School uniform sorting label generation method
By constructing a multidimensional data association model and a hierarchical framework to generate school uniform labels, the problem of low efficiency in traditional school uniform sorting is solved, and efficient and accurate school uniform sorting is achieved.
Patent Information
- Application Number
- CN202510515106.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-23
- Publication Date
- 2025-09-26
AI Technical Summary
Traditional school uniform sorting relies on manual verification, which has problems such as low sorting efficiency and easy misdelivery and omission. In addition, the existing label generation logic is rigid and cannot adapt to complex sorting needs.
Build a multidimensional data association model and hierarchical framework, integrate student biometrics, clothing ordering information and distribution strategies, generate school uniform labels through a configurable sorting engine, and achieve dynamic sorting and efficient sorting.
It improves the accuracy and efficiency of sorting, reduces manual misjudgment and redundancy of sorting paths, and supports adjustment of sorting paths according to scenarios.
Smart Images
Figure CN120696088A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of intelligent school uniform management technology, and specifically to a school uniform sorting label generation method based on multidimensional data association and dynamic sorting strategy, which is particularly suitable for large-scale customized production, sorting and distribution of student uniforms. Background Art
[0002] Traditional school uniform sorting usually relies on manual verification of student information and clothing specifications, which has problems such as low sorting efficiency and easy misdelivery and omission. In existing technologies, some systems use simple labels to record student names and sizes, but this has the following drawbacks:
[0003] 1. Data fragmentation: Clothing style numbers, student biometrics (such as body shape data), clothing attributes (such as neutral, male, female, and mixed male / female) and distribution strategies (such as by class or clothing attributes) have not formed an association model, resulting in isolated label information and difficulty adapting to complex sorting needs.
[0004] 2. Rigid label generation logic: The existing label generation rules are rigid and cannot dynamically adjust the sorting strategy according to the sorting scenario (such as prioritizing sorting by class or clothing attributes), resulting in redundant sorting paths and low sorting efficiency.
[0005] Therefore, there is an urgent need for a school uniform label generation method that supports dynamic integration of multi-dimensional data and efficient sorting. Summary of the Invention
[0006] The present invention aims to address the deficiencies in the existing technology and provides a method for generating school uniform sorting labels. By constructing a multidimensional data association model and a hierarchical framework, it dynamically integrates student biometrics, clothing ordering information, and distribution strategies, and generates attachable labels based on a configurable sorting engine, thereby achieving precise and efficient school uniform sorting.
[0007] Technical Solution
[0008] 1. Overall process
[0009] This method includes the following core steps:
[0010] Step S1: Construct a school uniform multidimensional data association model and establish a database containing the following subsets:
[0011] Clothing ordering information parameter set: defines the size label (e.g., 160 / S) of each school uniform, clothing attributes (e.g., unisex, male, female, mixed male / female), style code (e.g., TC-01 for sportswear, ZS-02 for formal wear), and number of styles (e.g., 2 sets of sportswear, 1 set of formal wear).
[0012] Student biometric data set: collects students' ergonomic data such as height, weight, shoulder width, and arm length;
[0013] Distribution strategy rule base: stores sorting priority rules, such as "central sorting by class" or "batch sorting by clothing attributes".
[0014] Step S2: Construct a hierarchical framework based on the data association model, which consists of the following levels:
[0015] Data storage layer: dynamically binds to the database and synchronizes student information, clothing ordering information parameter sets and policy rules in real time;
[0016] Data processing layer: The logic engine integrates clothing styles (such as yd133), distribution strategies (such as class priority), clothing attributes (such as neutral), clothing specifications (such as size), and student identity information (such as name) to generate a labeled data stream;
[0017] Visual rendering layer: maps the processed data to a preset label template and outputs a printable label entity containing student uniform ordering information.
[0018] 2. Refined structure of the hierarchical framework
[0019] The hierarchical framework is further divided into four levels of data units:
[0020] Level 1 data unit: consists of clothing order styles, including clothing style codes (e.g. TC-01 corresponds to "short-sleeved round neck").
[0021] Secondary data unit: contains the class code, which consists of the grade code (such as G01 for grade 1) and the class number (such as C05 for class 5), and is used to identify the institutional level to which the student belongs.
[0022] Level 3 data unit: contains clothing attribute codes (such as neutral clothing, men's clothing, women's clothing, men's and women's mixed clothing) and student name fields, and the attribute codes are associated with style codes (such as TC-01 corresponds to "short-sleeved round neck").
[0023] Level 4 data unit: integrates individual adaptation parameter groups, including:
[0024] Restrict sorting to specific clothing style codes;
[0025] Clothing size tags (e.g., 160 / S) are automatically matched based on students’ biometrics;
[0026] Gender identifier (M / F);
[0027] Ergonomic data (such as sleeve length and trouser length) for customized school uniform production;
[0028] Units at all levels establish cascading relationships through data-related fields (such as student name and clothing serial number) to ensure data consistency.
