CN116334822A

CN116334822A - Jacquard production control system and method

Info

Publication number: CN116334822A
Application number: CN202310448505.3A
Authority: CN
Inventors: 沈智杰; 黄泽华
Original assignee: WUJIANG GUANGYU TEXTILE CO Ltd
Current assignee: WUJIANG GUANGYU TEXTILE CO Ltd
Priority date: 2023-04-24
Filing date: 2023-04-24
Publication date: 2023-06-27
Anticipated expiration: 2043-04-24
Also published as: CN116334822B

Abstract

The embodiment of the specification provides a jacquard production control system, which comprises a jacquard host machine, a feeding module, a traction module, a driving module, a monitoring module and a processor. The feeding module is used for feeding materials, the traction module is used for conveying the materials, the driving module is used for providing power, the monitoring module is mechanically connected with the feeding module, the traction module and the driving module respectively, and the monitoring module is used for acquiring monitoring data and transmitting the monitoring data to the processor. The processor is respectively in communication connection with the jacquard host machine, the feeding module, the traction module, the driving module and the monitoring module, and is used for determining working parameters of the jacquard production control system based on the monitoring data, wherein the working parameters at least comprise weaving speed.

Description

Jacquard production control system and method

Technical Field

The specification relates to the technical field of jacquard machines, in particular to a jacquard machine production control system and a jacquard machine production control method.

Background

The jacquard knitting machine has a jacquard device for lifting yarns, and is a loom capable of knitting pattern on silk. The jacquard can store jacquard information, can weave hats, sweater and pants with patterns, and the like, and is widely applied in the textile field; the modern electronic jacquard machine weaves different patterns by lifting the positions of different warps and matching with a loom through the suction and release processes of an electromagnet. The detection analysis is carried out on the mechanical running state of the jacquard, sometimes the detection analysis cannot be accurately judged, and the jacquard is often operated in an unhealthy state until the jacquard fails to be overhauled.

CN113606478B discloses a lubricating oil coating device for jacquard draw bars, which can ensure the stable operation of the jacquard to a certain extent, but cannot monitor the weaving process.

Therefore, it is desirable to provide a jacquard production control system and method, which can realize intelligent monitoring of the jacquard working process so as to solve the problem of low jacquard state detection accuracy.

Disclosure of Invention

One or more embodiments of the present specification provide a jacquard machine production control system, the system comprising: the jacquard machine comprises a jacquard machine host, a feeding module, a traction module, a driving module, a monitoring module and a processor; the system comprises a feeding module, a traction module, a driving module, a monitoring module, a processor, a power supply module, a control module and a control module, wherein the feeding module is used for feeding materials, the traction module is used for conveying the materials, the driving module is used for providing power, the monitoring module is respectively and mechanically connected with the feeding module, the traction module and the driving module, and the monitoring module is used for acquiring monitoring data and transmitting the monitoring data to the processor; the processor is in communication connection with the jacquard host, the feeding module, the traction module, the driving module and the monitoring module respectively, and is used for: and determining working parameters of the jacquard production control system based on the monitoring data, wherein the working parameters at least comprise weaving speed.

One or more embodiments of the present specification provide a jacquard machine production control method, the method being performed by a processor, comprising: based on the monitoring data, operating parameters of the jacquard production control system are determined, said operating parameters comprising at least the weaving speed.

One or more embodiments of the present specification provide a jacquard machine production control device comprising at least one processor and at least one memory; the at least one memory is configured to store computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement the jacquard machine production control method of any one of the above embodiments.

One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions that, when read by a computer, perform the jacquard machine production control method according to any one of the above embodiments.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is an exemplary block diagram of a jacquard machine production control system shown in accordance with some embodiments of the present description;

FIG. 2 is an exemplary schematic diagram of a jacquard machine production control method according to some embodiments of the present disclosure;

FIG. 3 is an exemplary schematic diagram of a suction model shown in accordance with some embodiments of the present description;

FIG. 4 is an exemplary schematic diagram illustrating a determination of a second operating parameter of a lubrication module according to some embodiments of the present disclosure.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

FIG. 1 is an exemplary block diagram of a jacquard machine production control system according to some embodiments of the present description. As shown in FIG. 1, the jacquard production control system 100 may include a jacquard host 110, a supply module 120, a traction module 130, a drive module 140, a monitoring module 150, and a processor 160.

The jacquard main frame 110 refers to a main body portion for weaving pattern production cloth.

The feed module 120 refers to a device or component for feeding material. Wherein the material may comprise yarns, fibers, etc.

