Modular hoistway construction method

Abstract

The invention relates to a modular hoistway construction method, and belongs to the technical field of building construction. The method aims at solving the problems that in traditional modular hoistway hoisting splicing operation, due to the fact that real-time and accurate construction state sensing and remote intelligent regulation and control means are lacked, installation precision control is difficult, and safety risk early warning is lagged. According to the technical scheme, when a hoistway unit is prefabricated in a factory, a sensing device is fixedly installed in the hoistway unit; configuring an Internet of Things data acquisition and transmission module for each unit, and connecting the Internet of Things data acquisition and transmission module with a sensor so as to establish two-way communication with a remote construction management cloud platform in which design reference data is stored; during on-site hoisting and splicing, the sensor continuously collects space attitude and structure stress data and sends the data to the cloud platform through the Internet of Things module; and the cloud platform calculates and compares the real-time monitoring data with the design reference data. The method is mainly used for realizing real-time digital monitoring and intelligent operation guidance in the construction process of the modular hoistway.

Description

Technical Field

[0001] This invention relates to the field of building construction technology. More specifically, this invention relates to a modular shaft construction method. Background Technology

[0002] In the field of industrialized building, modular construction methods that involve factory prefabrication and on-site assembly have become an important development direction for improving the efficiency of shaft construction and ensuring the quality of the finished product. Among these methods, the on-site hoisting and assembly of prefabricated shaft units is a crucial step, and its operational precision and safety directly affect the overall quality and progress of the project. Current construction methods still primarily rely on traditional manual labor and experience-based judgment in this stage, presenting several technical challenges that urgently need to be addressed.

[0003] First, the real-time status perception and precise control capabilities during construction are insufficient. During hoisting and assembly, the spatial orientation (such as verticality and horizontality) of the shaft unit and the structural stress state of its key connections are core parameters for determining whether the installation meets standards and whether the structure is safe. Currently, obtaining these parameters relies heavily on construction personnel using instruments such as total stations and levels for offline, sampling measurements of key nodes, or on empirical estimations through observation. This method has significant time lag and cannot provide continuous, real-time monitoring of the entire hoisting process. Since hoisting is a dynamic process, the orientation and stress of the unit change constantly. Data obtained at only a few discrete moments is insufficient to fully reflect its motion and mechanical behavior, making it difficult to predict potential instantaneous deviations or stress exceedances. The fundamental reason is the lack of an embedded monitoring method that can move synchronously with the shaft unit and continuously collect multi-dimensional status data. Even attempts to install sensors fail to achieve reliable and effective integration due to the difficulties described later.

[0004] Secondly, the survivability and measurement effectiveness of sensing and monitoring devices in prefabricated and construction environments are difficult to guarantee. Theoretically, tilt and strain sensors could be installed on the shaft unit to detect these conditions. However, shaft units are precast concrete components, and their production involves pouring, vibration, and curing. On-site construction involves complex conditions such as transportation, hoisting, high-altitude movement, and splicing adjustments. If precision sensors are directly attached to the surface of the component or simply embedded, they are easily damaged by grout during concrete vibration and are susceptible to detachment or inaccuracy due to vibration, impact, and collision during hoisting. More importantly, the data collected by sensors is only valuable for safety assessment when installed in the most sensitive and representative "stress-critical areas" of the structure's mechanical response (such as near splicing surfaces and lifting points). However, achieving secure installation and effective protection of sensors in such locations, while ensuring the stability of their measurement benchmarks under harsh environments, is an engineering challenge. Common protection methods often compromise on one aspect while addressing another; strengthening protection may lead to distortion of strain transmission between the sensor and the structure, while pursuing measurement accuracy may sacrifice physical reliability. This contradiction between reliability (survivability) and effectiveness (accuracy) makes it difficult to implement reliable embedded sensing and monitoring solutions in prefabricated components.

[0005] Furthermore, a monitoring model that relies entirely on centralized data processing is inherently vulnerable. While the development of wireless communication technology has led to the idea of ​​remotely transmitting sensor data to a backend for analysis—essentially creating a centralized monitoring model—this model heavily depends on a continuous and stable network connection between the construction site and the remote server. Construction site environments are complex, and wireless signals are easily blocked or interfered with, especially in areas where large metal components are being hoisted. Once the network is interrupted, the data upload link fails, and the remote monitoring center becomes "blind" in real time, unable to perceive the situation on-site. At this point, regardless of whether the front-end sensors are functioning properly, the entire monitoring system is essentially paralyzed, unable to provide any status feedback or risk warnings during the most critical hoisting operations. Therefore, a monitoring system that only possesses remote data transmission capabilities but lacks the ability to survive and respond to emergencies under adverse network conditions cannot meet the high safety requirements of hoisting operations in terms of availability and reliability. Solving this problem requires moving beyond the mindset of solely centralized processing and considering how to ensure a minimum level of continuous monitoring functionality even under unreliable network conditions.

[0006] In summary, current monitoring of modular shaft construction during the hoisting phase faces three interconnected technical challenges: poor real-time status awareness, difficulty in reliably and effectively integrating sensor systems into components, and excessive reliance on stable networks for monitoring links. These challenges limit the development of refined and intelligent management of the construction process and make preventative safety control difficult to achieve. Therefore, it is necessary to seek new technical approaches to construct a construction status management method that can withstand harsh working conditions, adapt to unstable communication environments, and achieve a closed loop from data acquisition to risk response, without affecting the production and construction process of prefabricated components. Summary of the Invention

[0007] One object of the present invention is to solve at least the above-mentioned problems and to provide at least the advantages that will be described later.

[0008] To achieve these objectives and other advantages according to the present invention, a modular shaft construction method is provided, comprising the following steps: Step 1: In the factory, prefabricate the shaft unit and fix the tilt sensor for measuring spatial attitude data, the strain sensor for measuring structural stress data, and the positioning module for obtaining real-time three-dimensional spatial coordinates as sensing devices inside the shaft unit. Step 2: Configure an IoT data acquisition and transmission module for each shaft unit, and electrically connect the module to the tilt sensor and strain sensor in the shaft unit, so that the sensing device can establish two-way data communication with the remote construction management cloud platform through the IoT data acquisition and transmission module. The construction management cloud platform stores the design reference data corresponding to each shaft unit, including spatial coordinates and structural stress parameters. Step 3: Transport the shaft unit to the construction site and carry out hoisting and splicing operations. The tilt sensor continuously collects the spatial attitude data of the shaft unit, and the strain sensor continuously collects the structural stress data of the key connection parts of the shaft unit. The spatial attitude data and structural stress data are sent to the construction management cloud platform as real-time monitoring data through the Internet of Things data acquisition and transmission module. Step 4: After receiving the real-time monitoring data, the construction management cloud platform retrieves the design reference data corresponding to the current working shaft unit and calculates and compares the real-time monitoring data with the design reference data. When the calculation and comparison results show that the deviation value of the real-time monitoring data relative to the design reference data exceeds the preset allowable range, the construction management cloud platform generates specific operation instructions based on the deviation value and sends the operation instructions to the corresponding operation control terminal at the construction site.

[0009] Preferably, the installation steps of the sensing device are as follows: According to the structural design drawings of the shaft unit, multiple sensor installation positions are determined on the prefabricated template. The installation positions correspond to the key stress areas formed after the shaft unit is assembled. The key stress areas include the vertical docking surface between units, the horizontal docking surface, the surrounding structure of the hoisting stress point, and the edge of the reserved hole. The rigid protective housing is detachably fixed to the installation position of the precast template using connectors; the concrete pouring and curing process of the shaft unit is completed, so that the protective housing is embedded inside the structure of the shaft unit; after the curing process is completed, the tilt sensor and strain sensor are placed inside the rigid protective housing; the remaining space inside the rigid protective housing is filled with elastic potting material to fix all the internal components; the opening of the rigid protective housing is closed with a sealing cover plate to complete the installation.

[0010] Preferably, an equipment installation cavity is pre-set on the inner wall of the shaft unit, and an openable metal inspection door is provided at the opening of the equipment installation cavity; The IoT data acquisition and transmission module and a rechargeable battery that powers the module, the tilt sensor and the strain sensor are fixedly installed in the device mounting cavity. Before pouring the well unit, multiple metal conduits are pre-embedded in its structure. One end of each metal conduit leads to the interior of a rigid protective shell, and the other end leads to the equipment installation cavity. By laying connecting cables through the metal conduit, the signal output terminals of each tilt sensor and strain sensor are electrically connected to the corresponding data input ports of the IoT data acquisition and transmission module inside the device mounting cavity. The rechargeable battery powers the tilt sensor and the strain sensor via the IoT data acquisition and transmission module.

[0011] Preferably, the IoT data acquisition and transmission module has a built-in data storage unit and is configured to perform the following communication assurance steps: The IoT data acquisition and transmission module continuously monitors the wireless network connection status between itself and the construction management cloud platform; when the wireless network connection status is normal, the IoT data acquisition and transmission module sends the real-time monitoring data collected by the tilt sensor and the strain sensor to the construction management cloud platform in real time. When the wireless network connection is interrupted, the IoT data acquisition and transmission module automatically stores the real-time monitoring data collected by the tilt sensor and the strain sensor in its built-in data storage unit. When the wireless network connection is restored, the IoT data acquisition and transmission module automatically resends the real-time monitoring data temporarily stored in the data storage unit to the construction management cloud platform.

[0012] Preferably, the IoT data acquisition and transmission module is further configured with a local data processing unit and an audible and visual alarm, and a local early warning threshold is set; the module is configured to perform the following local security early warning steps: The local data processing unit receives and processes the raw data collected by the tilt sensor and strain sensor in real time to obtain the locally calculated spatial attitude parameters and structural force parameters. The local data processing unit continuously compares the locally calculated spatial attitude parameters and structural force parameters with the attitude warning threshold and force warning threshold in the local warning threshold, respectively. When any of the locally calculated spatial attitude parameters or structural force parameters exceeds its corresponding local warning threshold, the IoT data acquisition and transmission module immediately drives the audible and visual alarm to issue a field warning signal.

[0013] Preferably, the spatial coordinate deviation threshold and structural stress deviation threshold set in the construction management cloud platform for calculation and comparison are greater than the attitude warning threshold and stress warning threshold set in the Internet of Things data acquisition and transmission module, respectively. In step four, when the construction management cloud platform determines that the deviation value exceeds the preset allowable range and generates an operation instruction, the operation instruction will override and replace the on-site early warning signal triggered by the IoT data acquisition and transmission module, serving as the final basis for on-site execution.

