An intelligent exhibition stand design method and system

Abstract

The application discloses a kind of intelligent booth design methods, including receiving user input booth design requirement information, the design requirement information includes structured parameter and natural language description;The natural language description is analyzed semantically, and key semantic entity is identified and extracted;At least one quantization design constraint parameter is mapped to the key semantic entity identified based on pre-constructed design semantic material library;The at least one design constraint parameter obtained by mapping is fused with the structured design parameter input by user through interface selection, and booth design scheme is generated.The application fundamentally solves the problems of requirement distortion and high communication cost in traditional booth design by converting the natural language description of exhibitor into structured and quantifiable design constraint parameters and fusing with the structured parameters input by user, thereby greatly reducing communication cost and time loss, and laying a solid foundation for subsequent accurate and efficient booth design scheme generation.

Description

Technical Field

[0001] This invention relates to the field of intelligent interactive technology, and in particular to an intelligent exhibition booth design method and system. Background Technology

[0002] Booth design is a key element for exhibitors to successfully attract visitors, convey brand information, and achieve business goals.

[0003] Currently, mainstream booth design is usually based on communication between exhibitors and designers. In this process, exhibitors need to describe their vague and emotional needs in natural language, while designers rely on personal experience, subjective understanding, and reference to past cases to transform these non-standardized descriptions into concrete design drafts.

[0004] This traditional model, heavily reliant on natural language communication and the designer's personal experience, suffers from a core problem: severely distorted communication of requirements and uncontrollable high communication costs. Specifically, this manifests in two ways: First, there is often a significant discrepancy between the client's and the designer's understanding of the same terms, and this subjective difference is difficult to align and quantify before a solution is presented. Second, due to a lack of structured guidance tools, clients are typically unable to systematically and completely express their true thoughts in the initial stages, causing the designer's preliminary solutions to easily deviate from potential expectations. The direct consequence of these problems is a high degree of uncertainty and iterative nature in the design process. Initial solutions completed by designers based on incomplete or ambiguous information are highly likely to be rejected or require significant revisions when presented to exhibitors, thus trapping the designer in a multi-cycle cycle of "design-feedback-revision." Each cycle is accompanied by substantial time consumption, increased manpower costs, and a erosion of patience for both parties.

[0005] A content production system for exhibition engineering based on AIGC technology, with authorization announcement number CN117115843B, includes: a creative input unit, which is a platform for inputting creative design information; a creative production unit, which identifies the creative design information and processes it to generate initial material images; an effect evaluation unit, which corrects the initial material images and generates corrected material; a creative adjustment unit, which performs detail adjustments on the corrected material; and a server, which is connected to the creative input unit, the creative production unit, and the creative adjustment unit, and the server transmits data information to the connected units and outputs the final material.

[0006] However, while the aforementioned existing technologies have introduced AIGC technology for the generation of materials in exhibition projects, their core focus is on the automated production and optimization of creative materials, such as the generation, correction, and adjustment of image materials, without deeply addressing the key issue of parameterized definition of requirements in the early stages of booth design.

[0007] Therefore, there is an urgent need in the existing technology for a method and system that can transform exhibitors’ vague and subjective intentions into objective, structured and quantifiable design input parameters. This would allow for the alignment of understanding between the two parties to the greatest extent possible before designers intervene or solutions are generated, avoiding ineffective design iterations caused by misunderstandings of requirements, significantly reducing communication costs and time losses, and laying a solid foundation for the accurate generation of subsequent design solutions. Summary of the Invention

[0008] To address the above issues and overcome the shortcomings of existing technologies, this invention provides a smart booth design method, comprising the following steps:

[0009] A smart booth design method includes the following steps:

[0010] Based on the customer's initial intentions, the designer transforms them into specific booth design requirements and inputs them into the interactive interface of the intelligent booth design system. The design requirements include structured parameters set by the designer based on professional judgment, as well as natural language descriptions that record the customer's initial intentions.

[0011] The intelligent booth design system performs multi-dimensional semantic analysis on the natural language description, including intent recognition, key semantic entity extraction, and quantitative assessment of sentiment level.

[0012] The intelligent booth design system, based on a pre-built design semantic material library, extracts key semantic entities and combines them with intent recognition results and emotional intensity quantitative evaluation results to map them into at least one understandable quantitative design constraint parameter; wherein, each of the quantitative design constraint parameters defines specific technical indicators of booth design in terms of color, material, lighting, spatial form or interaction method.

