Batch clothing customization method, device, electronic device and storage medium

Abstract

The present application relates to a method for mass customization of clothing, wherein the method comprises: obtaining historical garment size data and customer order data; inputting the historical garment size data into a size archiving model to obtain multiple archive values, wherein the size archiving model is used to obtain statistical characteristics of the historical garment sizes and generate the archive values, wherein the archive values ​​are sizes corresponding to historical garment sizes within a preset range; matching the customer order data with the archive values ​​to obtain order size data; and making clothing based on the order size data to obtain target customized clothing. Through the present application, the problem of low efficiency in mass customization of clothing is solved, archiving is achieved based on existing garment size data, and a garment size database that is closer to the customer's personal customization data is established, thereby achieving efficient personalized clothing customization.

Description

Technical Field

[0001] The present application relates to the field of clothing production, and in particular to a method, device, electronic device and storage medium for mass clothing customization. Background Art

[0002] Currently, most garments are produced directly in factories in bulk quantities, that is, the same type of clothing is produced in large quantities. This method produces the same popular sizes, without the concept of customization, and will not produce corresponding sizes according to the requirements of different companies. This method has the highest production speed, but because the distribution data of human body characteristics such as height, weight, and three-dimensional features will change with the development of the country's economic level, it cannot meet customers' customized clothing needs.

[0003] To meet customer demand for customized clothing, some factories have adopted a custom production method based entirely on the order size. However, this customization method requires a single person to work on a single board, so workers and machines need to switch back and forth between different sizes. This personalized customization method is too expensive and cannot cope with large-scale group orders.

[0004] With regard to the problem of low efficiency in mass customization of clothing in related technologies, no effective solution has been proposed so far. Summary of the Invention

[0005] In this embodiment, a method, device, electronic device and storage medium for batch clothing customization are provided to solve the problem of low efficiency of batch clothing customization in related technologies.

[0006] First, in this embodiment, a method for mass customization of clothing is provided, comprising:

[0007] Obtain historical garment size data and customer order data;

[0008] Inputting the historical garment size data into a size archiving model to obtain a plurality of archiving values, wherein the size archiving model is used to obtain statistical features of the historical garment size and generate the archiving values, wherein the archiving values ​​are sizes corresponding to the historical garment size within a preset range;

[0009] Matching the customer order data with the archived value to obtain order size data;

[0010] Garments are made based on the order size data to obtain target customized garments.

[0011] In one embodiment, inputting the historical garment size data into a size archiving model to obtain a plurality of archiving values ​​includes: obtaining a size archiving dimension and an archiving step based on the size archiving model; obtaining the maximum and minimum values ​​of the historical garment size data corresponding to the size archiving dimension to determine a data archiving range; and determining a plurality of the archiving values ​​according to the data archiving range and the archiving step.

[0012] In one embodiment, obtaining the archiving step based on the size archiving model also includes: sorting the historical garment size data in ascending order of values ​​to obtain first statistical data; dividing the first statistical data into multiple groups in the order according to the data volume of the historical garment size data to obtain second statistical data; and taking the difference between the historical garment size data with the largest value in two adjacent groups of the second statistical data as the archiving step.

[0013] In one embodiment, the inputting of the historical garment size data into a size archiving model to obtain a plurality of archived values ​​further includes: the historical garment data includes reference part size data and restricted part size data, the reference part size data is used to determine the overall size of the garment, the restricted part size data is used to determine the local size of a specific part of the garment, and the reference part size data corresponds to the restricted part size data; the reference part size data is input into the size archiving model to obtain a plurality of reference part size archived values; the restricted part size data is input into the size archiving model to obtain a plurality of restricted part size archived values; each of the reference part size archived values ​​corresponds to at least one of the restricted part size archived values.

[0014] In one embodiment, matching the customer order data with the archived value to obtain order size data further includes: numbering the archived value to generate a size code; and generating order size data based on the size code matched with the customer order data.

[0015] In one embodiment, inputting the historical garment size data into a size archiving model to obtain a plurality of archive values ​​further comprises: sorting the historical garment size data in ascending order of value to obtain statistical data; based on the statistical data, removing the historical garment size data having a value greater than a first preset threshold, and / or removing the historical garment size data having a value less than a second preset threshold to obtain preliminary screening data; and obtaining a plurality of the archive values ​​based on the preliminary screening data.

