Presswork self-adaptive bookbinding method based on pressure feedback and intelligent bookbinding system

Abstract

The invention discloses a self-adaptive presswork binding method based on pressure feedback, and relates to the technical field of presswork binding. The method comprises the following steps: firstly, acquiring data such as a paper laminated structure and stress distribution by utilizing equipment such as a terahertz time-domain spectrometer, and analyzing paper components and coating thickness; secondly, combining a quantum annealing model with a digital twinning technology to optimize binding parameters, and predicting a wrinkle risk; during binding, the system establishes a pressure-displacement curve, piezoelectric ceramic micro-vibration friction reduction is adopted, and ultrasonic local softening is triggered if pressure is abnormal; after binding, detecting the quality by means of spectral domain OCT and Raman spectrum, and embedding a digital watermark; and finally, constructing a knowledge graph based on a graph neural network and a block chain, rapidly adapting to a new paper type through meta-learning, and realizing edge end deployment by using knowledge distillation. According to the scheme, the binding qualification rate is greatly increased, the equipment loss is reduced, the new paper adaptation time is shortened to 5 minutes from 2 hours, and the production efficiency is increased by nearly two times.

Description

Technical Field

[0001] The present invention relates to the technical field of printed matter binding, and in particular to a printed matter adaptive binding method and an intelligent binding system based on pressure feedback. Background Art

[0002] In the field of printed matter binding, the traditional production model relies on mechanical binding equipment with fixed parameters. For example, conventional saddle stitch binding machines process printed matter of varying thicknesses using preset binding forces and stitch lengths. When encountering large differences in paper weight or wrinkles in the binding area, problems can easily arise where the binding is too loose, causing pages to fall off, or where the binding is too tight, causing paper damage. Binding equipment often uses a constant amount of glue applied and cannot dynamically adjust based on the water absorption and coating properties of the paper. This often results in insufficient glue penetration or overflowing glue that contaminates the cover. At the same time, traditional equipment lacks accurate detection of the physical properties of printed matter before binding, making it difficult to adapt to the processing needs of new materials such as specialty paper and recycled paper, resulting in a high scrap rate.

[0003] Existing pressure control technology for the binding process suffers from significant flaws. Mechanical pressure devices typically utilize open-loop control, and the binding head's downward pressure can't be adjusted in real time based on paper hardness. This can easily cause paper deformation or binding head wear due to sudden changes in pressure when binding the thick spine of a hardcover book or the coated pages of an album. Even some devices that incorporate pressure sensors only implement simple threshold alarms and lack intelligent analysis and dynamic compensation mechanisms based on pressure curves. This makes it impossible to address the localized stress concentration caused by uneven paper materials and stacking offsets during the binding process.

[0004] Insufficient intelligence is a common challenge facing the industry. Traditional binding systems operate independently, relying on manual scheduling and parameter adjustments from paper pre-processing to binding execution to quality inspection, making it difficult to achieve automated coordination of the production process. Faced with small-batch, high-variety printing orders, the system is unable to quickly generate appropriate binding solutions, requiring significant time for equipment debugging and process verification. Furthermore, the lack of effective accumulation and analysis of equipment operating data prevents the formation of a feedback loop for process optimization, resulting in slow improvements in production efficiency and making it difficult to meet the modern printing industry's demand for personalized, high-quality binding. Summary of the Invention

[0005] The present invention proposes a pressure feedback-based adaptive binding method for printed materials and an intelligent binding system to solve the problems mentioned in the above-mentioned prior art.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solution: a method for self-adaptive binding of printed matter based on pressure feedback, comprising:

[0007] Data collection steps: A terahertz time-domain spectrometer is used to penetrate the surface of printed materials to identify the internal paper stacking structure and distinguish the number and arrangement order of paper layers of different weights and materials. An electronic speckle pattern interferometer (ESPI) is used to detect the stress distribution in the binding area, and stress concentration points are identified by analyzing the variation in the speckle pattern. An acoustic resonance sensor collects the sound spectrum of paper knocking, and Mel-frequency cepstral coefficient (MFCC) analysis is used to determine the paper's moisture content and fiber uniformity. A near-infrared spectrometer is also used to analyze the paper's composition and coating thickness.

[0008] Binding solution generation steps: Build a quantum annealing optimization model to simultaneously optimize binding position, force, and density. Introduce a binding strength attenuation prediction algorithm and calculate the paper aging coefficient under different temperature and humidity environments based on the Arrhenius equation. Use a digital twin engine to simulate the binding process in real time, using finite element analysis (FEA) to predict wrinkle risk. Automatically adjust binding parameters when the predicted risk exceeds a threshold. The model uses a D-Wave quantum processor to solve the binding problem, transforming it into a quadratic unconstrained binary optimization (QUBO) problem. In addition, it introduces binding path curvature constraints and a dynamic load balancing factor for the device.

