Modular box body segmented hoisting construction method suitable for narrow and small site

Abstract

The invention discloses a modular box segmented hoisting construction method suitable for a narrow site, and belongs to the technical field of building construction. The technical problem that in-place precision control is difficult in the hoisting process of the large prefabricated part on a space-limited construction site is solved. The method comprises the following steps: horizontally assembling a plurality of prefabricated box body modules into an integral hoisting section; after the hoisting section is horizontally lifted through hoisting equipment, the hoisting section is controlled to rotate around the lower end fulcrum to be in a vertical state; hoisting in place and completing connection and fixation with a lower part and an adjacent structure; the steps are cyclically repeated until hoisting and connecting of all the prefabricated box body modules are completed, and a complete shear wall structure is formed; a monitoring system comprising a sensor and an autonomous energy and communication relay module is arranged on the hoisting section, energy and communication guarantee can be provided in the hoisting rotation and in-place process, and a real-time monitoring task is executed. The method is mainly used for safely, accurately and efficiently completing hoisting construction and quality control of the modular box under the narrow site condition.

Description

Technical Field

[0001] This invention belongs to the field of building construction technology and relates to a modular box-type segmented hoisting construction method suitable for confined spaces. Background Technology

[0002] In the field of modular construction, especially in the construction of stacked box shear wall structures, to adapt to site constraints, a common construction method is to prefabricate large structures into multiple box modules in a factory, and then assemble and install them in sections on site. However, existing technologies face a series of specific technical challenges and limitations when applied to small or complex construction sites.

[0003] First, the hoisting operation faces significant spatial constraints due to the limited space. Traditional methods often require finding a sufficiently large open area next to the hoisting operation area to pre-assemble multiple prefabricated box modules into a single hoisting section with sufficient rigidity. When the open space on site is extremely limited, this ground assembly operation is difficult to carry out, or the assembled hoisting section may be too large, making it highly susceptible to collisions with adjacent existing structures, temporary facilities, or site boundaries during subsequent hoisting, rotation, and positioning operations in a confined space, posing a high safety risk. Furthermore, to ensure the integrity of the hoisting section during horizontal transportation and initial lifting, temporary reinforcement measures are usually required at the junctions between modules. However, existing temporary connection methods may be inefficient or lack adaptability, affecting the overall construction schedule.

[0004] Secondly, during the critical lifting posture transition process—the rotation of the lifting section from a horizontal to a vertical position—existing construction methods primarily rely on the experience of the crane operator and ground control personnel. Due to a lack of continuous, quantitative perception of the real-time posture of the lifting section (especially the elevation angle) and its precise distances to surrounding critical obstacles, operators often have to rely on estimations and experience-based judgments. Particularly when the lifting section rotates around its lower support point, its trajectory and speed control are dynamic and uncertain. Precise control of its angular velocity through visual observation alone is difficult, easily leading to sudden changes in internal structural stress or instability of the lifting section due to excessive rotation, or inefficiency due to overly cautious approaches. Furthermore, misjudging safe distances is a major cause of collision accidents. Current technology lacks an automated monitoring and guidance system capable of integrating key state parameters and comparing them in real-time with a pre-set safety model, making risk control throughout the lifting process dependent on continuous human vigilance, resulting in uncertain reliability.

[0005] Furthermore, during the stage of hoisting the section and connecting and fixing it to the substructure and adjacent structures, existing technologies often lag in controlling the connection quality. The common practice is to assess the connection quality through manual inspection or random checks using specialized instruments after the initial connection and final fixing are completed, such as checking bolt torque or weld appearance. This method cannot provide comprehensive and quantitative feedback on the tightening status immediately after the connection construction is completed, making it impossible to detect and correct potential quality defects in a timely manner. Once subsequent processes begin or adjacent components are installed, reworking concealed connection nodes becomes extremely difficult, costly, and compromises overall structural safety. In addition, during the construction of subsequent adjacent hoisting sections, the installed sections and their connection nodes will be subjected to new construction loads (such as impact and vibration), and existing methods lack effective means for continuous, online monitoring of the stress state of these connection nodes. This makes it difficult to assess the cumulative impact of construction activities on the local safety of the existing structure and to achieve risk warnings based on real-time data.

[0006] The aforementioned problems and shortcomings stem from two main reasons. Firstly, traditional construction methods lack sufficient integration of information and automation technologies, failing to deeply embed high-precision sensor networks, real-time data processing, and feedback mechanisms into the hoisting operation process. Secondly, deploying long-term, stable monitoring systems on precast concrete components presents practical difficulties, such as energy supply issues, sensor survival in harsh construction environments, reliable signal transmission, and the technical challenge of adaptively adjusting monitoring functions according to the needs of different construction stages. These difficulties often limit existing technologies to single-point, offline detection, hindering the formation of an intelligent support system that is synchronized with the hoisting construction process and covers the entire process from dynamic hoisting to static connection. Summary of the Invention

[0007] One object of the present invention is to solve at least the above-mentioned problems and / or defects, and to provide at least the advantages described below.

[0008] Another objective of this invention is to provide a modular box-type segmented hoisting construction method suitable for confined spaces.

[0009] This addresses the challenges of low construction safety, difficulty in controlling positioning accuracy, and the lack of real-time, full-process status perception and quality monitoring methods that are deeply integrated with the hoisting process when performing modular box-type segmented hoisting in confined spaces.

[0010] This addresses the problem that relying on human experience to judge angular velocity and safe distance during the high-risk dynamic process of attitude rotation (0° to 90°) in the hoisting section makes it difficult to achieve automatic comparison, early warning and guidance based on real-time data and preset safety models, thus making it difficult to effectively prevent the risk of collision and loss of control.

[0011] This addresses the issues of the inability to conduct immediate and comprehensive digital acceptance of the tightness of temporary and permanent connection nodes after the hoisting section is in place, and the lack of continuous monitoring of the stress state of the connection nodes of the installed section during subsequent construction to achieve early risk warning.

[0012] This addresses the challenge of ensuring a continuous energy supply for monitoring systems attached to precast components during long-term operation (especially after the components are in place and stationary), as well as the need for intelligent energy scheduling and management to adapt to different construction stages (dynamic high power consumption and static low power consumption).

[0013] This addresses the challenge of how a monitoring system can reliably and automatically identify operating conditions and seamlessly switch between working modes, network topology, and power consumption strategies based on changes in the construction phase (dynamic hoisting and static monitoring) to optimize system functionality and resource allocation.

[0014] This addresses the global management challenge of going beyond monitoring a single hoisting process, utilizing multiple historical and real-time data to reverse-engineer subsequent hoisting techniques, and proactively assessing and providing early warnings of the safety status of existing partial or overall structures during the construction period.

[0015] This addresses the emergency survival challenges of monitoring systems under extreme conditions (such as severe power shortages or communication link interruptions), including ensuring the preservation of critical monitoring data, achieving minimum status reporting, and maintaining the system's basic survivability.

[0016] This addresses the challenge of upgrading passive threshold alarms to proactive risk prediction based on dynamic models in hoisting status monitoring, and generating operationally guiding adjustment guidelines to enhance the intelligence and pre-control capabilities of hoisting control.

[0017] This addresses the problem that the health status (sensors, communication, power supply) of complex embedded monitoring systems is difficult to perceive and assess in real time, which may lead to distorted monitoring data or system failure due to hidden faults, affecting the reliability of the entire method.

[0018] This addresses the issue of mismatch between the preset general lifting path and the control model caused by individual differences in the lifting section (mass distribution, lifting point settings) and environmental changes, requiring rapid online identification of system characteristics and personalized adjustment of control parameters before actual lifting.

[0019] Therefore, the technical solution provided by this invention is as follows: A modular box-type segmented hoisting construction method suitable for confined spaces includes the following steps: S1: In the limited open space next to the hoisting operation area, assemble at least two prefabricated box modules in the horizontal direction to form an integral hoisting section, and install temporary reinforcing connection components at the joint of adjacent box modules; S2: Use hoisting equipment to connect the lifting gear, and first lift the hoisting section horizontally to the height above the ground; S3: Control the hoisting mechanism and the luffing mechanism to work together, so that the hoisting section rotates around its lower pivot point, and control the hoisting section to complete the tilting from 0° to 45° with a first angular velocity, and then complete the vertical state transition from 45° to 90° with a second angular velocity less than the first angular velocity. S4: After the hoisting section is in a vertical state, hoist it to the top of the installed lower structure, first make a preliminary connection between its lower end and the lower structure, and then finally fix its upper end to the adjacent structure. Steps S1 to S4 are repeated in this way until all prefabricated box modules are hoisted and connected to form a complete shear wall structure. A monitoring system is installed on the hoisting section. The monitoring system includes at least two sensors deployed on the hoisting section and an autonomous energy and communication relay module integrated on the prefabricated box module. The autonomous energy and communication relay module provides energy and communication support for the sensor during the rotation and positioning of the hoisting section; The monitoring system is configured to perform monitoring tasks in steps S3 and / or S4.

[0020] Preferably, in the modular box-type segmented hoisting construction method suitable for confined spaces, in step S3, the monitoring system operates as a hoisting status monitoring and guidance system, including the following steps: The at least two sensors constitute a sensor network, which includes at least an angle sensor for measuring the real-time elevation angle of the hoisting section and a distance sensor for measuring the distance between the hoisting section and key points of the adjacent structure. The data processing and communication unit of the autonomous energy and communication relay module receives and processes real-time data from the sensor network. A field control terminal establishes a wireless communication link with the data processing and communication unit to receive and graphically display the real-time elevation angle and real-time distance, and compare them with a preset hoisting path parameter model. When the real-time elevation angle is in the range of 0° to 45°, it compares with a first angular velocity threshold; when it is in the range of 45° to 90°, it compares with a second angular velocity threshold, and at the same time compares with a safety distance threshold with adjacent structures. When the data processing and communication unit or the field control terminal detects that the real-time angular velocity deviates from the corresponding threshold, or the real-time distance is less than the safe distance threshold, it immediately generates and sends an alarm signal and adjustment guidance to the field control terminal and / or the control system of the hoisting equipment.