[0014] Preferably, in step four, the construction management cloud platform calculates and compares the real-time monitoring data with the design baseline data, specifically as follows: The design benchmark data stored in the construction management cloud platform is a dynamic benchmark model, which pre-stores the theoretical spatial coordinate range and allowable structural stress parameter range corresponding to different construction stages. The construction management cloud platform identifies the target construction stage of the current shaft unit based on the real-time monitoring data; Compare the spatial attitude data in the real-time monitoring data with the theoretical spatial coordinate range corresponding to the target construction stage; Simultaneously, the structural stress data in the real-time monitoring data is compared with the allowable structural stress parameter range corresponding to the target construction stage.

[0015] Preferably, the dynamic reference model is further configured to store the instantaneous theoretical spatial coordinates, instantaneous allowable attitude angle range, and instantaneous theoretical force range corresponding to a series of continuous target points throughout the entire process from the start of hoisting to the completion of splicing. In step four, the construction management cloud platform calculates the mapping position of the spatial coordinates in the real-time monitoring data on the theoretical hoisting path to determine the current target point. Subsequently, the instantaneous theoretical spatial coordinates, instantaneous allowable attitude angle range, and instantaneous theoretical force range that match the target point are retrieved from the dynamic benchmark model and used as the comparison benchmark at the current moment.

[0016] Preferably, the dynamic benchmark model is constructed in the following manner: Before construction, a construction process simulation is conducted based on the design parameters and hoisting scheme of the shaft unit. The construction process simulation includes mechanical simulation analysis and motion trajectory planning. Based on the simulation results, for different construction stages or continuous target points, the corresponding theoretical spatial coordinate range, allowable attitude angle range and theoretical force range are calculated and assigned. The motion trajectory planning is used to define a theoretical hoisting path, which consists of a series of critical path points, including at least the hoisting start point, the aerial turning point, and the final splicing point.

[0017] Preferably, the method for calculating the mapping position of spatial coordinates on the theoretical hoisting path is as follows: The construction management cloud platform calculates the shortest distance from the actual spatial coordinates of the current shaft unit to all line segments on the theoretical hoisting path; The perpendicular point on the theoretical line segment corresponding to the shortest distance is determined as the mapping position of the current actual spatial coordinates on the theoretical hoisting path.

[0018] This invention offers at least the following advantages: The modular shaft construction method of this invention integrates tilt sensors, strain sensors, IoT communication modules, and a remote construction management cloud platform into a prefabricated shaft unit and establishes a data closed loop. This enables real-time, continuous monitoring and remote intelligent control of the entire hoisting and splicing process, changing the traditional operation mode that relies on manual experience and offline sampling inspection. It provides accurate data and automated decision support for construction management. By pre-embedding rigid protective shells in the key stress areas of the shaft unit to install sensors, the physical damage to precision sensors caused by concrete pouring vibration and hoisting impact is effectively resisted. Simultaneously, it ensures that the sensors are fixed in positions that best reflect the structural mechanical state, thereby guaranteeing the authenticity and validity of the collected attitude and stress data. The architecture of pre-embedding the sensor probes while separating the IoT core module and battery within an openable equipment mounting cavity, connected by pre-embedded conduits, allows the entire monitoring system to maintain its functionality while possessing maintainability, repairability, and rechargeability. It also improves wireless communication conditions and solves the inherent problems of unmaintainability, short battery life, and signal shielding caused by burying all equipment in concrete at once. The IoT module's network status monitoring and data caching / resume functions ensure that monitoring data is not lost in unstable network environments at construction sites and can be fully uploaded after connection restoration. This guarantees the continuity and integrity of data analysis on the cloud platform and improves the reliability of the entire system in harsh communication environments. The IoT module is endowed with local data processing and alarm capabilities, enabling it to independently perform data calculations and compare them with local warning thresholds when the wireless network is interrupted or the cloud platform is unavailable. This promptly triggers on-site audible and visual alarms, establishing a primary safety barrier independent of remote systems and achieving basic on-site rapid risk response. The thresholds used for cloud analysis are explicitly set to be more lenient than local warning thresholds, and cloud commands can override local alarm signals. This establishes a clear collaborative rule: local alarms serve as sensitive warnings, while cloud commands are the final decision-making mechanism. This avoids potential confusion in on-site commands caused by dual judgments and allows the system to operate in a coordinated and orderly manner. By transforming static design baseline data into dynamic baseline models that match different construction stages, the construction management cloud platform can identify the current operation stage based on real-time data and compare it with the corresponding theoretical parameter range. This allows safety criteria to adapt to the dynamic changes in the hoisting process, significantly improving the scientific rigor and accuracy of status assessment. Building upon the dynamic model, an instantaneous baseline based on a series of continuous target points is further defined. Precise matching is achieved by calculating the mapping position of real-time coordinates on the theoretical hoisting path. This enables the system to perform millisecond-level high-precision status comparison and feedback control, elevating the refined management of the construction process to a new level.The data for the dynamic benchmark model is derived from pre-construction mechanical simulation and motion trajectory planning simulation. The key points constituting the theoretical hoisting path are defined, revealing the technical origin and construction method of the core model. This enhances the scientific basis and feasibility of the solution, transforming it from an abstract functional concept. A specific geometric algorithm is provided to determine the mapping position by calculating the shortest distance and the perpendicular point, providing stable and reliable underlying computational support for the aforementioned high-precision instantaneous matching. This ensures that the crucial mapping judgment step has a clear and implementable technical path.

[0019] 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

[0020] The present invention will now be described in further detail with reference to specific embodiments, so that those skilled in the art can implement it based on the description.

[0021] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0022] It should be noted that, unless otherwise specified, the experimental methods described in the following implementation plan are all conventional methods, and the reagents and materials described are all commercially available unless otherwise specified.

[0023] This invention provides a modular shaft construction method, comprising the following steps: Step 1: In the factory, prefabricate the shaft unit and fix the tilt sensor for measuring spatial attitude data, the strain sensor for measuring structural stress data, and the positioning module for obtaining real-time three-dimensional spatial coordinates as sensing devices inside the shaft unit. Step 2: Configure an IoT data acquisition and transmission module for each shaft unit, and electrically connect the module to the tilt sensor and strain sensor in the shaft unit, so that the sensing device can establish two-way data communication with the remote construction management cloud platform through the IoT data acquisition and transmission module. The construction management cloud platform stores the design reference data corresponding to each shaft unit, including spatial coordinates and structural stress parameters. Step 3: Transport the shaft unit to the construction site and carry out hoisting and splicing operations. The tilt sensor continuously collects the spatial attitude data of the shaft unit, and the strain sensor continuously collects the structural stress data of the key connection parts of the shaft unit. The spatial attitude data and structural stress data are sent to the construction management cloud platform as real-time monitoring data through the Internet of Things data acquisition and transmission module. Step 4: After receiving the real-time monitoring data, the construction management cloud platform retrieves the design reference data corresponding to the current working shaft unit and calculates and compares the real-time monitoring data with the design reference data. When the calculation and comparison results show that the deviation value of the real-time monitoring data relative to the design reference data exceeds the preset allowable range, the construction management cloud platform generates specific operation instructions based on the deviation value and sends the operation instructions to the corresponding operation control terminal at the construction site.

[0024] In this technical solution, during the factory prefabrication of the shaft unit, tilt sensors for measuring spatial attitude data and strain sensors for measuring structural stress data are fixedly installed as sensing devices. The tilt sensors can be digital output sensors based on MEMS principles, and the strain sensors can be foil resistance strain gauges. Before installation, the signal lines of these sensors can be soldered or crimped onto waterproof connectors. Based on the structural design drawings of the shaft unit, installation points are determined in critical stress areas, such as the four corners of the upper and lower flanges. At the corresponding positions on the prefabricated template, a square metal protective box with an inner cavity slightly larger than the sensor can be pre-fixed with bolts; the protective box can be made of stainless steel. Subsequently, concrete is poured and vibrated to embed the protective box inside the component. After curing and demolding, the tilt sensors and steel gaskets with attached strain gauges are placed inside the protective box, filled with room temperature curing silicone rubber for fixation, and finally sealed with metal cover bolts with rubber sealing rings. After installation, the tilt sensor can measure the tilt angle of the well unit relative to the horizontal plane, and its output value is in degrees. The strain gauge measures the micro-strain value of the critical part by sensing the micro-deformation of the liner, and its output value is in με.

[0025] Each shaft unit is equipped with an IoT data acquisition and transmission module, which is electrically connected to sensors to establish bidirectional communication with the remote construction management cloud platform. The IoT module can be an industrial-grade data acquisition terminal integrating a microprocessor, wireless communication unit, and digital or analog input interfaces. The connection between this terminal and the aforementioned sensors is achieved via a four-core shielded cable pre-embedded in concrete. One end of the cable connects to the sensor connector inside a protective box, and the other end converges to a dedicated waterproof junction box on the inner wall of the shaft unit, connecting to the IoT terminal. The IoT terminal and its rechargeable lithium battery pack are installed together in this junction box. Through its wireless communication unit, the terminal supports 4G or NB-IoT networks and, at set time intervals (e.g., every 200ms), packages and sends the angle and strain data acquired by the sensors to a server with a fixed public IP address; this server is the construction management cloud platform. The cloud platform's software receives and parses these data packets, associating them with the design parameters of the shaft unit pre-stored in a database.

[0026] During hoisting and assembly operations, the construction management cloud platform performs real-time data comparison and command feedback. After the shaft unit is transported to the site, its built-in sensors continuously operate during hoisting. The tilt sensor measures attitude data 20 times per second, and the strain sensor collects strain data 100 times per second. This real-time monitoring data is sent to the cloud platform via an IoT terminal. The cloud platform software retrieves the unit's design reference data; for example, the design verticality deviation requirement is 0.1%, corresponding to an allowable attitude angle deviation threshold of 0.057°; the maximum allowable strain is 150 με. The platform compares the real-time data with these thresholds. The software algorithm of the construction management cloud platform is set so that if the real-time monitoring data shows that the attitude angle deviation exceeds the allowable threshold for 2 seconds or the strain value exceeds the allowable threshold for 0.1 seconds, the platform determines that the deviation exceeds the limit. Subsequently, the platform generates specific text commands based on the deviation type and value, such as "pause hoisting, adjust eastward by 5cm" or "stress exceeds limit, stop work immediately". The instruction is sent via the Internet of Things (IoT) network to a dedicated industrial tablet computer held by the hoisting commander at the construction site, guiding him to perform the next step of the operation.

[0027] Definition and Types of Operation Control Terminals The "operation control terminal" refers to various devices located at the construction site used to receive and execute (or transmit) operation instructions issued by the construction management cloud platform. Its specific forms include, but are not limited to, the following types, which can be used individually or in combination: Handheld smart terminal: an industrial-grade explosion-proof tablet computer, smartphone, or dedicated handheld device held by the hoisting commander, slinger, or safety officer, running a dedicated construction command application (App). This program is used to receive, highlight, and broadcast operation instructions issued by the cloud platform via voice; it is the primary device for receiving and viewing instructions.

