters and the process nodes, the production process is monitored, and a production monitoring data source is obtained, and step S300 of the present application further includes:
step S310: obtaining an associated flow according to the flow node;
step S320: obtaining production equipment numbers and processing parameters based on the process nodes and the associated processes;
step S330: and according to the production equipment numbers and the processing parameters, matching the monitoring equipment and the monitoring parameters, monitoring the control parameters and the workpiece parameters of the production equipment in each flow in real time, and obtaining the production monitoring data source.
Specifically, based on the production equipment, the workpiece parameters and the process nodes, matching and determining the monitoring equipment and the monitoring parameters, carrying out real-time production monitoring, obtaining monitoring data, carrying out production equipment mapping correspondence of each process node, and generating the production monitoring data source.
Specifically, the process nodes are analyzed after traversing the finished production flow of the plastic bags, influence nodes of the process nodes are determined, for example, for nodes without production flow correlation, the influence of the process nodes is to a certain extent avoided, and the nodes are used as the correlation flow. By means of node association analysis, completeness of performance influence coverage can be effectively improved, and integrity of information collected by subsequent monitoring is guaranteed. And taking the process node and the associated process as processes to be monitored, identifying the number of corresponding production equipment, acquiring the number of the production equipment, and determining processing control parameters corresponding to each production equipment as the processing parameters. Because the monitoring equipment is arranged on a complete production line, the production equipment number and the processing parameters are used as identification matching basis, and the monitoring equipment is matched, wherein the monitoring equipment can be in various types, such as monitoring equipment, sensing equipment and the like; the monitoring parameters are execution parameters of the monitoring equipment, such as monitoring time nodes, monitoring positions and the like, and the production equipment, the monitoring equipment and the monitoring parameters are mapped and identified, so that a data source can be conveniently determined subsequently. And further performing monitoring control of the monitoring equipment based on the monitoring parameters, collecting the production equipment control parameters and the workpiece parameters in the real-time production process, performing time sequence identification on the collected results, and generating the production monitoring data source.
Step S400: performing data preprocessing on the production monitoring data source, and extracting target monitoring data;
further, as shown in fig. 3, the data preprocessing is performed on the production monitoring data source to extract target monitoring data, and step S400 of the present application further includes:
step S410: normalizing the production monitoring data source;
step S420: determining relevant index features according to the correlation between the performance parameter information to be tested and the production flow;
step S430: based on the related index features, a feature mapping space is established;
step S440: projecting the normalized production monitoring data source into the feature mapping space, and eliminating overlapped monitoring data in each feature mapping space to obtain the target monitoring data.
Specifically, the production monitoring data source is obtained by performing real-time production monitoring. Due to the diversity of the monitoring devices, there may be multiple acquisitions of the same content data, with only differences in data format. And carrying out data format unification and overlapping data rejection on the production monitoring data source, generating the target detection data, rejecting invalid data, and avoiding data redundancy.
Specifically, the production monitoring data source is normalized, and is converted into the same data format state through data unified dimension conversion, so that the basis is tamped for subsequent overlapping data detection judgment. Based on the correlation between the performance index information to be measured and the production process, such as compression resistance, comprehensive evaluation can be performed based on hardness, toughness, structural characteristics and the like, and the comprehensive evaluation can be used as the correlation index characteristics. And further defining a functional execution space for carrying out data matching recognition and judgment, embedding the related index features into the functional execution space, carrying out function assignment, and generating the feature mapping space. Preferably, the feature mapping space includes a plurality of subspaces, which respectively correspond to the related index features. Projecting the normalized production monitoring data source into the feature mapping space, carrying out data identification and attribution to the corresponding subspace, carrying out overlapping data identification extraction and elimination, and taking the processed production monitoring data source as the target monitoring data.
Step S500: and inputting the target monitoring data into an evaluation model to obtain a performance evaluation result.
Further, the target monitoring data is input into an evaluation model to obtain a performance evaluation result, and step S500 of the present application further includes:
step S510: inputting the target monitoring data into a monitoring data identification sub-model, and identifying and marking the target monitoring data according to the process node, the state parameters of the workpiece and the production influence parameters;
step S520: inputting the output result of the monitoring data identification sub-model into a prediction chain construction sub-model, constructing a model based on a data identification tag according to a preset model data format, and predicting performance by using the constructed prediction chain model;
step S530: and outputting an output result of the prediction chain construction submodel through an output layer to obtain the performance evaluation result.
