CN115421465A - Optimized self-adaptive control method and system for textile equipment - Google Patents
Optimized self-adaptive control method and system for textile equipment Download PDFInfo
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- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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Abstract
The invention discloses an optimized self-adaptive control method and system for textile equipment, and relates to the field of equipment control, wherein the method comprises the following steps: constructing a mapping identifier of a textile product; carrying out environmental fluctuation analysis on the environmental acquisition data, and generating processing influence data based on an environmental fluctuation analysis result; inputting the operation parameters of the textile equipment into a parameter correction model to obtain initial operation optimization control parameters; correcting the quality detection result according to the mapping identification to obtain a corrected quality detection result, and analyzing the quality influence association parameters by combining the processing influence data to obtain an association parameter analysis result; and correcting the equipment control parameters of the initial operation optimization control parameters to obtain self-adaptive control parameters, and controlling the equipment of the textile equipment according to the self-adaptive control parameters. The technical problem of the equipment control effect of weaving equipment among the prior art not good is solved. The technical effects of improving the equipment control effect of the textile equipment and the like are achieved.
Description
Technical Field
The invention relates to the field of equipment control, in particular to an optimized self-adaptive control method and system for textile equipment.
Background
Along with the continuous development of scientific technology and the improvement of the living standard of people, the demand market of textile products is continuously expanded, the types and the quantity of the textile products are rapidly increased, the processing parameters of the textile products are more and more complicated, the equipment control diversity and the equipment control complexity of textile equipment are increased, and the requirements on the equipment control of the textile equipment at higher levels are provided. How to carry out optimization control on textile equipment is widely concerned by people.
In the prior art, the technical problems that the equipment control accuracy of the spinning equipment is not enough, and then the equipment control effect of the spinning equipment is not good and the equipment production quality is not high exist.
Disclosure of Invention
The application provides an optimized self-adaptive control method and system for textile equipment. The technical problems that in the prior art, the equipment control accuracy of the spinning equipment is not enough, and then the equipment control effect of the spinning equipment is poor and the production quality of the equipment is not high are solved.
In view of the above problems, the present application provides an optimized adaptive control method and system for a textile apparatus.
In a first aspect, the present application provides an optimized adaptive control method for a textile apparatus, wherein the method is applied to an optimized adaptive control system for a textile apparatus, and the method comprises: acquiring basic information of a textile material, and constructing a mapping identifier of a textile product based on the basic information; acquiring environmental data of the textile product processing through the environmental monitoring device to obtain environmental acquisition data; analyzing the environment fluctuation of the environment acquisition data, and generating processing influence data based on the environment fluctuation analysis result; obtaining historical operation data of the textile equipment, and constructing a parameter correction model of the textile equipment according to the historical operation data; collecting the operating parameters of the textile equipment, and inputting the operating parameters into the parameter correction model to obtain initial operation optimization control parameters; detecting the quality of the textile product through the quality measuring device, and correcting the quality influence of the quality detection result according to the mapping identification to obtain a corrected quality detection result; performing quality influence correlation parameter analysis according to the corrected quality detection result and the processing influence data to obtain a correlation parameter analysis result; and correcting the equipment control parameters of the initial operation optimization control parameters according to the correlation parameter analysis result to obtain self-adaptive control parameters, and controlling the equipment of the textile equipment according to the self-adaptive control parameters.
In a second aspect, the present application also provides an optimized adaptive control system for a textile apparatus, wherein the system comprises: the basic information acquisition module is used for acquiring basic information of the textile material and constructing a mapping identifier of a textile product based on the basic information; the environment data acquisition module is used for acquiring the environment data of the textile product processing through the environment monitoring device to obtain environment acquisition data; the environment fluctuation analysis module is used for carrying out environment fluctuation analysis on the environment acquisition data and generating processing influence data based on an environment fluctuation analysis result; the construction module is used for obtaining historical operation data of the textile equipment and constructing a parameter correction model of the textile equipment according to the historical operation data; the control parameter obtaining module is used for acquiring the operating parameters of the textile equipment, inputting the operating parameters into the parameter correction model and obtaining initial operation optimization control parameters; the quality influence correction module is used for detecting the quality of the textile product through the quality measuring device, correcting the quality influence of the quality detection result according to the mapping identification and obtaining a corrected quality detection result; the quality influence correlation parameter analysis module is used for carrying out quality influence correlation parameter analysis according to the corrected quality detection result and the processing influence data to obtain a correlation parameter analysis result; and the equipment control module is used for correcting the equipment control parameters of the initial operation optimization control parameters according to the correlation parameter analysis result to obtain self-adaptive control parameters, and performing equipment control on the textile equipment through the self-adaptive control parameters.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
constructing a mapping identifier of a textile product through basic information of the textile; acquiring environmental data of textile product processing through an environmental monitoring device to obtain environmental acquisition data; carrying out environmental fluctuation analysis on the environmental acquisition data, and generating processing influence data based on an environmental fluctuation analysis result; obtaining historical operation data of the textile equipment, and constructing a parameter correction model of the textile equipment according to the historical operation data; collecting the operating parameters of the textile equipment, and inputting the operating parameters into a parameter correction model to obtain initial operation optimization control parameters; detecting the quality of the textile product through a quality measuring device, and correcting the quality influence of the quality detection result according to the mapping identification to obtain a corrected quality detection result; performing quality influence correlation parameter analysis according to the corrected quality detection result and the processing influence data to obtain a correlation parameter analysis result; and correcting the equipment control parameters of the initial operation optimization control parameters according to the correlation parameter analysis result to obtain self-adaptive control parameters, and controlling the equipment of the textile equipment through the self-adaptive control parameters. The accuracy of equipment control is carried out to weaving equipment to the improvement has been reached, the equipment control of the intellectuality of realization weaving equipment, high accuracy to improve weaving equipment's equipment control effect, promote weaving equipment's equipment production quality's technical effect.
