A weaving production shaft optimization system and method

Abstract

The present invention belongs to the technical field of intelligent weaving, and discloses a weaving production shaft grouping optimization system and method, including: collecting order data; preprocessing the order data to obtain valid order data; obtaining the warp yarn temperature influence coefficient based on a constructed warp yarn production influence coefficient model, obtaining the weft yarn humidity influence coefficient based on a constructed weft yarn production influence coefficient model, performing demand calculation through the warp yarn temperature influence coefficient, the weft yarn humidity influence coefficient and the valid order data to obtain the warp yarn demand and the weft yarn demand; performing shaft grouping allocation based on the warp yarn demand and the weft yarn demand to obtain the optimal shaft grouping solution; greatly reducing labor costs.

Description

Technical Field

[0001] The present invention relates to the field of intelligent weaving technology, and more particularly, to a weaving production shaft grouping optimization system and method. Background Art

[0002] The patent application publication number CN118134144A discloses a production management method, system and storage medium for knitted fabrics. The method includes the following steps: obtaining the quantity of materials to be detected corresponding to the detection step; comparing the quantity of materials to be detected with a preset quantity, and determining whether the quantity of materials to be detected is similar to the preset quantity; if the quantity of materials to be detected is similar to the preset quantity, generating a corresponding transport signal, and sending the transport signal to a vehicle control module to control the transport vehicle to automatically transport the corresponding materials to be detected; by generating a transport signal and sending the transport signal to a vehicle control module to control the transport vehicle to automatically transport the corresponding materials to be detected, manual handling is reduced, time is saved, and the production efficiency of knitted fabrics is improved.

[0003] With the advancement of science and technology, the weaving industry is undergoing profound changes. Digital transformation has become a key trend in the industry's development. Through the application of industrial Internet technologies, the weaving industry's production efficiency has been significantly improved. For example, by increasing the machine utilization rate from 60% to 90%, not only has the factory's order volume increased, but it has also enabled upstream manufacturers to accurately predict the amount of yarn required by downstream manufacturers in the coming week, thereby reducing operating costs through precise production. This digital transformation not only improves production efficiency but also optimizes resource allocation, enabling the weaving industry to adapt more quickly to market changes. Intelligent production is also a key aspect of the weaving industry's background technology. In modern textile factories, smart spindles, industrial robots, and automated warehouses have become the norm. The application of these technologies has greatly improved production efficiency and product quality. In addition, the application of big data platforms has also brought revolutionary changes to the weaving industry. By building a textile industry big data platform, textile enterprises can be digitally transformed, industrial big data information can be obtained, and this data can be fed back into the platform's capacity allocation, thus forming a closed loop from raw material supply to finished product trade, realizing a model of industrial digital reconstruction of resource allocation.

[0004] However, as the weaving industry becomes larger and larger, the number of product varieties increases, and labor becomes increasingly insufficient, the traditional shaft assembly method is time-consuming, labor-intensive, and experience-based, and is no longer applicable. When there are many order varieties, the traditional shaft assembly method will inevitably cause some production waste, and it is difficult to find the optimal solution for shaft assembly by manual labor.

[0005] In view of this, the present invention proposes a weaving production shaft optimization system and method to solve the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a weaving production shaft group optimization system and method;

[0007] The weaving production shaft group optimization system of the present invention includes: a data acquisition module for collecting order data;

[0008] Data processing module: pre-process order data to obtain valid order data;

[0009] Demand generation module: Based on the established warp yarn production influence coefficient model, the warp yarn temperature influence coefficient is obtained. Based on the established weft yarn production influence coefficient model, the weft yarn humidity influence coefficient is obtained. Demand is calculated using the warp yarn temperature influence coefficient, weft yarn humidity influence coefficient, and valid order data to obtain the warp yarn demand and weft yarn demand.

[0010] Shaft grouping scheme generation module: allocates shaft groups based on warp yarn demand and weft yarn demand to obtain the optimal shaft grouping scheme.

[0011] Furthermore, the order data includes: finished fabric length, fabric width, warp density, weft density, warp spacing and order time.

