CN111519294A - Yarn breakage early warning monitoring method, system and device and readable storage medium - Google Patents
Yarn breakage early warning monitoring method, system and device and readable storage medium Download PDFInfo
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- CN111519294A CN111519294A CN202010385853.7A CN202010385853A CN111519294A CN 111519294 A CN111519294 A CN 111519294A CN 202010385853 A CN202010385853 A CN 202010385853A CN 111519294 A CN111519294 A CN 111519294A
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- D—TEXTILES; PAPER
- D01—NATURAL OR MAN-MADE THREADS OR FIBRES; SPINNING
- D01H—SPINNING OR TWISTING
- D01H13/00—Other common constructional features, details or accessories
- D01H13/14—Warning or safety devices, e.g. automatic fault detectors, stop motions ; Monitoring the entanglement of slivers in drafting arrangements
- D01H13/16—Warning or safety devices, e.g. automatic fault detectors, stop motions ; Monitoring the entanglement of slivers in drafting arrangements responsive to reduction in material tension, failure of supply, or breakage, of material
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- D—TEXTILES; PAPER
- D01—NATURAL OR MAN-MADE THREADS OR FIBRES; SPINNING
- D01H—SPINNING OR TWISTING
- D01H13/00—Other common constructional features, details or accessories
- D01H13/32—Counting, measuring, recording or registering devices
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Abstract
The invention relates to a yarn breakage early warning monitoring method, a system, a device and a readable storage medium, wherein the method comprises the following steps: data acquisition: acquiring production data of various types of spinning equipment, wherein the production parameters comprise but are not limited to yarn breakage confirmation information, yarn thickness and yarn pressure borne by a yarn feeding roller; data processing: processing and classifying the acquired production data according to a general mathematical processing method, and performing rationality judgment on the processed and classified data according to a preset judgment engine; and (3) data analysis: classifying and judging the data judged to be reasonable according to a preset mathematical model to obtain result data; and (3) result feedback: and feeding back the obtained result data to a controller of the spinning equipment, wherein the result data comprises but is not limited to normal parameter instructions, stop instructions, alarm signals, early warning signals and trigger signals for controlling the wiring robot to be in place. The invention can early warn the broken wire condition and effectively prevent the broken wire.
Description
Technical Field
The invention relates to the technical field of textile processing, in particular to a yarn breakage early warning monitoring method, a yarn breakage early warning monitoring system, a yarn breakage early warning monitoring device and a readable storage medium.
Background
The yarn is a product with certain fineness processed by various textile fibers, is used for weaving, rope making, thread making, knitting, embroidery and the like, and is divided into staple fiber yarn, continuous filament yarn and the like. In the actual textile production operation, yarns and textile machinery are needed, and before the yarns enter the textile machinery, the yarns often exceed the bearing capacity of the yarns due to the fact that the drawing force or speed of the yarns is too high, so that the yarns are stretched and broken, and production interruption is caused; it is therefore common to provide textile machines with thread breakage detection means for warning and/or stopping the operation of the textile machine in the event of a thread breakage.
For example, the conventional chinese patent publication No. CN110592784A provides a method for detecting a broken yarn in a loom, which includes: s100, identifying an operator of the loom; s200, detecting the yarn through a photoelectric detection device, outputting a photoelectric detection signal, and judging whether the yarn is broken or not through comparative analysis of the photoelectric detection signal; and step S300, stopping the operation of the loom in time when the yarn breakage occurs and informing an operator of yarn continuation in time. The device realizes timely detection and judgment of the disconnection phenomenon and timely informs the staff of line continuation.
However, the above prior art solutions have the following drawbacks: according to the technical scheme, the alarm can be only carried out after the broken yarn occurs, the early warning can not be carried out on the condition of possible broken yarn, so that the adjustment and processing opportunities cannot be given to workers, and once the broken yarn accident occurs, in order to enable the textile production to be normally carried out, the workers not only need to adjust and process equipment, but also need to spend a large amount of time to complete the wiring work of the broken yarn part, and the production efficiency is seriously influenced.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a yarn breakage early warning monitoring method, a system, a device and a readable storage medium.
