Textile production line real-time monitoring system and method based on Internet of Things

Abstract

The invention discloses a textile production line real-time monitoring system and method based on the Internet of Things, the system comprises a data monitoring unit, a central processing unit, an early warning prompt unit and a regulation and control management unit, textile production data are remotely monitored and collected through the Internet of Things and analyzed, and the real-time monitoring of the textile production line is realized from the two aspects of textile production quality and the resource consumption degree of the textile production line. The textile production efficiency is comprehensively evaluated, the integrity and comprehensiveness of data analysis are guaranteed, an early warning prompt signal is generated for real-time monitoring and risk prompt, and timely management of a textile production line is achieved, so that the production quality of textiles and the continuous stability of the production efficiency are continuously improved, and the production efficiency is improved. And the optimal management scheme of textile production is dynamically obtained, so that the textile production line is regulated and controlled, flexible management design of the textile production line and effective configuration of textile quality and resource consumption are realized, and finally, green textile production with high textile quality and low resource consumption is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of textile production, and in particular to a real-time monitoring system and method for a textile production line based on the Internet of Things. Background Art

[0002] The Internet of Things refers to connecting any object to the network through information sensing devices according to the agreed protocol. Objects exchange and communicate information through information dissemination media to achieve intelligent identification, positioning, tracking, supervision and other functions. In the application scenario of textile production and processing, the Internet of Things can realize remote data monitoring and real-time risk warning in the textile production process, so as to timely regulate and manage the textile production line; However, during the operation and processing of textile production lines, there will be defects such as unstable textile product quality and high resource consumption. In the textile production process, the safety and color fastness of textile products belong to routine textile quality inspections. When the textile quality is not up to standard, in order to improve the quality of textile products, the textiles need to be reworked, which will further increase the resource consumption. Raw materials, energy and water resources are not fully utilized. Resource waste will lead to increased production costs and a burden on the environment. Therefore, it is difficult to ensure a balance between textile quality and resource consumption, and it is impossible to monitor and manage risks in real time, and it is difficult to ensure the continuous stability of textile production. In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to solve the defects of unstable textile product quality and high resource consumption in the operation and processing of textile production lines, collect and analyze textile production data through remote monitoring, comprehensively evaluate textile production efficiency from two perspectives: textile production quality and resource consumption of textile production lines, ensure the integrity and comprehensiveness of data analysis, generate early warning prompt signals for real-time monitoring and risk prompts, and realize timely management of textile production lines, thereby continuously improving the production quality of textiles and the sustained stability of production efficiency, and dynamically obtain the optimal management plan for textile production, so as to regulate the textile production line, realize flexible management design of textile production lines, and effectively configure textile quality and resource consumption, and finally realize green textile production with high textile quality and low resource consumption.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: The real-time monitoring method of a textile production line based on the Internet of Things includes the following steps: Step 1: Remotely monitor and collect textile production data: Textile production data includes product quality information and resource consumption information. Set the data collection period Tc to collect textile production data regularly. Step 2: Preliminary analysis of textile production data to assess textile production quality and production line resource consumption: the textile production quality is assessed through product quality information analysis; the production line resource consumption is assessed through resource consumption information analysis; Step 3: Comprehensively evaluate the efficiency of textile production and generate early warning signals: By combining the quality of textile production and the degree of resource consumption of the production line, the efficiency of textile production is comprehensively evaluated, and then the corresponding early warning signals are generated by comparison, and the optimal management plan for textile production is obtained; Step 4: Conduct corresponding risk warning and control management operations on the textile production line: conduct real-time monitoring and risk warning of the textile production line through early warning prompt signals; conduct control management operations on the textile production line through the optimal management plan for textile production; Step 5: Send management instructions regularly and conduct cyclic monitoring: Set the instruction generation cycle Tz to regularly generate and send management instructions, re-collect and analyze textile production data through management instructions, thereby refreshing the optimal management plan for textile production and performing corresponding regulation and management of the textile production line.

