A 3D online self-service packaging system based on artificial intelligence

Abstract

The present invention discloses a 3D online self-service packaging system based on artificial intelligence, which belongs to the field of packaging design technology. It includes a material storage module for establishing a material library and storing component element data of packaging materials; a text input module for inputting text information of packaging requirements; a text analysis module for analyzing text information to obtain an evaluation value; a packaging material generation module for selecting a material generation mode according to the size of the evaluation value, and generating a packaging material set using the selected material generation mode; a material editing module for editing and downloading the component element data of the packaging material selected in the packaging material set. The present invention can automatically generate a packaging material source file that can be edited and downloaded according to the text information input by the user, which provides users with an efficient, flexible and personalized 3D online self-service packaging solution.

Description

Technical Field

[0001] The present invention relates to the technical field of packaging design, and in particular to a 3D online self-service packaging system based on artificial intelligence. Background Art

[0002] With the rapid development of e-commerce and online retail, the packaging industry is undergoing unprecedented changes. The traditional manual packaging design process is cumbersome and inefficient, and it is difficult to meet the market's demand for rapid response and personalized customization. In order to meet this challenge, an online packaging system based on artificial intelligence came into being.

[0003] However, most AI graphics generation tools on the market today tend to focus on quickly generating visually appealing content, while ignoring the user's need for source files and the flexibility of subsequent editing. The traditional packaging material selection process is usually based on the designer's experience and aesthetics, lacking scientific data support and intelligent decision-making. This makes it difficult for designers to quickly and accurately find suitable packaging materials when faced with a large number of goods and ever-changing packaging needs, which in turn affects packaging efficiency and effectiveness. In addition, traditional packaging methods often ignore the refined management and optimization of packaging materials, resulting in low utilization of packaging materials and waste of resources. Summary of the invention

[0004] To solve the above problems, the present invention provides a 3D online self-service packaging system based on artificial intelligence. By integrating artificial intelligence technology, material library resources and data analysis, it can automatically generate editable and downloadable packaging material source files that meet the requirements according to the text information input by the user, providing users with an efficient, flexible and personalized 3D online self-service packaging solution.

[0005] The above objectives can be achieved through the following solutions:

[0006] A 3D online self-service packaging system based on artificial intelligence includes a material storage module, a text input module, a text analysis module, a packaging material generation module and a material editing module. Specifically: the material storage module is used to establish a material library and store component element data of packaging materials, and periodically clean the component element data; the text input module is used to input text information of packaging requirements; the text analysis module is used to analyze the text information to obtain an evaluation value; the packaging material generation module is used to select a material generation mode according to the size of the evaluation value, and generate a packaging material set using the selected material generation mode; the material editing module is used to edit and download the component element data of the packaging material selected from the packaging material set.

[0007] Furthermore, the text analysis module includes: a preprocessing unit and an evaluation unit; the preprocessing unit is used to clean, segment and vectorize the text information to obtain a feature information set; the evaluation unit is used to count the number of feature information in the feature information set and the matching degree between the feature information set and the material library, and calculate an evaluation value.

[0008] Furthermore, the counting of the number of feature information in the feature information set and the degree of matching between the feature information set and the material library to obtain the evaluation value comprises: counting the number of feature information in the feature information set to obtain the number of feature information; counting the number of component element data in the material library having the feature information in the feature information set to obtain the matching value between the feature information set and the material library, and calculating the matching value. ,have:

[0009] ;

[0010] In the formula, is the number of feature information, The material library has the first The number of element data that make up the feature information, is the total number of component element data in the material library; using the number of feature information and the matching value, the evaluation value is calculated. ,have:

[0011] ;

[0012] In the formula, is the expansion factor.

[0013] Furthermore, the packaging material generation module includes an evaluation value judgment unit, a material library generation unit, a hybrid generation unit and an artificial intelligence generation unit; the evaluation value judgment unit is used to judge the size of the evaluation value and a preset first threshold and a preset second threshold; the material library generation unit is used to generate a first material set using a material library generation mode when the evaluation value is greater than the first threshold; the hybrid generation unit is used to generate a second material set using a material library and an artificial intelligence generation mode when the evaluation value is less than or equal to the first threshold and the evaluation value is greater than the second threshold; the artificial intelligence generation unit is used to generate a third material set using an artificial intelligence generation mode when the evaluation value is less than the second threshold.

[0014] Furthermore, the generating the first material set by using the material library generating mode includes: acquiring component element data having characteristic information in the characteristic information set in the material library to obtain a first element set; generating a plurality of packaging materials by using the component element data in the first element set to obtain a first material set.

