Intelligent adjustment method and system for process parameters of artificial leather production equipment

Abstract

The present invention discloses an intelligent adjustment method and system for process parameters of artificial leather production equipment, and relates to the field of intelligent adjustment technology. The method comprises: performing production design analysis on target artificial leather based on artificial leather production process element information, performing artificial leather production based on artificial leather production process parameter information obtained by analysis, and obtaining artificial leather production detection data stream based on detection extraction of detection node sensor network generated by layout; performing quality analysis on artificial leather production detection data stream based on artificial leather quality factor index set, and tracing back to obtain process control parameters of related equipment when the artificial leather quality detection result does not meet the standard; performing parameter optimization analysis based on process control parameters of related equipment, and adjusting production parameters based on process optimization parameters of production equipment obtained by optimization. The technical effect of realizing intelligent artificial leather quality detection and equipment process parameter adjustment, improving parameter adjustment accuracy and adjustment efficiency, and thus ensuring the production quality of artificial leather is achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent adjustment technology, and in particular to an intelligent adjustment method and system for process parameters of artificial leather production equipment. Background Art

[0002] Artificial leather is made of foamed or coated PVC and PU with various formulations on a textile or non-woven base. It can be processed according to different strength, color, gloss, pattern and other requirements. It has the characteristics of a wide variety of colors, good waterproof performance, neat edges, high utilization rate and relatively low price compared to genuine leather. With the popularity of artificial leather, the quality requirements for artificial leather production are becoming more and more important, so it is necessary to accurately control the process parameters of artificial leather production equipment. However, the process parameters of artificial leather production equipment in the prior art are not accurately adjusted, and the adjustment efficiency is low, which affects the quality of artificial leather production. Summary of the invention

[0003] The present application solves the technical problems of insufficient adjustment accuracy and low adjustment efficiency of the process parameters of artificial leather production equipment in the prior art, which affect the quality of artificial leather production, by providing a method and system for intelligently adjusting the process parameters of artificial leather production equipment. The application achieves the technical effect of intelligently realizing artificial leather quality detection and equipment process parameter adjustment, improving parameter adjustment accuracy and adjustment efficiency, and thus ensuring the quality of artificial leather production.

[0004] In view of the above problems, the present invention provides a method and system for intelligently adjusting process parameters of artificial leather production equipment.

[0005] In a first aspect, the present application provides an intelligent adjustment method for process parameters of artificial leather production equipment, the method comprising: obtaining artificial leather production process element information, the artificial leather production process element information including production process information and production equipment control information; performing production design analysis on a target artificial leather based on the artificial leather production process element information to determine artificial leather production process parameter information; deploying a sensor network based on the artificial leather production process element information to generate a detection node sensor network; producing the target artificial leather based on the artificial leather production process parameter information, and extracting and detecting the produced artificial leather based on the detection node sensor network to obtain an artificial leather production detection data stream; obtaining an artificial leather quality factor index set, performing quality analysis on the artificial leather production detection data stream based on the artificial leather quality factor index set to generate an artificial leather quality detection result; when the artificial leather quality detection result does not meet the artificial leather production standard, tracing the production parameters of the target artificial leather to obtain the process control parameters of the associated equipment; performing parameter optimization analysis based on the process control parameters of the associated equipment to obtain the process optimization parameters of the production equipment, and adjusting the production parameters of the target artificial leather based on the process optimization parameters of the production equipment.

[0006] On the other hand, the present application also provides an intelligent adjustment system for process parameters of artificial leather production equipment, the system comprising: a process factor information acquisition module, used to acquire process factor information of artificial leather production, the process factor information of artificial leather production including production process information and production equipment control information; a production design analysis module, used to perform production design analysis on target artificial leather based on the process factor information of artificial leather production, and determine the process parameter information of artificial leather production; a sensor network deployment module, used to deploy a sensor network based on the process factor information of artificial leather production, and generate a detection node sensor network; a detection data stream acquisition module, used to produce the target artificial leather based on the process parameter information of artificial leather production, and based on the detection node The sensor network extracts and detects the artificial leather produced to obtain an artificial leather production detection data stream; the artificial leather quality analysis module is used to obtain an artificial leather quality factor indicator set, and based on the artificial leather quality factor indicator set, the artificial leather production detection data stream is subjected to quality analysis to generate an artificial leather quality detection result; the production parameter tracing module is used to trace the production parameters of the target artificial leather when the artificial leather quality detection result does not meet the artificial leather production standard, and obtain the process control parameters of the associated equipment; the production parameter adjustment module is used to perform parameter optimization analysis based on the process control parameters of the associated equipment, obtain the process optimization parameters of the production equipment, and adjust the production parameters of the target artificial leather based on the process optimization parameters of the production equipment.

