Textile production detection method and system based on data feedback

Abstract

The invention discloses a textile production detection method and system based on data feedback, and relates to the technical field of textile production.The method comprises the steps that textile production raw materials are screened based on textile production raw material information, loom parameters are adjusted according to textile quality prediction indexes, and the real-time working environment is optimized; the real-time working environment information, the yarn type information and the yarn environment influence coefficient prediction model of the to-be-processed textile are obtained, the yarn environment real-time influence coefficient is obtained, the real-time yarn state index is obtained through the initial yarn state index and the yarn environment real-time influence coefficient, and finally the real-time yarn quality index is obtained based on the real-time yarn quality index. The textile quality prediction index is obtained, the loom parameters are adjusted according to the textile quality prediction index, the real-time working environment is optimized, the loom can adjust the state of the yarn according to the real-time working environment in the weaving process, the quality of the woven fabric is improved, and therefore the working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of textile production, and in particular to a textile production detection method and system based on data feedback. Background Art

[0002] As the global textile industry's requirements for production efficiency and product quality increase, traditional detection methods face challenges and are difficult to meet the needs of rapid response and precise control. Therefore, a textile production detection method based on data feedback has emerged. By real-time monitoring of key parameters in the production process, the system uses data analysis and intelligent algorithms to promptly detect and correct potential problems and ensure product consistency and quality. This method not only improves production efficiency and reduces scrap rates, but also promotes the transformation of enterprises to intelligent weaving and enhances market competitiveness. Its application significance lies in optimizing resource allocation, promoting sustainable development, and making the textile industry more innovative and flexible.

[0003] In the existing textile production detection methods based on data feedback, the types of yarns in the textile production raw materials are diverse and the quality is uneven, making it difficult to select high-quality yarns from the textile production raw materials. At the same time, different types of yarns are affected differently by the working environment during the weaving process, resulting in the loom being difficult to adjust the state of the yarn according to the real-time working environment during the weaving process, thereby affecting the quality of the textile after weaving is completed and reducing work efficiency. Therefore, it is necessary to provide a textile production detection method and system based on data feedback to solve the above-mentioned problems. Summary of the invention

[0004] In order to solve the above technical problems, a textile production detection method and system based on data feedback are provided. The technical solution solves the problem that the existing textile production detection method based on data feedback proposed in the above background technology, the types of yarns in the textile production raw materials are diverse, and the quality is also uneven, and it is difficult to select high-quality yarns from the textile production raw materials. At the same time, different types of yarns are affected differently by the working environment during the weaving process, resulting in the loom being difficult to adjust the state of the yarn according to the real-time working environment during the weaving process, thereby affecting the quality of the textile after weaving is completed and reducing work efficiency.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A textile production detection method based on data feedback, comprising:

[0007] Obtain information on textile production and determine information on raw materials used in textile production;

[0008] Screening textile production raw materials based on textile production raw material information to obtain preferred raw materials for textile production;

[0009] Selecting yarn to be processed from preferred raw materials for textile production and determining information of the yarn to be processed;

[0010] Determining yarn installation parameter information based on the yarn information to be processed and the textile production information;

[0011] installing the yarn in the loom according to the yarn installation parameter information and collecting the initial working environment information;

[0012] Determining preset weaving parameter information based on the yarn installation parameter information and the initial working environment information;

[0013] Adjust loom parameters according to preset weaving parameter information, collect real-time working environment information, and obtain real-time yarn quality index;

[0014] Based on the real-time yarn quality index, obtain the textile quality prediction index;

[0015] Adjust loom parameters according to the textile quality prediction index and optimize the real-time working environment to obtain the textiles to be processed.

[0016] In an optional embodiment, the screening of textile production raw materials based on textile production raw material information to obtain preferred textile production raw materials specifically includes:

[0017] Determine the information on textile performance requirements and textile appearance requirements based on textile production information;

[0018] Select i batches of textile production raw materials that meet the requirements based on the textile performance requirement information and the textile appearance requirement information, and determine the textile production raw material information;

[0019] According to the information of textile production raw materials, sample and test i batches of textile production raw materials in turn to obtain the physical property index and chemical property index of i batches of textile production raw materials;

[0020] Obtain the comprehensive performance index matrix of batch i of textile production raw materials;

[0021] According to the comprehensive performance index matrix of the i batches of textile production raw materials, the actual performance indexes of the i batches of textile production raw materials are obtained in sequence;

[0022] Based on the information of textile production raw materials, i batches of textile production raw materials are classified, and textile production raw materials of the same type are divided into the same area to obtain textile production raw materials of j areas;

[0023] According to the actual performance index of i batches of textile production raw materials, the textile production raw materials in j regions are sorted, and the textile production raw materials with the highest actual performance index in j regions are screened to obtain the preferred raw materials for textile production.