[0021] Preferably, in the modular box-type segmented hoisting construction method suitable for confined spaces, the at least two sensors include multiple intelligent sensing nodes, which are respectively installed at the temporary reinforcing connection member and at the connection interface with the lower structure and adjacent structures. Each intelligent sensing node integrates at least a micro pressure sensor or strain sensor for sensing the stress state at the connection. The node data aggregation unit of the autonomous energy and communication relay module is connected to each of the intelligent sensing nodes via a wireless personal area network and is used to receive, temporarily store and forward the sensing data of each node. The monitoring system operates as a distributed connection node monitoring system and also includes a data analysis platform, which is connected to the node data aggregation unit via a wireless wide area network to receive the sensor data and compare and analyze it with the design and construction parameter model of the corresponding connection node. The data analysis platform is configured as follows: a. After the initial connection and final fixation are completed, analyze the data of each node in real time to determine whether the connection tightness has reached the design threshold, and generate a digital acceptance report; b. During the construction of subsequent adjacent sections, the stress changes of each node are continuously monitored. If the stress data of any node exceeds the preset safety envelope or an abnormal change occurs, an early warning message is sent to the field terminal through the aggregation unit.

[0022] Preferably, the modular box-type segmented hoisting construction method suitable for confined spaces includes the following autonomous energy and communication relay module: A hybrid energy and intelligent management unit comprising: The energy harvesting assembly includes piezoelectric transducers distributed on the inner wall or structural frame of the prefabricated box module for converting mechanical vibration energy from hoisting, transportation and the environment into electrical energy; and thin-film photovoltaic modules attached to the unshaded area of ​​the outer surface of the box module for harvesting ambient light energy. A high-capacity energy storage unit, connected to the energy harvesting assembly, is used to store the electrical and light energy; An intelligent power management unit is electrically connected to the energy harvesting component and the energy storage unit. It has a pre-set energy management strategy that is linked to the hoisting construction phase. The strategy is configured as follows: during the hoisting movement phase in steps S2 to S3, the electrical energy generated by the piezoelectric transducer is preferentially used and stored, and full power is provided to the sensor network and communication unit. After the unit is fixed in place in step S4 and during subsequent long-term monitoring, it automatically switches to a hybrid power supply mode, primarily powered by the thin-film photovoltaic module and environmental micro-vibration, supplemented by energy release from the energy storage unit. An intermittent, low-power power supply strategy matching the monitoring task is also configured for the sensor network and communication unit. An anti-interference communication relay unit is electrically connected to the intelligent power management unit and adopts adaptive frequency hopping spread spectrum technology to dynamically adjust the communication power and frequency band according to link quality and power consumption strategies.

[0023] Preferably, the modular box-type segmented hoisting construction method suitable for confined spaces further includes a mode management and strategy configuration unit in the monitoring system. This unit is integrated into the edge computing core of the autonomous energy and communication relay module and performs the following steps: Intelligent identification of working conditions through multi-source information fusion: Real-time analysis of the comprehensive data stream from the sensor network to construct a fusion decision model that includes tilt angle, strain, acceleration, and hook load signals from the hoisting equipment control system; When the real-time elevation angle of the hoisting section is continuously detected to be stable at 90°±Δθ for more than a first preset time, and the strain sensor data indicates that the connection interface begins to bear pressure and tends to stabilize, and the hook load signal drops below the preset safe unloading threshold, the fusion decision model automatically generates a working condition identifier of "hoisting in place, entering the connection and fixing stage", where Δθ is a preset tolerance angle; Dynamic loading of parameter strategies: Based on the operating condition identifier, the system configuration strategy package matching the target operating mode is automatically called from the pre-stored strategy library; wherein, the strategy library includes at least: a first strategy package corresponding to the hoisting status monitoring and guidance mode; a second strategy package corresponding to the distributed connection node monitoring mode; the configuration of the second strategy package includes: reconstructing the network topology into a low-power monitoring network, and instructing the intelligent power management unit to execute a hybrid power supply strategy matching long-term monitoring; Seamless system switching and model update: The mode management and strategy configuration unit issues a reconfiguration command based on the loaded second strategy package, enabling the monitoring system to autonomously enter the long-term monitoring state of the connection node; at the same time, the key sensor data before and after the "lifting in place" moment are transmitted back to the data analysis platform or the construction global intelligent management platform to update the initial state of the digital twin of the lifting section.

[0024] Preferably, the modular box-type segmented hoisting construction method suitable for confined spaces further includes a global analysis and predictive optimization step based on digital twins, implemented through a global intelligent construction management platform, which performs the following: Full-element data fusion modeling: Access data from all monitoring systems on the current hoisting section and its adjacent installed sections, combine the design BIM model and actual hoisting process parameters to construct and update in real time a high-fidelity digital twin reflecting the entire process from single-section hoisting dynamics to multi-section structural statics. Reverse optimization of hoisting process parameters: Analyze the correlation between the actual motion data (including real-time angular velocity and structural vibration response) of the historical hoisting section in step S3 and the final positioning accuracy and the initial stress of the connection node. Through machine learning model training, dynamically generate and recommend the optimized first and second angular velocity parameters for subsequent hoisting sections, as well as the corresponding hoisting path parameter model, and push them to the on-site control terminal. Overall structural safety status assessment and early warning: After multiple hoisting sections form a local or overall structure, based on the digital twin, the structural response under different construction loads (such as subsequent hoisting impact and wind load) is simulated, and the simulated data is compared and analyzed in real time with the measured data of the distributed connection node monitoring system of each section. When the deviation between measured data and simulated data exceeds the tolerance limit, or when the simulation results show that the stress at key connection nodes exceeds the warning threshold, an overall structural safety warning is generated, and the highest risk hoisting section or connection node is accurately located, while construction adjustment suggestions are given.

[0025] Preferably, the modular box-type segmented hoisting construction method suitable for confined spaces further includes a data preservation and emergency control unit in the autonomous energy and communication relay module. This unit performs the following steps: real-time monitoring of the voltage level of the energy storage unit; when the voltage is lower than a first threshold, a low-power mode is activated and non-core sensors are shut down; when the voltage is lower than a critical second threshold for maintaining minimum functional operation, a data preservation emergency process is triggered; during communication link interruption, the data processing and communication unit is controlled to convert real-time monitoring data into encrypted data packets with high-precision timestamps, sequentially stored in local non-volatile memory, and automatically retransmitted with priority after communication is restored; when both communication interruption and power supply lower than the second threshold are detected simultaneously, an emergency mode is activated, the energy harvesting unit is controlled to harvest energy at full capacity, and an independent ultra-low power backup communication channel is activated to send an emergency beacon signal containing the hoisting segment number, real-time tilt angle, and voltage status to the field control terminal at a preset minimum time interval.

[0026] Preferably, in the modular box-type segmented hoisting construction method suitable for confined spaces, the data processing and communication unit of the hoisting status monitoring and guidance system also runs an adaptive early warning and predictive control engine. This engine performs the following steps: based on the preset hoisting path parameter model, it combines the current movement speed, attitude angular velocity, and real-time wind speed data of the hoisting segment in real time, and calculates and generates an adaptive safety envelope that changes with the hoisting stage and working conditions through a dynamic model; based on the real-time data stream of the sensor network, it predicts the movement trajectory and attitude of the hoisting segment within a short time window in the future, compares the predicted trajectory with the dynamic safety envelope to identify potential risks in advance and calculate the risk level; when a potential risk is predicted, it generates a graded alarm according to the risk level, and based on the prediction model and the response characteristics of the hoisting equipment, it reverse-calculates and generates a set of operation sequences including lead time and step-by-step execution as adjustment guidelines, and sends them to the control system of the hoisting equipment.

[0027] Preferably, the modular box-type segmented hoisting construction method suitable for confined spaces includes a monitoring system further comprising a system self-diagnosis and status management unit embedded in the autonomous energy and communication relay module. This unit performs the following steps: The built-in self-test program is run periodically to inject calibration signals and analyze the response of the at least two sensors in order to diagnose whether the sensors have excessive deviation, response delay or complete failure. Real-time monitoring of key performance indicators of the wireless communication unit's link, including signal strength, bit error rate, and connection stability, and assessment of communication health level; Collect and analyze the charge-discharge cycle count, current effective capacity, and internal resistance parameters of the energy storage unit to predict its remaining reliable operating time; The diagnostic results of the above sensor health status, communication health status, and power reliability status are packaged together with the monitoring data, or uploaded in real time to the field control terminal or remote management platform through an independent system status message. When any critical component is diagnosed to be below a preset safety threshold, a significant confidence degradation flag is automatically added to the monitoring data or system status message, and an independent system anomaly alarm is triggered.

[0028] Preferably, the modular box-type segmented hoisting construction method suitable for confined spaces further includes an online identification and model update unit in the hoisting status monitoring and guidance system, which performs the following steps: During the hovering and stabilization phase after the hoisting section is horizontally lifted off the ground in step S2 and before the rotation begins in step S3, the hoisting equipment is controlled to execute a set of small-amplitude, safe excitation motion sequences, while the dynamic response data of the hoisting section is collected at high frequency through the sensor network. Based on the input signal of the excitation motion sequence and the dynamic response output signal of the hoisting section, a system identification algorithm is used to estimate the key dynamic parameters of the current hoisting section-spreading system in real time online. The parameters include at least the equivalent pendulum length, damping coefficient, and coupling gain with the hoisting equipment control system. By utilizing the dynamic parameters identified online, the preset hoisting path parameter model is locally modified to generate a personalized hoisting path model that matches the individual characteristics and environment of the current hoisting segment. During the rotation process in the subsequent step S3, the adaptive early warning and predictive control engine generates an adaptive safety envelope and predictive guidance based on the personalized hoisting path model.