The traction module 130 refers to an apparatus or component for transporting material.

The drive module 140 refers to a device or component for providing power.

The monitoring module 150 refers to a device or component for acquiring monitoring data. In some embodiments, the monitoring module 150 may transmit the acquired monitoring data to the processor 160. In some embodiments, the monitoring module 150 may be mechanically coupled to the feeding module 120, the traction module 130, and the drive module 140, respectively. The connection of the monitoring module 150 to the feeding module 120, the traction module 130 and the driving module 140 may be other connection methods.

In some embodiments, the monitoring module 150 may also include a noise monitoring module and a cloth appearance monitoring module.

A noise monitoring module refers to a device or component for acquiring noise data. For example, the noise monitoring module may include a pickup, a microphone, and the like.

The cloth appearance monitoring module refers to a device or component for acquiring cloth appearance data. For example, the cloth appearance monitoring module may include a camera or the like.

Processor 160 refers to a device or component for processing data and/or information obtained from other devices or system components. Processor 160 may execute program instructions to perform one or more of the functions described herein based on such data, information, and/or processing results. For example, the processor 160 may determine operating parameters of the jacquard machine production control system based on the monitoring data. In some embodiments, the processor 160 may be communicatively coupled to the jacquard host 110, the supply module 120, the traction module 130, the drive module 140, and the monitoring module 150.

In some embodiments, the jacquard production control system 100 may also include a humidification module (not shown).

A humidification module refers to a device or component for humidification.

In some embodiments, the jacquard production control system 100 may also include a cleaning module (not shown).

A cleaning module refers to a device or component for cleaning the produced cloth. For example, the cleaning module may include a negative pressure suction device, a cold plasma electrostatic cleaning device, and the like.

Negative pressure suction means the equipment or components used to process the fly batting or dust. In some embodiments, the negative pressure pumping device may also be used to monitor the pressure differential between the air inside and outside the filter element. In some embodiments, the negative pressure suction device and the humidification module can be respectively arranged in two different directions of the jacquard host machine, and air circulation is promoted through negative pressure suction, so that the humidification efficiency of the humidification module is improved, and energy conservation is facilitated.

Cold plasma static cleaning means refers to a device or component for performing an antistatic treatment. In some embodiments, a cold plasma static cleaning device may be installed near the feed module 120 for continuously generating a proper cold plasma wind when the jacquard machine 110 is operated, and performing an antistatic treatment on the transported material, so as to reduce the static influence, and thus reduce the occurrence of problems such as flying flocks, yarn knots, and the like.

In some embodiments, the jacquard production control system 100 may also include a lubrication module (not shown).

A lubrication module refers to a device or component for applying lubricating oil. In some embodiments, a lubrication module may be used to monitor the volume of lubrication oil applied each time. The lubrication module can comprise a plurality of oil conveying parts at different positions, and the opening or closing of the oil conveying holes is controlled based on the electromagnetic valve, so that the automatic application of lubricating oil of the lubrication module is realized.

For more on the monitoring data see fig. 2 and its related description. For more on noise data, cloth appearance data see fig. 4 and its associated description.

It should be noted that the above description of the jacquard machine production control system and its modules is for convenience of description only and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the feeding module 120, the traction module 130, the driving module 140, the monitoring module 150, the humidifying module, the cleaning module, the lubricating module, etc. disclosed in fig. 1 may be different modules in one system, or may be one module to implement the functions of two or more modules. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.

FIG. 2 is an exemplary schematic diagram of a jacquard machine production control method according to some embodiments of the present disclosure. In some embodiments, the method may be performed by a processor.

In some embodiments, processor 160 may determine operating parameters 220 of the jacquard machine production control system based on monitoring data 210. For more on jacquard machine production control systems see fig. 1 and its related description.

The monitoring data is data reflecting the working state of the jacquard machine host. For example, the monitoring data may include, but is not limited to, temperature of the jacquard machine host, weaving speed, running time, etc. The monitoring data may include current monitoring data and historical monitoring data, among others.

In some embodiments, the processor 160 may obtain the monitoring data in a variety of ways. For example, the processor 160 may obtain the monitoring data by accessing a memory device of the monitoring module 150.

The operating parameters refer to parameters related to the operation of the jacquard machine production control system. For example, the operating parameters may include, but are not limited to, operating time, operating power, and the like.

