A temperature controller data acquisition method and system for a setting machine

Abstract

The present invention relates to the technical field of textile printing and dyeing setting machines, and proposes a data acquisition method and system for a temperature controller of a setting machine. The specific implementation steps include: first, calculating the initial first temperature sampling frequency in the preheating stage through the ambient temperature and the target temperature; then, calculating the temperature increase in the temperature control area, combining the temperature state estimation equation, predicting the temperature increase vector at the next sampling moment, and controlling the first temperature sampling frequency in real time through a temperature sampling adaptive controller; then, calculating the initial second temperature sampling frequency in the setting stage, and using an adaptive fuzzy controller to automatically adjust the second temperature sampling frequency according to the temperature change and heating power change rate of the temperature control area; finally, according to the real-time controlled temperature sampling frequency, collect temperature data in the temperature control area, and determine whether to issue an early warning. The present invention can dynamically adjust the sampling frequency according to real-time temperature data during the preheating and setting stages of the setting machine, thereby achieving an efficient and accurate temperature control process.

Description

Technical Field

[0001] The invention relates to the technical field of textile printing and dyeing setting machines, in particular to a data acquisition method and system for a temperature controller of a setting machine. Background Art

[0002] Setting machines are widely used in the textile industry due to their high efficiency, precise control and ability to adapt to different fabrics. During the setting process of textiles, the setting machine can ensure the stability of the size and structure of the textiles through precise temperature control and appropriate pressure adjustment. Among them, temperature control is one of the core factors for the setting machine to achieve high efficiency and precise control. Accurate temperature adjustment can not only avoid damage to the performance of textiles caused by overheating or insufficient heating, but also ensure the consistency of the setting effect. Therefore, by collecting data from the temperature controller in real time, not only can the temperature status during the setting process be fed back in real time, but also the stability of textile quality can be enhanced and production efficiency can be improved.

[0003] At present, the existing technology mainly realizes temperature control based on the real-time temperature data of the setting machine, ignoring the control of the acquisition frequency of the temperature data acquisition process, thus affecting the real-time and accuracy of the temperature response. For example, when the acquisition frequency is too high, the temperature controller needs to process more temperature sensor data, which may cause data processing delays, thereby affecting the transmission efficiency of the temperature data; when the acquisition frequency is too low, some key data cannot be fed back, causing the temperature to be out of control during the setting process, resulting in the setting effect of the textile not meeting the standard.

[0004] Therefore, a data acquisition method and system for a temperature controller of a setting machine are proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a temperature controller data acquisition method and system for a setting machine, which is used to collect temperature controller parameters in real time. In order to solve the problems existing in the prior art, the present invention first calculates the initial first temperature sampling frequency in the preheating stage through the ambient temperature and the target temperature; then, calculates the temperature increase in the temperature control area, combines the temperature state estimation equation, predicts the temperature increase vector at the next sampling moment, and controls the first temperature sampling frequency in real time through the temperature sampling adaptive controller; then, calculates the initial second temperature sampling frequency in the setting stage, and uses an adaptive fuzzy controller to automatically adjust the second temperature sampling frequency according to the temperature change and heating power change rate of the temperature control area; finally, according to the real-time controlled temperature sampling frequency, collects temperature data in the temperature control area, and determines whether to issue an early warning. The present invention can dynamically adjust the sampling frequency according to real-time temperature data during the preheating and setting stages of the setting machine, thereby realizing an efficient and accurate temperature control process.

[0006] A data acquisition method for a temperature controller of a setting machine, comprising:

[0007] Acquire textile process parameters, ambient temperature and operating parameters of the target setting machine; collect regional air temperatures and surface temperatures of heat source components in several temperature control areas in the target setting machine before startup;

[0008] Determine the first temperature sampling frequency at the initial moment according to the ambient temperature, the expected air temperature in the preheating phase, and the expected component temperature; construct a temperature state estimation equation in combination with the regional air temperature and the surface temperature of the heat source component at the first temperature sampling frequency, and predict the first temperature estimation value at the next sampling moment; input the first temperature estimation value, the regional air temperature, and the surface temperature of the heat source component at the same sampling moment into the temperature sampling adaptive controller, and update the first temperature sampling frequency in real time;

[0009] According to the textile process parameters and the operating parameters, the thermal response time of the textile, the ideal temperature change rate and the temperature sampling frequency correction index are calculated to determine the initial second temperature sampling frequency in the finalization stage; the second air temperature change rate, the second component temperature change rate and the heating power change rate are calculated and input into the adaptive fuzzy controller; the adaptive fuzzy controller determines whether to update the second temperature sampling frequency according to the fuzzy control rules; and the second temperature sampling frequency is adaptively controlled in combination with the membership function and the temperature sampling frequency modulation function;

[0010] According to the first temperature sampling frequency and the second temperature sampling frequency, the regional air temperature and the surface temperature of the heat source component of several temperature control areas are displayed in real time, and it is determined whether to issue an early warning to the management personnel.

[0011] Preferably, the specific implementation process of determining the first temperature sampling frequency at the initial moment includes:

[0012] According to the ambient temperature, the expected air temperature and the expected component temperature, a first air temperature difference and a first component temperature difference are obtained; a temperature sensor response period and a temperature controller processing period are obtained to calculate a maximum sampling frequency; and the first temperature sampling frequency at the initial moment is obtained by combining the first air temperature difference and the first component temperature difference. The specific calculation formula is:

[0013] ;

[0014] in, represents the first temperature sampling frequency at the initial moment; represent the desired air temperature and the desired component temperature, respectively; represents the ambient temperature; They represent the temperature sensor response period and the thermostat processing period respectively.