Specifically, the evaluation model is constructed, the target monitoring data is input into the evaluation model, data analysis and evaluation are carried out based on a model training mechanism, and the performance evaluation result is output.
Specifically, the evaluation model is built, the interior of the evaluation model comprises the monitoring data identification sub-model and the prediction chain building sub-model, and specific execution functions are different and are respectively used for carrying out data identification and performance prediction. Determining a plurality of groups of production monitoring record data by carrying out big data investigation and statistics, carrying out data mapping matching identification on the plurality of groups of production monitoring record data aiming at different attribution flow nodes, and correspondingly connecting the plurality of groups of production monitoring record data with node data matching identification results to generate first construction sample data; and further performing performance effect determination on the data matching identification result, directly extracting and determining based on the recorded data, performing serialization mapping adjustment on the node data matching identification result based on the production process node, and correspondingly associating the performance effects corresponding to the nodes to generate a plurality of sample evaluation prediction chains as second construction sample data, wherein each sample evaluation prediction chain corresponds to a group of production monitoring recorded data. And respectively performing neural network training based on the first construction sample data and the second construction sample data to generate the monitoring data identification sub-model and the prediction chain construction sub-model. And connecting an output layer of the monitoring data identification sub-model with an input layer of the prediction chain construction sub-model to form the evaluation model. The evaluation model is an auxiliary tool for evaluating application performance, so that the analysis rate can be effectively ensured, and the accuracy and objectivity of an evaluation result are improved.
Further, inputting the target monitoring data into the evaluation model, carrying out data identification integration based on the monitoring data identification sub-model, matching the process nodes corresponding to the determined data, the working state parameters and the production influence parameters, carrying out data identification, further transmitting identification data into the prediction chain construction sub-model, determining performance prediction results corresponding to each process node, and outputting results based on the output layer to obtain the performance evaluation results. And the performance evaluation result is an evaluation result of the performance parameter information to be tested, which is analyzed in combination with the correlation production flow.
Further, step S600 also exists in the present application, including:
step S610: acquiring size information and material information of the case;
step S620: performing influence analysis based on the luggage size information, luggage material information and preset performance parameter information, and determining performance influence parameters and influence coefficients;
step S630: and carrying out corresponding adjustment and supplementation on the performance evaluation result by utilizing the performance influence parameters and the influence coefficients.
Specifically, due to the differences in the size, the material, etc. of the plastic bags, the application performance is affected to some extent. For example, for compression resistance, for bags of 50cm×50cm and 100cm×100cm, there is a certain performance difference when being produced based on the same process flow, the middle position is the maximum difference position, and different materials also affect the performance due to the difference of strength and toughness, and based on the performance evaluation result, the accuracy and the product fit degree of the performance evaluation result are further improved. Specifically, based on the case size information, the case material information and the preset performance parameters, performance influence degree analysis is performed, and the performance influence parameters and the influence coefficients are generated. Preferably, the sizes and the materials of the various bags produced and prepared are determined, the two materials are combined one by one to determine the corresponding performance influence degree, for example, the corresponding performance influence degree can be determined through historical application records of bags made of the same specification, a plurality of influence analysis sequences which are characterized as size-material-influence parameters-influence coefficients are generated, and an influence reference table is generated. Wherein the influencing parameter is the influencing direction of the performance, such as strength, toughness, etc., and the influencing coefficient characterizes the specific influencing degree. Based on the impact reference table, the identification extraction of the performance impact parameters and the impact coefficients can be directly performed. And further, based on the performance influence parameters and the influence coefficients, determining an adjustment direction and an adjustment scale, and carrying out adaptation adjustment supplementation on the performance evaluation result to further improve the accuracy of the performance evaluation result.
Example two
Based on the same inventive concept as the application performance evaluation method of a plastic bag in the foregoing embodiments, as shown in fig. 4, the present application provides an application performance evaluation system of a plastic bag, the system comprising:
the information extraction module 11 is used for extracting preset performance parameter information, wherein the preset performance parameter information is used for representing application performance indexes of the plastic bags;
the correlation analysis module 12 is configured to determine performance parameter information to be tested according to the preset performance parameter information, perform production process correlation analysis, and obtain related production process information, where the related production process information includes production equipment, workpiece parameters, and process nodes;
the production monitoring module 13 is used for monitoring the production process based on the production equipment, the parameters of the machined parts and the flow nodes, and obtaining a production monitoring data source;
the data preprocessing module 14 is used for preprocessing the data of the production monitoring data source and extracting target monitoring data;
and the model evaluation module 15 is used for inputting the target monitoring data into an evaluation model to obtain a performance evaluation result.