[0013] The intelligent booth design system will visualize the mapped quantitative design constraint parameters to the designer through the interactive interface.

[0014] Based on the visual presentation, the designer confirms with the customer and submits the final confirmation or correction instructions from the customer through the interactive interface;

[0015] In response to receiving the confirmation command, the intelligent booth design system integrates the finally confirmed quantitative design constraint parameters with the structured design parameters to generate a booth design preview.

[0016] Further preferably, the method further includes: recording interactive operations on the visualization presentation, the interactive operations including: selecting mapping suggestions, correcting design constraint parameters, and expressing preferences for the final design scheme; and updating the association between abstract design concepts, style terms, and specific visualization quantitative design constraint parameters in the design semantic material library based on the recorded data.

[0017] More preferably, the structured parameters include: parameters received through the selection interface from preset options, including: budget range, industry, booth type, design style, functional areas, and design focus.

[0018] More preferably, when the design semantic material library does not contain a mapping relationship between the quantitative design constraint parameters corresponding to the identified key semantic entities:

[0019] Calculate the semantic similarity between the key semantic entity to be matched and each candidate semantic entity in the material library.

[0020] The corresponding quantitative design constraint parameters of several candidate entities with the highest similarity are selected as the mapping results and associated with the key semantic entities to be matched to form several mapping suggestions;

[0021] The mapping suggestions are presented to the user interface, prompting the user to select the closest mapping suggestion.

[0022] Establish a temporary mapping relationship between the key semantic entities confirmed by the user and the corresponding quantitative design constraint parameters, and store them in the temporary extension area of ​​the design semantic material library. This temporary mapping relationship will be called during the subsequent design scheme generation process.

[0023] In a further preferred embodiment, after establishing a temporary mapping relationship between the user-confirmed key semantic entities and the corresponding quantitative design constraint parameters and storing them in the temporary extension area of ​​the design semantic material library, when a natural language description containing the key semantic entities is received again, the temporary mapping relationship is preferentially called from the temporary extension area to map the key semantic entities to the corresponding quantitative design constraint parameters, without having to repeat the similarity calculation and user confirmation steps.

[0024] Further preferably, weights are assigned to each of the key semantic entities obtained based on the sentiment analysis of the natural language description, wherein the sentiment analysis is performed by quantitatively evaluating the positive, negative or neutral sentiment words and their intensity contained in the natural language description.

[0025] More preferably, the method further includes:

[0026] The system provides feedback to users by displaying design constraint parameters or corresponding design examples mapped from the natural language description.

[0027] Receive confirmation or correction instructions from the user regarding the feedback, and adjust the design constraint parameters according to the instructions.

[0028] Further optimization also includes learning steps:

[0029] Record user interactions with the feedback and their preferences for the generated booth design schemes;

[0030] Based on the recorded data, update the association between abstract design concepts, style terms, and specific design parameters in the design semantic material library.

[0031] A second aspect of the present invention provides an intelligent booth design system, the system comprising:

[0032] The input interface is used to receive natural language descriptions of the booth design.

[0033] The natural language parsing unit, connected to the input interface, is used to perform semantic analysis on the natural language description in order to identify design intent and extract key semantic entities;

[0034] The design semantic resource library stores the relationships between abstract design concepts, style terms, and specific design parameters;

[0035] The parameter mapping engine is connected to the natural language parsing unit and the design semantic material library, respectively, and is used to map the identified design intent and / or key semantic entities into at least one quantifiable design constraint parameter.

[0036] The parameter fusion unit is used to fuse the at least one design constraint parameter obtained by mapping with the structured design parameters selected and input by the user through the interface, so as to generate a booth design scheme.

[0037] The interactive feedback unit is used to provide users with visual feedback on the design constraint parameters or design examples mapped from the natural language description, and to receive confirmation or correction instructions from users.

[0038] The learning optimization unit is used to optimize and update the design semantic material library based on the user's historical interactions and solution selection data.

[0039] More preferably, the natural language parsing unit includes:

[0040] An intent recognition subunit is used to determine the appeal category to which the natural language description belongs; and / or

[0041] An entity extraction subunit is used to extract design-related entity words from the natural language description.