[0012] Furthermore, the specific method of pre-processing the order data includes:

[0013] The order demand data is standardized using the Z-score algorithm to obtain standardized order data. The scale parameter set A is preset and the standardized order data is filtered and transformed. The formula for the filtering transformation is: Where a represents the scale parameter, and a∈A, x i represents the i-th standardized order data, τ represents the translation parameter of the standardized order data, W a (x i ) represents the filtered value of the i-th standardized order data when the scale parameter is a, t represents the order time, e represents the base of the natural logarithm, and i represents the i-th standardized order data;

[0014] Calculate the dynamic adaptability weight of the standardized order data under different scale parameters. The calculation formula of the dynamic adaptability weight is: Among them, η a represents the dynamic adaptability weight corresponding to the scale parameter a, α is the adjustment factor, Var(W a (x i )) represents the variance of the filtered value of the i-th standardized order data when the scale parameter is a;

[0015] The filter value is weighted according to the dynamic adaptive weight. The weighted calculation formula is: in Represents the weighted sum of the filtered values ​​of the i-th standardized order data after filtering transformation; the residual of the standardized order data is calculated based on the weighted sum, and the residual calculation formula is: where r i Represents the residual of the i-th standardized order data; sort all the calculated residuals from large to small, and obtain the median Mid of the residuals;

[0016] Preset the abnormal sensitivity parameter β and calculate the dynamic threshold. The calculation formula of the dynamic threshold is: R i =β×|r i -Mid|; where R i Represents the dynamic threshold of the i-th standardized order data; the dynamic threshold is used to eliminate anomalies and obtain valid order data; the methods of eliminating anomalies include: when r i >R i When , the order data corresponding to the i-th standardized order data is marked as abnormal data, and all order data marked as abnormal data are removed.

[0017] Furthermore, the method for obtaining the warp yarn temperature influence coefficient includes:

[0018] Collecting group B historical warp yarn data, the historical warp yarn data includes historical ambient temperature and historical warp yarn temperature influence coefficient, constructing a warp yarn production influence coefficient model based on the historical warp yarn data, using the historical ambient temperature as the input of the warp yarn production influence coefficient model, using the warp yarn temperature influence coefficient as the output of the warp yarn production influence coefficient model, using the historical warp yarn temperature influence coefficient as the prediction target of the warp yarn production influence coefficient model, using the root mean square error function as the loss function of the warp yarn production influence coefficient model, and minimizing the loss function value of the warp yarn production influence coefficient model as the training target, and stopping training when the loss function value of the warp yarn production influence coefficient model is less than or equal to a preset target loss value;

[0019] The warp yarn production influence coefficient model is a multiple regression model or a logistic regression model, and the root mean square error function formula is: Among them, RMSE is the root mean square error, Su is the total amount of historical warp data, F b is the bth historical warp yarn temperature influence coefficient, F' b is the bth warp yarn temperature influence coefficient;

[0020] The real-time ambient temperature is collected, and based on the constructed warp yarn production influence coefficient model, the real-time ambient temperature is used as input to obtain the warp yarn temperature influence coefficient.

[0021] Furthermore, the method for obtaining the weft yarn humidity influence coefficient includes:

[0022] Collecting a group C of historical weft yarn data, the historical weft yarn data including historical ambient humidity and historical weft yarn humidity influence coefficients, constructing a weft yarn production influence coefficient model based on the historical weft yarn data, using the historical ambient humidity as the input of the weft yarn production influence coefficient model, using the weft yarn humidity influence coefficient as the output of the weft yarn production influence coefficient model, using the historical weft yarn humidity influence coefficient as the prediction target of the weft yarn production influence coefficient model, using the recall function as the loss function of the weft yarn production influence coefficient model, and minimizing the loss function value of the weft yarn production influence coefficient model as the training target, and stopping training when the loss function value of the weft yarn production influence coefficient model is less than or equal to a preset target loss value;

[0023] The weft yarn production influence coefficient model is a linear regression model, and the recall function formula is:

[0024] Where Recall is the recall rate, TP is the number of samples correctly predicted by the weft yarn production influence coefficient model, and FN is the number of samples incorrectly predicted by the weft yarn production influence coefficient model;

[0025] The real-time ambient humidity is collected, and based on the constructed latitude production impact coefficient model, the real-time ambient humidity is used as input to obtain the weft yarn humidity impact coefficient.