[0015] Furthermore, the method of generating a second material set using a material library and an artificial intelligence generation mode includes: obtaining component element data having feature information in the feature information set in the material library to obtain a first local element set; using artificial intelligence to generate component element data corresponding to the feature information in the feature information set, and associating the feature information in the feature information set with the corresponding component element data generated by artificial intelligence to obtain a first intelligent element set; integrating the first local element set and the first intelligent element set to obtain a second element set; and using the component element data in the second element set to generate multiple packaging materials to obtain a second material set.

[0016] Furthermore, the method of generating a third material set using an artificial intelligence generation mode includes: generating component element data corresponding to the feature information in the feature information set using artificial intelligence, and associating the feature information in the feature information set with the corresponding component element data generated by artificial intelligence to obtain a third element set; generating multiple packaging materials using the component element data in the third element set to obtain a third material set.

[0017] Furthermore, the material editing module includes: a search unit, an editing unit, a storage and downloading unit; the search unit is used to input search information to search and generate new component element data when editing the component element data of the generated packaging material to obtain a fourth element set; the editing unit is used to edit the component element data of the currently selected packaging material; the editing unit is also used to add the component element data in the fourth element set to the currently selected packaging material for editing; the storage and downloading unit is used to save the component element data in the currently edited packaging material after a preset time interval; the storage and downloading unit is also used to download the saved component element data of the packaging material.

[0018] Furthermore, the input search information searches and generates new component element data to obtain a fourth element set, including: cleaning, segmenting and vectorizing the input search information to obtain a search feature set; obtaining the component element data in the material library that has the feature information in the search feature set to obtain a second local element set; using artificial intelligence to generate component element data corresponding to the feature information in the search feature set, and associating the feature information in the search feature set with the corresponding component element data generated by artificial intelligence to obtain a second intelligent element set; integrating the second local element set and the second intelligent element set to obtain a fourth element set.

[0019] Furthermore, the storage and downloading unit also includes: when saving the component element data in the currently edited packaging material, obtaining the number of characteristic information; judging whether the number of characteristic information is greater than a preset third threshold; if so, storing the component element data generated by artificial intelligence in the saved packaging material into the material library; if not, calculating the occurrence rate of each characteristic information possessed by each component element data in the saved packaging material; associating the characteristic information corresponding to the occurrence rate greater than the preset fourth threshold with each component element data generated by artificial intelligence, and storing them in the material library; wherein, the characteristic information associated with the component element data will not be repeated.

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] 1. The present invention can automatically generate packaging materials that meet user needs by integrating artificial intelligence technology, material library resources and data analysis, greatly improving the efficiency of packaging design; compared with the traditional manual packaging design process, the present invention significantly reduces time and labor costs; the system supports users to edit and recombine the generated packaging materials according to actual needs, thereby meeting the user's personalized customization needs; this flexibility enables users to easily obtain packaging design solutions that are both in line with the brand image and unique;

[0022] 2. The present invention can generate highly personalized packaging materials based on the text information input by the user through in-depth text analysis and material generation mode selection; this means that each user can obtain a unique packaging design, thereby better meeting the diversified needs of the market;

[0023] 3. The system uses data analysis methods to quantitatively evaluate the user's text information and intelligently selects the material generation mode based on the evaluation results. This data-driven and intelligent decision-making approach makes packaging design more scientific and accurate, avoiding the subjectivity of traditional design based on designer experience and aesthetics.

[0024] 4. The present invention improves the utilization rate of materials and the response speed of the system by continuously updating and optimizing the material library; the system can automatically store the data of the constituent elements in the packaging materials that have been edited by the user and meet specific conditions into the material library, thereby continuously enriching the content of the material library and providing more choices and references for subsequent packaging design; at the same time, this optimization also reduces resource waste and improves the overall efficiency of the system.

[0025] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 It is a framework diagram of an artificial intelligence-based 3D online self-service packaging system according to an embodiment of the present invention.

[0028] Figure 2 It is a structural schematic diagram of an artificial intelligence-based 3D online self-service packaging system according to an embodiment of the present invention.

[0029] Figure 3 It is an execution flow chart of an artificial intelligence-based 3D online self-service packaging system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0031] Reference Figure 1 An embodiment of the present invention proposes an artificial intelligence-based 3D online self-service packaging system, which can automatically generate editable and downloadable packaging material source files that meet the requirements according to the text information input by the user by integrating artificial intelligence technology, material library resources and data analysis, thereby providing users with an efficient, flexible and personalized 3D online self-service packaging solution.

[0032] The system of this embodiment specifically includes: a material storage module, a text input module, a text analysis module, a packaging material generation module and a material editing module, specifically:

[0033] The material storage module is used to establish a material library and store the component element data of the packaging material, and periodically clean the component element data;

[0034] The text input module is used to input text information of packaging requirements;

[0035] The text analysis module is used to analyze the text information to obtain an evaluation value;

[0036] The packaging material generation module is used to select a material generation mode according to the size of the evaluation value, and generate a packaging material set using the selected material generation mode;

[0037] The material editing module is used to edit and download the component element data of the packaging material selected from the packaging material set.