In some embodiments, the processor 160 may implement determining the operating parameters of the jacquard machine production control system based on the monitoring data in a variety of ways. For example, the processor 160 may determine the operating parameters of the jacquard machine production control system based on the monitored data via a preset data look-up table. The preset data comparison table records working parameters of the jacquard production control system corresponding to different monitoring data and the like. The preset data comparison table can be preset based on priori knowledge or historical data.

In some embodiments, the operating parameters may include at least a weaving speed.

In some embodiments, the processor 160 may determine the fabric speed based on the monitored data in a variety of ways. For example, when the temperature of the jacquard host machine is greater than the temperature threshold, the processor 160 may decrease the weaving speed to a first preset speed. The temperature threshold and the first preset speed may be an empirical value, a default value, a preset value, etc.

The first preset speed may also be determined in other ways, for example, the first preset speed may be related to the temperature of the jacquard machine host. The higher the temperature of the jacquard machine, the lower the first preset speed can be set.

For another example, when the weave speed is greater than the speed threshold, the processor 160 may begin timing, and if the jacquard host machine is running at a weave speed that exceeds the speed threshold for a time that exceeds the first time threshold, the processor 160 may decrease the weave speed to a second preset speed. The speed threshold, the first time threshold, the second preset speed may be a default value, a preset value, etc. In some embodiments, the speed threshold, the first time threshold and the second preset speed may be set into multiple groups, and each group of speed threshold and the first time threshold corresponds to one second preset speed.

In some embodiments of the present disclosure, when the jacquard machine is used to produce a fabric, the fabric may increase in temperature due to frictional heat, and excessive temperature may cause deformation of the material (especially for some fiber materials), which may affect the aesthetic appearance of the product, and may damage the machine body itself. In addition, if the jacquard host machine runs at high power for a long time, the loss of the machine body can be accelerated, the future weaving speed can be adjusted based on the historical weaving speed and the machine running time, and the service life of the jacquard host machine can be prolonged.

In some embodiments, the operating parameters may also include a time of creel replacement.

The bobbin replacement time refers to the time for replacing the bobbin.

In some embodiments, the processor 160 may implement determining the time of creel replacement based on the monitoring data in a variety of ways. For example, processor 160 may construct a first target vector based on jacquard suit, jacquard pattern, and rotational speed of the jacquard host machine; determining, by the first vector database, a first association vector based on the first target vector; and determining the reference yarn barrel replacement time corresponding to the first association vector as the yarn barrel replacement time corresponding to the first target vector.

The first target vector refers to a vector constructed based on the jacquard suit, the jacquard pattern, and the rotational speed of the jacquard machine main frame.

The first vector database comprises a plurality of first reference vectors, and each first reference vector has a corresponding reference yarn barrel replacement time.

The first reference vector is a vector constructed based on the historical jacquard flower color, the historical jacquard pattern and the historical rotation speed of the jacquard host machine when the cloth is produced in the historical time period, and the reference yarn cylinder replacement time corresponding to the first reference vector can be the historical yarn cylinder replacement time when the cloth is produced in the historical time period.

In some embodiments, the processor 160 may calculate the vector distance between the first target vector and the first reference vector, respectively, and determine the bobbin change time for the first target vector. For example, a first reference vector whose vector distance from the first target vector satisfies a preset condition is used as a first correlation vector, and a reference yarn bobbin replacement time corresponding to the first correlation vector is used as a yarn bobbin replacement time corresponding to the first target vector. The preset conditions may be set according to circumstances. For example, the preset condition may be that the vector distance is minimum or that the vector distance is less than a distance threshold, or the like. Vector distances may include, but are not limited to, cosine distances, mahalanobis distances, euclidean distances, and the like.

In some embodiments, after changing a yarn package of a certain jacquard weave pattern, the processor 160 needs to recalculate the package change time of the yarn package of that jacquard weave pattern.

In some embodiments of the present disclosure, the time for replacing the yarn package is determined based on the monitoring data by the processor, so that not only can the yarn package be replaced in time, but also labor force can be liberated, and labor cost generated by manual monitoring can be reduced.

In some embodiments, the processor 160 may also monitor the ambient humidity of the target area, determine whether the ambient humidity meets a preset condition, and in response to no, activate the humidification module to humidify one or more of the target areas. For more details on the humidification module, see fig. 1 and its associated description.

The target area refers to an area where the humidity of the environment needs to be monitored.

The preset condition refers to a condition that the ambient humidity needs to meet. For example, the preset condition may include a value or a range of values corresponding to the ambient humidity.