[0024] In an optional embodiment, the step of obtaining a comprehensive performance index matrix of i batches of textile production raw materials specifically includes:

[0025] Respectively obtain the raw material quality values ​​of i batches of textile production raw materials and the sample quality values ​​of the i batches of textile production raw materials sampled and tested in sequence;

[0026] The ratio of the sample mass value of the i-th batch of textile production raw materials sampled and tested to the raw material mass value of the i-th batch of textile production raw materials is used as the performance weight coefficient of the i-th batch of textile production raw materials;

[0027] The product of the mth physical property index of the i-th batch of textile production raw materials and the performance weight coefficient of the i-th batch of textile production raw materials is taken as the xth element of the physical property index matrix of the i-th batch of textile production raw materials;

[0028] The product of the nth chemical performance index of the i-th batch of textile production raw materials and the performance weight coefficient of the i-th batch of textile production raw materials is taken as the yth element of the chemical performance index matrix of the i-th batch of textile production raw materials;

[0029] Among them, the physical property index matrix and chemical property index matrix of the i-th batch of textile production raw materials are both row matrices;

[0030] Respectively obtain the column numbers of the physical property index matrix and the chemical property index matrix of the i-th batch of textile production raw materials to obtain the column numbers of the physical property index matrix and the chemical property index matrix;

[0031] Compare the number of columns of the physical performance index matrix and the number of columns of the chemical performance index matrix, and use the value 0 as a supplementary element to make the number of columns of the physical performance index matrix and the chemical performance index matrix the same;

[0032] Performing matrix transposition on the chemical performance index matrix to obtain a chemical performance index transposed matrix;

[0033] The physical performance index matrix and the chemical performance index transposed matrix are multiplied to obtain a comprehensive performance index matrix.

[0034] In an optional embodiment, the determining of the yarn installation parameter information based on the yarn information to be processed and the textile production information specifically includes:

[0035] According to the actual performance index of the i batch of textile production raw materials, query the actual performance index corresponding to the yarn to be processed to obtain the actual performance index of the yarn to be processed;

[0036] The textile production raw material information and the actual performance index of the yarn to be processed are correlated and integrated to obtain the yarn information to be processed;

[0037] Determining textile production pattern information based on the textile production information, and determining textile organization structure information;

[0038] Obtaining dyeing type information of the yarn to be processed through the yarn to be processed information and the textile production information, and determining the dyeing type of the yarn to be processed, wherein the dyeing type of the yarn to be processed includes pre-dyeing and post-dyeing;

[0039] Processing the yarn according to the dyeing type information of the yarn to be processed to obtain the yarn to be arranged;

[0040] Determine yarn arrangement information based on textile production pattern information and textile organization structure information;

[0041] The yarn information to be processed and the yarn arrangement information are associated and integrated to obtain the yarn installation parameter information.

[0042] In an optional embodiment, the step of determining the preset weaving parameter information based on the yarn installation parameter information and the initial working environment information specifically includes:

[0043] Determine yarn type information, yarn size information, warp yarn quantity information and weft yarn quantity information according to yarn installation parameter information;

[0044] The yarn type is represented by label encoding, and the yarn type classification variable of each yarn in the yarn installation parameter information is obtained;

[0045] Determine the initial temperature index, initial humidity index, initial dust concentration index and initial light intensity index of each yarn based on the initial working environment information;

[0046] The yarn type classification variable of each yarn, as well as the initial temperature index, initial humidity index, initial dust concentration index and initial light intensity index of each yarn are sequentially input into the yarn environmental impact coefficient prediction model;

[0047] Obtaining the yarn environmental impact coefficient of each yarn output by the yarn environmental impact coefficient prediction model;

[0048] Based on the yarn size information, the warp yarn quantity information, the weft yarn quantity information and the yarn environmental impact coefficient corresponding to each yarn, the preset weaving parameter information is set.

[0049] In an optional embodiment, the steps for obtaining the yarn environmental impact coefficient prediction model are:

[0050] S601, constructing a multivariate linear regression model, and collecting historical working environment data, historical yarn type data, and historical yarn strength data;

[0051] S602, extracting the historical temperature index, the historical humidity index, the historical dust concentration index and the historical light intensity index from the historical working environment data as continuous variables, determining the historical yarn type classification variable from the historical yarn type data, determining the historical yarn strength from the historical yarn strength data as the control variable, and taking the yarn environmental impact coefficient as the target variable;

[0052] S603, obtaining multiple groups of continuous variables, categorical variables, control variables and target variables from historical working environment data, historical yarn type data and historical yarn strength data, and inputting them into a multivariate regression model for training to obtain an initial model for predicting the yarn environmental impact coefficient;

[0053] S604, performing a model evaluation on the initial model for predicting the yarn environmental impact coefficient, and optimizing the initial model for predicting the yarn environmental impact coefficient according to the evaluation result to obtain a yarn environmental impact coefficient prediction model;

[0054] Among them, the expression of the yarn environmental impact coefficient prediction model is:

[0055]

[0056] In the formula, S α is the historical yarn strength in the αth group of historical yarn strength data, Y α is the yarn environmental impact coefficient output by the prediction model, T α is the historical yarn type classification variable in the αth group of historical yarn type data, θ α is the historical temperature index in the αth group of historical working environment data, H α is the historical humidity index in the αth group of historical working environment data, D α is the historical dust concentration index in the αth group of historical working environment data, L α is the historical light intensity index in the αth group of historical working environment data, β0 and ω0 are constant terms, β1, β2, β3, β4, β5, ω1, ω2, ω3, ω4 and ω5 are weight coefficients, ∈1 and ∈2 are error terms.