Drawings
FIG. 1 is a schematic flow diagram of an optimized adaptive control method for textile equipment according to the present application;
FIG. 2 is a schematic flow chart of a parameter correction model constructed in an optimized adaptive control method for textile equipment according to the present application;
FIG. 3 is a schematic flow chart of the environment control optimization by controlling the environment control device in the adaptive optimization control method for textile equipment according to the present application;
fig. 4 is a schematic structural diagram of an optimized adaptive control system for textile equipment according to the present application.
Description of the reference numerals: the system comprises a basic information acquisition module 11, an environmental data acquisition module 12, an environmental fluctuation analysis module 13, a construction module 14, a control parameter obtaining module 15, a quality influence correction module 16, a quality influence correlation parameter analysis module 17 and an equipment control module 18.
Detailed Description
The application provides an optimized self-adaptive control method and system for textile equipment. The technical problems that in the prior art, the equipment control accuracy of the spinning equipment is not enough, and then the equipment control effect of the spinning equipment is poor and the production quality of the equipment is not high are solved. The accuracy of equipment control is carried out to weaving equipment to the improvement has been reached, the equipment control of the intellectuality of realization weaving equipment, high accuracy to improve weaving equipment's equipment control effect, promote weaving equipment's equipment production quality's technological effect.
Example one
Referring to fig. 1, the present application provides an optimized adaptive control method for textile equipment, wherein the method is applied to an optimized adaptive control system for textile equipment, the system is in communication connection with an environment monitoring device, a quality determination device and an environment regulation device, and the method specifically comprises the following steps:
step S100: acquiring basic information of a textile material, and constructing a mapping identifier of a textile product based on the basic information;
step S200: acquiring environmental data of the textile product processing through the environmental monitoring device to obtain environmental acquisition data;
step S300: carrying out environmental fluctuation analysis on the environmental collected data, and generating processing influence data based on an environmental fluctuation analysis result;
specifically, basic information of a textile material processed by the textile product is collected to obtain the basic information, and the textile product is mapped and identified according to the basic information to obtain a mapping identifier. And furthermore, collecting real-time environment data of textile product processing by using an environment monitoring device to obtain environment collected data, carrying out environment fluctuation analysis on the environment collected data to obtain an environment fluctuation analysis result, and further determining processing influence data. Illustratively, in determining the processing impact data, the optimal temperature range for the processing of the textile product is a and the optimal humidity range is B as determined by analyzing a plurality of historical data of the processing of the textile product. And adding the plurality of real-time environment temperature information which do not meet the optimal temperature range A and the plurality of real-time environment humidity information which do not meet the optimal humidity range B in the environment acquisition data to the processing influence data so as to obtain the processing influence data.
The basic information comprises data information such as the number, the size parameters, the colors, the material composition and the material performance characteristics of a plurality of textile materials processed by the textile products. The mapping identification is identification information used for representing the corresponding relation between the basic information of the textile material and the textile product. The textile product may be not only a single textile product but also a plurality of textile products. The environment monitoring device comprises environment monitoring equipment such as an environment temperature monitor and an environment humidity monitor in the prior art. The environment acquisition data comprises a plurality of real-time environment temperature information and a plurality of real-time environment humidity information of textile product processing. The environment fluctuation analysis result comprises a real-time environment temperature fluctuation analysis result and a real-time environment humidity fluctuation analysis result. The real-time environment temperature fluctuation analysis result comprises a plurality of pieces of real-time environment temperature information and a plurality of pieces of environment temperature change trend information corresponding to the real-time environment temperature information. The real-time environment humidity fluctuation analysis result comprises a plurality of real-time environment humidity information and environment humidity change trend information corresponding to the real-time environment humidity information. The processing influence data comprises real-time environment temperature information and real-time environment humidity information which influence textile products and tools in environment acquisition data. The method achieves the technical effects of mapping identification of the textile product according to the basic information, analyzing the environment acquisition data of textile product processing, and obtaining reliable processing influence data, thereby improving the accuracy and the scientificity of the follow-up optimization self-adaptive control of the textile equipment.
Step S400: obtaining historical operation data of the textile equipment, and constructing a parameter correction model of the textile equipment according to the historical operation data;
further, as shown in fig. 2, step S400 of the present application further includes:
step S410: extracting the deviation parameter characteristics of equipment operation according to the historical operation data to obtain a deviation parameter characteristic extraction set;
step S420: constructing a compensation parameter set of a deviation parameter feature extraction set, wherein the compensation parameter set and the deviation parameter feature extraction set have a corresponding relation;
step S430: and constructing the parameter correction model through the deviation parameter feature extraction set and the compensation parameter set.
Step S500: collecting the operating parameters of the textile equipment, and inputting the operating parameters into the parameter correction model to obtain initial operation optimization control parameters;
specifically, a plurality of historical operating parameters of the textile equipment are collected, and historical operating data are obtained. And further extracting the device operation deviation parameter characteristics in the historical operation data to obtain a deviation parameter characteristic extraction set. Namely, historical operating data is analyzed, and historical fault operating parameters when the textile equipment breaks down are added to the deviation parameter feature extraction set. And further analyzing the historical operating data, and adding a plurality of historical textile equipment correction parameters corresponding to the deviation parameter feature extraction set to the compensation parameter set. The compensation parameter set and the deviation parameter feature extraction set have a corresponding relation. Wherein the historical operating data comprises a plurality of historical operating parameters of the textile apparatus. The deviating parameter feature extraction set includes a plurality of historical faulty operating parameters in historical operating data. The compensation parameter set comprises a plurality of historical spinning equipment correction parameters corresponding to the deviation parameter feature extraction set in historical operation data. Illustratively, the historical operating data includes that the textile equipment fails after the textile equipment is controlled for 3 hours by using a historical operating parameter c under a certain historical time node, and the textile equipment normally operates after the textile equipment is optimally controlled by using a historical operating parameter d. Then, the historical operating parameter c is a historical failure operating parameter. The historical operating parameter d is a historical spinning device correction parameter. The obtained deviation parameter feature extraction set comprises historical operating parameters c. The obtained set of compensation parameters includes the historical operating parameter d.
