CN116427074B - Elasticizer and control system - Google Patents

Abstract

The embodiment of the specification provides a elasticizer and control system, and this elasticizer includes: the first heating device comprises a plurality of heating units and is used for heating the fiber to be processed; the second heating device is used for shaping the fiber to be processed; a cooling part for cooling the heated fiber to be processed; at least one set of monitoring devices for acquiring monitoring data and transmitting the monitoring data to the processor; the smoke suction device is used for sucking smoke generated in the heating process of the first heating device and/or the second heating device; a processor configured to be in communication with the first heating device, the second heating device, the cooling component, the at least one set of monitoring devices, and the smoke aspiration device for controlling an operational state of the at least one device and/or component of the elasticizer; and determining the working parameters of the texturing machine based on the raw material characteristics of the fiber to be processed and the monitoring data.

Description

Elasticizer and control system

Technical Field

The specification relates to the technical field of textile machinery, in particular to a texturing machine and a control system.

Background

With the development of the age and the progress of technology, the requirements of people on textile materials are higher and higher, and the texturing machine can process Polyester (POY), polypropylene and other untwisted yarns into textured yarns with good bulkiness and dimensional stability through false twist texturing. How to further improve the heating efficiency, the cleaning efficiency, the cooling efficiency and the quality of the elasticized finished product of the elasticizer becomes a problem to be solved.

CN107515636B discloses a temperature control system of a texturing machine, which can improve the crimping quality of a stretch textured yarn and reduce the power consumption of a heating box by controlling the heat dissipation of a cooling plate of the system to be not influenced by the ambient temperature. The heating and heat dissipation of the elasticizer are improved, and the cleaning of the elasticizer is not involved.

Therefore, it is necessary to provide a elasticizer and a control system, which can improve the heating efficiency, cooling efficiency and cleaning efficiency of the elasticizer, ensure the uniformity of heating and cooling, and improve the quality of elasticized finished products.

Disclosure of Invention

One of the embodiments of the present disclosure provides a method for a elasticizer. The elasticizer comprises a first heating device, a second heating device, a cooling part, at least one group of monitoring devices, a smoke suction device and a processor; the first heating device comprises a plurality of heating units and is used for heating the fiber to be processed; the second heating device is used for shaping the fiber to be processed; the cooling component is used for cooling the heated fiber to be processed; the at least one group of monitoring devices are used for acquiring monitoring data and transmitting the monitoring data to the processor; the smoke suction device is used for sucking smoke generated in the heating process of the first heating device and/or the second heating device; the processor is configured to be in communication with the first heating device, the second heating device, the cooling component, the at least one set of monitoring devices, and the fume suction device for controlling an operational state of at least one device and/or component of the elasticizer; and determining at least one of heating parameters of the plurality of heating units of the first heating device, cooling parameters of the cooling component, and suction parameters of the flue gas suction device based on the raw material characteristics of the fiber to be processed and the monitoring data.

One of the embodiments of the present specification provides a control system of a bullet feeding machine, the control system includes a first heating module, a second heating module, a cooling module, a monitoring module, a smoke suction module and a processing module, the control system is used for controlling the operation of the bullet feeding machine, and the control system includes: at least one group of rollers in the box body of the control box are used for conveying fibers to be processed to the first heating module for heating; controlling the cooling module to cool and shape the heated fiber to be processed; controlling the at least one group of rollers to convey the fiber to be processed to the second heating module for heating and shaping; and controlling at least one group of rollers to transmit the heated and shaped fiber to be processed to the oiling module for further processing, so as to obtain the processed fiber yarn.

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 a schematic illustration of a construction of a elasticizer according to some embodiments of the present disclosure;

FIG. 2 is a schematic flow chart illustrating the adjustment of the operating state of a heating unit according to some embodiments of the present disclosure;

FIG. 3 is a schematic flow chart of an early warning based on a second fiber temperature according to some embodiments of the present disclosure;

FIG. 4 is a schematic diagram of predicting unexpected faults, shown in accordance with some embodiments of the present description.

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 a schematic structural view of a elasticizer according to some embodiments of the present disclosure.

As shown in FIG. 1, in some embodiments, components of the elasticizer 100 may include at least a first heating device 110, a second heating device 120, a cooling component 130, at least one set of monitoring devices 140, a smoke aspiration device 150, and a processor 160.

In some embodiments, the first heating device 110 may include a plurality of heating units for heating the fibers to be processed. By way of example only, the first heating device may heat the fibers to be processed in a manner of vacuum sealing biphenyl vapor and electric heating combined heating.

In some embodiments, the plurality of heating units of the first heating device 110 may be operated independently, respectively, and sequentially arranged in the conveying direction of the fiber to be processed. The heating units which are independently operated can be started and/or closed according to the need when the operation of the texturing machine is problematic, so that flexible adjustment of texturing yarn production can be realized, the production efficiency is ensured, and the loss caused by shutdown is reduced.

In some embodiments, the first heating device 110 is mechanically coupled to the cooling device 130.

In some embodiments, the second heating device 120 may be used to heat-set the fibers to be processed. In some embodiments, the second heating device may also be referred to as a sizing hot box, which shapes the fiber to be processed after cooling by the cooling means by non-contact air heating.

In some embodiments, the second heating device is mechanically coupled to the false twisting device.

In some embodiments, the cooling component 130 may be used to cool the fiber to be processed after being heated by the first heating device. The thermal deformation of the fiber to be processed can be fixed and the thermoplasticity of the fiber to be processed can be reduced through the treatment of the cooling component, so that the fiber to be processed has certain rigidity, and the false twist forming is facilitated.

