Paper book customization and intelligent distribution method and system based on multi-modal AI

Abstract

The invention discloses a paper book customization and intelligent distribution method and system based on multi-modal AI. The method comprises the steps that S1, multi-modal input data of a user is acquired through a multi-source interaction interface; and S2, carrying out fusion analysis on the multi-modal input data by adopting a multi-modal AI model to generate multi-dimensional customized parameters. And S3, driving cooperative work of the natural language generation model and the image generation model according to content preferences in the multi-dimensional customization parameters, and generating image-text content meeting personalized requirements of the user. And S4, generating a cross-device compatible layout design scheme through an adaptive layout algorithm according to typesetting requirements in the multi-dimensional customized parameters. And S5, sending the electronic manuscript to the distributed printing nodes according to a preset triggering condition, and forming a paper book. And S6, triggering augmented reality interaction through the embedded intelligent identification identifier. The problems that traditional paper book customization depends on manual communication and design, and composite requirements expressed through multiple channels such as texts, voices and images by a user are difficult to efficiently process can be solved.

Description

Technical Field

[0001] The present invention relates to the intersection of artificial intelligence and printing and publishing technology, and specifically to a paper book customization and intelligent distribution method based on multimodal AI. Background Art

[0002] Traditional paper book customization relies mainly on manual demand communication and layout design, and has defects such as long response cycle and single personalized dimension. In existing technologies: (1) Recommendation systems based on user portraits are mostly limited to digital content push and cannot connect to physical book production; (2) Although digital printing technology has improved the speed of personalized printing, it lacks intelligent adaptation to material characteristics and reading scenarios; (3) Logistics distribution mostly adopts static path planning, without considering the dynamic relationship between the physical properties of books and environmental factors; (4) The application of augmented reality technology in the publishing industry is limited to the superposition of independent digital content, and has not formed a closed-loop feedback with the use behavior of paper books. In addition, the application of multimodal data in the field of paper books still remains at the single functional level, and has failed to build a full-process intelligent system covering creation, production, distribution, and interaction. Summary of the Invention

[0003] In view of the shortcomings of the above-mentioned existing technologies, the purpose of the present invention is to provide a multimodal AI-based method for paper book customization and intelligent distribution. This method addresses the problem that traditional paper book customization relies on manual communication and design, making it difficult to efficiently handle the complex needs expressed by users through multiple channels such as text, voice, and images. Through multimodal data fusion and analysis technology, the present invention deeply combines the user's voice emotional characteristics and image composition preferences with the text semantics to generate a three-dimensional production instruction set that includes content architecture, layout parameters, and logistics requirements.

[0004] The present invention provides a method for customizing and intelligently distributing paper books based on multimodal AI, comprising:

[0005] S1: Obtain multimodal input data from the user through a multi-source interactive interface, where the data includes at least two forms of text, voice, and image;

[0006] S2: Use a multimodal AI model to integrate and analyze multimodal input data to generate multi-dimensional customization parameters that include content preferences, layout requirements, and logistics needs;

[0007] S3: Drives the collaboration between the natural language generation model and the image generation model based on the content preferences in the multi-dimensional customization parameters to generate graphic and text content that meets the user's personalized needs;

[0008] S4: Based on the typesetting requirements in the multi-dimensional customized parameters and through the adaptive layout algorithm, a cross-device compatible layout design scheme is generated, a mapping relationship between the printing parameters and the characteristics of the paper material is established, and an electronic book manuscript is generated based on the layout design scheme and the mapping relationship;

[0009] S5: The e-book manuscript is sent to the distributed printing node according to the preset trigger conditions and turned into a paper book. The logistics route planning is dynamically optimized based on the user reading behavior data.

[0010] S6: After the paper book is delivered, the embedded smart identification tag triggers augmented reality interaction, and the recommendation model is iteratively updated based on user feedback data.

[0011] In one embodiment of the present invention, the multi-source interactive interface implements the following operations synchronously during a voice conversation: establishing user identity association through voiceprint recognition, real-time analysis of emotional feature values ​​in voice content, and dynamic adjustment of the framing and composition strategy of the image acquisition device.

[0012] In one embodiment of the present invention, the fusion analysis of the multimodal AI model includes the following technical features: establishing a cross-modal mapping relationship between the text semantic vector space and the visual feature space, using the attention mechanism to dynamically weight the feature contribution of different modalities, and the output dimensions include reading scene classification labels and content depth grading parameters.