[0026] Furthermore, the specific method of performing demand calculation includes:

[0027] The formula for calculating the warp yarn demand is: Among them, D l represents the warp yarn demand corresponding to the lth valid order data, Wid represents the fabric width, which means the horizontal width of the fabric, Dmi represents the warp yarn density, Swi represents the warp yarn spacing, α T represents the warp yarn temperature influence coefficient, T represents the real-time ambient temperature, T0 represents the standard ambient temperature, and l represents the index of valid order data;

[0028] The formula for calculating the weft yarn demand is: Among them, E l represents the weft yarn demand corresponding to the lth valid order data, Len represents the length of the finished cloth, Wmi represents the weft yarn density, β H Represents the weft yarn humidity influence coefficient.

[0029] Furthermore, the specific steps of performing group axis allocation include:

[0030] Construct an axle assembly optimization model, which includes the target optimization function Mdest and constraint conditions; perform optimization and solution based on the axle assembly optimization model to obtain the optimal axle assembly solution.

[0031] Furthermore, the objective optimization function

[0032] Where λ1 represents the warp balance coefficient, λ2 represents the weft balance coefficient; N j Represents the total amount of warp yarn provided by the jth group of shafts, N max Represents the maximum number of warps for a single device; Q j represents the total amount of weft yarn provided by the jth group of shafts, Q max Represents the maximum number of wefts in a single group of shafts, m represents the total number of shafts, j represents the index of the shaft, and n represents the amount of valid order data;

[0033] The constraints include: a constraint on the total amount of warp yarns, a constraint on the total amount of weft yarns, a constraint on the demand for warp yarns, and a constraint on the demand for weft yarns;

[0034] The total warp yarn quantity constraint is N j ≤N max ; The total amount of weft yarn is constrained by Q j ≤Q max ; The warp yarn demand constraint is The weft yarn demand constraint is

[0035] Furthermore, the specific steps of performing the optimization solution include:

[0036] Step 1: Preset K groups of random axis grouping schemes. Each group of axis grouping schemes is represented as a chromosome. The genes in the chromosome correspond to the axis grouping assignment of each valid order data.

[0037] Step 2: For each chromosome, calculate the fitness of each chromosome according to the target optimization function, and select the parent chromosome through the tournament selection algorithm according to the fitness value;

[0038] Step 3: Based on the parent chromosome, the Uth gene is preset as the exchange node, and the genes after the exchange node of the parent chromosome are exchanged to generate the daughter chromosome;

[0039] Step 4: Using a random mutation algorithm, randomly select a gene from the offspring chromosome and replace it with the gene at the corresponding position on the pre-stored chromosome;

[0040] Step 5: Calculate the fitness of the offspring chromosome according to the target optimization function. When the fitness value of the offspring chromosome is greater than the fitness value of the pre-existing chromosome, replace the pre-existing chromosome with the offspring chromosome as the new pre-existing chromosome. When the maximum number of iterations has not been reached and the fitness value of the pre-existing chromosome is less than the preset fitness threshold, return to step 2 to continue iterating. When the maximum number of iterations is reached or the fitness value of the pre-existing chromosome is greater than the preset fitness threshold, stop the iteration and output the pre-existing chromosome as the optimal axis grouping solution.

[0041] The present invention provides a weaving production shaft optimization method, comprising: S1, collecting order data;

[0042] S2. Preprocess the order data to obtain valid order data;

[0043] S3. Obtain the warp yarn temperature influence coefficient based on the constructed warp yarn production influence coefficient model, and obtain the weft yarn humidity influence coefficient based on the constructed weft yarn production influence coefficient model. Perform demand calculation based on the warp yarn temperature influence coefficient, weft yarn humidity influence coefficient, and valid order data to obtain the warp yarn demand and weft yarn demand.

[0044] S4. Perform grouping allocation based on the warp yarn demand and the weft yarn demand to obtain the optimal grouping solution.

[0045] The technical effects and advantages of the weaving production shaft optimization system and method of the present invention are as follows:

[0046] The present invention ensures the validity and accuracy of input data by preprocessing order data, reduces the impact of errors on the final shaft assembly plan, and improves the stability and reliability of the entire system; by building a model to predict the impact of ambient temperature and humidity on warp and weft yarn demand, the actual demand for warp and weft yarns is calculated more accurately, reducing waste and unnecessary adjustments in the production process; by calculating the demand for warp and weft yarns and combining it with an optimization algorithm, the optimal shaft assembly plan is generated, dynamically adapting to different production needs, ensuring maximum production efficiency, and can be adjusted according to actual conditions, avoiding production problems caused by unreasonable shaft assembly, and reducing production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Schematic diagram of a weaving production shaft optimization system of the present invention;