The invention aims to provide a yarn breakage early warning and monitoring method, which has the advantages that the yarn breakage condition can be early warned in advance, and the reduction of yarn breakage is facilitated;
the invention also aims to provide a yarn breakage early warning and monitoring system which can early warn breakage in real time according to production parameters of an equipment end, so that the production efficiency is effectively ensured;
the invention aims to provide a yarn breakage early warning monitoring device which can perform early warning analysis on the yarn breakage condition and reduce the occurrence of yarn breakage accidents;
the fourth purpose of the invention is to provide a readable storage medium which can store corresponding programs and has the characteristic of early warning on the line breakage condition.
The above object of the present invention is achieved by the following technical solutions:
a yarn breakage early warning monitoring method comprises the following steps:
s100, data acquisition: the method comprises the steps of acquiring production data of various types of spinning equipment, wherein the production data of various types comprises but is not limited to yarn breakage confirmation information when yarn is broken, thickness data of the yarn and yarn pressure data borne by a yarn feeding roller;
s200, data processing: processing and classifying the production data acquired in the step S100 according to a general mathematical processing method, and performing rationality judgment on the processed and classified data according to a preset judgment engine;
s300, data analysis: classifying and judging the data judged to be reasonable in the step S200 according to a preset mathematical model to obtain result data;
s400, result feedback: and feeding back result data obtained in the step S300 to a controller of the spinning equipment, wherein the result data comprises but is not limited to normal parameter commands, stop commands, alarm signals, early warning signals and trigger signals for controlling the wiring device to be in place.
By adopting the technical scheme, the production condition of the spinning equipment can be analyzed in real time according to the collected production data of the spinning equipment, when the production data of the spinning equipment is abnormal, such as the thickness data of the yarn and the yarn pressure data borne by the yarn feeding roller are out of the range of the preset threshold value, the spinning equipment can be controlled to be stopped immediately and an alarm or early warning signal is sent out, early warning before the yarn breakage is realized, and the occurrence of the yarn breakage condition is favorably reduced.
The present invention in a preferred example may be further configured to: in step S100, the manner of acquiring the production data of the spinning apparatus includes:
obtaining the broken yarn confirmation information when the yarn is broken through the broken yarn detector; acquiring thickness data of the yarn through a visual detection device; and obtaining yarn pressure data borne by the yarn feeding roller through a yarn tension detector;
the visual detection device comprises a plurality of machine vision industrial cameras arranged on the outer side of the yarn, and the machine vision industrial cameras are uniformly distributed along the circumferential direction of the yarn.
By adopting the technical scheme, whether the yarn is broken or not, whether the yarn is too thin or broken, and whether the yarn pressure is too large or not in the production process of the spinning equipment can be accurately detected, so that the data support is well made for the yarn breakage early warning judgment.
The present invention in a preferred example may be further configured to: step S200 includes the following substeps:
s201, data classification: classifying and marking the production data acquired in the step S100 according to categories;
s202, data acquisition: sequentially or simultaneously acquiring the characteristic parameters of each type of production data in the marked production data of multiple types;
s203, data inspection: detecting the validity and integrity of the characteristic parameters obtained in the step S202 according to a preset judgment engine and the classification marks in the step S201, and eliminating invalid parameters and damaged parameters, wherein the remaining characteristic parameters are reasonable characteristic parameters;
s204, data transmission: and binding the reasonable characteristic parameters obtained in the step S203 with the corresponding classification marks of the production data, and then entering the step S300.
By adopting the technical scheme, invalid and damaged data in the data acquisition process can be removed, the pressure of data processing is reduced, and misjudgment on the production condition of equipment is also avoided.
The present invention in a preferred example may be further configured to: the substep S204 specifically includes:
binding the reasonable characteristic parameters obtained in the step S203 with the corresponding classification marks of the production data, and then entering the step S300 and simultaneously sending the reasonable characteristic parameters to a preset cloud for storage;
the mathematical model used in step S300 is issued by the cloud, and the cloud periodically corrects the established mathematical model according to all stored characteristic parameter data, and applies the corrected mathematical model to step S300 after the model correction is completed.
By adopting the technical scheme, the mathematical model can be corrected and updated according to the equipment data in actual industrial operation, so that the matching degree of the mathematical model and the actual application environment is better.