[0029] 3. Implementation of dynamic sorting engine
[0030] The visual rendering layer integrates a dynamic sorting engine, whose core functions include:
[0031] Sorting strategy configuration module: provides a graphical configuration interface, allowing administrators to select the following modes according to the sorting scenario:
[0032] Institution-Prioritized Mode: Generates a label sequence based on the hierarchy of clothing style code, class code, clothing attribute code, and clothing size. For example, data is first generated by clothing style code (e.g., TC-01 corresponds to "short-sleeved round neck"), then by class (G01-C01). Within the same class, data is then sorted by clothing attribute (neutral clothing, men's clothing, women's clothing, mixed men's and women's clothing), and finally grouped and sorted by size (160 / S, 165 / M). This mode is suitable for class-by-class distribution.
[0033] Clothing attribute priority mode: Generates a label sequence based on the hierarchy of clothing style code, clothing attributes, clothing size, and class code. For example, orders for all classes are first aggregated by clothing style code (TC-01), then categorized by clothing attributes (neutral clothing, men's clothing, women's clothing, mixed men's and women's clothing), grouped and sorted by size (160 / S, 165 / M), and finally sorted by class (G01-C01, G01-C02). This mode is suitable for optimizing scenarios where orders are sorted in batches based on clothing style code combined with clothing attributes, and then distributed individually by class.
[0034] Dynamic adjustment function: The sorting strategy configuration module responds to external instructions (such as administrator operations), adjusts the sorting weights in real time and generates new label sequences without remodeling the data.
[0035] Technical Effects
[0036] 1. Improved sorting accuracy: By associating multi-dimensional data with individual adaptation parameters, sizing errors caused by human misjudgment can be avoided.
[0037] 2. Optimize sorting efficiency: The dynamic sorting engine supports switching sorting paths according to scenarios, reducing the sorter's movement path and shortening operation time. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION
[0039] Example 1: Label generation in institution-priority mode
[0040] 1. Data Modeling:
[0041] Collect biometric data of student Li Si: height 165cm, weight 50kg, shoulder width 38cm;
[0042] The order is associated with the autumn sportswear (clothing attribute code, neutral), style code "long-sleeved hooded"), and matching size 165 / M.
[0043] Hierarchical framework construction:
[0044] The first-level unit generates clothing style codes (TC-02);
[0045] The second-level unit generates the class code G02-C08 (second grade, class 8);
[0046] The third-level unit binds the student name "Li Si" with the clothing attribute code (neutral);
[0047] The fourth-level unit matches the size 165 / M, associated with the gender identifier F and ergonomic data (such as sleeve length 58 cm).
[0048] 2. Dynamic sorting and label generation:
[0049] If the administrator selects "Institution Priority Mode", the system generates a label sequence according to the hierarchy of TC-02 → G02-C08 → Neutral → 165 / M;
[0050] The visual rendering layer outputs labels, and the label sequence is arranged by clothing style and class, which makes it easier for sorters to distribute by class.
[0051] Example 2: Label generation in clothing attribute priority mode
[0052] 1. Configuration switching: The administrator switches to "clothing style combined with clothing attribute priority mode" through the graphical interface.
[0053] 2. Label reorganization: The system automatically adjusts the label sequence to TC-02 → Neutral → 165 / M → G02-C08, achieving the following results:
[0054] A scenario where all orders for the same clothing style (TC-02) are sorted in batches based on clothing attributes and then distributed one by one by class.
[0055] After grouping by clothing attributes (neutral) and size (165 / M), they are sorted by class to optimize sorting efficiency.
[0056] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.
[0057] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.
Claims
1. A method for generating school uniform sorting labels, characterized in that: include: Step S1: Construct a school uniform multidimensional data association model and establish a database containing student biometric data sets, clothing ordering information parameter sets, and distribution strategy rule base; Step S2: Constructing a hierarchical framework based on the data association model, the framework comprising: A data storage layer dynamically bound to the database; a data processing layer that realizes the logical integration of student identity information, clothing ordering information parameters and distribution strategy; and a visual rendering layer that outputs printable label templates.
2. The method according to claim 1, wherein The hierarchical framework includes: Level 1 data unit: consists of clothing order styles, including clothing style codes; Secondary data unit: an institutional level identifier containing a class code, wherein the class code includes a grade code and a class serial number; Level 3 data unit: contains clothing attribute code and student name field, the clothing attribute code is associated with the style code; Level 4 data unit: contains an individual adaptation parameter group, which integrates clothing size tags, gender identifiers, and ergonomic data, and establishes a cascade relationship through data association fields.
3. The method according to claim 1 or 2, wherein: The label entity generated by the visualization rendering layer forms a one-to-one mapping relationship between the total amount of label generation and the number of clothing items ordered by students; Each label entity comprises an attachable label carrier, wherein the carrier is embedded with student uniform ordering information.
4. The method according to claim 3, wherein The visualization rendering layer is provided with a dynamic sorting engine, which includes: a sorting strategy configuration module, which supports generating a tag sequence according to a preset hierarchical sorting weight, and has: a) Institutional priority mode: Generate weights by hierarchical ranking of clothing style code → class code → clothing attribute code → clothing size individual adaptation parameters; b) Clothing attribute priority mode: Generate hierarchical ranking weights according to clothing style code → clothing attribute code → clothing size individual adaptation parameter → class code; The sorting strategy configuration module dynamically adjusts the sorting strategy in response to the configuration instruction and switches the mode through the configuration interface.