Basement template splicing and node processing management system

Abstract

The invention belongs to the technical field of basement template management, and particularly relates to a basement template splicing and node processing management system which comprises a multi-source heterogeneous sensing fusion module, a self-adaptive sensor network optimization unit, a multi-physical field data fusion engine and a digital twinning-based closed-loop control unit. The multi-source heterogeneous sensing fusion module integrates various sensors, can comprehensively collect the friction force, node strain distribution and stratum three-dimensional structure data of a splicing interface in real time, and provides accurate basic data for subsequent analysis and control; the self-adaptive sensor network optimization unit dynamically adjusts the layout of sensors based on a topological optimization algorithm, so that redundant sensors are reduced, meanwhile, automatic fault reconstruction is realized by adopting a self-repairing sensor array, the reliability and the data acquisition efficiency of the sensor network are improved on the basis, and the cost of the sensor network is reduced.

Description

Technical Field

[0001] This invention belongs to the field of basement formwork management technology, specifically a basement formwork splicing and node processing management system. Background Technology

[0002] In underground engineering construction, the splicing of basement formwork and the treatment of joints are key aspects to ensure structural safety and construction efficiency.

[0003] Traditional methods often rely on single sensors or manual inspection, failing to comprehensively and in real-time acquire key data such as interface friction, nodal strain distribution, and three-dimensional geological structure, leading to inaccurate understanding of the template splicing status. For example, relying solely on manual inspection of nodal strain is not only inefficient but also fails to detect subtle potential strain changes, easily leaving safety hazards. Secondly, the sensor network layout lacks dynamic optimization mechanisms, typically employing a fixed arrangement that can lead to sensor redundancy or coverage blind spots, hindering flexible adjustments based on actual construction conditions and impacting data acquisition accuracy and efficiency. Traditional methods struggle to integrate multi-physics data to generate template-sediment interaction field distribution maps, failing to deeply understand the complex interactions between the template and the formation, and hindering precise optimization of splicing parameters. Furthermore, traditional methods lack closed-loop control based on digital twins, unable to dynamically adjust splicing parameters and execute timely compensation actions based on real-time sensing data, resulting in inconsistent splicing accuracy and outdated risk prevention and control measures, relying mainly on manual experience and simple monitoring indicators, making it difficult to accurately predict splicing failure risks and trigger effective early warning mechanisms in a timely manner, increasing risks during construction.

[0004] To address this, the present invention provides a basement formwork splicing and node processing management system. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: the basement formwork splicing and node processing management system of this invention, comprising: A multi-source heterogeneous sensing fusion module is used to collect data on splicing interface friction, nodal strain distribution, and three-dimensional geological structure in real time. The adaptive sensor network optimization unit dynamically adjusts the sensor layout based on the topology optimization algorithm and uses a self-healing sensor array in high-stress areas to achieve automatic fault reconstruction. A multiphysics data fusion engine is used to fuse mechanical, geological and geometric data through deep neural networks to generate template-stratum interaction field distribution maps; A closed-loop control unit based on digital twins is used to construct a high-precision template-stratum coupling model, dynamically optimize splicing parameters by combining real-time sensing data, and drive the hydraulic servo system to perform compensation actions. The intelligent risk prevention and control subsystem uses graph neural networks to analyze historical accident data, predict splicing failure risks, and trigger multi-level early warning mechanisms. The autonomous collaborative operation module includes a magnetic adsorption inspection robot and an adaptive breaker, which are used to achieve autonomous identification and non-destructive removal of node defects; The full lifecycle trusted management platform, based on blockchain and PUF technology, is used to ensure the secure traceability of template data throughout the entire process from design to disposal.

[0007] Furthermore, the multi-source heterogeneous sensing fusion module includes: A piezoelectric ceramic sensor array monitors changes in friction at the splicing interface in real time through the piezoelectric effect, with a resolution of ±0.1N; Distributed fiber optic sensors are arranged along the template splicing seams to detect the strain distribution at the nodes, with a sampling frequency of 1kHz. The 3D geological scanner uses synthetic aperture radar technology to generate a 3D structural model of the stratigraphy with an accuracy of ±5mm.