The second aim of the invention is realized by the following technical scheme:
a yarn breakage early warning monitoring system comprises:
the device end is used for producing yarns and detecting various types of production parameters in the production process, wherein the various types of production parameters comprise but are not limited to yarn breakage confirmation information, yarn thickness data and yarn pressure data borne by a yarn feeding roller;
the edge terminal is used for acquiring the production parameters acquired by the equipment terminal, obtaining result data according to a preset mathematical model and the acquired production parameters and feeding the result data back to a controller of the spinning equipment, wherein the result data comprises but is not limited to a shutdown instruction, an alarm signal, an early warning signal and a trigger signal for controlling the wiring device to be in place; and the number of the first and second groups,
the cloud end is used for establishing a mathematical model used for judging result data corresponding to the production parameters according to the stored historical data of the production parameters and sending the established mathematical model to the edge end for application;
the edge end is further used for processing and classifying the acquired production parameters according to a general mathematical processing method after the production parameters acquired by the equipment end are acquired, and sending the processed and classified production parameters to the cloud end for storage; and the cloud periodically corrects the established mathematical model according to all the stored production parameter data, and updates the corrected mathematical model to the edge terminal after the model correction is completed.
By adopting the technical scheme, the system is divided into three layers of the equipment end, the edge end and the cloud end, the edge end can detect the operation condition of the equipment end in real time, real-time early warning can be carried out before the broken line occurs, the cloud end can correct the mathematical model in time according to actual equipment operation data and update the mathematical model to the edge end, the matching degree of the mathematical model and actual spinning equipment is ensured, and the accuracy of an early warning judgment result is also ensured.
The present invention in a preferred example may be further configured to: the edge end comprises a data acquisition module, a data processing module and a data analysis module;
the data acquisition module is in communication connection with a controller of spinning equipment at an equipment end and is used for acquiring production parameters of the spinning equipment, and the production parameters corresponding to the spinning equipment are stored in the controller of the spinning equipment;
the data processing module is connected with the data acquisition module and used for processing and classifying the production parameters acquired by the data acquisition module, performing rationality judgment on the processed and classified data according to a preset judgment engine, and sending the rational data to the data analysis module after the rationality judgment;
the data analysis module is used for classifying and judging the received reasonable data according to a preset mathematical model to obtain result data, and meanwhile, the result data are fed back to a controller corresponding to the spinning equipment.
By adopting the technical scheme, the data processing module can eliminate some data which do not accord with industrial practice and then send the data to the data analysis module, so that the judgment error caused by obvious data error is eliminated, and the accuracy of section line early warning judgment is ensured.
The present invention in a preferred example may be further configured to: the cloud comprises:
the database is stored with production parameter historical data and is used for storing production parameters sent by the edge terminal;
the model building module is used for building a learning mathematical model according to the parameter characteristics of the spinning equipment; and the number of the first and second groups,
the model training module is used for training the learning mathematical model established by the model establishing module according to all production parameter data in the database and a preset training algorithm and obtaining parameter items of the mathematical model;
and after the model training module obtains the parameter items of the model, updating the trained mathematical model to the edge end in real time.
By adopting the technical scheme, a learning mathematical model can be established according to historical data of production parameters stored in the database and trained, so that the matching degree of the finally obtained mathematical model and the actual equipment operation scene is higher, and the final early warning judgment accuracy of the system is higher.
The present invention in a preferred example may be further configured to: the cloud end also comprises a model verification module and a cache library; the model verification module is used for judging whether the parameter items of the mathematical model obtained by the model training module are reasonable or not, updating the mathematical model judged to be reasonable to the edge end, and storing the mathematical model judged to be unreasonable to the cache library.
By adopting the technical scheme, the mathematical model obtained after correction can be verified after the mathematical model is corrected, and the mathematical model passing the verification can be updated to the edge end, so that the normal work of the edge end is ensured.
The third object of the invention is realized by the following technical scheme:
a yarn breakage early warning monitoring device comprises a memory and a processor, wherein a computer program which can be loaded by the processor and can execute any one of the yarn breakage early warning monitoring methods is stored in the memory.
Through adopting above-mentioned technical scheme, it has the characteristics that can carry out early warning to the broken string condition of spinning equipment in advance, makes things convenient for the staff in time to know the equipment abnormal conditions, has improved work efficiency.
