Garment factory production intelligent management method and system based on artificial intelligence

Abstract

The invention discloses a garment factory production intelligent management method and system based on artificial intelligence, and belongs to the technical field of production management. The method is used for solving the technical problems that in an existing scheme, the autonomous supervision and analysis effect of tailored fabric generated by garment production in a garment factory is poor, and the autonomous optimization and management effect of fabric tailoring is poor. Local tailoring supervision of tailored fabrics is performed on batch production of different garments, and local tailoring supervision analysis data in different aspects are integrated, calculated and analyzed, so that local tailored fabric states corresponding to different tailored fabrics are obtained; and performing targeted production optimization management on subsequent production of the clothes according to all the abnormal tailored fabrics obtained through analysis. According to the method, data processing and calculation analysis in different aspects are carried out on all tailored fabrics in garment production, simultaneous analysis is carried out on data analysis results in different aspects, the overall tailoring state of different tailored fabrics is determined, and targeted tailored fabric production optimization management is carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of production management, and in particular to an intelligent production management method and system for a garment factory based on artificial intelligence. Background Art

[0002] The production process in a garment factory refers to the process of transforming raw materials (such as fabrics, threads, buttons, etc.) into finished garments through a series of processing steps. This process can be divided into several main stages, each of which contains specific tasks and technical requirements.

[0003] When implementing the existing production management plan for garment factories, it is impossible to conduct local cutting supervision and processing analysis of cut fabrics for batch production of different garments, as well as overall cutting supervision and processing analysis of all cut fabrics corresponding to different garments, and to dynamically optimize the cutting of different fabrics from different aspects based on the analysis results. As a result, the autonomous supervision and analysis of cut fabrics generated by garment production in garment factories is not effective, as is the autonomous optimization management of fabric cutting. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent management method and system for garment factory production based on artificial intelligence, which is used to solve the technical problems in the existing solutions that the autonomous supervision and analysis of the cutting fabrics produced by garment factories in the production of garments are not effective, and the autonomous optimization management of fabric cutting is not effective.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The intelligent management method of clothing factory production based on artificial intelligence includes:

[0007] Carry out local cutting supervision of cutting fabrics for batch production of different garments produced by garment factories, and implement multi-dimensional data processing and analysis on local cutting supervision data, determine the corresponding marks of different cutting fabrics produced in the production process of different garment batches, and implement targeted production optimization management for subsequent production of different garments based on the cutting fabric marks;

[0008] According to all the cutting fabrics appearing in clothing production, different aspects of data processing and computational analysis are implemented, and the data analysis results from different aspects are jointly analyzed to determine the overall cutting status of different cutting fabrics and implement targeted cutting fabric production optimization management.

[0009] Preferably, batch production of different garments produced by a garment factory is supervised and numbered as i, i=1, 2, 3, ..., n; n is a positive integer;

[0010] When collecting and processing different cutting fabrics for batch production of different garments produced by a garment factory, all cutting fabrics j corresponding to different garments produced in the batch production and the cutting fabric weights ZCij of the different cutting fabrics are obtained; j = 1, 2, 3, ..., m; m is a positive integer;

[0011] When conducting partial cutting supervision analysis on different garments produced in different batches, the cutting value impact value CJij corresponding to different cutting fabrics of the produced garments is calculated by the formula CJij=ZCij×DCij; where DCij is the unit price of fabrics corresponding to different cutting fabrics;

[0012] And, the cutting weight influence value CZij of the production clothing corresponding to different cutting fabrics is calculated by the formula CZij=ZCij / Ni; wherein Ni is the total production weight of the clothing of the corresponding batch of production clothing.

[0013] Preferably, when performing data analysis on the state of local cutting fabrics according to the cutting value influence values ​​and cutting weight influence values ​​obtained by corresponding processing of different cutting fabrics, the cutting value influence values ​​and cutting weight influence values ​​corresponding to different cutting fabrics are sequentially converted into the formula: Calculate and obtain the corresponding local cutting fabric abnormal value CYij; where A and B are the first local cutting influence standard value and the second local cutting influence standard value corresponding to clothing production respectively; [*] is the rounding function, which means obtaining the maximum integer that does not exceed the real number *; max() means obtaining the maximum value among different values;

[0014] Data analysis and dynamic marking of outliers in locally cut fabrics.