[0013] In one embodiment of the present invention, the graphic content generation process includes: accessing the domain knowledge graph to verify the content credibility, automatically matching the education / literature / professional material database according to the user's identity attributes, and generating an extensible content architecture with chapter navigation tags.

[0014] In one embodiment of the present invention, when the adaptive layout algorithm is executed: the page size is dynamically adjusted based on the grammage-transmittance parameters in the paper material property database, the minimum line spacing threshold is calculated according to the ink adsorption characteristics, and the font contrast curve is optimized in combination with the ambient lighting model.

[0015] In one embodiment of the present invention, the preset trigger condition includes a combined application of a geographic location fence trigger mechanism and a time series prediction model: the printing job is started when the user's mobile terminal enters the service radius of the target printing node and the predicted arrival time window matches the node's idle capacity.

[0016] In one embodiment of the present invention, the logistics route planning optimization process includes: establishing a matching model between the vibration spectrum characteristics of the transport vehicle and the strength of the book binding, dynamically adjusting the moisture-proof packaging solution based on real-time meteorological data, and maintaining the topological relationship of multiple packages during transportation through radio frequency tag groups.

[0017] In one embodiment of the present invention, augmented reality interaction is implemented by printing a dot pattern of a specific frequency band on the inner pages of a book, capturing it through a mobile terminal camera and activating the three-dimensional content presentation, and the arrangement density of the dot pattern changes dynamically according to the page content theme.

[0018] In one embodiment of the present invention, user feedback data collection includes: monitoring the frequency distribution pattern of flipping physical book pages, recording the color space distribution characteristics of highlighter marks, capturing the position clustering characteristics of sticky note stickers, and constructing a multi-dimensional reading behavior analysis matrix.

[0019] The present invention also includes a paper book customization and intelligent distribution system based on multimodal AI, comprising:

[0020] A user data acquisition module, the user data acquisition module is configured with a multimodal sensor array and an interactive interface;

[0021] Demand analysis engine, which integrates visual language model and speech semantic understanding model;

[0022] A content generation device, the content generation device includes a configurable text generation unit and an image synthesis unit;

[0023] Intelligent typesetting system, which has adaptive layout algorithm and material property database;

[0024] Distributed printing network, which includes geographically distributed smart printing terminals and IoT monitoring devices;

[0025] Logistics optimization platform, which integrates real-time path planning algorithms and transportation resource scheduling systems;

[0026] An augmented reality interaction module, which includes a graphic recognition unit and a three-dimensional content presentation device;

[0027] Continuous learning unit,The continuous learning unit is configured with a user behavior analysis model and a recommendation model update mechanism.

[0028] The present invention provides a method and system for paper book customization and intelligent distribution based on multimodal AI. Through multimodal data fusion and analysis technology, it deeply combines the user's voice emotion characteristics, image composition preferences and text semantics to generate a three-dimensional production instruction set containing content architecture, layout parameters, and logistics requirements. The innovative introduction of material property database and adaptive algorithm enables the typesetting design to dynamically match the physical properties of paper weight, light transmittance, etc., and optimizes the font display effect in combination with the ambient lighting model to achieve precise coupling of digital design and physical media. In the logistics link, by establishing a correlation model between the strength of book binding and the vibration spectrum of the transport vehicle, the packaging plan and distribution route are dynamically adjusted to significantly reduce the transportation loss rate. More importantly, the invisible dot matrix coding technology is used to realize the dynamic binding of physical books and augmented reality content. The pattern density is automatically adjusted according to the page theme, and by capturing the user's physical behavior data such as page turning frequency and annotation position, a closed-loop feedback is formed from physical use to digital service optimization, which completely breaks the data island status of each link in the traditional publishing industry. The system ultimately builds a complete ecological chain covering demand perception, intelligent production, precise distribution, and two-way interaction, providing a new technical paradigm for the functional evolution of paper books in the era of artificial intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 A flowchart of a method for customizing and intelligently distributing paper books based on multimodal AI;

[0031] Figure 2 System architecture diagram of the paper book customization and intelligent distribution system based on multimodal AI. DETAILED DESCRIPTION