[0028] Airborne display terminal: A touch screen or dedicated monitor integrated into the control panel of the operator's cab of cranes, tower cranes, and other lifting equipment. Commands can be displayed directly in front of the operator's line of sight or linked with the equipment's monitoring system to achieve simultaneous display of command information and equipment status, guiding the operator to perform precise operations.

[0029] Dedicated wireless alarm device: An audible, visual, and vibration alarm device that can be installed or worn independently. When it receives a specific level of alarm command (such as "emergency stop" or "major risk"), it can trigger strong audible, visual, or even vibration signals to ensure that critical personnel are immediately alerted even in noisy and chaotic construction site environments.

[0030] Content and form of operation instructions The "operation instructions" generated by the construction management cloud platform are decision results based on the comparison and analysis of real-time monitoring data and dynamic benchmark models. To ensure the accurate and efficient transmission and execution of these instructions, their content and presentation have the following characteristics: 1. Structured Content: Instructions are not only natural language text, but more preferably use structured data formats, including clear instruction types, target objects, specific parameters, and priorities. For example, the data structure of a fine-tuning instruction may include: {“Instruction Type”: “Position Fine-tuning”, “Target Unit ID”: “U15”, “Direction”: “East”, “Distance”: “5cm”, “Priority”: “High”, “Effective Duration”: “10s”}.

[0031] 2. Diverse Formats: Instructions can be presented in various formats depending on the terminal type and operational scenario. Text and Voice: Displayed in a prominent text pop-up on handheld or airborne terminals, accompanied by clear voice broadcast.

[0032] Graphical guidance: In the BIM (Building Information Modeling) or 3D visualization interface integrated into the terminal, instructions can be transformed into visual elements such as graphic arrows, highlighted areas, and dynamic path lines, which are intuitively superimposed on the shaft unit model, providing immersive operation guidance.

[0033] Direct control signals: For intelligent hoisting equipment with automated interfaces, structured instructions can be directly converted into control signals that the equipment can recognize through agreed communication protocols (such as Modbus, CAN bus), so as to realize the automatic execution of some operations (such as automatic shutdown, micro-motion).

[0034] 3. Command Coordination: The same command can be issued to multiple relevant terminals simultaneously. For example, a "pause lifting" command can be sent to the crane operator (airborne terminal), the commander (handheld terminal), and trigger audible and visual alarms in high-risk areas, enabling multi-position coordinated response.

[0035] Through the specific implementation methods described above, digital tracking of the intrinsic status of prefabricated shaft units throughout their entire lifecycle, from factory fabrication to on-site hoisting, is achieved. This transforms the previous model of intermittent spot checks relying on external instruments into continuous, real-time sensing of key mechanical and geometric parameters achieved through built-in sensors. By separating and standardizing the connection methods between IoT terminals and sensors, the survivability of the sensing system is ensured while also considering communication reliability and equipment maintainability. Ultimately, by establishing a complete link of "real-time monitoring - cloud-based intelligent comparison - closed-loop command feedback," construction management can make decisions based on precisely quantified data. This effectively assists hoisting personnel in controlling installation accuracy and structural safety, reducing the uncertainty of human experience-based judgment, and providing a feasible technical path for the refined and intelligent management of modular construction.

[0036] In another technical solution, the installation steps of the sensing device are as follows: According to the structural design drawings of the shaft unit, multiple sensor installation positions are determined on the prefabricated template. The installation positions correspond to the key stress areas formed after the shaft unit is assembled. The key stress areas include the vertical docking surface between units, the horizontal docking surface, the surrounding structure of the hoisting stress point, and the edge of the reserved hole. The rigid protective housing is detachably fixed to the installation position of the precast template using connectors; the concrete pouring and curing process of the shaft unit is completed, so that the protective housing is embedded inside the structure of the shaft unit; after the curing process is completed, the tilt sensor and strain sensor are placed inside the rigid protective housing; the remaining space inside the rigid protective housing is filled with elastic potting material to fix all the internal components; the opening of the rigid protective housing is closed with a sealing cover plate to complete the installation.

[0037] In this technical solution, the installation positions of the sensing devices are first determined based on the structural design drawings of the shaft unit. The design drawings are typically CAD electronic drawings, which allow construction personnel to mark multiple installation positions on the inner surface of the precast template to be fabricated using ink lines. These installation positions correspond to critical stress areas after the shaft unit is assembled, such as the four symmetrical points within a 20cm radius around the bolt holes of the flanges connecting the upper and lower units, the center of the lateral connecting plate, and the four corners of the reserved doorway. A rigid protective housing can be selected as a hollow, 3mm thick square stainless steel box. An M6 threaded sleeve can be pre-drilled and embedded in the center of the ink line markings on the precast template. Subsequently, the stainless steel protective housing is detachably fastened to the inner surface of the precast template using matching M6 stainless steel bolts, ensuring that the housing opening faces inward and fits tightly against the template.

[0038] After the above fixation is completed, the concrete for the shaft unit is poured and cured. C40 grade ready-mixed concrete can be used. During pouring, the concrete will completely enclose the outer wall of the stainless steel protective shell located within the formwork. Use an immersion concrete vibrator to thoroughly compact the concrete, eliminating air bubbles and ensuring density. After pouring, cure in a standard curing room at a temperature of 20±2°C and a relative humidity of 95% or higher for at least 7 days. Demolding is performed after the concrete strength reaches at least 70% of the design value. At this point, the stainless steel protective shell is firmly embedded in the structure of the shaft unit through the bonding and gripping effect of the concrete, forming a sealed cavity isolated from the external concrete.

[0039] After the curing process, the final installation of the sensing device is carried out. The operator opens the pre-sealed protective housing cover. A dual-axis digital tilt sensor can be selected, and a 10mm gauge length foil resistance strain gauge can be used. This strain gauge is pre-attached to a matching stainless steel sheet gasket with epoxy resin. These two sensor elements are carefully placed into the bottom of the inner cavity of the protective housing. Subsequently, a two-component room-temperature curing silicone rubber can be used as the elastic potting material. Components A and B are mixed in a 1:1 weight ratio and poured into the housing until the sensor is completely covered and more than 95% of the remaining space is filled. This silicone rubber requires approximately 24 hours to fully cure at 25°C, forming an elastomer with cushioning and fixing functions. Finally, a metal cover plate with an annular rubber sealing ring is selected and connected to the threaded holes on the protective housing using four M4 stainless steel bolts, and tightened evenly to complete the installation of the sensing device.

[0040] It is worth noting that the encapsulation process using elastic potting materials (such as silicone rubber) not only solves the sensor's "survivability" problem, but also ensures its "measurement effectiveness" through material selection and structural design, especially for strain sensors. Material properties ensure that the selected elastic potting material (such as specially formulated silicone rubber) has a designed appropriate Young's modulus and viscoelasticity. It can provide effective cushioning and fixation for the internal sensor components, resisting impact vibration, and also has sufficient stiffness and adhesion to transfer the small strains of the concrete structure to the rigid protective shell, and further effectively to the strain sensor pads inside the shell.

[0041] Strain Transmission Path Guarantee: As mentioned earlier, strain sensors (strain gauges) are pre-attached to a steel pad, which maintains close contact or is fixedly connected to the inner wall of the rigid protective housing. The protective housing is then embedded in concrete, forming an integral part of the shaft unit structure. Therefore, the strain of the structure travels through the path of concrete → protective housing → steel pad, ultimately being sensed by the strain gauge. The elastic potting material primarily serves to fix, dampen, and seal in this path. Its own deformation under small strains is rationally designed, and its attenuation of the overall strain transmission is negligible, thus ensuring the accuracy and representativeness of the strain measurement data.

[0042] By pre-fixing the rigid protective shell to the template during the prefabrication stage and ultimately embedding it in the concrete, a robust and precisely positioned mounting foundation is created for the precision sensor. This effectively resists the impact and pressure during concrete pouring and vibration, preventing direct damage or displacement of the sensor during prefabrication. The process of first pouring to form a cavity, then installing and sealing the sensor later avoids long-term exposure of the sensor to high-temperature and high-humidity curing environments, improving its long-term reliability. The elastic potting material, while fixing the sensor, absorbs some of the vibration transmitted from the structure, reducing mechanical interference to the sensor itself. Combined with the sealing cover, it forms a dustproof and moisture-proof sealed environment, ensuring the sensor can operate continuously and stably under subsequent complex construction conditions, accurately collecting structural response data from key components.

[0043] In another technical solution, an equipment installation cavity is pre-set on the inner wall of the shaft unit, and an openable metal inspection door is provided at the opening of the equipment installation cavity; The IoT data acquisition and transmission module and a rechargeable battery that powers the module, the tilt sensor and the strain sensor are fixedly installed in the device mounting cavity. Before pouring the well unit, multiple metal conduits are pre-embedded in its structure. One end of each metal conduit leads to the interior of a rigid protective shell, and the other end leads to the equipment installation cavity. By laying connecting cables through the metal conduit, the signal output terminals of each tilt sensor and strain sensor are electrically connected to the corresponding data input ports of the IoT data acquisition and transmission module inside the device mounting cavity. The rechargeable battery powers the tilt sensor and the strain sensor via the IoT data acquisition and transmission module.

[0044] In this technical solution, firstly, an equipment mounting cavity is pre-set on the inner wall of the shaft unit, and the core module is installed there. Inside the concrete formwork of the precast shaft unit, a plastic mold shell approximately 300mm wide, 200mm high, and 150mm deep can be pre-fixed to form a concave cavity on the inner wall of the shaft unit after pouring. A rectangular door frame, formed by bending stainless steel plates, can be pre-embedded at the opening edge of this cavity. After the shaft unit has cured and been demolded, a stainless steel inspection door, connected to the door frame via hinges and equipped with a rotating lock handle, is installed on the door frame, forming an openable closed structure. The IoT data acquisition and transmission module can be an industrial-grade wireless data terminal, and the rechargeable battery can be a lithium battery pack with a nominal voltage of 12V and a capacity of 20Ah. The wireless data terminal and the lithium battery pack are then fixed side-by-side to the mounting backplate inside the equipment mounting cavity using screws, completing the installation of the core module.

[0045] Secondly, metal conduits are pre-embedded before pouring concrete and connecting cables are laid later. Conduit pre-embedding can be performed while binding the reinforcing steel frame of the shaft unit. Galvanized steel pipes with an outer diameter of 20mm can be used as metal conduits. According to the design drawings, one end of several galvanized steel pipes is connected to a metal junction box pre-fixed to the outer wall of the stainless steel protective shell on the formwork using threaded connections. The other ends are gathered and passed through the plastic formwork, extending into the future equipment installation cavity, and secured to the reinforcing steel frame using pipe clamps. Concrete is then poured, permanently embedding the conduits within the structure. After curing and demolding, operators open the access door and lay connecting cables through the pre-embedded galvanized steel pipes. 0.75mm diameter wires can be used. 2 A four-core shielded cable is used as the connecting cable. The cable is run from inside the equipment mounting cavity through the corresponding galvanized steel pipe to the protective housing at the other end, ensuring sufficient wiring slack.