[0005] Furthermore, the textile production data is analyzed and processed by constructing a textile efficiency analysis model, wherein the specific construction process of the textile efficiency analysis model is as follows: The textile efficiency analysis model includes quality analysis sub-model, resource analysis sub-model and comprehensive analysis sub-model; Analyze product quality information through the quality analysis sub-model, obtain textile quality assessment coefficients, and evaluate textile production quality; Analyze resource consumption information through the resource analysis sub-model, obtain resource consumption evaluation coefficients, and evaluate the resource consumption level of the production line; The textile quality assessment coefficient and resource consumption assessment coefficient are combined through a comprehensive analysis sub-model to obtain the textile production efficiency index and evaluate the textile production efficiency.

[0006] Furthermore, the specific construction process of the quality analysis sub-model is as follows: Analyze product quality information through the quality analysis sub-model, obtain textile quality assessment coefficients, and evaluate textile production quality; Product quality information includes safety factor, color fastness, tensile strength and tear strength; Sampling and testing the textiles in the textile production line, marking the number of textile samples as N0, marking any textile sample as a, marking the product quality information of textile sample a, and marking the safety factor, color fastness, tensile strength and tear strength of textile sample a as QAa, SLa, QLa, QSa respectively; The parameter matrix Ja is established through the product quality information of N0 textile samples: ; By calculating the average value of the column vector of the parameter matrix Ja, the mean factor δ1 is obtained, and then the standard deviation of the column vector is calculated to obtain the difference factor δ2. Then, the reference coefficient Ψ of the column vector is obtained by combining the mean factor δ1 with the difference factor δ2. The reference coefficients of safety factor QAa, color fastness SLa, tensile strength QLa and tear strength QSa are marked as Ψ1, Ψ2, Ψ3 and Ψ4 respectively; and the textile quality assessment coefficient Pzl is comprehensively obtained through the reference coefficients corresponding to the four groups of column vectors of the parameter matrix Ja; The evaluation interval of the textile quality evaluation coefficient Pzl is set, and the textile production quality is evaluated by interval comparison.

[0007] Furthermore, the specific construction process of the resource analysis sub-model is as follows: Analyze resource consumption information through the resource analysis sub-model, obtain resource consumption evaluation coefficients, and evaluate the resource consumption level of the production line; Resource consumption information includes consumable waste rate, energy usage and waste generation; The consumables utilization, energy usage, and waste generation are marked as LH, LN, and LF, respectively; The resource consumption information is measured and collected regularly through the data collection period Tc, so as to obtain the resource consumption information corresponding to N1 data collection periods; The resource consumption information of N1 data collection cycles is used to construct the historical matrix Va:

[0008] By measuring the difference between adjacent vectors of the column vector of the historical matrix Va and taking the average value, the increase factor σ1 is obtained, and then the variance of N1 column vectors is measured to obtain the fluctuation factor σ2. Then, the evaluation coefficient φ of the column vector is obtained by combining the increase factor σ1, the fluctuation factor σ2 and the parameter value of the current data collection cycle; The evaluation coefficients of the consumable waste rate LB, energy usage LN, and waste generation LF are marked as φ1, φ2, and φ3 respectively; then, the resource consumption evaluation coefficient Pxh is obtained comprehensively through the evaluation coefficients corresponding to the three groups of column vectors of the historical matrix Va; Set the evaluation interval of the resource consumption evaluation coefficient Pxh, and evaluate the resource consumption level of the production line by comparing the intervals.

[0009] Furthermore, the specific construction process of the comprehensive analysis sub-model is as follows: The textile production efficiency index Fxn is obtained by combining the textile quality assessment coefficient Pzl and the resource consumption assessment coefficient Pxh through a comprehensive analysis sub-model; Set the risk interval of textile production efficiency index Fxn, evaluate textile production efficiency through interval comparison, and generate corresponding early warning signals; Set the data calculation cycle Ts to perform regular calculation of the textile production efficiency index Fxn, and sort the calculated textile production efficiency index Fxn in descending order, obtain the textile production line management plan corresponding to the maximum value of the textile production efficiency index Fxn, and mark it as the optimal management plan for textile production.