[0048] Figure 2 This is a schematic diagram of a weaving production shaft optimization method of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0050] Example 1

[0051] See also Figure 1 As shown, the weaving production shaft group optimization system described in this embodiment includes: a data acquisition module for collecting order data;

[0052] Data processing module: pre-process order data to obtain valid order data;

[0053] Demand generation module: Based on the established warp yarn production influence coefficient model, the warp yarn temperature influence coefficient is obtained. Based on the established weft yarn production influence coefficient model, the weft yarn humidity influence coefficient is obtained. Demand is calculated using the warp yarn temperature influence coefficient, weft yarn humidity influence coefficient, and valid order data to obtain the warp yarn demand and weft yarn demand.

[0054] Shafting scheme generation module: allocates shafts based on warp and weft yarn requirements to obtain the optimal shafting scheme;

[0055] Each module is connected via wired and / or wireless means to achieve data transmission between modules;

[0056] Order data includes: finished fabric length, fabric width, warp density, weft density, warp spacing and order time;

[0057] Methods for preprocessing order data include:

[0058] The order demand data is standardized by the Z-score algorithm to eliminate the differences between different data levels and obtain standardized order data. The scale parameter set A is preset and the standardized order data is filtered to retain the main trend of the standardized order data. The formula for the filtering transformation is:

[0059] Where a represents the scale parameter, and a∈A, x i represents the i-th standardized order data, τ represents the translation parameter of the standardized order data, which controls the position of the standardized order data in the time domain after filtering and transformation, W a (x i ) represents the filtered value of the i-th standardized order data when the scale parameter is a, t represents the order time, e represents the base of the natural logarithm, and i represents the i-th standardized order data;

[0060] Calculate the dynamic adaptability weight of standardized order data under different scale parameters to improve the adaptability and accuracy of the processing results. The calculation formula of the dynamic adaptability weight is:

[0061] Among them, η a Represents the dynamic adaptive weight corresponding to the scale parameter a. α is the adjustment factor used to control the weight difference between different scales. The larger the value, the more obvious the weight difference. Var(W a (x i )) represents the variance of the filtered value of the i-th standardized order data when the scale parameter is a;

[0062] The filter value is weighted according to the dynamic adaptive weight. The weighted calculation formula is: in, Represents the weighted sum of the filtered values ​​of the i-th standardized order data after filtering with all scale parameters; the residual of the standardized order data is calculated based on the weighted sum. The residual calculation formula is: where r i Represents the residual of the i-th standardized order data; sort all the calculated residuals from large to small, and obtain the median Mid of the residuals;

[0063] Preset the abnormal sensitivity parameter β and calculate the dynamic threshold. The calculation formula of the dynamic threshold is: R i =β×|r i -Mid|;where R i Represents the dynamic threshold of the i-th standardized order data; the abnormality is eliminated through the dynamic threshold to obtain valid order data; when r i >R i When , the order data corresponding to the i-th standardized order data is marked as abnormal data, and all order data marked as abnormal data are removed;

[0064] The construction methods of the warp yarn production influence coefficient model include:

[0065] Collecting group B historical warp yarn data, the historical warp yarn data includes historical ambient temperature and historical warp yarn temperature influence coefficient, constructing a warp yarn production influence coefficient model based on the historical warp yarn data, using the historical ambient temperature as the input of the warp yarn production influence coefficient model, using the warp yarn temperature influence coefficient as the output of the warp yarn production influence coefficient model, using the historical warp yarn temperature influence coefficient as the prediction target of the warp yarn production influence coefficient model, using the root mean square error function as the loss function of the warp yarn production influence coefficient model, and minimizing the loss function value of the warp yarn production influence coefficient model as the training target, and stopping training when the loss function value of the warp yarn production influence coefficient model is less than or equal to a preset target loss value;

[0066] The warp yarn production influence coefficient model is a multiple regression model or a logistic regression model, and the root mean square error function formula is: Where RMSE is the root mean square error, Su is the total amount of historical warp data, and F b is the bth historical warp yarn temperature influence coefficient, F' b is the bth warp yarn temperature influence coefficient;