[0008] Furthermore, the adaptive sensor network optimization unit includes: The topology optimization submodule calculates the optimal sensor layout based on the variable density method, reducing redundant sensors by more than 30%. A self-healing sensor array uses microelectromechanical systems (MEMS) technology to achieve automatic isolation and functional reconfiguration of fault nodes; The dynamic calibration submodule uses a Kalman filter algorithm to eliminate environmental interference and ensure the long-term stability of sensor data.

[0009] Furthermore, the digital twin-based closed-loop control unit includes: A high-precision coupled model construction submodule is used to combine the Mohr-Coulomb criterion with contact mechanics theory to simulate the dynamic interaction between template and formation. The real-time optimization submodule uses a model predictive control algorithm to dynamically adjust the splicing angle and pressure based on the sensing data. The adjustment range is ±5° and the pressure is 0-25MPa. The hydraulic servo actuator controls the pressure of the support column through a proportional valve to achieve millimeter-level precision compensation.

[0010] Furthermore, the intelligent risk prevention and control subsystem includes: The graph neural network risk prediction module is used to construct a template splicing node knowledge graph, analyze historical accident data, and predict failure probability. A multi-level early warning mechanism is used to trigger audible and visual alarms, partial shutdowns, or global system lockouts based on the risk level. The emergency decision support module is used to generate optimal response plans based on reinforcement learning, thereby shortening the accident response time.

[0011] Furthermore, the autonomous collaborative operation module includes: The magnetic adsorption inspection robot, equipped with lidar and infrared thermal imager, can autonomously detect node defects. The adaptive hydraulic breaker uses force-position hybrid control technology to dynamically adjust the impact energy according to the material properties of the nodes, with an adjustment range of 0-1000J. The collaborative task scheduler optimizes robot paths and task allocation through multi-agent reinforcement learning.

[0012] Furthermore, the magnetic adsorption inspection robot includes: The permanent magnet adsorption chassis provides an adsorption force of ≥50N / cm², making it suitable for inspection of curved templates. A six-degree-of-freedom robotic arm, modeled using the DH parameter method, has an end effector load capacity of ≥5kg. The defect identification algorithm, combining YOLOv7 and the Transformer architecture, enables real-time classification of defects such as misalignment and leakage.

[0013] Furthermore, the full lifecycle trusted management platform includes: The PUF identification module is used to generate a unique digital fingerprint for each template to prevent forgery and tampering. The blockchain data upload unit adopts a consortium blockchain architecture to store template design, production and usage data, and supports national cryptographic standards SM2 / SM3 encryption. The smart contract execution module automatically triggers events such as template maintenance and scrapping, and generates unalterable operation logs.

[0014] Furthermore, the PUF identification module includes: A random dielectric layer, fabricated using nanoscale processes, generates unique optical response characteristics; The feature extraction submodule uses principal component analysis for dimensionality reduction to generate a 64-bit digital fingerprint. The security authentication submodule, based on hash algorithms and zero-knowledge proofs, enables fast verification of template identities.

[0015] Furthermore, it also includes: edge computing nodes, deployed at construction sites, to enable localized processing of perceived data; 5G / 6G communication module, supporting multi-robot collaborative operation and remote monitoring; The digital twin visualization platform uses the Unity engine to build a 3D interactive interface, displaying template status and risk warning information in real time.