[0016] In one embodiment, inputting the historical clothing size data into the size archiving model further includes: obtaining the customer region and customer age corresponding to the historical clothing size data; grouping the historical clothing size data based on the customer region to obtain regional grouping data; grouping the historical clothing size data based on the customer age to obtain age grouping data; and inputting the regional grouping data and the age grouping data into the size archiving model respectively.

[0017] In a second aspect, a batch clothing customization device is provided in this embodiment, comprising:

[0018] An acquisition unit, used to obtain historical garment size data and customer order data;

[0019] an archiving unit, configured to input the historical garment size data into a size archiving model to obtain a plurality of archiving values, wherein the size archiving model is configured to obtain statistical features of the historical garment sizes and generate the archiving values, wherein the archiving values ​​are sizes corresponding to the historical garment sizes within a preset range;

[0020] a matching unit, configured to match the customer order data with the archived value to obtain order size data;

[0021] The clothing making unit is used to make clothing based on the order size data to obtain target customized clothing.

[0022] In a third aspect, an electronic device is provided in this embodiment, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the batch clothing customization method described in the first aspect is implemented.

[0023] In a fourth aspect, a storage medium is provided in this embodiment, on which a computer program is stored. When the program is executed by a processor, the batch clothing customization method described in the first aspect is implemented.

[0024] Compared with the related art, the batch clothing customization method provided in this embodiment obtains historical clothing size data and customer order data; inputs the historical clothing size data into a size archiving model to obtain multiple archiving values, and the size archiving model is used to obtain the statistical characteristics of the historical clothing size and generate the archiving value, and the archiving value is the size corresponding to the historical clothing size within a preset range; matches the customer order data with the archiving value to obtain order size data; and makes clothing based on the order size data to obtain target customized clothing, which solves the problem of low efficiency of batch clothing customization, realizes archiving based on existing clothing size data, and establishes a clothing size database that is closer to the customer's personal customization data, thereby realizing efficient batch clothing customization.

[0025] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0027] Figure 1 This is a hardware structure block diagram of a terminal for the batch clothing customization method according to an embodiment of the present application;

[0028] Figure 2 is a flow chart of a method for mass customization of clothing according to an embodiment of the present application;

[0029] Figure 3 This is a structural block diagram of the batch clothing customization device of this embodiment. DETAILED DESCRIPTION

[0030] In order to more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0031] Unless otherwise defined, the technical terms or scientific terms involved in this application should have the general meaning understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "an", "a", "the", "these" and the like in this application do not indicate quantitative restrictions, and they can be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusions; for example, a process, method and system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "plurality" involved in this application refers to two or more. "And / or" describes the relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, and B exists alone. Generally, the character " / " indicates that the related objects are in an "or" relationship. The terms "first," "second," "third," etc. used in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0032] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 1 is a block diagram of the hardware structure of the terminal of the batch clothing customization method according to the embodiment of the present application. Figure 1 As shown, the terminal may include one or more ( Figure 1 Only one is shown) a processor 102 and a memory 104 for storing data, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0033] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the batch clothing customization method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above-mentioned method. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0034] The transmission device 106 is used to receive or send data via a network. The network may include a wireless network provided by the terminal's telecommunications provider. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0035] When clothing manufacturers produce custom clothing on a large scale, they first need to take measurements of the customers who want the custom clothing, and then make the clothing according to the measured structure. However, when customers customize clothing in large quantities, in order to facilitate mass production, not only do they need to take measurements of each person who is being customized, but they also need to analyze the data after the measurements, that is, to merge the parts with the same body shape together. When merging parts of the cut pieces with the same signals, the existing technology usually adopts a manual method for numbering, but this method is labor-intensive and inefficient, cannot adapt to current intelligent cutting machines, and is prone to errors. The present application provides a method, device, electronic device and storage medium for mass customization of clothing to solve the problem of low efficiency in mass customization of clothing.

[0036] In this embodiment, a method for batch clothing customization is provided. Figure 2 Flowchart of the method for mass customization of clothing according to an embodiment of the present application. Figure 2 As shown, the process includes the following steps:

[0037] Step S201, obtaining historical garment size data and customer order data.