[0057] In an optional embodiment, the adjusting the loom parameters according to the preset weaving parameter information and collecting the real-time working environment information to obtain the real-time yarn quality index specifically includes:

[0058] Collecting initial yarn state index;

[0059] According to the real-time working environment information, yarn type information and yarn environment impact coefficient prediction model, the real-time impact coefficient of the yarn environment is obtained;

[0060] The product of the initial yarn state index and the yarn environment real-time influence coefficient is taken as the real-time yarn state index.

[0061] Furthermore, a textile production detection system based on data feedback is proposed, which is used to implement any of the detection methods described above, including:

[0062] A main control module, the main control module is used to receive textile production information, initial working environment information, real-time working environment information and initial yarn state index, determine yarn installation parameter information based on the yarn information to be processed and the textile production information, determine preset weaving parameter information based on the yarn installation parameter information and the initial working environment information, and is used to obtain a real-time yarn quality index and a textile quality prediction index, screen textile production raw materials based on textile production raw material information to obtain preferred raw materials for textile production, select yarn to be processed from the preferred raw materials for textile production, and determine the yarn information to be processed, install the yarn in a loom according to the yarn installation parameter information, adjust loom parameters according to the preset weaving parameter information, adjust the loom parameters according to the textile quality prediction index, and optimize the real-time working environment;

[0063] A detection module, the detection module is used to collect initial working environment information, real-time working environment information and initial yarn state index;

[0064] A transmission module, which is used to transmit data and information between the main control module, the detection module and the display module;

[0065] The display module is used to display the data processing results of the main control module, the detection process and results of the detection module, and the process and results of data transmission by the transmission module.

[0066] In an optional embodiment, the main control module includes:

[0067] A receiving unit, the receiving unit is used to receive textile production information, initial working environment information, real-time working environment information and initial yarn state index;

[0068] A processing unit, the processing unit is used to determine yarn installation parameter information based on the yarn information to be processed and the textile production information, and to determine preset weaving parameter information based on the yarn installation parameter information and the initial working environment information, so as to obtain a real-time yarn quality index and a textile quality prediction index;

[0069] A screening unit, the screening unit is used to screen the textile production raw materials based on the textile production raw material information to obtain the preferred textile production raw materials, select the yarn to be processed from the preferred textile production raw materials, and determine the yarn information to be processed;

[0070] An adjustment unit is used to install the yarn in the loom according to the yarn installation parameter information, adjust the loom parameters according to the preset weaving parameter information, adjust the loom parameters according to the textile quality prediction index, and optimize the real-time working environment.

[0071] In an optional embodiment, the detection module includes:

[0072] A first detection unit, the first detection unit is used to collect initial working environment information and real-time working environment information;

[0073] The second detection unit is used to collect the initial yarn state index.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] This scheme proposes a textile production detection method and system based on data feedback, which obtains the physical performance index and chemical performance index of textile production raw materials and establishes a comprehensive performance index matrix to obtain the actual performance index of textile production raw materials. At the same time, the actual performance index is used to screen the textile production raw materials to obtain the preferred raw materials for textile production, thereby obtaining high-quality yarns, thereby avoiding the quality of woven textiles being reduced due to uneven yarn quality;

[0076] This scheme proposes a textile production detection method and system based on data feedback, which obtains the real-time impact coefficient of the yarn environment through real-time working environment information, yarn type information and a yarn environment impact coefficient prediction model, and obtains the real-time yarn state index through the initial yarn state index and the real-time impact coefficient of the yarn environment. Finally, based on the real-time yarn quality index, the textile quality prediction index is obtained, and the loom parameters are adjusted according to the textile quality prediction index to optimize the real-time working environment, so that the loom can adjust the yarn state according to the real-time working environment during the weaving process, improve the quality of the textile after weaving is completed, and thus improve work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 A flowchart of a textile production detection method based on data feedback proposed by the present invention;

[0078] Figure 2 A flow chart for obtaining preferred raw materials for producing textiles in the present invention;

[0079] Figure 3 A flowchart for obtaining yarn installation parameter information in the present invention;

[0080] Figure 4 This is a system framework diagram of a textile production detection system based on data feedback proposed by the present invention. DETAILED DESCRIPTION

[0081] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0082] Reference Figure 1 - Figure 4 As shown, a textile production detection method based on data feedback includes:

[0083] Obtain information on textile production and determine information on raw materials used in textile production;

[0084] Screening textile production raw materials based on textile production raw material information to obtain preferred raw materials for textile production;

[0085] Selecting yarn to be processed from preferred raw materials for textile production and determining information of the yarn to be processed;

[0086] Determining yarn installation parameter information based on the yarn information to be processed and the textile production information;

[0087] installing the yarn in the loom according to the yarn installation parameter information and collecting the initial working environment information;

[0088] Determining preset weaving parameter information based on the yarn installation parameter information and the initial working environment information;

[0089] Adjust loom parameters according to preset weaving parameter information, collect real-time working environment information, and obtain real-time yarn quality index;

[0090] Based on the real-time yarn quality index, obtain the textile quality prediction index;

[0091] Adjust loom parameters according to the textile quality prediction index and optimize the real-time working environment to obtain the textiles to be processed.