[0029] The present invention has at least the following beneficial effects: This invention provides a segmented hoisting construction method integrating a dedicated monitoring system. In confined spaces, multiple prefabricated box modules are assembled on the ground and then safely rotated into position using staged angular velocity control. Through built-in sensing and communication capabilities, this method transforms the hoisting process from relying solely on manual experience into a standardized workflow with visible data and controllable processes, effectively improving construction safety, positioning accuracy, and operational continuity under complex conditions.

[0030] This invention utilizes a monitoring and guidance system for the rotational phase of the hoisting section. It automatically compares and visualizes real-time collected elevation angle and distance data with a pre-set safety model, and provides timely alarms when parameters exceed limits. This offers operators precise and intuitive decision support, transforming safety judgments from vague, experience-based estimations into clear, quantitative comparisons. This significantly reduces the probability of collisions or loss of control due to misjudgments, enhancing the controllability of high-risk dynamic processes.

[0031] This invention enables the monitoring system to switch to a distributed connection node monitoring mode after the components are in place, allowing for real-time digital acceptance and long-term online monitoring of the stress state of temporary and permanent connection nodes. This shifts quality control from post-construction sampling to real-time process verification, ensuring that connection quality meets standards on the first attempt and continuously monitoring the impact of subsequent construction on the installed structure. It provides a data foundation for early detection of loose connections or overload risks, thus improving the overall structural safety during the construction period.

[0032] This invention provides a sustainable energy solution for attached monitoring systems by employing a hybrid energy harvesting scheme that combines piezoelectric and photovoltaic technologies, along with an energy management strategy that is intelligently linked to the construction phase. The system efficiently harvests and utilizes energy during the high-vibration phase of hoisting, while maintaining low-power operation during static monitoring phases by relying on photovoltaics and environmental micro-vibrations. This ingeniously overcomes the challenge of powering monitoring equipment for extended periods without an external power source, ensuring the continuity of monitoring tasks.

[0033] This invention introduces a mode management and strategy configuration unit, utilizing multi-source information fusion technology to reliably identify construction conditions and automatically invoke pre-set strategy packages, achieving seamless switching between monitoring system operating modes, network topology, and power consumption strategies. This endows the system with adaptability akin to "autonomy," enabling it to optimally configure its resources according to the task requirements at different stages. It can smoothly transition from a high-intensity hoisting guidance mode to a long-term low-power monitoring mode without manual intervention, significantly improving the system's intelligence and operational efficiency.

[0034] This invention constructs a management and control platform connecting physical construction and a virtual model by introducing global analysis and predictive optimization steps based on digital twins. This platform can not only reverse-engineer subsequent hoisting process parameters using historical data to achieve self-improvement through continuous optimization, but also simulate and assess the safety status of the completed construction structure and provide early warnings. This transcends the limitations of single-point monitoring, achieving closed-loop optimization of the construction process and proactive, full-cycle, global control over structural safety.

[0035] This invention integrates a data preservation and emergency control unit into an autonomous energy and communication relay module, endowing the monitoring system with strong robustness and survivability. In extreme situations such as insufficient power supply or communication interruption, the system can automatically degrade and preserve data, and send minimum status information through an emergency channel, ensuring that critical information is not lost and the system status remains perceptible. This greatly enhances the reliability and availability of the entire method in complex and harsh construction environments.

[0036] This invention introduces an adaptive early warning and predictive control engine into hoisting status monitoring, extending the monitoring dimension from the current state to short-term future trends. Through prediction based on a dynamic model and dynamic comparison of the safety envelope, potential risks can be identified in advance, and operational guidance adjustment instructions can be generated. This represents a leap from "post-event alarm" to "pre-event early warning" and then to "proactive guidance," significantly improving the risk prevention and control capabilities and operational intelligence level of the hoisting process.

[0037] This invention, by adding a system self-diagnosis and status management unit, endows complex monitoring systems with self-health perception and assessment capabilities. It can continuously diagnose the status of key components such as sensors, communications, and power supplies, and report health information. This allows managers to clearly understand the reliability of the monitoring system itself, and to quickly distinguish between actual structural responses and system malfunctions when data anomalies occur, greatly enhancing the reliability of the output data and the effectiveness of decision support.

[0038] This invention adds an online identification and model update unit to the hoisting status monitoring system. Before hoisting, it quickly identifies the individual dynamic characteristics of the current hoisting segment through safety stimuli and adjusts the hoisting path model accordingly. This effectively solves the problem of mismatch between the general model and the hoisting equipment caused by differences in components and changes in hoisting tools. This makes subsequent monitoring, early warning, and control guidance more closely aligned with the actual working conditions, improving the accuracy, adaptability, and reliability of the entire guidance system.

[0039] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0040] The present invention will now be described in further detail so that those skilled in the art can implement it based on the description.

[0041] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not imply the presence or addition of one or more other elements or combinations thereof.

[0042] According to one embodiment of the present invention, a modular box-type segmented hoisting construction method suitable for confined spaces includes the following steps: S1: In the limited open space next to the hoisting operation area, assemble at least two prefabricated box modules in the horizontal direction to form an integral hoisting section, and install temporary reinforcing connection components at the joint of adjacent box modules; S2: Use hoisting equipment to connect the lifting gear, and first lift the hoisting section horizontally to the height above the ground; S3: Control the hoisting mechanism and the luffing mechanism to work together, so that the hoisting section rotates around its lower pivot point, and control the hoisting section to complete the tilting from 0° to 45° with a first angular velocity, and then complete the vertical state transition from 45° to 90° with a second angular velocity less than the first angular velocity. S4: After the hoisting section is in a vertical state, hoist it to the top of the installed lower structure, first make a preliminary connection between its lower end and the lower structure, and then finally fix its upper end to the adjacent structure. Steps S1 to S4 are repeated in this way until all prefabricated box modules are hoisted and connected to form a complete shear wall structure. A monitoring system is installed on the hoisting section. The monitoring system includes at least two sensors deployed on the hoisting section and an autonomous energy and communication relay module integrated on the prefabricated box module. The autonomous energy and communication relay module provides energy and communication support for the sensor during the rotation and positioning of the hoisting section; The monitoring system is configured to perform monitoring tasks in steps S3 and / or S4.

[0043] Specifically, in a modular building project with limited space in a city center, stacked box girder shear walls needed to be installed. The construction site was surrounded by existing buildings and temporary facilities, leaving extremely limited open space for ground assembly. The workers first assembled two precast concrete box girder modules horizontally into a single hoisting section on a small open area adjacent to the hoisting area, measuring only 1.2 times the length of the hoisting section. To ensure the integrity of the horizontal transport and initial hoisting phases, temporary steel reinforcement components were installed at the joints between the box girder modules.

[0044] A mobile crane was used for the hoisting operation. The crane hook was connected to a specialized lifting device. After hooking, the horizontal hoisting section was smoothly lifted to a height of approximately 0.5 meters off the ground. Subsequently, the operator coordinated the crane's hoisting and luffing mechanisms to cause the hoisting section to begin rotating around a pre-set pivot point at its lower end. During the initial elevation phase (0 to 45 degrees), the hoisting section was controlled to rotate at a uniform first angular velocity. When the tilt sensor indicated an angle of 45 degrees, the operator reduced the angular velocity to a smaller second angular velocity and continued to control the hoisting section to rotate slowly until it reached a completely vertical position.

[0045] Throughout the rotation, the monitoring system installed on the hoisting section operates continuously. This system consists of tilt sensors, distance sensors, and an autonomous power and communication relay module integrated within the housing module. The sensors transmit real-time elevation angle of the hoisting section and its real-time distance to the exterior walls of adjacent buildings wirelessly to a ground-based control terminal. The terminal screen graphically displays this data, along with a preset safe hoisting path. Operators perform precise operations based on the comparison between real-time data and the preset model.

[0046] Once the hoisting section is in a vertical position, the crane lifts it onto the already installed substructure. Installers first initially connect and secure its lower end to the substructure using pre-installed bolts. Then, using an aerial work platform, they finally weld its upper end to the adjacent installed shear wall section. Throughout the entire hoisting and placement process, the monitoring system continuously provides status information.

[0047] Traditional construction methods, even in confined spaces, also employ a ground-based assembly followed by segmented hoisting. However, significant differences exist in the implementation process. In traditional methods, the rotation of the hoisting segment relies entirely on the crane operator's personal experience and the hand gestures and commands of ground control personnel. The operator visually judges the approximate range of the hoisting segment's elevation angle and estimates its distance from surrounding obstacles based on experience. Lacking real-time, quantifiable attitude and distance data, angular velocity control during the horizontal-to-vertical rotation is often based on intuition, easily leading to inefficiency in the early stages and disjointed operation due to excessive tension in the later stages to avoid collisions. Regarding the connection quality after the hoisting segment is in place, traditional methods require quality inspectors to conduct spot checks using torque wrenches and other tools after the segment is fully fixed, failing to provide comprehensive and objective feedback on the tightness status immediately upon connection completion. The safety and quality of the entire hoisting process highly depend on the immediate judgment and sense of responsibility of all parties involved, introducing inherent uncertainty and lag.

[0048] This embodiment provides a systematic solution. The method first completes the horizontal assembly and temporary reinforcement of modules in a limited open space, creating conditions for subsequent overall hoisting. Its core lies in clearly defining a two-stage rotational process with angular velocity control, and innovatively integrating a monitoring system containing autonomous power into the hoisting section itself, making it an integral part of the hoisting process. The advantage of this method is that it transforms the complex hoisting process, which originally relied on scattered manual operations and experience-based judgment, into a standardized process that is perceptible, quantifiable, and partially guided. Its beneficial effects are obvious. It not only standardizes high-risk actions and reduces operational arbitrariness through clear segmented angular velocity control, but more importantly, it achieves real-time capture and feedback of key state parameters during the hoisting process through the built-in monitoring system. This provides a solid technical foundation for improving operational safety, positioning accuracy, and overall process controllability in the specific and demanding construction environment of confined spaces, effectively addressing the core challenges of high safety risks, lagging quality control, and excessive reliance on personnel experience inherent in traditional methods.