[0042] More preferably, the system further includes:

[0043] The interactive feedback unit is used to provide users with visual feedback on the design constraint parameters or design examples mapped from the natural language description, and to receive confirmation or correction instructions from users.

[0044] The learning optimization unit is used to optimize and update the design semantic material library based on the user's historical interactions and solution selection data.

[0045] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the intelligent booth design method as described in any of the preceding claims.

[0046] Compared with existing technologies, this invention fundamentally solves the problems of distorted demand communication and high communication costs in traditional booth design by transforming exhibitors' natural language descriptions into structured and quantifiable design constraint parameters and integrating them with structured parameters input by users. This significantly reduces communication costs and time consumption, laying a solid foundation for the generation of accurate and efficient booth design solutions. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0048] Figure 2 This is a schematic diagram of the system structure of the present invention.

[0049] Figure 3 This is a schematic diagram illustrating the usage process of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] To facilitate understanding of the methods and systems provided in the embodiments of this application, the background of the embodiments of this application will be introduced before introducing the embodiments of this application.

[0052] Booth design is a key element for exhibitors to successfully attract visitors, convey brand information, and achieve business goals.

[0053] Currently, mainstream booth design is usually based on communication between exhibitors and designers. In this process, exhibitors need to describe their vague and emotional needs in natural language, while designers rely on personal experience, subjective understanding, and reference to past cases to transform these non-standardized descriptions into concrete design drafts.

[0054] This traditional model, heavily reliant on natural language communication and the designer's personal experience, suffers from a core problem: severely distorted communication of requirements and uncontrollable high communication costs. Specifically, this manifests in two ways: First, there is often a significant discrepancy between the client's and the designer's understanding of the same terms, and this subjective difference is difficult to align and quantify before a solution is presented. Second, due to a lack of structured guidance tools, clients are typically unable to systematically and completely express their true thoughts in the initial stages, causing the designer's preliminary solutions to easily deviate from potential expectations. The direct consequence of these problems is a high degree of uncertainty and iterative nature in the design process. Initial solutions completed by designers based on incomplete or ambiguous information are highly likely to be rejected or require significant revisions when presented to exhibitors, thus trapping the designer in a multi-cycle cycle of "design-feedback-revision." Each cycle is accompanied by substantial time consumption, increased manpower costs, and a erosion of patience for both parties.

[0055] A content production system for exhibition engineering based on AIGC technology, with authorization announcement number CN117115843B, includes: a creative input unit, which is a platform for inputting creative design information; a creative production unit, which identifies the creative design information and processes it to generate initial material images; an effect evaluation unit, which corrects the initial material images and generates corrected material; a creative adjustment unit, which performs detail adjustments on the corrected material; and a server, which is connected to the creative input unit, the creative production unit, and the creative adjustment unit, and the server transmits data information to the connected units and outputs the final material.

[0056] However, while the aforementioned existing technologies have introduced AIGC technology for the generation of materials in exhibition projects, their core focus is on the automated production and optimization of creative materials, such as the generation, correction, and adjustment of image materials, without deeply addressing the key issue of parameterized definition of requirements in the early stages of booth design.

[0057] Therefore, there is an urgent need in the existing technology for a method and system that can fundamentally revolutionize the initial requirements communication process in booth design. This method and system need to be able to transform exhibitors' vague and subjective intentions into objective, structured, and quantifiable design input parameters. This would allow for maximum alignment of understanding between both parties before designer involvement or solution generation, avoiding ineffective design iterations caused by misunderstandings of requirements, significantly reducing communication costs and time waste, and laying a solid foundation for the accurate generation of subsequent design solutions.

[0058] like Figures 1-3 As shown, this embodiment of the invention provides an intelligent booth design method. This method aims to systematically transform vague, subjective verbal or written descriptions from users, especially non-professional exhibitors, into quantifiable and executable design constraints, combined with their clearly defined structured parameter selections, thereby generating a precise booth design solution. The method includes the following steps S101 to S106:

[0059] S101: The designer receives and understands the client's initial design intentions for the booth;

[0060] The designer transforms the initial intentions into specific booth design requirements through the interactive interface of the intelligent booth design system and inputs them. These design requirements include structured parameters set by the designer based on professional judgment, as well as a natural language description recording the customer's initial intentions.