[0015] Preferably, if the abnormal value of the local cut fabric is 0, the corresponding cut fabric is associated with the local cut normal label, and the cut fabric is marked as normal cut fabric;

[0016] If the local cut fabric abnormality value is not 0, the corresponding cut fabric is associated with the local cut abnormality label, and the cut fabric is marked as abnormal cut fabric.

[0017] Preferably, all the normal cut fabrics and abnormal cut fabrics obtained by processing are sorted and combined to obtain local cut fabric status analysis data, and targeted production optimization management is implemented for subsequent production of the corresponding garments for all abnormal cut fabrics in the local cut fabric status analysis data.

[0018] Preferably, different cutting fabrics corresponding to different garment production are obtained, and the total weight ZZk of the cutting fabrics corresponding to the cutting fabrics and the total number of occurrences of abnormal cutting fabrics NYk are obtained; k is the different cutting fabrics corresponding to different garment production, k=1, 2, 3, ..., p; p is a positive integer;

[0019] The total weight of the cut fabrics corresponding to different cut fabrics is calculated in turn through the first cut influence formula Calculate and obtain the first cutting influence degree YCk of the corresponding cutting fabric; where A′ and B′ are the first overall cutting influence standard value and the second overall cutting influence standard value corresponding to clothing production, respectively;

[0020] And, the total number of occurrences of abnormally cut fabrics corresponding to different cut fabrics is calculated through the second cutting influence formula Calculate and obtain the second cutting influence ECk of the corresponding cutting fabric; where NB is the total number of occurrences of all cutting fabrics; C is the third overall cutting influence standard value;

[0021] The first cutting influence degree and the second cutting influence degree obtained by calculation are sorted and combined to obtain a cutting influence processing sequence corresponding to the corresponding cutting fabric.

[0022] Preferably, when performing data analysis on the cutting impact processing sequence to determine the overall cutting state corresponding to the corresponding cutting fabric, the cutting impact identification sequence is traversed and analyzed to see whether there is an element with a value of 1, and targeted management is implemented for the subsequent clothing production of the corresponding cutting fabric based on the analysis results.

[0023] Preferably, if there is no element with a value of 1, a normal overall cutting state label of the corresponding cutting fabric is generated, and the corresponding cutting fabric is maintained for design and use in subsequent garment production according to the normal overall cutting state label;

[0024] If there is an element with a value of 1, an overall cutting state abnormality label of the corresponding cutting fabric is generated, and the design and use of the corresponding cutting fabric in subsequent clothing production are optimized and managed based on the overall cutting state abnormality label.

[0025] The intelligent management system for garment factory production based on artificial intelligence includes:

[0026] The local processing management module for cutting fabrics is used to supervise the local cutting of cutting fabrics for batch production of different garments produced by the garment factory, and implement multi-dimensional data processing and analysis on the local cutting supervision data, determine the corresponding marks of different cutting fabrics produced in the production process of different garment batches, and implement targeted production optimization management for the subsequent production of different garments based on the cutting fabric marks;

[0027] The overall processing management module for cutting fabrics is used to implement different aspects of data processing and calculation analysis based on all the cutting fabrics that appear in clothing production, and to jointly analyze the data analysis results from different aspects to determine the overall cutting status of different cutting fabrics and implement targeted cutting fabric production optimization management.

[0028] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0029] The present invention performs local cutting supervision on cutting fabrics for batch production of different garments, integrates, calculates and analyzes local cutting supervision analysis data of different aspects, obtains local cutting fabric states corresponding to different cutting fabrics, and implements targeted production optimization management for subsequent production of the corresponding garments based on all abnormal cutting fabrics obtained by analysis, thereby realizing multi-dimensional data analysis and optimization management of different cutting fabrics generated in different garment production processes, and improving the autonomous supervision and analysis effect of cutting fabrics generated by garment factories in producing garments.