[0038] Specifically, the material storage module is used to establish a dynamically updated material library, store rich packaging material composition element data, and provide users with a variety of choices; the text input module allows users to input text information describing packaging needs, providing a basis for subsequent text analysis and material generation; the text analysis module conducts in-depth analysis of the input text information, evaluates user needs and preferences, and provides a basis for subsequent material generation mode selection; the packaging material generation module intelligently selects the material generation mode based on the results of the text analysis, generates a packaging material set that meets user needs, and ensures that the generated materials have source file support; the packaging material editing module provides flexible editing functions, allowing users to deeply edit the generated packaging materials and recombine them after cutting to meet the user's personalized customization needs; by integrating artificial intelligence technology, material library resources and data analysis, the automation, personalization and efficiency of packaging design are realized; at the same time, the system also integrates multiple modes such as material library generation, artificial intelligence generation and hybrid generation, and flexibly selects the most appropriate generation method according to the complexity and specificity of user needs to ensure that the generated packaging materials meet user expectations and are highly innovative and practical.

[0039] Furthermore, if Figure 2 As shown, the text analysis module includes: a preprocessing unit and an evaluation unit;

[0040] The preprocessing unit is used to clean, segment and vectorize the text information to obtain a feature information set;

[0041] Specifically, the preprocessing unit is the first part of the text analysis module, which is mainly responsible for preprocessing the text information input by the user for subsequent analysis and evaluation; cleaning is used to remove irrelevant characters and noise in the text information, such as punctuation marks, special symbols, blank characters, etc. The purpose of cleaning is to make the text information cleaner and more regular for subsequent processing; word segmentation is to split the cleaned text information into independent words; since Chinese text does not have spaces as natural separators for words like English, special word segmentation processing is required; the result of word segmentation directly affects the subsequent feature extraction and evaluation; vectorization processing is to convert the words obtained by word segmentation into a numerical form that can be processed by a computer, that is, a feature vector; this process can use technologies such as word frequency statistics, TF-IDF (term frequency-inverse document frequency) calculation, and word embedding (such as Word2Vec); after vectorization processing, each word is mapped to a point in a high-dimensional space, so that the computer can perform operations and comparisons on the words.

[0042] The evaluation unit is used to count the number of feature information in the feature information set and the matching degree between the feature information set and the material library, and calculate an evaluation value.

[0043] Specifically, the evaluation unit is the second part of the text analysis module, which is mainly responsible for statistics and analysis of the feature information set obtained by the preprocessing unit to calculate an evaluation value; this evaluation value will be used for the selection of subsequent packaging material generation mode; different evaluation value ranges may correspond to different material generation modes, thereby realizing personalized packaging material generation.

[0044] Furthermore, the counting of the number of feature information in the feature information set and the matching degree between the feature information set and the material library to calculate the evaluation value includes:

[0045] Counting the number of characteristic information in the characteristic information set to obtain the number of characteristic information;

[0046] Count the number of constituent element data of the feature information in the feature information set in the material library, calculate the matching value between the feature information set and the material library, and ,have:

[0047] ;

[0048] In the formula, is the number of feature information, The material library has the first The number of element data that make up the feature information, is the total number of component element data in the material library;

[0049] Specifically, the matching value is the average matching degree of each feature in the feature information set in the material library; if there are many component element data in the material library that contain the features in the feature information set, then the matching value will be higher.

[0050] The evaluation value is calculated using the feature information quantity and the matching value. ,have:

[0051] ;

[0052] In the formula, is the expansion factor.

[0053] Specifically, the evaluation value is obtained by comprehensively considering the matching value and the amount of feature information. The higher the matching value and the more appropriate the amount of feature information, the higher the evaluation value will be. The introduction of makes the evaluation value amplified, thereby amplifying subtle changes in the evaluation value and making the adjustment more flexible.

[0054] For example, the text information input by the user is preprocessed to obtain 5 feature information ( =5); There are 1000 component element data in the material library ( =1000); In the material library, there are 20 component element data containing the first feature information ( = 20, 15 contain the second feature information ( = 15), 15 contain the third feature information ( =15), 25 contain the fourth feature information ( =25), the fifth feature information contains 10 component element data (Y5=10); the expansion coefficient Set to 1, that is, no additional amplification; first calculate the matching value to get =0.17, and then calculate the evaluation value to get =1.034; this value will be used to select the subsequent packaging material generation mode. Depending on the system design, different evaluation value ranges may correspond to different material generation strategies; at the same time, since the evaluation value involves 3 decimal places, the expansion coefficient needs to be increased in actual applications to make the changes in the evaluation value more obvious.