Further, the system further comprises:
the luggage basic information acquisition module is used for acquiring luggage size information and luggage material information;
the influence analysis module is used for carrying out influence analysis based on the luggage size information, luggage material information and preset performance parameter information, and determining performance influence parameters and influence coefficients;
and the result adjustment module is used for carrying out corresponding adjustment supplement on the performance evaluation result by utilizing the performance influence parameters and the influence coefficients.
Further, the system further comprises:
the order requirement acquisition module is used for acquiring the order requirement of the plastic case;
the order demand decomposition module is used for performing multidimensional parameter decomposition on the order demand and comprises application scene information, material information and structure information;
the performance demand analysis module is used for carrying out performance demand analysis based on the application scene information, the material information and the structure information respectively to obtain a plastic case performance parameter set;
and the parameter clustering module is used for clustering the plastic case performance parameter set to obtain the preset performance parameter information.
Further, the system further comprises:
the defect information acquisition module is used for acquiring the performance defect information of the plastic case through big data;
the target performance parameter determining module is used for determining target performance parameters based on the performance defect information, and taking the target performance parameters as a clustering center;
the preset performance parameter information acquisition module is used for clustering the plastic case performance parameter sets based on each clustering center, and taking the target performance parameters with the clustering quantity reaching preset requirements as the preset performance parameter information.
Further, the system further comprises:
the association flow acquisition module is used for acquiring an association flow according to the flow node;
the process information acquisition module is used for acquiring production equipment numbers and processing parameters based on the process nodes and the associated processes;
the monitoring data source acquisition module is used for matching the monitoring equipment and the monitoring parameters according to the production equipment numbers and the processing parameters, and carrying out real-time monitoring on the control parameters and the workpiece parameters of the production equipment in each flow to obtain the production monitoring data source.
Further, the system further comprises:
the data source processing module is used for carrying out normalization processing on the production monitoring data source;
the characteristic determining module is used for determining related index characteristics according to the correlation between the performance parameter information to be detected and the production flow;
the space establishing module is used for establishing a feature mapping space based on the related index features;
and the overlapping data eliminating module is used for projecting the normalized production monitoring data source into the feature mapping space, eliminating the overlapping monitoring data in each feature mapping space and obtaining the target monitoring data.
Further, the system further comprises:
the data input identification module is used for inputting the target monitoring data into a monitoring data identification sub-model and identifying the target monitoring data according to flow nodes, workpiece state parameters and production influence parameters;
the performance prediction module is used for inputting the output result of the monitoring data identification sub-model into a prediction chain construction sub-model, constructing a model based on a data identification label according to a preset model data format, and performing performance prediction by utilizing the constructed prediction chain model;
and the performance evaluation result acquisition module is used for outputting the output result of the prediction chain construction submodel through an output layer to obtain the performance evaluation result.
The foregoing detailed description of the application performance evaluation method of a plastic bag will be clear to those skilled in the art, and the application performance evaluation method and system of a plastic bag in this embodiment are described more simply for the device disclosed in the embodiments, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (5)
1. A method for evaluating application performance of a plastic bag, the method comprising:
extracting preset performance parameter information, wherein the preset performance parameter information is used for representing application performance indexes of the plastic bags;
determining performance parameter information to be detected according to the preset performance parameter information, and carrying out production flow correlation analysis to obtain related production flow information, wherein the related production flow information comprises production equipment, workpiece parameters and flow nodes;
monitoring the production process based on the production equipment, the parameters of the machined parts and the process nodes to obtain a production monitoring data source;
performing data preprocessing on the production monitoring data source, and extracting target monitoring data;
inputting the target monitoring data into an evaluation model to obtain a performance evaluation result, wherein the performance evaluation result comprises the following steps: inputting the target monitoring data into a monitoring data identification sub-model, and identifying and marking the target monitoring data according to the process node, the state parameters of the workpiece and the production influence parameters; inputting the output result of the monitoring data identification sub-model into a prediction chain construction sub-model, constructing a model based on a data identification tag according to a preset model data format, and predicting performance by using the constructed prediction chain model; outputting the output result of the prediction chain construction submodel through an output layer to obtain the performance evaluation result;
the method for monitoring the production process based on the production equipment, the workpiece parameters and the process nodes to obtain a production monitoring data source comprises the following steps:
obtaining an associated flow according to the flow node;
obtaining production equipment numbers and processing parameters based on the process nodes and the associated processes;
according to the production equipment numbers and the processing parameters, matching the monitoring equipment and the monitoring parameters, monitoring the control parameters and the workpiece parameters of the production equipment of each flow in real time, and obtaining the production monitoring data source;
the data preprocessing is performed on the production monitoring data source, and the extraction of target monitoring data comprises the following steps:
normalizing the production monitoring data source;
determining relevant index features according to the correlation between the performance parameter information to be tested and the production flow;
based on the related index features, a feature mapping space is established;
projecting the normalized production monitoring data source into the feature mapping space, and eliminating overlapped monitoring data in each feature mapping space to obtain the target monitoring data.