Further, based on the neural network, continuously self-training and learning the deviation parameter feature extraction set and the compensation parameter set until a convergence state is obtained to obtain a parameter correction model. And then, collecting real-time operation parameters of the textile equipment to obtain the operation parameters, inputting the operation parameters as input information into a parameter correction model, and obtaining initial operation optimization control parameters. Wherein the operating parameters comprise a plurality of real-time operating parameters of a textile apparatus for processing textile products based on basic information of the textile material. The weaving equipment can be any weaving equipment which is intelligently controlled by using the optimized adaptive control system for the weaving equipment. The parameter correction model has the functions of intelligently analyzing the input operation parameters and intelligently correcting the parameter matching. The initial operation optimization control parameters comprise correction parameters corresponding to the operation parameters. The technical effects that a parameter correction model with high accuracy and high generalization performance is constructed through historical operation data of the textile equipment, and the operation parameters of the textile equipment are analyzed through the parameter correction model to obtain adaptive and reasonable initial operation optimization control parameters, so that the accuracy of equipment control on the textile equipment is improved are achieved.
Step S600: detecting the quality of the textile product through the quality measuring device, and correcting the quality influence of the quality detection result according to the mapping identification to obtain a corrected quality detection result;
step S700: performing quality influence correlation parameter analysis according to the corrected quality detection result and the processing influence data to obtain a correlation parameter analysis result;
specifically, the quality of the textile product is detected by using a quality measuring device, a quality detection result is obtained, and the quality detection result is corrected according to the mapping identifier, so that the interference of basic information of the textile material on the quality detection result is eliminated, a corrected quality detection result is obtained, and the accuracy of the subsequent equipment control parameter correction on the textile equipment is improved. The quality measuring device comprises textile special quality measuring equipment such as a textile printing and dyeing fastness detecting instrument, a general textile detecting instrument, a textile functional detecting instrument and the like in the prior art. The quality detection result comprises quality detection data information corresponding to the textile product, such as dyeing fastness, blemish, heat preservation, air permeability, protective performance, dyeing uniformity and the like. And correcting the quality detection result comprises quality detection data information obtained after correcting the quality detection result according to the mapping identifier. Illustratively, when the corrected quality detection result is obtained, the basic information of the textile material shows that the textile material e is low in price and easy to fade, and textile products produced by using the textile material have the defects of poor color fastness and easy fading. The textile material E is used for production, and the obtained quality detection result Ee comprises the characteristics of easy fading of the textile product E, poor color fastness of printing and dyeing, high air permeability and good wear resistance. And deleting the easy fading and poor dyeing fastness from the quality detection result Ee according to the mapping identifier to obtain a corrected quality detection result corresponding to the quality detection result Ee.
And further, performing quality influence correlation parameter analysis on the corrected quality detection result based on the processing influence data to obtain a correlation parameter analysis result. The correlation parameter analysis result comprises a processing influence data correlation parameter analysis result, a textile equipment quality influence correlation parameter analysis result and a parameter influence fluctuation value. The processing influence data correlation parameter analysis result comprises processing influence data quality influence information and processing influence data quality influence correlation parameters. The processing impact data quality impact information comprises quality impacts of the processing impact data on the textile product. And the processing influence data quality influence associated parameters comprise quality detection data information corresponding to the processing influence data quality influence information in the corrected quality detection result. The analysis result of the textile equipment quality influence associated parameters comprises the elimination of the interference of processing influence data, namely, the corresponding relation and the association parameters between the residual correction quality detection results and the operation parameters of the textile equipment after the processing influence data quality influence associated parameters are deleted from the correction quality detection results. The parameter influence fluctuation value comprises the interference of eliminating processing influence data, namely the textile product quality change information corresponding to the residual correction quality detection result after deleting the processing influence data quality influence associated parameters from the correction quality detection result.
The quality detection device achieves the technical effects that the textile product is subjected to quality detection through the quality detection device, the obtained quality detection result is corrected according to the mapping identification, the reliable corrected quality detection result is determined, the quality influence associated parameter analysis is carried out on the corrected quality detection result according to the processing influence data, the associated parameter analysis result is obtained, and therefore the accuracy and the comprehensiveness of the equipment control parameter correction of the initial operation optimization control parameter are improved.
Step S800: and correcting the equipment control parameters of the initial operation optimization control parameters according to the correlation parameter analysis result to obtain self-adaptive control parameters, and controlling the equipment of the textile equipment according to the self-adaptive control parameters.