[0015] Preferably, the process of implementing real-time control of the first temperature sampling frequency includes:

[0016] According to the regional air temperature and the surface temperature of the heat source component at the first temperature sampling frequency, the first air temperature increase and the first component temperature increase are calculated; the temperature state estimation equation is constructed, and the first temperature increase at the next sampling moment is obtained through the first air temperature increase and the first component temperature increase; the first temperature estimation value is obtained by combining the regional air temperature, the surface temperature of the heat source component and the first temperature increase; according to the first temperature sampling frequency and the first temperature estimation value, the temperature sampling adaptive controller is constructed to update the first temperature sampling frequency in real time.

[0017] Preferably, the process of updating the first temperature sampling frequency in real time by the temperature sampling adaptive controller includes:

[0018] Get The first temperature estimate value, the regional air temperature and the surface temperature of the heat source component at the sampling time are used to calculate the temperature sampling frequency confidence; The temperature sampling frequency confidence at the sampling time and The first temperature sampling frequency at the sampling time is input to the temperature sampling adaptive controller, and the output The first temperature sampling frequency at the sampling moment.

[0019] Preferably, the process of calculating the thermal response time of the textile, the ideal temperature change rate and the temperature sampling frequency correction index, and determining the second temperature sampling frequency at the beginning of the shaping stage includes:

[0020] Acquire textile process parameters, including textile thermal conductivity, textile specific heat capacity, textile thickness and textile moisture content; acquire the operating parameters of the target setting machine, including setting machine heating power, textile operating speed and area length of the temperature control area; calculate the textile thermal response time according to the textile thermal conductivity, the textile thickness and the textile moisture content; acquire the ideal temperature change rate by combining the heating power and the textile specific heat capacity; calculate the temperature sampling frequency correction index by the textile operating speed and the area length; calculate the initial temperature sampling frequency of the setting stage by combining the first temperature sampling frequency, the textile thermal response time, the ideal temperature change rate and the temperature sampling frequency correction index to obtain the second temperature sampling frequency.

[0021] Preferably, the process of adaptively controlling the second temperature sampling frequency by the adaptive fuzzy controller includes:

[0022] According to the regional air temperature and the surface temperature of the heat source component of the second temperature sampling frequency, the temperature changes at adjacent sampling time points are calculated to obtain the second air temperature change rate and the second component temperature change rate; the heating power change rate of the target molding machine at adjacent sampling time points is obtained; an adaptive fuzzy controller is constructed, and the second air temperature change rate, the second component temperature change rate and the heating power change rate are input into the adaptive fuzzy controller, and the fuzzy control rule is used to determine whether to update the second temperature sampling frequency; the input data is mapped through the membership function to obtain the corresponding membership weight; the second temperature sampling frequency is adaptively adjusted in combination with the temperature sampling frequency modulation function.

[0023] Preferably, the fuzzy control rules include:

[0024] If the second air temperature change rate and the second component temperature change rate are both less than the predetermined first temperature change rate threshold, and the heating power change rate is less than the predetermined power change threshold, the second temperature sampling frequency is reduced; if the second air temperature change rate and / or the second component temperature change rate is greater than the predetermined second temperature change rate threshold, and the heating power change rate is greater than or equal to the predetermined power change threshold, the second temperature sampling frequency is increased; if the second air temperature change rate and the second component temperature change rate are greater than or equal to the predetermined first temperature change rate threshold, and less than or equal to the predetermined second temperature change rate threshold, the second temperature sampling frequency remains unchanged.

[0025] Preferably, the process of determining whether to issue an early warning to a manager according to the first temperature sampling frequency includes:

[0026] According to the first temperature sampling frequency, the regional air temperature and the surface temperature of the heat source component in several temperature control areas in the target setting machine during the preheating stage are collected in real time; the temperature fluctuations of the regional air temperature and the surface temperature of the heat source component in adjacent time periods are calculated to obtain air temperature fluctuations and component temperature fluctuations; if the regional air temperature is greater than a predetermined first regional temperature threshold or the surface temperature of the heat source component is greater than a predetermined first component temperature threshold, an alarm is issued to the management personnel; if both the air temperature fluctuation and the component temperature fluctuation are less than a predetermined temperature increment threshold, an alarm is issued to the management personnel.

[0027] Preferably, the process of determining whether to issue an early warning to a manager according to the second temperature sampling frequency includes:

[0028] The regional air temperature and the surface temperature of the heat source component in the finalization stage are collected in real time through the second temperature sampling frequency; the temperature change rates of the regional air temperature and the surface temperature of the heat source component in adjacent time periods are calculated to obtain the air temperature change rate and the component temperature change rate; if the regional air temperature is greater than a predetermined second regional temperature threshold or the surface temperature of the heat source component is greater than a predetermined second component temperature threshold, an alarm is issued to the management personnel; if the air temperature change rate is less than a predetermined air temperature change threshold or the component temperature change rate is less than a predetermined component temperature change threshold, an alarm is issued to the management personnel.

[0029] A temperature controller data acquisition system for a setting machine, comprising:

[0030] The setting machine data acquisition module obtains textile process parameters, the ambient temperature and operating parameters of the target setting machine; collects the regional air temperature and the surface temperature of the heat source component of several temperature control areas in the target setting machine before startup;

[0031] The preheating temperature acquisition control module determines the first temperature acquisition frequency at the initial moment according to the ambient temperature, the expected air temperature and the expected component temperature in the preheating stage; constructs a temperature state estimation equation in combination with the regional air temperature and the surface temperature of the heat source component at the first temperature acquisition frequency, and predicts the first temperature estimation value at the next sampling moment; inputs the first temperature estimation value, the regional air temperature and the surface temperature of the heat source component at the same sampling moment into the temperature acquisition adaptive controller, and updates the first temperature acquisition frequency in real time;

