Paper book dynamic content optimization and environment-friendly publishing system based on artificial intelligence

Abstract

The paper book dynamic content optimization and environment-friendly publishing system based on artificial intelligence comprises an artificial intelligence content optimization engine, an environment-friendly material adaptation module, a dynamic printing control unit and a user feedback analysis network. An artificial intelligence content optimization engine analyzes reader behavior data, market trends and text semantic features, dynamically generates personalized content templates and optimizes typesetting and layout; the environment-friendly material adaptation module is used for matching an optimal environment-friendly paper combination through a multi-objective optimization algorithm on the basis of book types, printing batches and regional resource data; the dynamic printing control unit is connected with the digital printing equipment and receives the combination of the content template and the optimal environment-friendly paper in real time; the user feedback analysis network is deployed in an NFC chip or a two-dimensional code embedded in a book, and continuously collects reader page stay duration, two-dimensional code scanning frequency and physical page breakage data. According to the method, the problems of paper book content staticization and low efficiency in an environment-friendly material selection process can be solved.

Description

Technical Field

[0001] The present invention relates to the intersection of intelligent publishing and green manufacturing, and in particular to an artificial intelligence-based dynamic content optimization and environmentally friendly publishing system for paper books. Background Art

[0002] In the traditional publishing industry, the disconnect between content generation, printing production, and reader feedback has long constrained industry development. Taking educational publishing as an example, textbook content updates often rely on manual research and editorial committee decisions, taking upwards of 18 months. This results in knowledge iteration lagging far behind disciplinary development. Due to the lengthy development cycle, a well-known publisher's "Introduction to Artificial Intelligence" textbook had to upgrade its deep learning framework from TensorFlow 1.x to 2.0 by the time it was released, resulting in 30% of its code examples being invalid. This static content model not only degrades the reader experience but also leads to a large number of unsold books being pulped. According to statistics, in 2022 alone, my country's textbook waste volume reached 87,000 tons, equivalent to consuming 12,000 cubic meters of virgin forest resources. When it comes to the use of environmentally friendly materials, the industry suffers from a widespread problem of indiscriminate selection: publishers often simply use FSC-certified paper, ignoring the specificities of regional supply chains. A children's picture book project in northwest China forcibly promoted the use of a high percentage of recycled paper (70%), without considering the inadequate production processes of local paper mills. This resulted in excessive surface roughness (Ra value of 4.3μm), uneven ink penetration during printing, and ultimately, a recall of 300,000 copies due to illegible text, resulting in direct losses exceeding 5 million yuan. This case highlights the lack of systematic evaluation of existing environmental practices.

[0003] The limitations of existing technological attempts are equally evident: some companies have introduced digital printing to achieve short-run customization, but this hasn't solved the problem of intelligent content optimization. For example, one customized graduation album project still requires users to manually upload photos and text. A few advanced printing plants have deployed energy consumption monitoring systems, but these systems only display data and lack closed-loop control capabilities. While blockchain technology has found some application in copyright protection, it's largely limited to e-books and lacks deep integration with the physical characteristics of print books (such as material batches and printing processes). These fragmented improvements have failed to build a comprehensive solution encompassing "content generation - material adaptation - printing optimization - reader interaction - and version iteration," leading to bottlenecks in the industry's transformation. Summary of the Invention

[0004] In light of the shortcomings of the aforementioned prior art, the present invention aims to provide an AI-based system for dynamic content optimization and environmentally friendly publishing of printed books, addressing the issues of static content and inefficient environmentally friendly material selection. By deeply integrating AI, environmentally friendly material science, IoT perception, and blockchain technology, the present invention builds a complete technological ecosystem encompassing content generation, production optimization, reader interaction, and version traceability.

[0005] The present invention provides an artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system, comprising:

[0006] AI content optimization engine: This engine analyzes reader behavior data, market trends, and text semantic features to dynamically generate personalized content templates and optimize layouts. Content templates include replaceable text blocks, illustrations, and interactive QR codes.