In some embodiments, the processor 160 may determine whether the ambient humidity of the target area meets the preset condition by comparing whether the ambient humidity of the target area is within a value or range of values corresponding to the preset condition.

In some embodiments, the operating power of the humidification module may be related to ambient temperature. For example, the higher the ambient temperature, the greater the operating power of the humidification module may be when the ambient humidity is the same.

In some embodiments of the present disclosure, by monitoring the ambient humidity of the target area, and performing humidification processing appropriately, and correlating the operating power of the humidification module to the ambient temperature, the production environment can be prevented from generating a large amount of static electricity due to drying, so as to reduce the problems of flying flocks and the like.

In some embodiments, the processor 160 may determine a cleaning time when the negative pressure suction device performs cleaning based on the monitoring data. For more details on the negative pressure suction device, see fig. 1 and the description related thereto.

The cleaning time refers to the time when the produced cloth is cleaned. The cleaning time may include various manners, for example, the cleaning time may be expressed as "the number of fabrics reaches N to open the cleaning module to clean". Wherein, the number of the woven fabrics reaches N, which means that the number of the woven fabrics after the previous cleaning reaches N.

In some embodiments, the processor 160 may be configured in a variety of ways to determine a cleaning time when the negative pressure suction device is performing a cleaning based on the monitoring data. For example, the processor 160 may obtain the average value of the historic weaving speed, the historic running time after the last cleaning, and the historic yarn bobbin starting amount by accessing the storage device, obtain the current weaving amount by the formula "weaving amount=average value of historic weaving speed×historic running time after the last cleaning×historic yarn bobbin starting amount", and determine the cleaning time when the negative pressure suction device performs cleaning by presetting the cleaning time based on the current weaving amount and yarn type. For example only, the preset cleaning moment may be "number of cloths reaches 100 units to start the cleaning module for cleaning" for yarn 1.

The yarn package activation amount refers to the number of yarn packages used in weaving. The yarn type refers to the kind of yarn, such as polyester, etc.

In some embodiments, the processor 160 may predict cleaning characteristics at future times via a cleaning characteristics model based on the current and historical time monitoring data; the cleaning time at which the cleaning module performs cleaning is determined based on the monitored data and the cleaning characteristics at the future time. For more details on the cleaning module, see fig. 1 and its associated description.

The cleaning feature model may be a time series model for determining cleaning features. The cleaning feature model may be a natural regression (Autoregressive model, AR) model or other model. For example, an autoregressive moving average (Autoregressive moving average model, ARMA) model, and the like.

In some embodiments, the inputs to the cleaning feature model may include current weaving speed, historical weaving speed average after last cleaning, yarn type, yarn drum start-up, current run time, and historical run time after last cleaning; the output may include cleaning characteristics at a future time.

The cleaning characteristics at a future time refer to characteristics related to flying flocks of cloth and the like. For example, cleaning features at a future time may include, but are not limited to, accumulation of fly, dust, etc. on the fabric at the future time. The future time may include a plurality of future times, such as 2 hours in the future, 3 hours in the future, and so on.

In some embodiments, the cleaning feature model may be trained from a plurality of first training samples with first labels.

In some embodiments, the first training sample may include a sample weaving speed, a sample yarn type, a sample package start-up amount, a sample run time at a first historical time. The first tag may include a sample cleaning feature at a second historical time. Wherein the first historical moment is before the second historical moment. In some embodiments, the first training sample may be obtained via historical data (e.g., historical weaving speed, etc.). The first label may be obtained by measuring a sample cloth at the second historical moment.

In some embodiments, the processor 160 may determine the cleaning time at which the cleaning module performs cleaning based on the monitored data and the cleaning characteristics at the future time in a variety of ways. For example, the processor 160 may determine a cleaning time at which the cleaning module performs cleaning by a preset rule based on the monitored data and the cleaning characteristics at the future time.

For example only, the preset rules may include: if the accumulation of fly wadding at the current time is less than the first accumulation threshold and the accumulation of fly wadding at the future time is greater than the second accumulation threshold, the cleaning time may be set at any time between the current time and the future time. Wherein the first accumulation amount threshold value and the second accumulation amount threshold value may be default values, preset values, or the like.

In some embodiments of the present disclosure, the cleaning feature model is used to process the monitoring data, determine the cleaning feature at a future time, and further determine the cleaning time when the cleaning module performs cleaning, so that the influence of multiple factors can be considered at the same time, so that the determination of the cleaning feature is efficient and accurate, and the error of manual determination is avoided.