[0032] The shaping temperature acquisition control module calculates the thermal response time of the textile, the ideal temperature change rate and the temperature sampling frequency correction index according to the textile process parameters and the operating parameters, and determines the second temperature sampling frequency at the beginning of the shaping stage; calculates the second air temperature change rate, the second component temperature change rate and the heating power change rate, and inputs them into the adaptive fuzzy controller; the adaptive fuzzy controller determines whether to update the second temperature sampling frequency according to the fuzzy control rules; and adaptively controls the second temperature sampling frequency in combination with the membership function and the temperature sampling frequency modulation function;

[0033] The display and warning module displays the regional air temperature and the surface temperature of the heat source component of several temperature control areas in real time according to the first temperature sampling frequency and the second temperature sampling frequency, and determines whether to issue a warning to the management personnel.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. The temperature state estimation equation proposed in the present invention combines the changes of regional air temperature and heat source component surface temperature at the first temperature sampling frequency to achieve accurate prediction of temperature changes. This method can effectively predict the temperature change at the next sampling moment by calculating the increase of air temperature and heat source component temperature, providing a more reliable basis for subsequent temperature control; at the same time, by adaptively adjusting the temperature sampling frequency, the sampling frequency can be dynamically adjusted according to the real-time temperature estimation value, thereby improving the response speed and accuracy of temperature control.

[0036] 2. The temperature sampling adaptive controller proposed by the present invention can effectively improve the temperature sampling accuracy and response speed of the system by updating the temperature sampling frequency in real time. The controller can dynamically adjust the temperature sampling frequency at the current moment through the temperature sampling frequency confidence to adapt to the temperature changes in different working environments, thereby ensuring the data accuracy and real-time performance during the temperature monitoring process; the temperature sampling frequency confidence and the first temperature sampling frequency at the previous sampling moment are input into the controller and optimized, which can balance the temperature sampling accuracy and real-time performance, and reduce the system performance degradation caused by temperature sampling delay or error.

[0037] 3. The adaptive fuzzy controller proposed in the present invention can accurately adjust the temperature sampling frequency in the shaping stage. Taking into account the thermal response characteristics of textiles, heating power, operating speed and other factors, the initial temperature sampling frequency in the shaping stage can be accurately calculated; through the adjustment of the adaptive fuzzy controller, the temperature sampling frequency can be automatically optimized according to the actual operating conditions (such as temperature change rate and heating power change), ensuring the accuracy and stability of the temperature control process and improving the shaping quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flow chart of a data collection method for a temperature controller of a setting machine proposed in an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of the structure of an adaptive fuzzy controller proposed in an embodiment of the present invention;

[0040] Figure 3 The present invention is a flowchart of a temperature controller data acquisition system for a setting machine proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The process of shaping textiles by the shaping machine involves the morphological changes and performance stability of the textiles. Temperature fluctuations may directly affect the quality and production efficiency of the textiles. If the temperature is too high or too low, the textiles may not achieve the ideal shaping effect, resulting in defects in product appearance, feel, and dimensional stability, and even equipment failure. In order to ensure precise control of the shaping process, it is particularly important to collect data from the temperature control machine of the shaping machine. By real-time collection and monitoring of the data of the temperature control machines in each temperature control area of ​​the shaping machine, abnormal temperature fluctuations can be discovered in time, the temperature adjustment strategy can be optimized, and data support can be provided for subsequent fault diagnosis and predictive maintenance to avoid equipment shutdown or damage due to abnormal temperature.

[0043] The present invention provides a temperature controller data acquisition method and system for a setting machine to collect temperature changes in a temperature control area of ​​the setting machine in real time to improve the accuracy of temperature monitoring. In order to illustrate the effectiveness of the method and system of the present invention, a specific description will be given in combination with the accompanying drawings of this embodiment and the following two embodiments.

[0044] Embodiment 1

[0045] The embodiment of the present application discloses a method for collecting data from a temperature controller of a setting machine, and collects data from a temperature controller of a setting machine. Figure 1 The specific implementation steps of the method proposed in the present invention include: S1, obtaining textile process parameters, the ambient temperature and operating parameters of the target setting machine, collecting regional air temperature and the surface temperature of the heat source component; S2, calculating the initial first temperature sampling frequency of the preheating stage, and constructing a temperature state estimation equation to predict the first temperature estimation value at the next sampling moment; S3, inputting the first temperature estimation value, the regional air temperature and the surface temperature of the heat source component into the temperature sampling adaptive controller, and updating the first temperature sampling frequency in real time; S4, calculating the thermal response time of the textile, the ideal temperature change rate and the temperature sampling frequency correction index, and determining the initial second temperature sampling frequency of the setting stage; S5, calculating the second air temperature change rate, the second component temperature change rate and the heating power change rate, and inputting them into the adaptive fuzzy controller to control the second temperature sampling frequency in real time; S6, according to the first temperature sampling frequency and the second temperature sampling frequency, real-time collecting the temperature of several temperature control areas, and judging whether to issue an early warning to the management personnel.

[0046] Furthermore, the textile process parameters of the textile to be set, the ambient temperature before the start-up of the A setting machine and the operating parameters after the start-up are obtained; the regional air temperature and the surface temperature of the heat source component in several temperature control areas in the A setting machine before starting are collected, corresponding to the above-mentioned step S1; wherein the temperature control area includes a preheating area, a heating area, a holding area and a cooling area.