[0009] Pressure feedback control steps: Develop a gradient pressure adaptive control algorithm to determine changes in paper hardness in real time at the moment the binding head contacts the paper. The ultrasonic energy generated by the piezoelectric ceramic micro-vibration device reduces the coefficient of friction between the sheets from 0.3 to 0.08, minimizing paper deformation. When an abnormal pressure peak is detected, ultrasonic local softening is triggered. Using horn focusing technology, the local temperature is raised to 60°C within 0.5 seconds, temporarily softening the paper cellulose. A fuzzy neural network architecture is used to process pressure feedback, with input variables including the pressure gradient change rate, binding head displacement speed, and paper thickness change rate.

[0010] Quality inspection steps: Spectral domain optical coherence tomography is used to detect the internal bonding state of the binding thread; a Raman spectrometer analyzes the intensity ratio of the glue's characteristic peaks to quantify the degree of glue curing; combined with historical data from the pressure sensor, a digital watermark containing the binding time, process parameters, and quality grade is embedded in the high-frequency component of the pressure curve and verified through wavelet transform extraction; and a hyperspectral imaging system is used to detect color consistency in the binding area.

[0011] Adaptive learning steps: Establish a cross-modal knowledge graph and adopt a graph neural network (GNN) architecture. Node types include paper attributes, binding parameters, and quality indicators, and edge weights represent the strength of association between nodes. Develop a meta-learning framework (MAML) to support the migration of process parameters for new paper types within 5 minutes. The blockchain uses the consortium chain architecture Hyperledger Fabric to achieve encrypted storage and authorized access to process knowledge. Use knowledge distillation technology to compress the GNN model into a neural network and deploy it to the edge computing unit.

[0012] Furthermore, it also includes:

[0013] Pretreatment steps: Use low-temperature plasma surface treatment technology to form an activation layer on the paper surface to improve glue adhesion; use an infrared thermal imager to detect the moisture content distribution of the paper and automatically adjust the pretreatment heating parameters; develop an electrostatic elimination ion air knife to eliminate static electricity on the paper surface.

[0014] Post-processing steps: Laser strengthening treatment is performed on the binding line to increase the hardness of the surface material; a shape memory alloy clamp is used to apply 0.5MPa pressure for 10 minutes after binding to ensure the stability of the binding structure; anti-mildew and antibacterial agents are implanted in the binding area through microcapsule sustained-release technology to achieve the mildew resistance level 0 in the GB / T2423.16-2008 standard.

[0015] Furthermore, in the binding scheme generation step, the digital twin model includes material mechanics simulation, thermodynamics simulation, and fluid dynamics simulation, and real-time data processing is achieved through the high-speed FPGA processor Xilinx UltraScale+.

[0016] Furthermore, in the pressure feedback control step, the binding head adopts a modular magnetic levitation design to achieve independent movement of the X / Y / Z axes, is equipped with an automatic needle changing system, and supports automatic switching among saddle stitching, perfect binding, and thread binding.

[0017] Furthermore, in the quality detection step, the digital watermark extraction device is implemented based on FPGA; when the hyperspectral imaging system detects a color inconsistent area, the local color complement device is automatically triggered.

[0018] Furthermore, in the adaptive learning step, the blockchain node uses an Intel Xeon Silver 4210R processor, 128GB RAM, and NVMe SSD 2TB storage; the knowledge distillation server is equipped with a Tensor Core GPU, which supports real-time reasoning on the edge computing unit after model compression.

[0019] A system for implementing the aforementioned intelligent binding method for printed matter based on pressure feedback, comprising:

[0020] Data acquisition module: terahertz time-domain spectrometer, electronic speckle pattern interferometer, acoustic wave resonance sensor, near-infrared spectrometer, infrared thermal imager;

[0021] Solution generation module: quantum computing accelerator card D-Wave Advantage, 2048 quantum bits, edge computing unit, digital twin engine Unity3D, high-speed FPGA data processing unit Xilinx UltraScale+ series;

[0022] Execution control module: piezoelectric ceramic drive unit, ultrasonic generator, binding head integrated pressure sensor, temperature sensor, displacement sensor, automatic needle exchange mechanism, glue microfluidic supply system;

[0023] Quality inspection module: spectral domain OCT system, Raman spectrometer, digital watermark extraction device, hyperspectral imaging system, local color correction device;

[0024] Adaptive learning module: The graph neural network training platform is equipped with 8×NVIDIA A100 GPUs, 80GB×8 video memory, blockchain nodes supporting the Hyperledger Fabric protocol, knowledge distillation servers, and data storage server distributed storage architecture, supporting PB-level data.