[0032] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0033] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0034] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0035] See Figure 1 , shown is the paper book customization and intelligent distribution method based on multimodal AI of the present invention. The paper book customization and intelligent distribution method based on multimodal AI of the present invention includes: S1: obtaining the user's multimodal input data through a multi-source interactive interface, and the data includes at least two forms of text, voice, and image. S2: using a multimodal AI model to fuse and analyze the multimodal input data, and generate multidimensional customization parameters including content preferences, typesetting requirements, and logistics requirements. S3: driving the natural language generation model and the image generation model to work together according to the content preferences in the multidimensional customization parameters to generate graphic content that meets the user's personalized needs. S4: based on the typesetting requirements in the multidimensional customization parameters and through an adaptive layout algorithm, a cross-device compatible layout design scheme is generated, a mapping relationship between printing parameters and paper material characteristics is established, and an electronic book manuscript is formed according to the plate design scheme and the mapping relationship. S5: The electronic book manuscript is sent to the distributed printing node according to the preset trigger conditions and forms a paper book, and the logistics path planning is dynamically optimized according to the user's reading behavior data. S6: After the paper book is delivered, the embedded smart identification tag triggers augmented reality interaction, and the recommendation model is iteratively updated based on user feedback data.

[0036] like Figure 1As shown, this solution builds an intelligent system for the entire process, from demand collection to physical delivery. Initially, a multi-source interaction interface captures user intent in a three-dimensional manner through heterogeneous data channels. Its technical implementation comprises three parallel processing channels: the text input interface uses a natural language understanding model to parse key requirements from user-submitted electronic documents in real time, extracting structured data such as author preferences and subject areas through named entity recognition. The voice interaction channel deploys a microphone array with noise suppression, employing beamforming technology to enhance the primary sound source signal while intelligently filtering out ambient noise through a voice activity detection module. The image acquisition unit integrates a high dynamic range camera with automatic white balance adjustment and multiple exposure synthesis, ensuring accurate capture of detailed features of user-provided physical book reference samples or handwritten notes under various lighting conditions. These three data channels maintain temporal consistency through a timestamp synchronization mechanism. When semantic conflicts in multimodal input are detected, a confidence-weighted arbitration strategy is initiated. For example, if a voice command states "requires large font size" and the uploaded reference image displays dense typesetting, the system will prioritize based on historical user behavior data (such as font size selection records from past orders). The multimodal AI fusion parsing process employs a hierarchical feature extraction and dynamic weight assignment strategy. In the low-level feature processing stage, text data undergoes word segmentation and word vector embedding before being fed into a bidirectional long short-term memory network to capture contextual semantics. Speech signals undergo mel-spectrogram conversion and are then fed into a convolutional recurrent neural network to extract temporal features. Image data is processed using a residual network to extract multi-scale visual features. In the mid-level fusion stage, a cross-modal attention matrix is ​​constructed, where each word vector in the text is compared to a spatial region in the image feature map to generate a heatmap of semantic correlations between the modalities. For example, when a user describes the user's voice as "I want this shade of blue on the cover," the system increases the attention weight for the blue region in the image features and establishes a mapping relationship with the color descriptor in the speech signal. In the high-level decision-making stage, a gating mechanism is introduced to dynamically adjust the contribution of each modality to the final parameters. This is achieved through a differentiable decision tree. When the image quality score falls below a threshold, the visual modality is downgraded; when the speech emotion intensity exceeds a set value, the decision priority for the corresponding semantic modality is increased. The generated customized parameter set contains a three-dimensional vector space: the content preference dimension quantifies users' demand tendencies for knowledge depth, narrative style, and visual complexity through latent semantic analysis; the typesetting requirement dimension converts abstract descriptions (such as "academic style") into specific layout parameter combinations (1.5x line spacing, IEEE citation format); and the logistics demand dimension combines real-time traffic data and user historical choices to generate a delivery strategy feature vector.

