Intelligent precast concrete module construction system and method

Abstract

The invention discloses an intelligent precast concrete module construction system and method. Comprising a digital pre-deformation design module used for generating an optimal three-dimensional geometric model of a precast concrete module; a shape memory alloy wire mesh, a distributed MEMS sensor and a temperature sensor are pre-embedded in the precast concrete module, the surface of a non-splicing area is coated with a super-hydrophobic nano coating, and the surface of a splicing area is provided with a multi-scale bionic connection interface structure; and the monitoring module is used for monitoring the precast concrete module according to the real-time information collected by the distributed MEMS sensor and the temperature sensor and controlling the electrifying and heating of the shape memory alloy wire through a PID control method. According to the embedded shape memory alloy wire mesh, the multi-modal sensor group and the prefabricated concrete module with the multi-scale bionic connection interface structure, a digital pre-deformation design method, an edge calculation method and a PID control method are combined, and rapid construction and dynamic operation and maintenance of underground engineering are achieved at the same time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent construction, in particular to an intelligent prefabricated concrete module construction system and method. BACKGROUND

[0002] With the acceleration of urbanization, the development and utilization of underground space are becoming more and more frequent. Underground engineering construction urgently needs to achieve higher construction efficiency and intelligent operation and maintenance capability under the premise of ensuring structural safety. However, the existing construction mode faces many restrictions in actual application and cannot meet the multiple requirements of rapidity, adaptability and long-term performance in complex scenarios. The specific problems are as follows: 1. Construction efficiency bottleneck: The traditional cast-in-place concrete process generally has the disadvantages of long construction period, complex formwork support, and large influence of wet work. Especially in urban traffic-intensive areas, the traffic diversion time is long, the construction disturbance is large, and the urban operation efficiency is significantly affected; 2. Insufficient structural adaptability: In the face of heterogeneous geological conditions and irregular space environment, conventional prefabricated components lack targeted design, which is easy to form local rigid concentration, induce cracks, deformation and even structural instability, and the safety hidden danger is prominent; 3. Weak module connection: In the prefabricated assembly process, the interface treatment is extensive, which often leads to problems such as joint cracking, water leakage, and poor adhesion, etc., and it is difficult to meet the high requirements of underground engineering on sealing and durability; 4. Conflict between support and operation: The traditional support structure usually occupies the tunnel clearance, which not only hinders the construction space, but also affects the later traffic capacity, and lacks a construction-operation integrated compatible mechanism; 5. Monitoring response lag: The current monitoring system relies on manual inspection and separate sensor layout, and the information is lagging and the response is slow, which makes it difficult to discover structural abnormalities and intervene actively in time, resulting in high maintenance cost and low efficiency.

[0003] Although prefabricated assembly technology has been gradually promoted in recent years, it still has deficiencies in stress coordination, self-adaptability and intelligent linkage to a certain extent. SUMMARY

[0004] In view of the technical problems of long construction period, poor adaptability, frequent structural damage and lagging operation in existing underground engineering construction, the present application aims to provide an intelligent prefabricated concrete module construction system and method. The present application not only can reduce the interference of underground engineering construction on urban traffic and environment, but also can promote the transformation and upgrading of underground engineering construction to high integration, high adaptability and high intelligence.

[0005] The technical scheme adopted by the present application is: One, an intelligent prefabricated concrete module construction system The intelligent prefabricated concrete module construction system comprises: A digital pre-deformation design module comprising a three-dimensional laser scanning unit, a finite element analysis unit and an optimization unit; the three-dimensional laser scanning unit is used to collect three-dimensional geological point cloud data of a construction area, and the optimization unit is coupled with the finite element analysis unit and used to generate an optimal three-dimensional geometric model of a prefabricated concrete module; The prefabricated concrete module is internally embedded with a shape memory alloy wire mesh, a distributed MEMS sensor and a distributed temperature sensor, the surface of a non-splicing area is coated with a super-hydrophobic nano coating, and the surface of a splicing area is provided with a multi-scale biomimetic connecting interface structure; the shape memory alloy wire mesh is used to cause the prefabricated concrete module to produce controllable deformation after being heated by power supply, thereby realizing structural self-adjustment. A monitoring module is used to monitor the state of the prefabricated concrete module according to real-time information collected by the distributed MEMS sensor and the distributed temperature sensor and to control the power supply heating of the shape memory alloy wire through a PID control method.