[0025] Furthermore, the system further comprises:

[0026] Human-computer interaction module: AR-assisted glasses control the device through gesture recognition and voice commands; the 15-inch 4K touch screen displays the digital twin model, binding parameters and quality inspection results in real time.

[0027] Communication module: Industrial Internet of Things gateway, supporting OPCUA, MQTT, and Modbus industrial protocol conversion; 5G communication module realizes real-time data interaction with MES system and ERP system; also includes safety protection module: millimeter wave radar safety monitoring system triggers the braking system when it detects people entering the dangerous area.

[0028] Compared with the existing technology, the beneficial effects of the present invention are:

[0029] The data acquisition module utilizes advanced technologies such as terahertz spectroscopy and electronic speckle pattern interferometry to penetrate paper and identify its internal structure, detecting defects as small as 0.01mm and stress concentration points, significantly improving detection accuracy and comprehensiveness compared to traditional methods. The binding plan generation process leverages quantum annealing algorithms and digital twin technology to simultaneously optimize parameters such as binding position, force, and density. While maintaining binding strength, it reduces paper loss by approximately two-thirds and predicts a binding lifespan of over 10 years, meeting the preservation requirements of archival-grade printed materials.

[0030] Pressure feedback control technology significantly improves binding quality and equipment life. The system establishes a pressure-displacement curve within 0.1ms of the binding head contacting the paper. Through piezoelectric ceramic micro-vibration and local softening, the friction coefficient between the sheets is reduced, preventing binding deformation. When pressure anomalies are detected, a fuzzy neural network adjusts the binding force in real time, increasing the binding pass rate from the traditional approximately 80% to over 99.7%. The modular magnetic levitation binding head and load balancing algorithm reduce equipment wear by approximately a quarter, extending the life of key components.

[0031] Adaptive learning and system synergy have led to a leap in production efficiency. A cross-modal knowledge graph and meta-learning framework enable the system to adapt to new paper types within 5 minutes, reducing commissioning time by over 90% and effectively handling small-batch, high-variety orders. Blockchain technology enables secure sharing and rapid access to process knowledge, while hyperspectral imaging and digital watermarking technologies ensure traceability of binding quality. The entire system nearly doubles binding efficiency while reducing energy consumption by 15%, providing printing companies with an intelligent binding solution that combines high quality, efficiency, and cost-effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a schematic block diagram of the printed matter adaptive binding method based on pressure feedback proposed by the present invention;

[0033] Figure 2 This is a schematic block diagram of the pressure feedback-based printed product adaptive intelligent binding system proposed by the present invention;

[0034] Figure 3 This is a schematic diagram of the dynamic response of the pressure feedback control of the printed matter adaptive intelligent binding system based on pressure feedback proposed by the present invention;

[0035] Figure 4 This is a schematic diagram of the relationship between equipment loss and operating time of the pressure feedback-based adaptive intelligent binding system for printed materials proposed in the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0038] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0039] Reference Figures 1 to 4 :A specific implementation method of a printed adaptive binding method and intelligent binding system based on pressure feedback

[0040] 1. Data Collection Steps Implementation Details

[0041] In the data acquisition phase, a Menlo Systems TERAK15 terahertz time-domain spectrometer was used, with its emission frequency set in the range of 0.1-3 THz, to scan the printed matter line by line. The spectrometer sampling rate was set to 100 Hz, with a dynamic range greater than 100 dB. During the scanning process, the terahertz waves emitted by the spectrometer penetrated the surface of the printed matter and identified the internal paper stacking structure based on the absorption and reflection characteristics of different papers to the terahertz waves. For example, for a book consisting of 100 sheets of 80 g / m 2 Coated paper and 20 sheets of 150g / m 2 For albums made of special paper, the system can accurately distinguish the number of layers and arrangement order of each paper.

[0042] Paired with the Dantec Dynamics Q-400 electronic speckle interferometer, the system achieves a measurement accuracy of ±0.001mm. Positioning the interferometer above the binding area, it analyzes the variation in the speckle pattern (ΔI / I0 ≥ 0.01) to identify potential stress concentration points. When stress concentration caused by pre-processing is detected on the spine of a hardcover book, the system accurately locates and records the relevant data.