[0016] The beneficial effects of this invention are as follows: 1. The multi-source heterogeneous sensing fusion module in this invention integrates multiple sensors, enabling real-time and comprehensive acquisition of data on splicing interface friction, node strain distribution, and three-dimensional geological structure. This provides accurate foundational data for subsequent analysis and control. The adaptive sensor network optimization unit dynamically adjusts the sensor layout based on a topology optimization algorithm, thereby reducing redundant sensors. Simultaneously, a self-healing sensor array is used to achieve automatic fault reconstruction, thereby improving the reliability and data acquisition efficiency of the sensor network and reducing its cost. The multi-physics data fusion engine fuses mechanical, geological, and geometric data through deep neural networks to generate a template-stratum interaction field distribution map. This allows construction personnel to gain a more intuitive and in-depth understanding of the interaction between the template and the stratum, providing a scientific basis for optimizing splicing parameters. Furthermore, a high-precision template-stratum coupling model is constructed based on a digital twin-based closed-loop control unit. This model dynamically optimizes splicing parameters using real-time sensing data and drives a hydraulic servo system to perform compensation actions, achieving millimeter-level precision compensation and improving the accuracy and quality of template splicing. 2. The intelligent risk pre-control subsystem in this invention uses graph neural networks to analyze historical accident data, predict splicing failure risks, and trigger multi-level early warning mechanisms. This enables proactive measures to prevent accidents and ensure construction safety. Simultaneously, the emergency decision support module generates optimal handling solutions based on reinforcement learning, shortening accident response time. The autonomous collaborative operation module's magnetic adsorption inspection robot and adaptive breaker enable autonomous identification and non-destructive demolition of node defects, improving operational efficiency. The magnetic adsorption inspection robot, equipped with multiple sensors, accurately identifies defects such as misalignment and leakage, while the adaptive breaker dynamically adjusts impact energy based on node material characteristics, reducing damage to the template. The full lifecycle trusted management platform, based on blockchain and PUF technology, generates a unique digital fingerprint for each template, uses a consortium blockchain architecture to store data, and supports national cryptographic encryption, ensuring secure traceability of template data from design to disposal, preventing data tampering and forgery. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is the system architecture diagram of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 , This embodiment provides a basement formwork splicing and joint processing management system, including: A multi-source heterogeneous sensing fusion module is used to collect data on splicing interface friction, nodal strain distribution, and three-dimensional geological structure in real time. In the basement formwork splicing and node processing system, accurate data acquisition is fundamental. A detailed description of the sensor parameters and operating methods helps technicians understand and implement the module, ensuring the quality of the acquired data. Therefore, this invention further describes the multi-source heterogeneous sensing fusion module as including: A piezoelectric ceramic sensor array monitors changes in friction at the splicing interface in real time through the piezoelectric effect, with a resolution of ±0.1N; Distributed fiber optic sensors are arranged along the template splicing seams to detect the strain distribution at the nodes, with a sampling frequency of 1kHz. The 3D geological scanner uses synthetic aperture radar technology to generate a 3D structural model of the stratigraphy with an accuracy of ±5mm.

[0021] The adaptive sensor network optimization unit dynamically adjusts the sensor layout based on the topology optimization algorithm and uses a self-healing sensor array in high-stress areas to achieve automatic fault reconstruction. Sensor networks play a crucial role in data acquisition and transmission within the system, and their performance directly impacts the overall system operation. A detailed description of the technical aspects of each submodule demonstrates the scientific rigor and advancement of this unit in optimizing sensor networks, such as achieving layout optimization and fault repair through specific algorithms and technologies. Furthermore, these details help address potential issues in practical applications, such as sensor redundancy, faults, and data interference, thereby improving system stability and practicality. Therefore, this invention further illustrates that the adaptive sensor network optimization unit includes: The topology optimization submodule calculates the optimal sensor layout based on the variable density method, reducing redundant sensors by more than 30%. A self-healing sensor array uses microelectromechanical systems (MEMS) technology to achieve automatic isolation and functional reconfiguration of fault nodes; The dynamic calibration submodule uses a Kalman filter algorithm to eliminate environmental interference and ensure the long-term stability of sensor data.