[0037] Furthermore, the graphic and text content generation process utilizes a hybrid architecture combining generative adversarial networks and reinforcement learning. The text generator performs domain-adaptive fine-tuning based on a pretrained language model. During the generation process, it accesses a database of specialized terminology for compliance verification in real time. For example, when generating legal texts, it automatically links to a database of relevant legal provisions to ensure content accuracy. The image synthesis module utilizes a cascade diffusion model. In the first stage, a low-resolution sketch is generated and semantically aligned with the text. In the second stage, high-resolution rendering is performed to simultaneously optimize visual aesthetics and typographical compatibility. The collaborative control mechanism is embodied in three aspects: First, text and images share a latent space representation, ensuring cross-modal semantic consistency through a contrastive learning loss function. Second, the layout constraint module transforms typographical parameters into hard constraints during the generation process, such as limiting the aspect ratio of illustrations to no more than 40% of the page area. Third, the real-time rendering engine provides an interactive preview, allowing users to instantly modify layout elements during the generation process through gestures (such as pinch-to-zoom and drag-to-adjust). This operational data is fed back into the generation model for online fine-tuning. The system architecture achieves full-chain technical integration through a modular design. The hardware layer of the user data acquisition module includes a multispectral imaging unit capable of capturing visible and near-infrared image data to analyze paper material properties. The haptic feedback device uses a pressure-sensitive screen to record the force distribution characteristics of user interactions. The core of the demand analysis engine is a distributed computing framework. The visual language model utilizes a dual-tower architecture: the image tower uses EfficientNet-V2 to extract multi-granular features, the text tower utilizes the ALBERT model for lightweight semantic encoding, and the cross-modal fusion layer employs a multi-head attention mechanism to establish fine-grained associations. The speech processing pipeline comprises three processing units: a front-end processing unit implements speech enhancement and speaker separation, a core recognition unit uses an end-to-end streaming transcription model for real-time transcription, and a post-processing unit uses semantic completion technology enhanced by a knowledge graph to improve command parsing accuracy. The content generation unit utilizes a microservices architecture. The text generation unit deploys multiple expert models operating in parallel. Based on content type (novel, textbook, report), content is automatically routed to the corresponding domain generator and the output is merged using a weighted integration strategy. The image synthesis unit is equipped with a style transfer subsystem, whose workflow includes extracting style features from user reference images through a pre-trained VGG network, injecting these style features into the generation process using adaptive instance normalization, and finally ensuring compatibility between the output image and the target layout through adversarial training. The core algorithm of the intelligent typesetting system comprises a dual mechanism: a constraint solver that translates typesetting requirements into mathematical constraints (e.g., minimum line spacing ≥ 1.2 times the font height), and a physical simulator that simulates ink penetration on different paper types through finite element analysis. The two work together to produce a layout solution that meets design specifications and adapts to material properties.

[0038] like Figure 1As shown, the intelligent terminals in the distributed printing network are equipped with an autonomous decision-making system, including a material identification module (analyzing paper composition through laser scattering spectroscopy), a print quality inspection unit (using machine vision for online defect detection), and an adaptive calibration system (automatically adjusting printing pressure parameters based on ambient temperature and humidity). The logistics optimization platform builds a digital twin model, combining multi-physics coupled simulations of the vibration characteristics of transport vehicles (using Fourier transform to extract characteristic frequencies), the cushioning properties of packaging materials (based on stress-strain curve modeling), and the strength of book bindings (deriving limit parameters through destructive testing), dynamically generating the optimal shockproofing solution. The image recognition unit of the augmented reality interaction module employs a layered decoding strategy: first, YOLOv5 is used to detect the presence of dot patterns, followed by a U-Net network for high-precision positioning, and finally, a Transformer architecture is used to parse the encoded information. The 3D content rendering engine supports a dynamic loading mechanism, automatically selecting the rendering quality level based on device performance (GPU computing power and memory capacity), and achieving persistent alignment of virtual and real scenes through visual inertial odometry. The voice interaction enhancement mechanism integrates multi-dimensional biometric features. The voiceprint recognition subsystem employs a hybrid feature extraction approach: in time-domain analysis, the dynamic time warping distance of the pitch contour is calculated to capture pronunciation characteristics. In frequency-domain processing, a gammatone filter bank simulates the human hearing characteristics and extracts 24-dimensional cepstral coefficients. The identity binding process implements dual verification: real-time voice features are matched against pre-stored voiceprint templates for similarity, while the user's device MAC address and login credentials are cross-validated. The sentiment analysis algorithm constructs a multidimensional feature space, including prosodic features (intonation slope), sound quality features (harmonic noise ratio), and temporal features (rate of change of speech). A support vector machine classifier outputs a sentiment label and its confidence score. The dynamic control strategy for image acquisition demonstrates spatial perception intelligence. When the voice interaction involves visual content (such as a user's description of "the cover should resemble this book"), the system initiates collaborative acquisition mode: sound source localization determines the spatial position of the physical book held by the user, controls the pan-tilt camera for autofocus and perspective correction, and dynamically adjusts the fill light brightness based on the ambient light intensity (measured in lux). The composition optimization algorithm integrates aesthetic principles with user preferences: It applies the rule of thirds to initially identify areas of interest, then uses a style transfer network to mimic the compositional characteristics of the user's historically preferred works, ultimately generating a personalized framing scheme that adheres to the golden ratio. The real-time processing pipeline includes enhancements such as background blur and color stylization, and the processed image is instantly displayed on the user's terminal for confirmation or modification.