[0092] Specifically, the loom parameters are adjusted according to the textile quality prediction index, and the real-time working environment is optimized to obtain the textiles to be processed. The loom parameters can be adjusted according to the textile quality prediction index to keep the yarn installed on the loom in good condition. Real-time parameter adjustment can effectively reduce the defect rate and improve the quality of the final product. By optimizing the real-time working environment, the settings of the air conditioner, humidifier or dehumidifier can be adjusted as needed.

[0093] Furthermore, the textile production raw materials are screened based on the textile production raw material information to obtain the preferred raw materials for textile production, specifically including:

[0094] Determine the information on textile performance requirements and textile appearance requirements based on textile production information;

[0095] Select i batches of textile production raw materials that meet the requirements based on the textile performance requirement information and the textile appearance requirement information, and determine the textile production raw material information;

[0096] According to the information of textile production raw materials, sample and test i batches of textile production raw materials in turn to obtain the physical property index and chemical property index of i batches of textile production raw materials;

[0097] Obtain the comprehensive performance index matrix of batch i of textile production raw materials;

[0098] According to the comprehensive performance index matrix of the i batches of textile production raw materials, the actual performance indexes of the i batches of textile production raw materials are obtained in sequence;

[0099] Based on the information of textile production raw materials, i batches of textile production raw materials are classified, and textile production raw materials of the same type are divided into the same area to obtain textile production raw materials of j areas;

[0100] According to the actual performance index of i batches of textile production raw materials, the textile production raw materials in j regions are sorted, and the textile production raw materials with the highest actual performance index in j regions are screened to obtain the preferred raw materials for textile production.

[0101] Furthermore, the comprehensive performance index matrix of the i batch of textile production raw materials is obtained, including:

[0102] Respectively obtain the raw material quality values ​​of i batches of textile production raw materials and the sample quality values ​​of the i batches of textile production raw materials sampled and tested in sequence;

[0103] The ratio of the sample mass value of the i-th batch of textile production raw materials sampled and tested to the raw material mass value of the i-th batch of textile production raw materials is used as the performance weight coefficient of the i-th batch of textile production raw materials;

[0104] The product of the mth physical property index of the i-th batch of textile production raw materials and the performance weight coefficient of the i-th batch of textile production raw materials is taken as the xth element of the physical property index matrix of the i-th batch of textile production raw materials;

[0105] The product of the nth chemical performance index of the i-th batch of textile production raw materials and the performance weight coefficient of the i-th batch of textile production raw materials is taken as the yth element of the chemical performance index matrix of the i-th batch of textile production raw materials;

[0106] Among them, the physical property index matrix and chemical property index matrix of the i-th batch of textile production raw materials are both row matrices;

[0107] Respectively obtain the column numbers of the physical property index matrix and the chemical property index matrix of the i-th batch of textile production raw materials to obtain the column numbers of the physical property index matrix and the chemical property index matrix;

[0108] Compare the number of columns of the physical performance index matrix and the number of columns of the chemical performance index matrix, and use the value 0 as a supplementary element to make the number of columns of the physical performance index matrix and the chemical performance index matrix the same;

[0109] Performing matrix transposition on the chemical performance index matrix to obtain a chemical performance index transposed matrix;

[0110] The physical performance index matrix and the chemical performance index transposed matrix are multiplied to obtain a comprehensive performance index matrix.

[0111] Further, based on the yarn information to be processed and the textile production information, the yarn installation parameter information is determined, specifically including:

[0112] According to the actual performance index of the i batch of textile production raw materials, query the actual performance index corresponding to the yarn to be processed to obtain the actual performance index of the yarn to be processed;

[0113] The textile production raw material information and the actual performance index of the yarn to be processed are correlated and integrated to obtain the yarn information to be processed;

[0114] Determining textile production pattern information based on the textile production information, and determining textile organization structure information;

[0115] Obtaining dyeing type information of the yarn to be processed through the yarn information to be processed and the textile production information, and determining the dyeing type of the yarn to be processed, where the dyeing type of the yarn to be processed includes pre-dyeing and post-dyeing;

[0116] Processing the yarn according to the dyeing type information of the yarn to be processed to obtain the yarn to be arranged;

[0117] Determine yarn arrangement information based on textile production pattern information and textile organization structure information;

[0118] The yarn information to be processed and the yarn arrangement information are associated and integrated to obtain the yarn installation parameter information.