[0049] According to one embodiment of the present invention, preferably, a modular box-type segmented hoisting construction method suitable for confined spaces includes the following steps in step S3: The monitoring system operates as a hoisting status monitoring and guidance system. The at least two sensors constitute a sensor network, which includes at least an angle sensor for measuring the real-time elevation angle of the hoisting section and a distance sensor for measuring the distance between the hoisting section and key points of the adjacent structure. The data processing and communication unit of the autonomous energy and communication relay module receives and processes real-time data from the sensor network. A field control terminal establishes a wireless communication link with the data processing and communication unit to receive and graphically display the real-time elevation angle and real-time distance, and compare them with a preset hoisting path parameter model. When the real-time elevation angle is in the range of 0° to 45°, it compares with a first angular velocity threshold; when it is in the range of 45° to 90°, it compares with a second angular velocity threshold, and at the same time compares with a safety distance threshold with adjacent structures. When the data processing and communication unit or the field control terminal detects that the real-time angular velocity deviates from the corresponding threshold, or the real-time distance is less than the safe distance threshold, it immediately generates and sends an alarm signal and adjustment guidance to the field control terminal and / or the control system of the hoisting equipment.

[0050] For example, during the process of rotating the assembled hoisting section from a horizontal to a vertical position, the monitoring system operates as a hoisting status monitoring and guidance system according to the scheme of this embodiment. The tilt sensor installed on the hoisting section measures its real-time elevation angle at a frequency of ten times per second. At the same time, two laser rangefinders installed at the top and bottom corners of the hoisting section continuously scan and measure the real-time distance between them and the adjacent existing structural columns and key points of the scaffolding.

[0051] This real-time data is transmitted via wireless communication link to the data processing and communication unit within the autonomous energy and communication relay module for initial integration and verification. Subsequently, the data stream is sent to the field control terminal next to the crane operator's cab. On the terminal screen, a dynamic 3D simulation interface clearly displays a simplified model of the hoisting section, with its current elevation angle updated in real-time as numbers and progress bars, and the closest distance to surrounding obstacles also prominently marked.

[0052] The interface presets an ideal hoisting path parameter model. When the real-time elevation angle is within the range of 0 to 45 degrees, the system compares the actual angular velocity calculated at that moment with the first angular velocity threshold specified by the model. When the elevation angle enters the range of 45 to 90 degrees, it compares it with a smaller second angular velocity threshold. At the same time, the data transmitted back by the ranging sensor is constantly compared with the preset safe distance threshold.

[0053] During a hoisting operation, when the elevation angle of the hoisting section reached approximately 50 degrees, the on-site control terminal suddenly issued a continuous visual flashing alarm and audible alert. The reason was that the system detected a real-time angular velocity slightly exceeding the second angular velocity threshold, and the distance between a corner of the bottom of the hoisting section and the scaffolding pipes was approaching the safety threshold. Simultaneously, a text adjustment guide popped up on the terminal screen, suggesting the operator slightly reduce the luffing speed and fine-tune the slewing direction. The operator immediately followed the guide, and the motion parameters of the hoisting section quickly returned to normal, the alarm was cleared, and the vertical transition was completed smoothly.

[0054] This embodiment constructs a real-time data perception, processing, and decision support closed loop for the dynamic and high-risk process of hoisting attitude transformation. Its function is to liberate operators from the heavy burden of relying entirely on experience-based estimations and strained visual judgment, replacing them with clear, quantifiable, and intuitive data feedback. The system automatically and continuously compares real-time collected key parameters such as elevation angle, distance, and angular velocity with preset scientific models, enabling immediate detection and alarm of abnormal states. This not only significantly improves the accuracy and timeliness of state perception but also shifts the trigger point for safety control from "post-event remediation" to "in-process warning" and even "pre-event prevention." Its beneficial effects include significantly reducing the probability of collisions and operational loss of control due to human error or poor communication, making the entire rotation process visible, quantifiable, and predictable, greatly enhancing the controllability and safety of performing precision hoisting actions in complex spatial environments.

[0055] According to one embodiment of the present invention, preferably, a modular box-type segmented hoisting construction method suitable for confined spaces is provided. At least two sensors include multiple smart sensing nodes, which are respectively installed at the temporary reinforcing connection member and at the connection interface with the lower structure and adjacent structure. Each smart sensing node integrates at least a micro pressure sensor or strain sensor for sensing the force state at the connection. The node data aggregation unit of the autonomous energy and communication relay module is connected to each of the intelligent sensing nodes via a wireless personal area network and is used to receive, temporarily store and forward the sensing data of each node. The monitoring system operates as a distributed connection node monitoring system and also includes a data analysis platform, which is connected to the node data aggregation unit via a wireless wide area network to receive the sensor data and compare and analyze it with the design and construction parameter model of the corresponding connection node. The data analysis platform is configured as follows: a. After the initial connection and final fixation are completed, analyze the data of each node in real time to determine whether the connection tightness has reached the design threshold, and generate a digital acceptance report; b. During the construction of subsequent adjacent sections, the stress changes of each node are continuously monitored. If the stress data of any node exceeds the preset safety envelope or an abnormal change occurs, an early warning message is sent to the field terminal through the aggregation unit.

[0056] For example, after the hoisting section completes its vertical rotation and is lifted to the designated position, the installers begin connecting it to the substructure. At this time, the monitoring system operates as a distributed connection node monitoring system according to the scheme of this embodiment. Multiple pre-installed intelligent sensing nodes begin to function at the temporary reinforcing connection members between the two box modules of the hoisting section, and at the permanent connection interfaces between the lower end of the hoisting section and the foundation, and the upper end and the adjacent wall. These nodes integrate micro-pressure sensors capable of sensing the tightening force of the connecting bolts or the contact pressure of the mating surfaces.

[0057] When installers use a hydraulic wrench to initially tighten the lower connecting bolts, the pressure data captured by the relevant intelligent sensor nodes begins to change. The data is then transmitted via a low-power wireless personal area network to the node data aggregation unit within the enclosure module for temporary storage. Subsequently, the aggregation unit packages and uploads this data to a remote data analysis platform via a wireless wide area network.

[0058] After the initial connection at the lower end and the final welding and fixing at the upper end are completed, the data analysis platform immediately analyzes the sensor data of all received connection nodes. The platform compares the measured pressure value of each node with the threshold value in the corresponding node's design and construction parameter model. Within ten minutes, the platform generates a digital acceptance report showing that the tightness of all monitored nodes has reached or exceeded the design threshold, and notifies the construction team of acceptance via the on-site terminal.

[0059] Over the following week, the adjacent section of the shear wall began installation, and this work generated vibrations and impacts on the already installed section. The data analysis platform continuously received periodic monitoring data from each connection node on the installed section. One day, the platform algorithm detected an abnormal sudden change in the node pressure data on one of the temporary connection components, exceeding the safety envelope set for that node. The platform immediately sent a warning message to the on-site engineer's terminal through the aggregation unit, indicating a potential anomaly in the connection status at that location. The engineer promptly inspected and implemented necessary reinforcements, eliminating the potential hazard.

[0060] This embodiment constructs a comprehensive digital monitoring system covering the entire connection construction process and its subsequent impacts. Its function is to fundamentally change the connection quality control model, transforming it from relying on post-construction sampling and final acceptance to real-time verification and long-term protection covering all key nodes, conducted simultaneously with the installation work. By deploying sensor nodes at various key interfaces, the system can objectively and quantitatively capture the instantaneous completion status of the fastening state and achieve rapid digital acceptance. More valuable is that the system continues to operate after acceptance, acting like a perpetual "stethoscope" for the connection system, capable of sensing the impact of subsequent construction loads in real time. Its beneficial effects are twofold: First, it ensures that the connection construction quality meets standards on the first attempt, achieving process excellence and avoiding the risk of rework; second, it provides an unprecedented data foundation for proactive and preventative management of structural safety during construction, enabling early detection of connection performance degradation or potential risks caused by construction disturbances, thereby elevating structural safety assurance from static "design safety" to a new level of dynamic "process safety."

[0061] According to one embodiment of the present invention, preferably, a modular box-type segmented hoisting construction method suitable for confined spaces includes an autonomous energy and communication relay module comprising: A hybrid energy and intelligent management unit comprising: The energy harvesting assembly includes piezoelectric transducers distributed on the inner wall or structural frame of the prefabricated box module for converting mechanical vibration energy from hoisting, transportation and the environment into electrical energy; and thin-film photovoltaic modules attached to the unshaded area of ​​the outer surface of the box module for harvesting ambient light energy. A high-capacity energy storage unit, connected to the energy harvesting assembly, is used to store the electrical and light energy; An intelligent power management unit is electrically connected to the energy harvesting component and the energy storage unit. It has a pre-set energy management strategy that is linked to the hoisting construction phase. The strategy is configured as follows: during the hoisting movement phase in steps S2 to S3, the electrical energy generated by the piezoelectric transducer is preferentially used and stored, and full power is provided to the sensor network and communication unit. After the unit is fixed in place in step S4 and during subsequent long-term monitoring, it automatically switches to a hybrid power supply mode, primarily powered by the thin-film photovoltaic module and environmental micro-vibration, supplemented by energy release from the energy storage unit. An intermittent, low-power power supply strategy matching the monitoring task is also configured for the sensor network and communication unit. An anti-interference communication relay unit is electrically connected to the intelligent power management unit and adopts adaptive frequency hopping spread spectrum technology to dynamically adjust the communication power and frequency band according to link quality and power consumption strategies.