In some embodiments of the present disclosure, based on the processor, the cleaning time when the negative pressure suction device performs cleaning is determined by monitoring data, so that the fabric is ensured not to accumulate too much flying flocks or dust, and the quality of the produced fabric is improved.

In some embodiments, the processor may determine the negative pressure suction duration and the negative pressure suction power based on the suction model. For more on the suction model see fig. 3 and its related description.

The negative pressure suction period refers to the period during which the negative pressure suction device is operated.

The negative pressure suction power refers to the power level at which the negative pressure suction device operates.

In some embodiments, the cycle of replacement/cleaning of the filter cartridge of the negative pressure suction device may be related to the negative pressure suction duration and the negative pressure suction power of the negative pressure suction device. For example, the filter cartridge of the negative pressure suction device can be replaced/cleaned with a negative pressure suction time longer than the second time threshold. For another example, the cartridge of the negative pressure suction device may be replaced/cleaned with a negative pressure suction power greater than the power threshold and a negative pressure suction time greater than the third time threshold. Wherein the second time threshold, the third time threshold, and the power threshold may be default values, empirical values, or the like. In some embodiments, the third time threshold and the power threshold may be set in multiple groups, with each group of power thresholds corresponding to one third time threshold.

In some embodiments, the processor 160 may count the total operation time of the negative pressure suction device after the last replacement/cleaning, obtain the average operation power after the last replacement/cleaning, and calculate the weighted average operation power and the total operation time to obtain an evaluation value, and replace/clean the filter element of the negative pressure suction device if the evaluation value is greater than the evaluation threshold. Wherein, the evaluation threshold value can be preset in advance.

In some embodiments, the processor 160 may determine a first operating parameter of the cold plasma electrostatic cleaning device based on the monitored data. For more details on cold plasma electrostatic cleaning devices, see fig. 1 and its associated description.

The first operating parameter refers to a parameter related to the operation of the cold plasma electrostatic cleaning apparatus. For example, the first operating parameter may include an operating frequency and plasma power.

The operating frequency refers to the frequency at which the cold plasma electrostatic cleaning device operates. For example, the operating frequency may be that the cold plasma electrostatic cleaning device is turned on every 4 seconds, each for 2 seconds.

In some embodiments of the present disclosure, by setting the cold plasma electrostatic cleaning device to operate at a frequency, not only can the device be prevented from overheating due to long-term operation, but also energy conservation is facilitated.

Plasma power refers to the amount of power at which the cold plasma electrostatic cleaning apparatus operates.

In some embodiments, the processor 160 may be configured to determine the first operating parameter of the cold plasma electrostatic cleaning device based on the monitored data in a number of ways. For example, the memory device may store in advance correspondence between different monitoring data of the cold plasma static electricity removal device and the first operation parameter, and the processor 160 may access the memory device based on the obtained monitoring data, and determine the first operation parameter of the cold plasma static electricity removal device through the correspondence.

In some embodiments, processor 160 may determine the operating frequency and plasma power based on yarn type, antistatic agent type (e.g., alkyl sulfonate, alkyl sulfate, etc.), whether the yarn is antistatic treated, the speed of the jacquard mainframe, and the amount of yarn drum activation.

In some embodiments, the processor 160 can implement the determination of the operating frequency and plasma power in a variety of ways based on the yarn type, the type of antistatic agent, whether the yarn is antistatic treated, the rotational speed of the jacquard machine, and the amount of yarn drum activation. For example, the processor 160 may construct the second target vector based on the yarn type, the type of antistatic agent, whether the yarn is antistatic treated, the rotational speed of the jacquard host, and the yarn drum activation amount; determining, by the second vector database, a second association vector based on the second target vector; and determining the reference operating frequency and the reference plasma power corresponding to the second correlation vector as the operating frequency and the plasma power corresponding to the second target vector.

The second target vector refers to a vector constructed based on the yarn type, the antistatic agent type, whether the yarn is antistatic treated, the rotational speed of the jacquard machine, and the yarn drum activation amount.

The second vector database contains a plurality of second reference vectors, and each of the plurality of second reference vectors has a corresponding reference operating frequency and reference plasma power.

The second reference vector is a vector constructed based on the type of the history yarn, the type of the history antistatic agent, whether the history yarn is antistatic treated, the history rotation speed of the jacquard host machine and the history yarn drum activation amount when the cloth is produced in the history period, and the reference operation frequency and the reference plasma power corresponding to the second reference vector can be the corresponding history operation frequency and the history plasma power when the quality of the cloth produced in the history period is higher.