[0007] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0008] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

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

[0010] The invention adopts the method of conducting production design analysis on the target artificial leather based on the artificial leather production process element information, determining the artificial leather production process parameter information, and laying out the detection node sensor network based on the artificial leather production process element information; conducting target artificial leather production based on the artificial leather production process parameter information, and extracting and detecting the produced artificial leather based on the detection node sensor network to obtain the artificial leather production detection data stream; conducting quality analysis on the artificial leather production detection data stream based on the artificial leather quality factor index set to generate the artificial leather quality detection result; when the artificial leather quality detection result does not meet the artificial leather production standard, tracing the production parameters of the target artificial leather, obtaining the process control parameters of the associated equipment, and thereby conducting parameter optimization analysis, obtaining the process optimization parameters of the production equipment, and adjusting the production parameters of the target artificial leather based on the process optimization parameters of the production equipment. Thus, the technical effect of realizing the intelligent artificial leather quality detection and equipment process parameter adjustment, improving the parameter adjustment accuracy and adjustment efficiency, and thus ensuring the production quality of artificial leather is achieved.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of the process of the applicant's method for intelligently adjusting process parameters of artificial leather production equipment;

[0013] Figure 2 This is a schematic diagram of the process of obtaining artificial leather production detection data flow in the applicant's artificial leather production equipment process parameter intelligent adjustment method;

[0014] Figure 3 This is a schematic diagram of the structure of the intelligent adjustment system for process parameters of artificial leather production equipment of the applicant;

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

[0016] Explanation of the reference numerals: process element information acquisition module 11, production design analysis module 12, sensor network deployment module 13, detection data stream acquisition module 14, artificial leather quality analysis module 15, production parameter tracing module 16, production parameter adjustment module 17, bus 1110, processor 1120, transceiver 1130, bus interface 1140, memory 1150, operating system 1151, application 1152 and user interface 1160. DETAILED DESCRIPTION

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

[0018] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of 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, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0019] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

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

[0021] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes 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 device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

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

[0023] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0024] The present application is described below in conjunction with the drawings in the present application. Example

[0025] like Figure 1 As shown, the present application provides an intelligent adjustment method for process parameters of artificial leather production equipment, the method comprising:

[0026] Step S1: Acquire artificial leather production process element information, wherein the artificial leather production process element information includes production process information and production equipment control information;

[0027] Specifically, artificial leather is made of foamed or laminated PVC and PU with different formulations on a textile or non-woven base. It can be processed according to different strength, color, gloss, pattern and other requirements. It has the characteristics of a wide variety of patterns, good waterproof performance, neat edges, high utilization rate and relatively low price compared to genuine leather. With the popularity of artificial leather, the quality requirements for artificial leather production are becoming more and more important, so it is necessary to accurately control the process parameters of artificial leather production equipment.

[0028] Firstly, the information of artificial leather production process elements is obtained, which includes production process information, such as stirring and mixing, calendering film forming, heating and foaming, embossing and winding, etc., and production equipment control information, that is, the corresponding control parameter information of each production equipment. The production quality of artificial leather is accurately controlled by determining the production process elements.

[0029] Step S2: performing production design analysis on the target artificial leather based on the artificial leather production process element information to determine artificial leather production process parameter information;

[0030] Furthermore, the steps of determining the artificial leather production process parameter information in this application also include:

[0031] Constructing an artificial leather property classifier, wherein the artificial leather property classifier includes synthetic resin, production base material, production method, and production purpose;

[0032] Marking the target artificial leather with attributes based on the artificial leather attribute classifier to obtain artificial leather attribute information;

[0033] Acquire an artificial leather production design database by using data mining technology, wherein the artificial leather production design database includes artificial leather properties and artificial leather production process elements;

[0034] The artificial leather production process parameter information is determined by performing production data traversal matching with the artificial leather production design database according to the artificial leather property information.

[0035] Specifically, a production design analysis is performed on the target artificial leather based on the artificial leather production process element information, wherein the target artificial leather is the artificial leather to be produced required by the customer. First, an artificial leather attribute classifier is constructed, and the artificial leather attribute classifier is used to comprehensively classify artificial leather. The classification elements include synthetic resins, such as polyvinyl chloride artificial leather, polyamide artificial leather, etc.; production base materials, such as cotton cloth base, synthetic fiber base, etc.; production methods, such as direct scraping artificial leather, transfer scraping artificial leather, etc.; production purposes, such as civil use and industrial use. Based on the artificial leather attribute classifier, the production requirements of the target artificial leather are attribute classified and marked to obtain the corresponding artificial leather attribute information.