In some embodiments, the cooling member 130 is mechanically coupled to the second heating device 120.

In some embodiments, at least one set of monitoring devices 140 may be used to monitor the monitoring data for multiple zones of the elasticizer and/or different locations of the fiber to be processed. For example only, the at least one set of monitoring devices may include a thermometer, a thermal imaging device, etc. for acquiring temperature changes in various areas of the elasticizer and/or at different locations of the fiber to be processed.

The monitoring data may include data regarding the elasticizer acquired based on the monitoring device. For example, the monitoring data may include fiber temperature distribution data, ambient temperature, temperature in the first heating device, temperature in the second heating device, temperature of the cooling member, temperature after oiling the fiber to be processed, flue gas concentration, etc.

The monitoring data may be obtained based on a variety of possible ways. In some embodiments, fiber temperature profile data, the temperature after oiling the fiber to be processed, may be obtained based on an infrared camera. In some embodiments, the ambient temperature, the temperature within the first heating device, the temperature within the second heating device, the temperature of the cooling component, may be obtained by temperature sensors mounted to each device and/or component. In some embodiments, the flue gas concentration may be obtained based on a flue gas monitoring device.

In some embodiments, at least one set of monitoring devices may be respectively connected to the cabinet body, the first heating device 110, the second heating device 120, the cooling component 130, and the smoke sucking device 150, for acquiring monitoring data of different components and transmitting the monitoring data to the processor.

In some embodiments, the smoke suction device 150 may be used to suck smoke generated during heating by the first heating device and/or the second heating device.

In some embodiments, the flue gas suction device 150 is mechanically coupled to the first and second heating devices 110, 120, respectively.

In some embodiments, the processor 160 is configured to be communicatively coupled to the first heating device 110, the second heating device 120, the cooling component 130, the at least one set of detection devices 140, and the smoke aspiration device 150 for controlling an operational state of at least one device and/or component of the elasticizer; and determining the working parameters of the texturing machine based on the raw material characteristics of the fiber to be processed and the monitoring data. In some embodiments, the operating parameters may include at least one of heating parameters of the plurality of heating units of the first heating device, cooling parameters of the cooling component, and suction parameters of the flue gas suction device.

Wherein, the heating parameters of the heating device can comprise the heating temperature of each heating unit and the like; the cooling parameters of the cooling member may include a cooling temperature of the cooling unit, etc.; the suction parameters of the smoke suction device may include the strength of the suction, etc.

The raw material characteristics of the fibers to be processed may include the material characteristics, thickness, length, etc. of the raw material of the fibers.

In some embodiments, the processor 160 may determine the operating parameters of the loader at the current and future times by consulting a reference operating parameter table based on the material characteristics of the fibrous raw material, the monitored data. The reference working parameter table comprises fiber raw materials, reference monitoring data and reference working parameters of the elasticizer in a period of time corresponding to the reference monitoring data.

For more description of the functionality of the processor 160, see fig. 2-4 and their associated description herein.

In some embodiments, the elasticizer may further comprise an interactive screen 170. The interactive screen 170 may be used to implement human-machine interaction, whose functions include, but are not limited to, displaying monitoring data, obtaining instructions entered by a user, displaying information on the operating state of the elasticizer and its various components, sending pre-warnings to the user based on the monitoring data, etc.

In some embodiments, the interactive screen 170 may be communicatively coupled to the at least one monitoring device 140, the processor 160.

In some embodiments, the texturing machine may further comprise a chassis box, a heat preservation device, a false twisting device, an oiling component and the like. The case body can comprise at least one group of rollers, and the rollers can be used for drawing and outputting fibers to be processed. The heat preservation device may be disposed outside the housing of the first heating device 110 and the second heating device 120, for maintaining the internal temperature of the two heating devices stable. The false twisting device can perform false twisting on the fiber to be processed. The oiling device can be used for oiling the fiber filaments after heating and shaping to prevent static electricity.

It should be understood that the system shown in fig. 1 and its modules may be implemented in a variety of ways.

It should be noted that the above description of the elasticizer and its components is for descriptive purposes only and is not intended to limit the present disclosure to 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 components disclosed in fig. 1 may be different modules in a system, or may be one module to implement the functions of two or more modules.

Fig. 2 is a schematic flow chart illustrating the operation of the heating unit according to some embodiments of the present disclosure. In some embodiments, the process 200 may be performed by a processor.

In some embodiments, the processor may acquire the first fiber temperature distribution data 210 of the plurality of heating units through the monitoring device, determine whether the first fiber temperature distribution data 210 satisfies the first preset condition 220, and if not, issue the adjustment command 230.

The first fiber temperature profile data 210 may refer to the fiber temperature of various regions of the fiber to be processed after heating by the first heating device. In some embodiments, the fibers to be processed may be divided by long strip regions, and the first fiber temperature distribution data 210 may refer to the average temperature of each long strip region after heating. Wherein the elongated area may refer to a rectangular area capable of covering a certain number of fibers to be processed. In some embodiments, the length, width, and number of sliver areas may be preset based on historical experience. In some embodiments, the length, width, and number of sliver areas may also be determined based on characteristics (e.g., volume, density, etc.) of the fibers themselves to be processed.

The processor may acquire the first fiber temperature distribution data 210 in a number of possible ways.

In some embodiments, the processor may determine at least one set of first fiber temperature profile data via the monitoring device. For example, the temperature of the fiber to be processed corresponding to each heating unit is detected by the monitoring device, and the first fiber temperature distribution data is determined based on the temperature of the fiber to be processed of each heating unit in each region and is transmitted to the processor. For a further description of the heating unit, the fibre to be processed and the monitoring device, reference can be made to fig. 1 and its related description.