[0046] Finally, make the electrical connections between the sensor and the module and power the sensor. Inside the protective housing, connect the positive and negative terminals of the tilt sensor's power supply, data line A, and data line B to the four cores of a four-core shielded cable. Inside the equipment mounting cavity, connect the other ends of the four cores of the same cable to the terminal blocks labeled "Sensor Power Output" and "Digital Input Channel" on the industrial wireless data terminal. The wireless data terminal integrates a power management circuit; the voltage at its "Sensor Power Output" terminal is 5VDC, which is derived from a 12V lithium battery pack connected in parallel, converted by the terminal's internal DC-DC circuit. The positive and negative terminals of the lithium battery pack are connected via a 2.5mm² cross-sectional area... 2 The wires are connected to the terminal marked "Main Power Input" on the wireless data terminal, thus forming a complete power supply circuit. When the lithium battery pack switch is turned on, the wireless data terminal is powered on and provides a stable operating voltage to the remote tilt sensor and strain sensor through its output terminal. The data collected by the sensors is then read by the terminal through the digital input channel.

[0047] The IoT data acquisition and transmission module integrates a power management circuit. The rechargeable battery provides the main power for the IoT data acquisition and transmission module; the power management circuit inside the module converts and regulates the input voltage, and then provides a stable and compliant operating voltage for the tilt sensor and strain sensor through its dedicated sensor power supply interface.

[0048] By centrally installing the core IoT module and battery within an openable, independent device mounting cavity on the inner wall, these components requiring regular maintenance, inspection, or replacement are physically separated from the sensor probes embedded in the concrete. This significantly improves the maintainability and accessibility of the entire monitoring system without affecting sensing functionality. Pre-embedded metal conduits provide robust physical protection for connecting cables, effectively preventing damage or breakage during concrete vibration and subsequent use. Centralized power management simplifies wiring, with the IoT module uniformly powering the distributed sensors, improving the reliability and neatness of system integration. This modular, maintainable architecture solves the fundamental problems of not being able to replace, charge, or suffer severe signal shielding caused by embedding all electronic equipment in concrete at once, ensuring the long-term effective operation of the intelligent monitoring system throughout the building's entire lifecycle.

[0049] In another technical solution, the IoT data acquisition and transmission module has a built-in data storage unit and is configured to perform the following communication assurance steps: The IoT data acquisition and transmission module continuously monitors the wireless network connection status between itself and the construction management cloud platform; when the wireless network connection status is normal, the IoT data acquisition and transmission module sends the real-time monitoring data collected by the tilt sensor and the strain sensor to the construction management cloud platform in real time. When the wireless network connection is interrupted, the IoT data acquisition and transmission module automatically stores the real-time monitoring data collected by the tilt sensor and the strain sensor in its built-in data storage unit. When the wireless network connection is restored, the IoT data acquisition and transmission module automatically resends the real-time monitoring data temporarily stored in the data storage unit to the construction management cloud platform.

[0050] In this technical solution, the IoT data acquisition and transmission module needs a built-in data storage unit and continuous network status monitoring. A suitable IoT module is an industrial IoT gateway, whose core board integrates a microprocessor, wireless communication module, and embedded storage chip. The suitable data storage unit is an eMMC chip soldered onto the core board, with a capacity of up to 8GB. This IoT gateway is installed in the device mounting cavity on the inner wall of the shaft unit. After the module powers on, its built-in software program begins running. The wireless communication module, such as a module supporting 4G networks, continuously attempts to establish and maintain a TCP / IP connection with the server of the remote construction management cloud platform. The software program monitors the connection status in real time by querying the module's network status register, which can be quantified as a received signal strength indicator value. The program is configured to determine that the wireless network connection is interrupted when the RSSI remains below -90dBm and the platform server cannot be pinged for more than 5 seconds; and that the connection is normal when the RSSI is above -85dBm and a TCP handshake is successfully established.

[0051] When the network connection is normal, the module performs real-time data transmission. The microprocessor of the IoT gateway reads data from the tilt sensor 10 times per second and from the strain sensor 100 times per second via its digital input channel. This data is timestamped to milliseconds and temporarily stored in the microprocessor's memory buffer. The software program, following a set 200ms data packet cycle, sends the packaged real-time monitoring data from the memory buffer to the designated IP address and port of the construction management cloud platform via a 4G wireless module with a normal connection. After successful transmission, the successfully transmitted data in the memory buffer is cleared to free up space.

[0052] When the network connection is interrupted, the module initiates a data storage mechanism and resends the data upon restoration. Once the software program determines that the network is interrupted, it immediately transfers the data packets that were originally intended to be sent from the memory buffer to the eMMC chip, which serves as the data storage unit. During storage, the data packets and their timestamps are written to the file system and arranged in chronological order. During the network outage, all newly acquired sensor data is temporarily stored in the eMMC chip in this manner. The module continuously attempts to restore the connection. When the network is restored and the program re-determines the connection status as normal, the software starts a resend thread. This thread first checks if there is any temporary data stored in the eMMC chip, and then retrieves these data packets sequentially in ascending order of timestamp, sending them to the cloud platform through the restored network connection. Each successfully resent data packet is deleted from the eMMC chip until all temporary data has been resent, after which the system resumes real-time transmission mode.

[0053] By integrating data storage units into the IoT module and designing corresponding status monitoring and logic control programs, the entire monitoring system is made resilient to network fluctuations at construction sites. This mechanism ensures that critical data collected by sensors is not lost due to simple communication link interruptions under any network conditions, but is reliably stored locally. Once the network is restored, the system can automatically and systematically re-upload historical data to the cloud, thus forming a continuous and complete data record in the cloud. This enables the analysis and decision-making of the remote construction management cloud platform to be based on complete information, avoiding judgment errors or monitoring blind spots caused by data omissions, and significantly improving the practicality and reliability of the intelligent construction monitoring system in real and complex industrial environments.

[0054] In another technical solution, the IoT data acquisition and transmission module is further configured with a local data processing unit and an audible and visual alarm, and a local early warning threshold is set; the module is configured to perform the following local security early warning steps: The local data processing unit receives and processes the raw data collected by the tilt sensor and strain sensor in real time to obtain the locally calculated spatial attitude parameters and structural force parameters. The local data processing unit continuously compares the locally calculated spatial attitude parameters and structural force parameters with the attitude warning threshold and force warning threshold in the local warning threshold, respectively. When any of the locally calculated spatial attitude parameters or structural force parameters exceeds its corresponding local warning threshold, the IoT data acquisition and transmission module immediately drives the audible and visual alarm to issue a field warning signal.

[0055] In this technical solution, the IoT data acquisition and transmission module needs to be configured with a local data processing unit and an audible and visual alarm, and local warning thresholds need to be set. The local data processing unit can be a microcontroller integrated on the IoT gateway motherboard, such as a chip based on the ARM Cortex-M core. The audible and visual alarm can be an integrated device with multi-color LEDs and a high-decibel buzzer. The audible and visual alarm is connected to the digital output interface on the IoT gateway motherboard via wires and is installed together with the gateway in the equipment mounting cavity on the inner wall of the shaft unit. The local warning thresholds are pre-written into the microcontroller's non-volatile memory via configuration software. The settable attitude warning threshold is 0.1° for monitoring tilt angle; the settable force warning threshold is 200με for monitoring structural strain. These thresholds are set according to the warning values ​​in the construction safety specifications, which are lower than the control values ​​used by the cloud platform.

[0056] The aforementioned local and cloud-based early warning thresholds were not arbitrarily set, but rather derived from a comprehensive assessment based on the following multi-layered criteria: Final Design Acceptance Criteria: First, the final acceptance criteria are determined based on the design requirements of the shaft unit. For example, the design documents specify a verticality deviation requirement of 0.1%, which corresponds to a final allowable attitude angle deviation of 0.057°; simultaneously, the design specifies a maximum allowable strain value of 150 με. This is the quality acceptance standard after the component installation is completed.

[0057] Process monitoring threshold setting: To ensure the safety and controllability of the hoisting process, thresholds need to be set for real-time monitoring. These thresholds are typically more lenient than the final acceptance criteria to allow for dynamic adjustments. The basis for setting these thresholds is as follows: Specifications and design standards: First, refer to the general requirements for the verticality, flatness and construction safety monitoring of components in relevant national and industry standards such as the "Code for Acceptance of Construction Quality of Concrete Structures" (GB 50204) and the "General Technical Conditions for Safety Monitoring System of Construction Lifting Machinery" (GB / T 28264).

[0058] Mechanical simulation pre-analysis: Before construction, finite element software is used to simulate and calculate the mechanical behavior of the shaft unit under various working conditions such as hoisting and transportation, predict the maximum stress / strain range and attitude angle change range that may occur in its key parts, and provide theoretical estimates for threshold setting.

[0059] Safety redundancy design: Based on simulation estimates, considering factors such as sensor measurement errors, data acquisition and transmission delays, and uncertainties in on-site dynamic loads, a reasonable safety factor (typically 1.2-1.5) is introduced. First, a relatively sensitive local early warning threshold is determined for early risk alerts; then, this local early warning threshold is multiplied by the aforementioned safety factor to obtain a relatively lenient cloud control threshold, which serves as the boundary for triggering remote precision intervention commands.

[0060] For example, based on simulation and experience, the local attitude warning threshold is set to 0.1°. Multiplying this by a safety factor of 1.2, we obtain the cloud control threshold of 0.12°. Similarly, setting the local strain warning threshold to 200με and multiplying it by a safety factor of 1.25, we obtain the cloud control threshold of 250με.

[0061] Engineering experience correction: Based on construction experience data from similar modular hoisting projects, the above thresholds are fine-tuned to ensure that they are sensitive enough to provide timely warnings of risks, while avoiding frequent false alarms due to excessive sensitivity, which could interfere with normal operations.

[0062] The local data processing unit continuously receives, processes, and compares data. The microcontroller, through its integrated analog-to-digital converter or digital interface, receives voltage signals from the tilt sensor and bridge output signals from the strain sensor in real time at a sampling frequency of 50Hz. The program running inside the microcontroller processes this raw data. For the tilt signal, the voltage value is converted to an angle value using a lookup table; for the strain signal, the micro-strain value is calculated using the Wheatstone bridge formula and calibration coefficients. These calculated local spatial attitude parameters and structural stress parameters are temporarily stored in the microcontroller's memory. Subsequently, the processing program compares each newly calculated angle value with an attitude warning threshold of 0.1° and each newly calculated strain value with a stress warning threshold of 200με.