[0036] The artificial leather production design database is obtained by data mining technology. The artificial leather production design database is a historical artificial leather production data set, including various artificial leather attributes and corresponding artificial leather production process elements. According to the artificial leather attribute information, the production data is traversed and matched with the artificial leather production design database to determine the artificial leather production process parameter information matching the target artificial leather attribute, including production process parameters and production equipment control parameters. The rapid matching of artificial leather production process parameters and the accuracy of design analysis are improved, thereby ensuring the quality of artificial leather production.

[0037] Step S3: deploying a sensor network based on the artificial leather production process element information to generate a detection node sensor network;

[0038] Step S4: producing the target artificial leather based on the artificial leather production process parameter information, and performing extraction detection on the produced artificial leather based on the detection node sensor network to obtain an artificial leather production detection data stream;

[0039] like Figure 2 As shown, further, the steps of obtaining the artificial leather production detection data stream in this application also include:

[0040] Extracting node devices from the detection node sensor network to obtain a quality detection sensor device set and a CMOS image sensor;

[0041] Performing quality inspection on the produced artificial leather according to the quality inspection sensor device set and the CMOS image sensor to obtain a structured inspection data stream and an image inspection data stream;

[0042] Based on the data preprocessing dual channels, the structured detection data stream and the image detection data stream are preprocessed to generate a standard structured detection data stream and a standard image detection data stream;

[0043] The standard structured detection data stream and the standard image detection data stream are integrated to determine the artificial leather production detection data stream.

[0044] Furthermore, the steps of generating the standard structured detection data stream and the standard image detection data stream also include:

[0045] Setting a dual data preprocessing channel, wherein the dual data preprocessing channel includes a structured data processing channel and an image data processing channel;

[0046] Based on the structured data processing channel, data cleaning and standardization processing are performed on the structured detection data stream to obtain the standard structured detection data stream;

[0047] Performing image filtering on the image detection data stream through the image data processing channel to generate a denoised image detection data stream;

[0048] Construct an extreme attention mechanism module, and perform regional enhancement on the denoised image detection data stream based on the extreme attention mechanism module to obtain the standard image detection data stream.

[0049] Specifically, a sensor network is deployed based on the artificial leather production process element information, that is, the artificial leather quality detection sensor is determined according to the production process elements, and a detection node sensor network is generated for artificial leather production quality detection. The target artificial leather is produced based on the artificial leather production process parameter information, and artificial leather in the production process is extracted and detected based on the detection node sensor network. First, node equipment is extracted from the detection node sensor network to obtain the artificial leather quality detection equipment information, including a set of quality detection sensor equipment, such as a leather dry and wet friction tester, a fabric wear and fading tester, a color fastness and discoloration degree detector, a hardness detector, etc., and a CMOS image sensor for leather appearance detection.

[0050] According to the quality inspection sensor device set and the CMOS image sensor, the quality of the artificial leather produced is inspected respectively, and the structured inspection data stream detected by the quality inspection sensor device set is obtained, including data such as apparent density, light transmittance, swelling ratio, water content, ignition point, hardness, wear resistance, and the artificial leather image inspection data stream acquired by the CMOS image sensor. The structured inspection data stream and the image inspection data stream are preprocessed based on the data preprocessing dual channel, specifically, the data preprocessing dual channel is set, and the data preprocessing dual channel includes a structured data processing channel and an image data processing channel. Based on the structured data processing channel, the structured inspection data stream is cleaned, invalid data in the inspection data is removed, and standardization is performed, that is, the inspection data dimension and format are unified to obtain the preprocessed standard structured inspection data stream.

[0051] At the same time, the image detection data stream is subjected to image filtering through the image data processing channel, and image filtering algorithms such as median filtering, Gaussian filtering and other algorithms can be used for denoising preprocessing to generate denoised image detection data stream. An extreme attention mechanism module is constructed through artificial leather image detection data training. The extreme attention mechanism module is used to enhance the area of ​​interest in the image. Based on the extreme attention mechanism module, the denoised image detection data stream is regionally enhanced, that is, the artificial leather detection area is image enhanced, and the background image data therein is weakened to obtain the preprocessed standard image detection data stream. The standard structured detection data stream and the standard image detection data stream are integrated to form a determined artificial leather production detection data stream. By preprocessing the quality detection data stream through dual channels, data processing standardization is achieved, data preprocessing efficiency is improved, and the accuracy of artificial leather quality detection is thereby improved.