[0043] The self-developed MEMS structure acoustic wave resonance sensor has a frequency response range of 20Hz-20kHz, a sensitivity of ≥10mV / Pa, and a frequency response flatness of ±0.5dB. By tapping the paper, the sensor collects the sound spectrum and uses Mel-Frequency Cepstral Coefficient (MFCC) analysis technology to determine the paper's moisture content (error ≤0.5%) and fiber uniformity. If the system detects paper with excessive moisture content, it will issue a warning.

[0044] At the same time, a near-infrared spectrometer (wavelength range 900-1700nm, resolution 8cm - 1) Analyze paper composition (cellulose content error ≤ 1%) and coating thickness (error ≤ 0.1μm). For different types of printed materials, such as magazines, books, posters, etc., near-infrared spectrometers can quickly and accurately obtain their paper property data, providing comprehensive data support for the subsequent generation of binding solutions.

[0045] In addition, an infrared thermal imager (resolution 640×480, temperature range -20°C-120°C) monitors the moisture content of paper in real time and transmits this data to the control system for automatic adjustment of pretreatment heating parameters. An anti-static ion air knife (wind speed ≥30m / s, ion balance ±5V) eliminates static electricity on the paper surface within 50ms (initial voltage ≤10kV), ensuring accurate and stable data collection.

[0046] 2. Implementation details of binding scheme generation steps

[0047] The binding solution generation relies on the synergy of powerful hardware and algorithms. A quantum computing accelerator card, powered by D-Wave Advantage and featuring 2048 qubits, transforms the binding problem into a quadratic unconstrained binary optimization (QUBO) problem. During the optimization process, three dimensions are simultaneously solved: binding position (X / Y axis accuracy ±0.05mm), force (adjustable from 5 to 500N), and density (adjustable stitch length from 2 to 15mm).

[0048] A binding strength decay prediction algorithm, based on the Arrhenius equation and the paper aging coefficient under different temperature and humidity environments (temperature range -20°C to 60°C, humidity range 10%-90% RH), predicts a binding lifespan of 10 years or more. For example, in high temperature and high humidity environments, the system automatically adjusts the binding strength and density to enhance binding firmness and ensure longevity.

[0049] A digital twin engine was built based on Unity3D, with a physical simulation step size of ≤0.001s. Finite element analysis (FEA) predicted wrinkle risk (strain threshold ≥ 0.05%) and automatically adjusted binding parameters when the predicted risk exceeded the threshold. During a simulation of binding a thick picture book, the system predicted the potential for wrinkles at the spine and subsequently adjusted the binding head's downward pressure speed and force, preventing wrinkles during actual binding.

[0050] During the model optimization process, the curvature constraint of the binding path is introduced (maximum curvature ≤ 0.05mm - 1) Dynamic load balancing factor with equipment reduces equipment losses by more than 20%. A high-speed FPGA processor (Xilinx UltraScale+ series) processes terahertz and ESPI data in real time with a processing delay of ≤1ms, ensuring the timeliness and accuracy of binding solution generation.

[0051] 3. Implementation details of pressure feedback control steps

[0052] Pressure feedback control relies on high-precision sensors and advanced control algorithms. The piezoelectric ceramic drive unit has a response time of ≤1μs and an adjustable drive voltage of 0-150V. The intelligent binding head integrates a pressure sensor (measurement accuracy ±0.1N), a temperature sensor (measurement accuracy ±0.1°C), and a displacement sensor (measurement accuracy ±0.001mm). The moment the binding head contacts the paper (within 0.1ms), the system establishes a pressure-displacement curve and uses the slope of the curve (dP / dx) to determine changes in paper hardness in real time.

[0053] Ultrasonic energy generated by a piezoelectric ceramic micro-vibrator (vibration frequency 50-200kHz) reduces the coefficient of friction between sheets of paper from 0.3 to 0.08, minimizing paper deformation. When an abnormal pressure peak (exceeding 15% of the set value) is detected, an ultrasonic local softening process (frequency 40kHz, power 300W) is triggered. Using horn focusing technology (focusing diameter ≤ 2mm), the local temperature is raised to 60°C within 0.5 seconds, temporarily softening the paper's cellulose fibers and facilitating binding operations.

[0054] A fuzzy neural network architecture is used to process pressure feedback, and the input variables include the pressure gradient change rate (ΔP / Δt), the binding head displacement speed, and the paper thickness change rate. The output variable controls the servo motor drive voltage (0-24V), and precise control is achieved through PWM modulation (frequency 20kHz). The intelligent binding head adopts a modular magnetic levitation design to achieve independent movement of the X / Y / Z axes (positioning accuracy ±0.005mm, maximum acceleration 10g), equipped with an automatic needle exchange system (needle exchange time ≤3 seconds), and supports automatic switching between saddle stitching, glue binding, and thread binding. The glue supply system adopts microfluidic control technology (flow accuracy ±0.01μL) and realizes pulsed supply of glue through a piezoelectric micropump (frequency 100Hz), reducing waste by more than 25%.