[0038] Specifically, historical garment size data refers to garment size data for a large number of customers, stored based on their historical order data. In one embodiment, this historical garment size data is also associated with the customer identity, age, and region of the order to which it belongs. Customer order data refers to data related to the garments currently ordered by a customer, including but not limited to the body size data, customer identity data, customer age data, and customer region information in the current customer order.

[0039] Step S202: inputting the historical garment size data into a size archiving model to obtain a plurality of archiving values. The size archiving model is used to obtain statistical features of the historical garment size and generate the archiving values.

[0040] Specifically, the size archiving model utilizes historical garment size data and an automated archiving model trained using a Bayesian network. This model can perform operations such as data classification, clustering, and association rules. Using pre-set archiving rules, massive amounts of historical garment size data can be archived. Compared to national standard garment size values, data analysis using the size archiving model can yield garment size archiving results, or archiving values, that are closer to customized patterns.

[0041] Step S203: Match the customer order data with the archived value to obtain order size data.

[0042] Specifically, based on the customer order data currently placed by the customer, the body data in the customer order is matched with the archived values ​​in the size library obtained by archiving the size archiving model, thereby converting the body data in the customer order into the order size data. In one embodiment, the body data in the customer order can also be rounded up or down to match the archived values ​​based on customer information in the customer order data, such as customer age and customer region information, to obtain the final order size data.

[0043] Step S204: making clothes based on the order size data to obtain target customized clothes.

[0044] Specifically, the order size data is sent to the garment factory, which can process the garments according to the order size data and the garment pattern selected by the customer to make customized garments.

[0045] Through the above steps, the batch clothing customization method of the present application collects the size characteristics of historically ordered garments, archives the sizes, and obtains a size library calculated based on the historical garment sizes. Garment factories produce based on this size library and can perform some production line classification before production, reducing manual and machine switching between different sizes to achieve batch customization group order business and improve the efficiency of batch clothing customization.

[0046] In one embodiment, inputting the historical garment size data into a size archiving model to obtain a plurality of archiving values ​​includes: obtaining a size archiving dimension and an archiving step based on the size archiving model; obtaining the maximum and minimum values ​​of the historical garment size data corresponding to the size archiving dimension to determine a data archiving range; and determining a plurality of the archiving values ​​according to the data archiving range and the archiving step.

[0047] Specifically, archiving dimensions include, but are not limited to, garment type and garment part. By inputting historical garment size data into the size archiving model, statistical results for historical garment size data for different garment types and garment parts can be obtained. By obtaining the maximum and minimum values ​​of the historical garment size data corresponding to the size archiving dimension and a preset archiving step size, the historical garment size data within the archiving range is divided into multiple equal parts, and the maximum size value in each equal part is used as the archiving value.

[0048] In one embodiment, obtaining the archiving step based on the size archiving model also includes: sorting the historical garment size data in ascending order of values ​​to obtain first statistical data; dividing the first statistical data into multiple groups in the order according to the data volume of the historical garment size data to obtain second statistical data; and taking the difference between the historical garment size data with the largest value in two adjacent groups of the second statistical data as the archiving step.

[0049] In one embodiment, the archiving step size can also be dynamically configured based on the data distribution of the historical garment size data. For example, if the current historical garment size data includes 100 data, and its size values ​​are normally distributed between sizes 60 and 80, then a relatively large first archiving step size is used at both ends of the size archiving range, for example, a step size of 4 yards, and a file is taken every 4 yards; a relatively small second archiving step size is used in the middle of the size archiving range, for example, a step size of 1, and the archiving values ​​can be 60, 64, 68, 69, 70, 71, 72, 76, and 80. Using the size archiving method of this embodiment, the binning step size can be adjusted according to the distribution of the historical garment size data within the archiving range. A small step size binning is used for the part of the historical garment data with a larger data distribution, which is conducive to the refined archiving of sizes and further refined differentiation of similar size data to make it more in line with customized business requirements.