In some embodiments, the processor 160 may calculate the vector distance between the second target vector and the second reference vector, respectively, and determine the operating frequency and the plasma power of the second target vector. Regarding the manner of determining the operating frequency and the plasma power of the second target vector, reference may be made to the manner of determining the bobbin replacement time of the first target vector described above.

In some embodiments of the present disclosure, the first operation parameter of the cold plasma static electricity removing device is determined by monitoring data, and characteristics such as yarn type, antistatic agent type, whether the yarn is subjected to antistatic treatment, rotation speed of a jacquard host machine, yarn drum starting amount and the like are considered, so that the finally determined operation frequency and plasma power are more reasonable, the reduction of static electricity as much as possible while saving energy is achieved, and the quality of the produced cloth is improved.

In some embodiments, the processor 160 may determine the second operating parameter of the lubrication module based on the monitored data. For more on lubrication modules see fig. 1 and its associated description.

The second operating parameter refers to a parameter related to the operation of the lubrication module. For example, the second operating parameter may include, but is not limited to, a timing of application of the lubricating oil, a volume of application of the lubricating oil, a location of application of the lubricating oil (e.g., tie rod, lift, guide plate, etc.), and the like.

In some embodiments, the processor 160 may implement determining the second operating parameter of the lubrication module based on the monitored data in a variety of ways. For example, the processor 160 may determine the second operating parameter of the lubrication module from a preset data look-up table based on the monitored data. The preset data comparison table is recorded with second operation parameters corresponding to different monitoring data.

In some embodiments, the processor 160 may determine the second operating parameter of the lubrication module based on the noise data and the cloth appearance data. For more on determining the second operating parameter of the lubrication module, see fig. 4 and its associated description.

In some embodiments of the present disclosure, the second operating parameter of the lubrication module is determined based on the processor via the monitoring data, thereby enabling automatic application of the lubricating oil and ensuring good lubricity of the various components of the jacquard machine.

Fig. 3 is an exemplary schematic diagram of a suction model shown in accordance with some embodiments of the present description.

The suction model 330 may be a machine learning model for determining negative suction duration and negative suction power. The suction model may be a Neural Networks (NN) model or other model. For example, a recurrent neural network (Recurrent Neural Network, RNN) model, and the like.

In some embodiments, the suction model 330 may include a flyweight determination layer 331 and a power determination layer 332.

The flyer determination layer may be a machine learning model for determining a flyer severity level. For example, the flyaway decision layer may include a recurrent neural network model or the like.

In some embodiments, the inputs to the batting decision layer 331 may include differential pressure data 310 and ambient humidity 320; the output may include a fly batting severity level 342.

The differential pressure data refers to data related to the internal and external pressure differences of the negative pressure pumping device. In some embodiments, the processor 160 may obtain the differential pressure data by accessing a memory device of the negative suction negative pressure device.

The wadding severity level refers to a parameter related to the wadding severity. The severity of the flyings may be expressed in terms of a number, with a greater number representing a greater severity of the flyings and a greater severity of the flyings.

The power determination layer may be a machine learning model for determining the negative pressure suction duration and the negative pressure suction power. For example, the power determination layer may include a neural network model or the like.

In some embodiments, the inputs to the power determination layer 332 may include yarn type 341, wadding severity level 342, and jacquard host runtime 343; the output may include a negative pressure pumping duration 351 and a negative pressure pumping power 352. For more on yarn type, duration of suction under negative pressure and suction power under negative pressure, see fig. 2 and the description related thereto.

In some embodiments, the negative pressure suction power output by the power determination layer may be the negative pressure suction power of a future period of time or the negative pressure suction power of a plurality of future periods of time. For example, the negative pressure suction device has a negative pressure suction power of a one hour in the future and a negative pressure suction power of b 1 to 2 hours in the future.

In some embodiments, the input to the power determination layer 332 may also include an operating rate 344 of the humidification module.

In some embodiments of the present disclosure, by taking the operation rate of the humidification module as the input of the power determining layer, the influence on the air flow rate when the negative pressure suction device performs suction is considered, so that the humidification efficiency of the humidification module is affected, and the finally determined negative pressure suction duration and the finally determined negative pressure suction power are more reasonable.

In some embodiments, the flyweight determination layer 331 and the power determination layer 332 may be obtained by joint training.