[0039] In one embodiment of the present invention, the fusion parsing mechanism of the multimodal AI model achieves deep demand mining by constructing a cross-modal semantic association network. The model uses a dual-encoder architecture to process text and visual inputs separately, where the text encoder extracts semantic vectors based on a pre-trained language model, and the visual encoder extracts image feature maps through a convolutional neural network. During the feature fusion stage, a cross-modal attention alignment module is designed to calculate the similarity matrix between text word vectors and image region features, and dynamically allocate the contribution ratio of different modal features through a learnable weight matrix. For example, when a user uploads a reference book image and verbally says "I want a similar style but more concise", the model will prioritize enhancing the weight of the layout-related channels in the image features, while increasing the text vector dimension corresponding to "concise" in the voice command. The fused feature vector is decoded by a multi-layer perceptron and outputs a structured parameter set, which includes reading scene classification labels (such as commuting scenes and academic research scenes) and content depth classification parameters (such as popular science level and professional level). Scene classification uses a hierarchical labeling system, with a multi-label classifier identifying complex scenarios (e.g., labeling "nighttime reading" and "outdoor use" simultaneously). Content depth grading uses a regression model to output a continuous value from 0 to 1, controlling the density of specialized terminology and the level of detail in the generated content. To improve parsing accuracy, a contrastive learning strategy is introduced during model training, constructing a cross-modal dataset containing positive and negative sample pairs. A triplet loss function is used to shorten the distance between semantically matching image and text features while simultaneously increasing the similarity of features between unrelated combinations. During real-time inference, the system continuously monitors the confidence score of each modal data. When the input quality of a modality is detected to be below a threshold (e.g., image blur is too high), a redundant modality compensation mechanism is automatically triggered, such as by supplementing design details through voice dialogue to ensure the robustness of parameter generation. A domain knowledge-driven quality control system is established for the image and text content generation process. When the system accesses the domain knowledge graph, it uses a graph neural network for multi-hop reasoning to verify the logical consistency of the generated content. Taking the generation of medical books as an example, the knowledge graph contains entity nodes such as diseases, symptoms, and treatment plans, as well as their relationships. During content generation, a real-time check is performed to determine whether the drug indications mentioned in the generated text have a "treatment" relationship with the current disease node. If a conflict is detected (such as "antibiotics are used to treat viral infections" appearing in the generated content), a correction mechanism is triggered to regenerate compliant content. The matching process of the material database uses attribute-aware retrieval technology, constructing dynamic filters based on user identity characteristics (such as occupation and educational background). For example, when the user is identified as an architect, the specifications and standards in the building materials database are automatically linked to case atlases, and CAD drawing styles are preferred when generating illustrations. The implementation of an extensible content architecture relies on a modular content organization strategy. The generated book framework contains pluggable chapter units, each with standardized interfaces (such as abstract slots and reference anchors).Chapter navigation markup is achieved through implicit coding, embedding machine-readable hierarchical tags in the e-manuscript (such as using specific XML tags to mark key paragraphs). These tags are converted into physical identifiers (such as micro-QR codes on the edge of the chapter start page) during the printing stage, allowing for quick content location via mobile devices. In addition, the generation system is equipped with a style transfer engine that can deconstruct user-provided reference styles (such as the typesetting style of a classic literary work) into style parameters (character spacing, paragraph indentation, illustration scale), and transfer them to newly generated content through a generative adversarial network to ensure personalized and unified visual presentation.