In some embodiments, the first preset condition 220 may include that the temperature in the first fiber temperature distribution data is within a preset temperature interval. For example, 175℃to 185 ℃. In some embodiments, the first preset condition may be determined based on historical temperature data.

In some embodiments, the first preset condition may further include the degree of uniformity of the first fiber temperature distribution meeting a uniformity requirement.

The degree of uniformity may refer to the degree of difference in fiber temperature at different locations. For example, the degree of uniformity may refer to the temperature differential values for different regions of the fiber to be processed.

In some embodiments, the degree of uniformity is related to the difference in fiber temperature between the different locations. The smaller the difference in fiber temperature at different locations, the better the uniformity.

In some embodiments, the processor may determine that the first fiber temperature distribution is non-uniform in response to not meeting the uniformity requirement by determining whether the uniformity of the first fiber temperature distribution meets the uniformity requirement. Exemplary uniformity requirement: the fiber temperature at the different locations is not greater than the difference threshold (e.g., 5 ℃). The uniformity requirement may be set based on historical temperature data.

In some embodiments, the variance threshold may be related to a distance interval between different locations. The larger the distance separation between different locations, the larger the difference threshold may be. For example, during the heating process, the fiber to be processed may be divided into 5 preset parallel strip areas, which are the strip areas A, B, C, D, E in sequence from left to right, and the first fiber temperature distribution data is the temperatures of the 5 areas; if the preset conditions are: "the difference in fiber temperature at different positions is not greater than the difference threshold", the difference threshold of adjacent regions may be set smaller, for example, the difference threshold between a and B may be set smaller; instead, the difference threshold of two non-adjacent regions may be increased appropriately based on the threshold setting described above, for example, the difference threshold between a and C may be greater than the difference threshold between a and B.

In some embodiments of the present disclosure, the uniformity of the first fiber temperature distribution is considered, which helps the processor to more reasonably adjust the on/off of the heating unit. In addition, when setting the difference threshold of uniformity, the distance interval between different positions is also considered, and the influence of weakening of the heat transfer effect on fibers which are far apart is reduced or avoided.

In some embodiments, the degree of uniformity of the first fiber temperature distribution further comprises a degree of uniformity of the first fiber temperature distribution at a future time, the operating condition of the at least one heating unit being indicated as satisfactory when the first fiber temperature distribution at the future time satisfies the requirement. It can be understood that the working state of the at least one heating unit meets the requirement that the on-off of the at least one heating unit can meet the heating requirement of the fiber to be processed, i.e. the first fiber temperature distribution of the fiber to be processed in the subsequent heating temperature can be ensured to meet the first preset condition.

The degree of uniformity of the first fiber temperature distribution at a future time instant can be determined in various possible ways. In some embodiments, the degree of uniformity of the first fiber temperature distribution at a future time may be determined by a uniformity model.

In some embodiments, the uniformity model may be a machine learning model. The uniformity 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 input of the uniformity model may include first fiber temperature distribution data at a current time, a current operating state of the plurality of heating units, an ambient temperature, an estimated degree of fouling of the plurality of heating units; the output may include a degree of uniformity of the first fiber temperature distribution at a future time.

The operating state of the heating unit may refer to the on and off condition of the heating unit. In some embodiments, the on-off state of the heating unit may be represented by a control parameter (e.g., 0 or 1) of the heating unit. If 0 indicates that the heating unit is off; 1 indicates that the heating unit is on.

In some embodiments, the processor may obtain the current operating state of the plurality of heating units via the monitoring device.

The ambient temperature may refer to a temperature value of the texturing machine environment. Such as the temperature between the processes. In some embodiments, ambient temperature may include, but is not limited to, acquisition by a thermometer.

The estimated level of fouling may refer to an estimated cleanliness of the heating unit at a future time. In some embodiments, the processor may construct the fouling feature vector based on temperature and time dependent data of the fouling level of the heating unit over a period of time. The processor may retrieve in the dirty feature vector database based on the dirty feature vector, and determine a corresponding reference dirty level for the reference dirty feature vector having a similarity greater than the preference similarity threshold as the estimated dirty level. The dirty feature vector database includes a plurality of reference dirty feature vectors. Each reference soil characteristic vector corresponds to a reference soil level.

The dirty feature vector database refers to a database for storing a plurality of reference dirty feature vectors and their corresponding reference dirty levels. In some embodiments, the processor may construct the reference cleaning feature vector based on historical monitoring data. For example, the change data of the dirt degree of the heating unit along with the temperature and time in a historical period of time is used for constructing a reference dirt characteristic vector, and the reference dirt degree corresponding to the historical monitoring data in a period of time after the period of time is used as the reference dirt degree to construct a dirt characteristic vector database.

In some embodiments, the uniformity model may be trained by gradient descent or other possible methods, and a first training sample for uniformity model training may be obtained based on historical data over a first historical time. The first training sample may include historical first fiber temperature distribution data, operating states of the plurality of historical heating units, historical ambient temperatures, and estimated soil levels of the historical heating units. The first label may be determined based on the degree of uniformity of the actual first fiber temperature distribution at each instant in time during the second historical time. Wherein the first historical time is before the second historical time.

In some embodiments of the present disclosure, the uniformity model is used to process the first fiber temperature distribution data at the current moment, the opening and closing conditions of the current multiple heating units, the ambient temperature, and the estimated dirt degrees of the multiple heating units, so as to determine the uniformity degree of the first fiber temperature distribution at the future moment, and can consider the influence of multiple factors at the same time, so that the determination of the uniformity degree of the first fiber temperature distribution at the future moment is efficient and accurate.