Further, step S800 of the present application further includes:
step S810: judging whether the parameter influence fluctuation value in the correlation parameter analysis result meets a preset fluctuation threshold value or not;
step S820: when the parameter influence fluctuation value does not meet the preset fluctuation threshold value, generating a continuous quality monitoring instruction;
step S830: continuously acquiring quality detection results according to the continuous quality monitoring instructions to obtain a corrected quality detection set;
specifically, whether a parameter influence fluctuation value in the correlation parameter analysis result meets a preset fluctuation threshold value or not is judged, if the parameter influence fluctuation value does not meet the preset fluctuation threshold value, a continuous quality monitoring instruction is obtained, continuous quality detection is carried out on the textile product according to the continuous quality monitoring instruction, and a correction quality detection set is obtained. Wherein the preset fluctuation threshold comprises a preset determined parameter influence fluctuation threshold. The continuous quality monitoring instruction is instruction information used for representing that the parameter influence fluctuation value does not meet a preset fluctuation threshold value and continuous quality detection needs to be carried out on the textile product. The correction quality detection set comprises a plurality of quality detection results, and each quality detection result comprises quality detection data information of the textile product, such as the dyeing fastness, the defect property, the heat preservation property and the like, which is obtained by performing quality detection on the textile product according to the continuous quality monitoring instruction. When the parameter influence fluctuation value does not meet the preset fluctuation threshold value, the quality detection result is continuously acquired according to the continuous quality monitoring instruction, a corrected quality detection set is obtained, and a foundation is laid for the subsequent generation of the continuous associated parameter analysis result.
Step S840: analyzing the quality influence correlation parameters according to the corrected quality detection set to generate a continuous correlation parameter analysis result;
further, step S840 of the present application further includes:
step S841: analyzing quality influence associated parameters according to the corrected quality detection set to obtain a quality influence associated parameter set, wherein the quality influence associated parameter set is provided with a time identifier;
step S842: carrying out time axis sequence sorting on the quality influence associated parameter set according to the time identification to obtain a sequence sorting result;
step S843: analyzing the same-parameter change trend of the sequence sorting result to obtain a same-parameter change trend curve;
step S844: judging whether the same-parameter change trend curve meets a window trend constraint value or not;
step S845: and when the same-parameter variation trend curve meets the window trend constraint value, generating the continuous correlation parameter analysis result according to the trend variation value.
Step S850: and correcting the equipment control parameters according to the continuous correlation parameter analysis result.
Specifically, based on basic information of a textile material, processing influence data and operating parameters of textile equipment, quality influence associated parameter analysis is carried out on each quality detection result in the corrected quality detection set to obtain a quality influence associated parameter set with a time identifier, and time axis sequence sorting is carried out on the quality influence associated parameter set according to the time identifier to obtain a sequence sorting result. And further, carrying out same-parameter variation trend analysis on the sequence sequencing results to obtain a same-parameter variation trend curve, judging whether the same-parameter variation trend curve meets a window trend constraint value, and if the same-parameter variation trend curve meets the window trend constraint value, generating a continuous correlation parameter analysis result according to the trend variation value. And then, correcting the initial operation optimization control parameters according to the continuous correlation parameter analysis result to obtain self-adaptive control parameters, and controlling the textile equipment according to the self-adaptive control parameters. And the quality influence correlation parameter set comprises a plurality of correlation parameters between the operation parameters of the textile equipment and each quality detection result in the corrected quality detection set after the basic information of the textile material and the interference of the processing influence data are eliminated. The time identification comprises acquisition time information of the quality detection result of the corrected quality detection set corresponding to the plurality of relevance parameters in the quality influence relevance parameter set. And the sequence sorting result comprises the quality influence associated parameter set with time axis sequence sorting obtained after the quality influence associated parameter set is subjected to time axis sequence sorting according to the time identification. The same-parameter variation trend curve comprises variation trend curves corresponding to a plurality of relevance parameters of the same type in the sequence sorting result. The window trend constraint value comprises preset curve trend change information corresponding to a preset time interval. The trend variation value comprises curve trend variation parameter information corresponding to the parameter variation trend curve. The continuous associated parameter analysis result comprises a quality influence associated parameter set and a trend change value corresponding to a parameter change trend curve. The self-adaptive control parameters comprise optimized correction control parameters of the textile equipment obtained after the initial operation optimized control parameters are corrected according to the continuous correlation parameter analysis result. The method achieves the technical effects that the correlation parameter analysis of quality influence is carried out on the corrected quality detection set to generate the continuous correlation parameter analysis result, and the initial operation optimization control parameter is corrected according to the continuous correlation parameter analysis result, so that the accuracy of equipment control on the textile equipment is improved, and the equipment control quality of the textile equipment is improved.
Further, step S820 of the present application further includes:
step S821: judging whether the correction quality detection result does not meet an expected quality constraint threshold value;
step S822: when the corrected quality detection result does not meet the expected quality constraint threshold, generating a fit deviation influence coefficient according to the environment acquisition data and the operation parameters;
step S823: and carrying out self-adaptive restriction on the textile equipment based on the fit deviation influence coefficient.
Specifically, when the parameter influence fluctuation value does not meet a preset fluctuation threshold value, whether the corrected quality detection result does not meet an expected quality constraint threshold value or not is judged, if the corrected quality detection result does not meet the expected quality constraint threshold value, environment collected data and operation parameters of the textile equipment are evaluated, a matching deviation influence coefficient is obtained, and control parameter adjustment and self-adaptive restriction are carried out on the textile equipment according to the matching deviation influence coefficient. Wherein the expected quality constraint threshold comprises preset quality detection result constraint information. For example, the expected quality constraint threshold includes a colorfastness threshold, a dyeing uniformity threshold, and the like. The fit deviation influence coefficient is parameter information used for representing the fit degree and the fit effect between the environment acquisition data and the operation parameters of the textile equipment. The higher the adaptation degree between the environment acquisition data and the operating parameters of the textile equipment, the better the matching effect, and the smaller the corresponding matching deviation influence coefficient.
The method achieves the technical effects of obtaining accurate and reliable matching deviation influence coefficients by evaluating environment acquisition data and operating parameters of the textile equipment when the corrected quality detection result does not meet the expected quality constraint threshold, and improving the equipment control adaptability and accuracy of the textile equipment according to the self-adaptive constraint influence coefficients of the environment acquisition data and the operating parameters of the textile equipment.