[0050] In one embodiment, the archiving value can also be calculated based on the data distribution of historical garment size data and the percentage of the data volume of the historical garment size data. For example, if the current historical garment size data includes 100 data points, whose size values ​​are distributed between sizes 60 and 80, and the archiving step size is configured to be 10% of the data volume, then, if the data volume is evenly distributed, the archiving values ​​are 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, and 80. In the actual size archiving process, the size distribution often appears in the form of a normal distribution, with more data near the middle of the archiving range and less data near the sizes at the ends of the archiving range. When the archiving step size is set based on the percentage of the data volume, due to the data distribution characteristics, the archiving step size naturally becomes larger near the ends of the archiving range and smaller near the middle of the archiving range. In one embodiment, if two adjacent archiving values ​​with the same archiving step size are the same, the latter archiving value is increased by a preset value, such as +1, to avoid the situation where two archiving values ​​are the same.

[0051] In one embodiment, the inputting of the historical garment size data into a size archiving model to obtain a plurality of archived values ​​further includes: the historical garment data includes reference part size data and restricted part size data, the reference part size data is used to determine the overall size of the garment, the restricted part size data is used to determine the local size of a specific part of the garment, and the reference part size data corresponds to the restricted part size data; the reference part size data is input into the size archiving model to obtain a plurality of reference part size archived values; the restricted part size data is input into the size archiving model to obtain a plurality of restricted part size archived values; each of the reference part size archived values ​​corresponds to at least one of the restricted part size archived values.

[0052] Specifically, the reference part is the foundation of the garment to be produced, used to determine the 3D skeleton of the service. The reference part is determined based on the pattern maker's experience, and then these parts are divided into fixed multiple levels. The restricted parts are the detailed parts of the garment, used to determine the size of each part of the garment. The restricted parts are interval values ​​calculated based on the reference parts. By determining the reference and restricted parts, it is beneficial to achieve hierarchical and sequential size archiving of various positions of customized garments. This not only clarifies the overall structure of the garment formation, but also takes into account the detailed dimensions of the garment customization. Moreover, by corresponding the archiving values ​​of the reference parts with the archiving values ​​of the restricted parts, the efficiency of garment pattern customization is greatly improved. Moreover, in the actual factory production process, the reference parts and restricted parts can be produced on different production lines, which is conducive to improving garment production efficiency.

[0053] In one embodiment, matching the customer order data with the archived value to obtain order size data further includes: numbering the archived value to generate a size code; and generating order size data based on the size code matched with the customer order data.

[0054] Specifically, since the archived values ​​include multiple levels and there are archived steps between different archived values, the customer's order size may not be completely consistent with the archived value. Therefore, by matching the customer order data with the archived value, the archived value corresponding to the customer order size, that is, the archived size, can be obtained. Since the archived value has been pre-numbered, the customer order data can be converted into a size code through the above process, and the size code is used as the order size data. In one embodiment, a mapping relationship can also be established between the size code and the cut piece code of the corresponding part. Through the size matching step of this embodiment, the customer's size data can be directly converted into the cut piece size of the pattern selected by the user, and the cut piece data of the customer's target can be directly generated, shortening the time from the user placing an order to the generation of the pattern and size required for factory production, thereby improving clothing production efficiency.

[0055] In one embodiment, inputting the historical garment size data into a size archiving model to obtain a plurality of archive values ​​further comprises: sorting the historical garment size data in ascending order of value to obtain statistical data; based on the statistical data, removing the historical garment size data having a value greater than a first preset threshold, and / or removing the historical garment size data having a value less than a second preset threshold to obtain preliminary screening data; and obtaining a plurality of the archive values ​​based on the preliminary screening data.

[0056] Specifically, individual data is eliminated based on the limit values ​​of each part, which are maintained manually based on experience. For example, based on region and age, "reference part values" are extracted from historical order data, and the multiple extracted "reference part values" are sorted, and the first 2% and last 2% are eliminated for data cleaning. This helps to make the archived sizes more suitable for the general body shape of the public. It also improves the fit between batch customization group order business and the body shape of customer groups. Preferably, the eliminated data can also be archived separately, and a special body archive size library is established based on the eliminated data to accommodate customized business for special body customers.

[0057] In one embodiment, inputting the historical clothing size data into the size archiving model further includes: obtaining the customer region and customer age corresponding to the historical clothing size data; grouping the historical clothing size data based on the customer region to obtain regional grouping data; grouping the historical clothing size data based on the customer age to obtain age grouping data; and inputting the regional grouping data and the age grouping data into the size archiving model respectively.