[0063] When the monitored data exceeds the local warning threshold, the module immediately activates the audible and visual alarm. The comparison logic runs continuously. Once the microcontroller detects that the absolute value of the latest calculated angle parameter continuously exceeds the preset local attitude warning threshold (e.g., 0.1°) for three consecutive sampling cycles, or the absolute value of the latest calculated strain parameter continuously exceeds the preset local force warning threshold (e.g., 200με) for five consecutive sampling cycles, the program determines that a local over-limit event has occurred. At this time, the microcontroller immediately sends a high-level trigger signal to the digital output pin of the audible and visual alarm connected to it. Upon receiving the signal, the audible and visual alarm activates, its red LED begins flashing at a frequency of 2Hz, and the buzzer emits an intermittent alarm sound of no less than 80dB. This alarm process is entirely autonomously completed by the local IoT module, independent of the network connection status with the remote cloud platform, thus providing immediate, visible, and audible risk warnings on-site.

[0064] By endowing the IoT module with local data processing and logical judgment capabilities and equipping it with an independent audible and visual alarm device, a complete and autonomous rapid safety early warning subsystem is built within the IoT module. This subsystem can collaborate with the cloud when network connectivity is good, and more importantly, it can continue to operate independently even in extreme situations where wireless network signals are interrupted or the remote construction management cloud platform is temporarily unavailable. It continuously compares sensor data with preset local safety warning thresholds to quickly detect abnormal posture or structural stress in the shaft unit and immediately trigger on-site audible and visual alarms. This provides an indispensable and highly reliable underlying safety defense for the construction site, ensuring that even in unexpected situations of communication interruption, the ability to instantly perceive and warn of major safety risks is not lost. This effectively compensates for the inherent vulnerability of pure cloud monitoring models and improves the robustness and security of the overall construction safety monitoring system.

[0065] In another technical solution, the spatial coordinate deviation threshold and structural stress deviation threshold set in the construction management cloud platform for calculation and comparison are respectively greater than the attitude warning threshold and stress warning threshold set in the Internet of Things data acquisition and transmission module. In step four, when the construction management cloud platform determines that the deviation value exceeds the preset allowable range and generates an operation instruction, the operation instruction will override and replace the on-site early warning signal triggered by the IoT data acquisition and transmission module, serving as the final basis for on-site execution.

[0066] In this technical solution, the thresholds used for calculation and comparison in the construction management cloud platform need to be greater than the local early warning thresholds. The cloud platform is deployed on a server in a remote data center, and its software manages a parameter database. For the local attitude early warning threshold of 0.1° and the local stress early warning threshold of 200με set in claim 5, the spatial coordinate deviation threshold set for the same shaft unit in the cloud platform's parameter database can be set to 0.12°, and the structural stress deviation threshold can be set to 250με. The logic for setting these cloud thresholds is to add a safety margin to the local warning value. This margin can be determined by considering sensor errors, data transmission delays, and the dynamic characteristics of the hoisting process, for example, by increasing the local threshold by 20%. Therefore, the cloud thresholds are clearly greater than the local thresholds, forming a two-layer monitoring range from tight to loose.

[0067] In step four of the hoisting operation, the construction management cloud platform makes judgments and generates instructions. The analysis program on the cloud platform server continuously receives real-time monitoring data from the IoT module. The program compares the received real-time attitude angle with a cloud-based attitude threshold of 0.12° and the real-time strain value with a cloud-based force threshold of 250με. When the program determines that the real-time data continuously exceeds these cloud-based thresholds, for example, if the attitude angle is greater than 0.12° for 2 consecutive seconds, it generates a specific operation instruction based on a pre-set rule base. This instruction is encapsulated as a data message containing the instruction code, target unit ID, and detailed parameters.

[0068] The generated cloud-based command will override the local early warning signal as the final execution basis. This data message is sent to the construction site via a mobile network. The work control terminal can be an industrial explosion-proof tablet or a dedicated wireless command receiver, held by the hoisting supervisor. When the cloud platform command reaches the terminal, the terminal will display the command through vibration, a strong pop-up window, and text prompts, such as "Cloud platform command: Stop hoisting, perform position fine-tuning." Simultaneously, the audible and visual alarm triggered by the IoT module on the shaft unit may still be sounding. This coordination rule requires that when on-site personnel receive conflicting cloud commands and local audible and visual alarms simultaneously, the cloud command should be the final action basis. After detecting that the cloud platform command has been issued, the IoT module can also automatically mute its audible and visual alarm or switch to low-frequency flashing through logic design to indicate that control has been transferred to the cloud, thus achieving both physical and operational logic-level coverage and replacement of the local early warning signal by the cloud command. This coordination rule relies on two-way communication: when the construction management cloud platform determines that an operation command needs to be issued, the command data packet may contain a control field for the current alarm status. Upon receiving instructions from the cloud platform, the IoT data acquisition and transmission module's internal program immediately parses and executes the instructions. Simultaneously, if the instructions indicate that the current status has been taken over by the cloud or the risk has been eliminated, the module will proactively drive its connected audible and visual alarms to stop sounding (or switch to a warning flash). This physically achieves cloud-based command coverage of local warning signals, ensuring that the audible and visual signals received by on-site personnel are always consistent with authoritative cloud commands, avoiding command confusion.

[0069] The IoT data acquisition and transmission module works in conjunction with the construction management cloud platform to form a hierarchical monitoring system with clear responsibilities and levels of authority. Its core behavioral logic under different network conditions can be summarized as follows: 1. Normal network connection scenario: Data flow: Sensor data → real-time upload to the construction management cloud platform.

[0070] Core processing: The cloud platform performs high-precision dynamic comparison and intelligent decision-making.

[0071] Command Priority: Operation commands issued by the cloud platform have the highest execution priority. On-site execution is based on the cloud-based commands.

[0072] 2. Network connection interruption scenario: Data protection: Sensor data is temporarily stored in the local storage unit of the IoT module.

[0073] Local emergency response: The local data processing unit of the IoT module synchronously performs data calculations and compares them with the local early warning threshold.

[0074] Primary warning: If the local calculation parameters exceed the local warning threshold, the IoT module immediately drives the audible and visual alarm to issue an on-site warning signal, achieving a safety backup that does not rely on the network.

[0075] 3. Network connection restoration scenario: Data continuation: The IoT module automatically resends the cached monitoring data to the construction management cloud platform.

[0076] Re-analysis in the cloud: The cloud platform performs retrospective analysis and current status assessment on complete data (including cached data).

[0077] Command normalization: If the cloud platform determines that intervention is required and issues a new operation command, the command will override (or suspend) any warning signals triggered locally during the network outage, and re-establish the cloud command as the sole authoritative basis for on-site execution.

[0078] Through the above logical design, the system ensures that monitoring data is not lost and safety warnings are not absent even when network conditions fluctuate. Ultimately, the construction control is always uniformly held by the cloud platform, which has overall information and better decision-making capabilities, thus avoiding on-site chaos that may be caused by multiple instructions.

[0079] By setting the judgment threshold of the construction management cloud platform to be numerically greater than the warning threshold of the local IoT module, a tiered monitoring system with distinct internal and external responses is constructed. The local module, with its more sensitive threshold, enables early and rapid risk warnings, while the cloud platform, with its more lenient threshold and more comprehensive information, makes the final and accurate decision. This setting effectively avoids frequent conflicting alarms between the local and cloud platforms due to identical thresholds or unclear logic. It is clearly stipulated that cloud commands override and replace local alarm signals, establishing a clear and unambiguous master-slave collaboration rule. This ensures that in complex field environments, when the multi-level monitoring system is triggered, on-site personnel receive a single, authoritative decision command, avoiding confusion or misoperation caused by receiving multiple different signals, thereby improving the coordination, order, and reliability of the final decision of the entire intelligent hoisting monitoring system.

[0080] In another technical solution, the construction management cloud platform in step four calculates and compares the real-time monitoring data with the design benchmark data, specifically as follows: The design benchmark data stored in the construction management cloud platform is a dynamic benchmark model, which pre-stores the theoretical spatial coordinate range and allowable structural stress parameter range corresponding to different construction stages. The construction management cloud platform identifies the target construction stage of the current shaft unit based on the real-time monitoring data; Compare the spatial attitude data in the real-time monitoring data with the theoretical spatial coordinate range corresponding to the target construction stage; Simultaneously, the structural stress data in the real-time monitoring data is compared with the allowable structural stress parameter range corresponding to the target construction stage.

[0081] In this technical solution, the design benchmark data stored in the construction management cloud platform is a dynamic benchmark model. This model pre-stores theoretical parameter ranges corresponding to different construction stages. This model can be built into a relational database on the cloud platform server as an independent data table. The database server can be a general-purpose x86 architecture server, and the data stored in it includes multiple predefined construction stages, such as "lifting off the ground," "horizontal movement," "high-altitude hovering," and "precise docking." For the "lifting off the ground" stage, its theoretical spatial coordinate range can be defined as a cubic space region with a base side length of 2m and a height of 3m; for the "precise docking" stage, its theoretical spatial coordinate range is reduced to a narrow region with a base side length of 0.1m and a height of 0.05m. The corresponding allowable structural stress parameter range also changes with the stage. For example, the allowable dynamic strain range for the "horizontal movement" stage can be set to ±300με, while the allowable static strain range for the "high-altitude hovering" stage can be set to ±150με.

[0082] The construction management cloud platform identifies the target construction stage of the current shaft unit based on real-time monitoring data. The cloud platform's background analysis service program receives real-time data streams from the IoT module several times per second. The identification logic can be triggered based on characteristic values ​​in the real-time data. For example, the program continuously monitors the ground clearance data of the bottom of the shaft unit. When the height increases from less than 0.1m to more than 1m, combined with a horizontal movement speed of less than 0.1m / s, the program can determine that it has entered the "lifting off the ground" stage. When the height is greater than 20m and the horizontal movement speed is greater than 0.5m / s, it determines that it has entered the "horizontal movement" stage. The program matches all real-time data (including position, velocity, acceleration, and strain history) with predefined data characteristic patterns for each stage, identifies the stage with the highest matching degree as the current target construction stage, and outputs this stage identifier to the subsequent comparison module.