[0067] The historical warp yarn temperature influence coefficient is obtained by collecting the historical predicted warp yarn demand data and the historical actual warp yarn demand data of the same historical ambient temperature in group P, using the historical warp yarn temperature influence coefficient as the optimization variable, and solving the optimal historical warp yarn temperature influence coefficient through the Bayesian optimization algorithm;

[0068] The construction methods of the weft yarn production influence coefficient model include:

[0069] Collecting a group C of historical weft yarn data, the historical weft yarn data including historical ambient humidity and historical weft yarn humidity influence coefficients, constructing a weft yarn production influence coefficient model based on the historical weft yarn data, using the historical ambient humidity as the input of the weft yarn production influence coefficient model, using the weft yarn humidity influence coefficient as the output of the weft yarn production influence coefficient model, using the historical weft yarn humidity influence coefficient as the prediction target of the weft yarn production influence coefficient model, using the recall function as the loss function of the weft yarn production influence coefficient model, and minimizing the loss function value of the weft yarn production influence coefficient model as the training target, and stopping training when the loss function value of the weft yarn production influence coefficient model is less than or equal to a preset target loss value;

[0070] The weft yarn production influence coefficient model is a linear regression model, and the recall function formula is:

[0071] Where Recall is the recall rate, TP is the number of samples correctly predicted by the weft yarn production influence coefficient model, and FN is the number of samples incorrectly predicted by the weft yarn production influence coefficient model;

[0072] The historical weft yarn humidity influence coefficient is obtained by collecting the historical predicted weft yarn demand data and the historical actual weft yarn demand data under the same historical environmental humidity of group P, using the historical weft yarn humidity influence coefficient as the optimization variable and solving the optimal historical weft yarn humidity influence coefficient through the Bayesian optimization algorithm;

[0073] Ways to perform demand calculations include:

[0074] The real-time ambient temperature is collected and, based on the established warp yarn production impact coefficient model, the real-time ambient temperature is used as input to obtain the warp yarn temperature impact coefficient. The impact of ambient temperature on warp yarn demand is considered to ensure the accuracy of the production plan. The warp yarn demand is calculated based on the warp yarn temperature impact coefficient and valid order data. The calculation formula for the warp yarn demand is: Among them, D l represents the warp yarn demand corresponding to the lth valid order data, Wid represents the fabric width, which means the horizontal width of the fabric, Dmi represents the warp yarn density, Swi represents the warp yarn spacing, α T represents the warp yarn temperature influence coefficient, T represents the real-time ambient temperature, T0 represents the standard ambient temperature, and l represents the index of valid order data;

[0075] The real-time ambient humidity is collected and, based on the constructed latitude production impact coefficient model, the real-time ambient humidity is used as input to obtain the weft yarn humidity impact coefficient. Based on the weft yarn humidity impact coefficient and valid order data, the weft yarn demand is calculated. The calculation formula for the weft yarn demand is:

[0076] Among them, E l represents the weft yarn demand corresponding to the lth valid order data, Len represents the length of the finished cloth, Wmi represents the weft yarn density, β H Represents the influence coefficient of weft yarn humidity; considering the influence of environmental factors, the influence coefficient is dynamically adjusted through the model to reduce production errors caused by environmental changes;

[0077] The specific methods for generating the optimal axis combination solution include:

[0078] Define the target optimization function Mdest,

[0079] Where λ1 represents the warp balance coefficient, λ2 represents the weft balance coefficient; N j Represents the total amount of warp yarn provided by the jth group of shafts, N max Represents the maximum number of warps for a single device (device parameters are obtained by reading device information); Q j represents the total amount of weft yarn provided by the jth group of shafts, Q max Represents the maximum number of wefts for a single group of shafts (device parameters are obtained by reading device information), m represents the total number of shafts, j represents the number of shafts, and n represents the amount of valid order data;

[0080] The constraints of the objective optimization function include: total warp yarn quantity constraint, total weft yarn quantity constraint, warp yarn demand constraint and weft yarn demand constraint.

[0081] The total warp yarn quantity constraint is N j ≤N max ; The total amount of weft yarn is constrained by Q j ≤Q max ; The warp yarn demand constraint is The weft yarn demand constraint is

[0082] The warp balance coefficient and weft balance coefficient are adjusted according to different production demands to ensure the flexibility and balance of the production plan under different demands. When the matching degree of the warp demand is greater than or equal to the matching degree of the weft demand, λ1 is greater than λ2; when the matching degree of the warp demand is less than the matching degree of the weft demand, λ1 is less than λ2. The matching degree of the warp demand is the degree of consistency between the warp demand and the actual number of warp yarns provided, and the matching degree of the weft demand is the degree of consistency between the weft demand and the actual number of weft yarns provided.