[0030] The present invention implements different aspects of data processing and computational analysis on all cutting fabrics that appear in clothing production, and jointly analyzes the data analysis results of different aspects, determines the overall cutting status of different cutting fabrics and implements targeted cutting fabric production optimization management, thereby realizing overall dimensional supervision and processing analysis of all cutting fabrics for different production services in the early stage, and dynamically manages the subsequent use of different cutting fabrics according to the analysis results, thereby improving the autonomous optimization management effect of cutting fabrics in clothing factory production. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] Figure 1 This is a flowchart of the intelligent management method for garment factory production based on artificial intelligence of the present invention.

[0033] Figure 2 It is a flowchart of the steps of performing data analysis on abnormal values ​​of locally cut fabrics in the present invention.

[0034] Figure 3 It is a flowchart of the steps of performing data analysis on the clipping impact processing sequence in the present invention.

[0035] Figure 4 This is a module block diagram of the clothing factory production intelligent management system based on artificial intelligence in the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Example 1: Figure 1As shown, the present invention is an intelligent management method for garment factory production based on artificial intelligence, comprising:

[0038] Carry out local cutting supervision of cutting fabrics for batch production of different garments produced by garment factories, and implement multi-dimensional data processing and analysis on local cutting supervision data, determine the corresponding marks of different cutting fabrics produced in the production process of different garment batches, and implement targeted production optimization management for subsequent production of different garments based on the cutting fabric marks; including:

[0039] The batch production of different garments produced by the garment factory is supervised and numbered as i, i = 1, 2, 3, ..., n; n is a positive integer;

[0040] Among them, the number markings of different garments can be determined according to the order of production time;

[0041] When collecting and processing different cutting fabrics for batch production of different garments produced by a garment factory, all cutting fabrics j corresponding to different garments produced in the batch production and the cutting fabric weights ZCij of the different cutting fabrics are obtained; j = 1, 2, 3, ..., m; m is a positive integer;

[0042] When conducting partial cutting supervision analysis on different garments produced in different batches, the cutting value impact value CJij corresponding to different cutting fabrics of the produced garments is calculated by the formula CJij=ZCij×DCij; where DCij is the unit price of fabrics corresponding to different cutting fabrics;

[0043] And, the cutting weight influence value CZij of the production clothing corresponding to different cutting fabrics is calculated by the formula CZij=ZCij / Ni; wherein Ni is the total production weight of the clothing of the corresponding batch of production clothing;

[0044] In an embodiment of the present invention, by monitoring, counting, processing and calculating different cutting fabrics generated by producing clothing, cutting value impact values ​​and cutting weight impact values ​​corresponding to different aspects of different cutting fabrics are obtained, which can provide reliable multi-dimensional processing data support for subsequent local cutting fabric status data analysis of different cutting fabrics.

[0045] When analyzing the data of the local cutting fabric status according to the cutting value influence value and cutting weight influence value obtained by corresponding processing of different cutting fabrics, the cutting value influence value and cutting weight influence value corresponding to different cutting fabrics are successively calculated by formula Calculate and obtain the corresponding local cutting fabric abnormal value CYij; where A and B are the first local cutting influence standard value and the second local cutting influence standard value corresponding to clothing production, respectively, which can be determined according to the production quality design requirements corresponding to clothing production, or according to the median of all historical cutting value influence values ​​and the median of all historical cutting weight influence values; [*] is a rounding function, which means obtaining the maximum integer that does not exceed the real number *; max() means obtaining the maximum value among different values;

[0046] In the embodiment of the present invention, the data obtained from the early processing of different aspects of the cut fabric are integrated and calculated to obtain the corresponding local cut fabric abnormal value, which not only realizes the expansion and extension of the early processing data of different aspects, but also provides reliable data support for the subsequent local cut fabric state analysis of the cut fabric;