[0083] The platform compares real-time monitoring data with the theoretical range corresponding to the identified stage. After determining the target construction stage, the comparison program retrieves the theoretical spatial coordinate range and allowable structural stress parameter range corresponding to that stage from the data table of the dynamic benchmark model. The program compares the spatial attitude data in the real-time monitoring data stream, mainly the three-dimensional coordinates fed back by GPS or total station, with the retrieved theoretical spatial coordinate range to check whether the current coordinates fall within the cube area. Simultaneously, the program compares the structural stress data in the real-time monitoring data, i.e., the micro-strain values ​​uploaded by strain sensors, with the retrieved allowable structural stress parameter range to check whether the current strain value is within the allowable positive and negative range. These two comparisons are performed synchronously; any data exceeding the allowable range for its corresponding stage will be recorded as a deviation event, triggering subsequent evaluation and instruction generation processes.

[0084] By extending static design benchmarks into dynamic benchmark models associated with different construction stages, the analysis and judgment of the construction management cloud platform can be closely integrated with the actual dynamic progress of hoisting operations. The system can automatically identify the specific operational stage of a hoistway unit and compare it with corresponding theoretical parameter ranges of varying stringency, allowing safety criteria and accuracy requirements to be dynamically adjusted according to the operational content. This avoids unreasonable warnings that might result from using a single, stringent final installation standard to measure the entire hoisting process, and also avoids the risk of ignoring critical stages by using overly lenient uniform standards. This method improves the rationality and intelligence of status monitoring during the hoisting of large-sized components, making the decisions of the remote management platform more aligned with the phased characteristics of on-site construction, and providing a more adaptable analytical framework for the digital management of complex dynamic operations.

[0085] In another technical solution, the dynamic reference model is further configured to store the instantaneous theoretical spatial coordinates, instantaneous allowable attitude angle range, and instantaneous theoretical force range of a series of continuous target points throughout the entire process from the start of hoisting to the completion of splicing. In step four, the construction management cloud platform calculates the mapping position of the spatial coordinates in the real-time monitoring data on the theoretical hoisting path to determine the current target point. Subsequently, the instantaneous theoretical spatial coordinates, instantaneous allowable attitude angle range, and instantaneous theoretical force range that match the target point are retrieved from the dynamic benchmark model and used as the comparison benchmark at the current moment.

[0086] In this technical solution, the dynamic benchmark model is further configured to store instantaneous theoretical parameters corresponding to a series of continuous target points. This model can be built into the database of a construction management cloud platform server, and its data structure can be a data table containing multiple rows of records. Each record represents a preset target point and includes the following fields: target point ID, theoretical cumulative time, theoretical spatial coordinates, allowable attitude angle range, and theoretical force range. For example, for a typical hoisting operation with a total duration of approximately 10 minutes and a low average speed (e.g., below 0.5 m / s), to balance model accuracy and computational efficiency, a target point can be set at regular intervals (e.g., 1 second) along the theoretical hoisting path. A 10-minute path will generate approximately 600 continuous target points. In practical applications, the density of target points (i.e., the setting interval) is not fixed but is set and adjusted according to the theoretical speed curve, acceleration changes, and control precision requirements of different construction stages (e.g., smooth movement, start-up, deceleration, and precise alignment) of the specific hoisting scheme. For example, during critical phases of rapid dynamic change, the interval between target points can be increased to 0.1 seconds or higher to more accurately describe the instantaneous state.

[0087] It should be noted that the dynamic benchmark model stores a series of discrete target point parameters. During actual mapping and comparison, the algorithm of the construction management cloud platform can use these discrete points as a basis to estimate the theoretical spatial coordinates, allowable attitude angle range, and theoretical stress range at any given moment through interpolation calculations (such as linear interpolation and spline interpolation). This enables the system to achieve near-continuous, high-resolution comparisons with real-time monitoring data based on discretely stored data, thereby accurately matching the dynamic changes in the hoisting process. Specifically, the theoretical spatial coordinates of the 100th point (corresponding to the 10th second) can be set as (X=15.20m, Y=8.50m, Z=2.10m), its allowable attitude angle range can be set as ±0.5° around the X-axis and ±0.5° around the Y-axis, and the theoretical stress range of this point can be set as -50με to +100με. The parameters of these continuous points are pre-obtained through interpolation calculations of the parameters of key points on the hoisting path (such as the starting point, inflection point, and ending point).

[0088] The construction management cloud platform determines the current hoisting progress based on spatial coordinates or timestamps in real-time monitoring data. The real-time monitoring data packets received by the cloud platform contain timestamps collected and uploaded by the IoT module and calculated spatial coordinates. There are two methods for determining progress. One method is time matching: the platform reads the timestamp in the data packet, for example, "125.36 seconds after the start of work," and compares it with the "theoretical cumulative time" field of each target point in the dynamic benchmark model to find the target point with the closest time, such as point 1254 (corresponding to 125.4 seconds). The other method is spatial coordinate matching: the platform calculates the received real-time spatial coordinates (X=23.15m, Y=12.77m, Z=18.06m) with the theoretical coordinates of all target points stored in the model, and uses spatial geometric algorithms (such as finding the point with the closest Euclidean distance) to determine the closest target point. These two methods can be used individually or in combination, ultimately outputting a currently matched target point ID, which represents the current precise hoisting progress.

[0089] The platform retrieves instantaneous parameters matching the current progress from the dynamic benchmark model as a comparison benchmark. After determining the current target point ID (e.g., point 1254), the platform's analysis program immediately queries all instantaneous theoretical parameters corresponding to that ID from the model data table in the database. These parameters include: the instantaneous theoretical coordinates of the point, the instantaneous allowable attitude angle range, and the instantaneous theoretical stress range. Subsequently, the program calculates the difference between the real-time spatial coordinates received at the same moment and the retrieved instantaneous theoretical coordinates to obtain the position deviation; it compares the real-time attitude angle with the retrieved instantaneous allowable attitude angle range to determine if it exceeds the limit; and it compares the real-time strain value with the retrieved instantaneous theoretical stress range to determine if it exceeds the limit. Since the target points in the model are spaced very closely (e.g., 0.1s), the retrieved parameters can be regarded as the current "instantaneous" theoretical state, thus achieving high-frequency point-to-point comparisons almost synchronously with the hoisting process.

[0090] Acquisition of spatial coordinates: To achieve high-precision dynamic comparison, the positioning module integrated on the well unit is preferably an ultra-wideband (UWB) positioning tag or a GNSS / IMU fusion positioning terminal, forming an independent high-precision real-time positioning system.

[0091] Specific Implementation Method 1 (UWB System): A UWB positioning base station network is pre-deployed at the construction site to form a three-dimensional coordinate reference covering the hoisting operation area. Each hoistway unit is equipped with a UWB positioning tag, which can be integrated into the housing of the IoT data acquisition and transmission module or installed independently and communicate with the module via a wired connection. The positioning tag transmits signals in real time, which are received and processed by the base station to obtain the high-frequency (typically 10-100Hz), high-precision (static accuracy down to centimeter level) three-dimensional spatial coordinates (X, Y, Z) of the hoistway unit. This coordinate data is synchronously uploaded to the construction management cloud platform via the positioning system network or directly through the communication link of the IoT module, and is timestamped with data from sensors such as tilt angle and strain to form complete real-time monitoring data.

[0092] Specific Implementation Method Two (GNSS / IMU Fusion Terminal): A fusion positioning terminal integrating a GNSS receiver and an IMU is installed on the top of the hoistway unit. In outdoor areas or areas with good signal, GNSS data is used primarily; in areas with signal obstruction or when high dynamic response is required, IMU data is used for dead reckoning. The high-frequency, high-precision position, velocity, and attitude data calculated by this terminal are transmitted to the IoT data acquisition and transmission module via its communication interface (such as RS-485, CAN bus), and then packaged and sent to the cloud platform by this module.

[0093] By defining a high-density sequence of continuous target points in the dynamic benchmark model, the construction process is discretized into a series of instantaneous ideal states, thus achieving a leap from "stage-based" to "instantaneous" benchmarking. The construction management cloud platform, through precise matching of real-time data with these continuous target points (whether based on time or spatial location), can determine the current hoisting progress with extremely high temporal and state resolution, and invoke corresponding, highly specific instantaneous theoretical parameters. This makes the comparison behavior of the remote monitoring system no longer based on a broad "stage range," but on an "instantaneous point" that infinitely approximates the continuous process, greatly improving the precision and timeliness of state judgment. This millisecond-level, centimeter-level precise matching mechanism enables the system to more sensitively capture minute anomalies in motion trajectory and structural stress, providing the possibility for generating extremely precise fine-tuning instructions, thereby raising the level of intelligent control of modular hoisting to a new height and effectively supporting the requirements of high-precision and high-safety construction.

[0094] In another technical solution, the dynamic benchmark model is constructed in the following manner: Before construction, a construction process simulation is conducted based on the design parameters and hoisting scheme of the shaft unit. The construction process simulation includes mechanical simulation analysis and motion trajectory planning. Based on the simulation results, for different construction stages or continuous target points, the corresponding theoretical spatial coordinate range, allowable attitude angle range and theoretical force range are calculated and assigned. The motion trajectory planning is used to define a theoretical hoisting path, which consists of a series of critical path points, including at least the hoisting start point, the aerial turning point, and the final splicing point.

[0095] In this technical solution, before construction, a construction process simulation is performed based on the design parameters of the shaft unit and the hoisting plan. The design parameters can be derived from the building's BIM model or CAD drawings, while the hoisting plan includes the selected crane model, hoisting point locations, and planned movement path. The mechanical simulation analysis software used is a general-purpose finite element analysis software, running on the designer's computer workstation. In the software, a three-dimensional solid model of the shaft unit is established based on the design parameters, and it is assigned actual material properties, such as setting the elastic modulus of concrete to 30 GPa. According to the hoisting plan, corresponding loads are applied at the hoisting point locations of the model, such as simulating the force generated by the component's self-weight of 1.5 tons, and constraint conditions are set. By running static and dynamic analyses, the software can calculate the stress and strain distribution cloud maps and variation curves of various parts of the component, especially the key connection areas, throughout the assumed hoisting process; for example, the maximum tensile stress is found to be 2.5 MPa. Motion trajectory planning can be accomplished with the help of BIM construction simulation software or a dedicated path planning algorithm library. In three-dimensional space, based on the obstacle information on site, a collision-free motion path from the stacking point to the installation point is planned, and the theoretical spatial coordinates of the component's center of gravity at each time point under ideal conditions are simulated and calculated.

[0096] Based on the simulation results, corresponding theoretical parameter ranges are calculated and assigned to different construction stages or continuous target points. Data output from the mechanical simulation analysis, such as the maximum stress and strain extremes under different working conditions, are exported and post-processed. Combining the material design safety factor (e.g., 1.5), the calculated maximum stress of 2.5 MPa is divided by the safety factor to deduce the allowable stress limit during construction, which is then converted into an allowable micro-strain range, such as -120 με to +100 με. The path point sequence output by the motion trajectory planning software, i.e., the list of component centroid coordinates changing over time, is directly used as the reference for theoretical spatial coordinates. The planning software can also calculate the theoretical attitude required for the component to maintain stability at each point along the path, such as a vertical position, and, considering disturbances such as wind load, assign an allowable attitude angle fluctuation range, such as ±0.8°. These calculated ranges of "coordinate-attitude-force" parameters are linked to the corresponding construction stages such as "lifting" and "translation" according to the simulated timeline or path progress, or directly assigned to each continuous target point at a 0.1s interval, forming a structured data table.