[0006] Specifically, the shape memory alloy wire is a millimeter-level NiTi alloy wire, has a reversible phase transition temperature range of 55-65 DEG C, can produce an axial strain of about 4% after being heated to about 60 DEG, and is used for active release of structural stress and attitude adjustment.

[0007] Preferably, the wire diameter of the NiTi alloy wire is 3 mm, and the layout spacing is 150 mm x 150 mm.

[0008] Specifically, in the digital pre-deformation design module, the finite element analysis unit loads the structural dead weight, the surrounding rock pressure and the vehicle dynamic load working condition into a three-dimensional geological model constructed according to the three-dimensional geological point cloud data, performs mechanical response simulation, and outputs the steady-state deformation error after installation. Specifically, the optimization unit uses a genetic algorithm, takes the unit length pre-deformation of the prefabricated concrete module in X, Y and Z directions as the to-be-optimized variables, takes the minimization of the steady-state deformation error after installation as the optimization target, is coupled with the finite element analysis unit, obtains the pre-deformation correction amount of the prefabricated concrete module after multiple rounds of iteration, and generates the optimal three-dimensional geometric model according to the pre-deformation correction amount using a modeling software such as CAD.

[0009] Specifically, the intelligent monitoring platform comprises an edge computing unit, a PID current control unit and a dual-redundancy power supply unit; the edge computing unit receives real-time information collected by distributed MEMS sensors and distributed temperature sensors, and processes the information through an extended Kalman filtering method and a state space model, so as to realize data filtering, anomaly detection and state prediction; the anomaly detection includes stress anomaly, temperature mutation or displacement anomaly; the PID current control unit controls the input current of the shape memory alloy wire according to the target temperature and the real-time temperature of the shape memory alloy wire after receiving instructions from the edge computing unit; the dual-redundancy power supply unit is electrically connected with and supplies power to the shape memory alloy wire net, the edge computing unit and the PID current control unit, and comprises a main power supply, a backup power supply and a UPS power management.

[0010] Specifically, the multi-scale biomimetic connecting interface structure comprises a mechanical interlocking structure, a groove array and a photosensitive adhesive.

[0011] Specifically, the multi-scale biomimetic connecting interface structure comprises a macroscopic mechanical interlocking structure, a mesoscopic groove array and a microscopic photosensitive adhesive.

[0012] Further, the mechanical interlocking structure includes but is not limited to trapezoidal mortise and tenon slots and dovetail slots.

[0013] II. A construction and maintenance method applied to the intelligent precast concrete module construction system The construction and maintenance method comprises the following steps: S1) generating an optimal three-dimensional geometric model of a precast concrete module through a digital pre-deformation design module, and manufacturing the precast concrete module through casting.

[0014] Specifically, the step S1 comprises: S1.1) generating an optimal three-dimensional geometric model through three-dimensional scanning and mechanical simulation; S1.2) embedding an alloy wire net and a sensor group in a mold, and after casting concrete, performing laser grooving on the surface of the obtained precast concrete module to form grooves; S1.3) coating an ultrahydrophobic nano coating on the surface of a non-splicing area of the precast concrete module, and pre-coating a photosensitive adhesive on the surface of a splicing area and covering a protective film.

[0015] S2) installing the precast concrete module in a construction area.

[0016] Specifically, the step S2 comprises: S2.1) hoisting the precast concrete module, and installing the precast concrete module in a corresponding position by positioning according to real-time MEMS data; S2.2) peeling off the interface protective film, and curing the photosensitive adhesive; S2.3) Power heating shape memory alloy wire mesh, triggering shape memory effect to complete initial stress adjustment.