The fourth object of the invention is realized by the following technical scheme:
a computer readable storage medium storing a computer program capable of being loaded by a processor and executing any of the yarn breakage warning monitoring methods described above.
By adopting the technical scheme, the corresponding program can be stored, and the yarn breaking condition early warning device has the characteristic of early warning on the yarn breaking condition of spinning equipment.
In summary, the invention includes at least one of the following beneficial technical effects:
1. the method has the advantages that the production data of various types of the spinning equipment are collected and analyzed in real time, and result data reflecting the running condition of the spinning equipment can be rapidly obtained by combining a mathematical model trained according to historical production parameter data of the spinning equipment, for example, when the thickness data of yarn or the pressure data of the yarn borne by a yarn feeding roller is out of a preset threshold range, the spinning equipment can be controlled to be stopped immediately and an alarm or early warning signal is sent out, the early warning of the yarn breakage condition is realized, the reduction of the yarn breakage condition is facilitated, and the working efficiency of spinning production is effectively improved;
2. the production parameters of the spinning equipment are sent to the cloud and stored, the cloud can periodically correct the established mathematical model according to all stored characteristic parameter data, and the corrected mathematical model is applied to the edge end used for performing real-time early warning and judgment on the spinning equipment after the model correction is completed, so that a broken yarn early warning and judging mechanism of the edge end is more in line with the actual production environment of the spinning equipment, and the accuracy of broken yarn early warning and judgment is improved.
Drawings
FIG. 1 is a block diagram illustrating a yarn breakage warning and monitoring system according to an embodiment;
fig. 2 is a block diagram of a connection relationship between a yarn device and an intelligent mobile terminal according to an embodiment;
FIG. 3 is a flow chart of a method for monitoring yarn breakage warning in accordance with the second embodiment;
fig. 4 is a schematic structural diagram for showing the positional relationship between the machine vision industrial camera and the yarn in the second embodiment.
In the figure, 1, a spinning apparatus; 11. a controller; 12. a machine vision industrial camera; 13. a solid color baffle; 14. an early warning device; 15. an alarm device; 16. a wiring device; 17. a communication module; 2. an equipment end; 3. an edge end; 31. a data acquisition module; 32. a data processing module; 33. a data analysis module; 4. a cloud end; 41. a database; 42. a model building module; 43. a model training module; 44. a model verification module; 45. a cache bank; 5. an intelligent mobile terminal; 51. a display screen is touched.
Detailed Description
In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples thereof.
The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings.
Example one
Referring to fig. 1, the yarn breakage early warning monitoring system disclosed by the invention comprises an equipment end 2, an edge end 3 and a cloud end 4. The equipment end 2 is used for carrying out yarn production and detecting various types of production parameters in the production process. The various production parameters include but are not limited to yarn breakage confirmation information, yarn thickness data and yarn pressure data of the yarn feeding roller.
Referring to fig. 1 and 2, the edge terminal 3 is configured to obtain various types of production parameters collected by the device terminal 2, obtain result data according to a preset mathematical model and the obtained production parameters, and feed back the result data to the controller 11 of the spinning device 1 of the device terminal 2. The resulting data from the edge terminal 3 includes, but is not limited to, parameter normal commands, shutdown commands, alarm signals, pre-alarm signals, and trigger signals to control the wiring device 16 in place. Specifically, when the edge end 3 acquires the yarn breakage confirmation information of the yarn, the result data obtained by the edge end 3 is a stop instruction, an alarm signal and a trigger signal for controlling the wiring device 16 to be in place; when the edge end 3 acquires that the thickness value of the yarn is outside the preset thickness threshold range and/or the yarn pressure value borne by the yarn feeding roller is outside the preset pressure threshold range, the result data obtained by the edge end 3 are a shutdown instruction, an early warning signal and a trigger signal for controlling the wiring device 16 to be in place; when the edge end 3 does not acquire the yarn breakage confirmation information and the thickness value of the yarn and the yarn pressure value borne by the yarn feeding roller are both within the normal range, the result data obtained by the edge end 3 is a parameter normal instruction.