[0047] Specifically, the A setting machine is turned on, and in the preheating stage, the textile to be set is transported to the preheating zone, and the textile to be set is preheated to reach a uniform temperature state to improve the effect of subsequent heating and setting; in the setting stage, the textile to be set is transported to the heating zone for heating to reach the setting temperature, and then the textile to be set is transported to the holding zone to maintain its setting temperature to achieve the setting effect; finally, it is determined whether it is necessary to enter the cooling zone according to the type of the textile to be set to fix the shape of the textile to be set. Among them, high-temperature sensitive textiles (including synthetic fibers and textile composite materials), natural fibers, and fabrics that require specific structures or sizes are sensitive to temperature changes and require a cooling zone to ensure the stability of the shape and size of the textile; textiles such as cotton, linen fibers, silk, and thin fabrics have low setting temperatures or are not sensitive to high temperatures, and can have stable sizes and structures without a cooling zone.

[0048] Further, the first temperature sampling frequency at the initial moment is determined according to the ambient temperature, the expected air temperature in the preheating stage, and the expected component temperature; corresponding to the above step S2, the specific implementation process includes:

[0049] According to the ambient temperature, the expected air temperature and the expected component temperature, a first air temperature difference and a first component temperature difference are obtained; a temperature sensor response period and a temperature controller processing period are obtained to calculate a maximum sampling frequency; and the first temperature sampling frequency at the initial moment is obtained by combining the first air temperature difference and the first component temperature difference. The specific calculation formula is:

[0050] ;

[0051] in, represents the first temperature sampling frequency at the initial moment; represent the desired air temperature and the desired component temperature, respectively; represents the ambient temperature; They represent the temperature sensor response period and the thermostat processing period respectively.

[0052] The embodiment of the present application comprehensively considers the ambient temperature, the expected air temperature, the expected component temperature, and the response characteristics of the temperature sensor and the thermostat to accurately calculate the temperature sampling frequency at the initial moment. This method can improve the response speed and stability of the system while ensuring the temperature control accuracy, avoid over-frequent or over-sparse sampling, thereby achieving a more efficient and accurate temperature control process, reducing energy consumption, and improving the overall performance of the system.

[0053] Furthermore, combining the regional air temperature and the surface temperature of the heat source component at the first temperature sampling frequency, a temperature state estimation equation is constructed to predict the first temperature estimation value at the next sampling moment; corresponding to the above steps S2-S3, the specific implementation process includes:

[0054] According to the regional air temperature and the surface temperature of the heat source component at the first temperature sampling frequency, the first air temperature increase and the first component temperature increase are calculated; the temperature state estimation equation is constructed, and the first temperature increase at the next sampling moment is obtained through the first air temperature increase and the first component temperature increase. The calculation formula is expressed as follows:

[0055] ;

[0056] in, express At sampling time The first temperature increase, is the air temperature of the area or the surface temperature of the heat source component; Respectively and At sampling time The value of express At sampling time The weight coefficient of express At sampling time The first temperature increase; Represents an exponential function with a natural constant as base;

[0057] The first temperature estimation value is obtained by combining the regional air temperature, the surface temperature of the heat source component and the first temperature increase; the temperature sampling adaptive controller is constructed according to the first temperature sampling frequency and the first temperature estimation value, and the first temperature sampling frequency is updated in real time.

[0058] The embodiment of the present application can accurately predict the changes in regional air temperature and heat source component surface temperature through the temperature state estimation equation, thereby realizing accurate estimation and control of the temperature state. The method dynamically generates the temperature increase by calculating the air temperature increase and the component temperature increase, and updates it in real time in combination with the actual temperature data, making the temperature acquisition more sensitive and adaptable.

[0059] Furthermore, the first temperature estimation value, the regional air temperature and the surface temperature of the heat source component at the same sampling time are input into the temperature sampling adaptive controller to update the first temperature sampling frequency in real time; the specific implementation process includes:

[0060] Get The first temperature estimate value at the sampling time, the regional air temperature and the surface temperature of the heat source component are used to calculate the temperature sampling frequency confidence. The specific formula is:

[0061] ;

[0062] in, represents the confidence level of the temperature sampling frequency; Representation vector The standard deviation of Respectively The air temperature of the area and the surface temperature of the heat source component at the sampling time, Respectively A first temperature increase of the air temperature of the area and the surface temperature of the heat source component at the sampling time; Representation vector The standard deviation of Respectively The air temperature of the area or the surface temperature of the heat source component at the sampling time;

[0063] Will The temperature sampling frequency confidence at the sampling time and The first temperature sampling frequency at the sampling time is input to the temperature sampling adaptive controller, and the output The first temperature sampling frequency at the sampling time is specifically calculated as follows:

[0064] ;

[0065] in, express The first temperature sampling frequency at the sampling moment; express The first temperature sampling frequency at the sampling moment; represents a predetermined confidence threshold; represents the adjustment factor and , obtained by fitting a large amount of experimental data; They represent the temperature sensor response period and the thermostat processing period respectively.

[0066] The temperature sampling adaptive controller proposed in the embodiment of the present application can dynamically adjust the temperature sampling frequency according to the temperature estimation information, regional air temperature and surface temperature of the heat source component at the sampling time in real time. This real-time adjustment improves the system's responsiveness to temperature changes, and can effectively improve the temperature control accuracy and stability, thereby optimizing the temperature control effect during the textile shaping process, reducing energy consumption and improving product quality.