In some embodiments, the training samples of the joint training may include sample differential pressure data, sample ambient humidity, sample yarn type, sample run time of the jacquard host. The label may include a sample negative pressure suction duration and a sample negative pressure suction power corresponding to the set of training samples. In some embodiments, the training samples may be obtained based on historical data (e.g., historical differential pressure data at the time of historical production, etc.), the tags may be obtained by manual labeling, and the tags may be obtained by other means. For example, the processor 160 may simulate sample data corresponding to the set of training samples, perform simulated cleaning on the sample data, and take the negative pressure suction duration and the negative pressure suction power with the best cleaning effect as the labels.

In some embodiments, the sample differential pressure data and the sample ambient humidity are input into the flying cotton judging layer to obtain flying cotton severity level output by the flying cotton judging layer, the flying cotton severity level is used as a training sample, and the training sample, the sample yarn type and the sample running time of the jacquard host are input into the power determining layer together to obtain the negative pressure suction duration and the negative pressure suction power output by the power determining layer. Based on the sample negative pressure suction time length and the sample negative pressure suction power, and the negative pressure suction time length and the negative pressure suction power output by the power determination layer, constructing a loss function, synchronously updating parameters of the flying flocculation judgment layer and the power determination layer, and obtaining the trained flying flocculation judgment layer and the power determination layer through parameter updating.

In some embodiments of the specification, the negative pressure suction duration and the negative pressure suction power are determined by processing differential pressure data, ambient humidity and the like through the suction model, and the influence of various factors can be simultaneously considered, so that the determination of the negative pressure suction duration and the negative pressure suction power is efficient and accurate, and errors of manual determination are avoided. In addition, the combined training flying cotton judgment layer and the power determination layer are beneficial to solving the problem that labels are difficult to obtain during independent training, so that the number of required samples can be reduced, and the training efficiency can be improved.

In some embodiments, the processor 160 may determine the second operating parameter 430 of the lubrication module based on the noise data 410 and the cloth appearance data 420. For more on lubrication modules see fig. 1 and its associated description. For more details regarding the second operating parameter, see fig. 2 and its associated description.

Noise data refers to data related to sound. For example, the noise data may include, but is not limited to, the size of the noise, the type of noise duration, and the like. The noise duration type refers to a type related to a noise duration. For example, the type of noise sustained may include a brief noise, a sustained noise, and the like.

In some embodiments, the processor 160 may implement the acquisition of noise data in a variety of ways. For example, the processor 160 may process the sound data using an audio analysis algorithm based on the sound data collected by the noise monitoring module to obtain noise data. For more details on the noise monitoring module, see fig. 1 and its associated description.

The cloth appearance data refers to data information related to the appearance state of the cloth. For example, cloth appearance data may include, but is not limited to, oil stain conditions on the cloth, and the like.

In some embodiments, processor 160 may implement the acquisition of cloth appearance data in a variety of ways. For example, the processor 160 may capture images of the produced cloth based on the cloth appearance monitoring module to obtain cloth appearance data. For more on the cloth appearance monitoring module, see fig. 1 and its related description.

In some embodiments, the processor 160 may implement determining the second operating parameter of the lubrication module based on the noise data and the cloth appearance data in a variety of ways. For example, the processor 160 may construct a third target vector based on the noise data and the cloth appearance data; determining a third association vector from a third vector database based on the third target vector; and determining the reference second operation parameter corresponding to the third association vector as the second operation parameter corresponding to the third target vector.

The third target vector refers to a vector constructed based on noise data and cloth appearance data.

The third vector database contains a plurality of third reference vectors, and each third reference vector of the plurality of third reference vectors has a corresponding reference second operating parameter.

The third reference vector is a vector constructed based on the historical noise data and the historical cloth appearance data when the cloth is produced in the historical time period, and the reference second operation parameter corresponding to the third reference vector may be a historical second operation parameter corresponding to the case that the quality of the cloth produced in the historical time period is high.

In some embodiments, processor 160 may calculate a vector distance between the third target vector and the third reference vector, respectively, and determine a second operating parameter for the third target vector. As for the manner of determining the second operation parameter of the third target vector, reference may be made to the manner of determining the bobbin replacement time of the first target vector in fig. 2.

In some embodiments, the processor 160 may determine the second operating parameter of the lubrication module based on the lubrication model.

The lubrication model may be a machine learning model for determining a second operating parameter of the lubrication module. For example, the lubrication model may be a Neural Networks (NN) model or other model. For example, a recurrent neural network (Recurrent Neural Network, RNN) model, and the like.