Referring to fig. 1, the cloud 4 is configured to establish a mathematical model for determining result data corresponding to the production parameters according to stored historical data of the production parameters, and the cloud 4 sends the established mathematical model to the edge 3 for application in real time after establishing the mathematical model.
Referring to fig. 1 and 2, a controller 11 of the spinning apparatus 1 is connected with an early warning device 14, an alarm device 15, and a wiring device 16. When the controller 11 receives the normal parameter command from the edge terminal 3, the controller does not perform the actions of early warning and/or alarming and/or stopping, and at this time, the spinning device 1 keeps normal production operation. When the controller 11 receives the stop command, the controller 11 controls the spinning device 1 to stop operation. When the controller 11 receives the alarm signal, the controller 11 controls the alarm device 15 to alarm. When the controller 11 receives the warning signal, the controller 11 controls the warning device 14 to warn. When the controller 11 receives a trigger signal that controls the wiring device 16 to be in place, the controller 11 controls the wiring device 16 to activate and/or controls the transport cart carrying the wiring device 16 to move to a designated equipment location. The alarm signal from the alarm device 15 and the warning signal from the warning device 14 are one or a combination of a long-time illumination of an alarm lamp, a flashing alarm lamp, a buzzer signal, and the like, and in the present embodiment, the yarn connecting device 16 is a yarn cutting and connecting machine that facilitates connection in the event of a yarn break.
Referring to fig. 2, the device side 2 further includes an intelligent mobile terminal 5, and a touch display screen 51 is disposed on the intelligent mobile terminal 5. The controller 11 is further connected with a communication module 17, and the controller 11 is in wireless communication connection with the intelligent mobile terminal 5 through the communication module 17. When the controller 11 controls the operation of the early warning device 14 and/or the operation of the alarm device 15 and/or the operation of the wiring device 16, corresponding display information is simultaneously sent to the intelligent mobile terminal 5 through the communication module 17. After receiving the display information sent by the communication module 17, the intelligent mobile terminal 5 can display the fault condition of the corresponding spinning device 1 on the touch display screen 51, and the intelligent mobile terminal 5 can simultaneously send out prompt signals such as the illumination of an alarm lamp and the sound of a buzzer, so that the worker can quickly know the abnormal condition of the device to quickly process the abnormal condition.
Referring to fig. 1, the edge terminal 3 includes a data acquisition module 31, a data processing module 32, and a data analysis module 33. Referring to fig. 2, the data collection module 31 is in communication connection with the controller 11 of the spinning device 1 of the device end 2 through the communication module 17 and is used for acquiring the production parameters of the spinning device 1, and the production parameters corresponding to the spinning device 1 are stored in the controller 11 and are sent to the data collection module 31 of the edge end 3 by the controller 11 through the communication module 17.
Referring to fig. 1, the data processing module 32 is connected to the data acquisition module 31, and is configured to process and classify the production parameters acquired by the data acquisition module 31 according to a preset general mathematical processing method and a judgment engine, then perform rationality judgment on the data, and send the rational data to the data analysis module 33 after judging that the data is rational. Specifically, the general mathematical processing method adopts clustering, regression or fitting, and the judgment engine adopts an upper and lower limit rule engine. Taking the yarn pressure data received by the yarn feeding roller as an example, when the yarn pressure value exceeds the upper and lower pressure limit values in the upper and lower limit rule engines and/or the growth rate of the yarn pressure value in unit time exceeds the upper and lower pressure increase rate limit values in the upper and lower limit rule engines, the judgment is reasonable, otherwise, the judgment is unreasonable. It should be noted that values outside the range of the upper and lower pressure limit values and values outside the range of the upper and lower pressure increase rate limit values included in the upper and lower rule engines are values that do not occur in the industrial production process of the yarn.
Referring to fig. 1, the data analysis module 33 is connected to the data processing module 32, and is configured to classify and judge the received reasonable data according to the mathematical model sent from the cloud 4, obtain result data, and feed back the result data to the controller 11 of the corresponding spinning device 1. When the data processing module 32 sends the data judged to be reasonable to the data analysis module 33, the data is sent to the cloud end 4 for storage, the cloud end 4 periodically corrects the established mathematical model according to all stored production parameter data, and the corrected mathematical model is updated to the data analysis module 33 of the edge end 3 after the model correction is completed.