[0067] Further, according to the textile process parameters and the operating parameters, the thermal response time of the textile, the ideal temperature change rate and the temperature sampling frequency correction index are calculated to determine the initial second temperature sampling frequency of the finalization stage; corresponding Figure 1 The specific implementation process of step S4 includes:

[0068] The process parameters of the textile are obtained, including thermal conductivity of the textile, specific heat capacity of the textile, thickness of the textile and moisture content of the textile; the operating parameters of the target setting machine are obtained, including heating power of the setting machine, operating speed of the textile and length of the temperature control area; the thermal response time of the textile is calculated according to the thermal conductivity of the textile, the thickness of the textile and the moisture content of the textile, and the specific formula is:

[0069] ;

[0070] in, represents the thermal response time of the textile; Indicates the moisture content of the textile; Exponential functions with natural constants as bases; Indicates the thickness of the textile; represents the thermal conductivity of the textile; is an empirical parameter representing an adjustment factor of the thermal response time of the textile;

[0071] Combined with the heating power and the specific heat capacity of the textile ,pass Calculate the ideal temperature change rate ;in, Textile quality;

[0072] The rate at which the textile runs and the region length ,pass Calculate the temperature frequency correction index ; Combine the first temperature sampling frequency, the textile thermal response time, the ideal temperature change rate and the temperature sampling frequency correction index to calculate the initial temperature sampling frequency in the finalization stage to obtain the second temperature sampling frequency, and the calculation formula is expressed as:

[0073] ;

[0074] in, The second temperature sampling frequency represents the initial moment of the shaping stage; represents the thermal response time of the textile; represents the ideal temperature change rate; represents the temperature sampling frequency correction index; is an empirical parameter, indicating the influence of the running speed of the textile on the temperature acquisition frequency.

[0075] In the embodiment of the present application, by combining the physical properties of the textile (such as thermal conductivity, specific heat capacity, thickness and water content) with the operating parameters of the setting machine (such as heating power, operating speed and temperature control area length), the initial temperature sampling frequency in the setting stage can be accurately adjusted. This method can effectively improve the accuracy and response speed of temperature monitoring during the setting process, ensure that the textile achieves the best heat treatment effect during the setting process, avoid excessive or insufficient heat treatment, and reduce energy consumption and material waste.

[0076] Further, the second air temperature change rate, the second component temperature change rate and the heating power change rate are calculated and input into the adaptive fuzzy controller; the adaptive fuzzy controller determines whether to update the second temperature sampling frequency according to the fuzzy control rule; the second temperature sampling frequency is adaptively controlled by combining the membership function and the temperature sampling frequency modulation function; corresponding Figure 1 The specific implementation process of step S5 includes:

[0077] First, according to the regional air temperature and the surface temperature of the heat source component at the second temperature sampling frequency, Calculate the temperature change at adjacent sampling time points to obtain the second air temperature change rate and the second component temperature change rate , is a positive integer; They are and At sampling time The numerical value of is the air temperature of the area or the surface temperature of the heat source component;

[0078] Next, through Obtain the heating power change rate of the target setting machine at adjacent sampling time points , is a positive integer; They are and The heating power at the sampling moment;

[0079] Finally, construct an adaptive fuzzy controller, see Figure 2 , the second air temperature change rate, the second component temperature change rate and the heating power change rate are input into the adaptive fuzzy controller, and the fuzzy control rule is used to determine whether to update the second temperature sampling frequency; the input data is mapped to obtain the corresponding membership weight through the membership function; the membership function is expressed as:

[0080] ;

[0081] in, represents the membership function; is the second air temperature change rate , the second component temperature change rate or the heating power change rate ; Indicates the lower threshold, below which the change level is low; Indicates the upper threshold, when it is greater than this threshold, it indicates a high degree of change;

[0082] Combined with the temperature sampling frequency modulation function, the second temperature sampling frequency is adaptively adjusted; the temperature sampling frequency modulation function is expressed as:

[0083] ;

[0084] in, express The second temperature sampling frequency at the sampling moment; express The second temperature sampling frequency at the sampling moment; express The sampling interval of the sampling moment; represents the adjustment parameters, The value of is in the interval Inner , used to measure the influence of the regional air temperature, the surface temperature of the heat source component and the heating power on the temperature change.

[0085] The embodiment of the present application controls the second temperature sampling frequency through the above-mentioned adaptive fuzzy control method, and can dynamically adjust the temperature sampling frequency according to the real-time changes of the regional air temperature, the surface temperature of the heat source component and the heating power change rate. This process can effectively improve the accuracy and response speed of temperature sampling, optimize the temperature control effect during the heating process, ensure the stability and energy efficiency of the setting machine under different working conditions, and thus improve production efficiency and reduce energy waste.

[0086] Furthermore, the fuzzy control rules include:

[0087] If the second air temperature change rate and the second component temperature change rate are both less than the predetermined first temperature change rate threshold, and the heating power change rate is less than the predetermined power change threshold, the second temperature sampling frequency is reduced; if the second air temperature change rate and / or the second component temperature change rate is greater than the predetermined second temperature change rate threshold, and the heating power change rate is greater than or equal to the predetermined power change threshold, the second temperature sampling frequency is increased; if the second air temperature change rate and the second component temperature change rate are greater than or equal to the predetermined first temperature change rate threshold, and less than or equal to the predetermined second temperature change rate threshold, the second temperature sampling frequency remains unchanged.

[0088] In order to clearly illustrate the logical relationship of the fuzzy control rules, the embodiments of the present application provide examples of fuzzy rule judgment. Refer to Table 1 for the input variable level division results and Table 2 for the fuzzy rule judgment examples.

[0089] Table 1. Input variable classification results

[0090]

[0091] Table 2. Fuzzy rule judgment examples

[0092]

[0093] According to the above content, the fuzzy control rules of the embodiment of the present application can dynamically adjust the second temperature sampling frequency according to the real-time changes in the temperature and heating power of the system, effectively optimize the temperature control process, and improve the energy efficiency and accuracy of the shaping stage. When the temperature changes are small and the heating power changes smoothly, the temperature sampling frequency is reduced to reduce unnecessary measurements and controls, thereby reducing the energy consumption of the system; when the temperature changes are large or the heating power changes significantly, increasing the temperature sampling frequency helps to respond to temperature fluctuations more accurately and ensure the stability and consistency of the shaping effect.