[0062] One specific implementation involves pre-calculating the final three-dimensional model of the stacked modules during the factory production stage. Thin-film photovoltaic modules are then attached to unshaded areas on the outer surface of the modules that will remain exposed to natural light for extended periods after stacking, such as the top edge or specially designed lateral protrusions. Simultaneously, piezoelectric transducer sheets are distributed and attached to the main load-bearing framework inside the module. These energy harvesting components, along with a high-capacity lithium-ion capacitor energy storage unit and an intelligent power management unit, are integrated into a protective housing, forming the core of the autonomous energy and communication relay module.

[0063] On the day of hoisting, from the time the hoisting section arrived at the site until it began to rotate, the hoisting section experienced continuous vibration. Piezoelectric transducers efficiently converted this mechanical vibration energy into electrical energy. The intelligent power management unit prioritized storing this electrical energy in the energy storage unit, while simultaneously providing sufficient power to the tilt sensor, ranging sensor, and interference-resistant communication relay unit, which maintains high data throughput under heavy workloads. This communication unit employs adaptive frequency-hopping spread spectrum technology to combat wireless interference at the construction site.

[0064] Once the hoisting section is in place and finally secured, the construction site transitions to a relatively static phase. At this point, the intelligent power management unit detects changes in motion and automatically executes pre-set strategies. It first switches the system's main power source to thin-film photovoltaic modules. Although the enclosures are stacked, due to the pre-planned arrangement, some photovoltaic areas can still receive sunlight for several hours, continuously collecting solar energy. The system also doesn't completely abandon collecting residual micro-vibrations from the environment. Based on this hybrid power supply mode, the power management unit issues new configuration instructions to the sensor network and communication unit: reducing the sampling rate of most intelligent sensor nodes from once per second to once every ten minutes and switching to event-triggered mode; the communication relay unit also reduces its transmission power, activating only briefly during data uploads. The energy storage unit acts as a buffer, supplementing power on cloudy days or at night, thus creating a low-power operating state suitable for long-term monitoring tasks.

[0065] This embodiment creatively proposes and implements a hybrid autonomous energy system deeply coupled with the construction environment and stages. It fundamentally solves the problem of sustained power supply faced by monitoring systems attached to precast concrete components. The solution ingeniously combines high-energy-density vibration power generation with sustainable ambient light energy harvesting, supplemented by an intelligent, phased energy management strategy. This allows the monitoring system to efficiently store energy during the hoisting phase when energy acquisition is most convenient, and intelligently switch to a low-power maintenance mode primarily based on photovoltaics during the static monitoring phase when energy acquisition is relatively difficult. The beneficial effect of this adaptive energy supply method is that it frees the monitoring system from dependence on a fixed power grid or limited, one-time batteries, achieving near-perpetual operational potential. This not only ensures the continuity of data throughout the entire process from dynamic hoisting to long-term health monitoring, but more importantly, it makes it possible to deploy permanent sensing nodes early in the component's lifecycle, laying a solid physical foundation for integrated digital management of structural construction and operation.

[0066] According to one embodiment of the present invention, preferably, a modular box-type segmented hoisting construction method suitable for confined spaces includes a monitoring system further comprising a mode management and strategy configuration unit. This unit is integrated into the edge computing core of the autonomous energy and communication relay module and performs the following steps: Intelligent identification of working conditions through multi-source information fusion: Real-time analysis of the comprehensive data stream from the sensor network to construct a fusion decision model that includes tilt angle, strain, acceleration, and hook load signals from the hoisting equipment control system; When the real-time elevation angle of the hoisting section is continuously detected to be stable at 90°±Δθ (Δθ is a preset tolerance angle) for more than a first preset time, and the strain sensor data indicates that the connection interface begins to bear pressure and tends to stabilize, and the hook load signal drops below the preset safe unloading threshold, the fusion decision model automatically generates a working condition identifier of "hoisting in place, entering the connection and fixing stage"; Dynamic loading of parameter strategies: Based on the operating condition identifier, the system configuration strategy package matching the target operating mode is automatically called from the pre-stored strategy library; wherein, the strategy library includes at least: a first strategy package corresponding to the hoisting status monitoring and guidance mode; a second strategy package corresponding to the distributed connection node monitoring mode; the configuration of the second strategy package includes: reconstructing the network topology into a low-power monitoring network, and instructing the intelligent power management unit to execute a hybrid power supply strategy matching long-term monitoring; Seamless system switching and model update: The mode management and strategy configuration unit issues a reconfiguration command based on the loaded second strategy package, enabling the monitoring system to autonomously enter the long-term monitoring state of the connection node; at the same time, the key sensor data before and after the "lifting in place" moment are transmitted back to the data analysis platform or the construction global intelligent management platform to update the initial state of the digital twin of the lifting section.

[0067] In one specific implementation, as the hoisting section is lifted above the installation position and begins its slow descent for alignment, the mode management and strategy configuration unit, integrated into the edge computing core of the autonomous energy and communication relay module, begins high-speed operation of a multi-source information fusion-based intelligent condition identification program. It receives a comprehensive data stream from the sensor network in real time: the tilt sensor reports an elevation angle of 89.5 degrees for the hoisting section; the strain sensor installed at the connection interface shows that as the bottom of the hoisting section approaches the foundation pre-embedded parts, the contact pressure is steadily increasing and the rate of change is gradually approaching zero; simultaneously, the hook load signal obtained from the crane control system via a wireless data link shows that the load value is steadily decreasing.

[0068] The integrated decision model within the unit continuously analyzes these signals. When the elevation angle of the lifting section remains stable for more than 30 seconds within a tolerance range of 90 degrees ± 0.5 degrees, the strain data curve clearly shows that the bottom has firmly settled under pressure and entered a stable state, and the hook load drops to the safe unloading threshold equivalent to only the self-weight of the lifting equipment, the model comprehensively judges that all conditions are met. It automatically generates a clear working condition indicator indicating that the lifting is in place and has entered the connection and fixing stage.

[0069] Subsequently, the parameter strategy dynamic loading function was triggered. Based on the working condition identifier, the unit automatically retrieved and loaded the second strategy package from the locally stored strategy library, namely the distributed connection node monitoring mode configuration package. After loading, the unit immediately issued a series of reconfiguration instructions to the entire monitoring network: commanding the wireless personal area network to reconstruct the topology, switching the star network that transmits attitude data at high speed to a low-power tree network with the node data aggregation unit as the root and only periodically polling; at the same time, sending instructions to the intelligent power management unit, requiring it to execute a hybrid power supply strategy that matches long-term monitoring. The entire system completed the conversion within seconds, silently transitioning from the high-intensity hoisting guidance state to the quiet long-term monitoring state of the connection nodes. Meanwhile, the mode management and strategy configuration unit timestamped the newly identified positioning time and the key sensor raw data for one minute before and after, and transmitted it back to the remote construction global intelligent management platform to update the precise initial state of the digital twin of this hoisting section.

[0070] This embodiment endows the monitoring system with a high degree of environmental awareness and autonomous decision-making capabilities, achieving intelligent adaptation in complex construction scenarios. It constructs a sophisticated logical judgment mechanism based on multi-source information fusion, enabling it to comprehensively assess whether the hoisting section is truly and safely in place, much like an experienced engineer, and thus drive a global mode transition of the entire system at the most appropriate time. This scheme not only considers the stability of spatial attitude but also incorporates key evidence such as the mechanical contact state and the unloading state of the hoisting equipment, forming a robust multi-condition criterion that ensures the accuracy and robustness of condition identification. Its beneficial effects include a significant improvement in the intelligence level and practical value of the monitoring system. The system can seamlessly, smoothly, and reliably transition from dynamic guidance to static monitoring without manual intervention, avoiding problems caused by incorrect switching or improper switching timing. This adaptive capability ensures the efficient utilization and accurate deployment of monitoring resources, allowing a single hardware system to perfectly serve two distinct construction phases. It guarantees the safety and controllability of the hoisting process while achieving long-term protection of the connection status, embodying true intelligent system integration.

[0071] According to one embodiment of the present invention, preferably, a modular box-type segmented hoisting construction method suitable for confined spaces is provided. The method further includes a global analysis and predictive optimization step based on digital twins, implemented through a global intelligent construction management platform, which performs the following: Full-element data fusion modeling: Access data from all monitoring systems on the current hoisting section and its adjacent installed sections, combine the design BIM model and actual hoisting process parameters to construct and update in real time a high-fidelity digital twin reflecting the entire process from single-section hoisting dynamics to multi-section structural statics. Reverse optimization of hoisting process parameters: Analyze the correlation between the actual motion data (including real-time angular velocity and structural vibration response) of the historical hoisting section in step S3 and the final positioning accuracy and the initial stress of the connection node. Through machine learning model training, dynamically generate and recommend the optimized first and second angular velocity parameters for subsequent hoisting sections, as well as the corresponding hoisting path parameter model, and push them to the on-site control terminal. Overall structural safety status assessment and early warning: After multiple hoisting sections form a local or overall structure, based on the digital twin, the structural response under different construction loads (such as subsequent hoisting impact and wind load) is simulated, and the simulated data is compared and analyzed in real time with the measured data of the distributed connection node monitoring system of each section. When the deviation between measured data and simulated data exceeds the tolerance limit, or when the simulation results show that the stress at key connection nodes exceeds the warning threshold, an overall structural safety warning is generated, and the highest risk hoisting section or connection node is accurately located, while construction adjustment suggestions are given.