[0061] The system receives booth design requirements from users through a user interface (such as a web interface or mobile application interface). This information is hybrid, consisting of two parts:

[0062] Structured parameters: These are parameters that users select from a pre-defined list or range of options by clicking, checking, or swiping. These parameters provide users with clear and unambiguous guidance, including at least: Budget range: such as "less than 100,000", "100,000-200,000", "200,000-300,000", "more than 300,000", etc., divided by total amount or cost per unit area.

[0063] Industry category: such as "Technology and Electronics", "Automobile Manufacturing", "Healthcare", "Education and Training", "Fast Moving Consumer Goods Retail", etc., accompanied by industry icons.

[0064] Booth type: Defined according to the booth's location in the exhibition hall and the number of openings, such as "standard opening type (single-sided opening)", "peninsula type (double-sided opening)", "island type (four-sided opening)", etc.

[0065] Design styles: such as "modern minimalism", "industrial style", "technological futurism", "natural ecology", "high-end luxury" and other predefined aesthetic style tags.

[0066] Functional zoning: The booth space is broken down into multiple standard modules for users to confirm their needs, such as "reception area", "core product display area", "interactive experience area", "business negotiation area", "storage area", and "rest area".

[0067] Design Highlights: Offers multiple design priority dimensions for users to choose from, such as "highlighting visual effects", "emphasizing functional practicality", "strictly controlling costs", "prioritizing the use of environmentally friendly materials", and "facilitating quick assembly and disassembly".

[0068] Natural Language Description: Users can describe their emotional and personalized needs that cannot be fully summarized by the above options by entering them in a dedicated text box or via voice input. For example: "I want a warm feeling of home," "I want something memorable and unforgettable," "It should reflect our brand's spirit of exploring the unknown."

[0069] S102: The intelligent booth design system performs multi-dimensional semantic analysis on the natural language description, including intent recognition, key semantic entity extraction, and emotional intensity quantification assessment.

[0070] Deep semantic analysis is performed on the natural language description input by the user. This step is executed by the system's natural language parsing unit and specifically includes:

[0071] Intent recognition: Determine the core category of the user's stated need. For example, for "the warmth of home", the intent is identified as "creating a specific atmosphere"; for "to be unforgettable", the intent is identified as "pursuing visual impact and uniqueness".

[0072] Entity and Attribute Extraction: Using techniques such as named entity recognition and keyword extraction, core words related to the design are extracted from the description. For example, the emotional attribute entity "warmth" is extracted from "warmth"; the abstract concept entities "exploration" and "unknown" are extracted from "exploring the unknown".

[0073] Sentiment and Intensity Analysis: This analyzes the sentiment and intensity modifiers in the descriptions. For example, it identifies "very much hopeful" as a high-intensity positive sentiment and "somewhat" as a low-intensity need. The results of this analysis will be used to assign weights to the mapped design parameters.

[0074] S103: Based on the pre-built design semantic material library, the extracted key semantic entities, combined with the intent recognition results and the emotional degree quantitative evaluation results, are jointly mapped into at least one visual quantitative design constraint parameter that can be understood by the user; wherein, each of the visual quantitative design constraint parameters defines the specific technical indicators of the booth design in terms of color, material, lighting, spatial form or interaction method.

[0075] The system pre-builds a vast design semantic resource library, which is essentially a structured knowledge base that stores the mapping relationships between abstract concepts, style terms, and specific, quantifiable design parameters. This step is completed by the parameter mapping engine:

[0076] Query Mapping: Using key semantic entities extracted from S102 (such as "warmth," "unforgettable," and "exploration") as query keys, the system searches for associated design parameters in the design semantic material library. This material library contains items such as:

[0077] "Warm and Cozy" -> {Main Color Scheme: Warm colors (e.g., around RGB(240, 220, 180)); Materials: Wood, Fabric, Matte Paint; Lighting: 2700K-3500K Warm Yellow Light; Shapes: Curved, Rounded Corners}

[0078] "Unforgettable" -> {Design Goal: Stunning Publicity; Elements: Giant Curved LED Screen, Central Iconic Sculpture, High-Contrast Colors; Technique: Immersive Interactive Installation}

[0079] "Explore" -> {Theme elements: starry sky, path, microscope; interactivity: high; can be set with exploration clues or treasure hunt mechanics}

[0080] Generate derived constraints: Transform the query results into a series of quantifiable derived design constraint parameters. For example, for "warmth", the output parameter set is: {color_palette: "warm", primary_material: ["wood", "fabric"], lighting_temp: 3000K}.