[0058] Specifically, "reference part values" can be extracted from historical order data based on region and age. This is lower than categorizing by province, with age groups categorized by five-year intervals. Order size data can be broken down by province and age group based on clothing category. This is because different regions have different length requirements for individual parts, for example, northern customers prefer longer garments, while southern customers prefer a more fitted fit. Grouping customer order data by region and age allows for further refinement of customer segments, allowing for adjustments to archived sizes based on customer requirements to better meet their needs.

[0059] Through the above steps, the batch clothing customization method of the present application reduces the archive size library by analyzing the historical garment size data. Compared with the existing national standard size chart, the present application sets different size archiving dimensions and archiving steps in the size return model. The final archive value is more refined and more in line with the business needs of batch customization group orders. By associating the reference part archive value and the restricted part archive value, it is beneficial to improve the conversion efficiency of the size customer order size into the final garment size required by the factory, thereby improving the garment making speed. In addition, grouping the historical garment size data according to the customer's region and age is beneficial to further adjust the size according to the archive value, and finally obtain a size library suitable for different regions and people in different regions. The problem of low efficiency of batch clothing customization is solved, archiving based on existing garment size data is realized, and a garment size database that is closer to the customer's personal customization data is established, thereby realizing efficient batch clothing customization.

[0060] The present embodiment is described and illustrated below through preferred embodiments.

[0061] First, historical order data for all customers is obtained from a database or cloud. Based on the customer's region and age information, a "reference part value" is extracted from this historical order data. Region information is divided by province, with each province being a region. In one embodiment, multiple provinces can be grouped into a single region. Age information is grouped into tiers of three or five years.

[0062] The extracted multiple reference part values ​​are sorted, and the top 2% and bottom 2% of the historical order data corresponding to the reference part values ​​are eliminated. According to the program, the "reference part values" are automatically archived according to 5% of the data volume, and 20 "reference part archive values" are obtained. After archiving, individual parts can be further adjusted through manual work. For example, the hip circumference of "women's one-step skirt" is divided into: 83, 85, 87, 89..., 117, 119.

[0063] Based on the correspondence between the reference and restricted areas, the full dimensional values ​​of the "restricted areas" were extracted from historical order data according to the "reference area archive value" and converted into a file that can be recognized by Weka. Weka, short for Waikato Intelligent Analysis Environment, is an open data mining platform that integrates a large number of machine learning algorithms for data mining tasks, including data preprocessing, classification, regression, clustering, association rules, and visualization in a new interactive interface.

[0064] The reference part is a file that converts historical order sizes into a format recognizable by Weka. Weka then uses a Bayesian network to train a model. This model trains the limit values ​​based on the reference values. The model then removes the 90% limit value range from the reference part values. The limit value range is then divided into steps of 1 or 2, depending on the size of the interval. In one embodiment, Weka reads the file and extracts the "limited part values" corresponding to the "reference part value" based on the 95% range. For example, if the 95% waist circumference data for a hip circumference of 87 is between 60 and 68, the waist circumference range of the "limited part value" is set to 60, 62, 64, 66, and 68.

[0065] After archiving, each gear is coded and selected and matched in the coding according to the user size. In one embodiment, the user size data can also be added and expanded in the process of matching to the corresponding gear.

[0066] The factory's production line consists of a cutting machine, sewing machine, and quality inspection process. After the garment size is archived, the cutting machine can pre-set a set of cutting point data according to the code. This allows the corresponding code to be entered, and the machine can automatically cut, reducing the need for manual fabric movement and enabling customized cutting. Similarly, in the sewing process and cutting machine process, the same archived pieces can be completed by a single team of people and machines as much as possible, reducing the need for processing personnel to constantly review process drawings when switching between sewing of different sizes. In the quality inspection process, since there are corresponding archive codes for comparison, there is no need to measure each finished garment, which improves the production efficiency of customized clothing.

[0067] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0068] In this embodiment, a mass clothing customization device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments. The terms "module", "unit", "sub-unit", etc. used below can be a combination of software and / or hardware that implements the predetermined functions. Although the devices described in the following embodiments are preferably implemented in software, implementation by hardware, or a combination of software and hardware, is also possible and conceivable.