[0072] In a construction project comprising twenty stacked box girder shear walls, with the completion of the third segment's installation, the overall intelligent construction management platform began full-element data fusion modeling. The platform not only integrated real-time micro-pressure data from all connecting nodes of the newly installed third segment, but also continuously monitored data from the previously installed first and second segments, while simultaneously receiving dynamic motion data from the ongoing installation of the fourth segment. The platform merged this continuous stream of measured data with the original shear wall design BIM model and the actual process parameters used for each segment's installation, constructing and updating a high-fidelity digital twin in real time. This twin not only includes the structure's geometric and physical properties but also reflects the actual stress state of the connected sections and the dynamic installation process of the unfinished sections.

[0073] The platform initiated its reverse optimization function for hoisting process parameters. It analyzed historical data from the first three stages of rotation between 0 and 90 degrees, including real-time angular velocity at each moment and vibration acceleration at key points on the housing. This motion data was then correlated with the final positioning accuracy deviation of each stage and the initial stress curves of the connection nodes within 24 hours of tightening. Through training with a built-in machine learning model, the platform discovered that the positioning accuracy was highest and the stress distribution at the connection interface was most uniform when the first angular velocity was controlled at 1.5 degrees per second and the second angular velocity at 0.8 degrees per second. Therefore, the platform dynamically generated and pushed these two optimized angular velocity parameters and the corresponding complete hoisting path model to the on-site control terminal responsible for the fourth stage of hoisting, guiding its subsequent operations.

[0074] After the sixth section was hoisted and formed a local three-layer structure, the platform performed an overall structural safety assessment and early warning. Based on the latest digital twin, the platform simulated the impact of potential impact loads on the existing local structure during the planned hoisting of the seventh section the following day. The simulation results showed that at a certain connection node between the second and third sections, the simulated stress value was close to the warning threshold. The platform immediately compared and analyzed this simulation result in real time with the measured stress data transmitted back from the distributed node monitoring system on the second and third sections. Although the current measured data had not yet exceeded the standard, the platform identified the risk based on the deviation trend, generated an overall structural safety warning, accurately located the risk node, and provided a construction adjustment suggestion to postpone the hoisting of the seventh section and first re-inspect the node.

[0075] This embodiment constructs a global intelligent brain for construction that transcends single-point, single-segment monitoring. It deeply integrates discrete hoisting operations, isolated monitoring data, and structural design models to create a continuously evolving, reality-reflecting digital twin. This twin not only records the past but also simulates the future, enabling construction management to shift from passive response to proactive prevention. Its beneficial effects include achieving closed-loop optimization of the construction process and advanced insight into structural safety. The system can reverse-engineer better process parameters using historical measured data, making the construction process increasingly refined and demonstrating its self-learning and improvement capabilities. More importantly, it can predict the impact of construction loads through high-fidelity simulation before they are actually applied and verify this in real time using a sensor network distributed throughout the structure. This allows for accurate early warnings and location of potential risks before they develop into substantial hazards. This fundamentally changes the paradigm of construction safety management, shifting from post-event remediation to pre-event prevention, greatly improving the overall controllability and scientific decision-making level of complex projects.

[0076] According to one embodiment of the present invention, preferably, a modular box-type segmented hoisting construction method suitable for confined spaces includes an autonomous energy and communication relay module further comprising a data preservation and emergency control unit. This unit performs the following steps: real-time monitoring of the voltage level of the energy storage unit; when the voltage is lower than a first threshold, initiating a low-power mode and shutting down non-core sensors; when the voltage is lower than a critical second threshold for maintaining minimum functional operation, triggering a data preservation emergency process; during a communication link interruption, controlling the data processing and communication unit to convert real-time monitoring data into encrypted data packets with high-precision timestamps, sequentially storing them in a local non-volatile memory, and automatically prioritizing retransmission after communication is restored; when both communication interruption and power supply being lower than the second threshold are detected simultaneously, activating an emergency mode, controlling the energy harvesting unit to harvest energy at full capacity, and initiating an independent ultra-low-power backup communication channel to send an emergency beacon signal containing the hoisting segment number, real-time tilt angle, and voltage status to the field control terminal at a preset minimum time interval.

[0077] During a period of continuous rainy weather, the monitoring system on a certain hoisting section had been operating for several days. Due to severely insufficient sunlight, the power generation of the thin-film photovoltaic modules was negligible, and the system mainly relied on the energy storage unit for power. The data preservation and emergency control unit integrated within the autonomous energy and communication relay module began to function. It continuously monitored the voltage level of the energy storage unit. When the voltage dropped to the first threshold of 3.3 volts, the unit automatically activated a low-power mode, immediately shutting down non-core auxiliary sensors, leaving only the tilt sensor and critical strain nodes operating at the lowest possible frequency.

[0078] However, the weather remained severe, and the voltage continued to drop. When the voltage fell to the critical second threshold of 3.0 volts, the unit triggered the data preservation emergency procedure. Adding insult to injury, the system's main wireless communication link was interrupted due to the obstruction of the tower crane's tall metal structure and the severe weather. During the communication interruption, the control unit instructed the data processing unit to attach high-precision timestamps to the limited amount of key monitoring data collected in real time, encrypt it, convert it into compact data packets, and store them sequentially in local non-volatile memory.

[0079] A more serious situation arose when the unit simultaneously detected a communication link interruption and a power supply voltage below the critical second threshold of 3.0 volts. It immediately activated the highest-level emergency mode. On one hand, it controlled the energy harvesting unit to adjust to maximum sensitivity, making every effort to capture any slight mechanical vibration in the environment to harvest energy. On the other hand, it activated an independent, extremely low-power backup communication channel, such as narrowband communication using a different frequency band or modulation method. In this mode, the system sends a very simple emergency beacon signal to the field control terminal at preset, as long as possible intervals, such as every thirty minutes. This signal only contains the unique number of the hoisting section, the current tilt angle, and the voltage value. This weak beacon was finally detected by the field terminal at some point, alerting management personnel that the hoisting section's monitoring system was in an extremely low power and communication failure state, but was still operating, and that critical data had been locally preserved.

[0080] This embodiment endows the monitoring system with strong resilience and survival intelligence, establishing a tiered response and multi-layered protection emergency mechanism to ensure that the system can maintain minimum functionality even under extreme conditions and maximize the preservation of core data assets. By intelligently monitoring voltage and communication status, this solution can proactively and gradually reduce system power consumption, delaying the onset of crises. When a crisis is unavoidable, it ensures reliable local data storage. In the worst-case scenario, it can still activate backup mechanisms and send out life signals indicating its existence. Its beneficial effect is a significant enhancement of the reliability and availability of the entire monitoring system in real, complex, and unpredictable construction site environments. It avoids sudden system failures and data black holes, allowing managers to always understand the system's baseline status, even under extreme conditions. This design philosophy of "preserving data and sending signals as long as there is a sliver of energy and opportunity" elevates the reliability of the monitoring system to a new level, ensuring the integrity of the data chain for critical construction processes and providing a solid guarantee for project safety assessment and traceability. According to one embodiment of the present invention, preferably, a modular box-type segmented hoisting construction method suitable for confined spaces includes a data processing and communication unit of a hoisting status monitoring and guidance system that also runs an adaptive early warning and predictive control engine. This engine performs the following steps: based on the preset hoisting path parameter model, it combines the current movement speed, attitude angular velocity, and real-time wind speed data of the hoisting segment in real time, and calculates an adaptive safety envelope that changes with the hoisting stage and working conditions using a dynamic model; based on the real-time data stream of the sensor network, it predicts the movement trajectory and attitude of the hoisting segment within a short future time window, compares the predicted trajectory with the dynamic safety envelope to identify potential risks in advance and calculate the risk level; when a potential risk is predicted, it generates a graded alarm based on the risk level, and based on the prediction model and the response characteristics of the hoisting equipment, it reverse-calculates and generates a set of operation sequences including lead time and step-by-step execution as adjustment guidelines, which are then sent to the control system of the hoisting equipment.

[0081] During a hoisting operation, a gust of wind arose when the fifth stacked shear wall section was being lifted. The adaptive early warning and predictive control engine was operating within the data processing and communication unit of the hoisting status monitoring and guidance system. Based on the current 30-degree elevation angle of the hoisting section and a preset hoisting path parameter model, the engine combined the section's current velocity, attitude angular velocity, and real-time wind speed data from the on-site anemometer, performing rapid calculations through a built-in dynamic model. It generated a dynamic safety envelope that varied with the current stage and wind conditions; this envelope was narrower than the static safety range under calm conditions.

[0082] The engine doesn't just monitor the current state; based on the high-speed real-time data stream from the sensor network, it predicts the trajectory and attitude of the hoisting section within the next five seconds. The prediction model shows that, under the combined effects of the current operating speed and wind force, when the upper edge of the hoisting section reaches a 55-degree elevation angle, its predicted trajectory will exceed the dynamic safety envelope, getting too close to the standard tower crane section on one side, and the risk level is calculated as medium. Therefore, three seconds before the hoisting section actually reaches the dangerous position, the on-site control terminal issues a graded alarm, and the screen highlights the risk area. More importantly, based on its understanding of the hoisting equipment's response characteristics, the engine reverse-calculates and generates an operational sequence as adjustment guidance: it recommends that the operator reduce the luffing speed by 20% within the next two seconds and make a slight adjustment in the opposite direction by about one degree in the third second. Following the guidance, the operator's actual trajectory of the hoisting section narrowly avoids the predicted risk point and smoothly enters the next stage.

[0083] This embodiment upgrades hoisting status monitoring from static, threshold-based, and passive alarms to dynamic, model-driven, and proactive prediction and guidance. Its function is to equip the operator with an intelligent co-pilot with forward-looking capabilities. This engine, by combining real-time environmental data and dynamic models, can predict short-term movement trends and assess risks in advance. Its beneficial effect is a significant expansion of the safety control buffer and decision-making time window. It transforms warnings from alarming cries of impending danger into calm prompts with sufficient lead time and specific, quantifiable operational suggestions. This not only more effectively avoids accidents but also helps drivers achieve more precise and smoother control by generating smooth operational sequence guidance. This improves both safety and the smoothness and efficiency of hoisting operations, representing a significant evolution of hoisting operation assistance systems from perception to cognition, and from reaction to pre-control.