[0083] Optimize and solve the target optimization function. The specific steps of optimization and solution include:

[0084] Step 1: Preset K groups of random axis grouping schemes. Each group of axis grouping schemes is represented as a chromosome. The genes in the chromosome correspond to the axis grouping assignment of each valid order data.

[0085] Step 2: For each chromosome, calculate the fitness of each chromosome according to the target optimization function. The chromosome with the larger fitness value is better. The parent chromosome is selected by the tournament selection algorithm according to the fitness value.

[0086] Step 3: Based on the parent chromosome, the Uth gene is preset as the exchange node, and the genes after the exchange node of the parent chromosome are exchanged to generate the daughter chromosome;

[0087] Step 4: Using a random mutation algorithm, randomly select a gene from the offspring chromosome and replace it with the gene at the corresponding position on the pre-stored chromosome (the preset initial solution);

[0088] Step 5: Calculate the fitness of the offspring chromosome according to the target optimization function. When the fitness value of the offspring chromosome is greater than the fitness value of the pre-stored chromosome, replace the pre-stored chromosome with the offspring chromosome as the new pre-stored chromosome. When the maximum number of iterations has not been reached and the fitness value of the pre-stored chromosome is less than the preset fitness threshold, return to step 2 to continue iterating. When the maximum number of iterations is reached or the fitness value of the pre-stored chromosome is greater than the preset fitness threshold, stop the iteration and output the pre-stored chromosome as the optimal axis combination scheme.

[0089] The fitness calculation method includes: based on the target optimization function, the fitness is calculated using the inverse proportional fitness function. The fitness calculation formula is: Where MinF represents the value of the target optimization function, Fit represents the fitness, and ε is a very small positive number used to avoid the division by zero error caused by MinF being too small;

[0090] Through optimization and multiple iterations, the final output shaft assembly solution is guaranteed to be the global optimal solution, avoiding the trap of local optimal solutions. The optimal shaft assembly solution is found quickly and effectively to meet the ever-changing needs and environmental conditions in production.

[0091] This embodiment ensures the validity and accuracy of input data by preprocessing order data, reduces the impact of errors on the final shaft assembly plan, and improves the stability and reliability of the entire system; by building a model to predict the impact of ambient temperature and humidity on warp and weft yarn demand, the actual demand for warp and weft yarns is calculated more accurately, reducing waste and unnecessary adjustments in the production process; by calculating the demand for warp and weft yarns and combining the optimization algorithm to generate the optimal shaft assembly plan, dynamically adapt to different production needs, ensure maximum production efficiency, and can be adjusted according to actual conditions, avoiding production problems caused by unreasonable shaft assembly and reducing production costs.

[0092] Example 2

[0093] See also Figure 2 As shown, for the parts not described in detail in this embodiment, please refer to the description of embodiment 1. A method for optimizing the group axis of a weaving production is provided, comprising: S1, collecting order data;

[0094] S2. Preprocess the order data to obtain valid order data;

[0095] S3. Obtain the warp yarn temperature influence coefficient based on the constructed warp yarn production influence coefficient model, and obtain the weft yarn humidity influence coefficient based on the constructed weft yarn production influence coefficient model. Perform demand calculation based on the warp yarn temperature influence coefficient, weft yarn humidity influence coefficient, and valid order data to obtain the warp yarn demand and weft yarn demand.

[0096] S4. Perform grouping allocation based on the warp yarn demand and the weft yarn demand to obtain the optimal grouping solution.

[0097] Example 3

[0098] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the above-mentioned method for optimizing the group axis of weaving production is realized.

[0099] Since the electronic device described in this embodiment is an electronic device used to implement a weaving production shaft optimization method in the embodiment of this application, based on the weaving production shaft optimization method described in the embodiment of this application, those skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application will not be described in detail here. As long as those skilled in the art implement the electronic device used in the weaving production shaft optimization method in the embodiment of this application, it falls within the scope of protection of this application.