[0022] For example, in a large basement construction site, an excessive number of sensors might have been deployed, resulting in wasted resources. The topology optimization submodule can reduce redundant sensors, making the sensor layout more rational and efficient. The self-healing sensor array, employing microelectromechanical systems (MEMS) technology, can automatically isolate and reconfigure the faulty node when it fails. For instance, if a sensor is damaged by an external impact during construction, the self-healing sensor array can quickly recover, ensuring the normal operation of the sensor network. The dynamic calibration submodule uses a Kalman filter algorithm to eliminate environmental interference, ensuring long-term stability of sensor data. For example, construction sites may be affected by environmental factors such as temperature and humidity, causing fluctuations in sensor data. The dynamic calibration submodule can effectively eliminate these interferences, making the data more accurate and reliable. A multiphysics data fusion engine is used to fuse mechanical, geological and geometric data through deep neural networks to generate template-stratum interaction field distribution maps; A closed-loop control unit based on digital twins is used to construct a high-precision template-stratum coupling model, dynamically optimize splicing parameters by combining real-time sensing data, and drive the hydraulic servo system to perform compensation actions. Closed-loop control is a crucial step in ensuring the quality of template splicing. A detailed description of the technical content and parameters of each sub-module demonstrates the scientific and precise nature of the system's control. For example, clearly defining the adjustment range and precision compensation values ​​allows technicians to understand the system's control capabilities and accuracy levels, facilitating adjustments and optimizations during actual construction to ensure the template splicing meets design requirements. Therefore, a further description of this invention reveals that the closed-loop control unit based on digital twins includes: For example, if a large deviation in the splicing angle is found during the actual splicing process, the real-time optimization submodule can adjust it in time to ensure the accuracy of the splicing. The hydraulic servo actuator controls the pressure of the support column through a proportional valve to achieve millimeter-level precision compensation. For example, if a slight displacement deviation occurs during the splicing process, the hydraulic servo actuator can react quickly and make precise compensation to ensure the quality of the template splicing; A high-precision coupled model construction submodule is used to combine the Mohr-Coulomb criterion with contact mechanics theory to simulate the dynamic interaction between template and formation. The real-time optimization submodule uses a model predictive control algorithm to dynamically adjust the splicing angle and pressure based on the sensing data. The adjustment range is ±5° and the pressure is 0-25MPa. The hydraulic servo actuator controls the pressure of the support column through a proportional valve to achieve millimeter-level precision compensation.

[0023] The intelligent risk prevention and control subsystem uses graph neural networks to analyze historical accident data, predict splicing failure risks, and trigger multi-level early warning mechanisms. Various risks exist during the construction of basement formwork assembly, making risk prevention and control crucial. This invention details the functions and implementation methods of each module, demonstrating the system's comprehensiveness and effectiveness in risk prediction, early warning, and handling. For example, by using specific technical means to predict risks and generate response plans, the system's ability to cope with risks is improved, reducing the likelihood of accidents and losses. Therefore, this invention further illustrates that the intelligent risk prevention and control subsystem includes: The graph neural network risk prediction module constructs a knowledge graph of template splicing nodes, analyzes historical accident data, and predicts failure probabilities. For example, by analyzing splicing node accident data from similar past projects, it is found that splicing nodes are prone to failure under certain specific conditions. When the current construction conditions are close to these conditions, the system can predict the failure probability in advance. A multi-level early warning mechanism is used to trigger audible and visual alarms, partial shutdowns, or global system lockouts based on the risk level. For example, when a high risk level is predicted, the system will immediately trigger a global system lockout, stopping all construction operations to prevent accidents from occurring. The emergency decision support module is used to generate optimal handling solutions based on reinforcement learning, thereby shortening accident response time. For example, when a sudden accident occurs, the emergency decision support module can quickly generate multiple handling solutions and select the optimal solution based on the actual situation to guide construction personnel in emergency handling. The autonomous collaborative operation module includes a magnetic adsorption inspection robot and an adaptive breaker, which are used to achieve autonomous identification and non-destructive removal of node defects; Autonomous collaborative operation is an important means to improve construction efficiency and quality. A detailed description of the technical details of each component reflects the system's level of automation and intelligence in operation. For example, clarifying the robot's adsorption force, robotic arm load capacity, and defect recognition algorithm helps technicians understand the robot's performance and operational capabilities, ensuring it can adapt to complex construction site environments and accurately complete node defect detection and removal tasks. Therefore, this invention further describes the autonomous collaborative operation module as including: The magnetic adsorption inspection robot, equipped with lidar and infrared thermal imager, can autonomously detect node defects. Furthermore, magnetic adsorption inspection robots include: The permanent magnet adsorption chassis provides an adsorption force of ≥50N / cm², making it suitable for inspection of curved templates. A six-degree-of-freedom robotic arm, modeled using the DH parameter method, has an end effector load capacity of ≥5kg. The defect identification algorithm, combining YOLOv7 and the Transformer architecture, enables real-time classification of defects such as misalignment and leakage.

[0024] The adaptive hydraulic breaker uses force-position hybrid control technology to dynamically adjust the impact energy according to the material properties of the nodes, with an adjustment range of 0-1000J. The collaborative task scheduler optimizes robot paths and task allocation through multi-agent reinforcement learning.