In some embodiments, the estimated level of fouling may be determined based on the cleaning cycle and the flue gas concentration data.

The cleaning cycle may refer to a time interval for cleaning the heating unit. For example, one week. For more on the cleaning cycle, see fig. 4 and its associated description.

The flue gas concentration data may refer to cumulative data of the flue gas concentration generated during heating by the heating unit during the cleaning cycle. The flue gas concentration data can be determined in a number of possible ways. In some embodiments, the processor may determine the smoke concentration data by a smoke aspiration device. The cumulative data may be determined based on the total amount of smoke drawn by the smoke suction device during the cleaning cycle. For example, the cumulative data is determined based on the ratio of the total amount of flue gas to the volume of the heating device.

In some embodiments, the processor may determine the estimated level of fouling based on the cleaning cycle and the flue gas concentration data by vector database matching. For example, the processor may construct a first target vector based on the clean up cycle and the flue gas concentration data; determining, by the first vector database, a first association vector based on the first target vector; and determining the reference estimated pollution degree corresponding to the first association vector as the estimated pollution degree corresponding to the first target vector.

In some embodiments, the estimated soil level may be determined by a soil level prediction model.

In some implementations, the soil level prediction model may be a machine learning model. The pollution level prediction 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 inputs to the pollution level prediction model may include cleaning cycle and flue gas concentration data; the output may include an estimated degree of fouling of the heating unit.

In some embodiments, the soil level prediction model may be trained by gradient descent or other possible methods. A second training sample for the soil prediction model training may be obtained based on historical data. The second training sample may include historical fume concentration data and a cleaning cycle of the historical heating unit. The second label may be determined based on the actual heating unit contamination level corresponding to the trained input data.

In some embodiments of the present disclosure, the cleaning cycle and the flue gas concentration data are processed through the pollution level prediction model, so as to determine the estimated pollution level of the heating unit, and meanwhile, the influence of multiple factors can be considered, so that the estimation of the pollution level is efficient and accurate.

The adjustment instruction refers to an instruction for controlling other components to perform adjustment change by the processor. In some embodiments, the adjustment instructions are for adjusting an operating state of the at least one heating unit. Wherein the operating state may comprise an on and/or off state of the heating unit.

In some embodiments, the processor may acquire the first fiber temperature distribution data determined by the monitoring device, determine whether the first fiber temperature distribution data meets a first preset condition, and when the first fiber temperature distribution data does not meet the preset condition, the processor issues an adjustment instruction, and determines to change the on/off of the at least one heating unit.

In some embodiments, the processor determines the degree of uniformity of the first fiber temperature distribution data at the future time based on the uniformity model, and in response to the degree of uniformity not meeting the first preset condition, the processor may regenerate a set of heating unit control parameters, process the new heating unit control parameters based on the uniformity model, generate the degree of uniformity of the first fiber temperature distribution data at the new future time, determine whether the degree of uniformity meets the first preset condition, and if so, the processor adjusts the heating unit according to the newly generated heating unit control parameters, and if not, repeat the steps until the first preset condition is met.

In some embodiments of the present disclosure, the heating unit control parameter is adjusted by determining whether the first fiber temperature distribution data meets the preset condition, so as to help determine a more accurate heating unit control parameter that meets the actual requirement.

FIG. 3 is a schematic flow chart of an early warning based on a second fiber temperature according to some embodiments of the present disclosure. As shown in fig. 3, the process 300 includes the following steps. In some embodiments, the process 300 may be performed by a processor.

In step 310, the processor obtains second fiber temperature distribution data for the plurality of heating units via the monitoring device.

The second fiber temperature distribution data may refer to temperature distribution data of each region of the fiber to be processed after being cooled by the cooling member. In some embodiments, the fibers to be processed may be divided by long strip regions, and the second fiber temperature distribution data may refer to an average temperature of each long strip region after cooling. For more description of the cooling member, the strip region, see the relevant description in fig. 1, 2.

The processor may obtain the second fiber temperature distribution data of the fiber to be processed after being cooled by the cooling member in a variety of possible ways. The second fiber temperature distribution data acquisition manner is similar to the first fiber temperature distribution data acquisition manner, and can be referred to in the related description in fig. 1.

In step 320, the processor determines whether the second fiber temperature distribution data satisfies a second preset condition.

In some embodiments, the second preset condition comprises a preset temperature requirement.

In some embodiments, the second preset condition is related to at least the first fiber temperature distribution data. For example, the preset temperature requirement may include that the temperature of the fiber to be processed in any one region is less than a temperature threshold (e.g., 80 ℃) and the temperature difference of the fiber to be processed in a different region is less than a temperature difference threshold (e.g., 3 ℃) in the second fiber temperature distribution data.

In some embodiments, the temperature threshold and the temperature difference threshold in the preset temperature requirement may be determined based on the first fiber temperature distribution data. For example, the difference threshold of the preset temperature requirement is equal to the average value of the difference in the fiber temperature to be processed between the regions of the first fiber temperature distribution data.

And 330, the processor responds that the second fiber temperature distribution data does not meet a second preset condition, and sends out early warning through the interactive screen.

The early warning can be used for reminding and warning of adverse conditions. In some embodiments, the pre-warning may include an audible pre-warning and/or a visual information pre-warning.

In some embodiments, the processor may acquire second fiber temperature distribution data transmitted by the monitoring device, determine whether the second fiber temperature distribution data meets a second preset condition, and when the second fiber temperature distribution data does not meet the second preset condition, the processor sends an early warning instruction to the interactive screen. In some embodiments, the interactive screen may display monitoring information and alert the user to make adjustments based on the early warning instructions. In some embodiments, the interactive screen may perform the pre-warning according to the number of pre-warning times and/or the pre-warning frequency.