[0017] S3) Realize post-installation maintenance through the monitoring module.

[0018] Specifically, the step S3 comprises: according to the stress threshold and the temperature threshold, starting the PID current control unit by the sensor data collected by the distributed MEMS sensor and the distributed temperature sensor in real time, and controlling the input current of the shape memory alloy wire by the PID current control unit according to the target temperature and the real-time temperature of the shape memory alloy wire, so as to realize adaptive heating of the shape memory alloy wire.

[0019] Preferably, the stress threshold is 110% of the design upper limit value, and the temperature threshold is 70 DEG C.

[0020] The beneficial effects of the present application are: According to the prefabricated concrete module provided with the embedded shape memory alloy wire mesh, the multi-modal sensor group and the multi-scale biomimetic connecting interface structure, the digital pre-deformation design method, the edge computing method and the PID control method are combined, so that the rapid construction and dynamic operation and maintenance of the underground engineering are realized. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 It is a schematic diagram of the system of the present application.

[0022] Figure 2 It is a construction flowchart of the method of the present application. DETAILED DESCRIPTION

[0023] The present application will be further described in detail below in combination with the drawings and specific embodiments.

[0024] The present application provides an intelligent prefabricated concrete module construction system. The intelligent prefabricated concrete module construction system comprises: A digital pre-deformation design module, comprising a three-dimensional laser scanning unit, a finite element analysis unit and an optimization unit; the three-dimensional laser scanning unit is used for collecting three-dimensional geological point cloud data of a construction area, and the optimization unit is coupled with the finite element analysis unit and used for generating an optimal three-dimensional geometric model of a prefabricated concrete module; A prefabricated concrete module, internally embedded with a shape memory alloy wire mesh, a distributed MEMS sensor and a distributed temperature sensor, the surface of a non-splicing area being coated with a super-hydrophobic nano coating, and the surface of a splicing area being provided with a multi-scale biomimetic connecting interface structure; the shape memory alloy wire mesh is used for producing controllable deformation of the prefabricated concrete module after power heating, thereby realizing structural self-adjustment; A monitoring module is configured to monitor the state of the prefabricated concrete module according to real-time information collected by the distributed MEMS sensor and the distributed temperature sensor, and to control the power heating of the shape memory alloy wire through a PID control method.

[0025] Specifically, the shape memory alloy wire is a millimeter-level NiTi alloy wire, has a reversible phase transition temperature range of 55-65 DEG C, and can generate an axial strain of about 4% after being heated to about 60 DEG C, and is used for active release of structural stress and attitude adjustment.

[0026] Preferably, the diameter of the NiTi alloy wire is 3 mm, and the layout spacing is 150 mm x 150 mm.

[0027] Specifically, in the digital pre-deformation design module, the finite element analysis unit loads the structural dead weight, surrounding rock pressure and vehicle dynamic load working conditions into the three-dimensional geological model constructed according to the three-dimensional geological point cloud data, performs mechanical response simulation, and outputs the steady-state deformation error after installation. Specifically, the optimization unit uses a genetic algorithm, takes the unit length pre-deformation of the prefabricated concrete module in X, Y and Z directions as the optimization variable, takes the minimization of the steady-state deformation error after installation as the optimization target, and is coupled with the finite element analysis unit to obtain the pre-deformation correction amount of the prefabricated concrete module after multiple iterations. The optimal three-dimensional geometric model is generated using CAD modeling software according to the pre-deformation correction amount.

[0028] Specifically, the intelligent monitoring platform includes an edge computing unit, a PID current control unit and a dual-redundancy power supply unit; the edge computing unit receives real-time information collected by the distributed MEMS sensor and the distributed temperature sensor, and processes through an extended Kalman filtering method and a state space model, thereby realizing data filtering, anomaly detection and state prediction; the anomaly detection includes stress anomaly, temperature mutation or displacement anomaly; the PID current control unit receives instructions from the edge computing unit, controls the input current of the shape memory alloy wire according to the target temperature and the real-time temperature of the shape memory alloy wire; the dual-redundancy power supply unit is electrically connected with and powers the shape memory alloy wire network, the edge computing unit and the PID current control unit, and includes a main power supply, a backup power supply and a UPS power management.