[0052] Step S5: obtaining an artificial leather quality factor index set, performing quality analysis on the artificial leather production test data stream based on the artificial leather quality factor index set, and generating an artificial leather quality test result;

[0053] Furthermore, the steps of generating the artificial leather quality test results in this application also include:

[0054] Acquire the artificial leather quality factor index set, wherein the artificial leather quality factor index set includes physical and chemical property testing, mechanical property testing, aging property testing, and appearance testing;

[0055] Acquire an artificial leather production and testing database through big data, wherein the artificial leather production and testing database includes production testing data and quality testing result data;

[0056] Classifying the artificial leather production inspection database based on the artificial leather quality factor index set to obtain a quality inspection factor data set;

[0057] Based on the quality detection factor data set, detection network training and fusion are performed to generate a quality detection adaptive network, and the artificial leather production detection data stream is analyzed through the quality detection adaptive network to output the artificial leather quality detection result.

[0058] Furthermore, the step of generating a quality detection adaptive network in this application further includes:

[0059] Model training is performed based on the quality detection factor data set to obtain a detection factor analysis branch network set;

[0060] Performing a criticality analysis on the artificial leather quality factor index set to determine the detection factor fusion coefficient information;

[0061] The detection factor analysis branch network set is fused based on the detection factor fusion coefficient information to generate the quality detection adaptive network.

[0062] Specifically, a set of artificial leather quality factor indicators is formulated, and the artificial leather quality factor indicator set includes physical and chemical performance testing, mechanical performance testing, aging performance testing, and appearance testing. Based on the artificial leather quality factor indicator set, the artificial leather production test data stream is subjected to quality analysis, and firstly, an artificial leather production test database is obtained through big data, and the artificial leather production test database is historical artificial leather quality test data, including production test data and corresponding quality test result data. Based on the artificial leather quality factor indicator set, the artificial leather production test database is classified to obtain a quality test factor data set after classification and integration according to the artificial leather quality factor indicator set.

[0063] Based on the quality detection factor data set, the detection network training and fusion are performed. First, the model training is performed based on the quality detection factor data set respectively to obtain the detection factor analysis branch network set corresponding to the training of each quality detection factor data set, which is used to perform artificial leather quality analysis based on each detection factor. Then, the criticality analysis is performed on the artificial leather quality factor index set, that is, the weight of the artificial leather quality factor index set is assigned, and the detection factor fusion coefficient information can be determined by assigning values ​​through objective production experience, that is, the factor index weight is used as the model fusion coefficient of the branch network corresponding to the detection factor. The larger the fusion coefficient, the greater the voting power of the branch network model.

[0064] Based on the detection factor fusion coefficient information, the detection factor analysis branch network set is fused to generate an integrated quality detection adaptive network. The artificial leather production detection data stream is analyzed by the quality detection adaptive network to output the artificial leather quality detection result corresponding to the produced artificial leather. The quality detection adaptive network is generated by multi-factor indicator training to improve the accuracy and comprehensiveness of the adaptive network model analysis, thereby improving the accuracy and efficiency of artificial leather quality analysis.

[0065] Step S6: when the artificial leather quality test result does not meet the artificial leather production standard, tracing the production parameters of the target artificial leather to obtain the process control parameters of the associated equipment;

[0066] Specifically, when the artificial leather quality test result does not meet the artificial leather production standard, the artificial leather production standard is the performance quality application standard of the target artificial leather, and the production record data of the target artificial leather is traced for production parameters, that is, the quality performance that does not meet the standard is traced for the associated production parameters to obtain the corresponding associated equipment process control parameters. For example, the color fastness quality performance of artificial leather does not meet the standard, and the traceability analysis determines that it is associated with the process control parameters such as calendering film forming and printing equipment.

[0067] Step S7: performing parameter optimization analysis based on the process control parameters of the associated equipment to obtain process optimization parameters of the production equipment, and adjusting the production parameters of the target artificial leather based on the process optimization parameters of the production equipment.

[0068] Furthermore, the steps of obtaining the process optimization parameters of the production equipment in this application also include:

[0069] Based on the artificial leather quality optimization rules and production cost rules, a parameter fitness function is constructed;

[0070] Taking the artificial leather equipment control parameter library as the optimization space, taking the process control parameters of the associated equipment as constraint parameters to perform iterative optimization in the optimization space, and using the parameter fitness function to perform optimization evaluation on the search parameters to obtain multiple control parameter optimization scores;

[0071] Based on the optimization scores of the multiple control parameters, comparison and screening are performed until a preset number of iterations is reached, and the process optimization parameters of the production equipment are output.