[0047] like Figure 2 As shown, data analysis is performed on the outliers of the locally cut fabrics and they are dynamically marked;

[0048] If the abnormal value of the local cut fabric is 0, the corresponding cut fabric is associated with the local cut normal label, and the cut fabric is marked as normal cut fabric;

[0049] If the abnormal value of the local cut fabric is not 0, the corresponding cut fabric is associated with the local cut abnormal label, and the cut fabric is marked as abnormal cut fabric;

[0050] All the normal cutting fabrics and abnormal cutting fabrics obtained by processing are sorted and combined to obtain local cutting fabric status analysis data, and targeted production optimization management is implemented for the subsequent production of the corresponding garments for all abnormal cutting fabrics in the local cutting fabric status analysis data;

[0051] Among them, when implementing targeted production optimization management for the subsequent production of all garments to which the abnormally cut fabrics belong, the local fabric designs corresponding to all the abnormally cut fabrics can be optimized.

[0052] In the embodiment of the present invention, local cutting supervision of cutting fabrics is performed on batch production of different garments, and local cutting supervision analysis data of different aspects are integrated, calculated and analyzed to obtain local cutting fabric states corresponding to different cutting fabrics, and targeted production optimization management is implemented for subsequent production of the corresponding garments based on all abnormal cutting fabrics obtained through analysis, thereby realizing multi-dimensional data analysis and optimization management of different cutting fabrics generated in different garment production processes, and improving the autonomous supervision and analysis effect of cutting fabrics generated by garment factories in producing garments.

[0053] According to all the cutting fabrics in clothing production, different aspects of data processing and calculation analysis are carried out, and the data analysis results of different aspects are analyzed jointly to determine the overall cutting status of different cutting fabrics and implement targeted cutting fabric production optimization management; including:

[0054] Obtain different cutting fabrics corresponding to different clothing production, and obtain the total weight ZZk of the cutting fabrics corresponding to the cutting fabrics and the total number of occurrences of abnormal cutting fabrics NYk; k is the different cutting fabrics corresponding to different clothing production, k = 1, 2, 3, ..., p; p is a positive integer;

[0055] The total weight of the cut fabrics corresponding to different cut fabrics is calculated in turn through the first cut influence formula Calculate and obtain the first cutting influence degree YCk of the corresponding cutting fabric; where A′ and B′ are the first overall cutting influence standard value and the second overall cutting influence standard value corresponding to clothing production, respectively, and both can be determined according to the production quality design requirements corresponding to clothing production;

[0056] And, the total number of occurrences of abnormally cut fabrics corresponding to different cut fabrics is calculated through the second cutting influence formula Calculate and obtain the second cutting influence ECk of the corresponding cutting fabric; where NB is the total number of occurrences of all cutting fabrics; C is the third overall cutting influence standard value, which can be determined according to the production quality design requirements corresponding to the clothing production;

[0057] The first cutting influence degree and the second cutting influence degree obtained by calculation are sorted and combined to obtain a cutting influence processing sequence corresponding to the corresponding cutting fabric;

[0058] In the embodiment of the present invention, by performing different aspects of cutting influence data processing and calculation on all cutting fabrics generated in different clothing production processes, the corresponding collection influence degrees of different aspects are obtained, which can not only realize the digital representation of the cutting influence states of different aspects corresponding to different cutting fabrics, but also provide reliable multi-dimensional data support for the data integration analysis and dynamic management of the overall cutting states corresponding to different cutting fabrics;

[0059] like Figure 3 As shown, when the data analysis of the cutting influence processing sequence is performed to determine the overall cutting state corresponding to the corresponding cutting fabric, the cutting influence identification sequence is traversed and analyzed to see whether there is an element with a value of 1;

[0060] If there is no element with a value of 1, a normal overall cutting state label of the corresponding cutting fabric is generated, and the design and use of the corresponding cutting fabric in subsequent clothing production is maintained according to the normal overall cutting state label; the normal overall cutting state label indicates that the overall negative impact of the corresponding cutting fabric meets the design requirements of clothing production cutting;

[0061] If there is an element with a value of 1, an abnormal label of the overall cutting state of the corresponding cutting fabric is generated, and the design and use of the corresponding cutting fabric in subsequent clothing production are optimized and managed according to the abnormal label of the overall cutting state;

[0062] Among them, the design and use of the cutting fabrics belonging to the overall cutting status abnormal label in subsequent clothing production are optimized and managed, and specifically, the use of the cutting fabrics can be replaced, or the corresponding design use of the cutting fabrics can be reduced.