[0040] Furthermore, the adaptive layout algorithm achieves a dynamic coupling between material properties and digital design. When the algorithm is running, it first queries the material database to obtain the physical parameters of the currently selected paper. The gram weight-transmittance parameter is represented by the experimentally measured transmittance curve. The algorithm calculates the constraints of the page size based on this. For example, when the paper weight is less than 80g / m 2And when the transmittance is higher than 30%, the page margins are automatically increased to avoid visual interference caused by the text showing through on the back page. The ink adsorption characteristics are processed using a fluid dynamics model. Based on the surface roughness of the paper (Ra value) and the ink viscosity parameters, the diffusion process of the ink between the fibers is simulated to derive the minimum line spacing threshold. Specifically, the maximum radial range of ink diffusion under different font sizes is calculated through finite element analysis to ensure that the spacing between the base areas of adjacent rows is greater than 1.5 times the diffusion extreme value. The integration of the ambient lighting model enables the typesetting system to dynamically adjust the display parameters. The algorithm accesses the ambient light sensor data of the terminal device and calculates the optimal contrast curve under the current lighting conditions in real time. This process involves color adaptation transformation. When the ambient color temperature is detected to be lower than 3000K (warm light environment), the brightness difference between the font and the background is automatically increased, while the saturation of the cool color is reduced to prevent visual fatigue. To meet cross-device compatibility requirements, the layout engine generates responsive layout templates, whose key parameters (such as column width and font size) are expressed as functions of the viewport size. When users preview the layout on different devices (mobile, tablet, PC), the system automatically solves constraint equations to generate a layout solution that adapts to the current screen. The printing parameter optimization module also includes a physical simulator that predicts the impact of different binding methods (saddle stitch, perfect binding, and thread binding) on ​​page flatness and adjusts the page offset accordingly to ensure visual balance in the bound content display area. Defined preset trigger conditions construct an intelligent printing decision model with spatiotemporal joint optimization. The geofence trigger mechanism uses hybrid positioning technology, combining GPS positioning, Wi-Fi fingerprinting, and Bluetooth beacon triangulation, to divide the service radius of the printing node into dynamically adjustable geofence areas (configurable radius of 500m-5km). When a user's mobile terminal enters the target node's fence, the system initiates multi-dimensional matching verification: first, confirming the user's identity and order association through NFC near-field communication, and then checking the inventory status of printed materials. The time series prediction model uses an LSTM neural network. Its input features include historical order flow, equipment maintenance records, holiday factors, etc., and it outputs predicted capacity utilization values ​​for each period in the next 24 hours. When it is detected that the user's estimated arrival time window (ETA±15 minutes) overlaps with the node's idle capacity period (utilization rate less than 70%), the printing job start instruction is triggered. This collaborative mechanism optimizes resource allocation through a queuing theory model. When selecting between multiple candidate nodes, it calculates the comprehensive matching score of each node (including distance factor, time fit, and carbon emission valuation), and selects the node with the highest total score to perform printing. At the job scheduling level, the system implements a dynamic priority adjustment strategy: when the same node receives multiple orders, the production queue is rearranged according to the user's membership level, the urgency of the order (such as the "expedited" label), and the logistics path aggregation degree (whether multiple orders can be combined for delivery).Trigger conditions also include exception handling rules. For example, if the predicted arrival time deviates from the actual arrival time by more than a 30-minute threshold, a secondary confirmation process is automatically initiated: a push notification is sent to obtain the user's latest itinerary and the optimal node selection is recalculated. For emergencies (such as printing equipment failure), the system activates an emergency switching protocol, rerouting the user to an alternate node based on real-time traffic data and adjusting the fence trigger radius to ensure service continuity.

[0041] like Figure 1As shown, the logistics route planning optimization mechanism establishes a dynamic decision-making system that couples multiple physical fields. In a matching model between the transport vehicle's vibration spectrum and the strength of book binding, the system uses triaxial accelerometers installed on the binding line to collect the binding structure's natural frequency characteristics. The system then uses a fast Fourier transform to convert the time-domain vibration signal into a frequency-energy distribution spectrum. This spectrum is convolved with the transport vehicle's historical vibration data (continuously recorded by the onboard IMU sensor) to calculate a potential resonance risk index. When an excitation frequency within ±5% of the main frequency of the book binding is detected on a road section, a route avoidance strategy is automatically triggered, prioritizing roads with a surface roughness score above AA when replanning the delivery route. Dynamic adjustments to the moisture-proof packaging solution rely on a meteorological data assimilation system that integrates satellite remote sensing data, weather station observations, and a network of micro-meteorological sensors (deployed inside shipping containers). Using an LSTM neural network, the system predicts temperature and humidity trends along the transport route over the next 12 hours. The packaging decision engine dynamically selects composite materials from the material library based on the prediction results: when the relative humidity exceeds 70%, the aluminum-coated film interlayer material is activated, and when the temperature fluctuation is greater than 15°C, a phase change material temperature control layer is added. The radio frequency tag group uses topological coding technology. The electronic tag of each package has an embedded adjacency relationship matrix. When the spatial position of the package changes during transportation, the topological connection status is updated through a directional radio frequency signal. The system implements three core functions: real-time monitoring of the relative position relationship between multiple packages (with an accuracy of centimeters), dynamic reconstruction of the three-dimensional stacking model of the transport unit, and automatic verification of the integrity of the package collection at the sorting node (verified by topological connectivity). The augmented reality interaction system realizes the adaptive binding of physical books and digital content. The dot pattern generation algorithm utilizes frequency division multiplexing technology. During the printing process, a high-precision inkjet device embeds an invisible code consisting of three information layers: a base layer consisting of a fixed-spaced reference dot matrix (spacing 0.5mm±0.02mm) for fast positioning; a content layer employs differential encoding, with tiny dot displacements (±50μm) conveying page identification information; and a dynamic layer calculates the optimal density based on the page's semantic content (keyword vectors extracted by a natural language processing model). Technical pages utilize a dense encoding of 400 dots per square centimeter to support complex models, while literary pages utilize a reduced density of 200 dots and incorporate artistic layouts. Efficient decoding is achieved via a customized image processing pipeline for mobile device recognition: perspective distortion correction is performed (using a camera parameter matrix based on checkerboard calibration), followed by frequency-domain filtering to separate the three information layers. Finally, a convolutional neural network verifies the encoding validity. The 3D content rendering engine utilizes a layered loading strategy, with basic geometry rendered in real time (≥60fps) and high-precision texture maps streamed on demand.The dynamic binding mechanism is reflected in three aspects: when users continuously flip through the physical book, the AR system automatically establishes scene context across pages (such as maintaining the visual coherence of 3D models between illustrations across pages); intelligently preloads associated digital resources (such as extended reading videos) based on the time spent on the page; and uses data from the physical book page curvature sensor (collected by embedded flexible strain gauges) to adjust the projection angle of virtual objects in real time to ensure the perspective consistency of the virtual and real visual fusion. The system also includes a self-healing mechanism: when the damage rate of the dot matrix pattern exceeds 15%, the image inpainting algorithm is activated (based on the generative adversarial network to complete the missing dot matrix) and cross-validated with the encoding information of the adjacent pages.