[0007] Environmentally friendly material adaptation module, which uses a multi-objective optimization algorithm to match the optimal environmentally friendly paper combination based on book type, printing batch and regional resource data;

[0008] Dynamic Print Control Unit: This unit connects to digital printing equipment and receives content templates and optimal environmentally friendly paper combinations in real time. It monitors the carbon footprint of the printing process through online sensors and dynamically adjusts printing speed, ink volume, and energy consumption to match preset environmental thresholds.

[0009] The user feedback analysis network is deployed in the NFC chip or QR code embedded in the book. It continuously collects data on how long readers stay on the page, the frequency of QR code scanning, and the depreciation of physical pages, and feeds it back to the artificial intelligence engine to iteratively optimize the content of subsequent versions.

[0010] In one embodiment of the present invention, the artificial intelligence content optimization engine integrates a collaborative filtering algorithm and a natural language processing model to generate a content association recommendation template across reader groups based on the chapter jump rate, annotation hotspots and semantic similarity of the historical reader groups. The content association recommendation template includes dynamically inserted extended reading chapters and interactive knowledge graph links.

[0011] In one embodiment of the present invention, when matching the optimal environmentally friendly paper combination, the environmentally friendly material adaptation module combines the regional waste paper recycling rate, the local paper mill carbon emission intensity data and the transportation radius, determines the balance point between the proportion of recycled fiber and the ink type through Pareto front analysis, and dynamically generates a material supply chain topology map.

[0012] In one embodiment of the present invention, the dynamic printing control unit deploys optical sensors and conductive ink detection devices to monitor the surface roughness of the printed paper, the ink penetration depth and the adhesive curing temperature in real time, and adjusts the printing pressure and drying time through a fuzzy logic controller to reduce the carbon emissions per unit area to below a preset threshold.

[0013] In one embodiment of the present invention, the user feedback analysis network integrates an edge computing module in the NFC chip embedded in the book. After locally preprocessing the reader's page turning acceleration and page corner angle data, only the feature vector is uploaded to the cloud analysis network, reducing the data transmission bandwidth usage.

[0014] In one embodiment of the present invention, the distributed version management database adopts a sharded blockchain structure to associate the environmental material traceability information of each book with the content hash value and verify the data integrity through zero-knowledge proof. The environmental material traceability information includes but is not limited to the paper mill batch number and ink heavy metal test report.

[0015] In one embodiment of the present invention, an artificial intelligence content optimization engine adjusts the typesetting layout according to a reader emotion recognition model. The reader emotion recognition model infers reading concentration and dynamically increases font spacing or inserts visual separators by analyzing the QR code scanning interval, the standard deviation of page dwell time, and the distribution of physical page creases.

[0016] In one embodiment of the present invention, when the environmental material adaptation module detects a sudden change in regional environmental protection policy, it initiates an emergency reconfiguration protocol, dynamically switches to a backup material combination plan based on the Markov decision process, and synchronizes change instructions to publishers and printing plants through the blockchain.

[0017] In one embodiment of the present invention, the edge computing module uses a lightweight encryption protocol to desensitize reader behavior data, generates a differentially private feature vector, and then uploads it to the cloud to prevent user privacy leakage.

[0018] In one embodiment of the present invention, the blockchain's smart contract includes automatic environmental compliance verification rules. When it is detected that the volatile organic compound (VOC) content of a batch of materials exceeds the standard, the printing authorization of the corresponding version content template is automatically frozen and an alarm process is triggered.

[0019] The present invention provides an artificial intelligence-based dynamic content optimization and environmentally friendly publishing system for paper books. By deeply coupling artificial intelligence, environmentally friendly material science, Internet of Things perception and blockchain technology, it constructs a complete technical ecosystem covering content generation, production optimization, reader interaction and version traceability. Among them, the combination of dynamic content templates and paper carriers breaks through the boundary between digital and physical media; the multi-objective optimization model and closed-loop control of printing parameters form a technical closed loop for green manufacturing; embedded sensing and blockchain traceability redefine the interactive capabilities and anti-counterfeiting mechanisms of paper books. This cross-domain technology integration not only solves the long-standing fragmentation problem in the industry, but also opens up a new path for the intelligent upgrade of paper publishing, and provides a reusable methodological framework for the digital transformation of traditional industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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.