[0025] The full lifecycle trusted management platform, based on blockchain and PUF technology, is used to ensure the secure traceability of template data throughout the entire process from design to disposal. In the full lifecycle management of templates, data security and trustworthiness are paramount. This paper details the technical aspects of each component of the platform, showcasing the system's advanced technology and reliability in data management. For example, by generating unique digital fingerprints, storing data, and implementing secure authentication through specific technical means, it effectively prevents data forgery and tampering, providing reliable data support for template management and maintenance. Therefore, this invention further illustrates that the full lifecycle trust management platform includes: The PUF identification module is used to generate a unique digital fingerprint for each template to prevent forgery and tampering. The blockchain data upload unit adopts a consortium blockchain architecture to store template design, production and usage data, and supports national cryptographic standards SM2 / SM3 encryption. The smart contract execution module automatically triggers events such as template maintenance and scrapping, and generates unalterable operation logs.

[0026] Furthermore, the PUF identification module includes: A random dielectric layer, fabricated using nanoscale processes, generates unique optical response characteristics; The feature extraction submodule uses principal component analysis for dimensionality reduction to generate a 64-bit digital fingerprint. The security authentication submodule, based on hash algorithms and zero-knowledge proofs, enables fast verification of template identities.

[0027] With the increasing complexity and informatization of construction sites, the system needs to have more efficient data processing, collaborative operation and visualization capabilities. At the same time, in order to facilitate managers to grasp the situation of the construction site in a timely manner and make accurate decisions, the present invention further includes: edge computing nodes, deployed at the construction site to realize localized processing of perception data. 5G / 6G communication module, supporting multi-robot collaborative operation and remote monitoring; The digital twin visualization platform uses the Unity engine to build a 3D interactive interface, displaying template status and risk warning information in real time.

[0028] The system's operation process is as follows: First, a comprehensive system deployment was carried out at the basement construction site. This involved the rational installation of various sensors included in the multi-source heterogeneous sensing fusion module. Specifically, a piezoelectric ceramic sensor array was responsible for monitoring frictional data at the splicing interface, distributed fiber optic sensors were arranged along the template splicing seams to detect nodal strain distribution, and a 3D geological scanner was used to generate a 3D geological structure model. After sensor installation was completed, an adaptive sensor network optimization unit was reliably connected to these sensors to achieve dynamic optimization of the sensor layout and automatic reconfiguration in case of failure.

[0029] Next, a closed-loop control unit based on digital twins, an intelligent risk pre-control subsystem, an autonomous collaborative operation module, and a full lifecycle trusted management platform are deployed in an orderly manner. At the same time, edge computing nodes, 5G / 6G communication modules, and a digital twin visualization platform are connected to ensure smooth communication between the various parts and lay the foundation for the normal operation of the system. Following this, the multi-source heterogeneous sensing fusion module begins real-time operation, continuously collecting key data such as interface friction, nodal strain distribution, and three-dimensional geological structure. At this point, the adaptive sensor network optimization unit comes into play, dynamically adjusting the sensor layout based on the collected data to obtain more accurate and comprehensive information. The self-healing sensor array constantly monitors the sensor status; once a faulty node is detected, it immediately performs automatic isolation and functional reconstruction to ensure the normal operation of the sensor network. The dynamic calibration submodule continuously eliminates environmental interference factors, ensuring the high stability of the collected data.