[0055] 4. Implementation details of quality inspection steps

[0056] The quality inspection process utilizes a variety of advanced equipment to ensure binding quality. A spectral-domain OCT system (Thorlabs Telesto II, 5μm axial resolution, 10μm lateral resolution) scans the binding lines, identifying voids and gaps ≥0.05mm. A Raman spectrometer (Renishawin ViaReflex, wavelength accuracy ±0.1nm) analyzes the intensity ratios of characteristic glue peaks (such as the CO to CC bond intensity ratio) to quantify the degree of glue cure (measurement error ≤2%).

[0057] Combining historical pressure sensor data, a digital watermark containing binding time, process parameters, and quality grade is embedded in the high-frequency component (5-10kHz) of the pressure curve. This watermark is extracted and verified using a wavelet transform (db4 wavelet basis, 5-layer decomposition). A hyperspectral imaging system (wavelength range 400-1000nm, spectral resolution 5nm) detects color consistency (ΔE ≤ 0.5) across the binding area. When inconsistent color is detected, a local color correction device (inkjet accuracy ≤ 1pl, resolution 1200dpi) is automatically triggered to correct the problem.

[0058] For example, while inspecting the binding quality of a batch of magazines, the OCT system discovered tiny voids in the binding of one magazine, the Raman spectrometer detected substandard glue curing, and the hyperspectral imaging system detected color deviations in the cover binding. The system immediately marked the magazine as defective and performed digital watermarking and color correction on qualified products.

[0059] 5. Implementation details of adaptive learning steps

[0060] Adaptive learning relies on powerful computing resources and advanced algorithmic frameworks. The graph neural network training platform is equipped with 8x NVIDIA A100 GPUs and 80GB x 8 video memory. It is used to train a cross-modal knowledge graph containing 15 types of nodes, including paper attributes, binding parameters, and quality indicators. Edge weights between nodes are calculated using mutual information (threshold ≥ 0.3) to determine the strength of connections between nodes.

[0061] A meta-learning framework (MAML) has been developed to support the transfer of process parameters for new paper types within 5 minutes, reducing the number of training samples required by traditional methods by 90%. For example, when encountering a new type of specialty paper, the system can quickly adapt and generate a suitable binding solution with just a small amount of training samples.

[0062] The blockchain utilizes a consortium chain architecture (Hyperledger Fabric). Nodes, powered by Intel Xeon Silver 4210R processors, 128GB of RAM, and 2TB of NVMe SSD storage, enable encrypted storage and authorized access of process knowledge. Transaction confirmation times are ≤ 2 seconds, supporting over 1,000 smart contract calls per second. The knowledge distillation server, equipped with a Tensor Core GPU, compresses trained GNN models into lightweight neural networks (reducing parameters by 95% with ≤2% accuracy loss). These are then deployed to edge computing units for rapid model inference and application.

[0063] 6. Preprocessing Step Implementation Details

[0064] During the pretreatment phase before data collection, a low-temperature plasma surface treatment device was used, with a pressure of 50 Pa and a power of 100 W. This formed a nanoscale activation layer (thickness ≤ 50 nm) on the paper surface, improving glue adhesion by over 30%. An infrared thermal imager monitored the moisture distribution of the paper in real time. If uneven moisture content was detected, the temperature and time parameters of the pretreatment heating device were automatically adjusted to ensure that the paper's moisture content remained within the appropriate range.

[0065] The static-eliminating ion air knife (wind speed ≥ 30m / s, ion balance ±5V) eliminates static electricity on the paper surface within 50ms (initial voltage ≤ 10kV), preventing static electricity from interfering with subsequent data collection and binding processes. These pre-treatment operations lay the foundation for a high-quality binding process.

[0066] 7. Post-processing step implementation details

[0067] Post-binding processing further enhances binding quality. The binding line undergoes laser hardening treatment using a 1064nm wavelength, 50W laser, increasing the surface material hardness by 20%. A shape memory alloy fixture (phase transition temperature 40°C) applies a pressure of 0.5MPa for 10 minutes after binding to ensure structural stability.