Referring to fig. 1, cloud 4 includes a database 41, a model building module 42, and a model training module 43. The database 41 stores history data of the production parameters and is used for storing the production parameters sent by the edge terminal 3. The model construction module 42 is used for establishing a learning mathematical model according to the parameter characteristics of the spinning device 1. The model training module 43 is configured to train the learning mathematical model established by the model establishing module 42 according to all production parameter data in the database 41 and a preset training algorithm, and obtain parameter items of the mathematical model. It should be noted that the learning mathematical model established by the model establishing module 42 is a linear regression model or a genetic algorithm mathematical model, and the model training module 43 is configured to train the established learning mathematical model by using a gradient descent algorithm according to all production parameter data in the database 41, and obtain parameter items of the model.
Referring to fig. 1, the cloud 4 further includes a model verification module 44 and a cache 45, where the model verification module 44 is connected to the model training module 43 and is configured to determine whether a parameter item of the mathematical model obtained by the model training module 43 is reasonable. Specifically, the model verification module 44 may obtain preset production parameters for the model verification when the mathematical model obtained by the model training module 43 is reasonable, and production parameters for the model verification when the spinning device 1 operates normally and production parameters for the model verification when the spinning device 1 operates abnormally, and if the results obtained by bringing the production parameters into the mathematical model are correct, the mathematical model is defined as reasonable, otherwise, the mathematical model is defined as unreasonable. Finally, the model verification module 44 updates the mathematical model judged to be reasonable to the data analysis module 33 of the edge 3 in real time, and stores the mathematical model judged to be unreasonable to the cache library 45 for subsequent analysis.
Example two
Based on the yarn breakage early warning monitoring system in the first embodiment, the first embodiment provides a yarn breakage early warning monitoring method, referring to fig. 2 and 3, which includes the following steps:
s100, data acquisition: production data of a plurality of types of the spinning apparatus 1 are acquired, including but not limited to yarn breakage confirmation information at the time of yarn breakage, thickness data of the yarn, and yarn pressure data received by the yarn feed roller.
S200, data processing: the production data acquired in step S100 is processed and classified according to a general mathematical processing method, and the processed and classified data is subjected to rationality judgment according to a preset judgment engine.
S300, data analysis: and classifying and judging the data judged to be reasonable in the step S200 according to a preset mathematical model to obtain result data.
S400, result feedback: the result data obtained in step S300, including but not limited to parameter normal command, stop command, alarm signal, pre-warning signal and trigger signal to control the wiring device 16 in place, is fed back to the controller 11 (refer to fig. 2) of the spinning apparatus 1.
Referring to fig. 3, in step S100, the manner of acquiring the production data of the spinning apparatus 1 includes: obtaining the broken yarn confirmation information when the yarn is broken through the broken yarn detector; acquiring thickness data of the yarn through a visual detection device; and acquiring the yarn pressure data borne by the yarn feeding roller through a yarn tension detector. Referring to fig. 4, the visual inspection apparatus includes a plurality of machine vision industrial cameras 12 disposed outside the yarn, and the plurality of machine vision industrial cameras 12 are uniformly distributed along the circumferential direction of the yarn.
Specifically, three machine vision industrial cameras 12 are arranged in the embodiment, pure color baffles 13 are connected between every two adjacent machine vision industrial cameras 12, and angles between every two adjacent pure color baffles 13 are 60 degrees, so that two sides of each machine vision industrial camera 12 are provided with one pure color baffle 13, and the opposite side of the machine vision industrial camera 12 is also provided with one pure color baffle 13 perpendicular to the shooting direction of the machine vision industrial camera. When the machine vision industrial camera 12 takes a picture of the yarn, it can take a picture of the yarn whose ground color is a pure color, which contributes to further determining the thickness of the yarn. And because machine vision industry camera 12 has set up three, the yarn thickness value that three machine vision industry camera 12 detected can calibrate each other to obtain comparatively accurate yarn thickness value. Specifically, the yarn thickness values obtained by the three machine vision industrial cameras 12 may be averaged to obtain a final yarn thickness value, and whether the yarn has yarn breakage or strand breakage may be determined by one or more yarn thickness values.