[0030] Subsequently, the multiphysics data fusion engine uses deep neural networks to deeply fuse the collected mechanical, geological and geometric data. After complex calculations and analysis, it generates a template-stratum interaction field distribution map, which provides an important basis for subsequent control and decision-making. Secondly, the closed-loop control unit based on digital twins starts working. The high-precision coupled model construction submodule combines the Mohr-Coulomb criterion and contact mechanics theory to accurately simulate the dynamic interaction between the template and the formation, providing theoretical support for the formulation of control strategies. The real-time optimization submodule adopts model predictive control algorithms to dynamically adjust the splicing angle (range ±5°) and pressure (range 0-25MPa) in real time based on the sensing data, ensuring the accuracy and quality of template splicing. The hydraulic servo actuator, according to the optimized parameters, precisely controls the pressure of the support column through a proportional valve to achieve millimeter-level accuracy compensation, so that the template splicing reaches the optimal state. Next, the intelligent risk prevention and control subsystem begins operation. The graph neural network risk prediction module constructs a knowledge graph of template splicing nodes, deeply analyzes historical accident data, accurately predicts the probability of splicing failure, and a multi-level early warning mechanism reacts rapidly based on the predicted risk level, triggering corresponding measures such as audible and visual alarms, partial shutdowns, or global system lockouts, promptly reminding construction personnel to take countermeasures. The emergency decision support module generates optimal handling solutions based on reinforcement learning algorithms, greatly shortening accident response time and effectively reducing accident losses. Following this, the magnetic adsorption inspection robot in the autonomous collaborative operation module begins its inspection work along the template surface. Equipped with a lidar and infrared thermal imager, it comprehensively detects the nodes, enabling autonomous detection of node defects. The magnetic adsorption inspection robot's permanent magnet chassis provides a powerful adsorption force of ≥50N / cm², adapting to the inspection needs of curved templates. The six-degree-of-freedom robotic arm is modeled using the DH parameter method, and the end effector has a load capacity of ≥5kg. Combined with a defect recognition algorithm (integrating YOLOv7 and Transformer architecture), it achieves real-time classification of misaligned sections and leakage defects.

[0031] Meanwhile, the adaptive hydraulic breaker employs force-position hybrid control technology, dynamically adjusting the impact energy (range 0-1000J) based on the material properties of the nodes, enabling precise treatment of defective nodes. The collaborative operation scheduler optimizes robot paths and task allocation through multi-agent reinforcement learning, making collaborative operations between devices more efficient.

[0032] Furthermore, the full lifecycle trusted management platform manages templates throughout their entire lifecycle. The PUF identification module generates a unique digital fingerprint for each template, preventing forgery and tampering at the source. The blockchain data on-chain unit adopts a consortium blockchain architecture, securely storing data from the entire process of template design, production, and use, and supports national cryptographic standards SM2 / SM3 encryption to ensure data security and confidentiality. The smart contract execution module automatically triggers events such as template maintenance and scrapping, generating immutable operation logs to achieve precise management and traceability of the entire template lifecycle. On the other hand, edge computing nodes deployed at the construction site localize the perceived data, improving data processing efficiency and response speed. 5G / 6G communication modules ensure collaborative operation between multiple robots and enable remote monitoring, allowing managers to monitor system operation even when not on-site. The digital twin visualization platform uses the Unity engine to build a 3D interactive interface, displaying template status and risk warning information in real time, providing managers with intuitive and clear decision-making support.

[0033] The terms "front," "back," "left," "right," "top," and "bottom" all refer to the figures in the accompanying drawings. Figure 1Based on the perspective of the observer, the side of the device facing the observer is defined as the front, the left side of the observer is defined as the left, and so on.

[0034] In the description of this invention, it should be understood that the terms "center", "longitudinal", "lateral", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the scope of protection of this invention.

[0035] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A basement formwork splicing and node processing management system, characterized in that, include: A multi-source heterogeneous sensing fusion module is used to collect data on splicing interface friction, nodal strain distribution, and three-dimensional geological structure in real time. The adaptive sensor network optimization unit dynamically adjusts the sensor layout based on the topology optimization algorithm and uses a self-healing sensor array in high-stress areas to achieve automatic fault reconstruction. A multiphysics data fusion engine is used to fuse mechanical, geological and geometric data through deep neural networks to generate template-stratum interaction field distribution maps; A closed-loop control unit based on digital twins is used to construct a high-precision template-stratum coupling model, dynamically optimize splicing parameters by combining real-time sensing data, and drive the hydraulic servo system to perform compensation actions. The intelligent risk prevention and control subsystem uses graph neural networks to analyze historical accident data, predict splicing failure risks, and trigger multi-level early warning mechanisms. The autonomous collaborative operation module includes a magnetic adsorption inspection robot and an adaptive breaker, which are used to achieve autonomous identification and non-destructive removal of node defects; The full lifecycle trusted management platform, based on blockchain and PUF technology, is used to ensure the secure traceability of template data throughout the entire process from design to disposal.