[0072] Specifically, based on the process control parameters of the associated equipment, parameter optimization analysis is performed. First, a parameter fitness function is constructed. The parameter fitness function is used to evaluate the optimization applicability of the equipment control parameters. It is determined based on the artificial leather quality optimization rules and the production cost rules, that is, through the parameter experience function of the artificial leather quality optimization factor and the parameter experience function of the production cost factor. The higher the fitness, the higher the optimization degree of the control parameter. The artificial leather equipment control parameter library is used as the optimization space, that is, the control parameter optimization range, and the associated equipment process control parameters are used as constraint parameters to perform iterative optimization in the optimization space, and the parameter fitness function is used to perform optimization evaluation on the multiple control parameters obtained by the search and optimization, and multiple control parameter optimization scores corresponding to the multiple control parameters are calculated.

[0073] Based on the optimization scores of the multiple control parameters, comparison and screening are performed until the optimization iteration reaches a preset number of iterations, wherein the preset number of iterations can be set by oneself, and then the process optimization parameters of the production equipment with the best fitness calculation score are output. Based on the process optimization parameters of the production equipment, the production parameters of the target artificial leather are adjusted, and the process parameter adjustment of the artificial leather equipment is intelligently realized, the parameter adjustment accuracy and adjustment efficiency are improved, and the production quality of the artificial leather is guaranteed.

[0074] In summary, the method and system for intelligently adjusting process parameters of artificial leather production equipment provided by the present application have the following technical effects:

[0075] The invention adopts the method of conducting production design analysis on the target artificial leather based on the artificial leather production process element information, determining the artificial leather production process parameter information, and laying out the detection node sensor network based on the artificial leather production process element information; conducting target artificial leather production based on the artificial leather production process parameter information, and extracting and detecting the produced artificial leather based on the detection node sensor network to obtain the artificial leather production detection data stream; conducting quality analysis on the artificial leather production detection data stream based on the artificial leather quality factor index set to generate the artificial leather quality detection result; when the artificial leather quality detection result does not meet the artificial leather production standard, tracing the production parameters of the target artificial leather, obtaining the process control parameters of the associated equipment, and thereby conducting parameter optimization analysis, obtaining the process optimization parameters of the production equipment, and adjusting the production parameters of the target artificial leather based on the process optimization parameters of the production equipment. Thus, the technical effect of realizing the intelligent artificial leather quality detection and equipment process parameter adjustment, improving the parameter adjustment accuracy and adjustment efficiency, and thus ensuring the production quality of artificial leather is achieved. Example

[0076] Based on the same inventive concept as the method for intelligently adjusting process parameters of artificial leather production equipment in the aforementioned embodiment, the present invention also provides an intelligent adjustment system for process parameters of artificial leather production equipment, such as Figure 3As shown, the system comprises:

[0077] The process element information acquisition module 11 is used to acquire the artificial leather production process element information, wherein the artificial leather production process element information includes production process information and production equipment control information;

[0078] A production design analysis module 12, configured to perform production design analysis on the target artificial leather based on the artificial leather production process element information, and determine the artificial leather production process parameter information;

[0079] A sensor network deployment module 13, configured to deploy a sensor network based on the artificial leather production process element information and generate a detection node sensor network;

[0080] A detection data stream acquisition module 14 is used to produce the target artificial leather based on the artificial leather production process parameter information, and to extract and detect the produced artificial leather based on the detection node sensor network to obtain an artificial leather production detection data stream;

[0081] The artificial leather quality analysis module 15 is used to obtain an artificial leather quality factor index set, perform quality analysis on the artificial leather production test data stream based on the artificial leather quality factor index set, and generate an artificial leather quality test result;

[0082] The production parameter tracing module 16 is used to trace the production parameters of the target artificial leather and obtain the process control parameters of the associated equipment when the quality test result of the artificial leather does not meet the production standard of the artificial leather;

[0083] The production parameter adjustment module 17 is used to perform parameter optimization analysis based on the process control parameters of the associated equipment, obtain process optimization parameters of the production equipment, and adjust the production parameters of the target artificial leather based on the process optimization parameters of the production equipment.

[0084] Furthermore, the system also includes:

[0085] An attribute classifier construction unit, used to construct an artificial leather attribute classifier, wherein the artificial leather attribute classifier includes synthetic resin, production base material, production method, and production purpose;

[0086] an attribute marking unit, used for marking the attribute of the target artificial leather based on the artificial leather attribute classifier to obtain the attribute information of the artificial leather;

[0087] A design database acquisition unit, used to acquire an artificial leather production design database by using data mining technology, wherein the artificial leather production design database includes artificial leather properties and artificial leather production process elements;

[0088] The data traversal matching unit is used to perform production data traversal matching with the artificial leather production design database according to the artificial leather property information, so as to determine the artificial leather production process parameter information.