[0094] Furthermore, according to the first temperature sampling frequency and the second temperature sampling frequency, the regional air temperature and the surface temperature of the heat source component of several temperature control areas are displayed in real time, and it is determined whether to issue an early warning to the management personnel. Figure 1 Step S6; the implementation process of determining whether to issue an early warning to the management personnel according to the first temperature collection frequency includes:

[0095] According to the first temperature sampling frequency, the regional air temperature and the surface temperature of the heat source component in several temperature control areas in the target setting machine during the preheating stage are collected in real time; the temperature fluctuations of the regional air temperature and the surface temperature of the heat source component in adjacent time periods are calculated to obtain air temperature fluctuations and component temperature fluctuations; if the regional air temperature is greater than a predetermined first regional temperature threshold or the surface temperature of the heat source component is greater than a predetermined first component temperature threshold, an alarm is issued to the management personnel; if both the air temperature fluctuation and the component temperature fluctuation are less than a predetermined temperature increment threshold, an alarm is issued to the management personnel.

[0096] The embodiment of the present application can timely monitor temperature anomalies during the shaping process and ensure the stability and accuracy of the temperature control system by collecting the air temperature and the surface temperature of the heat source component in multiple temperature control areas in the target shaping machine in real time and calculating their temperature fluctuations.

[0097] Furthermore, according to the second temperature sampling frequency, the process of determining whether to issue an early warning to the management personnel includes:

[0098] The regional air temperature and the surface temperature of the heat source component in the finalization stage are collected in real time through the second temperature sampling frequency; the temperature change rates of the regional air temperature and the surface temperature of the heat source component in adjacent time periods are calculated to obtain the air temperature change rate and the component temperature change rate; if the regional air temperature is greater than a predetermined second regional temperature threshold or the surface temperature of the heat source component is greater than a predetermined second component temperature threshold, an alarm is issued to the management personnel; if the air temperature change rate is less than a predetermined air temperature change threshold or the component temperature change rate is less than a predetermined component temperature change threshold, an alarm is issued to the management personnel.

[0099] The embodiment of the present application monitors the air temperature in the shaping stage area and the surface temperature of the heat source component in real time, and performs analysis based on the temperature change rate. When the temperature changes abnormally or the change rate does not meet the predetermined threshold, the system can promptly issue an alarm to ensure the temperature control stability of the production process and avoid quality problems or equipment failures caused by abnormal temperature.

[0100] The embodiment of the present application realizes the precise control of temperature sampling in the finalization stage through the application of temperature state estimation equation, temperature sampling adaptive controller and adaptive fuzzy controller, and ensures the stability and accuracy of the temperature control process. The specific implementation process mainly includes the following steps: (a) combining the regional air temperature and the surface temperature change of the heat source component, constructing the temperature state estimation equation, and accurately predicting the temperature change at the next sampling moment; (b) dynamically adjusting the first temperature sampling frequency according to the real-time temperature estimation value to adapt to the temperature changes in different working environments and improve the temperature control response speed and accuracy; (c) using an adaptive fuzzy controller to automatically adjust the temperature sampling frequency based on temperature changes and finalization machine operating parameters. The present invention proposes a temperature state estimation equation, a temperature sampling adaptive controller and an adaptive fuzzy controller for the above three processes respectively; the temperature state estimation equation realizes the precise prediction of temperature changes and improves the response accuracy of temperature control; the temperature sampling adaptive controller further improves the real-time performance and accuracy of temperature acquisition; the adaptive fuzzy controller further improves the accuracy and stability of temperature control in the finalization stage.

[0101] Embodiment 2

[0102] In the first embodiment, the method of the present invention realizes data collection of the temperature controller of the setting machine A. In the embodiment of the present application, the temperature controller data collection system of the setting machine proposed by the present invention is applied to the setting machine of the textile factory B to collect the temperature data of the temperature control area in the steam setting machine in real time; Figure 3 The data acquisition system includes: a setting machine data acquisition module, a preheating temperature acquisition control module, a setting temperature acquisition control module and a display and early warning module.

[0103] Furthermore, the setting machine data acquisition module obtains textile process parameters, ambient temperature and operating parameters of the target setting machine; and collects regional air temperatures and surface temperatures of heat source components in several temperature control areas in the target setting machine before startup.

[0104] Furthermore, the preheating temperature acquisition control module determines the first temperature acquisition frequency at the initial moment according to the ambient temperature, the expected air temperature and the expected component temperature in the preheating stage; constructs a temperature state estimation equation in combination with the regional air temperature and the surface temperature of the heat source component at the first temperature acquisition frequency, and predicts the first temperature estimation value at the next sampling moment; inputs the first temperature estimation value, the regional air temperature and the surface temperature of the heat source component at the same sampling moment into the temperature acquisition adaptive controller, and updates the first temperature acquisition frequency in real time; the specific implementation process includes:

[0105] Get The first temperature estimate value at the sampling time, the regional air temperature and the surface temperature of the heat source component are used to calculate the temperature sampling frequency confidence. The specific formula is:

[0106] ;

[0107] in, represents the confidence level of the temperature sampling frequency; Representation vector The standard deviation of Respectively The air temperature of the area and the surface temperature of the heat source component at the sampling time, Respectively A first temperature increase of the air temperature of the area and the surface temperature of the heat source component at the sampling time; Representation vector The standard deviation of Respectively The air temperature of the area or the surface temperature of the heat source component at the sampling time;

[0108] Will The temperature sampling frequency confidence at the sampling time and The first temperature sampling frequency at the sampling time is input to the temperature sampling adaptive controller, and the output The first temperature sampling frequency at the sampling time is specifically calculated as follows:

[0109] ;

[0110] in, express The first temperature sampling frequency at the sampling moment; express The first temperature sampling frequency at the sampling moment; represents a predetermined confidence threshold; represents the adjustment factor and , obtained by fitting a large amount of experimental data; They represent the temperature sensor response period and the thermostat processing period respectively; Indicates the maximum acquisition frequency.