2. The method of claim 1, wherein the method further comprises:
acquiring size information and material information of the case;
performing influence analysis based on the luggage size information, luggage material information and preset performance parameter information, and determining performance influence parameters and influence coefficients;
and carrying out corresponding adjustment and supplementation on the performance evaluation result by utilizing the performance influence parameters and the influence coefficients.
3. The method of claim 1, wherein extracting the preset performance parameter information comprises:
obtaining order demands of plastic bags;
carrying out multidimensional parameter decomposition on order demands, wherein the multidimensional parameter decomposition comprises application scene information, material information and structure information;
based on the application scene information, the material information and the structure information, performance requirement analysis is carried out to obtain a plastic case performance parameter set;
and clustering the plastic case performance parameter set to obtain the preset performance parameter information.
4. A method according to claim 3, wherein clustering the set of plastic bag performance parameters to obtain the preset performance parameter information comprises:
obtaining the performance defect information of the plastic case through big data;
determining target performance parameters based on the performance defect information, and taking the target performance parameters as a clustering center;
based on each clustering center, the plastic case performance parameter sets are clustered, and target performance parameters with the clustering quantity reaching the preset requirement are used as the preset performance parameter information.
5. A plastic bag application performance evaluation system, the system comprising:
the information extraction module is used for extracting preset performance parameter information, and the preset performance parameter information is used for representing application performance indexes of the plastic bags;
the correlation analysis module is used for determining performance parameter information to be detected according to the preset performance parameter information, carrying out production process correlation analysis and obtaining related production process information, wherein the related production process information comprises production equipment, workpiece parameters and process nodes;
the production monitoring module is used for monitoring the production process based on the production equipment, the parameters of the machined parts and the flow nodes to obtain a production monitoring data source;
the data preprocessing module is used for preprocessing the data of the production monitoring data source and extracting target monitoring data;
the model evaluation module is used for inputting the target monitoring data into an evaluation model to obtain a performance evaluation result;
the data input identification module is used for inputting the target monitoring data into a monitoring data identification sub-model and identifying the target monitoring data according to flow nodes, workpiece state parameters and production influence parameters;
the performance prediction module is used for inputting the output result of the monitoring data identification sub-model into a prediction chain construction sub-model, constructing a model based on a data identification label according to a preset model data format, and performing performance prediction by utilizing the constructed prediction chain model;
the performance evaluation result acquisition module is used for constructing an output result of the sub-model of the prediction chain and outputting the output result through an output layer to obtain the performance evaluation result;
the association flow acquisition module is used for acquiring an association flow according to the flow node;
the process information acquisition module is used for acquiring production equipment numbers and processing parameters based on the process nodes and the associated processes;
the monitoring data source acquisition module is used for matching the monitoring equipment and the monitoring parameters according to the production equipment numbers and the processing parameters, and carrying out real-time monitoring on the control parameters and the workpiece parameters of the production equipment of each flow to obtain the production monitoring data source;
the data source processing module is used for carrying out normalization processing on the production monitoring data source;
the characteristic determining module is used for determining related index characteristics according to the correlation between the performance parameter information to be detected and the production flow;
the space establishing module is used for establishing a feature mapping space based on the related index features;
and the overlapping data eliminating module is used for projecting the normalized production monitoring data source into the feature mapping space, eliminating the overlapping monitoring data in each feature mapping space and obtaining the target monitoring data.
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Denomination of invention: A Method and System for Evaluating the Application Performance of Plastic Luggage Granted publication date: 20231219 Pledgee: Daqitang Sub-branch of Zhejiang Pinghu Rural Commercial Bank Co.,Ltd. Pledgor: Pinghu xinshixin New Material Co.,Ltd. Registration number: Y2024980008939 |
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