[0021] Figure 1 This is the system architecture diagram of the AI-based paper book dynamic content optimization and environmentally friendly publishing system. DETAILED DESCRIPTION

[0022] 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.

[0023] 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.

[0024] 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.

[0025] The present invention relates to a dynamic content optimization and environmentally friendly publishing system for paper books based on artificial intelligence, which is mainly used in large-scale printing scenarios. The primary defects of the existing technology are poor environmental management and chaotic management. Carbon emission control in the printing process has been out of control for a long time. Traditional printing plants usually use fixed production parameters, such as setting the drying temperature to 85°C, without considering the difference in the ink absorption of paper. A high-end picture album was printed on high-bulk art paper (180gsm). Due to the failure to adjust the drying parameters, the water-based ink was not completely cured, resulting in a volatile organic compound (VOC) concentration of 45mg / m 3, exceeding workshop safety standards by three times, leading to employee health complaints. Industry data shows that my country's printing industry produces 120 million tons of carbon emissions annually, 38% of which comes from improperly managed equipment energy consumption. When it comes to user feedback, publishers often rely on slow manual surveys. For example, one educational publishing house collected teacher suggestions for revisions to a physics textbook through questionnaires, but after three months, only 1,200 valid responses were collected, and the cognitive load on readers in specific chapters could not be quantified. This inefficient feedback mechanism results in a lack of data support for content optimization in reprints. Statistics show that the average knowledge retention rate for traditional textbooks is only 41%, significantly lower than the 65% for digital learning platforms. The problem of chaotic version management is particularly prominent when distributing textbooks across regions. An English textbook publisher produced separate "basic" and "extended" editions for urban and rural markets. However, due to manual management errors, the edition containing advanced grammar content was mistakenly sent to a rural school, causing difficulties for teachers and students. More seriously, piracy causes the industry approximately 23 billion yuan in losses annually. Traditional anti-counterfeiting technologies (such as laser watermarks) are easily copied. The ratio of pirated copies of a certain best-selling novel reached as high as 1:3.5. Authentic copies must be sent back to the publisher for testing, a process that can take up to two weeks. Regarding policy responses, the EU suddenly raised the recycled content standard for paper from 50% to 65% in 2023. A failed adjustment to the material formula of an export textbook resulted in 120 tons of finished products being held up at customs, resulting in 2.8 million yuan in late payment fees. These pain points collectively point to systemic deficiencies in the traditional publishing industry's dynamic response, refined management, and sustainable development.

[0026] See Figure 1 , shown is the paper book dynamic content optimization and environmentally friendly publishing system based on artificial intelligence of the present invention. The paper book dynamic content optimization and environmentally friendly publishing system based on artificial intelligence of the present invention includes an artificial intelligence content optimization engine, an environmentally friendly material adaptation module, a dynamic printing control unit and a user feedback analysis network. The artificial intelligence content optimization engine analyzes reader behavior data, market trends and text semantic features, dynamically generates personalized content templates and optimizes typesetting layouts. The content templates include replaceable text blocks, illustrations and interactive QR codes; the environmentally friendly material adaptation module matches the optimal environmentally friendly paper combination through a multi-objective optimization algorithm based on book type, printing batch and regional resource data; the dynamic printing control unit connects to the digital printing equipment and receives the content template and the optimal environmentally friendly paper combination in real time, monitors the carbon footprint indicators during the printing process through online sensors, and dynamically adjusts the printing speed, ink volume and energy consumption to match the preset environmental protection threshold; the user feedback analysis network is deployed in the NFC chip or QR code embedded in the book, continuously collects the reader's page dwell time, QR code scanning frequency and physical book page depreciation data, and feeds back to the artificial intelligence engine to iteratively optimize the subsequent version content.