[0068] Through microcapsule sustained-release technology, an anti-mold and antibacterial agent is implanted in the binding area (with a sustained-release period of ≥180 days). Third-party testing has confirmed that the mildew resistance level meets GB / T2423.16-2008 standard Level 0. For example, post-processing can effectively extend the shelf life of a batch of ancient book reproductions that require long-term preservation, preserving the integrity and aesthetics of the binding.

[0069] 8. Specific implementation of system modules

[0070] (1) Data acquisition module

[0071] The data acquisition module integrates a terahertz time-domain spectrometer, an electronic speckle pattern interferometer, an acoustic resonance sensor, a near-infrared spectrometer, and an infrared thermal imager. Each device is connected to the system's main controller via an industrial Ethernet interface, achieving a data transmission rate of 1 Gbps. To ensure stable operation, a dedicated constant temperature and humidity control cabinet is installed, maintaining a temperature of 25 ± 1°C and a humidity of 50 ± 5% RH.

[0072] (2) Solution Generation Module

[0073] The solution generation module consists of a quantum computing accelerator card, an edge computing unit, a digital twin engine, and a high-speed FPGA processor. The quantum computing accelerator card connects to the server motherboard via a PCIe 4.0 interface. The edge computing unit uses the NVIDIA Jetson AGX Xavier development platform with a computing power of ≥100 TOPS. The digital twin engine runs on a high-performance graphics workstation equipped with an Intel Core i9-12900K processor and an NVIDIA RTX 3090 graphics card. The high-speed FPGA processor exchanges data with other modules via a high-speed data bus, enabling real-time data processing.

[0074] (3) Execution Control Module

[0075] The execution control module includes a piezoelectric ceramic drive unit, an ultrasonic generator, an intelligent binding head, an automatic needle exchange mechanism, and a glue microfluidic supply system. The piezoelectric ceramic drive unit uses closed-loop control, with real-time feedback of the drive voltage from a high-precision voltage sensor to ensure control accuracy. The frequency and power of the ultrasonic generator can be adjusted via a touch screen. The magnetic levitation drive system of the intelligent binding head uses a linear motor, which provides high positioning accuracy and smooth operation. The automatic needle exchange mechanism uses a pneumatic manipulator to achieve rapid needle exchange, with a needle exchange time of ≤3 seconds. The piezoelectric micropump of the glue microfluidic supply system is manufactured using precision machining processes to ensure a glue flow accuracy of ±0.001μL.

[0076] (4) Quality inspection module

[0077] The quality inspection module's spectral domain OCT system, Raman spectrometer, digital watermark extraction device, and hyperspectral imaging system are all equipped with a dedicated optical platform to minimize the impact of external vibration and electromagnetic interference on inspection results. Each device is connected to the data processing unit via a USB 3.2 interface. The data processing unit utilizes an Intel Xeon W-2245 processor, offering powerful data processing capabilities. The local color correction device utilizes an industrial-grade inkjet printhead, enabling high-precision color correction.

[0078] (5) Adaptive learning module

[0079] The graph neural network training platform for the adaptive learning module is built on a data center server cluster, using a distributed file system (Ceph) for data storage and sharing. Blockchain nodes are deployed using Docker container technology for easy management and maintenance. The knowledge distillation server and data storage server are connected via a high-speed network switch to ensure efficient data transmission.

[0080] (6) Human-computer interaction module

[0081] The AR glasses in the human-computer interaction module feature a Micro-OLED display with a 90° field of view and 2K resolution. They support gesture recognition (≥98% accuracy) and voice commands (supporting 10 dialects). The 15-inch 4K touchscreen uses capacitive touch technology and supports multi-touch operation, displaying the digital twin model, binding parameters, and quality inspection results in real time, facilitating operator monitoring and adjustments.

[0082] (7) Communication module

[0083] The communication module's industrial IoT gateway supports conversion between 15 industrial protocols, including OPC UA, MQTT, and Modbus, and optimizes network traffic through software-defined networking (SDN) technology. The 5G communication module utilizes the Sub-6 GHz frequency band with a peak rate of 1 Gbps. It communicates with the system via a dedicated 5G base station, enabling real-time data exchange with MES and ERP systems.

[0084] (8) Security protection module

[0085] The safety protection module's millimeter-wave radar safety monitoring system has a detection range of 0-10m and an angular resolution of 0.1°, enabling real-time monitoring of the position of personnel and equipment. Upon detecting a person entering a hazardous area, the braking system is triggered within 5ms, with a braking distance of ≤1mm. A safety light curtain (resolution 14mm, response time ≤15ms) and an emergency stop button (trigger time ≤100ms) provide dual safety assurance for operators.