[0097] The motion trajectory planning process defines the theoretical hoisting path, which consists of a series of critical path points. In the planning software, engineers can manually set these critical path points or have them automatically generated by an algorithm. These points include at least the hoisting start point, i.e., the center coordinates of the initial placement of the component, such as (X=10.0m, Y=5.0m, Z=0.5m); aerial turning points, i.e., the coordinates of inflection points in the path where the horizontal movement direction needs to be changed, such as (X=25.0m, Y=5.0m, Z=15.0m); and the final splicing point, i.e., the target coordinates for alignment with the lower structure, such as (X=30.0m, Y=5.0m, Z=30.0m). Using these critical path points as control points, the software can automatically generate a smooth, continuous theoretical motion trajectory through interpolation algorithms, such as cubic spline curve interpolation, and output a dense sequence of coordinate points on the trajectory at 0.1s intervals, providing basic data for building a high-precision continuous target point model.

[0098] By introducing pre-construction mechanical simulation and motion trajectory planning as the basis for constructing a dynamic benchmark model, the parameter settings of the model are transformed from experience-based estimation to scientific digital simulation and calculation. This provides a clear and quantifiable source for the theoretical coordinate range, allowable attitude angle range, and theoretical stress range stored in the construction management cloud platform. These values ​​are closely related to the component's own characteristics, specific hoisting schemes, and the site environment, significantly improving the accuracy and reliability of the benchmark data. The model constructed by this method is no longer a static, general reference value, but a "digital twin" benchmark that dynamically reflects the expected state of a specific component in a specific work process. This provides a solid and reliable benchmark for high-precision comparison of subsequent real-time monitoring data, enabling the analysis and decision-making of the entire intelligent hoisting monitoring system to be based on a more scientific and objective foundation, thereby improving the accuracy of early warning and control.

[0099] In another technical solution, the method for calculating the mapping position of spatial coordinates on the theoretical hoisting path is as follows: The construction management cloud platform calculates the shortest distance from the actual spatial coordinates of the current shaft unit to all line segments on the theoretical hoisting path; The perpendicular point on the theoretical line segment corresponding to the shortest distance is determined as the mapping position of the current actual spatial coordinates on the theoretical hoisting path.

[0100] In this technical solution, the construction management cloud platform calculates the shortest distance from the actual spatial coordinates of the current hoisting unit to all line segments on the theoretical hoisting path. The actual spatial coordinates are derived from real-time monitoring data reported by the IoT module, such as a three-dimensional coordinate point P (23.15, 12.77, 18.06) containing X, Y, and Z components. The theoretical hoisting path is stored in the cloud platform as a polyline composed of a series of continuous points, such as the point sequence S0, S1, S2, ..., S...n Each pair of adjacent points forms a path segment. During calculation, the analysis program on the platform first traverses each line segment S on the path. i S i+1 For each line segment, the program uses spatial analytic geometry to calculate the foot of the perpendicular from point P to the line containing the line segment, and determines whether the foot of the perpendicular lies on line segment S. i S i+1 Between the two endpoints. If the foot of the perpendicular lies on the line segment, then the distance from point P to the line segment is the same as the distance from point P to the foot of the perpendicular; if the foot of the perpendicular lies on the extension of the line segment, then the distance is taken as the distance from point P to the two endpoints S of the line segment. i or S i+1 The program compares and records the minimum distance D among all line segment distances using a loop. min For example, D min =0.08m, and simultaneously record the target line segment S that produced this minimum distance. k S k+1 .

[0101] The perpendicular point on the theoretical line segment corresponding to the shortest distance is determined as the mapping position. After finding the target line segment S... k S k+1 Next, the program needs to accurately calculate the perpendicular projection of point P onto the line segment, i.e., the foot of the perpendicular F. Let the starting point of the line segment be S. k Coordinates are (X k Y k Z k ), endpoint S k+1 Coordinates are (X k+1 Y k+1 Z k+1 The program calculates the vector u=S. k+1 -S k Vector v=PS k Then calculate the projection scale parameter t = (u·v) / (u·u), where "·" represents the dot product of vectors. The parameter t represents the perpendicular point F on line segment S. k S k+1 The program calculates the coordinates of the perpendicular point F based on the t-value: F = S k +t*u. Ultimately, this calculated 3D coordinate point F is determined as the precise mapping position of the current actual spatial coordinates P on the theoretical hoisting path. The platform will simultaneously record the path progress information corresponding to this mapping position, such as its cumulative distance from the path start point or the ID of the nearest key point.

[0102] This method, as a software functional module, runs on the backend server of the construction management cloud platform. The server can be a general-purpose cloud computing instance or a physical server, with its CPU handling the primary computational tasks. The calculation process is invoked by a background analysis service program on the platform, which can implement the aforementioned geometric algorithm using common programming languages. When a new real-time coordinate data packet arrives, a complete mapping position calculation process is triggered. The calculated perpendicular point coordinates and their corresponding path progress serve as crucial intermediate results, immediately used to retrieve the instantaneous theoretical parameters of the corresponding points from the dynamic benchmark model for high-precision comparison.

[0103] By employing a deterministic geometric algorithm that calculates the shortest distance and determines the perpendicular point, a stable, accurate, and repeatable technical implementation is provided for mapping discrete actual measured coordinates onto a continuous theoretical path. This algorithm is logically rigorous, yields unique results, and avoids errors or inconsistencies that may arise from fuzzy matching. It transforms complex spatial relationships into programmable mathematical operations, enabling the construction management cloud platform to efficiently and automatically determine the degree of deviation of the actual position of the hoistway unit from the ideal path and its precise projection point on the path. This accurate mapping result is a key technical support for realizing the "instantaneous matching" comparison logic described in claim 8, ensuring that the system can perform status monitoring and feedback control with extremely high spatial resolution, thus laying a reliable algorithmic foundation for the refined and intelligent management of hoisting operations.

[0104] The construction management cloud platform is a server-side system containing multiple software functional modules, and its core modules include at least: 1. Data Receiving and Fusion Module: Receives multi-data streams (spatial coordinates, attitude angles, strain values, timestamps, unit IDs, etc.) uploaded from IoT modules of each shaft unit in real time, performs time synchronization, data verification and formatting, and stores them in a real-time database.

[0105] 2. Construction Process Identification and State Mapping Module: This module incorporates the aforementioned dynamic baseline model. Its workflow is as follows: Stage identification: Based on features in real-time data (such as ground clearance, horizontal movement speed, and location area), a preset rule engine or machine learning classifier is invoked to automatically determine which macro construction stage the current shaft unit is in, such as "lifting", "horizontal movement", "high-altitude hovering" or "precise docking".

[0106] Instantaneous mapping: In the macroscopic stage, geometric algorithms are used to map real-time spatial coordinates onto the theoretical hoisting path, and the accurate path progress percentage or the nearest target point index is calculated.

[0107] 3. Intelligent Comparison and Decision Command Generation Module: This is the brain of the platform. This module: Comparative analysis: Based on the output of the state mapping module, the corresponding instantaneous theoretical parameters are retrieved from the dynamic benchmark model and compared with the real-time monitoring data at the millisecond level to calculate the deviation.

[0108] Rule Base / Model Decision-Making: A built-in hierarchical early warning and instruction rule base is provided. The rule base defines specific operation instructions (such as "stop", "fine-tune Y centimeters in the X direction", "decelerate") corresponding to different deviation types (e.g., position deviates eastward, attitude tilts forward, strain exceeds limits), different deviation magnitudes, and different construction stages. For complex situations, fuzzy control or predictive control algorithms can be used to predict future states based on deviation trends and generate optimized control instructions.

[0109] Instruction generation and distribution: Transform decision results into structured, unambiguous text or code instructions, and accurately distribute them to the designated job control terminal through the communication interface.

[0110] 4. Data storage, visualization and traceability module: Stores all historical data, model parameters and operation logs, provides a web-based graphical monitoring interface, displays the status of each unit, alarm information and command execution status in real time, and supports the traceability and analysis of construction data throughout the entire process.

[0111] Example 1 1. Project Preparation The elevator shaft was constructed using a modular construction method, with factory prefabrication and on-site hoisting and assembly. Each shaft unit measures 3.0m (length) × 2.8m (width) × 3.6m (height), using C40 concrete. Before construction, the technical team used finite element analysis software to conduct a full-process mechanical simulation of the hoisting process based on the BIM model and hoisting plan, and planned a theoretical hoisting path from the storage area to the installation position. The simulation results (such as stress and strain extremes at different stages and allowable attitude ranges) and the coordinates of a series of continuous target points along the path (set according to the planned path and velocity curve, for example, generated at 1-second intervals) were uploaded to the "Construction Intelligent Management Cloud Platform" deployed on Tencent Cloud, forming a dynamic benchmark model.

[0112] 2. Factory prefabrication and sensor system integration At the component factory, based on the design drawings, eight sensor installation positions were determined in the critical stress areas of the shaft unit—the four corners of the upper and lower mating surfaces and the concrete ribs next to the lifting points. At the corresponding positions on the steel formwork, square stainless steel rigid protective shells with embedded rubber sealing rings were fixed with bolts.

[0113] After the concrete pouring and standard curing were completed and the formwork was removed, the protective shell was firmly embedded in the component. Workers opened the shell cover, inserted the high-precision biaxial tilt sensor and the foil strain gauge that had been attached to the steel liner, and then filled and sealed it with flexible silicone rubber.

[0114] Meanwhile, within the equipment installation cavity (with a hinged stainless steel access door) pre-reserved on the inner wall of the unit, an IoT data acquisition and transmission module integrating a microprocessor, a 4G communication module, an 8GB storage chip, and a local data processing unit, as well as a set of 12V / 24Ah rechargeable lithium batteries, is installed. Galvanized steel pipes pre-embedded before pouring connect each protective shell to the equipment installation cavity; after curing, shielded cables are inserted to complete the electrical connection and power supply for all sensors and IoT modules.

[0115] 3. On-site hoisting and intelligent monitoring Shaft unit number 15 was transported to the site. Before hoisting began, the IoT module was activated and connected to the cloud management platform. The platform then loaded the corresponding dynamic baseline model for this unit.