In some embodiments, the early warning instructions may include interactive screen early warning times and/or early warning frequencies. The early warning times and/or the early warning frequency are related to the difference value between the second fiber temperature distribution data and the second preset condition. The fiber temperature to be processed in any area of the second fiber temperature distribution data is smaller than the temperature threshold value, and the larger the difference value between the fiber temperature distribution data and the temperature threshold value is, the larger the early warning frequency and/or the early warning frequency is.

In some embodiments, the processor may predict a cooling efficiency of the cooling component via the cooling efficiency prediction model based on the second fiber temperature distribution data, the ambient temperature, and the monitored temperature of the first heating device, and in response to the cooling efficiency not meeting a third preset condition, issue an early warning in advance.

Cooling efficiency may refer to the magnitude of the temperature decrease per unit time during cooling.

The cooling efficiency can be determined by a number of possible methods. In some embodiments, the cooling efficiency is determined based on the initial temperature before cooling, the current temperature, and the time taken for cooling. In some embodiments, modeling may be performed or various data analysis algorithms may be employed, such as regression analysis, discriminant analysis, etc., to analyze the second fiber temperature distribution data, the ambient temperature, and the monitored temperature of the heating device to determine cooling efficiency.

In some embodiments, the third preset condition may include that the cooling efficiency is between a minimum standard efficiency and a maximum standard efficiency. The minimum standard efficiency may be determined based on the pre-cool initial temperature, the target temperature threshold maximum, and the standard time. The target temperature threshold maximum value may refer to the highest allowable temperature of the fiber to be processed after cooling. The standard time may refer to a fixed time set to cool the fiber to be processed. The maximum standard efficiency may be determined based on the pre-cool initial temperature, the target temperature threshold minimum, and the standard time. Wherein the target temperature threshold minimum may refer to the minimum allowable temperature of the fiber to be processed after cooling.

In some embodiments, the processor may issue an early warning instruction to the interactive screen based on the cooling efficiency not meeting a third preset condition (e.g., below a minimum standard efficiency or above a maximum standard efficiency).

In some embodiments, the cooling efficiency may be determined by a cooling efficiency prediction model.

In some embodiments, the cooling efficiency prediction model may be a machine learning model of the custom structure hereinafter. The cooling efficiency prediction 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 inputs to the cooling efficiency prediction model may include first fiber temperature distribution data, second fiber temperature distribution data, ambient temperature, and a monitored temperature of the first heating device; the output may include whether the cooling efficiency is acceptable. In some embodiments, the cooling efficiency prediction model may include a feature extraction layer and an efficiency determination layer.

The input of the feature extraction layer may include at least one time of the first fiber temperature distribution data and at least one time of the second fiber temperature distribution data, and the output is a feature of the change in the fiber temperature distribution to be processed. In some embodiments, the feature extraction layer may include, but is not limited to, an LSTM network model.

The inputs to the efficiency determining layer may include the ambient temperature, the monitored temperature of the first heating device, and the temperature profile variation characteristics of the fiber to be processed, the output being whether the predicted cooling efficiency is acceptable. For example, the output may include: time point 1, qualification; time point 2, pass, time point 3, fail. Wherein time points 1, 2, 3 refer to time points during cooling.

In some embodiments, the cooling efficiency prediction model may be obtained by joint training. A third training sample for joint training may be obtained based on historical processing data. The third label of the third training sample may be based on whether the actual cooling efficiency corresponding to the input data is acceptable. For example only, the processor may determine whether the cooling efficiency at each time point is acceptable based on the historical data by counting the cooling efficiencies at each time point with the first fiber temperature distribution data, the second fiber temperature distribution data, the ambient temperature, and the first heating device monitoring temperature for different time sequences.

The joint training process of the cooling efficiency prediction model comprises the following steps: and inputting the one or more sample first fiber temperature distribution data and the one or more sample second fiber temperature distribution data into the feature extraction layer to obtain one or more sample fiber temperature distribution change features output by the feature extraction layer, and then inputting the one or more sample fiber temperature distribution change features, the sample environment temperature and the monitored temperature of the sample first heating device into the efficiency determination layer to obtain whether the cooling efficiency output by the efficiency determination layer is qualified. And establishing a loss function based on the output result of the tag and the efficiency determining layer, updating parameters of the feature extraction layer and the efficiency determining layer, and completing model training when the loss function meets preset conditions to obtain a trained cooling efficiency prediction model. The preset condition may be that the loss function converges, the iteration number reaches an iteration number threshold, and the like.

The trained cooling efficiency prediction model is obtained through combined training, so that the problem that the time stamp is difficult to obtain in a single training feature extraction layer is solved under some conditions. Meanwhile, a trained cooling efficiency prediction model is obtained based on the combined training, so that whether the cooling efficiency is qualified or not can be better judged by the cooling efficiency prediction model.

In some embodiments, when the cooling efficiency is determined to be failed by the cooling efficiency prediction model, the processor calculates a time length t from a future failure time point to the present time, and calculates an early warning frequency p, p=1/t. And the processor sends early warning instruction information to the interactive display screen according to the early warning frequency p.

In some embodiments of the present disclosure, determining the cooling efficiency and whether it is acceptable or not based on the cooling efficiency prediction model by considering the influence of the fiber temperature distribution data, the ambient temperature, and the monitored temperature of the first heating device on the cooling efficiency is helpful for improving the accuracy of predicting the cooling efficiency and judging whether it is acceptable or not.