[0055] Furthermore, if Figure 2 As shown, the packaging material generation module includes an evaluation value judgment unit, a material library generation unit, a hybrid generation unit and an artificial intelligence generation unit;

[0056] The evaluation value judgment unit is used to judge the difference between the evaluation value and a preset first threshold and a preset second threshold;

[0057] Specifically, the evaluation value judgment unit is responsible for receiving the evaluation value from the text analysis module and comparing it with the preset first threshold and second threshold; these two thresholds are preset according to system design and user needs, and are used to judge the size range of the evaluation value, so as to decide which material generation mode to adopt; the packaging material generation module can intelligently select the appropriate material generation mode according to the evaluation value input by the user, so as to provide users with efficient, flexible and personalized 3D online self-service packaging solutions.

[0058] The material library generation unit is configured to generate a first material set by using a material library generation mode when the evaluation value is greater than the first threshold;

[0059] Specifically, if the evaluation value is greater than the first threshold, it indicates that the text information input by the user has a high degree of match with the material library and the text has less feature information. In this case, the existing elements in the material library can be directly used to generate packaging materials to improve efficiency and reduce the use of computing resources.

[0060] The hybrid generation unit is configured to generate a second material set by using a material library and an artificial intelligence generation mode when the evaluation value is less than or equal to the first threshold and the evaluation value is greater than the second threshold;

[0061] Specifically, if the evaluation value is less than or equal to the first threshold but greater than the second threshold, it indicates that the match is moderate and it may be necessary to combine elements in the material library and new elements generated by artificial intelligence to meet the user's specific needs. At this time, the hybrid generation mode will be adopted to balance efficiency and innovation.

[0062] The artificial intelligence generation unit is used to generate a third material set using an artificial intelligence generation mode when the evaluation value is less than the second threshold.

[0063] Specifically, if the evaluation value is less than the second threshold, it indicates that the matching degree is low, or the text information input by the user is relatively complex and contains more feature information. In this case, it is necessary to rely more on artificial intelligence generation mode to create new packaging materials to meet the unique needs of users; this unit uses deep learning, generative adversarial networks and other advanced technologies to generate new packaging material elements based on the feature information input by the user, and combine them into a complete packaging material set; this mode is suitable for user needs with low matching degree with the material library or complex feature information, and can give full play to the creativity and flexibility of artificial intelligence.

[0064] Further, the generating the first material set by using the material library generating mode includes:

[0065] Acquire component element data having characteristic information in the characteristic information set in the material library to obtain a first element set;

[0066] Specifically, the system will retrieve component element data that matches the feature information set input by the user from the established material library; these feature information are usually obtained after preprocessing, segmenting and vectorizing the user's text requirements through the text analysis module; the system will search for component element data containing these feature information in the material library based on these feature information, and extract them to form a new set, namely the first element set; the key to this step is to ensure that the retrieved component element data is highly relevant to the user's needs; the system achieves this by comparing the feature information set with the feature information of each component element data in the material library, thereby ensuring that the elements in the first element set can meet the user's packaging needs.

[0067] A plurality of packaging materials are generated using the component element data in the first element set to a first material set.

[0068] Specifically, after obtaining the first element set, the system will then use these component element data to generate multiple packaging materials; this process may involve combining, arranging, adjusting the size or color of the component element data to form packaging materials that meet user needs; the system will automatically generate multiple packaging material solutions based on preset packaging material generation rules or algorithms, as well as the user's specific needs (such as packaging size, shape, style, etc.); the material library generation mode can effectively generate a packaging material set that meets the needs based on the feature information input by the user; this mode is particularly suitable for user needs that have a high degree of match with the material library and relatively simple feature information, because it can make full use of existing resources in the material library and quickly respond to user packaging needs.

[0069] For example, suppose the user is a designer of an e-commerce company and needs to design a packaging box for a new product that is about to be launched on the market; the user enters the following text information: "I need a paper packaging box with a simple design, blue as the main color, and a brand logo"; after analysis, the system extracts feature information such as "simple design", "blue as the main color", "brand logo", and "paper quality"; then, the system searches the material library for some component element data that matches these feature information, such as simple style patterns, blue gradient backgrounds, brand logo images provided by users, and paper textures; next, the system uses these element data to generate multiple packaging material solutions; one of the solutions is a packaging box with blue as the main color, printed with the brand logo provided by the user and simple style decorative patterns, and the overall design is simple and elegant, meeting the needs of users; users can preview these generated packaging materials in the system, and select the most satisfactory one for subsequent editing and downloading, and finally use it for product packaging design.

[0070] Further, the generating the second material set by using the material library and the artificial intelligence generation mode includes:

[0071] Acquire component element data having characteristic information in the characteristic information set in the material library to obtain a first local element set;

[0072] Specifically, the system will extract a set of feature information from the text information input by the user. This feature information may include packaging-related attributes such as color, shape, pattern, and material. Then, the system will search and obtain the component element data containing this feature information in the material library to form a first local element set. These local element sets are elements that already exist in the material library and match user needs, and can be directly used to generate packaging materials.