[0027] like Figure 1As shown, the proposed system first establishes a hardware foundation for heterogeneous collaboration among multiple encoders. In traditional publishing processes, content generation and recommendations have long relied on the empirical judgment of human editors, lacking in-depth insights into reader behavior patterns. This system integrates collaborative filtering algorithms with natural language processing to establish a dynamic content recommendation mechanism. As readers flip through a book, an embedded flexible sensor network captures physical interaction details, such as the angle at which pages unfold in a specific chapter, the frequency of pressure changes on the spine, and accompanying QR code scanning. After desensitizing this data, the system can identify key interests among different groups. For example, repeated folding of a chapter may indicate comprehension difficulties, while frequently scanned QR code links point to areas of knowledge readers are eager to explore. Based on these insights, the algorithm extracts relevant content snippets from a pre-set resource library, such as inserting visual derivation steps for theoretical difficulties or adding the latest industry developments to case study sections. This dynamic curation is not simply a pile of content; instead, semantic network analysis establishes logical connections between knowledge points, ensuring the coherence of the new content with the original. For example, when the system detects an unusually high frequency of readers looking up the concept of "quantum entanglement," it automatically embeds an expandable interactive diagram in the margin, linking to relevant experimental videos and academic paper abstracts, and adjusts the layout to optimize reading flow. This closed loop of real-time feedback and content iteration allows print publications to transcend the limitations of traditional static media and become intelligent media that engages in continuous dialogue with readers. The selection of environmentally friendly materials often presents a dilemma between performance and sustainability, forcing publishers to repeatedly balance paper strength, ink adhesion, and environmental performance. This system incorporates a multi-objective optimization model, transforming material selection into a dynamic equilibrium process. Rather than solely focusing on maximizing the proportion of recycled materials, the model considers regional resource characteristics, such as the maturity of the local waste paper recycling system, the production process level of paper mills, and the carbon emission coefficient of the transportation network. When environmental protection policies in a region are suddenly adjusted, the system can quickly activate an emergency response mechanism, reassess the compliance potential of each node in the supply chain, and automatically generate alternative solutions. For example, during a transitional period when policies mandate an increase in the proportion of recycled fiber, the model might recommend using high-quality recycled paper shipped over short distances paired with low-volatile inks, rather than blindly choosing a high proportion of recycled materials shipped over long distances. This flexible adaptability is visualized through a supply chain topology diagram. The system connects to the supplier database in real time, analyzing production capacity, lead times, and quality certification status to provide printers with actionable purchasing recommendations. In practice, this intelligent decision-making mechanism significantly reduces the risk of batch rejection due to substandard materials, while also avoiding the trade-off between environmental performance and product quality.

[0028] Furthermore, precise control of the printing process is crucial to achieving environmental goals. Traditional printing presses rely on fixed parameter settings, making them difficult to cope with the process challenges brought on by fluctuating material properties. This system deploys a multimodal sensor array on the printing line to continuously monitor the paper surface condition, ink penetration depth, and adhesive curing process. If uneven fiber distribution in the recycled paper is detected, the control unit adjusts the printing pressure in real time to ensure even ink adhesion. If changes in ambient temperature and humidity affect drying efficiency, the system dynamically adjusts the drying temperature and conveyor speed to avoid energy waste and poor curing. This adaptive control does not rely on preset empirical formulas, but instead uses a fuzzy logic algorithm to build a dynamic response rule base, nonlinearly mapping sensor data to equipment parameters. For example, for highly absorbent paper, the system may simultaneously reduce ink supply and increase printing pressure to ensure image clarity while minimizing ink consumption. This closed-loop control approach ensures that the printing process consistently maintains optimal efficiency while minimizing resource waste and pollution emissions. Another key breakthrough of this system is the immediate collection and efficient utilization of user feedback. Microsensor modules embedded in the bookbinding process continuously record the reader's interaction with the content without disrupting the reading experience. For example, by monitoring the distribution pattern of page folds, the system can infer the key chapters that readers repeatedly consult; by analyzing the distribution of QR code scanning times, it can identify peak hours and common points of confusion for knowledge retrieval. These data are not uploaded in full, but are preliminarily processed locally through edge computing technology - after extracting key feature vectors, only abstract behavior patterns are transmitted to the cloud, which not only ensures privacy security but also greatly reduces the data transmission load. When the reader group in a certain area generally shows comprehension difficulties in a specific chapter, the system will automatically trigger the content optimization process and add graphic annotations or expanded reading links to the chapter in the next printing batch. This transformation mechanism from individual behavior to group insights makes the content iteration of paper books no longer rely on lagging market research, but forms a dynamic system that evolves in sync with readers' needs.