[0081] Weighting: Based on the degree analysis results in S102, assign a weight value to each derived parameter. For example, for the "warmth" that is "very desirable", the associated "warm color tone" weight is set to 0.9 (out of 1.0); for the "technological feel" that is "slightly", the weight of "metallic material" may only be 0.3.

[0082] In a preferred embodiment, when there is no direct mapping relationship between a certain key semantic entity, such as the user-created neologism "cyber Zen," in the semantic material library, the system performs the following steps:

[0083] Semantic similarity calculation: Using a word vector model, calculate the semantic similarity between the unfamiliar entity and all known entities in the material library (such as "technological", "Zen", "minimalist").

[0084] Generate mapping suggestions: Select the top N (e.g., 3) known entities with the highest similarity, and combine their corresponding quantitative design constraint parameters to generate several mapping suggestions for the unfamiliar entity. For example, generate suggestion A (70% "tech-savvy" parameters + 30% "Zen-like" parameters) and suggestion B (60% "tech-savvy" parameters + 40% "simple" parameters).

[0085] User interaction confirmation: Present these mapping suggestions to the user in a visually appealing way (e.g., “Based on your ‘cyber Zen,’ we understand it to be similar to which of the following feelings?” and show style images corresponding to A and B), prompting the user to select the closest one.

[0086] S104: The intelligent booth design system will visualize the mapped quantitative design constraint parameters to the designer through the interactive interface.

[0087] User-confirmed mappings (such as the parameter combination for "Cyber ​​Zen" -> Suggestion A) are stored as temporary mappings in the temporary extension area of ​​the resource library. This mapping can be directly accessed in the current and subsequent sessions. After similar mappings have been verified multiple times, they can be upgraded to permanent knowledge and stored in the main library.

[0088] S105: Based on the visual presentation, the designer confirms with the customer and submits the final confirmation or correction instruction from the customer through the interactive interface.

[0089] S106: In response to receiving the confirmation command, the intelligent booth design system integrates the finally confirmed quantitative design constraint parameters with the structured design parameters to generate a booth design preview.

[0090] Conflict Validation and Optimization: The system will validate the merged parameter set. For example, if the budget in the structured parameters is "less than 100,000", but the derived parameters require "giant curved LED screen" (high cost), the system will trigger optimization rules, either prompting the user that there is a budget conflict or automatically downgrading and recommending "large high-definition light box" as an alternative.

[0091] Driving Solution Generation: This integrated Design Brief will serve as input, driving the solution generation layer (which can be a rule engine, an AIGC model, or a hybrid system combining both) to create solutions. The generated solutions will strictly adhere to the constraints outlined in the brief. For example, within a budget of 200,000-300,000 RMB and the "technology and electronics" industry, a "modern minimalist" style will be adopted, with a focus on creating a "stunning exposure" area (using parametric facades and a central screen), ensuring the "negotiation area" is fully functional, and incorporating a deep blue color scheme reflecting the "exploration" theme.

[0092] Output visualization solutions: Finally, generate one or more complete booth design solution packages, and output them to users in a visual form (such as 3D renderings, walkthrough animations, bill of materials, cost estimates) through the solution presentation module.

[0093] Furthermore, before the final solution is generated, an interactive confirmation step can be included: the system provides the user with feedback on the key design directions mapped by S103 (such as "warmth: warm colors, wood materials") in the form of example images or brief descriptions, asking, "Do you mean something like this direction?" After the user confirms or corrects, the system generates the final solution based on the corrected constraints, ensuring an accurate understanding of the requirements.

[0094] Furthermore, the method includes a learning optimization step: the system continuously records users' choices during the interactive confirmation process, their preferences for different solution versions, and the final solution adoption rate. The learning optimization unit analyzes this behavioral data to optimize the design semantic material library. For example, if a large number of users tend to adjust the mapped "metallic coldness" and increase "wood grain and soft light" when selecting solutions for the "high-end medical device" industry, the system will automatically enhance the association weights of the "warmth," "trust," and "wood / warm light" parameters under that industry.

[0095] like Figure 2 As shown, corresponding to the above method, this embodiment of the invention also provides an intelligent booth design system 200, which includes: an input interface 201 for receiving mixed design requirements input by a user through the interface; and a natural language parsing unit 202 connected to the input interface, including an intent recognition subunit and an entity extraction subunit, for performing semantic analysis in step S102.