In some embodiments, inputs to the lubrication model may include the grade of wadding severity, the time of last application of lubrication, the volume of last application of lubrication, the run time of the jacquard host; the output may include a second operating parameter. For more details regarding the severity level of the flyings, see fig. 3 and its associated description.

In some embodiments, the lubrication model may be trained from a plurality of second training samples with second labels.

In some embodiments, the second training samples may include a sample fly severity level, a sample time of last application of lubricating oil, a sample volume of last application of lubricating oil, a sample run time of the jacquard host machine. The second tag may include a sample second operating parameter. In some embodiments, the second training sample and the second label may be obtained via historical data (e.g., historical flying batting severity level, etc.).

In some embodiments, the processor 160 may also determine the second operating parameter based on the monitoring module acquiring a temperature condition of a lever, a lifting knife, etc. of the jacquard host. For example, after the jacquard host begins to operate, the processor 160 may control the lubrication module to apply lubrication oil if the temperature rise rate of the components of the jacquard host, such as the draw bar, the lifting knife, etc., is greater than the temperature rate threshold. Wherein the temperature rate threshold may be an empirical value, a preset value, etc. In some embodiments, the processor 160 controls the amount of lubricant applied by the lubrication module may be related to the magnitude of the rate of temperature rise. For example, the greater the rate of temperature rise, the greater the volume of lubricant applied may be.

It will be appreciated that when friction increases, the rate of temperature rise may increase, by monitoring the temperature change of the relevant parts of the jacquard machine, and when the temperature change exceeds a threshold, lubrication is performed in time, avoiding losses to the jacquard machine device itself and to the cloth, which is beneficial to prolonging the service life of the jacquard machine.

In some embodiments of the present disclosure, the second operation parameter is determined by processing the severity level of the flying batting through the lubrication model, which can consider the influence of multiple factors at the same time, so that the determination of the second operation parameter is efficient and accurate, and the error of manual determination is avoided.

In some embodiments of the present disclosure, the second operating parameter is determined by the noise data and the fabric appearance data, so that the jacquard host can be ensured to be in a working state with good lubricity, which is beneficial to improving the quality of the produced fabric and prolonging the service life of the jacquard host.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims

1. A jacquard production control system, which is characterized by comprising a jacquard host machine, a feeding module, a traction module, a driving module, a monitoring module and a processor;

the system comprises a feeding module, a traction module, a driving module, a monitoring module, a processor, a power supply module, a control module and a control module, wherein the feeding module is used for feeding materials, the traction module is used for conveying the materials, the driving module is used for providing power, the monitoring module is respectively and mechanically connected with the feeding module, the traction module and the driving module, and the monitoring module is used for acquiring monitoring data and transmitting the monitoring data to the processor;

the processor is in communication connection with the jacquard host, the feeding module, the traction module, the driving module and the monitoring module respectively, and is used for:

And determining working parameters of the jacquard production control system based on the monitoring data, wherein the working parameters at least comprise weaving speed.

2. The system of claim 1, wherein the jacquard machine production control system further comprises a cleaning module for cleaning produced cloth, the cleaning module comprising a negative pressure suction device, the processor further configured to:

and determining a cleaning time when the negative pressure suction device performs cleaning based on the monitoring data.

3. The system of claim 2, wherein the cleaning module further comprises a cold plasma static cleaning device for continuing the antistatic treatment, the processor further configured to:

based on the monitoring data, a first operating parameter of the cold plasma electrostatic cleaning apparatus is determined.

4. The system of claim 1, wherein the jacquard machine production control system further comprises a lubrication module for applying lubrication oil to one or more modules in the jacquard machine production control system, the processor further configured to:

A second operating parameter of the lubrication module is determined based on the monitoring data.

5. A jacquard machine production control method, the method being performed by a processor and comprising:

based on the monitoring data, operating parameters of the jacquard production control system are determined, said operating parameters comprising at least the weaving speed.

6. The method of claim 5, wherein the method further comprises:

and determining the cleaning time when the negative pressure suction device executes cleaning based on the monitoring data.

7. The method of claim 6, wherein the method further comprises:

8. The method of claim 5, wherein the method further comprises:

based on the monitoring data, a second operating parameter of the lubrication module is determined.

9. A jacquard machine production control device, characterized in that it comprises at least one processor and at least one memory;

the at least one memory is configured to store computer instructions;

the at least one processor is configured to execute at least some of the computer instructions to implement the method of any one of claims 5 to 8.

10. A computer readable storage medium storing computer instructions which, when read by a computer in the storage medium, perform the method of any one of claims 5 to 8.