Further, as shown in fig. 3, after step S800, the method further includes:
step S900: obtaining an environment regulation and control constraint interval, wherein the environment regulation and control constraint interval comprises a multi-stage regulation and control constraint interval;
step S1000: matching environment regulation and control parameters according to the environment acquisition data and the environment regulation and control constraint interval;
step S1100: and controlling the environment regulation and control device to carry out environment control optimization according to the environment regulation and control parameter matching result.
Specifically, the environment regulation and control parameters are matched with the environment regulation and control constraint interval through the environment acquisition data, an environment regulation and control parameter matching result is obtained, and the environment regulation and control device is controlled according to the environment regulation and control parameter matching result, so that the production environment of the textile equipment is optimized. Wherein the environmental regulation constraint interval comprises a multi-level regulation constraint interval. In the multi-stage regulation and control constraint interval, each stage of regulation and control constraint interval comprises preset real-time production environment information of the textile equipment, and preset optimized production environment information of the textile equipment corresponding to the preset real-time production environment information of the textile equipment. The environment regulation and control device comprises environment temperature regulation and control equipment and environment humidity regulation and control equipment in the prior art. And the environment regulation and control parameter matching result comprises the optimized environment temperature and the optimized environment humidity corresponding to the environment acquisition data. The technical effects of optimizing the production environment of the spinning equipment through the environment regulation and control constraint interval and improving the production quality of the spinning equipment are achieved.
Further, step S1100 of the present application further includes:
step S1110: when the environment regulation and control device is started, a feedback supervision instruction is generated;
step S1120: carrying out environment adjustment supervision on textile product processing according to the feedback supervision instruction;
step S1130: when the environmental adjustment supervision result does not meet a preset recovery value, generating abnormal environmental early warning information;
step S1140: and carrying out early warning processing according to the abnormal environment early warning information.
Specifically, when the environment regulation and control device is controlled according to the environment regulation and control parameter matching result, after the environment regulation and control device is started, the optimization self-adaptive control system for the textile equipment automatically obtains a feedback supervision instruction, supervises the environment adjustment process of textile product processing according to the feedback supervision instruction, and obtains an environment adjustment supervision result. And further, comparing the environment adjustment supervision result with a preset recovery value, if the environment adjustment supervision result does not meet the preset recovery value, acquiring abnormal environment early warning information, and early warning the environment for processing the textile products according to the abnormal environment early warning information. The feedback supervision instruction is instruction information used for representing that the environment regulation device is started and the real-time supervision is needed to be carried out on the environment regulation process of textile product processing. And the environment adjustment supervision result comprises corresponding real-time environment temperature and real-time environment humidity after the environment adjustment device is controlled according to the environment adjustment parameter matching result and the environment adjustment process of textile product processing is finished. The preset recovery value comprises the optimized environment temperature and the optimized environment humidity of the environment regulation and control parameter matching result. The abnormal environment early warning information is data information used for representing that an environment adjustment supervision result does not meet a preset recovery value. The technical effects of adaptively generating abnormal environment early warning information according to the environment adjustment monitoring result and the preset recovery value and improving the reliability of optimizing the production environment of the textile equipment are achieved.
In summary, the optimized adaptive control method for textile equipment provided by the present application has the following technical effects:
1. constructing a mapping identifier of a textile product through basic information of the textile; acquiring environmental data of textile product processing through an environmental monitoring device to obtain environmental acquisition data; carrying out environmental fluctuation analysis on the environmental collected data, and generating processing influence data based on an environmental fluctuation analysis result; obtaining historical operation data of the textile equipment, and constructing a parameter correction model of the textile equipment according to the historical operation data; collecting the operating parameters of the textile equipment, and inputting the operating parameters into a parameter correction model to obtain initial operation optimization control parameters; detecting the quality of the textile product through a quality measuring device, and correcting the quality influence of the quality detection result according to the mapping identification to obtain a corrected quality detection result; performing quality influence correlation parameter analysis according to the corrected quality detection result and the processing influence data to obtain a correlation parameter analysis result; and correcting the equipment control parameters of the initial operation optimization control parameters according to the correlation parameter analysis result to obtain self-adaptive control parameters, and controlling the equipment of the textile equipment through the self-adaptive control parameters. The accuracy of equipment control is carried out to weaving equipment to the improvement has been reached, the equipment control of the intellectuality of realization weaving equipment, high accuracy to improve weaving equipment's equipment control effect, promote weaving equipment's equipment production quality's technical effect.
2. The quality of the textile product is detected through the quality measuring device, the obtained quality detection result is corrected according to the mapping identification, the reliable corrected quality detection result is determined, quality influence associated parameter analysis is carried out on the corrected quality detection result according to the processing influence data, and an associated parameter analysis result is obtained, so that the accuracy and the comprehensiveness of equipment control parameter correction on the initial operation optimization control parameter in the follow-up process are improved.
3. The corrected quality detection set is subjected to quality influence associated parameter analysis to generate a continuous associated parameter analysis result, and the initial operation optimization control parameter is corrected according to the continuous associated parameter analysis result, so that the accuracy of equipment control on the textile equipment is improved, and the equipment control quality of the textile equipment is improved.