[0096] Design Semantic Material Library 203: Stores knowledge mapping abstract concepts to specific design parameters.

[0097] Parameter mapping engine 204: connects parsing unit 202 and material library 203, is used to perform query and mapping in step S103, and includes a semantic similarity calculation module for handling unknown entities.

[0098] Parameter fusion unit 205: used to fuse derived constraints with structured parameters.

[0099] Solution Generation Engine 206: Generates design solutions based on fused parameters.

[0100] Solution Output Module 207: Used to visualize the final solution.

[0101] Interactive feedback unit 208: Used to confirm with the user before generating the final solution.

[0102] Learning optimization unit 209: Used to optimize the design of semantic material library 203 based on user behavior data.

[0103] This invention also provides a computer-readable storage medium storing a computer program that, when executed by one or more processors (e.g., the CPU of a server or user terminal), causes the processor to perform the steps of the intelligent booth design method described in Embodiment 1 above.

[0104] This invention creatively solves the core pain point of "distorted communication of requirements" in the traditional exhibition booth design field by adopting a dual-track input method of "structured selection + natural language parsing and mapping" and by leveraging a design semantic material library and interactive learning mechanism. It efficiently and accurately transforms vague subjective intentions into objective design instructions, greatly improving the efficiency of design communication and the accuracy of solution generation.

[0105] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0106] The embodiments and functional operations of the subject matter described in this specification can be implemented in the following ways: digital electronic circuits, tangibly implemented computer software or firmware, computer hardware, including the structures disclosed in this specification and their equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, that is, one or more modules of computer program instructions encoded on one or more tangible non-transitory program carriers, for execution by a data processing device or to control the operation of the data processing device.

[0107] Alternatively or additionally, program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are then generated as coded information to be transmitted to an appropriate receiver device executed by data processing equipment. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or one or more combinations of the above.

[0108] The processing and logic flows described in this specification can be executed by one or more programmable computers, which execute one or more computer programs by processing input data and generating output to run functions. The processing and logic flows can also be executed by special-purpose logic circuitry, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits), and the device can also be implemented as special-purpose logic circuitry.

[0109] To transmit interactions with a user, embodiments of the subject matter described in this specification can be implemented on a computer having: a display device, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user; and a keyboard and a positioning device, such as a mouse or trackball, which the user can use to send input to the computer. Other types of devices can also be used to transmit interactions with the user; for example, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including sound input, voice input, or tactile input. Additionally, the computer can interact with the user by sending documents to and receiving documents from a device used by the user; for example, by sending a webpage to a web browser on the user's client device in response to a received request from a web browser.

[0110] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather as descriptions of features that can embody specific embodiments of a particular invention. Specific features described in this specification within the context of an independent embodiment may also be implemented in combination with a single embodiment. Conversely, various features described within the context of a single embodiment may also be implemented independently in multiple embodiments, or in any suitable sub-combination. Furthermore, while features may be described for combination and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and the claimed combination may be redirected to a sub-combination or a variation thereof.

[0111] Similarly, although operations are described in the accompanying drawings in a specific order, it should not be construed as requiring that such operations be performed in the specific order shown or in sequential order, or that all illustrated operations be performed, in order to achieve the desired result. In certain cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that program components and systems can generally be integrated into a single software product or packaged into multiple software products.

[0112] Specific implementations of the subject matter have been described. Other implementations are within the scope of the following claims. For example, the activities described in the claims can be performed in a different order and still achieve the desired result. As an example, the processes described in the drawings do not necessarily require a specific order or sequence to be shown in order to achieve the desired result. In certain implementations, multitasking and parallel processing may be advantageous.

Claims

1. A method for designing an intelligent exhibition booth, characterized in that, Includes the following steps: Based on the customer's initial intentions, the designer transforms them into specific booth design requirements and inputs them into the interactive interface of the intelligent booth design system. The design requirements include structured parameters set by the designer based on professional judgment, as well as natural language descriptions that record the customer's initial intentions. The intelligent booth design system performs multi-dimensional semantic analysis on the natural language description, including intent recognition, key semantic entity extraction, and quantitative assessment of sentiment level. The intelligent booth design system, based on a pre-built design semantic material library, extracts key semantic entities and combines them with intent recognition results and emotional intensity quantitative evaluation results to map them into at least one understandable quantitative design constraint parameter; wherein, each of the quantitative design constraint parameters defines specific technical indicators of booth design in terms of color, material, lighting, spatial form or interaction method. The intelligent booth design system will visualize the mapped quantitative design constraint parameters to the designer through the interactive interface. Based on the visual presentation, the designer confirms with the customer and submits the final confirmation or correction instructions from the customer through the interactive interface; In response to receiving the confirmation command, the intelligent booth design system integrates the finally confirmed quantitative design constraint parameters with the structured design parameters to generate a booth design preview.