[0010] A real-time monitoring system for a textile production line based on the Internet of Things, comprising a data monitoring unit, a central processing unit, an early warning prompt unit and a control management unit, wherein the central processing unit comprises a quality analysis module, a resource analysis module and a comprehensive analysis module, and the data monitoring unit, the central processing unit, the early warning prompt unit and the control management unit are communicatively connected, and the system applies the above-mentioned real-time monitoring method for a textile production line based on the Internet of Things; The data monitoring unit is used to remotely monitor and collect textile production data; The central processor performs preliminary analysis and comprehensive processing of textile production data through the textile efficiency analysis model, and generates corresponding early warning prompt signals and optimal management solutions for textile production; among them, the quality analysis module evaluates the quality of textile production through the quality analysis sub-model; the resource analysis module evaluates the resource consumption of the production line through the resource analysis sub-model; the comprehensive analysis module evaluates the efficiency of textile production through the comprehensive analysis sub-model; The early warning prompt unit is used to receive early warning prompt signals and issue corresponding risk prompts to the textile production line; The control and management unit is used to receive the optimal management plan for textile production, perform control and management operations on the textile production line, and send management instructions to the data monitoring unit at regular intervals.

[0011] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The present invention collects and analyzes textile production data through remote monitoring, comprehensively evaluates textile production efficiency from two perspectives: textile production quality and resource consumption of textile production lines, ensures the integrity and comprehensiveness of data analysis, and generates early warning prompt signals for real-time monitoring and risk prompts, thereby achieving timely management of textile production lines, thereby continuously improving the production quality of textiles and the sustained stability of production efficiency, and dynamically obtaining the optimal management plan for textile production, thereby regulating and controlling the textile production line, achieving flexible management design of the textile production line, and effective configuration of textile quality and resource consumption, and ultimately achieving green textile production with high textile quality and low resource consumption; The present invention conducts sampling inspection on multiple textile samples and textile quality assessment, integrates the overall level and difference degree of product quality information, conducts targeted analysis and assessment of the quality of textile samples, thereby comprehensively assessing the production quality of textiles and improving the integrity of data analysis; the present invention conducts time series analysis on resource consumption information of textile production lines, integrates the growth trend and fluctuation degree of resource consumption, realizes early warning management of resource consumption risks, and improves the accuracy of data warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram showing the steps of the method flow of the present invention is shown; Figure 2 A connection diagram of the system modules of the present invention is shown. DETAILED DESCRIPTION

[0013] 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.