[0111] In order to further illustrate the role of the temperature adaptive controller proposed in the present invention, the first temperature frequency of the B textile long setting machine in the preheating stage is given as an example for comparison, see Table 3; wherein the adjustment coefficient is 0.8; the predetermined confidence threshold is 0.8; the maximum acquisition frequency is 10 .

[0112] According to the data in Table 3, when the confidence level of the temperature sampling frequency is low, the frequency cannot be adjusted according to the sampling frequency at the previous sampling moment. When the temperature sampling confidence is 0.55, the first temperature sampling frequency is adjusted from 0.47 to 10; when the temperature sampling frequency confidence is high, it means that the sampling frequency of the previous sampling moment is trustworthy, and the sampling frequency of the current moment can be adjusted according to the previous sampling frequency, such as the sampling moment When the temperature sampling confidence is 0.85, the first temperature sampling frequency is adjusted from 0.5 to 0.47.

[0113] Table 3. The first temperature frequency of B textile long setting machine in the preheating stage

[0114]

[0115] Furthermore, the shaping temperature acquisition control module calculates the thermal response time of the textile, the ideal temperature change rate and the temperature sampling frequency correction index according to the textile process parameters and the operating parameters, and determines the initial second temperature sampling frequency in the shaping stage; calculates the second air temperature change rate, the second component temperature change rate and the heating power change rate, and inputs them into the adaptive fuzzy controller; the adaptive fuzzy controller determines whether to update the second temperature sampling frequency according to the fuzzy control rules; and adaptively controls the second temperature sampling frequency in combination with the membership function and the temperature sampling frequency modulation function.

[0116] Furthermore, the display and warning module displays the regional air temperature and the surface temperature of the heat source component of several temperature control areas in real time according to the first temperature sampling frequency and the second temperature sampling frequency, and determines whether to issue a warning to the management personnel.

[0117] The embodiment of the present application can accurately control the temperature change during the shaping process by dynamically adjusting the temperature acquisition frequency and real-time temperature prediction, ensuring uniform heating and optimized temperature control of the textile during the shaping stage. By combining the ambient temperature, operating parameters and textile process parameters, the system can intelligently adjust the temperature acquisition frequency and improve the temperature control accuracy, thereby improving the shaping quality and production efficiency.

[0118] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data acquisition method for a temperature controller of a setting machine, characterized in that: include: Acquire textile process parameters, ambient temperature and operating parameters of the target setting machine; collect regional air temperatures and surface temperatures of heat source components in several temperature control areas in the target setting machine before startup; Determine the first temperature sampling frequency at the initial moment according to the ambient temperature, the expected air temperature in the preheating phase, and the expected component temperature; construct a temperature state estimation equation in combination with the regional air temperature and the surface temperature of the heat source component at the first temperature sampling frequency, and predict the first temperature estimation value at the next sampling moment; input the first temperature estimation value, the regional air temperature, and the surface temperature of the heat source component at the same sampling moment into the temperature sampling adaptive controller, and update the first temperature sampling frequency in real time; Calculate the thermal response time, ideal temperature change rate and temperature sampling frequency correction index of the textile according to the textile process parameters and the operating parameters, and determine the initial second temperature sampling frequency in the finalization stage; Calculate the second air temperature change rate, the second component temperature change rate and the heating power change rate, and input them into the adaptive fuzzy controller; the adaptive fuzzy controller determines whether to update the second temperature sampling frequency according to the fuzzy control rule; and adaptively control the second temperature sampling frequency by combining the membership function and the temperature sampling frequency modulation function; According to the first temperature sampling frequency and the second temperature sampling frequency, the regional air temperature and the surface temperature of the heat source component of several temperature control areas are displayed in real time, and it is determined whether to issue an early warning to the management personnel.

2. The data acquisition method for a temperature controller of a setting machine according to claim 1, characterized in that: The specific implementation process of determining the first temperature sampling frequency at the initial moment includes: According to the ambient temperature, the expected air temperature and the expected component temperature, a first air temperature difference and a first component temperature difference are obtained; a temperature sensor response period and a temperature controller processing period are obtained to calculate a maximum sampling frequency; and the first temperature sampling frequency at the initial moment is obtained by combining the first air temperature difference and the first component temperature difference. The specific calculation formula is: in, represents the first temperature sampling frequency at the initial moment; , represent the desired air temperature and the desired component temperature, respectively; represents the ambient temperature; , They represent the temperature sensor response period and the thermostat processing period respectively.

3. The data acquisition method for a temperature controller of a setting machine according to claim 1, characterized in that: The process of updating the first temperature sampling frequency in real time includes: According to the regional air temperature and the surface temperature of the heat source component at the first temperature sampling frequency, the first air temperature increase and the first component temperature increase are calculated; the temperature state estimation equation is constructed, and the first temperature increase at the next sampling moment is obtained through the first air temperature increase and the first component temperature increase; the first temperature estimation value is obtained by combining the regional air temperature, the surface temperature of the heat source component and the first temperature increase; according to the first temperature sampling frequency and the first temperature estimation value, the temperature sampling adaptive controller is constructed to update the first temperature sampling frequency in real time.

4. The data acquisition method for the temperature controller of a setting machine according to claim 3, characterized in that: The process of implementing real-time updating of the first temperature sampling frequency by the temperature sampling adaptive controller includes: Get The first temperature estimate value, the regional air temperature and the surface temperature of the heat source component at the sampling time are used to calculate the temperature sampling frequency confidence; The temperature sampling frequency confidence at the sampling time and The first temperature sampling frequency at the sampling time is input to the temperature sampling adaptive controller, and the output The first temperature sampling frequency at the sampling moment.