2. The intelligent booth design method according to claim 1, characterized in that, The method further includes: recording interactive operations on the visualization presentation, including: selecting mapping suggestions, correcting design constraint parameters, and expressing preferences for the final design scheme; and updating the association between abstract design concepts, style terms, and specific visualization quantitative design constraint parameters in the design semantic material library based on the recorded data.

3. The intelligent exhibition booth design method according to claim 2, characterized in that, The structured parameters include: parameters received from preset options through the selection interface, including: budget range, industry, booth type, design style, functional areas, and design focus.

4. The intelligent booth design method according to claim 3, characterized in that, When the design semantic material library does not contain a mapping relationship between the quantitative design constraint parameters corresponding to the identified key semantic entities: Calculate the semantic similarity between the key semantic entity to be matched and each candidate semantic entity in the material library, select the quantitative design constraint parameters corresponding to the candidate entities with the highest similarity as the mapping result, and form several mapping suggestions; The aforementioned mapping suggestions are presented to the user interface, prompting the user to select the closest mapping suggestion; Establish a temporary mapping relationship between the key semantic entities confirmed by the user and the corresponding quantitative design constraint parameters, and store them in the temporary extension area of ​​the design semantic material library. This temporary mapping relationship will be called during the subsequent design scheme generation process.

5. The intelligent exhibition booth design method according to claim 4, characterized in that, After establishing a temporary mapping relationship between the user-confirmed key semantic entities and their corresponding quantitative design constraint parameters and storing it in the temporary extension area of ​​the design semantic material library, when a natural language description containing the key semantic entities is received again, the temporary mapping relationship is preferentially called from the temporary extension area to map the key semantic entities to their corresponding quantitative design constraint parameters, without having to repeat the similarity calculation and user confirmation steps.

6. The intelligent exhibition booth design method according to claim 5, characterized in that, Also includes: Based on the sentiment analysis of the natural language description, weights are assigned to each of the key semantic entities obtained. The sentiment analysis is performed by quantitatively evaluating the positive, negative, or neutral sentiment words and their intensity contained in the natural language description.

7. The intelligent booth design method according to claim 6, characterized in that, The method further includes: The system provides feedback to users by displaying design constraint parameters or corresponding design examples mapped from the natural language description. Receive confirmation or correction instructions from the user regarding the feedback, and adjust the design constraint parameters according to the instructions.

8. The intelligent booth design method according to claim 7, characterized in that, Also includes: Record user interactions with the feedback and their preferences for the generated booth design schemes; Based on the recorded data, update the association between abstract design concepts, style terms and specific design parameters in the design semantic material library.

9. An intelligent booth design system, employing the intelligent booth design method as described in any one of claims 1-8, characterized in that, include: The input interface is used to receive natural language descriptions of the booth design. The natural language parsing unit, connected to the input interface, is used to perform semantic analysis on the natural language description in order to identify design intent and extract key semantic entities; The design semantic resource library stores the relationships between abstract design concepts, style terms, and specific design parameters; The parameter mapping engine is connected to the natural language parsing unit and the design semantic material library, respectively, and is used to map the identified design intent and / or key semantic entities into at least one quantifiable design constraint parameter. The parameter fusion unit is used to fuse the at least one design constraint parameter obtained by mapping with the structured design parameters selected and input by the user through the interface, so as to generate a booth design scheme. The interactive feedback unit is used to provide users with visual feedback on the design constraint parameters or design examples mapped from the natural language description, and to receive confirmation or correction instructions from users. The learning optimization unit is used to optimize and update the design semantic material library based on the user's historical interactions and solution selection data.

10. The intelligent booth design system according to claim 9, characterized in that, The natural language parsing unit includes: An intent recognition subunit is used to determine the category of the request to which the natural language description belongs; The entity extraction subunit is used to extract design-related entity words from the natural language description.

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