Referring to fig. 3, step S200 includes the following sub-steps:
s201, data classification: the production data obtained in step S100 are classified and labeled according to categories according to a general mathematical processing method, which may be a clustering, regression, or fitting method.
S202, data acquisition: and sequentially or simultaneously acquiring the characteristic parameters of each type of production data in the marked various types of production data, for example, when the production data is yarn pressure data, the characteristic parameters are the pressure value in the yarn pressure data borne by the yarn feeding roller and the growth rate of the yarn pressure value in unit time.
S203, data inspection: detecting the validity and integrity of the characteristic parameters obtained in the step S202 according to a preset judgment engine and the classification marks in the step S201, and eliminating invalid parameters and damaged parameters, wherein the remaining characteristic parameters are reasonable characteristic parameters; in this embodiment, the judgment engine uses an upper and lower limit rule engine.
S204, data transmission: and (5) binding the reasonable characteristic parameters obtained in the step (S203) with the corresponding classification marks of the production data, and then entering the step (S300) and simultaneously sending the reasonable characteristic parameters to a preset cloud end 4 for storage.
Referring to fig. 1 and 3, the mathematical model used in step S300 is issued by the cloud end 4, the cloud end 4 periodically corrects the established mathematical model according to all stored characteristic parameter data, and the corrected mathematical model is applied to step S300 after the model correction is completed. Specifically, the model modification method is as follows: and retraining the established learning mathematical model according to all production parameter data stored in the cloud 4 and a preset training algorithm, so as to obtain a parameter item of the corrected mathematical model. It should be noted that the established learning mathematical model is a linear regression model or a genetic algorithm mathematical model, and a gradient descent algorithm is adopted to train the established learning mathematical model during model training.
EXAMPLE III
The yarn breakage early warning monitoring device comprises a memory and a processor, wherein the memory is stored with a computer program which can be loaded by the processor and can execute the yarn breakage early warning monitoring method in the second embodiment.
Example four
A computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the yarn breakage warning monitoring method according to the second embodiment, the computer-readable storage medium includes, for example: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above examples are only used to illustrate the technical solutions of the present invention, and do not limit the scope of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from these embodiments without making any inventive step, fall within the scope of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art may still make various combinations, additions, deletions or other modifications of the features of the embodiments of the present invention according to the situation without conflict, so as to obtain different technical solutions without substantially departing from the spirit of the present invention, and these technical solutions also fall within the protection scope of the present invention.
Claims (10)
1. A yarn breakage early warning monitoring method is characterized by comprising the following steps:
s100, data acquisition: acquiring various types of production data of the spinning equipment (1), wherein the various types of production data comprise but are not limited to yarn breakage confirmation information when the yarn is broken, thickness data of the yarn and yarn pressure data borne by a yarn feeding roller;
s200, data processing: processing and classifying the production data acquired in the step S100 according to a general mathematical processing method, and performing rationality judgment on the processed and classified data according to a preset judgment engine;
s300, data analysis: classifying and judging the data judged to be reasonable in the step S200 according to a preset mathematical model to obtain result data;
s400, result feedback: and feeding back result data obtained in the step S300 to a controller (11) of the spinning equipment (1), wherein the result data comprises but is not limited to normal parameter commands, stop commands, alarm signals, early warning signals and trigger signals for controlling the wiring device to be in place.
2. Method according to claim 1, characterized in that in step S100, the way of acquiring production data of the spinning apparatus (1) comprises:
obtaining the broken yarn confirmation information when the yarn is broken through the broken yarn detector; acquiring thickness data of the yarn through a visual detection device; and obtaining yarn pressure data borne by the yarn feeding roller through a yarn tension detector;
wherein the visual detection device comprises a plurality of machine vision industrial cameras (12) arranged on the outer side of the yarn, and the plurality of machine vision industrial cameras (12) are uniformly distributed along the circumferential direction of the yarn.