5. The data acquisition method for a temperature controller of a setting machine according to claim 1, characterized in that: The process of calculating the thermal response time of the textile, the ideal temperature change rate and the temperature sampling frequency correction index, and determining the second temperature sampling frequency at the beginning of the shaping stage includes: Acquire textile process parameters, including textile thermal conductivity, textile specific heat capacity, textile thickness and textile moisture content; acquire the operating parameters of the target setting machine, including setting machine heating power, textile operating speed and area length of the temperature control area; calculate the textile thermal response time according to the textile thermal conductivity, the textile thickness and the textile moisture content; acquire the ideal temperature change rate by combining the heating power and the textile specific heat capacity; calculate the temperature sampling frequency correction index by the textile operating speed and the area length; calculate the initial temperature sampling frequency of the setting stage by combining the first temperature sampling frequency, the textile thermal response time, the ideal temperature change rate and the temperature sampling frequency correction index to obtain the second temperature sampling frequency.

6. The data acquisition method for a temperature controller of a setting machine according to claim 1, characterized in that: The process of adaptively controlling the second temperature sampling frequency by the adaptive fuzzy controller includes: According to the regional air temperature and the surface temperature of the heat source component of the second temperature sampling frequency, the temperature changes at adjacent sampling time points are calculated to obtain the second air temperature change rate and the second component temperature change rate; the heating power change rate of the target molding machine at adjacent sampling time points is obtained; an adaptive fuzzy controller is constructed, and the second air temperature change rate, the second component temperature change rate and the heating power change rate are input into the adaptive fuzzy controller, and the fuzzy control rule is used to determine whether to update the second temperature sampling frequency; the input data is mapped through the membership function to obtain the corresponding membership weight; the second temperature sampling frequency is adaptively adjusted in combination with the temperature sampling frequency modulation function.

7. The data acquisition method for a temperature controller of a setting machine according to claim 6, characterized in that: The fuzzy control rules include: If the second air temperature change rate and the second component temperature change rate are both less than the predetermined first temperature change rate threshold, and the heating power change rate is less than the predetermined power change threshold, the second temperature sampling frequency is reduced; if the second air temperature change rate and / or the second component temperature change rate is greater than the predetermined second temperature change rate threshold, and the heating power change rate is greater than or equal to the predetermined power change threshold, the second temperature sampling frequency is increased; if the second air temperature change rate and the second component temperature change rate are greater than or equal to the predetermined first temperature change rate threshold, and less than or equal to the predetermined second temperature change rate threshold, the second temperature sampling frequency remains unchanged.

8. The data acquisition method for a temperature controller of a setting machine according to claim 1, characterized in that: The implementation process of determining whether to issue an early warning to the management personnel according to the first temperature sampling frequency includes: According to the first temperature sampling frequency, the regional air temperature and the surface temperature of the heat source component in several temperature control areas in the target setting machine during the preheating stage are collected in real time; the temperature fluctuations of the regional air temperature and the surface temperature of the heat source component in adjacent time periods are calculated to obtain air temperature fluctuations and component temperature fluctuations; if the regional air temperature is greater than a predetermined first regional temperature threshold or the surface temperature of the heat source component is greater than a predetermined first component temperature threshold, an alarm is issued to the management personnel; if both the air temperature fluctuation and the component temperature fluctuation are less than a predetermined temperature increment threshold, an alarm is issued to the management personnel.

9. The data acquisition method for a temperature controller of a setting machine according to claim 1, characterized in that: The process of determining whether to issue an early warning to the management personnel according to the second temperature sampling frequency includes: The regional air temperature and the surface temperature of the heat source component in the finalization stage are collected in real time through the second temperature sampling frequency; the temperature change rates of the regional air temperature and the surface temperature of the heat source component in adjacent time periods are calculated to obtain the air temperature change rate and the component temperature change rate; if the regional air temperature is greater than a predetermined second regional temperature threshold or the surface temperature of the heat source component is greater than a predetermined second component temperature threshold, an alarm is issued to the management personnel; if the air temperature change rate is less than a predetermined air temperature change threshold or the component temperature change rate is less than a predetermined component temperature change threshold, an alarm is issued to the management personnel.

10. A temperature controller data acquisition system for a setting machine, characterized in that: include: The setting machine data acquisition module obtains textile process parameters, the ambient temperature and operating parameters of the target setting machine; collects the regional air temperature and the surface temperature of the heat source component of several temperature control areas in the target setting machine before startup; The preheating temperature acquisition control module determines the first temperature acquisition frequency at the initial moment according to the ambient temperature, the expected air temperature and the expected component temperature in the preheating stage; constructs a temperature state estimation equation in combination with the regional air temperature and the surface temperature of the heat source component at the first temperature acquisition frequency, and predicts the first temperature estimation value at the next sampling moment; inputs the first temperature estimation value, the regional air temperature and the surface temperature of the heat source component at the same sampling moment into the temperature acquisition adaptive controller, and updates the first temperature acquisition frequency in real time; The shaping temperature acquisition control module calculates the thermal response time of the textile, the ideal temperature change rate and the temperature acquisition frequency correction index according to the textile process parameters and the operating parameters, and determines the initial second temperature acquisition frequency in the shaping stage; Calculate the second air temperature change rate, the second component temperature change rate and the heating power change rate, and input them into the adaptive fuzzy controller; the adaptive fuzzy controller determines whether to update the second temperature sampling frequency according to the fuzzy control rule; and adaptively control the second temperature sampling frequency by combining the membership function and the temperature sampling frequency modulation function; The display and warning module displays the regional air temperature and the surface temperature of the heat source component of several temperature control areas in real time according to the first temperature sampling frequency and the second temperature sampling frequency, and determines whether to issue a warning to the management personnel.

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