[0063] In the embodiment of the present invention, by implementing different aspects of data processing and computational analysis on all cutting fabrics that appear in clothing production, and jointly analyzing the data analysis results of different aspects, the overall cutting status of different cutting fabrics is determined and targeted cutting fabric production optimization management is implemented, thereby realizing overall dimensional supervision and processing analysis of all cutting fabrics for different production services in the early stage, and dynamically managing the subsequent use of different cutting fabrics based on the analysis results, thereby improving the autonomous optimization management effect of cutting fabrics in clothing factory production.

[0064] Example 2: Figure 4 As shown in the figure, the intelligent management system for garment factory production based on artificial intelligence includes:

[0065] The local processing management module for cutting fabrics is used to supervise the local cutting of cutting fabrics for batch production of different garments produced by the garment factory, and implement multi-dimensional data processing and analysis on the local cutting supervision data, determine the corresponding marks of different cutting fabrics produced in the production process of different garment batches, and implement targeted production optimization management for the subsequent production of different garments based on the cutting fabric marks;

[0066] The overall processing management module for cutting fabrics is used to implement different aspects of data processing and calculation analysis based on all the cutting fabrics that appear in clothing production, and to jointly analyze the data analysis results from different aspects to determine the overall cutting status of different cutting fabrics and implement targeted cutting fabric production optimization management.

[0067] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.

[0068] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0069] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0071] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An intelligent management method for garment factory production based on artificial intelligence, characterized in that: include: Carry out local cutting supervision of cutting fabrics for batch production of different garments produced by garment factories, and implement multi-dimensional data processing and analysis on local cutting supervision data, determine the corresponding marks of different cutting fabrics produced in the production process of different garment batches, and implement targeted production optimization management for subsequent production of different garments based on the cutting fabric marks; According to all the cutting fabrics appearing in clothing production, different aspects of data processing and computational analysis are implemented, and the data analysis results from different aspects are jointly analyzed to determine the overall cutting status of different cutting fabrics and implement targeted cutting fabric production optimization management.

2. The method for intelligent management of clothing factory production based on artificial intelligence according to claim 1 is characterized in that: The batch production of different garments produced by the garment factory is supervised and numbered as i, i = 1, 2, 3, ..., n; n is a positive integer; When collecting and processing different cutting fabrics for batch production of different garments produced by a garment factory, all cutting fabrics j corresponding to different garments produced in the batch production and the cutting fabric weights ZCij of different cutting fabrics are obtained; j = 1, 2, 3, ..., m; m is a positive integer; When conducting partial cutting supervision analysis on different garments produced in different batches, the cutting value impact value CJij of different cutting fabrics of the produced garments is calculated by the formula CJij=ZCij×DCij; In the formula, DCij is the unit price of fabrics corresponding to different cut fabrics; And, the cutting weight influence value CZij of the production clothing corresponding to different cutting fabrics is calculated by the formula CZij=ZCij / Ni; wherein Ni is the total production weight of the clothing of the corresponding batch of production clothing.

3. The method for intelligent management of clothing factory production based on artificial intelligence according to claim 2 is characterized in that: When analyzing the data of the local cutting fabric status according to the cutting value influence value and cutting weight influence value obtained by corresponding processing of different cutting fabrics, the cutting value influence value and cutting weight influence value corresponding to different cutting fabrics are successively calculated by formula Calculate and obtain the corresponding local cutting fabric abnormal value CYij; where A and B are the first local cutting influence standard value and the second local cutting influence standard value corresponding to clothing production respectively; [*] is the rounding function, which means obtaining the maximum integer that does not exceed the real number *; max() means obtaining the maximum value among different values; Data analysis is performed on outliers in locally cut fabrics and they are marked dynamically.