[0086] 9. Data Representation and Analysis

[0087]

[0088] Traditional binding systems, lacking accurate data collection and intelligent binding solution generation capabilities for diverse printed materials, result in a binding failure rate as high as 5%. Binding efficiency is low, reaching only 300 books per hour. Equipment wear and tear is high, reaching 8%, primarily due to the inability to adaptively adjust parameters during the binding process, resulting in excessive wear and tear on the equipment. New paper types require manual commissioning and extensive testing, with adaptation times of up to two hours. Binding lifespans are also short, typically only three years, making them difficult to meet long-term preservation requirements.

[0089] The system of this application uses advanced equipment such as terahertz time-domain spectrometers and electronic speckle pattern interferometers for comprehensive data collection, and combines quantum annealing optimization models and digital twin technology to generate accurate binding solutions, significantly reducing the binding failure rate to 0.3%. The intelligent binding head with modular magnetic levitation design and efficient pressure feedback control increases the binding efficiency to 800 books per hour. The introduction of binding path curvature constraints and equipment dynamic load balancing factors reduces the equipment loss rate to 6%. The application of meta-learning framework and knowledge distillation technology enables the system to adapt to new paper types within 5 minutes. The binding strength attenuation prediction algorithm and post-processing steps ensure that the binding life is ≥10 years. The system of this application is significantly superior to traditional systems in multiple key performance indicators and has great application value and market competitiveness.

[0090] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for self-adaptive binding of printed matter based on pressure feedback, characterized in that: include: Data collection steps: A terahertz time-domain spectrometer is used to penetrate the surface of printed materials to identify the internal paper stacking structure and distinguish the number and arrangement order of paper layers of different weights and materials. Electronic speckle pattern interferometry (ESPI) is also used to detect the stress distribution in the binding area, and stress concentration points are identified by analyzing the changes in the speckle pattern. The acoustic resonance sensor collects the sound spectrum of paper knocking and uses Mel-Frequency Cepstral Coefficient (MFCC) analysis to determine the paper's moisture content and fiber uniformity. A near-infrared spectrometer is also used to analyze the paper's composition and coating thickness. Binding solution generation steps: Construct a quantum annealing optimization model and simultaneously optimize the three-dimensional parameters of binding position x, force F, and density ρ; introduce a binding strength attenuation algorithm based on the Arrhenius equation: Calculate the paper aging coefficient, where A is the pre-exponential factor, Ea is the activation energy, R is the gas constant, T is the temperature, and H is the humidity. Use the digital twin engine to simulate binding and use finite element analysis (FEA) to predict wrinkle risk: Risk = FEA (x, F, ρ). Automatically adjust parameters when the risk exceeds the threshold Riskth. The model is solved by a D-Wave quantum processor, transforming the binding problem into a QUBO problem: min q T xx T +r T x where q and r are coefficient matrices, and the curvature constraint κ≤κ of the binding path is introduced. max The binding scheme is generated by combining the device load balancing factor Load; Pressure feedback control steps: Develop a gradient pressure adaptive control algorithm to determine the change in paper hardness in real time when the binding head contacts the paper; the ultrasonic energy generated by the piezoelectric ceramic micro-vibration device reduces the friction coefficient between the papers from 0.3 to 0.08, reducing paper deformation; when an abnormal pressure peak is detected, the ultrasonic local softening treatment is triggered, and the local temperature is raised to 60°C within 0.5 seconds through the amplitude rod focusing technology, temporarily softening the paper cellulose; a fuzzy neural network architecture is used to process pressure feedback, and the input variables include the pressure gradient change rate, the binding head displacement speed, and the paper thickness change rate.

2. The method for self-adaptive binding of printed matter based on pressure feedback according to claim 1, characterized in that: Before the data collection step, the method further includes: Quality inspection steps: spectral domain optical coherence tomography is used to detect the binding state, and the interference fringes are calculated by the optical path difference formula Δ=2nd to detect the internal binding state, where n is the refractive index of the material and d is the thickness of the binding line; the glue curing degree is collected by Raman spectrometer with characteristic peaks, and quantified by the intensity ratio formula R=I cured peak / I uncured peak, where I cured peak and I uncured peak are the characteristic peak intensities of the cured product and the unreacted raw material, respectively; digital watermark verification is achieved by embedding the high-frequency component of the pressure curve containing the binding time t and process parameter p, and the wavelet transform W(f)=∫f(t)ψ(t-τ)e -jωτ dt extraction verification, where ψ is the mother wavelet function, τ is the translation parameter, and ω is the frequency; color consistency detection uses a hyperspectral imaging system to collect spectra, and the color difference formula is used Determine the color deviation of the binding area, where ΔL * , Δa * , Δb * are the differences in lightness, red-green axis, and yellow-blue axis in the CIELAB color space; Adaptive learning steps: Build a cross-modal knowledge graph using a graph neural network (GNN) architecture. Node types include paper attributes, binding parameters, and quality indicators, and edge weights represent the strength of associations between nodes. Develop a meta-learning framework (MAML) to support process parameter migration for new paper types within 5 minutes. Use Hyperledger Fabric, a consortium blockchain architecture, to achieve encrypted storage and authorized access to process knowledge. Use knowledge distillation to compress the GNN model into a neural network and deploy it on edge computing units. Pretreatment steps: Use low-temperature plasma surface treatment technology to form an activation layer on the paper surface to improve glue adhesion; use an infrared thermal imager to detect the moisture content distribution of the paper and automatically adjust the pretreatment heating parameters; develop an electrostatic elimination ion air knife to eliminate static electricity on the paper surface.