[0069] Figure 3 This is a structural block diagram of the batch clothing customization device of this embodiment. Figure 3 As shown, the device includes:

[0070] An acquisition unit 10 is used to acquire historical garment size data and customer order data;

[0071] an archiving unit 20, configured to input the historical garment size data into a size archiving model to obtain a plurality of archiving values, wherein the size archiving model is configured to obtain statistical features of the historical garment sizes and generate the archiving values, wherein the archiving values ​​are sizes corresponding to the historical garment sizes within a preset range;

[0072] a matching unit 30, configured to match the customer order data with the archived value to obtain order size data;

[0073] The clothing making unit 40 is used to make clothing based on the order size data to obtain target customized clothing.

[0074] Among them, the archiving unit 20 is also used to obtain the size archiving dimension and the archiving step based on the size archiving model; obtain the maximum and minimum values ​​of the historical garment size data corresponding to the size archiving dimension to determine the data archiving range; and determine multiple archiving values ​​according to the data archiving range and the archiving step.

[0075] The archiving unit 20 is further used to sort the historical garment size data in ascending order of values ​​to obtain first statistical data; divide the first statistical data into multiple groups in the order according to the data volume of the historical garment size data to obtain second statistical data; and use the difference between the historical garment size data with the largest value in two adjacent groups of the second statistical data as the archiving step.

[0076] The archiving unit 20 is also used for the historical garment data including reference part size data and restricted part size data, the reference part size data is used to determine the overall size of the garment, the restricted part size data is used to determine the local size of a specific part of the garment, and the reference part size data corresponds to the restricted part size data; the reference part size data is input into the size archiving model to obtain a plurality of reference part size archiving values; the restricted part size data is input into the size archiving model to obtain a plurality of restricted part size archiving values; each of the reference part size archiving values ​​corresponds to at least one of the restricted part size archiving values.

[0077] The matching unit 30 is further configured to number the archived values ​​to generate size codes; and to generate order size data based on the size codes matched with the customer order data.

[0078] The archiving unit 20 is further used to sort the historical garment size data in ascending order of values ​​to obtain statistical data; based on the statistical data, remove the historical garment size data with a value greater than a first preset threshold, and / or remove the historical garment size data with a value less than a second preset threshold to obtain preliminary screening data; and obtain multiple archived values ​​based on the preliminary screening data.

[0079] The archiving unit 20 is further configured to obtain the customer region and customer age corresponding to the historical garment size data; group the historical garment size data based on the customer region to obtain regional grouping data; group the historical garment size data based on the customer age to obtain age grouping data; and input the regional grouping data and the age grouping data into the size archiving model respectively.

[0080] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0081] This embodiment further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0082] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0083] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0084] S1, obtain historical garment size data and customer order data.

[0085] S2, inputting the historical garment size data into a size archiving model to obtain a plurality of archiving values, wherein the size archiving model is used to obtain statistical features of the historical garment size and generate the archiving values, wherein the archiving values ​​are sizes corresponding to the historical garment size within a preset range.

[0086] S3, matching the customer order data with the archived value to obtain order size data.

[0087] S4, making clothes based on the order size data to obtain target customized clothes.

[0088] It should be noted that, for specific examples in this embodiment, reference may be made to the examples described in the above embodiments and optional implementation modes, and will not be repeated in this embodiment.

[0089] In addition, in conjunction with the batch clothing customization method provided in the above embodiments, a storage medium may also be provided in this embodiment to implement the method. The storage medium stores a computer program; when the computer program is executed by a processor, it implements any of the batch clothing customization methods in the above embodiments.

[0090] It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit it. Based on the embodiments provided in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0091] Obviously, the accompanying drawings are merely examples or embodiments of the present application. A person skilled in the art can also apply the present application to other similar situations based on these drawings without inventive effort. Furthermore, it is understandable that, although the work involved in this development process may be complex and lengthy, certain design, manufacturing, or production changes based on the technical content disclosed in this application are merely routine technical means for a person skilled in the art and should not be considered to constitute a deficiency in the disclosure of the present application.

[0092] The term "embodiment" as used in this application refers to specific features, structures, or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily mean that the embodiment is the same, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. It is understood, either explicitly or implicitly, by those skilled in the art that the embodiments described in this application can be combined with other embodiments when there is no conflict.