In some embodiments, the processor shortens the cleaning cycle of the cooling component based on the cooling being less efficient. For example, in response to the cooling efficiency being below a minimum standard efficiency, the processor may issue instructions to the interactive screen that display information that shortens the cleaning cycle of the cooling component. For more explanation of how the cleaning cycle is determined, see fig. 4 and its associated content.

In some embodiments of the present disclosure, by determining whether the second fiber temperature distribution data meets the second preset condition, an early warning is sent through the interactive screen when the second fiber temperature distribution data does not meet the second preset condition, which is helpful for reminding the user to process in time.

FIG. 4 is a schematic diagram of predicting unexpected faults, shown in accordance with some embodiments of the present description.

In some embodiments, the processor 160 may determine a dynamic cleaning cycle 420 of at least one device and/or component of the loader based on the monitoring data 410; based on the dynamic cleaning period 420, the likelihood of an unexpected failure of the elasticizer during the next dynamic cleaning period is predicted.

For more on the monitoring data 410, see FIG. 1 and its associated description.

By a device and/or component of a loader is meant a device and/or component of a loader that needs to be cleaned. For example, a first heating device, a second heating device and a cooling device of the elasticizer.

The cleaning cycle may refer to the time between the last cleaning and the next cleaning. Dynamic cleaning cycle 420 may refer to a cleaning cycle that may be adjusted based on the monitored data.

Unexpected faults refer to faults that are not within expected conditions. For example, faults caused by excessive temperatures.

The processor 160 may determine the dynamic cleaning cycle 420 of at least one device and/or component of the loader based on the monitoring data 410 in a number of possible ways.

In some embodiments, the processor 160 may construct the cleaning feature vector based on temperature data of the device and/or component of the elasticizer over time. Processor 160 may retrieve in the cleaning feature vector database based on the cleaning feature vector, and determine a corresponding reference dynamic cleaning period for the reference cleaning feature vector having a similarity greater than the preference similarity threshold as dynamic cleaning period 420. The cleaning feature vector database includes a plurality of reference cleaning feature vectors. Each reference cleaning feature vector corresponds to a reference dynamic cleaning cycle.

The cleaning feature vector database is a database for storing a plurality of reference cleaning feature vectors and corresponding reference dynamic cleaning periods. In some embodiments, processor 160 may construct the reference cleaning feature vector based on historical monitoring data. For example, the reference cleaning feature vector is constructed from the historical time-dependent data of the temperature of the device and/or the component of the elasticizer, and the cleaning period corresponding to the historical monitoring data is used as the reference dynamic cleaning period to construct the cleaning feature vector database.

In some embodiments, the processor 160 may also be configured to obtain a standard cleaning cycle; the dynamic cleaning period 420 is determined by a preset algorithm based on the standard cleaning period 440 and the monitoring data 410.

The standard cleaning cycle 440 refers to a preset standard cleaning interval. For example, the standard clean cycle 440 may be preset to "clean once every 45 days". In some embodiments, the standard cleaning cycle 440 may be determined based on historical cleaning experience, general knowledge of the cleaning.

In some embodiments, the processor 160 may periodically detect the operation of the smoke extractor, monitor based on the smoke concentration sensor to obtain smoke concentration data, obtain a smoke coefficient based on the smoke concentration data, and determine the dynamic cleaning period 420 based on the smoke coefficient and a preset algorithm.

In some embodiments, the smoke coefficient may be determined based on the smoke concentration retrieving a preset smoke coefficient table. In some embodiments, the smoke coefficient table may be constructed based on historical experience, with the basic principle that the greater the smoke concentration, the greater the smoke coefficient, and all the smoke coefficient sizes in the range of 0 to 1.

In some embodiments, the rules of the preset algorithm may include a higher smoke concentration, a shorter cleaning cycle. For example, the preset algorithm may be a dynamic purge cycle = standard purge cycle-a smoke coefficient-a number of days elapsed since the last purge.

In some embodiments, the preset algorithm may also be related to the number/frequency of pre-warnings. For more details on the number/frequency of pre-warnings, see fig. 3 and its associated description.

In some embodiments, the processor 160 may obtain the number/frequency of pre-warnings and determine the dynamic cleaning period 420 based on the number/frequency of pre-warnings, the smoke concentration, and the like according to a preset algorithm.

In some embodiments, the processor 160 may process the historical early warning times/frequency data, the historical smoke concentration data, and the historical dynamic period data by using a statistical analysis method (e.g., a regression analysis method, a principal component analysis method), and determine early warning weights and smoke weights respectively corresponding to the early warning times/frequency data and the smoke concentration data in a preset algorithm.

In some embodiments, the preset algorithm may be constructed from standard clean-up periods, pre-warning times/frequencies, pre-warning weights, and smoke concentrations and smoke weights. For example, the preset algorithm may be a dynamic cleaning cycle = (standard cleaning cycle smoke concentration smoke weight-days elapsed since last cleaning) -number of pre-warnings/frequency location coefficient. The position coefficient refers to a preset cleaning coefficient corresponding to different types of devices and/or components, which is determined manually according to historical experience and based on cleaning requirements of the different types of devices and/or components.

In some embodiments of the present disclosure, a statistical analysis method is used to determine a weight coefficient in a preset algorithm according to historical data, so that the occurrence of the situation that the majority swallows the decimal number can be prevented, and the construction of the preset algorithm is more comprehensive and stable, so that the dynamic cleaning period can be determined more accurately.

In some embodiments of the present disclosure, the dynamic cleaning period is determined based on the early warning times/frequencies and the flue gas concentration data, so that the determined dynamic cleaning period is more in line with the actual cleaning requirement situation, and the cleaning state of the bullet feeding machine is effectively ensured.