[0073] Generate component element data corresponding to the feature information in the feature information set by using artificial intelligence, and associate the feature information in the feature information set with the corresponding component element data generated by artificial intelligence to obtain a first intelligent element set;

[0074] Specifically, the system will use artificial intelligence technologies, such as deep learning, generative adversarial networks, etc., to generate new component element data based on a set of feature information; these newly generated element data are created based on artificial intelligence algorithms that learn and analyze large amounts of data, and are highly innovative and personalized; the system will associate these newly generated element data with corresponding feature information to form a first intelligent element set.

[0075] Integrate the first local element set and the first intelligent element set to obtain a second element set;

[0076] Specifically, after obtaining the first local element set and the first intelligent element set, the system will integrate them to form a richer second element set; this set includes both local elements already available in the material library and intelligent elements newly generated by artificial intelligence, providing users with more choices and possibilities.

[0077] A plurality of packaging materials are generated using the component element data in the second element set to obtain a second material set.

[0078] Specifically, the system will use the data of the constituent elements in the second element set to generate multiple packaging material solutions through combination, arrangement, adjustment and other operations; these solutions not only reflect the classic and practical nature of the existing elements in the material library, but also incorporate the novelty and individuality of the elements generated by artificial intelligence, thus forming a diverse second material set for users to choose from.

[0079] For example, suppose that the user is a designer of a fashion brand and needs to design a packaging box for a newly launched clothing product; the user enters the following text information: "I need a packaging box with a modern design, gold and black as the main colors, and abstract patterns"; after analysis, the system extracts feature information such as "modern design", "gold and black as the main colors", and "abstract patterns"; then, the system searches for some local elements that match these feature information in the material library, such as gold lines, black background, and some simple pattern elements, to form a first local element set; the system uses artificial intelligence to generate some smart elements corresponding to the feature information of "abstract pattern", such as some unique, artistic abstract graphics; these smart elements are associated with the feature information to form a first smart element set; next, the system integrates the local element set and the smart element set to obtain a second element set containing rich elements; then, the system uses these elements to generate multiple packaging material solutions, one of which is a packaging box design with a black background, gold lines outlining the brand logo, and an abstract pattern generated by artificial intelligence as decoration; this solution not only reflects the brand's sense of modernity, but also increases its uniqueness and attractiveness through the pattern generated by artificial intelligence. This balances the efficiency of utilizing existing material library resources and the innovation generated by artificial intelligence to meet users' diverse packaging needs.

[0080] Further, the generating the third material set by using the artificial intelligence generation mode includes:

[0081] Generate component element data corresponding to the feature information in the feature information set by using artificial intelligence, and associate the feature information in the feature information set with the corresponding component element data generated by artificial intelligence to obtain a third element set;

[0082] Specifically, the system will deeply analyze the text information input by the user and extract a set of key feature information, which may cover multiple aspects such as color, shape, pattern, material, style, etc.; then, the system will use advanced artificial intelligence technology, such as deep learning, generative adversarial network (GAN), neural style transfer, etc., to generate new component element data that is highly matched with user needs based on these feature information; these element data are created entirely based on artificial intelligence algorithms and are highly innovative and personalized; the system will associate each feature information in the feature information set with the corresponding component element data generated by artificial intelligence to ensure that the generated elements can accurately reflect user needs; in this way, a third element set containing rich and unique elements is obtained.

[0083] A plurality of packaging materials are generated using the component element data in the third element set to obtain a third material set.

[0084] Specifically, the system will use the data of the constituent elements in the third element set to generate multiple packaging material solutions that meet user needs through intelligent combination, arrangement, resizing, color matching and other operations; these solutions are completely based on elements generated by artificial intelligence, and therefore can demonstrate extremely high creativity and personalization; the system can use a variety of algorithms and strategies to generate these packaging materials, such as rule-based generation, template-based generation, genetic algorithm-based generation, etc., to ensure that the generated materials not only meet user needs but also have diversity and novelty; the system will combine these generated packaging material solutions into a set, namely the third material set, for users to select and use.

[0085] For example, suppose the user is a designer at a high-end gift company and needs to design a packaging box for a customized high-end gift. The user enters the following text information: "I need a packaging box with a unique design, dark blue and gold as the main colors, Chinese classical elements (such as dragon and phoenix patterns), and a company logo." After in-depth analysis, the system extracts key feature information such as "unique design," "dark blue and gold as the main colors," "Chinese classical elements (dragon and phoenix patterns)," and "company logo." Then, the system uses artificial intelligence technology, such as deep learning and generative adversarial networks, to generate new component element data based on these feature information, such as a unique dark blue and gold gradient. Changing backgrounds, delicate dragon and phoenix pattern elements, and decorative elements that are coordinated with the company's logo style; next, the system uses these generated element data to generate multiple high-end and unique packaging material solutions through intelligent combination and arrangement; one of the solutions may be a packaging box design with dark blue and gold as the main colors, with exquisite dragon and phoenix pattern elements incorporated on the box surface, and the company logo cleverly integrated into it. The overall design reflects the charm of Chinese classical culture and showcases the company's high-end brand image; users can preview these generated packaging material solutions in the system and choose the most satisfactory one for subsequent editing and customization, and finally get a packaging box design that meets their needs.