[0089] Furthermore, the system also includes:

[0090] A node device extraction unit, used to extract node devices from the detection node sensor network to obtain a quality detection sensor device set and a CMOS image sensor;

[0091] A quality detection unit, used to perform quality detection on the produced artificial leather according to the quality detection sensor device set and the CMOS image sensor, and obtain a structured detection data stream and an image detection data stream;

[0092] A data preprocessing unit, used for performing data preprocessing on the structured detection data stream and the image detection data stream based on a data preprocessing dual channel to generate a standard structured detection data stream and a standard image detection data stream;

[0093] The data stream integration unit is used to integrate the standard structured detection data stream and the standard image detection data stream to determine the artificial leather production detection data stream.

[0094] Furthermore, the system also includes:

[0095] A preprocessing dual-channel setting unit, used for setting a data preprocessing dual-channel, wherein the data preprocessing dual-channel includes a structured data processing channel and an image data processing channel;

[0096] A standardization processing unit, configured to perform data cleaning and standardization processing on the structured detection data stream based on the structured data processing channel to obtain the standard structured detection data stream;

[0097] An image filtering unit, used for performing image filtering on the image detection data stream through the image data processing channel to generate a denoised image detection data stream;

[0098] A regional enhancement unit is used to construct an extreme attention mechanism module, and to perform regional enhancement on the denoised image detection data stream based on the extreme attention mechanism module to obtain the standard image detection data stream.

[0099] Furthermore, the system also includes:

[0100] A quality factor index acquisition unit, used to acquire the artificial leather quality factor index set, wherein the artificial leather quality factor index set includes physical and chemical property testing, mechanical property testing, aging property testing and appearance testing;

[0101] A detection database acquisition unit, used to acquire an artificial leather production detection database through big data, wherein the artificial leather production detection database includes production detection data and quality detection result data;

[0102] A database classification unit, used for classifying the artificial leather production inspection database based on the artificial leather quality factor index set to obtain a quality inspection factor data set;

[0103] The quality detection result output unit is used to perform detection network training and fusion based on the quality detection factor data set to generate a quality detection adaptive network, and analyze the artificial leather production detection data stream through the quality detection adaptive network to output the artificial leather quality detection result.

[0104] Furthermore, the system also includes:

[0105] A model training unit, used to perform model training based on the quality detection factor data set to obtain a detection factor analysis branch network set;

[0106] A criticality analysis unit, used to perform criticality analysis on the artificial leather quality factor index set to determine detection factor fusion coefficient information;

[0107] The adaptive network generation unit is used to fuse the detection factor analysis branch network set based on the detection factor fusion coefficient information to generate the quality detection adaptive network.

[0108] Furthermore, the system also includes:

[0109] A fitness function building unit, used for building a parameter fitness function based on an artificial leather quality optimization rule and a production cost rule;

[0110] An iterative optimization unit, used to use the artificial leather equipment control parameter library as an optimization space, use the associated equipment process control parameters as constraint parameters to perform iterative optimization in the optimization space, and use the parameter fitness function to perform optimization evaluation on the search parameters to obtain multiple control parameter optimization scores;

[0111] The parameter comparison and screening unit is used to perform comparison and screening based on the optimization scores of the multiple control parameters until a preset number of iterations is reached, and output the process optimization parameters of the production equipment.

[0112] The foregoing Figure 1The various variations and specific examples of the intelligent adjustment method for process parameters of artificial leather production equipment in Example 1 are also applicable to the intelligent adjustment system for process parameters of artificial leather production equipment in this embodiment. Through the above detailed description of the intelligent adjustment method for process parameters of artificial leather production equipment, those skilled in the art can clearly know the implementation method of the intelligent adjustment system for process parameters of artificial leather production equipment in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0113] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and run on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method embodiment for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0114] Exemplary Electronic Devices

[0115] For details, see 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 .

[0116] In the present application, the electronic device further includes: a computer program stored in the memory 1150 and executable on the processor 1120, and when the computer program is executed by the processor 1120, each process of the above-mentioned method embodiment for controlling output data is implemented.

[0117] The transceiver 1130 is configured to receive and send data under the control of the processor 1120 .

[0118] In the present application, a bus architecture (represented by bus 1110 ) may include any number of interconnected buses and bridges. Bus 1110 connects various circuits including one or more processors represented by processor 1120 and a memory represented by memory 1150 .

[0119] Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. Such architectures include, by way of example and not limitation, an Industry Standard Architecture bus, a Micro Channel Architecture bus, an expansion bus, a Video Electronics Standards Association, a Peripheral Component Interconnect bus.