Example two
Based on the same inventive concept as the optimized adaptive control method for the textile equipment in the previous embodiment, the invention further provides an optimized adaptive control system for the textile equipment, and please refer to fig. 4, wherein the system comprises:
the basic information acquisition module 11 is used for acquiring basic information of the textile material, and constructing a mapping identifier of the textile product based on the basic information;
the environment data acquisition module 12, the environment data acquisition module 12 is used for acquiring the environment data of the textile product processing through the environment monitoring device to obtain environment acquisition data;
the environment fluctuation analysis module 13 is used for performing environment fluctuation analysis on the environment acquisition data and generating processing influence data based on an environment fluctuation analysis result;
the building module 14 is used for obtaining historical operating data of the textile equipment and building a parameter correction model of the textile equipment according to the historical operating data;
the control parameter obtaining module 15 is used for acquiring the operation parameters of the textile equipment, inputting the operation parameters into the parameter correction model and obtaining initial operation optimization control parameters;
the quality influence correction module 16 is used for detecting the quality of the textile product through the quality measuring device, correcting the quality influence of the quality detection result according to the mapping identification, and obtaining a corrected quality detection result;
a quality influence correlation parameter analysis module 17, wherein the quality influence correlation parameter analysis module 17 is configured to perform quality influence correlation parameter analysis according to the corrected quality detection result and the processing influence data to obtain a correlation parameter analysis result;
and the equipment control module 18 is used for correcting the equipment control parameters of the initial operation optimization control parameters according to the correlation parameter analysis result to obtain adaptive control parameters, and carrying out equipment control on the textile equipment according to the adaptive control parameters.
Further, the system further comprises:
the first judgment module is used for judging whether a parameter influence fluctuation value in the correlation parameter analysis result meets a preset fluctuation threshold value or not;
the monitoring instruction generating module is used for generating a continuous quality monitoring instruction when the parameter influence fluctuation value does not meet the preset fluctuation threshold value;
a corrected quality detection set obtaining module, configured to perform continuous acquisition of quality detection results according to the continuous quality monitoring instruction, to obtain a corrected quality detection set;
a continuous correlation parameter analysis result generation module, which is used for analyzing the quality influence correlation parameters according to the correction quality detection set to generate a continuous correlation parameter analysis result;
and the equipment control parameter correction module is used for correcting the equipment control parameters according to the continuous correlation parameter analysis result.
Further, the system further comprises:
a quality influence associated parameter set determining module, configured to perform quality influence associated parameter analysis according to the corrected quality detection set to obtain a quality influence associated parameter set, where the quality influence associated parameter set has a time identifier;
a time axis sequence sorting module, configured to perform time axis sequence sorting on the quality-impact associated parameter set according to the time identifier, and obtain a sequence sorting result;
the curve construction module is used for carrying out same-parameter change trend analysis on the sequence sorting result to obtain a same-parameter change trend curve;
the second judgment module is used for judging whether the same-parameter change trend curve meets a window trend constraint value or not;
and the continuous associated parameter analysis result determining module is used for generating a continuous associated parameter analysis result according to a trend change value when the same parameter change trend curve meets the window trend constraint value.
Further, the system further comprises:
a third determining module, configured to determine whether the revised quality detection result does not satisfy an expected quality constraint threshold;
a fit deviation influence coefficient generation module, configured to generate a fit deviation influence coefficient according to the environment acquisition data and the operation parameter when the corrected quality detection result does not satisfy the expected quality constraint threshold;
an adaptive beamforming module to adaptively beamform the textile device based on the fit deviation impact coefficient.
Further, the system further comprises:
the device comprises a constraint interval obtaining module, a constraint interval obtaining module and a constraint interval setting module, wherein the constraint interval obtaining module is used for obtaining an environment regulation and control constraint interval, and the environment regulation and control constraint interval comprises a multi-stage regulation and control constraint interval;
the environment regulation and control parameter matching module is used for matching environment regulation and control parameters according to the environment acquisition data and the environment regulation and control constraint interval;
and the environment control optimization module is used for controlling the environment regulation and control device to carry out environment control optimization according to the environment regulation and control parameter matching result.
Further, the system further comprises:
the feedback supervision instruction generation module is used for generating a feedback supervision instruction when the environment regulation and control device is started;
the environment adjusting and monitoring module is used for carrying out environment adjusting and monitoring on textile product processing according to the feedback monitoring instruction;
the early warning information generation module is used for generating abnormal environment early warning information when the environmental regulation monitoring result does not meet a preset recovery value;
and the early warning processing module is used for carrying out early warning processing according to the abnormal environment early warning information.
Further, the system further comprises:
the deviation parameter feature extraction set determination module is used for extracting the deviation parameter features of equipment operation according to the historical operation data to obtain a deviation parameter feature extraction set;
the compensation parameter set construction module is used for constructing a compensation parameter set of a deviation parameter feature extraction set, wherein the compensation parameter set and the deviation parameter feature extraction set have a corresponding relation;
a model construction module for constructing the parameter correction model from the deviating parameter feature extraction set and the compensation parameter set.
The application provides an optimized adaptive control method for a textile device, wherein the method is applied to an optimized adaptive control system for the textile device, and the method comprises the following steps: constructing a mapping identifier of a textile product through basic information of the textile; acquiring environmental data of textile product processing through an environmental monitoring device to obtain environmental acquisition data; carrying out environmental fluctuation analysis on the environmental collected data, and generating processing influence data based on an environmental fluctuation analysis result; obtaining historical operation data of the textile equipment, and constructing a parameter correction model of the textile equipment according to the historical operation data; collecting the operating parameters of the textile equipment, and inputting the operating parameters into a parameter correction model to obtain initial operation optimization control parameters; detecting the quality of the textile product through a quality measuring device, and correcting the quality influence of the quality detection result according to the mapping identification to obtain a corrected quality detection result; performing quality influence correlation parameter analysis according to the corrected quality detection result and the processing influence data to obtain a correlation parameter analysis result; and correcting the equipment control parameters of the initial operation optimization control parameters according to the correlation parameter analysis result to obtain self-adaptive control parameters, and controlling the equipment of the textile equipment through the self-adaptive control parameters. The technical problems that in the prior art, the equipment control accuracy of the spinning equipment is not enough, and then the equipment control effect of the spinning equipment is not good and the equipment production quality is not high are solved. The accuracy of equipment control is carried out to weaving equipment to the improvement has been reached, the equipment control of the intellectuality of realization weaving equipment, high accuracy to improve weaving equipment's equipment control effect, promote weaving equipment's equipment production quality's technical effect.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The specification and drawings are merely illustrative of the present application, and it is intended that the present invention cover modifications and variations of this invention provided they come within the scope of the invention and their equivalents.