In some embodiments, the processor 160 may determine the likelihood of an unexpected failure of the loader during the next dynamic cleaning period based on the probability of the unexpected failure of the loader during the same historical dynamic cleaning period as the next dynamic cleaning period. For example, the same historical dynamic cleaning cycles as the next dynamic cleaning cycle for a total of 10, wherein the historical dynamic cleaning cycles for the loader with unexpected faults are 3, the processor 160 determines that the loader with unexpected faults is 3/10=30% likely during the next dynamic cleaning cycle.

In some embodiments, the processor 160 may also determine the likelihood of an unexpected failure of the loader during the next dynamic cleaning period based on the problem model 430.

In some embodiments, the problem model 430 may be a machine learning model. For example, the problem model 430 may include any one or combination of a Neural Network model (NN), a roll-around Neural Network (Recurrent Neural Network, RNN) model, and the like.

In some embodiments, the inputs to the problem model 430 are first fiber temperature distribution data 450, second fiber temperature distribution data 460, dynamic cleaning period 420, flue gas concentration data 470; the output is the possibility of unexpected faults of the elasticizer in the next dynamic cleaning period.

In some embodiments, the flue gas concentration data 470 may include cumulative data of the flue gas concentration generated during heating by the heating unit during the cleaning cycle. For more on the first fiber temperature distribution data 450, see fig. 2 and its associated description; for more on the second fiber temperature distribution data 460, see fig. 3 and its associated description.

In some embodiments, the problem model 430 may be trained to obtain by gradient descent or other possible means based on the fourth training sample. The fourth training sample may be obtained based on historical data over the first historical time. The fourth training sample may include historical first fiber temperature profile data, historical second fiber temperature profile data, historical dynamic cleaning cycle, and historical smoke concentration data. The label of the fourth training sample may be based on whether unexpected failure acquisition occurred within a second historical time corresponding to the input data. Wherein the second historical time is later than the first historical time.

In some embodiments of the present disclosure, the dynamic cleaning period is determined according to the monitoring data, so that the cleaning period can more meet the actual cleaning requirement of the device and/or the component of the elasticizer, and the cleaning condition of the elasticizer is ensured; based on the problem model, the possibility of unexpected faults of the elasticizer in the next dynamic cleaning period is predicted, and the cleaning period can be further adjusted, the cleaning precision is improved, the labor cost is reduced, and the labor force is liberated.

Some embodiments of the present disclosure further provide a control system for a loader.

In some embodiments, the elasticizer control system comprises at least a first heating module, a second heating module, a cooling module, a monitoring module, a flue gas suction module, and a processing module.

The first heating module may comprise first heating means for heating the fibres to be processed. The second heating module may comprise a second heating means for shaping the fibres to be processed. The cooling module comprises at least one group of cooling devices for cooling the heated fiber to be processed. The monitoring module comprises at least one group of monitoring devices for acquiring monitoring data and transmitting the acquired monitoring data to the processor. The fume suction module comprises at least one set of fume suction means for sucking fume generated during the heating process. The processing module may include a processor for performing the functions referred to in one or more embodiments of the present description.

In some embodiments, the control system may be used to control the operation of the elasticizer, including:

at least one group of rollers in the box body of the control cabinet are used for conveying fibers to be processed to a first heating module for heating; controlling a cooling module to perform shaping cooling on the heated fiber to be processed; the false twisting module is controlled to perform false twisting on the shaped and cooled fiber to be processed, wherein the false twisting module can comprise a false twisting device; after the false twisting is finished, controlling at least one group of rollers to transmit the fiber to be processed to a second heating module for heating and shaping; and controlling at least one group of rollers to transmit the heated and shaped fiber to be processed to an oiling module for further processing, so as to obtain the processed fiber yarn.

In some embodiments, the control system is further configured to control the processing module to: acquiring first fiber temperature distribution data of a plurality of heating units of a first heating module through a monitoring device; and responding to the fact that the first fiber temperature distribution data does not meet the first preset condition, sending an adjusting instruction, wherein the adjusting instruction is used for adjusting the working state of at least one heating unit.

In some embodiments, the control system is further configured to control the processing module to: acquiring second fiber temperature distribution data of the plurality of heating units through a monitoring device; responding to the fact that the second fiber temperature distribution data does not meet a second preset condition, and sending out early warning through an interactive screen of the elasticizer; the second preset condition comprises a preset temperature requirement, and the preset temperature requirement is at least related to the first fiber temperature distribution data.

In some embodiments, the control system is further configured to control the processing module to: determining a dynamic cleaning period of at least one device and/or component of the elasticizer based on the monitoring data acquired by the monitoring device; based on the dynamic cleaning period, the possibility of unexpected faults of the elasticizer in the next dynamic cleaning period is predicted.

In some embodiments, the control system is further configured to control the processing module to: obtaining a standard cleaning period; and determining the dynamic cleaning period through a preset algorithm based on the standard cleaning period and the monitoring data.

It should be noted that the above description is for purposes of illustration and description only and is not intended to limit the scope of applicability of the present description. Various modifications and alterations will be apparent to those skilled in the art based upon the teachings herein in the examples provided. However, such modifications and variations are still within the scope of the present description.