[0119] Further, based on the yarn installation parameter information and the initial working environment information, the preset weaving parameter information is determined, specifically including:

[0120] Determine yarn type information, yarn size information, warp yarn quantity information and weft yarn quantity information according to yarn installation parameter information;

[0121] The yarn type is represented by label encoding, and the yarn type classification variable of each yarn in the yarn installation parameter information is obtained;

[0122] Determine the initial temperature index, initial humidity index, initial dust concentration index and initial light intensity index of each yarn based on the initial working environment information;

[0123] The yarn type classification variable of each yarn, as well as the initial temperature index, initial humidity index, initial dust concentration index and initial light intensity index of each yarn are sequentially input into the yarn environmental impact coefficient prediction model;

[0124] Obtaining the yarn environmental impact coefficient of each yarn output by the yarn environmental impact coefficient prediction model;

[0125] Based on the yarn size information, the warp yarn quantity information, the weft yarn quantity information and the yarn environmental impact coefficient corresponding to each yarn, the preset weaving parameter information is set.

[0126] Furthermore, the steps for obtaining the yarn environmental impact coefficient prediction model are as follows:

[0127] S601, constructing a multivariate linear regression model, and collecting historical working environment data, historical yarn type data, and historical yarn strength data;

[0128] S602, extracting the historical temperature index, the historical humidity index, the historical dust concentration index and the historical light intensity index from the historical working environment data as continuous variables, determining the historical yarn type classification variable from the historical yarn type data, determining the historical yarn strength from the historical yarn strength data as the control variable, and taking the yarn environmental impact coefficient as the target variable;

[0129] S603, obtaining multiple groups of continuous variables, categorical variables, control variables and target variables from historical working environment data, historical yarn type data and historical yarn strength data, and inputting them into a multivariate regression model for training to obtain an initial model for predicting the yarn environmental impact coefficient;

[0130] S604, performing a model evaluation on the initial model for predicting the yarn environmental impact coefficient, and optimizing the initial model for predicting the yarn environmental impact coefficient according to the evaluation result to obtain a yarn environmental impact coefficient prediction model;

[0131] Among them, the expression of the yarn environmental impact coefficient prediction model is:

[0132]

[0133] In the formula, S α is the historical yarn strength in the αth group of historical yarn strength data, Y α is the yarn environmental impact coefficient output by the prediction model, T α is the historical yarn type classification variable in the αth group of historical yarn type data, θα is the historical temperature index in the αth group of historical working environment data, H α is the historical humidity index in the αth group of historical working environment data, D α is the historical dust concentration index in the αth group of historical working environment data, L α is the historical light intensity index in the αth group of historical working environment data, β0 and ω0 are constant terms, β1, β2, β3, β4, β5, ω1, ω2, ω3, ω4 and ω5 are weight coefficients, ∈1 and ∈2 are error terms.

[0134] Furthermore, loom parameters are adjusted according to preset weaving parameter information, and real-time working environment information is collected to obtain real-time yarn quality index, specifically including:

[0135] Collecting initial yarn state index;

[0136] According to the real-time working environment information, yarn type information and yarn environment impact coefficient prediction model, the real-time impact coefficient of the yarn environment is obtained;

[0137] The product of the initial yarn state index and the yarn environment real-time influence coefficient is taken as the real-time yarn state index.

[0138] It can be understood that the initial yarn state index is used to describe the quantitative value of the initial state of the yarn when installed on the loom, including the tension of the yarn, etc.;

[0139] Specifically, the textile production information includes textile production raw material information, textile production type and textile production specifications, etc. For obtaining the textile quality prediction index based on the real-time yarn quality index, it can be understood that one corner of the loom (lower left corner) is taken as the origin, the first warp yarn is taken as the X-axis, and the starting point of the weft yarn is taken as the Y-axis perpendicular to the X-axis, and the X-axis and the Y-axis are both in the working plane of the loom (the surface where the textile is located). A plane coordinate system is established with the origin, the X-axis and the Y-axis, and the number of warp yarns and weft yarns installed on the loom is obtained to obtain the real-time warp yarn quantity information and the real-time weft yarn quantity information, and further obtain the real-time warp yarn distance coefficient and the real-time weft yarn distance coefficient. Then, according to the real-time warp yarn distance coefficient, the real-time warp yarn quantity information and the real-time yarn quality index corresponding to the real-time warp yarn, the real-time warp yarn quality prediction total index is obtained, and according to the real-time weft yarn distance coefficient, the real-time weft yarn quantity information and the real-time yarn quality index corresponding to the real-time weft yarn, the real-time weft yarn quality prediction total index is obtained, and finally the sum of the real-time warp yarn quality prediction total index and the real-time weft yarn quality prediction total index is used as the textile quality prediction index;

[0140] Among them, the calculation formula of the initial yarn state index is:

[0141]

[0142] In the formula, Q initial is the initial yarn state index, G max is the maximum load-bearing capacity of the yarn, L max is the maximum length of the yarn, D aver is the average diameter of the yarn measured 100 times, D stand is the mean square error of the average diameter of the yarn measured 100 times, Y weight is the weight of the yarn in its initial state, T number is the number of yarn turns, Y length is the length of the yarn in the initial state, τ1, τ2, τ3 and τ4 are all weight coefficients;