[0029] like Figure 1As shown, in traditional publication version management and anti-counterfeiting traceability systems, the physical properties of paper books and their digital information have long been disconnected. This system, through a distributed database architecture, deeply integrates each book's environmentally friendly material formula, printing process characteristics, and content version information. When the printing press starts production, the system automatically collects key parameters such as the paper mill's raw material batch and ink environmental testing report to generate a unique material fingerprint. Simultaneously, the content engine hashes the dynamically generated personalized template to form a content signature code. These two sets of data are distributedly stored using a sharded blockchain structure, with each node storing only a subset of the data. This ensures data security while minimizing storage pressure on a single node. For example, when an educational institution prints a rural edition of textbooks, the system records the source of the bamboo pulp paper used in that batch and the qualifications of the water-based ink supplier, linking these to the content signature code of a customized local agricultural case library. When market regulators conduct spot checks, they simply scan hidden identification within the pages with specialized equipment to verify the matching of materials and content using zero-knowledge proof technology, without exposing any raw data details. This mechanism effectively curbs piracy. For a best-selling book, counterfeiters were unable to replicate the blockchain-anchored material-content association features, significantly improving the accuracy of genuine product identification and providing unalterable legal evidence in copyright disputes. The conflict between collecting user behavior data and protecting privacy has always been a challenge in smart publishing. This system embeds a micro-sensor unit in the book binding process. A triaxial accelerometer captures the force and frequency of page flips, while a flexible sensor records the distribution of page creases. A lightweight encryption module is also embedded in the QR code on the inner pages. Instead of directly uploading this physical interaction data to the cloud, it is pre-processed locally on an edge computing unit. This data is denoised using a differential privacy algorithm to extract abstract behavioral pattern features, such as preferred knowledge retrieval paths and rereading patterns of key chapters. For example, if a reader repeatedly folds a page corner and frequently scans for extended video links, the edge unit converts this data into a feature vector of "deep reading behavior," rather than recording the specific number of folds and timestamps. This processed feature data is transmitted to the analysis network via an encrypted channel, preserving the accuracy of the user profile while minimizing the risk of privacy breaches. After adopting this mechanism, an academic monograph successfully identified the migration trend of research hotspots among readers across regions, providing direction for the optimization of reprinted content. At the same time, the user privacy complaint rate dropped to the lowest level in the industry.

[0030] In one embodiment of the present invention, faced with the impact of sudden environmental policy adjustments on the supply chain, the traditional publishing industry often falls into the dilemma of passive response. The emergency response mechanism of this system initiates a multi-dimensional assessment at the early stage of policy changes by constructing a dynamic decision-making model. When a region suddenly raises the standard for the use of recycled materials, the system first analyzes the technical reserves and production capacity potential of existing suppliers, and simulates the comprehensive benefits of different alternatives: including core indicators such as the probability of material performance meeting the standards, supply chain switching costs, and the difficulty of transport network reconstruction. For example, after the EU revised the packaging material recycling rate regulations, the model quickly locked in alternative suppliers with certification qualifications, evaluated the compatibility of their production processes with current equipment, and generated a phased switching plan - giving priority to the use of compliant materials in stock to maintain short-term production, while starting the certification process for new suppliers. The Markov chain model is introduced in the decision-making process to predict the buffer period of policy implementation and the trend of changes in regulatory intensity, and dynamically adjust the supply chain topology. After a multinational publishing group applied this function, it took only one-third of the time of the traditional solution during the upgrade of environmental protection regulations in Southeast Asia.