3. The method according to claim 1, characterized in that step S200 comprises the following sub-steps:
s201, data classification: classifying and marking the production data acquired in the step S100 according to categories;
s202, data acquisition: sequentially or simultaneously acquiring the characteristic parameters of each type of production data in the marked production data of multiple types;
s203, data inspection: detecting the validity and integrity of the characteristic parameters obtained in the step S202 according to a preset judgment engine and the classification marks in the step S201, and eliminating invalid parameters and damaged parameters, wherein the remaining characteristic parameters are reasonable characteristic parameters;
s204, data transmission: and binding the reasonable characteristic parameters obtained in the step S203 with the corresponding classification marks of the production data, and then entering the step S300.
4. The method according to claim 1, wherein the substep S204 specifically comprises:
binding the reasonable characteristic parameters obtained in the step S203 with the corresponding classification marks of the production data, then entering the step S300 and simultaneously sending the reasonable characteristic parameters to a preset cloud (4) for storage;
the mathematical model used in the step S300 is issued by the cloud (4), the cloud (4) periodically corrects the established mathematical model according to all stored characteristic parameter data, and the corrected mathematical model is applied to the step S300 after the model correction is completed.
5. The utility model provides a yarn broken string early warning monitoring system which characterized in that includes:
the device end (2) is used for producing yarns and detecting various types of production parameters in the production process, wherein the various types of production parameters comprise but are not limited to yarn breakage confirmation information, yarn thickness data and yarn pressure data borne by a yarn feeding roller;
the edge terminal (3) is used for acquiring the production parameters acquired by the equipment terminal (2), obtaining result data according to a preset mathematical model and the acquired production parameters and feeding the result data back to the controller (11) of the spinning equipment (1), wherein the result data comprises but is not limited to a shutdown instruction, an alarm signal, an early warning signal and a trigger signal for controlling the wiring device to be in place; and the number of the first and second groups,
the cloud end (4) is used for establishing a mathematical model used for judging result data corresponding to the production parameters according to the stored historical production parameter data and sending the established mathematical model to the edge end (3) for application;
the edge terminal (3) is further used for processing and classifying the acquired production parameters according to a general mathematical processing method after the production parameters acquired by the equipment terminal (2) are acquired, and sending the processed and classified production parameters to the cloud terminal (4) for storage; the cloud end (4) corrects the established mathematical model periodically according to all stored production parameter data, and updates the corrected mathematical model to the edge end (3) after model correction is completed.
6. The system according to claim 5, characterized in that said edge terminal (3) comprises a data acquisition module (31), a data processing module (32) and a data analysis module (33);
the data acquisition module (31) is in communication connection with a controller (11) of the spinning equipment (1) of the equipment end (2) and is used for acquiring production parameters of the spinning equipment (1), and the production parameters corresponding to the spinning equipment (1) are stored in the controller (11) of the spinning equipment;
the data processing module (32) is connected with the data acquisition module (31) and is used for processing and classifying the production parameters acquired by the data acquisition module (31), judging the rationality of the processed and classified data according to a preset judgment engine, and sending the rational data to the data analysis module (33) after judging that the rationality is reasonable;
the data analysis module (33) is used for carrying out classification judgment on the received reasonable data according to a preset mathematical model to obtain result data, and meanwhile, the result data are fed back to the controller (11) corresponding to the spinning equipment (1).
7. System according to claim 5, characterized in that the cloud (4) comprises:
the database (41) is stored with production parameter historical data and is used for storing production parameters sent by the edge terminal (3);
a model construction module (42) for establishing a learning mathematical model according to the parameter characteristics of the spinning apparatus (1); and the number of the first and second groups,
the model training module (43) is used for training the learning mathematical model established by the model establishing module (42) according to all production parameter data in the database (41) and a preset training algorithm, and obtaining parameter items of the mathematical model;
after the model training module (43) obtains the parameter items of the model, the trained mathematical model is updated to the edge end (3) in real time.
8. The system according to claim 7, characterized in that the cloud (4) further comprises a model verification module (44) and a cache (45); the model verification module (44) is used for judging whether the parameter items of the mathematical model obtained by the model training module (43) are reasonable or not, updating the mathematical model judged to be reasonable to the edge terminal (3), and storing the mathematical model judged to be unreasonable to the cache library (45).
9. A yarn breakage warning monitoring device, comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and that executes the method according to any one of claims 1 to 4.
10. A computer-readable storage medium, in which a computer program is stored which can be loaded by a processor and which executes the method of any one of claims 1 to 4.
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