[0143] Understandably, and They represent the strength, uniformity index, fineness and twist of the yarn respectively;

[0144] The calculation formula of real-time warp distance coefficient is:

[0145]

[0146] Where d(ρ,σ) is the real-time warp distance coefficient between the ρth warp yarn and the σth warp yarn, X ρ is the geometric distance of the ρth warp yarn from the origin, X σ is the geometric distance of the σth warp yarn from the origin, B ρ is the real-time fluctuation area value of the ρth warp yarn, B σ is the real-time fluctuation area value of the σth warp yarn;

[0147] The calculation formula of the real-time weft distance coefficient is:

[0148]

[0149] Where d(μ,π) is the real-time weft distance coefficient between the μth warp yarn and the πth weft yarn, Y μ is the geometric distance between the starting point of the μth weft yarn and the origin, Y π is the geometric distance between the starting point of the πth weft yarn and the origin, W μ is the geometric distance between the insertion end point of the μth weft yarn and the origin, W π is the geometric distance between the interlacing end point of the πth weft yarn and the origin;

[0150] The calculation formula of the real-time warp yarn quality prediction total index is:

[0151]

[0152] Where W ρσis the distance weighting factor between the ρth warp yarn and the σth warp yarn, Q warp is the real-time warp quality prediction total index, γ is a non-zero constant, Q ρ is the real-time yarn quality index of the ρth warp yarn, M is the total number of warp yarns installed on the loom;

[0153] The calculation formula of the total index of real-time weft yarn quality prediction is:

[0154]

[0155] Where W μπ is the distance weighting factor between the μth weft yarn and the πth weft yarn, Q weft is the total index of real-time weft yarn quality prediction, δ is a non-zero constant, Q μ is the real-time yarn quality index of the μth weft yarn, and N is the total number of weft yarns installed on the loom.

[0156] Furthermore, a textile production detection system based on data feedback is proposed, which is used to implement any of the above detection methods, including:

[0157] A main control module, the main control module is used to receive textile production information, initial working environment information, real-time working environment information and initial yarn state index, determine yarn installation parameter information based on the yarn information to be processed and the textile production information, determine preset weaving parameter information based on the yarn installation parameter information and the initial working environment information, and is used to obtain a real-time yarn quality index and a textile quality prediction index, screen textile production raw materials based on textile production raw material information to obtain preferred raw materials for textile production, select yarn to be processed from the preferred raw materials for textile production, and determine the yarn information to be processed, install the yarn in a loom according to the yarn installation parameter information, adjust loom parameters according to the preset weaving parameter information, adjust loom parameters according to the textile quality prediction index, and optimize the real-time working environment;

[0158] A detection module, the detection module is used to collect initial working environment information, real-time working environment information and initial yarn state index;

[0159] Transmission module, which is used for data and information transmission between the main control module, the detection module and the display module;

[0160] The display module is used to display the data processing results of the main control module, the detection process and results of the detection module, and the process and results of data transmission by the transmission module.

[0161] Furthermore, the main control module includes:

[0162] A receiving unit, the receiving unit is used to receive textile production information, initial working environment information, real-time working environment information and initial yarn state index;

[0163] A processing unit, the processing unit is used to determine yarn installation parameter information based on the yarn information to be processed and the textile production information, and to determine preset weaving parameter information based on the yarn installation parameter information and the initial working environment information, so as to obtain a real-time yarn quality index and a textile quality prediction index;

[0164] A screening unit, the screening unit is used to screen the textile production raw materials based on the textile production raw material information to obtain the preferred textile production raw materials, select the yarn to be processed from the preferred textile production raw materials, and determine the yarn information to be processed;

[0165] The adjustment unit is used to install the yarn in the loom according to the yarn installation parameter information, adjust the loom parameters according to the preset weaving parameter information, adjust the loom parameters according to the textile quality prediction index, and optimize the real-time working environment.

[0166] Furthermore, the detection module includes:

[0167] A first detection unit, the first detection unit is used to collect initial working environment information and real-time working environment information;

[0168] The second detection unit is used to collect the initial yarn state index.

[0169] In summary, the advantages of the present invention are: by obtaining the physical performance index and chemical performance index of the raw materials for textile production and establishing a comprehensive performance index matrix, the actual performance index of the raw materials for textile production is obtained, and the raw materials for textile production are screened by using the actual performance index to obtain the preferred raw materials for textile production, thereby obtaining high-quality yarns, and avoiding the quality reduction of the woven textiles due to the uneven quality of the yarns;

[0170] The real-time impact coefficient of the yarn environment is obtained through real-time working environment information, yarn type information and the yarn environment impact coefficient prediction model, and the real-time yarn state index is obtained through the initial yarn state index and the real-time impact coefficient of the yarn environment. Finally, based on the real-time yarn quality index, the textile quality prediction index is obtained, and the loom parameters are adjusted according to the textile quality prediction index to optimize the real-time working environment. The loom can adjust the yarn state according to the real-time working environment during the weaving process, improve the quality of the textile after weaving is completed, and thus improve work efficiency.