[0014] Embodiment 1: like Figure 1-Figure 2 As shown, the real-time monitoring method of a textile production line based on the Internet of Things includes the following steps: S1, remote monitoring and collection of textile production data: Textile production data includes product quality information and resource consumption information. The data collection period Tc is set to collect textile production data regularly; textile quality information is obtained by sampling and testing textiles through setting up a textile quality testing platform; resource consumption information of textile production is obtained by accessing the textile production line management platform; Product quality information and resource consumption information are integrated and marked as textile production data, and remotely transmitted to the central processor for analysis and processing through the Internet of Things technology; among them, the textile quality testing platform refers to an institutional platform that sets quality indicators of textile products in accordance with relevant national or international standards (such as ISO, ASTM, etc.) for quality testing; the textile production line management platform refers to the data management background of the factory production line that processes textiles; The textile production data is analyzed and processed by constructing a textile efficiency analysis model, wherein the specific construction process of the textile efficiency analysis model is as follows: The textile efficiency analysis model includes quality analysis sub-model, resource analysis sub-model and comprehensive analysis sub-model; Analyze product quality information through the quality analysis sub-model, obtain textile quality assessment coefficients, and evaluate textile production quality; Analyze resource consumption information through the resource analysis sub-model, obtain resource consumption evaluation coefficients, and evaluate the resource consumption level of the production line; The textile quality assessment coefficient and resource consumption assessment coefficient are combined through a comprehensive analysis sub-model to obtain the textile production efficiency index and evaluate the textile production efficiency; S2, preliminarily analyze textile production data and evaluate textile production quality and production line resource consumption: the textile production quality is evaluated through product quality information analysis; the production line resource consumption is evaluated through resource consumption information analysis; S2-1, the specific construction process of the quality analysis sub-model is as follows: Analyze product quality information through the quality analysis sub-model, obtain textile quality assessment coefficients, and evaluate textile production quality; Product quality information includes safety factor, color fastness, tensile strength and tear strength; Among them, the safety factor is obtained by testing the pollution risk value of textiles. The pollution risk value refers to the content of harmful chemicals contained in textiles, such as formaldehyde, heavy metals, dyes, etc. The textiles are treated with extraction liquid, and the corresponding material content is tested by spectral analysis, and then the pollution risk value is obtained by integration analysis; the pollution risk value is marked as FW, and the content of any pollutant r is marked as Cr, then the preset pollution risk value FW: , where ωr is the weight coefficient of the pollutant r, and ωr is greater than 0; the weight coefficient is obtained by preset experience after calculating a large amount of experimental data. When the content Cr of the pollutant r is higher, the pollution risk value FW is higher, which means that the content of harmful chemicals contained in the textile is higher, and the safety factor of the textile is lower. The safety factor is marked as QA: ; Color fastness refers to the color stability of textiles under conditions such as washing, light and sweat stains. The rating system is set up through the textile quality testing platform to test and score color fastness; tensile strength refers to the tensile strength and ductility of textiles, which is measured by setting up tensile testing equipment; tear strength refers to the maximum load value that textiles resist tearing when subjected to concentrated loads, which is measured by setting up a tear tester; Sampling and testing the textiles of the prevention and control production line, marking the number of textile samples as N0, marking any textile sample as a, marking the product quality information of textile sample a, and marking the safety factor, color fastness, tensile strength and tear strength of textile sample a as QAa, SLa, QLa, QSa respectively; The parameter matrix Ja is established through the product quality information of N0 textile samples: ; Among them, the four groups of column vectors of the parameter matrix Ja refer to the safety factor QAa, color fastness SLa, tensile strength QLa and tear strength QSa respectively; the N0 groups of row vectors of the parameter matrix Ja refer to the product quality information of N0 textile samples respectively; By calculating the average value of the column vector of the parameter matrix Ja, the mean factor δ1 is obtained, and then the standard deviation of the column vector is calculated to obtain the difference factor δ2. Then, the reference coefficient Ψ of the column vector is obtained by combining the mean factor δ1 with the difference factor δ2: , where ε1 and ε2 are the weight coefficients of the mean factor δ1 and the difference factor δ2, respectively, and both ε1 and ε2 are greater than 0. When the mean factor δ1 is higher, it means that the overall level of the parameter corresponding to the column vector is higher, and when the difference factor δ2 is lower, it means that the numerical difference of the parameter corresponding to the column vector is lower, which comprehensively indicates that the parameter representation corresponding to the column vector is better; Taking the first set of column vectors of the parameter matrix Ja as an example, any vector of the first set of column vectors of the parameter matrix Ja is marked as ; Then the mean factor δ1 is: ; Difference factor δ2: ; When the mean factor δ1 of the first group of column vectors is higher and the difference factor δ2 is lower, it means that the overall level of the safety factor QAa is higher and the degree of difference is lower, which comprehensively indicates that the higher the test result corresponding to the safety factor of the textile is, the better the safety of the textile is evaluated; The reference coefficients of safety factor QAa, color fastness SLa, tensile strength QLa and tear strength QSa are marked as Ψ1, Ψ2, Ψ3 and Ψ4 respectively; and the textile quality assessment coefficient Pzl is obtained comprehensively through the reference coefficients corresponding to the four groups of column vectors of the parameter matrix Ja: ; Among them, α1, α2, α3 and α4 are weight coefficients of safety factor QAa, color fastness SLa, tensile strength QLa and tear strength QSa, respectively, and α1, α2, α3 and α4 are all greater than 0; , ; , ; When the reference coefficients of safety factor QAa, color fastness SLa, tensile strength QLa and tear strength QSa are higher, the textile quality assessment coefficient Pzl is higher, and the assessed textile production quality is better; Set the evaluation interval of the textile quality evaluation coefficient Pzl, and evaluate the textile production quality through interval comparison; S2-2, the specific construction process of the resource analysis sub-model is as follows: Analyze resource consumption information through the resource analysis sub-model, obtain resource consumption evaluation coefficients, and evaluate the resource consumption level of the production line; Resource consumption information includes consumable waste rate, energy usage and waste generation; The waste rate of consumables is obtained through the utilization rate of consumables. The utilization rate of consumables refers to the proportion of textile consumables made into textile finished products. For example, in the production process of textile fabrics to textile clothing, the proportion of fabric consumables actually used in the processing process such as cutting; energy usage refers to the amount of energy such as electricity, natural gas, coal, and water resources required during the operation of the textile production line; waste generation refers to the content of solid, liquid and gaseous pollutant waste generated after processing on the textile production line; Among them, the consumables utilization rate, energy usage and waste generation are marked as LH, LN and LF respectively; Consumables utilization rate LH: , d1 is the actual amount of textile consumables, d2 is the total amount of textile consumables, and the consumables waste rate is marked as LB: ; Energy usage LN: , m1 is the number of energy types, En is the usage of any energy and En is greater than 0, is the weight coefficient of energy usage En, and Greater than 1; Waste generation LF: , m2 is the number of waste types, Fn is the amount of any waste generated and Fn is greater than 0, is the weight coefficient of waste generation Fn, and Greater than 1; The resource consumption information is measured and collected regularly through the data collection period Tc, so as to obtain the resource consumption information corresponding to N1 data collection periods; The resource consumption information of N1 data collection cycles is used to construct the historical matrix Va:

[0015] Among them, the three groups of column vectors of the history matrix Va refer to the consumable waste rate LB, energy usage LN and waste generation LF respectively; the N1 groups of row vectors of the history matrix Va refer to the resource consumption information of N1 data collection cycles respectively; By measuring the difference between adjacent vectors of the column vector of the historical matrix Va and taking the average value, the increase factor σ1 is obtained, and then the variance of N1 column vectors is measured to obtain the fluctuation factor σ2. Then, the evaluation coefficient φ of the column vector is obtained by combining the increase factor σ1, the fluctuation factor σ2 and the parameter value of the current data collection cycle: ,in, It refers to the column vector of the current data collection cycle, that is, the parameter value corresponding to the N1th data collection cycle. ρ1 and ρ2 are the weight coefficients of the increase rate factor σ1 and the volatility factor σ2, respectively, and ρ1 and ρ2 are both greater than 0. When the increase rate factor σ1 and the volatility factor σ2 are higher, it means that the growth trend of the parameter corresponding to the column vector is higher, and the volatility of the historical data is higher, which comprehensively indicates that the parameter risk corresponding to the column vector is more serious; Taking the first set of column vectors of the history matrix Va as an example, any vector marking the first set of column vectors of the history matrix Va is ; Then the increase rate factor σ1:

[0016] Fluctuation factor σ2: ; The evaluation coefficient of the first set of column vectors of the history matrix Va is φ1: ; When the increase rate factor σ1 and fluctuation factor σ2 of the first group of column vectors are higher, and the parameter value of the current data acquisition cycle is The higher the value, the higher the consumable waste risk corresponding to the comprehensive evaluation of the consumable waste rate. The evaluation coefficients of the consumable waste rate LB, energy usage LN, and waste generation LF are marked as φ1, φ2, and φ3 respectively; then, the resource consumption evaluation coefficient Pxh is obtained comprehensively through the evaluation coefficients corresponding to the three groups of column vectors of the historical matrix Va: ; Among them, β1, β2 and β3 are the weight coefficients of the consumable waste rate LB, energy usage LN and waste generation LF, respectively, and β1, β2 and β3 are all greater than 0; , , ; When the evaluation coefficients of the consumable waste rate LB, energy usage LN, and waste generation LF are higher, the resource consumption evaluation coefficient Pxh is higher, and the resource consumption of the production line is evaluated to be higher; Set the evaluation interval of the resource consumption evaluation coefficient Pxh, and evaluate the resource consumption of the production line by comparing the intervals; S3, Comprehensively evaluate the efficiency of textile production and generate early warning signals: By combining the quality of textile production and the resource consumption of the production line, the efficiency of textile production is comprehensively evaluated, and then the corresponding early warning signals are generated by comparison, and the optimal management plan for textile production is obtained; The specific construction process of the comprehensive analysis sub-model is as follows: The textile quality assessment coefficient Pzl and resource consumption assessment coefficient Pxh are combined through a comprehensive analysis sub-model to obtain the textile production efficiency index Fxn: ; Among them, the preset and are the weight coefficients of the textile quality assessment coefficient Pzl and the resource consumption assessment coefficient Pxh, respectively, and and Both are greater than 0; when the textile quality assessment coefficient Pzl is higher and the resource consumption assessment coefficient Pxh is lower, the textile production efficiency index Fxn is higher, and the assessed textile production efficiency is higher; Set the risk interval of textile production efficiency index Fxn, evaluate textile production efficiency through interval comparison, and generate corresponding early warning signals; There are U0 risk intervals of the preset textile production efficiency index Fxn, and any risk interval is marked as Qu, where u is the serial number of the risk interval and 1≤u≤U0. When the textile production efficiency index Fxn is in the risk interval Qu, the textile production efficiency is evaluated as u level, and a u level early warning prompt signal is generated; It should be noted that the interval comparison evaluation of textile production quality and production line resource consumption is similar to the interval comparison evaluation process of textile production efficiency. It can be achieved by calculating according to a large amount of experimental data and presetting the corresponding evaluation interval based on experience, which will not be elaborated here. When an early warning signal is received, the risk is visualized on the management platform of the textile production line. For example, the corresponding early warning text data is displayed, so as to prompt the back-end management personnel to conduct timely textile production parameter testing, including the equipment parameter adjustment and correction of textile printing, dyeing, cutting and other processing processes. The specific operation process is flexibly adjusted by professional technicians based on the actual situation of textiles, so as to continuously improve the quality of textile production; Set the data calculation cycle Ts to perform regular calculations on the textile production efficiency index Fxn, sort the calculated textile production efficiency index Fxn in descending order, obtain the textile production line management plan corresponding to the maximum value of the textile production efficiency index Fxn, and mark it as the optimal management plan for textile production; S4, carry out corresponding risk warning and control management operations on textile production lines: carry out real-time monitoring and risk warning of textile production lines through early warning warning signals; carry out control management operations on textile production lines through the optimal management plan for textile production; The textile production line management plan includes a resource allocation plan and a waste control plan. The resource allocation plan is a plan for managing the energy of the production line, such as the allocation ratio of electricity, natural gas, coal, water resources and other energy sources. By rationally allocating multiple energy sources for textile production, or reducing the idle time of textile equipment through automated production, and promoting water recycling management methods, resource utilization efficiency can be improved; the waste control plan is to carry out green transformation of textile materials, for example, using environmentally friendly and renewable materials such as organic cotton and water-based dyes to reduce the generation of waste pollutants; follow-up processing is carried out according to the textile production line management plan corresponding to the optimal point of the textile production efficiency index, and dynamic adjustments are made in combination with the subsequent production needs of textiles, so as to continuously and flexibly manage and operate the textile production line to achieve a balance between textile quality and resource consumption; S5, regularly sending management instructions and performing cyclic monitoring: setting the instruction generation cycle Tz to regularly generate and send management instructions, re-collecting and analyzing textile production data through management instructions, thereby refreshing the optimal management plan for textile production and performing corresponding regulation and management on the textile production line; By dynamically acquiring the optimal management plan for textile production, the textile production line can be regulated and managed accordingly, flexible management and design of the textile production line can be achieved, and the optimal configuration of textile quality and resource consumption can be achieved, ultimately achieving the transformation of green textile production with high textile quality and low resource consumption.

[0017] A real-time monitoring system for a textile production line based on the Internet of Things, comprising a data monitoring unit, a central processing unit, an early warning prompt unit and a control management unit, wherein the central processing unit comprises a quality analysis module, a resource analysis module and a comprehensive analysis module, and the data monitoring unit, the central processing unit, the early warning prompt unit and the control management unit are communicatively connected, and the system applies the above-mentioned real-time monitoring method for a textile production line based on the Internet of Things; The data monitoring unit is used to remotely monitor and collect textile production data; The central processor performs preliminary analysis and comprehensive processing of textile production data through the textile efficiency analysis model, and generates corresponding early warning prompt signals and optimal management solutions for textile production; among them, the quality analysis module evaluates the quality of textile production through the quality analysis sub-model; the resource analysis module evaluates the resource consumption of the production line through the resource analysis sub-model; the comprehensive analysis module evaluates the efficiency of textile production through the comprehensive analysis sub-model; The early warning prompt unit is used to receive early warning prompt signals and issue corresponding risk prompts to the textile production line; The control and management unit is used to receive the optimal management plan for textile production, perform control and management operations on the textile production line, and send management instructions to the data monitoring unit at regular intervals.