[0084] According to one embodiment of the present invention, preferably, a modular box-type segmented hoisting construction method suitable for confined spaces includes a monitoring system further comprising a system self-diagnosis and status management unit embedded in an autonomous energy and communication relay module, which performs the following steps: The built-in self-test program is run periodically to inject calibration signals and analyze the response of the at least two sensors in order to diagnose whether the sensors have excessive deviation, response delay or complete failure. Real-time monitoring of key performance indicators of the wireless communication unit's link, including signal strength, bit error rate, and connection stability, and assessment of communication health level; Collect and analyze the charge-discharge cycle count, current effective capacity, and internal resistance parameters of the energy storage unit to predict its remaining reliable operating time; The diagnostic results of the above sensor health status, communication health status, and power reliability status are packaged together with the monitoring data, or uploaded in real time to the field control terminal or remote management platform through an independent system status message. When any critical component is diagnosed to be below a preset safety threshold, a significant confidence degradation flag is automatically added to the monitoring data or system status message, and an independent system anomaly alarm is triggered.

[0085] Midway through a project, a monitoring system installed on a shear wall continuously transmits pressure data from the connection nodes. The system self-diagnosis and status management unit, embedded in its autonomous energy and communication relay module, is performing tasks according to a set cycle. Every 24 hours, it runs a built-in self-test program, injecting a tiny standard electrical calibration signal into a micro-pressure sensor mounted on a temporary connection component and analyzing its response. One self-test revealed a significant deviation in the sensor's response curve compared to the baseline, along with increased response delay. The unit diagnosed it as having excessive deviation and response delay faults, marking its reliability as low.

[0086] Meanwhile, the unit monitors the uplink metrics of the wireless communication unit in real time, detecting significant signal strength fluctuations and occasional spikes in the bit error rate within the past hour, assessing its communication health level as moderate. The unit also continuously collects voltage and internal resistance data from the energy storage unit, and, combined with a charge-discharge cycle model, predicts its remaining reliable operating time to be approximately thirty days. These diagnostic results, including sensor faults, communication quality fluctuations, and power lifespan estimates, are packaged into the uploaded data frames and sent as metadata along with actual node stress monitoring data to the remote management platform.

[0087] Upon receiving the data, the platform software detected a sensor reliability degradation flag. It automatically added a special visual marker to the pressure data displayed for that node and popped up a system anomaly alarm, prompting engineers that the node's data might be unreliable and recommending verification. Instead of blindly accepting the specific pressure fluctuations at that node, management arranged a targeted on-site inspection, ultimately discovering that moisture in the sensor's cable connection was causing the performance degradation.

[0088] This embodiment endows the intelligent monitoring system with crucial "self-awareness," constructing a continuous self-inspection and health assessment system covering core components such as sensors, communication, and power supply. This transforms the system from a mere data producer into one capable of evaluating its own data quality. It proactively diagnoses internal faults, assesses the impact of external conditions on performance, and transparently transmits this health information to users. The beneficial effect is a significant improvement in the data reliability and decision support value of the entire monitoring method. Managers no longer need to blindly trust data but can use it discerningly based on the system's health reports. When data is accompanied by a "high reliability" label, decision-making confidence is stronger; when accompanied by "reliability downgrade" or "system anomaly" alarms, priority can be given to investigating the equipment itself rather than structural problems. This is equivalent to equipping the monitoring system with a dedicated "quality inspector," ensuring the reliability of output information, preventing misjudgments caused by hidden equipment faults, and establishing data-driven construction safety management on a more solid and reliable foundation.

[0089] According to one embodiment of the present invention, preferably, a modular box-type segmented hoisting construction method suitable for confined spaces includes a hoisting status monitoring and guidance system further comprising an online identification and model updating unit, which performs the following steps: During the hovering and stabilization phase after the hoisting section is horizontally lifted off the ground in step S2 and before the rotation begins in step S3, the hoisting equipment is controlled to execute a set of small-amplitude, safe excitation motion sequences, while the dynamic response data of the hoisting section is collected at high frequency through the sensor network. Based on the input signal of the excitation motion sequence and the dynamic response output signal of the hoisting section, a system identification algorithm is used to estimate the key dynamic parameters of the current hoisting section-spreading system in real time online. The parameters include at least the equivalent pendulum length, damping coefficient, and coupling gain with the hoisting equipment control system. By utilizing the dynamic parameters identified online, the preset hoisting path parameter model is locally modified to generate a personalized hoisting path model that matches the individual characteristics and environment of the current hoisting segment. During the rotation process in the subsequent step S3, the adaptive early warning and predictive control engine generates an adaptive safety envelope and predictive guidance based on the personalized hoisting path model.

[0090] During one implementation, when hoisting the eighth section of the stacked shear wall, after the hoisting section was horizontally lifted one meter off the ground and stabilized, the online identification and model update unit in the hoisting status monitoring and guidance system began to operate. The field control terminal sent instructions to the crane control system, controlling the hook to perform a series of small-amplitude, safe reciprocating movements in the horizontal plane, forming an excitation motion sequence lasting approximately thirty seconds. Simultaneously, the sensor network, especially high-precision accelerometers and tilt sensors, frequently collected dynamic response data of the hoisting section to this excitation, including swing amplitude, frequency, and decay rate.

[0091] The online identification unit receives the input signal of the excitation motion sequence and the output signal of the dynamic response of the lifting section. It employs a system identification algorithm to perform rapid calculations in the background. Just one minute later, the algorithm outputs an estimation result: the equivalent pendulum length of the current lifting section and spreader combination system is 4.2 meters, the damping coefficient is a specific value, and the coupling gain with the crane control system is slightly different from the standard model. These differences may stem from subtle variations in the spreader or minor changes in the internal counterweight of the lifting box.

[0092] The unit then utilizes these online-identified dynamic parameters specific to this particular lifting segment to locally modify the preset general lifting path parameter model. It generates a personalized lifting path model, in which the safe angular velocity threshold and predicted swing amplitude are adjusted to match the equivalent swing length of 4.2 meters. During the subsequent rotation of the lifting segment from zero to ninety degrees, the adaptive warning and predictive control engine no longer relies on the general model, but instead uses this personalized model to generate a dynamic safety envelope and predict the motion trajectory. The entire lifting process thus becomes more realistic, and the accuracy of control guidance is improved.

[0093] This embodiment endows the monitoring and guidance system with the crucial "adaptive calibration" capability. Before the actual lifting operation begins, a brief safety pre-operation phase is used to proactively stimulate the system and collect its response, thereby quickly identifying the true dynamic characteristics of the lifting equipment assembly in the current lifting section online. Its beneficial effect is that it achieves a leap from "general preset" to "individual customization" in the lifting control model. By injecting the identified real parameters into the model, the system-generated warning thresholds, safety envelopes, and predicted trajectories can highly match the actual behavioral characteristics of the object being lifted. This significantly improves the accuracy of judging the lifting process status and predicting risks, avoiding efficiency losses caused by overly conservative models and eliminating safety blind spots caused by discrepancies between the model and the actual object. Like an experienced master craftsman, the system can "weigh" the characteristics of the object before taking action, thus making the most appropriate control strategy, demonstrating a highly intelligent level of situational awareness and adaptive control.

[0094] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A modular box-type segmented hoisting construction method suitable for confined spaces, characterized in that, Includes the following steps: S1: Next to the hoisting operation area, assemble at least two prefabricated box modules in the horizontal direction to form an integral hoisting section, and install temporary reinforcing connection components at the joint of adjacent box modules; S2: Use hoisting equipment to connect the lifting gear, and first lift the hoisting section horizontally to the height above the ground; S3: Control the hoisting mechanism and the luffing mechanism to work together, so that the hoisting section rotates around its lower pivot point, and control the hoisting section to complete the tilting from 0° to 45° with a first angular velocity, and then complete the vertical state transition from 45° to 90° with a second angular velocity less than the first angular velocity. S4: After the hoisting section is in a vertical state, hoist it to the top of the installed lower structure, first make a preliminary connection between its lower end and the lower structure, and then finally fix its upper end to the adjacent structure. Steps S1 to S4 are repeated in this way until all prefabricated box modules are hoisted and connected to form a complete shear wall structure. A monitoring system is installed on the hoisting section. The monitoring system includes at least two sensors deployed on the hoisting section and an autonomous energy and communication relay module integrated on the prefabricated box module. The autonomous energy and communication relay module provides energy and communication support for the sensor during the rotation and positioning of the hoisting section; The monitoring system is configured to perform monitoring tasks in steps S3 and / or S4.

2. The modular box-type segmented hoisting construction method suitable for confined spaces as described in claim 1, characterized in that, In step S3, the monitoring system operates as a hoisting status monitoring and guidance system, including the following steps: At least two sensors constitute a sensor network, which includes at least an inclination sensor for measuring the real-time elevation angle of the hoisting section and a distance sensor for measuring the distance between the hoisting section and key points of the adjacent structure. The data processing and communication unit of the autonomous energy and communication relay module receives and processes real-time data from the sensor network. A field control terminal establishes a wireless communication link with the data processing and communication unit to receive and graphically display the real-time elevation angle and real-time distance, and compare them with a preset hoisting path parameter model. When the real-time elevation angle is in the range of 0° to 45°, it compares with a first angular velocity threshold; when it is in the range of 45° to 90°, it compares with a second angular velocity threshold, and at the same time compares with a safety distance threshold with adjacent structures. When the data processing and communication unit or the field control terminal detects that the real-time angular velocity deviates from the corresponding threshold, or the real-time distance is less than the safe distance threshold, it immediately generates and sends an alarm signal and adjustment guidance to the field control terminal and / or the control system of the hoisting equipment.