[0086] Furthermore, if Figure 2 As shown, the material editing module includes: a search unit, an editing unit, a storage and downloading unit;

[0087] The search unit is used to input search information to search and generate new component element data when editing the component element data of the generated packaging material, so as to obtain a fourth element set;

[0088] Specifically, the main function of the search unit is to provide a convenient way to search and generate new component element data when the user edits the component element data of the generated packaging material; when the user needs to add or replace certain elements, the search function can be triggered by entering search information (such as keywords, colors, styles, etc.); the system will search for relevant element data in the internal database or external resources based on the user's search request, and generate a fourth element set containing these new elements.

[0089] For example, if the user is editing a spring-themed packaging box design but finds that the existing flower elements are not rich enough, the user can enter keywords such as "spring flowers" through the search unit, and the system will then search and display element data of various spring flowers for the user to choose from; the user can choose appropriate ones from these new elements and add them to their own packaging materials to further enrich and improve the design.

[0090] The editing unit is used to edit the component element data of the currently selected packaging material; the editing unit is also used to add the component element data in the fourth element set to the currently selected packaging material for editing;

[0091] Specifically, the editing unit is the core part of the material editing module. It provides rich editing tools and functions, allowing users to perform various editing operations on the component element data in the currently selected packaging material; these operations may include adjusting the size, position, color, transparency of the elements, adding or deleting elements, and applying various special effects and filters; in addition, the editing unit also supports users to add new elements from the fourth element set generated by the search unit to the currently selected packaging material, and perform further editing and customization.

[0092] The storage and downloading unit is used to save the component element data of the currently edited packaging material after a preset time interval; the storage and downloading unit is also used to download the saved component element data of the packaging material.

[0093] Specifically, the storage and downloading unit is responsible for automatically saving the data of the component elements in the packaging material currently edited by the user after a preset time interval to ensure that the user's work will not be lost due to unexpected circumstances; in addition, the unit also provides a download function, allowing the user to download the saved packaging material data to a local computer or mobile device after editing is completed for subsequent use and sharing.

[0094] Furthermore, the input search information searches and generates new component element data, and the fourth element set obtained includes:

[0095] Clean, segment and vectorize the input search information to obtain a search feature set;

[0096] Specifically, the system will clean the search information input by the user, remove insignificant characters, spaces, punctuation marks, etc., to ensure the purity of the information; then, the system will perform word segmentation on the cleaned search information, cutting continuous text strings into meaningful words or phrases; finally, the system will convert the segmented results into vector form for subsequent similarity calculation and search; this usually involves mapping each word or phrase to a vector in a high-dimensional space, where each dimension of the vector represents a specific feature or attribute.

[0097] Acquire component element data having feature information in the search feature set in the material library to obtain a second local element set;

[0098] Specifically, the system will search the material library for component element data with these characteristic information based on the obtained search feature set; for example, if the search feature set contains characteristic information such as "red", "circle" and "pattern", the system will search the material library for all element data with these characteristic information to form a second local element set.

[0099] Generate component element data corresponding to the feature information in the search feature set using artificial intelligence, and associate the feature information in the search feature set with the corresponding component element data generated by artificial intelligence to obtain a second intelligent element set;

[0100] Specifically, the system will also use artificial intelligence technology to generate new component element data based on the search feature set; these newly generated element data are based on the feature information in the search feature set, obtained through algorithm innovation and creation, and are highly matched with user needs; the system will associate these newly generated element data with the corresponding search feature information to form a second intelligent element set.

[0101] The second local element set and the second intelligent element set are integrated to obtain a fourth element set.

[0102] Specifically, the system will integrate the second local element set and the second intelligent element set to obtain a fourth element set containing rich and diverse elements; this set includes both element data already available in the material library that matches user needs, and element data newly generated by artificial intelligence that is innovative and personalized.

[0103] Furthermore, the storage and downloading unit also includes:

[0104] When saving the component element data in the currently edited packaging material, the quantity of the characteristic information is obtained;

[0105] Specifically, when a user finishes editing a packaging material and prepares to save it, the storage and downloading unit will first calculate the number of feature information that constitutes the element data in the material; this value reflects the richness and complexity of the element data in the material.