[0120] The processor 1120 may be an integrated circuit chip having signal processing capabilities. In the implementation process, each step of the above method embodiment may be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processors include: a general-purpose processor, a central processing unit, a network processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, a complex programmable logic device, a programmable logic array, a microcontroller unit or other programmable logic device, a discrete gate, a transistor logic device, and a discrete hardware component. The various methods, steps, and logic block diagrams disclosed in this application may be implemented or executed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated into a single chip or located on multiple different chips.

[0121] Processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in the present application may be directly executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a readable storage medium known in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, a register, etc. The readable storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0122] The bus 1110 may also connect various other circuits such as peripheral devices, voltage regulators or power management circuits, and the bus interface 1140 provides an interface between the bus 1110 and the transceiver 1130, which are well known in the art. Therefore, this application will not further describe them.

[0123] The transceiver 1130 may be one element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on a transmission medium. For example, the transceiver 1130 receives external data from other devices, and the transceiver 1130 is used to send 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 a touch screen, a physical keyboard, a display, a mouse, a speaker, a microphone, a trackball, a joystick, or a stylus.

[0124] It should be understood that in the present application, the memory 1150 may further include a memory remotely arranged relative to the processor 1120, and these remotely arranged memories may be connected to the server through a network. One or more parts of the above network may be a self-organizing 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 wireless fidelity network, and a combination of two or more of the above networks. For example, the cellular telephone network and the wireless network may be a global mobile communication device, a code division multiple access device, a global microwave interconnection access device, a general packet radio service device, a wideband code division multiple access device, a long term evolution device, a LTE frequency division duplex device, a LTE time division duplex device, an advanced long term evolution device, a universal mobile communication device, an enhanced mobile broadband device, a massive machine type communication device, an ultra-reliable low latency communication device, and the like.

[0125] It should be understood that the memory 1150 in the present application may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. Among them, the non-volatile memory includes: a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, or a flash memory.

[0126] 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, synchronously linked dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in the present application includes, but is not limited to, the above and any other suitable types of memory.

[0127] In the present 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.

[0128] Specifically, the operating system 1151 includes various device programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 1152 includes various application programs, such as a media player and a browser, which are used to implement various application services. The program implementing the method of the present application may be included in the application 1152. The application 1152 includes applets, objects, components, logic, data structures, and other computer device executable instructions that perform specific tasks or implement specific abstract data types.

[0129] 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, the various processes of the above-mentioned method embodiment for controlling output data are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0130] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent adjustment method for process parameters of artificial leather production equipment, characterized in that: The method comprises: Acquire artificial leather production process element information, wherein the artificial leather production process element information includes production process information and production equipment control information; Performing production design analysis on the target artificial leather based on the artificial leather production process element information to determine the artificial leather production process parameter information; Deploy a sensor network based on the artificial leather production process element information to generate a detection node sensor network; The target artificial leather is produced based on the artificial leather production process parameter information, and the produced artificial leather is sampled and tested based on the detection node sensor network to obtain an artificial leather production test data stream; Acquire an artificial leather quality factor indicator set, perform quality analysis on the artificial leather production test data stream based on the artificial leather quality factor indicator set, and generate an artificial leather quality test result; When the artificial leather quality test result does not meet the artificial leather production standard, tracing the production parameters of the target artificial leather to obtain the process control parameters of the associated equipment; Performing parameter optimization analysis based on the process control parameters of the associated equipment to obtain process optimization parameters of the production equipment, and adjusting production parameters of the target artificial leather based on the process optimization parameters of the production equipment; The step of obtaining the artificial leather production detection data stream comprises: Extracting node devices from the detection node sensor network to obtain a quality detection sensor device set and a CMOS image sensor; Performing quality inspection on produced artificial leather according to the quality inspection sensor device set and the CMOS image sensor to obtain a structured inspection data stream and an image inspection data stream; Based on the data preprocessing dual channels, the structured detection data stream and the image detection data stream are preprocessed to generate a standard structured detection data stream and a standard image detection data stream; Integrating the standard structured detection data stream and the standard image detection data stream to determine the artificial leather production detection data stream; The generating of the standard structured detection data stream and the standard image detection data stream comprises: Setting a dual data preprocessing channel, wherein the dual data preprocessing channel includes a structured data processing channel and an image data processing channel; Based on the structured data processing channel, data cleaning and standardization processing are performed on the structured detection data stream to obtain the standard structured detection data stream; Performing image filtering on the image detection data stream through the image data processing channel to generate a denoised image detection data stream; Construct an extreme attention mechanism module, and perform regional enhancement on the denoised image detection data stream based on the extreme attention mechanism module to obtain the standard image detection data stream.