[0093] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for mass customization of clothing, characterized in that: include: Obtain historical garment size data and customer order data; Inputting the historical garment size data into a size archiving model to obtain a plurality of archiving values, wherein the size archiving model is used to obtain statistical features of the historical garment size and generate the archiving values, wherein the archiving values ​​are sizes corresponding to the historical garment size within a preset range; Matching the customer order data with the archived value to obtain order size data; Making clothes based on the order size data to obtain target customized clothes; Inputting the historical garment size data into a size archiving model to obtain a plurality of archive values ​​further comprises: The historical garment data includes reference part size data and restricted part size data, wherein the reference part size data is used to determine the overall size of the garment, and the restricted part size data is used to determine the local size of a specific part of the garment, and the reference part size data corresponds to the restricted part size data; Inputting the reference part dimension data into the dimension archiving model to obtain a plurality of reference part dimension archiving values; The restriction part size data is input into the size archiving model to obtain a plurality of restriction part size archiving values; each of the reference part size archiving values ​​corresponds to at least one of the restriction part size archiving values.

2. The method for mass customization of clothing according to claim 1, characterized in that: The inputting of the historical garment size data into the size archiving model to obtain a plurality of archive values ​​comprises: Acquire a dimensional archiving dimension and an archiving step size based on the dimensional archiving model; Obtaining the maximum and minimum values ​​of the historical garment size data corresponding to the size archiving dimension to determine the data archiving range; A plurality of the archiving values ​​are determined according to the data archiving range and the archiving step length.

3. The method for mass customization of clothing according to claim 2, characterized in that: The acquiring of the archiving step size based on the size archiving model further comprises: sorting the historical garment size data in ascending order of value to obtain first statistical data; Dividing the first statistical data into multiple groups according to the order according to the amount of the historical garment size data, to obtain second statistical data; The difference between the historical garment size data with the largest value in two adjacent groups of the second statistical data is used as the archiving step length.

4. The method for mass customization of clothing according to claim 1, characterized in that: The step of matching the customer order data with the archived value to obtain order size data further includes: Numbering the archived values ​​to generate a size code; Order size data is generated based on the size code matched with the customer order data.

5. The method for mass customization of clothing according to claim 1, characterized in that: Inputting the historical garment size data into a size archiving model to obtain a plurality of archive values ​​further comprises: Sorting the historical garment size data in ascending order of value to obtain statistical data; Based on the statistical data, removing historical garment size data with values ​​greater than a first preset threshold, and / or removing historical garment size data with values ​​less than a second preset threshold, to obtain preliminary screening data; A plurality of archived values ​​are obtained based on the preliminary screening data.

6. The method for mass customization of clothing according to claim 1, characterized in that: The inputting the historical garment size data into the size archiving model further comprises: Obtaining the customer region and customer age corresponding to the historical clothing size data; Grouping the historical garment size data based on the customer region to obtain regional grouping data; Grouping the historical clothing size data based on the customer's age to obtain age grouping data; The region grouping data and the age grouping data are respectively input into the dimension archiving model.

7. A batch clothing customization device, characterized in that: include: An acquisition unit, used to obtain historical garment size data and customer order data; an archiving unit, configured to input the historical garment size data into a size archiving model to obtain a plurality of archiving values, wherein the size archiving model is configured to obtain statistical features of the historical garment sizes and generate the archiving values, wherein the archiving values ​​are sizes corresponding to the historical garment sizes within a preset range; a matching unit, configured to match the customer order data with the archived value to obtain order size data; a garment making unit, configured to make garments based on the order size data to obtain target customized garments; The historical garment data includes reference part size data and restricted part size data, wherein the reference part size data is used to determine the overall size of the garment, and the restricted part size data is used to determine the local size of a specific part of the garment, and the reference part size data corresponds to the restricted part size data; The archiving unit is further configured to input the reference part dimension data into the dimension archiving model to obtain a plurality of reference part dimension archiving values; The restriction part size data is input into the size archiving model to obtain a plurality of restriction part size archiving values; each of the reference part size archiving values ​​corresponds to at least one of the restriction part size archiving values.

8. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the mass clothing customization method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the mass clothing customization method according to any one of claims 1 to 7 are implemented.

Images