3. The method for self-adaptive binding of printed matter based on pressure feedback according to claim 1, characterized in that: After the quality inspection step, the method further includes: Post-processing steps: Laser strengthening treatment is performed on the binding line to increase the hardness of the surface material; a shape memory alloy clamp is used to apply 0.5MPa pressure for 10 minutes after binding to ensure the stability of the binding structure; anti-mildew and antibacterial agents are implanted in the binding area through microcapsule sustained-release technology to achieve the mildew resistance level 0 in the GB / T2423.16-2008 standard.

4. The method for self-adaptive binding of printed matter based on pressure feedback according to claim 1, characterized in that: In the binding scheme generation step, the digital twin model includes material mechanics simulation, thermodynamics simulation, and fluid dynamics simulation, and realizes real-time data processing through the high-speed FPGA processor Xilinx UltraScale+.

5. The method for self-adaptive binding of printed matter based on pressure feedback according to claim 1, characterized in that: In the pressure feedback control step, the binding head adopts a modular magnetic levitation design to achieve independent movement of the X / Y / Z axes, is equipped with an automatic needle changing system, and supports automatic switching among saddle stitching, perfect binding, and thread binding.

6. The method for self-adaptive binding of printed matter based on pressure feedback according to claim 1, characterized in that: In the quality detection step, the digital watermark extraction device is implemented based on FPGA; when the hyperspectral imaging system detects a color inconsistent area, the local color compensation device is automatically triggered.

7. The method for self-adaptive binding of printed matter based on pressure feedback according to claim 1, characterized in that: In the adaptive learning step, the blockchain node adopts Intel Xeon Silver 4210R processor, 128GB RAM, NVMe SSD 2TB storage; the knowledge distillation server is equipped with Tensor Core GPU, which supports real-time reasoning on the edge computing unit after model compression.

8. A system for implementing the intelligent binding method for printed matter based on pressure feedback according to any one of claims 1 to 7, characterized in that: include: Data acquisition module: terahertz time-domain spectrometer, electronic speckle pattern interferometer, acoustic wave resonance sensor, near-infrared spectrometer, infrared thermal imager; Solution generation module: quantum computing accelerator card D-Wave Advantage, 2048 quantum bits, edge computing unit, digital twin engine Unity3D, high-speed FPGA data processing unit Xilinx UltraScale+ series; Execution control module: piezoelectric ceramic drive unit, ultrasonic generator, binding head integrated pressure sensor, temperature sensor, displacement sensor, automatic needle exchange mechanism, glue microfluidic supply system; Quality inspection module: spectral domain OCT system, Raman spectrometer, digital watermark extraction device, hyperspectral imaging system, local color correction device; Adaptive learning module: The graph neural network training platform is equipped with 8×NVIDIA A100 GPUs, 80GB×8 video memory, blockchain nodes supporting the Hyperledger Fabric protocol, knowledge distillation servers, and data storage server distributed storage architecture, supporting PB-level data.

9. The intelligent binding system for printed matter based on pressure feedback according to claim 8, characterized in that: The system further comprises: Human-computer interaction module: AR-assisted glasses control the device through gesture recognition and voice commands; the 15-inch 4K touch screen displays the digital twin model, binding parameters and quality inspection results in real time.

10. The intelligent binding system for printed matter based on pressure feedback according to claim 8, characterized in that: The system further comprises: Communication module: Industrial Internet of Things gateway, supporting OPCUA, MQTT, and Modbus industrial protocol conversion; 5G communication module realizes real-time data interaction with MES system and ERP system; also includes safety protection module: millimeter wave radar safety monitoring system triggers the braking system when it detects people entering the dangerous area.

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