2. The basement formwork splicing and node processing management system according to claim 1, characterized in that: The multi-source heterogeneous sensing fusion module includes: A piezoelectric ceramic sensor array monitors changes in friction at the splicing interface in real time through the piezoelectric effect, with a resolution of ±0.1N; Distributed fiber optic sensors are arranged along the template splicing seams to detect the strain distribution at the nodes, with a sampling frequency of 1kHz. The 3D geological scanner uses synthetic aperture radar technology to generate a 3D structural model of the stratigraphy with an accuracy of ±5mm.

3. The basement formwork splicing and node processing management system according to claim 1, characterized in that: The adaptive sensor network optimization unit includes: The topology optimization submodule calculates the optimal sensor layout based on the variable density method, reducing redundant sensors by more than 30%. A self-healing sensor array uses microelectromechanical systems (MEMS) technology to achieve automatic isolation and functional reconfiguration of fault nodes; The dynamic calibration submodule uses a Kalman filter algorithm to eliminate environmental interference and ensure the long-term stability of sensor data.

4. The basement formwork splicing and node processing management system according to claim 1, characterized in that: The digital twin-based closed-loop control unit includes: A high-precision coupled model construction submodule is used to combine the Mohr-Coulomb criterion with contact mechanics theory to simulate the dynamic interaction between template and formation. The real-time optimization submodule uses a model predictive control algorithm to dynamically adjust the splicing angle and pressure based on the sensing data. The adjustment range is ±5° and the pressure is 0-25MPa. The hydraulic servo actuator controls the pressure of the support column through a proportional valve to achieve millimeter-level precision compensation.

5. The basement formwork splicing and node processing management system according to claim 1, characterized in that: The intelligent risk prevention and control subsystem includes: The graph neural network risk prediction module is used to construct a template splicing node knowledge graph, analyze historical accident data, and predict failure probability. A multi-level early warning mechanism is used to trigger audible and visual alarms, partial shutdowns, or global system lockouts based on the risk level. The emergency decision support module is used to generate optimal response plans based on reinforcement learning, thereby shortening the accident response time.

6. The basement formwork splicing and node processing management system according to claim 5, characterized in that: The autonomous collaborative operation module includes: The magnetic adsorption inspection robot, equipped with lidar and infrared thermal imager, can autonomously detect node defects. The adaptive hydraulic breaker uses force-position hybrid control technology to dynamically adjust the impact energy according to the material properties of the nodes, with an adjustment range of 0-1000J. The collaborative task scheduler optimizes robot paths and task allocation through multi-agent reinforcement learning.

7. The basement formwork splicing and node processing management system according to claim 6, characterized in that: The magnetic adsorption inspection robot includes: The permanent magnet adsorption chassis provides an adsorption force of ≥50N / cm², making it suitable for inspection of curved templates. A six-degree-of-freedom robotic arm, modeled using the DH parameter method, has an end effector load capacity of ≥5kg. The defect identification algorithm, combining YOLOv7 and the Transformer architecture, enables real-time classification of defects such as misalignment and leakage.

8. The basement formwork splicing and node processing management system according to claim 7, characterized in that: The full lifecycle trusted management platform includes: The PUF identification module is used to generate a unique digital fingerprint for each template to prevent forgery and tampering. The blockchain data upload unit adopts a consortium blockchain architecture to store template design, production and usage data, and supports national cryptographic standards SM2 / SM3 encryption. The smart contract execution module automatically triggers template maintenance and scrapping events, and generates unalterable operation logs.

9. The basement formwork splicing and node processing management system according to claim 8, characterized in that: The PUF identification module includes: A random dielectric layer, fabricated using nanoscale processes, generates unique optical response characteristics; The feature extraction submodule uses principal component analysis for dimensionality reduction to generate a 64-bit digital fingerprint. The security authentication submodule, based on hash algorithms and zero-knowledge proofs, enables fast verification of template identities.

10. The basement formwork splicing and node processing management system according to claim 1, characterized in that: Also includes: Edge computing nodes are deployed at construction sites to enable localized processing of perceived data. 5G / 6G communication module, supporting multi-robot collaborative operation and remote monitoring; The digital twin visualization platform uses the Unity engine to build a 3D interactive interface, displaying template status and risk warning information in real time.

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