[0106] Determining whether the amount of feature information is greater than a preset third threshold;

[0107] If yes, the component element data generated by artificial intelligence in the saved packaging material is stored in the material library;

[0108] Specifically, the system will determine whether the amount of feature information is greater than a preset third threshold; this threshold is set according to system design and user needs, and is used to distinguish hidden features of component element data; if the amount of feature information is greater than the third threshold, it means that the component element data generated by artificial intelligence selected by the user is consistent with the description of the text information content originally input by the user, but there is too much feature information, and it cannot be confirmed which feature information is more consistent. Therefore, the component element data only needs to have the feature information associated with the second intelligent element set, and it can be directly stored in the material library without additionally associating feature information.

[0109] If not, then calculate the occurrence rate of each characteristic information of each component element data in the saved packaging material;

[0110] The feature information corresponding to the occurrence rate greater than the preset fourth threshold is respectively associated with each component element data generated by artificial intelligence, and stored in the material library;

[0111] Among them, the characteristic information associated with the component element data will not be repeated.

[0112] Specifically, if the number of feature information is not greater than the third threshold, it means that the feature information initially input by the user contains relatively broad feature information. The system will then further analyze the occurrence rate of the feature information of each component element data in the packaging material. For those feature information whose occurrence rate is greater than the preset fourth threshold, the system will consider them to be representative, indicating that the feature information whose occurrence rate is greater than the preset fourth threshold is the hidden feature information of the component element data generated by artificial intelligence in the final packaging material. Therefore, the system will associate these feature information with each relevant component element data generated by artificial intelligence, and store these associated element data in the material library.

[0113] For example, when using the 3D online self-service packaging system, the user decides to design a packaging material with the theme of "love"; the user selects some components generated by artificial intelligence through the system interface, such as roses, rings, heart patterns, etc., and makes appropriate edits and adjustments; when the user completes the editing and is ready to save the packaging material, the storage and download unit starts working. The storage and download unit first calculates the number of characteristic information of the component element data in the "love" theme packaging material. For example, if it is 2 (less than the third threshold 4), the system will think that the characteristic information contained in the text information "love" initially input by the user is relatively broad, and the system will further analyze the various components in the packaging material. The occurrence rate of the characteristic information of the data, assuming that the occurrence rate of the two characteristic information "romance" and "love" is greater than the fourth threshold, then the system will associate the two characteristic information "romance" and "love" with roses, rings, and love patterns respectively, that is, roses can also represent "love" and "romance"; the system will store these component element data (that is, roses, rings, and love patterns) into the material library; if the characteristic information associated with the component element data "roses" are "red", "flowers", and "love", the system will associate the two characteristic information "romance" and "love" with the component element data "roses", and the characteristic information of the component element data "roses" will be "red", "flowers", "love", and "romance", and there will be no two characteristic information "love".

[0114] It should be noted that the feature information mentioned in the embodiments of the present invention is vectorized text information. For ease of understanding and explanation, the feature information is described in words, but it does not necessarily represent the information processing process in the actual processing process.

[0115] For example, Figure 3As shown, the user inputs text information describing packaging requirements through the text input module of the system; the text analysis module cleans, segments and vectorizes the input text information to obtain a feature information set; the evaluation unit counts the number of feature information in the feature information set and the matching degree between the feature information set and the material library, and calculates the evaluation value; the packaging material generation module selects a corresponding material generation mode according to the evaluation value. If the evaluation value is greater than a preset first threshold, the material library generation mode is used to generate a first material set. If the evaluation value is less than or equal to the first threshold but greater than a preset second threshold, the material library and the artificial intelligence generation mode are used to generate a second material set. If the evaluation value is less than the second threshold, the artificial intelligence generation mode is used entirely to generate a third material set. According to the selected material generation mode, multiple material sets that meet the user's needs are generated. The system can generate packaging materials required by users through the material editing module, including adjusting the size, position, color, etc. of the elements; during the editing process, users can also enter search information, search and generate new component element data, and further enrich the packaging materials; after editing, users can download the edited packaging material source file for subsequent use; when saving the packaging materials edited by users, the system will decide whether to store the newly generated component element data in the material library based on the number of characteristic information and the occurrence rate of characteristic information in the component element data in the material. This step helps to continuously update and optimize the material library and improve the efficiency and accuracy of the system; the system can automatically generate packaging materials that meet the requirements according to the text requirements of users, and meet the personalized customization needs of users through editing and downloading functions. At the same time, the system also improves the utilization rate of materials and the response speed of the system by continuously updating and optimizing the material library.

[0116] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean direct connection of the lines, and the indirect connection mode can be applied to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention.

[0117] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present invention. This application is intended to cover any variation, use or adaptive change of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the art that are not described in the present invention.