[0042] Furthermore, the user feedback data collection system has built a multimodal behavior perception network. The frequency monitoring of physical book page turning is achieved through a piezoelectric film sensor array implanted in the spine of the book. Each sensor node records the pressure change waveform at a sampling rate of 100Hz. Through time-frequency joint analysis (short-time Fourier transform combined with wavelet decomposition), the system accurately identifies the characteristics of page turning actions: normal reading and page turning are manifested as pressure fluctuations of 0.5-2Hz, while rapid search behavior presents sudden high-frequency components (>5Hz). The highlighter mark analysis system uses multispectral imaging technology. A miniature camera deployed in the user's writing area is combined with a specific band LED light source (alternating illumination of 365nm ultraviolet light and 850nm infrared light) to capture the characteristic reflectance spectrum of the marking ink. By establishing an RGB-CMYK-Lab multi-dimensional color space conversion matrix, the system can not only identify the mark color (distinguishing 12 standard color systems), but also analyze the number of overlapping layers (detecting up to 5 layers of overlapping marks). The location clustering analysis of sticky notes utilizes a density peak algorithm (DBSCAN), combining sticker shape features (circular / square / irregular) with the sticking direction (tilt angle detection) to construct a three-dimensional feature space (x-coordinate, y-coordinate, rotation angle). The construction of a multidimensional reading behavior analysis matrix encompasses both temporal and spatial dimensions. In the temporal dimension, a Markov chain model of reading conversations is established (predicting the next hot reading area); in the spatial dimension, kernel density estimation is used to generate a heat map of knowledge point attention. During the data fusion phase, tensor decomposition technology is employed to uniformly represent flipping frequency (time series), tag distribution (spatial data), and sticker clustering (topological relationships) as a third-order tensor. Tucker decomposition is then used to extract potential behavioral pattern features. These features are input into a reinforcement learning framework to drive recommendation model updates: When a user's dwell time on a chapter exceeds a threshold and is accompanied by frequent tagging, a list of extended reading materials in that field is automatically generated. If analysis reveals that multiple users have experienced binding cracking on the same page, binding process improvements are triggered to optimize adhesive formulation parameters.

[0043] The present invention provides a method and system for customizing and intelligently distributing paper books based on multimodal AI. Through multimodal data fusion and analysis technology, it deeply combines the user's voice emotion characteristics, image composition preferences, and text semantics to generate a three-dimensional production instruction set that includes content architecture, layout parameters, and logistics requirements.

[0044] Therefore, the multimodal AI-based paper book customization and intelligent distribution method and system of the present invention can solve the problem that traditional paper book customization relies on manual communication and design, and is difficult to efficiently handle the complex needs expressed by users through multiple channels such as text, voice, and images.