[0116] hoisting process: Data Acquisition and Transmission: Tilt and strain sensors continuously acquire data at frequencies of 20Hz and 100Hz, respectively. The IoT module uploads data to the cloud platform in real time via a 4G network. When the unit moves to an area with weak signal at a building corner, the module detects a network interruption and automatically activates a data caching mechanism to temporarily store the data locally.

[0117] Local early warning: The module's local data processing unit calculates data in real time and compares it with the preset local early warning thresholds (attitude angle 0.10°, strain 200με). If, at a certain moment of swaying, the locally calculated attitude angle reaches 0.16° for more than 3 cycles, the module immediately drives the audible and visual alarm installed outside the shaft unit to sound and flash, issuing a primary alarm to the site.

[0118] Precise Cloud-Based Comparison and Decision-Making: A few seconds later, the unit moves out of the signal blind spot, and the IoT module automatically retransmits cached data. The cloud platform receives the real-time data stream and first determines the current progress point (e.g., "at the 142nd second on the theoretical path") by calculating the shortest distance to each segment of the theoretical hoisting path and finding the perpendicular bisector based on the real-time 3D coordinates. Subsequently, it retrieves the corresponding instantaneous theoretical parameters (e.g., theoretical coordinates (X, Y, Z), allowable attitude angle ±0.13°, theoretical strain range -50 to +120 με) from the dynamic benchmark model for comparison. The instantaneous theoretical values ​​(±0.13°, -50 to +120 µε) in this example are the preset, allowable instantaneous state range for the specific target point at the 142nd second in the dynamic benchmark model. This range varies according to the theoretical force and attitude requirements at different locations on the hoisting path. This range differs from the general "cloud control threshold" (e.g., 0.12°, 250 µε) applicable to the entire process as described in claim 6 and above. The general threshold is a relatively loose boundary set to ensure safety throughout the entire process, while the instantaneous value in the dynamic benchmark model provides a more refined and rigorous theoretical reference for achieving high-precision, instantaneous matching and comparison.

[0119] Command Issuance and Execution: The platform comparison revealed a real-time attitude angle of 0.13°, exceeding the instantaneous allowable value but still within the higher cloud-based control threshold (0.25°). Simultaneously, the platform analyzed the movement trend and determined the risk was manageable. Therefore, the platform generated a command: "Attitude slightly exceeded, but can continue to ascend slowly, pay attention to maintaining stability," and issued it to the hoisting commander's smart wristband and handheld explosion-proof tablet. This cloud-based command overridden and replaced the still-ringing local alarm, and the commander continued operations according to the platform's instructions, ensuring orderly system coordination.

[0120] Final Assembly: During the precision docking phase, the platform applied even stricter instantaneous allowable ranges (attitude angle ±0.05°). Through real-time data feedback and millimeter-level fine-tuning commands issued by the platform, the final installation accuracy of the wellbore unit fully met the design requirements.

[0121] This embodiment deeply integrates sensing systems, IoT modules, and cloud platforms from the factory prefabrication stage, constructing an intelligent construction system of "real-time perception - local early warning - cloud decision-making - command closed loop." This method effectively solves problems such as lagging status perception, difficulty in controlling installation accuracy, and insufficient safety early warning in traditional hoisting, realizing controllable, visible, and intelligent management of the entire modular shaft construction process, significantly improving construction safety, efficiency, and quality.

[0122] 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 examples shown and described herein.

Claims

1. A modular shaft construction method, characterized in that, Includes the following steps: Step 1: In the factory, prefabricate the shaft unit and fix the tilt sensor for measuring spatial attitude data, the strain sensor for measuring structural stress data, and the positioning module for obtaining real-time three-dimensional spatial coordinates as sensing devices inside the shaft unit. Step 2: Configure an IoT data acquisition and transmission module for each shaft unit, and electrically connect the module to the tilt sensor and strain sensor in the shaft unit, so that the sensing device can establish two-way data communication with the remote construction management cloud platform through the IoT data acquisition and transmission module. The construction management cloud platform stores the design reference data corresponding to each shaft unit, including spatial coordinates and structural stress parameters. Step 3: Transport the shaft unit to the construction site and carry out hoisting and splicing operations. The tilt sensor continuously collects the spatial attitude data of the shaft unit, and the strain sensor continuously collects the structural stress data set in the key stress area of ​​the shaft unit. The spatial attitude data and structural stress data are sent to the construction management cloud platform as real-time monitoring data through the Internet of Things data acquisition and transmission module. Step 4: After receiving the real-time monitoring data, the construction management cloud platform retrieves the design reference data corresponding to the current working shaft unit and calculates and compares the real-time monitoring data with the design reference data. When the calculation and comparison results show that the deviation of the real-time monitoring data from the design benchmark data exceeds the preset allowable range, the construction management cloud platform generates an operation instruction based on the deviation value and the predetermined adjustment rules, and sends the operation instruction to the corresponding operation control terminal at the construction site.

2. The modular shaft construction method as described in claim 1, characterized in that, The specific installation steps for the sensing device are as follows: According to the structural design drawings of the shaft unit, multiple sensor installation positions are determined on the prefabricated template. The installation positions correspond to the key stress areas formed after the shaft unit is assembled. The key stress areas include the vertical docking surface between units, the horizontal docking surface, the surrounding structure of the hoisting stress point, and the edge of the reserved hole. The rigid protective housing is detachably fixed to the installation position of the precast template using connectors; the concrete pouring and curing process of the shaft unit is completed, so that the protective housing is embedded inside the structure of the shaft unit; after the curing process is completed, the tilt sensor and strain sensor are placed inside the rigid protective housing; the remaining space inside the rigid protective housing is filled with elastic potting material to fix all the internal components; the opening of the rigid protective housing is closed with a sealing cover plate to complete the installation.

3. The modular shaft construction method as described in claim 2, characterized in that, The inner wall of the shaft unit has a pre-set equipment installation cavity, and an openable metal inspection door is provided at the opening of the equipment installation cavity. The IoT data acquisition and transmission module and a rechargeable battery that powers the module, the tilt sensor and the strain sensor are fixedly installed in the device mounting cavity. Before pouring the well unit, multiple metal conduits are pre-embedded in its structure. One end of each metal conduit leads to the interior of a rigid protective shell, and the other end leads to the equipment installation cavity. By laying connecting cables through the metal conduit, the signal output terminals of each tilt sensor and strain sensor are electrically connected to the corresponding data input ports of the IoT data acquisition and transmission module inside the device mounting cavity. The rechargeable battery powers the tilt sensor and the strain sensor via the IoT data acquisition and transmission module.

4. The modular shaft construction method as described in claim 1, characterized in that, The IoT data acquisition and transmission module has a built-in data storage unit and is configured to perform the following communication guarantee steps: The IoT data acquisition and transmission module continuously monitors the wireless network connection status between itself and the construction management cloud platform; when the wireless network connection status is normal, the IoT data acquisition and transmission module sends the real-time monitoring data collected by the tilt sensor and the strain sensor to the construction management cloud platform in real time. When the wireless network connection is interrupted, the IoT data acquisition and transmission module automatically stores the real-time monitoring data collected by the tilt sensor and the strain sensor in its built-in data storage unit. When the wireless network connection is restored, the IoT data acquisition and transmission module automatically resends the real-time monitoring data temporarily stored in the data storage unit to the construction management cloud platform.

5. The modular shaft construction method as described in claim 4, characterized in that, The IoT data acquisition and transmission module is also equipped with a local data processing unit and an audible and visual alarm, and is set with a local early warning threshold; the module is configured to execute the following local security early warning steps: The local data processing unit receives and processes the raw data collected by the tilt sensor and strain sensor in real time to obtain the locally calculated spatial attitude parameters and structural force parameters. The local data processing unit continuously compares the locally calculated spatial attitude parameters and structural force parameters with the attitude warning threshold and force warning threshold in the local warning threshold, respectively. When any of the locally calculated spatial attitude parameters or structural force parameters exceeds its corresponding local warning threshold, the IoT data acquisition and transmission module immediately drives the audible and visual alarm to issue a field warning signal.

6. The modular shaft construction method as described in claim 5, characterized in that, The spatial coordinate deviation threshold and structural force deviation threshold set in the construction management cloud platform for calculation and comparison are respectively greater than the attitude warning threshold and force warning threshold set in the Internet of Things data acquisition and transmission module. In step four, when the construction management cloud platform determines that the deviation value exceeds the preset allowable range and generates an operation instruction, the operation instruction will override and replace the on-site early warning signal triggered by the IoT data acquisition and transmission module, serving as the final basis for on-site execution.

7. The modular shaft construction method as described in claim 1, characterized in that, In step four, the construction management cloud platform calculates and compares the real-time monitoring data with the design baseline data, specifically as follows: The design benchmark data stored in the construction management cloud platform is a dynamic benchmark model, which pre-stores the theoretical spatial coordinate range and allowable structural stress parameter range corresponding to different construction stages. The construction management cloud platform identifies the target construction stage of the current shaft unit based on the real-time monitoring data; Compare the spatial attitude data in the real-time monitoring data with the theoretical spatial coordinate range corresponding to the target construction stage; Simultaneously, the structural stress data in the real-time monitoring data is compared with the allowable structural stress parameter range corresponding to the target construction stage.

8. The modular shaft construction method as described in claim 7, characterized in that, The dynamic reference model is further configured to store the instantaneous theoretical spatial coordinates, instantaneous allowable attitude angle range, and instantaneous theoretical force range of a series of continuous target points throughout the entire process from the start of hoisting to the completion of splicing. In step four, the construction management cloud platform calculates the mapping position of the spatial coordinates in the real-time monitoring data on the theoretical hoisting path to determine the current target point. Subsequently, the instantaneous theoretical spatial coordinates, instantaneous allowable attitude angle range, and instantaneous theoretical force range that match the target point are retrieved from the dynamic benchmark model and used as the comparison benchmark at the current moment.

9. The modular shaft construction method as described in claim 8, characterized in that, The dynamic benchmark model is constructed in the following manner: Before construction, a construction process simulation is conducted based on the design parameters and hoisting scheme of the shaft unit. The construction process simulation includes mechanical simulation analysis and motion trajectory planning. Based on the simulation results, for different construction stages or continuous target points, the corresponding theoretical spatial coordinate range, allowable attitude angle range and theoretical force range are calculated and assigned. The motion trajectory planning is used to define a theoretical hoisting path, which consists of a series of critical path points, including at least the hoisting start point, the aerial turning point, and the final splicing point.

10. The modular shaft construction method as described in claim 8, characterized in that, The method for calculating the mapping position of spatial coordinates on the theoretical hoisting path is as follows: The construction management cloud platform calculates the shortest distance from the actual spatial coordinates of the current shaft unit to all line segments on the theoretical hoisting path; The perpendicular point on the theoretical line segment corresponding to the shortest distance is determined as the mapping position of the current actual spatial coordinates on the theoretical hoisting path.