Claims

1. A 3D online self-service packaging system based on artificial intelligence, characterized in that: The system includes: a material storage module, a text input module, a text analysis module, a packaging material generation module and a material editing module, specifically: The material storage module is used to establish a material library and store the component element data of the packaging material, and periodically clean the component element data; The text input module is used to input text information of packaging requirements; The text analysis module is used to analyze the text information to obtain an evaluation value; The packaging material generation module is used to select a material generation mode according to the size of the evaluation value, and generate a packaging material set using the selected material generation mode; The material editing module is used to edit and download the component element data of the packaging material selected from the packaging material set; Wherein, the text analysis module includes: a preprocessing unit and an evaluation unit; The preprocessing unit is used to clean, segment and vectorize the text information to obtain a feature information set; The evaluation unit is used to count the number of feature information in the feature information set and the matching degree between the feature information set and the material library, and calculate an evaluation value; The packaging material generation module includes an evaluation value judgment unit, a material library generation unit, a hybrid generation unit and an artificial intelligence generation unit; The evaluation value judgment unit is used to judge the difference between the evaluation value and a preset first threshold and a preset second threshold; The material library generation unit is configured to generate a first material set by using a material library generation mode when the evaluation value is greater than the first threshold; The hybrid generation unit is configured to generate a second material set by using a material library and an artificial intelligence generation mode when the evaluation value is less than or equal to the first threshold and the evaluation value is greater than the second threshold; The artificial intelligence generation unit is used to generate a third material set using an artificial intelligence generation mode when the evaluation value is less than the second threshold; The evaluation unit calculates the evaluation value specifically including: Counting the number of characteristic information in the characteristic information set to obtain the number of characteristic information; Count the number of constituent element data of the feature information in the feature information set in the material library, calculate the matching value between the feature information set and the material library, and ,have: ； In the formula, is the number of feature information, The material library has the first The number of element data that make up the feature information, is the total number of component element data in the material library; The evaluation value is calculated using the feature information quantity and the matching value. ,have: ； In the formula, is the expansion factor.

2. The artificial intelligence-based 3D online self-service packaging system according to claim 1, characterized in that: The generating the first material set by using the material library generating mode comprises: Acquire component element data having characteristic information in the characteristic information set in the material library to obtain a first element set; A plurality of packaging materials are generated using the component element data in the first element set to obtain a first material set.

3. The artificial intelligence-based 3D online self-service packaging system according to claim 1, characterized in that: The generating the second material set by using the material library and the artificial intelligence generating mode comprises: Acquire component element data having characteristic information in the characteristic information set in the material library to obtain a first local element set; Generate component element data corresponding to the feature information in the feature information set by using artificial intelligence, and associate the feature information in the feature information set with the corresponding component element data generated by artificial intelligence to obtain a first intelligent element set; Integrate the first local element set and the first intelligent element set to obtain a second element set; A plurality of packaging materials are generated using the component element data in the second element set to obtain a second material set.

4. The artificial intelligence-based 3D online self-service packaging system according to claim 1, characterized in that: The generating of the third material set by using the artificial intelligence generating mode includes: Generate component element data corresponding to the feature information in the feature information set by using artificial intelligence, and associate the feature information in the feature information set with the corresponding component element data generated by artificial intelligence to obtain a third element set; A plurality of packaging materials are generated using the component element data in the third element set to obtain a third material set.

5. The artificial intelligence-based 3D online self-service packaging system according to claim 1, characterized in that: The material editing module includes: a search unit, an editing unit, a storage and downloading unit; The search unit is used to input search information to search and generate new component element data when editing the generated component element data of the packaging material to obtain a fourth element set; The editing unit is used to edit the component element data of the currently selected packaging material; the editing unit is also used to add the component element data in the fourth element set to the currently selected packaging material for editing; The storage and downloading unit is used to save the component element data of the currently edited packaging material after a preset time interval; the storage and downloading unit is also used to download the saved component element data of the packaging material.

6. The artificial intelligence-based 3D online self-service packaging system according to claim 5, characterized in that: The input search information searches and generates new component element data, and the fourth element set obtained includes: Clean, segment and vectorize the input search information to obtain a search feature set; Acquire component element data having feature information in the search feature set in the material library to obtain a second local element set; Generate component element data corresponding to the feature information in the search feature set using artificial intelligence, and associate the feature information in the search feature set with the corresponding component element data generated by artificial intelligence to obtain a second intelligent element set; The second local element set and the second intelligent element set are integrated to obtain a fourth element set.

7. The artificial intelligence-based 3D online self-service packaging system according to claim 6, characterized in that: The storage and downloading unit also includes: When saving the component element data in the currently edited packaging material, the quantity of the characteristic information is obtained; Determining whether the amount of feature information is greater than a preset third threshold; If yes, the component element data generated by artificial intelligence in the saved packaging material is stored in the material library; If not, then calculate the occurrence rate of each characteristic information of each component element data in the saved packaging material; The feature information corresponding to the occurrence rate greater than the preset fourth threshold is respectively associated with each component element data generated by artificial intelligence, and stored in the material library; Among them, the characteristic information associated with the component element data will not be repeated.

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