[0045] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A paper book customization and intelligent distribution method based on multimodal AI, characterized by: include: S1: Acquire multimodal input data from a user through a multi-source interaction interface, where the data includes at least two forms of text, voice, and image; S2: Using a multimodal AI model to fuse and analyze the multimodal input data, generating multi-dimensional customization parameters including content preferences, layout requirements, and logistics needs; S3: driving the natural language generation model and the image generation model to work together according to the content preferences in the multi-dimensional customization parameters to generate graphic and text content that meets the user's personalized needs; S4: generating a cross-device compatible layout design scheme based on the typesetting requirements in the multi-dimensional customized parameters and using an adaptive layout algorithm, establishing a mapping relationship between printing parameters and paper material properties, and forming an electronic book manuscript based on the layout design scheme and the mapping relationship; S5: The e-book manuscript is sent to the distributed printing node according to the preset trigger conditions and is converted into a paper book. The logistics route planning is dynamically optimized based on the user reading behavior data. S6: After the paper book is delivered, the augmented reality interaction is triggered by the embedded intelligent recognition identifier, and the recommendation model is iteratively updated based on the user feedback data.

2. The method for customizing and intelligently distributing paper books based on multimodal AI according to claim 1, characterized in that: The multi-source interactive interface enables the following operations to be performed synchronously during a voice conversation: establishing user identity association through voiceprint recognition, analyzing emotional feature values ​​in voice content in real time, and dynamically adjusting the framing and composition strategy of the image acquisition device.

3. The method for customizing and intelligently distributing paper books based on multimodal AI according to claim 1, characterized in that: The fusion analysis of the multimodal AI model includes the following technical features: establishing a cross-modal mapping relationship between the text semantic vector space and the visual feature space, using the attention mechanism to dynamically weight the feature contribution of different modalities, and the output dimensions include reading scene classification labels and content depth grading parameters.

4. The method for customizing and intelligently distributing paper books based on multimodal AI according to claim 1, characterized in that: The graphic content generation process includes: accessing the domain knowledge graph to verify the content credibility, automatically matching the education / literature / professional material database according to the user's identity attributes, and generating an extensible content architecture with chapter navigation tags.

5. The method for customizing and intelligently distributing paper books based on multimodal AI according to claim 1, characterized in that: When the adaptive layout algorithm is executed: the page size is dynamically adjusted based on the grammage-transmittance parameters in the paper material property database, the minimum line spacing threshold is calculated according to the ink adsorption characteristics, and the font contrast curve is optimized in combination with the ambient lighting model.

6. The method for customizing and intelligently distributing paper books based on multimodal AI according to claim 1, characterized in that: The preset trigger condition includes a combined application of a geographic location fence trigger mechanism and a time series prediction model: when the user's mobile terminal enters the service radius of the target printing node and the predicted arrival time window matches the node's idle capacity, the printing job is started.

7. The method for customizing and intelligently distributing paper books based on multimodal AI according to claim 1, characterized in that: The logistics route planning optimization process includes: establishing a matching model between the vibration spectrum characteristics of the transport vehicle and the strength of the book binding, dynamically adjusting the moisture-proof packaging solution based on real-time meteorological data, and maintaining the topological relationship of multiple packages during transportation through radio frequency tag groups.

8. The method for customizing and intelligently distributing paper books based on multimodal AI according to claim 1, characterized in that: The augmented reality interaction is implemented by printing a dot pattern of a specific frequency band on the inner pages of a book, capturing it through a mobile terminal camera and activating the three-dimensional content presentation. The arrangement density of the dot pattern changes dynamically according to the page content theme.

9. The method for customizing and intelligently distributing paper books based on multimodal AI according to claim 1, characterized in that: The user feedback data collection includes: monitoring the frequency distribution pattern of physical book pages, recording the color space distribution characteristics of highlighter marks, capturing the position clustering characteristics of sticky notes, and constructing a multi-dimensional reading behavior analysis matrix.

10. A paper book customization and intelligent distribution system based on multimodal AI using any one of claims 1 to 9, characterized in that: include: A user data acquisition module, wherein the user data acquisition module is configured with a multimodal sensor array and an interactive interface; A demand analysis engine integrating a visual language model and a speech semantic understanding model; A content generation device, comprising a configurable text generation unit and an image synthesis unit; An intelligent typesetting system having an adaptive layout algorithm and a material properties database; A distributed printing network comprising geographically distributed smart printing terminals and IoT monitoring devices; A logistics optimization platform that integrates a real-time path planning algorithm and a transportation resource scheduling system; An augmented reality interaction module, comprising a graphic recognition unit and a three-dimensional content presentation device; A continuous learning unit is configured with a user behavior analysis model and a recommendation model update mechanism.

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