3. The modular box-type segmented hoisting construction method suitable for confined spaces as described in any one of claims 1 or 2, characterized in that, At least two sensors include multiple smart sensing nodes, which are respectively installed at the temporary reinforcing connection member and at the connection interface with the lower structure and adjacent structure. Each smart sensing node integrates at least a micro pressure sensor or strain sensor for sensing the force state at the connection. The node data aggregation unit of the autonomous energy and communication relay module is connected to each of the intelligent sensing nodes via a wireless personal area network and is used to receive, temporarily store and forward the sensing data of each node. The monitoring system operates as a distributed connection node monitoring system and also includes a data analysis platform, which is connected to the node data aggregation unit via a wireless wide area network to receive the sensor data and compare and analyze it with the design and construction parameter model of the corresponding connection node. The data analysis platform is configured as follows: a. After the initial connection and final fixation are completed, analyze the data of each node in real time to determine whether the connection tightness has reached the design threshold, and generate a digital acceptance report; b. During the construction of subsequent adjacent sections, the stress changes of each node are continuously monitored. If the stress data of any node exceeds the preset safety envelope or an abnormal change occurs, an early warning message is sent to the field terminal through the aggregation unit.

4. The modular box-type segmented hoisting construction method suitable for confined spaces as described in claim 3, characterized in that, The autonomous energy and communication relay module includes: A hybrid energy and intelligent management unit comprising: The energy harvesting assembly includes piezoelectric transducers distributed on the inner wall or structural frame of the prefabricated box module for converting mechanical vibration energy from hoisting, transportation and the environment into electrical energy; and thin-film photovoltaic modules attached to the unshaded area of ​​the outer surface of the box module for harvesting ambient light energy. A high-capacity energy storage unit, connected to the energy harvesting assembly, is used to store the electrical and light energy; An intelligent power management unit is electrically connected to the energy harvesting component and the energy storage unit. It has a pre-set energy management strategy that is linked to the hoisting construction phase. The strategy is configured as follows: during the hoisting movement phase in steps S2 to S3, the electrical energy generated by the piezoelectric transducer is preferentially used and stored, and full power is provided to the sensor network and communication unit. After the unit is fixed in place in step S4 and during subsequent long-term monitoring, it automatically switches to a hybrid power supply mode, primarily powered by the thin-film photovoltaic module and environmental micro-vibration, supplemented by energy release from the energy storage unit. An intermittent, low-power power supply strategy matching the monitoring task is also configured for the sensor network and communication unit. An anti-interference communication relay unit is electrically connected to the intelligent power management unit and adopts adaptive frequency hopping spread spectrum technology to dynamically adjust the communication power and frequency band according to link quality and power consumption strategies.

5. The modular box-type segmented hoisting construction method suitable for confined spaces as described in claim 3, characterized in that, The monitoring system also includes a mode management and policy configuration unit, which is integrated into the edge computing core of the autonomous energy and communication relay module and performs the following steps: Intelligent identification of working conditions through multi-source information fusion: Real-time analysis of the comprehensive data stream from the sensor network to construct a fusion decision model that includes tilt angle, strain, acceleration, and hook load signals from the hoisting equipment control system; when the real-time elevation angle of the hoisting section is continuously detected to be stable at 90°±Δθ for more than a first preset time, and the strain sensor data indicates that the connection interface begins to bear pressure and tends to stabilize, and the hook load signal drops below the preset safe unloading threshold, the fusion decision model automatically generates a working condition identifier of "hoisting in place, entering the connection and fixing stage", where Δθ is a preset tolerance angle; Dynamic loading of parameter strategies: Based on the operating condition identifier, the system configuration strategy package matching the target operating mode is automatically called from the pre-stored strategy library; wherein, the strategy library includes at least: a first strategy package corresponding to the hoisting status monitoring and guidance mode; a second strategy package corresponding to the distributed connection node monitoring mode; the configuration of the second strategy package includes: reconstructing the network topology into a low-power monitoring network, and instructing the intelligent power management unit to execute a hybrid power supply strategy matching long-term monitoring; Seamless system switching and model update: The mode management and strategy configuration unit issues a reconfiguration command based on the loaded second strategy package, enabling the monitoring system to autonomously enter the long-term monitoring state of the connection node; at the same time, the key sensor data before and after the "lifting in place" moment are transmitted back to the data analysis platform or the construction global intelligent management platform to update the initial state of the digital twin of the lifting section.

6. The modular box-type segmented hoisting construction method suitable for confined spaces as described in claim 5, characterized in that, The method also includes a global analysis and predictive optimization step based on digital twins, implemented through a construction global intelligent management platform, which performs the following: Full-element data fusion modeling: Access data from all monitoring systems on the current hoisting section and its adjacent installed sections, combine the design BIM model and actual hoisting process parameters to construct and update in real time a high-fidelity digital twin reflecting the entire process from single-section hoisting dynamics to multi-section structural statics. Reverse optimization of hoisting process parameters: Analyze the correlation between the actual motion data of historical hoisting sections in step S3 and the final positioning accuracy and the initial stress of the connection nodes. Through machine learning model training, dynamically generate and recommend optimized first and second angular velocity parameters for subsequent hoisting sections, as well as the corresponding hoisting path parameter model, and push them to the on-site control terminal. Overall structural safety status assessment and early warning: After multiple hoisting sections form a local or overall structure, based on the digital twin, the structural response under different construction loads is simulated, and the simulated data is compared and analyzed in real time with the measured data of the distributed connection node monitoring system of each section. When the deviation between measured data and simulated data exceeds the tolerance limit, or when the simulation results show that the stress at key connection nodes exceeds the warning threshold, an overall structural safety warning is generated, and the highest risk hoisting section or connection node is accurately located, while construction adjustment suggestions are given.

7. The modular box-type segmented hoisting construction method for confined spaces as described in claim 4, wherein the autonomous energy and communication relay module further includes a data preservation and emergency control unit, which performs the following steps: real-time monitoring of the voltage level of the energy storage unit; when the voltage is lower than a first threshold, initiating a low-power mode and shutting down non-core sensors; when the voltage is lower than a critical second threshold for maintaining minimum functional operation, triggering a data preservation emergency process; during communication link interruption, controlling the data processing and communication unit to convert real-time monitoring data into encrypted data packets with high-precision timestamps, sequentially storing them in local non-volatile memory, and automatically prioritizing retransmission after communication is restored; when both communication interruption and power supply being lower than the second threshold are detected simultaneously, activating an emergency mode, controlling the energy harvesting unit to harvest energy at full capacity, and initiating an independent ultra-low power backup communication channel to send an emergency beacon signal containing the hoisting segment number, real-time tilt angle, and voltage status to the field control terminal at a preset minimum time interval.

8. The modular box-type segmented hoisting construction method suitable for confined spaces as described in claim 4, characterized in that, The data processing and communication unit of the hoisting status monitoring and guidance system also runs an adaptive early warning and predictive control engine. This engine performs the following steps: based on the preset hoisting path parameter model, it combines the current movement speed, attitude angular velocity, and real-time wind speed data of the hoisting segment in real time, and calculates and generates an adaptive safety envelope that changes with the hoisting stage and working conditions through a dynamic model; based on the real-time data stream of the sensor network, it predicts the movement trajectory and attitude of the hoisting segment in the future short time window, compares the predicted trajectory with the dynamic safety envelope to identify potential risks in advance and calculate the risk level; when a potential risk is predicted, it generates a graded alarm according to the risk level, and based on the prediction model and the response characteristics of the hoisting equipment, it reverse-calculates and generates a set of operation sequences including lead time and step-by-step execution as adjustment guidelines, and sends them to the control system of the hoisting equipment.

9. The modular box-type segmented hoisting construction method suitable for confined spaces as described in claim 4, characterized in that, The monitoring system also includes a system self-diagnosis and status management unit embedded in the autonomous energy and communication relay module, which performs the following steps: The built-in self-test program is run periodically to inject calibration signals and analyze the response of the at least two sensors in order to diagnose whether the sensors have excessive deviation, response delay or complete failure. Real-time monitoring of key performance indicators of the wireless communication unit's link, including signal strength, bit error rate, and connection stability, and assessment of communication health level; Collect and analyze the charge-discharge cycle count, current effective capacity, and internal resistance parameters of the energy storage unit to predict its remaining reliable operating time; The diagnostic results of the above sensor health status, communication health status, and power reliability status are packaged together with the monitoring data, or uploaded in real time to the field control terminal or remote management platform through an independent system status message. When any critical component is diagnosed to be below a preset safety threshold, a significant confidence degradation flag is automatically added to the monitoring data or system status message, and an independent system anomaly alarm is triggered.

10. The modular box-type segmented hoisting construction method suitable for confined spaces as described in claim 2, characterized in that, The hoisting status monitoring and guidance system also includes an online identification and model update unit, which performs the following steps: During the hovering and stabilization phase after the hoisting section is horizontally lifted off the ground in step S2 and before the rotation begins in step S3, the hoisting equipment is controlled to execute a set of small-amplitude, safe excitation motion sequences, while the dynamic response data of the hoisting section is collected at high frequency through the sensor network. Based on the input signal of the excitation motion sequence and the dynamic response output signal of the hoisting section, a system identification algorithm is used to estimate the key dynamic parameters of the current hoisting section-spreading system in real time online. The parameters include at least the equivalent pendulum length, damping coefficient, and coupling gain with the hoisting equipment control system. By utilizing the dynamic parameters identified online, the preset hoisting path parameter model is locally modified to generate a personalized hoisting path model that matches the individual characteristics and environment of the current hoisting segment. During the rotation process in the subsequent step S3, the adaptive early warning and predictive control engine generates an adaptive safety envelope and predictive guidance based on the personalized hoisting path model.