Claims (8)
1. An optimized adaptive control method for textile equipment, wherein the method is applied to an optimized adaptive control system which is in communication connection with an environment monitoring device and a quality determination device, and the method comprises the following steps:
acquiring basic information of a textile material, and constructing a mapping identifier of a textile product based on the basic information;
acquiring environmental data of the textile product processing through the environmental monitoring device to obtain environmental acquisition data;
carrying out environmental fluctuation analysis on the environmental collected data, and generating processing influence data based on an environmental fluctuation analysis result;
obtaining historical operation data of the textile equipment, and constructing a parameter correction model of the textile equipment according to the historical operation data;
collecting the operating parameters of the textile equipment, and inputting the operating parameters into the parameter correction model to obtain initial operation optimization control parameters;
detecting the quality of the textile product through the quality detection device, and correcting the quality influence of a quality detection result according to the mapping identification to obtain a corrected quality detection result;
performing quality influence correlation parameter analysis according to the corrected quality detection result and the processing influence data to obtain a correlation parameter analysis result;
and correcting the equipment control parameters of the initial operation optimization control parameters according to the correlation parameter analysis result to obtain self-adaptive control parameters, and controlling the equipment of the textile equipment according to the self-adaptive control parameters.
2. The method of claim 1, wherein the method further comprises:
judging whether the parameter influence fluctuation value in the correlation parameter analysis result meets a preset fluctuation threshold value or not;
when the parameter influence fluctuation value does not meet the preset fluctuation threshold value, generating a continuous quality monitoring instruction;
continuously collecting quality detection results according to the continuous quality monitoring instructions to obtain a corrected quality detection set;
analyzing the quality influence correlation parameters according to the corrected quality detection set to generate a continuous correlation parameter analysis result;
and correcting the equipment control parameters according to the continuous correlation parameter analysis result.
3. The method of claim 2, wherein said performing a quality impact correlation parameter analysis based on said revised quality check set to generate a continuous correlation parameter analysis result, further comprises:
analyzing quality influence associated parameters according to the corrected quality detection set to obtain a quality influence associated parameter set, wherein the quality influence associated parameter set is provided with a time identifier;
performing time axis sequence sorting on the quality influence associated parameter set according to the time identification to obtain a sequence sorting result;
analyzing the same-parameter variation trend of the sequence sorting result to obtain a same-parameter variation trend curve;
judging whether the same-parameter change trend curve meets a window trend constraint value or not;
and when the same-parameter variation trend curve meets the window trend constraint value, generating the continuous associated parameter analysis result according to the trend variation value.
4. The method of claim 2, wherein when the parameter impact fluctuation value does not satisfy the preset fluctuation threshold, further comprising:
judging whether the correction quality detection result does not meet an expected quality constraint threshold value;
when the corrected quality detection result does not meet the expected quality constraint threshold, generating a matching deviation influence coefficient according to the environment acquisition data and the operation parameters;
and carrying out self-adaptive restriction on the textile equipment based on the fit deviation influence coefficient.
5. The method of claim 1, wherein the optimizing adaptive control system is communicatively coupled to an environmental conditioning device, the method comprising:
obtaining an environment regulation and control constraint interval, wherein the environment regulation and control constraint interval comprises a multi-stage regulation and control constraint interval;
matching environment regulation and control parameters according to the environment acquisition data and the environment regulation and control constraint interval;
and controlling the environment regulation and control device to carry out environment control optimization according to the environment regulation and control parameter matching result.
6. The method of claim 5, wherein the method comprises:
when the environment regulation and control device is started, a feedback supervision instruction is generated;
carrying out environment adjustment supervision on textile product processing according to the feedback supervision instruction;
when the environmental adjustment supervision result does not meet a preset recovery value, generating abnormal environmental early warning information;
and carrying out early warning processing according to the abnormal environment early warning information.
7. The method of claim 1, wherein the method comprises:
extracting the deviation parameter characteristics of equipment operation according to the historical operation data to obtain a deviation parameter characteristic extraction set;
constructing a compensation parameter set of a deviation parameter feature extraction set, wherein the compensation parameter set and the deviation parameter feature extraction set have a corresponding relation;
and constructing the parameter correction model through the deviation parameter feature extraction set and the compensation parameter set.