[0100] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0101] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for users of ordinary skill in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A weaving production shaft optimization system, characterized in that: include: Data collection module: collect order data; Data processing module: pre-process order data to obtain valid order data; Demand generation module: Based on the established warp yarn production influence coefficient model, the warp yarn temperature influence coefficient is obtained. Based on the established weft yarn production influence coefficient model, the weft yarn humidity influence coefficient is obtained. Demand is calculated using the warp yarn temperature influence coefficient, weft yarn humidity influence coefficient, and valid order data to obtain the warp yarn demand and weft yarn demand. Shafting scheme generation module: allocates shafts based on warp and weft yarn requirements to obtain the optimal shafting scheme; The specific method of pre-processing the order data includes: performing data standardization on the order demand data by using the Z-score algorithm to obtain standardized order data, and presetting the scale parameter set. , filter the standardized order data to obtain the filtered value of the standardized order data; calculate the dynamic adaptability weight of the standardized order data under different scale parameters; perform weighted calculation on the filtered value according to the dynamic adaptability weight to obtain the weighted sum of the filtered values ​​of the standardized order data after filtering; calculate the residual of the standardized order data based on the weighted sum; sort all the calculated residuals from large to small; preset the abnormal sensitivity parameter and calculate the dynamic threshold; eliminate abnormalities through the dynamic threshold to obtain valid order data; The specific method of performing demand calculation includes: The formula for calculating the warp yarn demand is: ;in, Representative The warp yarn demand corresponding to the valid order data, Represents the fabric width, indicating the horizontal width of the fabric. represents the warp density, represents the warp spacing, represents the warp yarn temperature influence coefficient, Represents the real-time ambient temperature, Represents standard ambient temperature, Represents the index of valid order data; The formula for calculating the weft yarn demand is: ;in, Representative The weft yarn demand corresponding to the valid order data, Represents the length of the finished cloth, represents the weft density, represents the weft yarn humidity influence coefficient; The specific steps of performing group axis allocation include: Construct an axis group optimization model, which includes the target optimization function and constraints; perform optimization based on the shaft assembly optimization model to obtain the optimal shaft assembly solution; The objective optimization function ;in represents the warp balance coefficient, represents the weft balance coefficient; Representative The total amount of warp yarn provided by the group beams, Represents the maximum number of warps for a single device; Representative The total amount of weft yarn provided by the group shaft, Represents the maximum number of weft yarns in a single group of shafts, Represents the total number of group axes, represents the index of the group axis, The amount of data representing valid order data; The constraints include: a constraint on the total amount of warp yarns, a constraint on the total amount of weft yarns, a constraint on the demand for warp yarns, and a constraint on the demand for weft yarns; The total warp yarn quantity constraint is ; The constraint condition for the total amount of weft yarn is ; The warp yarn demand constraint is ; The weft yarn demand constraint is .

2. A weaving production shaft optimization system according to claim 1, characterized in that: The order data includes: finished fabric length, fabric width, warp density, weft density, warp spacing and order time.

3. A weaving production shaft optimization system according to claim 2, characterized in that: The formula for performing the filtering transformation is: ;in, represents the scale parameter, and , Representative Standardized order data, represents the translation parameter of the normalized order data, The representative scale parameter is Time The filtered value of the standardized order data, Represents the order time, represents the base of natural logarithms; The calculation formula of the dynamic adaptability weight is: ;in, Representative scale parameter The corresponding dynamic adaptability weight is, is the regulating factor, The representative scale parameter is Time The variance of the filtered values ​​of the standardized order data; The formula for weighted calculation of the filter value is: ;in Representative The weighted sum of the filtered values ​​of the standardized order data after filtering transformation; the residual of the standardized order data is calculated based on the weighted sum, and the residual calculation formula is: ;in Representative The residuals of the standardized order data; sort all the calculated residuals from large to small and obtain the median of the residuals ; The calculation formula of the dynamic threshold is: ;in, Representative A dynamic threshold for standardized order data, represents the abnormal sensitivity parameter; The method of performing abnormal elimination includes: When The order data corresponding to the standardized order data are abnormal data, and all order data marked as abnormal data are eliminated.