[0171] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A textile production detection method based on data feedback, characterized in that: include: Obtain information on textile production and determine information on raw materials used in textile production; Screening textile production raw materials based on textile production raw material information to obtain preferred raw materials for textile production; Selecting yarn to be processed from preferred raw materials for textile production and determining information of the yarn to be processed; Determining yarn installation parameter information based on the yarn information to be processed and the textile production information; installing the yarn in the loom according to the yarn installation parameter information and collecting the initial working environment information; Determining preset weaving parameter information based on the yarn installation parameter information and the initial working environment information; Adjust loom parameters according to preset weaving parameter information, collect real-time working environment information, and obtain real-time yarn quality index; Based on the real-time yarn quality index, obtain the textile quality prediction index; Adjust loom parameters according to the textile quality prediction index and optimize the real-time working environment to obtain the textiles to be processed.

2. A textile production detection method based on data feedback according to claim 1, characterized in that: The screening of textile production raw materials based on textile production raw material information to obtain preferred textile production raw materials specifically includes: Determine the information on textile performance requirements and textile appearance requirements based on textile production information; Select i batches of textile production raw materials that meet the requirements based on the textile performance requirement information and the textile appearance requirement information, and determine the textile production raw material information; According to the information of textile production raw materials, sample and test i batches of textile production raw materials in turn to obtain the physical property index and chemical property index of i batches of textile production raw materials; Obtain the comprehensive performance index matrix of batch i of textile production raw materials; According to the comprehensive performance index matrix of the i batches of textile production raw materials, the actual performance indexes of the i batches of textile production raw materials are obtained in sequence; Based on the information of textile production raw materials, i batches of textile production raw materials are classified, and textile production raw materials of the same type are divided into the same area to obtain textile production raw materials of j areas; According to the actual performance index of i batches of textile production raw materials, the textile production raw materials in j regions are sorted, and the textile production raw materials with the highest actual performance index in j regions are screened to obtain the preferred raw materials for textile production.

3. A textile production detection method based on data feedback according to claim 2, characterized in that: The obtaining of the comprehensive performance index matrix of i batches of textile production raw materials specifically includes: Respectively obtain the raw material quality values ​​of i batches of textile production raw materials and the sample quality values ​​of the i batches of textile production raw materials sampled and tested in sequence; The ratio of the sample mass value of the i-th batch of textile production raw materials sampled and tested to the raw material mass value of the i-th batch of textile production raw materials is used as the performance weight coefficient of the i-th batch of textile production raw materials; The product of the mth physical property index of the i-th batch of textile production raw materials and the performance weight coefficient of the i-th batch of textile production raw materials is taken as the xth element of the physical property index matrix of the i-th batch of textile production raw materials; The product of the nth chemical performance index of the i-th batch of textile production raw materials and the performance weight coefficient of the i-th batch of textile production raw materials is taken as the yth element of the chemical performance index matrix of the i-th batch of textile production raw materials; Among them, the physical property index matrix and chemical property index matrix of the i-th batch of textile production raw materials are both row matrices; Respectively obtain the column numbers of the physical property index matrix and the chemical property index matrix of the i-th batch of textile production raw materials to obtain the column numbers of the physical property index matrix and the chemical property index matrix; Compare the number of columns of the physical performance index matrix and the number of columns of the chemical performance index matrix, and use the value 0 as a supplementary element to make the number of columns of the physical performance index matrix and the chemical performance index matrix the same; Performing matrix transposition on the chemical performance index matrix to obtain a chemical performance index transposed matrix; The physical performance index matrix and the chemical performance index transposed matrix are multiplied to obtain a comprehensive performance index matrix.

4. A textile production detection method based on data feedback according to claim 3, characterized in that: The determining of yarn installation parameter information based on the yarn information to be processed and the textile production information specifically includes: According to the actual performance index of the i batch of textile production raw materials, the actual performance index corresponding to the yarn to be processed is queried to obtain the actual performance index of the yarn to be processed; The textile production raw material information and the actual performance index of the yarn to be processed are correlated and integrated to obtain the yarn information to be processed; Determining textile production pattern information based on the textile production information, and determining textile organization structure information; Obtaining dyeing type information of the yarn to be processed through the yarn to be processed information and the textile production information, and determining the dyeing type of the yarn to be processed, wherein the dyeing type of the yarn to be processed includes pre-dyeing and post-dyeing; Processing the yarn according to the dyeing type information of the yarn to be processed to obtain the yarn to be arranged; Determine yarn arrangement information based on textile production pattern information and textile organization structure information; The yarn information to be processed and the yarn arrangement information are associated and integrated to obtain the yarn installation parameter information.