8. An optimized adaptive control system for textile equipment, said system being communicatively coupled to environmental monitoring means, quality determination means, said system comprising:
the basic information acquisition module is used for acquiring basic information of the textile material and constructing a mapping identifier of the textile product based on the basic information;
the environment data acquisition module is used for acquiring the environment data of the textile product processing through the environment monitoring device to obtain environment acquisition data;
the environment fluctuation analysis module is used for carrying out environment fluctuation analysis on the environment acquisition data and generating processing influence data based on an environment fluctuation analysis result;
the building module is used for obtaining historical operating data of the textile equipment and building a parameter correction model of the textile equipment according to the historical operating data;
the control parameter obtaining module is used for acquiring the operating parameters of the textile equipment, inputting the operating parameters into the parameter correction model and obtaining initial operation optimization control parameters;
the quality influence correction module is used for detecting the quality of the textile product through the quality measuring device, correcting the quality influence of the quality detection result according to the mapping identification and obtaining a corrected quality detection result;
the quality influence correlation parameter analysis module is used for carrying out quality influence correlation parameter analysis according to the corrected quality detection result and the processing influence data to obtain a correlation parameter analysis result;
and the equipment control module is used for correcting the equipment control parameters of the initial operation optimization control parameters according to the correlation parameter analysis result to obtain self-adaptive control parameters, and equipment control of the textile equipment is carried out through the self-adaptive control parameters.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116341771A (en) * | 2023-05-30 | 2023-06-27 | 江苏鹏润电缆科技有限公司 | Intelligent optimization method and system for low-temperature-resistant cable production process |
| CN116523144A (en) * | 2023-06-27 | 2023-08-01 | 天津建城基业集团有限公司 | TRD construction process control method and system in hard soil layer |
| CN116577995A (en) * | 2023-07-05 | 2023-08-11 | 广州惠特节能科技有限公司 | Intelligent control method and system for parameters of heat pump drying unit |
| CN116661402A (en) * | 2023-07-27 | 2023-08-29 | 北京盛世利科技有限公司 | Production control method and system for chemical materials |
| CN116957309A (en) * | 2023-09-21 | 2023-10-27 | 深圳市明心数智科技有限公司 | Textile scene dynamic monitoring method, system and medium |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103103661A (en) * | 2011-10-06 | 2013-05-15 | 立达英格尔施塔特有限公司 | Method for correcting a parameter dependent on the thickness of a fibre strand and textile machine with a device for stretching a fibre strand |
| CN112215488A (en) * | 2020-10-12 | 2021-01-12 | 宝鸡市大地纺织有限公司 | Weaving production line quality and fault detection statistical system |
| CN112819178A (en) * | 2021-02-05 | 2021-05-18 | 上海创能国瑞新能源科技股份有限公司 | Data prediction model training method, device and storage medium |
| US20210342705A1 (en) * | 2020-04-30 | 2021-11-04 | Maschinenfabrik Rieter Ag | Device and method for detecting a fault in a spinning mill and for estimating one or more sources of the fault |
| CN115201211A (en) * | 2022-09-15 | 2022-10-18 | 江苏牛掌柜科技有限公司 | Quality control method and system for intelligent visual textile product |
-
2022
- 2022-10-31 CN CN202211341240.9A patent/CN115421465B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103103661A (en) * | 2011-10-06 | 2013-05-15 | 立达英格尔施塔特有限公司 | Method for correcting a parameter dependent on the thickness of a fibre strand and textile machine with a device for stretching a fibre strand |
| US20210342705A1 (en) * | 2020-04-30 | 2021-11-04 | Maschinenfabrik Rieter Ag | Device and method for detecting a fault in a spinning mill and for estimating one or more sources of the fault |
| CN112215488A (en) * | 2020-10-12 | 2021-01-12 | 宝鸡市大地纺织有限公司 | Weaving production line quality and fault detection statistical system |
| CN112819178A (en) * | 2021-02-05 | 2021-05-18 | 上海创能国瑞新能源科技股份有限公司 | Data prediction model training method, device and storage medium |
| CN115201211A (en) * | 2022-09-15 | 2022-10-18 | 江苏牛掌柜科技有限公司 | Quality control method and system for intelligent visual textile product |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116341771A (en) * | 2023-05-30 | 2023-06-27 | 江苏鹏润电缆科技有限公司 | Intelligent optimization method and system for low-temperature-resistant cable production process |
| CN116341771B (en) * | 2023-05-30 | 2023-08-04 | 江苏鹏润电缆科技有限公司 | Intelligent optimization method and system for low-temperature-resistant cable production process |
| CN116523144A (en) * | 2023-06-27 | 2023-08-01 | 天津建城基业集团有限公司 | TRD construction process control method and system in hard soil layer |
| CN116523144B (en) * | 2023-06-27 | 2023-09-15 | 天津建城基业集团有限公司 | TRD construction process control method and system in hard soil layer |
| CN116577995A (en) * | 2023-07-05 | 2023-08-11 | 广州惠特节能科技有限公司 | Intelligent control method and system for parameters of heat pump drying unit |
| CN116577995B (en) * | 2023-07-05 | 2023-11-21 | 广州惠特节能科技有限公司 | Intelligent control method and system for parameters of heat pump drying unit |
| CN116661402B (en) * | 2023-07-27 | 2023-10-03 | 北京盛世利科技有限公司 | Production control method and system for chemical materials |
| CN116661402A (en) * | 2023-07-27 | 2023-08-29 | 北京盛世利科技有限公司 | Production control method and system for chemical materials |
| CN117077288A (en) * | 2023-08-21 | 2023-11-17 | 浙江大学 | Adaptive design analysis method and system for aviation aircraft element |
| CN117077288B (en) * | 2023-08-21 | 2024-03-29 | 浙江大学 | Adaptive design analysis method and system for aviation aircraft element |
| CN116957309A (en) * | 2023-09-21 | 2023-10-27 | 深圳市明心数智科技有限公司 | Textile scene dynamic monitoring method, system and medium |
| CN116957309B (en) * | 2023-09-21 | 2024-01-09 | 深圳市明心数智科技有限公司 | Textile scene dynamic monitoring method, system and medium |
| CN116966332A (en) * | 2023-09-22 | 2023-10-31 | 南通亚壹生物科技有限公司 | Textile automatic sterilization method and system based on intelligent recognition |
| CN116966332B (en) * | 2023-09-22 | 2023-12-01 | 南通亚壹生物科技有限公司 | Textile automatic sterilization method and system based on intelligent recognition |
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