[0031] This restructures the supply chain, preventing the scrapping of large quantities of inventory materials. The system also automatically updates procurement terms through blockchain smart contracts, ensuring that all parties have real-time access to the latest compliance requirements and reducing delays and errors caused by manual negotiation. Regarding process control of printing quality, the system transcends the limitations of traditional static parameter settings and dynamically optimizes process parameters through multi-source sensor data fusion. When a high percentage of recycled paper enters the printing line, optical sensors monitor fiber distribution uniformity in real time and simultaneously monitor the impact of ambient temperature and humidity changes on ink viscosity. This data is fed into an adaptive control model, dynamically adjusting printing pressure and drying temperature. For example, during the rainy season, when humidity rises, the system automatically extends the drying zone and reduces conveyor speed to ensure adequate curing of water-based inks. When surface roughness fluctuations in recycled paper are detected, the system fine-tunes roller pressure to maintain edge clarity. This real-time control capability maintains a high print yield while significantly reducing peak energy consumption. Using this technology, an art album printing project achieved professional-grade color reproduction standards even with environmentally friendly paper that has an 80% recycled content, dispelling the industry's preconception that recycled paper cannot be used for high-end printing. The lifecycle management of environmentally friendly materials achieves full traceability through blockchain technology. From raw material procurement at the paper mill, production and processing at the printing plant, to logistics and transportation during distribution, environmental impact data for each link is encrypted and recorded. Once the book reaches the reader, the built-in degradation indicator module begins operating, monitoring temperature and humidity changes through micro-environmental sensors to estimate the biodegradation progress of the binding adhesive. This data is cross-validated with the original production records on the blockchain, providing empirical evidence for environmental certification. For example, a textbook using a starch-based adhesive showed a degradation rate superior to laboratory data in actual use. The system automatically fed this field data back to the materials R&D department to drive formulation iteration. This closed-loop data system, from the laboratory to real-world scenarios, significantly improves the effectiveness of environmentally friendly material R&D and shortens the new product verification cycle.

[0032] Furthermore, the system's elastic scalability is fully demonstrated when faced with complex and ever-changing publishing needs. When a publishing house needs to simultaneously operate large-scale printing of academic monographs and personalized customization projects, the distributed version management database automatically divides the resource pool and assigns independent optimization threads to different projects. Academic monographs focus on intelligent updates of the citation system and cross-version content continuity, while customized projects strengthen user portrait analysis and rapid template generation. This parallel processing mechanism achieves resource isolation through containerization technology, ensuring that high-priority tasks are not interfered with. After applying this architecture, a publishing group successfully carried out thousands of customized commemorative album projects simultaneously during the traditional textbook printing peak season, maintaining stable production efficiency and project quality, and breaking through the capacity boundaries of traditional production models.

[0033] like Figure 1As shown, traditional privacy protection mechanisms for paper books often rely on simple encryption or physical isolation, making it difficult to balance data value mining with personal information security. This system embeds a microprocessor between the binding layers of a book, equipped with a lightweight privacy computing framework. As readers flip through the pages, raw behavioral data captured by flexible sensors (such as fingertip pressure distribution and page dwell time) is first processed locally with noise injection, and a fuzzified feature vector is generated using a differential privacy algorithm. For example, a reader's repeated annotations on the "cognitive bias" section of a psychology book will be converted into an abstract label such as "conceptual depth exploration" rather than recording the specific location and frequency of the annotations. This desensitized data is uploaded to the analysis network via a dynamic encrypted channel. Key management utilizes quantum random number generation technology, with each transmission using a unique session key. Even if a single communication is intercepted, historical data patterns cannot be inferred. Applying this solution to a legal publication project, a cross-regional lawyer community's search patterns were successfully identified, providing trend analysis for judicial practice research, while maintaining user anonymity and achieving zero privacy complaint records. Compliance verification of environmentally friendly materials has long relied on manual inspections and paper reports, posing risks of data tampering and timeliness. This system embeds an automated verification rule base within blockchain smart contracts, providing real-time access to a global database of environmental regulations. When a printing batch is initiated, the smart contract automatically triggers a multi-level verification process: first, verifying the validity of the material supplier's qualification certificates; then, comparing test report thresholds for factors such as tear strength and heavy metal content; and finally, assessing whether the transportation route's carbon footprint complies with regional policies. For example, a children's picture book destined for Northern Europe was found to have an adhesive biodegradation cycle that did not meet new regulatory requirements before shipment. The smart contract immediately froze the batch's shipping instructions and pushed a replacement material solution to the printing plant's control terminal. This real-time blocking mechanism effectively avoids the risk of batch scrapping caused by the traditional "produce first, submit for inspection later" model, helping a publishing group reduce its quality incident rate to a historic low.