4. A weaving production shaft optimization system according to claim 3, characterized in that: The method for obtaining the warp yarn temperature influence coefficient includes: Collecting group B historical warp yarn data, the historical warp yarn data includes historical ambient temperature and historical warp yarn temperature influence coefficient, constructing a warp yarn production influence coefficient model based on the historical warp yarn data, using the historical ambient temperature as the input of the warp yarn production influence coefficient model, using the warp yarn temperature influence coefficient as the output of the warp yarn production influence coefficient model, using the historical warp yarn temperature influence coefficient as the prediction target of the warp yarn production influence coefficient model, using the root mean square error function as the loss function of the warp yarn production influence coefficient model, and minimizing the loss function value of the warp yarn production influence coefficient model as the training target, and stopping training when the loss function value of the warp yarn production influence coefficient model is less than or equal to a preset target loss value; The warp yarn production influence coefficient model is a multiple regression model or a logistic regression model, and the root mean square error function formula is: ;in, is the root mean square error, is the total amount of historical warp data, It is The historical warp temperature influence coefficient, It is Warp yarn temperature influence coefficient; The real-time ambient temperature is collected, and based on the constructed warp yarn production influence coefficient model, the real-time ambient temperature is used as input to obtain the warp yarn temperature influence coefficient.

5. A weaving production shaft optimization system according to claim 4, characterized in that: The method for obtaining the weft yarn humidity influence coefficient includes: Collecting a group C of historical weft yarn data, the historical weft yarn data including historical environmental humidity and historical weft yarn humidity influence coefficients, constructing a weft yarn production influence coefficient model based on the historical weft yarn data, using the historical environmental humidity as the input of the weft yarn production influence coefficient model, using the weft yarn humidity influence coefficient as the output of the weft yarn production influence coefficient model, using the historical weft yarn humidity influence coefficient as the prediction target of the weft yarn production influence coefficient model, using the recall function as the loss function of the weft yarn production influence coefficient model, and minimizing the loss function value of the weft yarn production influence coefficient model as the training target, and stopping the training when the loss function value of the weft yarn production influence coefficient model is less than or equal to a preset target loss value; The weft yarn production influence coefficient model is a linear regression model, and the recall function formula is: ;in, is the recall rate, The correct number of samples for predicting the weft yarn production influence coefficient model, The number of samples for which the weft yarn production influence coefficient model predicts errors; The real-time ambient humidity is collected, and based on the constructed latitude production impact coefficient model, the real-time ambient humidity is used as input to obtain the weft yarn humidity impact coefficient.

6. A weaving production shaft optimization system according to claim 5, characterized in that: The specific steps of performing the optimization solution include: Step 1: Preset K groups of random axis grouping schemes. Each group of axis grouping schemes is represented as a chromosome. The genes in the chromosome correspond to the axis grouping assignment of each valid order data. Step 2: For each chromosome, calculate the fitness of each chromosome according to the target optimization function, and select the parent chromosome through the tournament selection algorithm according to the fitness value; Step 3: Based on the parent chromosome, the Uth gene is preset as the exchange node, and the genes after the exchange node of the parent chromosome are exchanged to generate the daughter chromosome; Step 4: Using a random mutation algorithm, randomly select a gene from the offspring chromosome and replace it with the gene at the corresponding position on the pre-stored chromosome; Step 5: Calculate the fitness of the offspring chromosome according to the target optimization function. When the fitness value of the offspring chromosome is greater than the fitness value of the pre-existing chromosome, replace the pre-existing chromosome with the offspring chromosome as the new pre-existing chromosome. When the maximum number of iterations has not been reached and the fitness value of the pre-existing chromosome is less than the preset fitness threshold, return to step 2 to continue iterating. When the maximum number of iterations is reached or the fitness value of the pre-existing chromosome is greater than the preset fitness threshold, stop the iteration and output the pre-existing chromosome as the optimal axis grouping solution.

7. A weaving production shaft optimization method, which is implemented based on a weaving production shaft optimization system according to any one of claims 1 to 6, characterized in that: include: S1. Collect order data; S2. Preprocess the order data to obtain valid order data; S3. Obtain the warp yarn temperature influence coefficient based on the constructed warp yarn production influence coefficient model, and obtain the weft yarn humidity influence coefficient based on the constructed weft yarn production influence coefficient model. Perform demand calculation based on the warp yarn temperature influence coefficient, weft yarn humidity influence coefficient, and valid order data to obtain the warp yarn demand and weft yarn demand. S4. Perform grouping allocation based on the warp yarn demand and the weft yarn demand to obtain the optimal grouping solution.

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