5. A textile production detection method based on data feedback according to claim 4, characterized in that: The step of determining the preset weaving parameter information based on the yarn installation parameter information and the initial working environment information specifically includes: Determine yarn type information, yarn size information, warp yarn quantity information and weft yarn quantity information according to yarn installation parameter information; The yarn type is represented by label encoding, and the yarn type classification variable of each yarn in the yarn installation parameter information is obtained; Determine the initial temperature index, initial humidity index, initial dust concentration index and initial light intensity index of each yarn based on the initial working environment information; The yarn type classification variable of each yarn, as well as the initial temperature index, initial humidity index, initial dust concentration index and initial light intensity index of each yarn are sequentially input into the yarn environmental impact coefficient prediction model; Obtaining the yarn environmental impact coefficient of each yarn output by the yarn environmental impact coefficient prediction model; Based on the yarn size information, the warp yarn quantity information, the weft yarn quantity information and the yarn environmental impact coefficient corresponding to each yarn, the preset weaving parameter information is set.

6. A textile production detection method based on data feedback according to claim 5, characterized in that: The steps for obtaining the yarn environmental impact coefficient prediction model are as follows: S601, constructing a multivariate linear regression model, and collecting historical working environment data, historical yarn type data, and historical yarn strength data; S602, extracting the historical temperature index, the historical humidity index, the historical dust concentration index and the historical light intensity index from the historical working environment data as continuous variables, determining the historical yarn type classification variable from the historical yarn type data, determining the historical yarn strength from the historical yarn strength data as the control variable, and taking the yarn environmental impact coefficient as the target variable; S603, obtaining multiple groups of continuous variables, categorical variables, control variables and target variables from historical working environment data, historical yarn type data and historical yarn strength data, and inputting them into a multivariate regression model for training to obtain an initial model for predicting the yarn environmental impact coefficient; S604, performing a model evaluation on the initial model for predicting the yarn environmental impact coefficient, and optimizing the initial model for predicting the yarn environmental impact coefficient according to the evaluation result to obtain a yarn environmental impact coefficient prediction model; Among them, the expression of the yarn environmental impact coefficient prediction model is: In the formula, S α is the historical yarn strength in the αth group of historical yarn strength data, Y α is the yarn environmental impact coefficient output by the prediction model, T α is the historical yarn type classification variable in the αth group of historical yarn type data, θ α is the historical temperature index in the αth group of historical working environment data, H α is the historical humidity index in the αth group of historical working environment data, D α is the historical dust concentration index in the αth group of historical working environment data, L α is the historical light intensity index in the αth group of historical working environment data, β0 and ω0 are constant terms, β1, β2, β3, β4, β5, ω1, ω2, ω3, ω4 and ω5 are weight coefficients, ∈1 and ∈2 are error terms.

7. A textile production detection method based on data feedback according to claim 6, characterized in that: The method of adjusting loom parameters according to preset weaving parameter information, collecting real-time working environment information, and obtaining real-time yarn quality index specifically includes: Collecting initial yarn state index; According to the real-time working environment information, yarn type information and yarn environment impact coefficient prediction model, the real-time impact coefficient of the yarn environment is obtained; The product of the initial yarn state index and the yarn environment real-time influence coefficient is taken as the real-time yarn state index.

8. A textile production detection system based on data feedback, used to implement the detection method according to any one of claims 1 to 9, characterized in that: include: A main control module, the main control module is used to receive textile production information, initial working environment information, real-time working environment information and initial yarn state index, determine yarn installation parameter information based on the yarn information to be processed and the textile production information, determine preset weaving parameter information based on the yarn installation parameter information and the initial working environment information, and is used to obtain a real-time yarn quality index and a textile quality prediction index, screen textile production raw materials based on textile production raw material information to obtain preferred raw materials for textile production, select yarn to be processed from the preferred raw materials for textile production, and determine the yarn information to be processed, install the yarn in a loom according to the yarn installation parameter information, adjust loom parameters according to the preset weaving parameter information, adjust the loom parameters according to the textile quality prediction index, and optimize the real-time working environment; A detection module, the detection module is used to collect initial working environment information, real-time working environment information and initial yarn state index; A transmission module, which is used to transmit data and information between the main control module, the detection module and the display module; The display module is used to display the data processing results of the main control module, the detection process and results of the detection module, and the process and results of data transmission by the transmission module.

9. A textile production detection system based on data feedback according to claim 8, characterized in that: The main control module comprises: A receiving unit, the receiving unit is used to receive textile production information, initial working environment information, real-time working environment information and initial yarn state index; A processing unit, the processing unit is used to determine yarn installation parameter information based on the yarn information to be processed and the textile production information, and to determine preset weaving parameter information based on the yarn installation parameter information and the initial working environment information, so as to obtain a real-time yarn quality index and a textile quality prediction index; A screening unit, the screening unit is used to screen the textile production raw materials based on the textile production raw material information to obtain the preferred textile production raw materials, select the yarn to be processed from the preferred textile production raw materials, and determine the yarn information to be processed; The adjusting unit is used to install the yarn in the loom according to the yarn installation parameter information, adjust the loom parameters according to the preset weaving parameter information, adjust the loom parameters according to the textile quality prediction index, and optimize the real-time working environment.

10. A textile production detection system based on data feedback according to claim 8, characterized in that: The detection module comprises: A first detection unit, the first detection unit is used to collect initial working environment information and real-time working environment information; The second detection unit is used to collect the initial yarn state index.

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