[0018] In summary, the present invention collects and analyzes textile production data through remote monitoring of the Internet of Things, comprehensively evaluates textile production efficiency from two perspectives: textile production quality and resource consumption of textile production lines, ensures the integrity and comprehensiveness of data analysis, and generates early warning prompt signals for real-time monitoring and risk prompts, thereby achieving timely management of textile production lines, thereby continuously improving the production quality of textiles and the sustained stability of production efficiency, and dynamically obtaining the optimal management plan for textile production, thereby regulating and controlling the textile production line, achieving flexible management design of the textile production line, and effective configuration of textile quality and resource consumption, and ultimately achieving green textile production with high textile quality and low resource consumption; The present invention conducts sampling inspection on multiple textile samples and textile quality assessment, integrates the overall level and difference degree of product quality information, conducts targeted analysis and assessment of the quality of textile samples, thereby comprehensively assessing the production quality of textiles and improving the integrity of data analysis; the present invention conducts time series analysis on resource consumption information of textile production lines, integrates the growth trend and fluctuation degree of resource consumption, realizes early warning management of resource consumption risks, and improves the accuracy of data warning.

[0019] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0020] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A real-time monitoring method for a textile production line based on the Internet of Things, characterized in that: The following steps are involved: Step 1: Remotely monitor and collect textile production data: Textile production data includes product quality information and resource consumption information. Set the data collection period Tc to collect textile production data regularly. Step 2: Preliminary analysis of textile production data to assess textile production quality and production line resource consumption: the textile production quality is assessed through product quality information analysis; the production line resource consumption is assessed through resource consumption information analysis; Step 3: Comprehensively evaluate the efficiency of textile production and generate early warning signals: By combining the quality of textile production and the degree of resource consumption of the production line, the efficiency of textile production is comprehensively evaluated, and then the corresponding early warning signals are generated by comparison, and the optimal management plan for textile production is obtained; Step 4: Conduct corresponding risk warning and control management operations on the textile production line: conduct real-time monitoring and risk warning of the textile production line through early warning prompt signals; conduct control management operations on the textile production line through the optimal management plan for textile production; Step 5: Send management instructions regularly and conduct cyclic monitoring: Set the instruction generation cycle Tz to regularly generate and send management instructions, re-collect and analyze textile production data through management instructions, thereby refreshing the optimal management plan for textile production and performing corresponding regulation and management of the textile production line.

2. The real-time monitoring method for a textile production line based on the Internet of Things according to claim 1 is characterized in that: The textile production data is analyzed and processed by constructing a textile efficiency analysis model, wherein the specific construction process of the textile efficiency analysis model is as follows: The textile efficiency analysis model includes quality analysis sub-model, resource analysis sub-model and comprehensive analysis sub-model; Analyze product quality information through the quality analysis sub-model, obtain textile quality assessment coefficients, and evaluate textile production quality; Analyze resource consumption information through the resource analysis sub-model, obtain resource consumption evaluation coefficients, and evaluate the resource consumption level of the production line; The textile quality assessment coefficient and resource consumption assessment coefficient are combined through a comprehensive analysis sub-model to obtain the textile production efficiency index and evaluate the textile production efficiency.