4. The method for intelligent management of clothing factory production based on artificial intelligence according to claim 3 is characterized in that: If the abnormal value of the local cut fabric is 0, the corresponding cut fabric is associated with the local cut normal label, and the cut fabric is marked as normal cut fabric; If the local cut fabric abnormality value is not 0, the corresponding cut fabric is associated with the local cut abnormality label, and the cut fabric is marked as abnormal cut fabric.

5. The method for intelligent management of clothing factory production based on artificial intelligence according to claim 4 is characterized in that: All the normal cut fabrics and abnormal cut fabrics obtained by processing are sorted and combined to obtain local cut fabric status analysis data, and targeted production optimization management is implemented for the subsequent production of the corresponding garments for all abnormal cut fabrics in the local cut fabric status analysis data.

6. The method for intelligent management of clothing factory production based on artificial intelligence according to claim 5 is characterized in that: Obtain different cutting fabrics corresponding to different clothing production, and obtain the total weight ZZk of the cutting fabrics corresponding to the cutting fabrics and the total number of occurrences of abnormal cutting fabrics NYk; k is the different cutting fabrics corresponding to different clothing production, k = 1, 2, 3, ..., p; p is a positive integer; The total weight of the cut fabrics corresponding to different cut fabrics is calculated in turn through the first cut influence formula Calculate and obtain the first cutting influence degree YCk of the corresponding cutting fabric; where A′ and B′ are the first overall cutting influence standard value and the second overall cutting influence standard value corresponding to clothing production, respectively; And, the total number of occurrences of abnormally cut fabrics corresponding to different cut fabrics is calculated through the second cutting influence formula Calculate and obtain the second cutting influence ECk of the corresponding cutting fabric; where NB is the total number of occurrences of all cutting fabrics; C is the third overall cutting influence standard value; The first cutting influence degree and the second cutting influence degree obtained by calculation are sorted and combined to obtain a cutting influence processing sequence corresponding to the corresponding cutting fabric.

7. The method for intelligent management of garment factory production based on artificial intelligence according to claim 6 is characterized in that: When performing data analysis on the cutting impact processing sequence to determine the overall cutting status corresponding to the corresponding cutting fabric, the cutting impact identification sequence is traversed and analyzed to see if there is an element with a value of 1, and targeted management is implemented for the subsequent clothing production of the corresponding cutting fabric based on the analysis results.

8. The method for intelligent management of garment factory production based on artificial intelligence according to claim 7 is characterized in that: If there is no element with a value of 1, a normal label for the overall cutting state of the corresponding cutting fabric is generated, and the corresponding cutting fabric is maintained for design and use in subsequent garment production according to the normal label for the overall cutting state; If there is an element with a value of 1, an overall cutting state abnormality label of the corresponding cutting fabric is generated, and the design and use of the corresponding cutting fabric in subsequent clothing production are optimized and managed based on the overall cutting state abnormality label.

9. An intelligent management system for garment factory production based on artificial intelligence, applying the intelligent management method for garment factory production based on artificial intelligence as described in any one of claims 1 to 8, characterized in that: include: The local processing management module for cutting fabrics is used to supervise the local cutting of cutting fabrics for batch production of different garments produced by the garment factory, and implement multi-dimensional data processing and analysis on the local cutting supervision data, determine the corresponding marks of different cutting fabrics produced in the production process of different garment batches, and implement targeted production optimization management for the subsequent production of different garments based on the cutting fabric marks; The overall processing management module for cutting fabrics is used to implement different aspects of data processing and calculation analysis based on all the cutting fabrics that appear in clothing production, and to jointly analyze the data analysis results from different aspects to determine the overall cutting status of different cutting fabrics and implement targeted cutting fabric production optimization management.

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