2. The method according to claim 1, characterized in that The step of determining the artificial leather production process parameter information includes: Constructing an artificial leather property classifier, wherein the artificial leather property classifier includes synthetic resin, production base material, production method, and production purpose; Marking the target artificial leather with attributes based on the artificial leather attribute classifier to obtain artificial leather attribute information; Acquire an artificial leather production design database by using data mining technology, wherein the artificial leather production design database includes artificial leather properties and artificial leather production process elements; The artificial leather production process parameter information is determined by performing production data traversal matching with the artificial leather production design database according to the artificial leather property information.

3. The method according to claim 1, characterized in that The generating of the artificial leather quality test result comprises: Acquire the artificial leather quality factor index set, wherein the artificial leather quality factor index set includes physical and chemical property testing, mechanical property testing, aging property testing, and appearance testing; Acquire an artificial leather production and testing database through big data, wherein the artificial leather production and testing database includes production testing data and quality testing result data; Classifying the artificial leather production inspection database based on the artificial leather quality factor index set to obtain a quality inspection factor data set; Based on the quality detection factor data set, detection network training and fusion are performed to generate a quality detection adaptive network, and the artificial leather production detection data stream is analyzed through the quality detection adaptive network to output the artificial leather quality detection result.

4. The method according to claim 3, characterized in that The generating quality detection adaptive network comprises: Model training is performed based on the quality detection factor data set to obtain a detection factor analysis branch network set; Performing a criticality analysis on the artificial leather quality factor index set to determine the detection factor fusion coefficient information; The detection factor analysis branch network set is fused based on the detection factor fusion coefficient information to generate the quality detection adaptive network.

5. The method according to claim 1, characterized in that The obtaining of the process optimization parameters of the production equipment comprises: Based on the artificial leather quality optimization rules and production cost rules, a parameter fitness function is constructed; Taking the artificial leather equipment control parameter library as the optimization space, taking the process control parameters of the associated equipment as constraint parameters to perform iterative optimization in the optimization space, and using the parameter fitness function to perform optimization evaluation on the search parameters to obtain multiple control parameter optimization scores; Based on the optimization scores of the multiple control parameters, comparison and screening are performed until a preset number of iterations is reached, and the process optimization parameters of the production equipment are output.

6. Intelligent adjustment system for process parameters of artificial leather production equipment, characterized in that: The system comprises: A process element information acquisition module, used to acquire artificial leather production process element information, wherein the artificial leather production process element information includes production process information and production equipment control information; A production design analysis module, used to perform production design analysis on the target artificial leather based on the artificial leather production process element information, and determine the artificial leather production process parameter information; A sensor network deployment module, used to deploy a sensor network based on the artificial leather production process element information to generate a detection node sensor network; A detection data stream acquisition module, used to produce the target artificial leather based on the artificial leather production process parameter information, and to extract and detect the produced artificial leather based on the detection node sensor network to obtain the artificial leather production detection data stream; An artificial leather quality analysis module, used to obtain an artificial leather quality factor index set, perform quality analysis on the artificial leather production test data stream based on the artificial leather quality factor index set, and generate an artificial leather quality test result; A production parameter tracing module is used to trace the production parameters of the target artificial leather and obtain the process control parameters of the associated equipment when the quality test result of the artificial leather does not meet the production standard of the artificial leather; A production parameter adjustment module, used for performing parameter optimization analysis based on the process control parameters of the associated equipment, obtaining process optimization parameters of the production equipment, and adjusting the production parameters of the target artificial leather based on the process optimization parameters of the production equipment; A node device extraction unit, used to extract node devices from the detection node sensor network to obtain a quality detection sensor device set and a CMOS image sensor; A quality detection unit, used to perform quality detection on the produced artificial leather according to the quality detection sensor device set and the CMOS image sensor, and obtain a structured detection data stream and an image detection data stream; A data preprocessing unit, used for performing data preprocessing on the structured detection data stream and the image detection data stream based on a data preprocessing dual channel to generate a standard structured detection data stream and a standard image detection data stream; A data stream integration unit, used to integrate the standard structured detection data stream and the standard image detection data stream to determine the artificial leather production detection data stream; A preprocessing dual-channel setting unit, used for setting a data preprocessing dual-channel, wherein the data preprocessing dual-channel includes a structured data processing channel and an image data processing channel; A standardization processing unit, configured to perform data cleaning and standardization processing on the structured detection data stream based on the structured data processing channel to obtain the standard structured detection data stream; An image filtering unit, used for performing image filtering on the image detection data stream through the image data processing channel to generate a denoised image detection data stream; A regional enhancement unit is used to construct an extreme attention mechanism module, and to perform regional enhancement on the denoised image detection data stream based on the extreme attention mechanism module to obtain the standard image detection data stream.

7. An electronic device for intelligently adjusting process parameters of artificial leather production equipment, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 5 are implemented.

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