3. The real-time monitoring method for a textile production line based on the Internet of Things according to claim 2 is characterized in that: The specific construction process of the quality analysis sub-model is as follows: Analyze product quality information through the quality analysis sub-model, obtain textile quality assessment coefficients, and evaluate textile production quality; Product quality information includes safety factor, color fastness, tensile strength and tear strength; Sampling and testing the textiles in the textile production line, marking the number of textile samples as N0, marking any textile sample as a, marking the product quality information of textile sample a, and marking the safety factor, color fastness, tensile strength and tear strength of textile sample a as QAa, SLa, QLa, QSa respectively; The parameter matrix Ja is established through the product quality information of N0 textile samples: ； By calculating the average value of the column vector of the parameter matrix Ja, the mean factor δ1 is obtained, and then the standard deviation of the column vector is calculated to obtain the difference factor δ2. Then, the reference coefficient Ψ of the column vector is obtained by combining the mean factor δ1 with the difference factor δ2. The reference coefficients of safety factor QAa, color fastness SLa, tensile strength QLa, and tear strength QSa are marked as Ψ1, Ψ2, Ψ3, and Ψ4, respectively; Then, the textile quality assessment coefficient Pzl is comprehensively obtained through the reference coefficients corresponding to the four groups of column vectors of the parameter matrix Ja; The evaluation interval of the textile quality evaluation coefficient Pzl is set, and the textile production quality is evaluated by interval comparison.

4. The real-time monitoring method for a textile production line based on the Internet of Things according to claim 3 is characterized in that: The specific construction process of the resource analysis sub-model is as follows: Analyze resource consumption information through the resource analysis sub-model, obtain resource consumption evaluation coefficients, and evaluate the resource consumption level of the production line; Resource consumption information includes consumable waste rate, energy usage and waste generation; The consumables utilization, energy usage, and waste generation are marked as LH, LN, and LF, respectively; The resource consumption information is measured and collected regularly through the data collection period Tc, so as to obtain the resource consumption information corresponding to N1 data collection periods; The resource consumption information of N1 data collection cycles is used to construct the historical matrix Va: By measuring the difference between adjacent vectors of the column vector of the historical matrix Va and taking the average value, the increase factor σ1 is obtained, and then the variance of N1 column vectors is measured to obtain the fluctuation factor σ2. Then, the evaluation coefficient φ of the column vector is obtained by combining the increase factor σ1, the fluctuation factor σ2 and the parameter value of the current data collection cycle; The evaluation coefficients of the consumable waste rate LB, energy usage LN, and waste generation LF are marked as φ1, φ2, and φ3, respectively; Then, the resource consumption evaluation coefficient Pxh is comprehensively obtained through the evaluation coefficients corresponding to the three groups of column vectors of the historical matrix Va; Set the evaluation interval of the resource consumption evaluation coefficient Pxh, and evaluate the resource consumption level of the production line by comparing the intervals.

5. The real-time monitoring method for a textile production line based on the Internet of Things according to claim 4 is characterized in that: The specific construction process of the comprehensive analysis sub-model is as follows: The textile production efficiency index Fxn is obtained by combining the textile quality assessment coefficient Pzl and the resource consumption assessment coefficient Pxh through a comprehensive analysis sub-model; Set the risk interval of textile production efficiency index Fxn, evaluate textile production efficiency through interval comparison, and generate corresponding early warning signals; Set the data calculation cycle Ts to perform regular calculation of the textile production efficiency index Fxn, and sort the calculated textile production efficiency index Fxn in descending order, obtain the textile production line management plan corresponding to the maximum value of the textile production efficiency index Fxn, and mark it as the optimal management plan for textile production.

6. Real-time monitoring system for textile production lines based on the Internet of Things, characterized by: The system comprises a data monitoring unit, a central processing unit, an early warning prompt unit and a control management unit, wherein the central processing unit comprises a quality analysis module, a resource analysis module and a comprehensive analysis module, and the data monitoring unit, the central processing unit, the early warning prompt unit and the control management unit are communicatively connected, and the system applies the real-time monitoring method for a textile production line based on the Internet of Things as described in any one of claims 1 to 5 above; The data monitoring unit is used to remotely monitor and collect textile production data; The central processor performs preliminary analysis and comprehensive processing of textile production data through the textile efficiency analysis model, and generates corresponding early warning prompt signals and optimal management solutions for textile production; among them, the quality analysis module evaluates the quality of textile production through the quality analysis sub-model; the resource analysis module evaluates the resource consumption of the production line through the resource analysis sub-model; the comprehensive analysis module evaluates the efficiency of textile production through the comprehensive analysis sub-model; The early warning prompt unit is used to receive early warning prompt signals and issue corresponding risk prompts to the textile production line; The control and management unit is used to receive the optimal management plan for textile production, perform control and management operations on the textile production line, and send management instructions to the data monitoring unit at regular intervals.

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