[0034] The present invention's AI-based dynamic content optimization and environmentally friendly publishing system for paper books deeply couples AI, environmentally friendly material science, IoT perception, and blockchain technology to build a complete technical ecosystem covering content generation, production optimization, reader interaction, and version traceability.

[0035] Therefore, the artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system of the present invention can solve the problems of static paper book content and inefficient environmentally friendly material selection process.

[0036] 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. Artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system, characterized by: include: An AI content optimization engine that analyzes reader behavior data, market trends, and text semantics to dynamically generate personalized content templates and optimize layouts. The templates include replaceable text blocks, illustrations, and interactive QR codes. An environmentally friendly material adaptation module, which uses a multi-objective optimization algorithm to match the optimal environmentally friendly paper combination based on book type, printing batch, and regional resource data; A dynamic printing control unit connected to a digital printing device and receiving the content template and the optimal environmentally friendly paper combination in real time, monitoring the carbon footprint of the printing process through online sensors, and dynamically adjusting the printing speed, ink volume, and energy consumption to match a preset environmental threshold; A user feedback analysis network is deployed in the NFC chip or QR code embedded in the book, continuously collecting data on how long readers stay on the page, the frequency of QR code scanning, and physical page depreciation, and feeding it back to the artificial intelligence engine to iteratively optimize the content of subsequent versions.

2. The artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system according to claim 1 is characterized in that: The artificial intelligence content optimization engine integrates collaborative filtering algorithms and natural language processing models to generate content association recommendation templates across reader groups based on the chapter jump rates, annotation hotspots and semantic similarities of historical reader groups. The content association recommendation templates include dynamically inserted extended reading chapters and interactive knowledge graph links.

3. The artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system according to claim 1 is characterized in that: When matching the optimal environmentally friendly paper combination, the environmentally friendly material adaptation module combines the regional waste paper recycling rate, the carbon emission intensity data of local paper mills and the transportation radius, determines the balance point between the proportion of recycled fiber and the ink type through Pareto front analysis, and dynamically generates a material supply chain topology map.

4. The artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system according to claim 1 is characterized in that: The dynamic printing control unit deploys optical sensors and conductive ink detection devices to monitor the surface roughness of the printed paper, the ink penetration depth and the adhesive curing temperature in real time, and adjusts the printing pressure and drying time through a fuzzy logic controller to reduce carbon emissions per unit area to below a preset threshold.

5. The artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system according to claim 1 is characterized in that: The user feedback analysis network integrates an edge computing module in the NFC chip embedded in the book. After local pre-processing of the reader's page turning acceleration and page corner angle data, only feature vectors are uploaded to the cloud analysis network, reducing data transmission bandwidth usage.

6. The artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system according to claim 1 is characterized in that: The distributed version management database adopts a sharded blockchain structure to associate and store the environmental material traceability information of each book with the content hash value, and verifies the data integrity through zero-knowledge proof. The environmental material traceability information includes but is not limited to the paper mill batch number and ink heavy metal test report.

7. The artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system according to claim 1 is characterized in that: The artificial intelligence content optimization engine adjusts the typesetting layout according to the reader emotion recognition model. The reader emotion recognition model infers reading concentration and dynamically increases font spacing or inserts visual separators by analyzing the QR code scanning interval, the standard deviation of page dwell time and the distribution of physical page creases.

8. The artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system according to claim 1 is characterized in that: When the environmental protection material adaptation module detects sudden changes in regional environmental protection policies, it initiates the emergency reconfiguration protocol, dynamically switches to the backup material combination plan based on the Markov decision process, and synchronizes the change instructions to the publisher and the printing factory through the blockchain.

9. The artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system according to claim 1 is characterized in that: The edge computing module uses a lightweight encryption protocol to desensitize reader behavior data, generates a differentially private feature vector, and then uploads it to the cloud to prevent user privacy leakage.

10. The artificial intelligence-based paper book dynamic content optimization and environmentally friendly publishing system according to claim 1, characterized in that: The blockchain's smart contract includes automatic environmental compliance verification rules. When it is detected that the volatile organic compound (VOC) content of a batch of materials exceeds the standard, the printing authorization of the corresponding version content template is automatically frozen and an alarm process is triggered.

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