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 (6)

1. The bullet adding machine is characterized by comprising an interactive screen, a first heating device, a second heating device, a cooling part, at least one group of monitoring devices, a smoke sucking device and a processor; wherein,

The interaction screen is used for displaying the working state information of the bullet feeding machine;

the first heating device comprises a plurality of heating units for heating the fiber to be processed, and the heating units respectively and independently run and are sequentially arranged along the conveying direction of the fiber to be processed;

the second heating device is used for shaping the fiber to be processed;

the cooling component is used for cooling the heated fiber to be processed;

the at least one group of monitoring devices are used for acquiring monitoring data and transmitting the monitoring data to the processor;

the smoke suction device is used for sucking smoke generated in the heating process of the first heating device and/or the second heating device;

the processor is configured to be communicatively connected to the first heating device, the second heating device, the cooling component, the at least one set of monitoring devices, and the flue gas pumping device for:

controlling the operating state of at least one device and/or component of the elasticizer; the method comprises the steps of,

determining at least one of heating parameters of the plurality of heating units of the first heating device, cooling parameters of the cooling component, and suction parameters of the flue gas suction device based on the raw material characteristics of the fibers to be processed and the monitoring data;

The monitoring device is further configured to:

acquiring first fiber temperature distribution data and second fiber temperature distribution data of the plurality of heating units, wherein the first fiber temperature distribution data refers to fiber temperatures of all areas of the fiber to be processed after being heated by a first heating device, and the second fiber temperature distribution data refers to temperature distribution data of the fiber to be processed after being cooled by the cooling component;

the processor is further configured to:

responding to the first fiber temperature distribution data not meeting a first preset condition, sending an adjustment instruction, wherein the adjustment instruction is used for adjusting the working state of at least one heating unit, the first preset condition comprises that the uniformity degree of the first fiber temperature distribution meets the uniformity degree requirement, the uniformity degree refers to the difference degree of fiber temperatures at different positions, the uniformity degree of the first fiber temperature distribution comprises the uniformity degree of the first fiber temperature distribution at the future moment, and the uniformity degree of the first fiber temperature distribution at the future moment is determined through a uniformity model, wherein the uniformity model is a machine learning model;

responding to the second fiber temperature distribution data not meeting a second preset condition, and sending out early warning through the interactive screen; the second preset condition includes a preset temperature requirement, the preset temperature requirement being related to at least the first fiber temperature distribution data, and,

And responding to the cooling efficiency of the cooling component not meeting the third preset condition, and sending out early warning in advance, wherein the cooling efficiency of the cooling component is predicted by a cooling efficiency prediction model based on the second fiber temperature distribution data, the ambient temperature and the monitored temperature of the first heating device.

2. The elasticizer of claim 1, wherein the processor is further configured to:

determining a dynamic cleaning cycle of at least one device and/or component of the elasticizer based on the monitoring data;

and predicting the possibility of unexpected faults of the bullet adding machine in the next dynamic cleaning period based on the dynamic cleaning period.

3. The elasticizer of claim 2, wherein the processor is further configured to:

obtaining a standard cleaning period;

and determining a dynamic cleaning period through a preset algorithm based on the standard cleaning period and the monitoring data.

4. The utility model provides a elasticizer control system, its characterized in that, control system includes interactive screen module, first heating module, second heating module, cooling module, monitoring module, flue gas suction module and processing module, control system is used for controlling elasticizer operation, includes:

Controlling the interactive screen module to display the working state information of the bullet feeding machine;

at least one group of rollers in the box body of the control cabinet are used for conveying fibers to be processed to the first heating module for heating, the first heating module comprises a plurality of heating units, and the heating units respectively and independently run and are sequentially arranged along the conveying direction of the fibers to be processed;

controlling the cooling module to cool and shape the heated fiber to be processed;

controlling the at least one group of rollers to convey the fiber to be processed to the second heating module for heating and shaping;

controlling at least one group of rollers to transmit the heated and shaped fiber to be processed to an oiling module for further treatment to obtain processed fiber filaments;

the monitoring module is further to:

acquiring first fiber temperature distribution data and second fiber temperature distribution data of the plurality of heating units, wherein the first fiber temperature distribution data refers to fiber temperatures of all areas of the fiber to be processed after being heated by a first heating module, and the second fiber temperature distribution data refers to temperature distribution data of the fiber to be processed after being cooled by a cooling module;

The processing module is further to:

responding to the first fiber temperature distribution data not meeting a first preset condition, sending an adjustment instruction, wherein the adjustment instruction is used for adjusting the working state of at least one heating unit, the first preset condition comprises that the uniformity degree of the first fiber temperature distribution meets the uniformity degree requirement, the uniformity degree refers to the difference degree of fiber temperatures at different positions, the uniformity degree of the first fiber temperature distribution comprises the uniformity degree of the first fiber temperature distribution at the future moment, and the uniformity degree of the first fiber temperature distribution at the future moment is determined through a uniformity model, wherein the uniformity model is a machine learning model;

responding to the second fiber temperature distribution data not meeting a second preset condition, and sending out early warning through the interactive screen module; the second preset condition includes a preset temperature requirement, the preset temperature requirement being related to at least the first fiber temperature distribution data, and,

and responding to the cooling efficiency of the cooling module not meeting the third preset condition, and sending out early warning in advance, wherein the cooling efficiency of the cooling module is predicted by a cooling efficiency prediction model based on the second fiber temperature distribution data, the ambient temperature and the monitored temperature of the first heating module.

5. The elasticizer control system of claim 4, wherein the control system is further configured to control the processing module to:

determining a dynamic cleaning period of at least one device and/or component of the elasticizer based on the monitoring data acquired by the monitoring device;

and predicting the possibility of unexpected faults of the bullet adding machine in the next dynamic cleaning period based on the dynamic cleaning period.

6. The elasticizer control system of claim 5, wherein the control system is further configured to control the processing module to:

obtaining a standard cleaning period;

and determining a dynamic cleaning period through a preset algorithm based on the standard cleaning period and the monitoring data.