[0029] Specifically, the multi-scale biomimetic connecting interface structure includes a mechanical interlocking structure, a groove array and a photosensitive adhesive. In the groove array, the width of the groove is millimeter-level.

[0030] Specifically, the multi-scale biomimetic connecting interface structure includes a macroscopic mechanical interlocking structure, a mesoscopic groove array and a microscopic photosensitive adhesive.

[0031] Further, the mechanical interlocking structure includes but is not limited to a trapezoidal mortise and tenon slot and a dovetail slot.

[0032] This invention also provides a construction and maintenance method for the aforementioned intelligent precast concrete module construction system. Specifically, it includes the following steps: S1) Generate the optimal three-dimensional geometric model of the precast concrete module through the digital pre-deformation design module, and manufacture the precast concrete module by pouring.

[0033] Specifically, step S1 includes: S1.1) Generate the optimal three-dimensional geometric model through three-dimensional scanning and mechanical simulation; S1.2) An alloy wire mesh and sensor assembly are pre-embedded in the mold. After pouring concrete, the surface of the resulting precast concrete module is laser-grooved to form grooves. S1.3) A superhydrophobic nano-coating is applied to the non-joining areas of the precast concrete module, and a photosensitive adhesive is pre-coated and a protective film is applied to the joint areas.

[0034] S2) Install precast concrete modules in the construction area.

[0035] Specifically, step S2 includes: S2.1) Hoist the precast concrete modules and install them in the corresponding positions by positioning based on real-time MEMS data; S2.2) Peel off the interface protective film and cure the photosensitive adhesive; S2.3) The shape memory alloy wire mesh is heated by electricity to trigger the shape memory effect and complete the initial stress adjustment.

[0036] S3) Post-installation maintenance is achieved through a monitoring module.

[0037] Specifically, step S3 includes: using sensor data collected in real time by distributed MEMS sensors and distributed temperature sensors, activating the PID current control unit based on stress threshold and temperature threshold, and using the PID current control unit to control the input current of the shape memory alloy wire according to the target temperature and real-time temperature of the shape memory alloy wire, thereby realizing adaptive heating of the shape memory alloy wire.

[0038] Preferably, the stress threshold is 110% of the design upper limit and the temperature threshold is 70°C.

[0039] The specific embodiments of the present invention are as follows: like Figure 2 As shown in the figure, this embodiment provides a construction and maintenance method for a modular tunnel widening system, which specifically includes the following steps: Phase 1: Intelligent Module Prefabrication and Integration Design 1. Digital pre-deformation design before module prefabrication The goal of this stage is to conduct digital pre-adjustment and shape correction of the geometric parameters of the module in combination with the actual geological conditions of the construction site, so as to ensure the stability of the structure after installation and its high adaptability to the deformation of the site.

[0040] Firstly, the point cloud data of the construction area is obtained by using high-precision three-dimensional laser scanning equipment Trimble X7, and is imported into building information modeling software Revit for geological modeling to obtain a three-dimensional geological model.

[0041] Based on the three-dimensional geological model, numerical analysis and parameter optimization are carried out in sequence: (1) Mechanical loading analysis: According to the constructed three-dimensional model, the mechanical response of the structure under typical working conditions is simulated by using finite element analysis software. The loading conditions include: structural self-weight stress, surrounding rock lateral pressure and vehicle dynamic load. In specific implementation, the numerical values of various load parameters can be set according to domestic and foreign engineering design specifications and site experience to cover the main stress states that the structure may bear.

[0042] (2) Dynamic modal analysis and buckling analysis: Based on the mechanical loading analysis, dynamic modal analysis is carried out to extract the natural frequency and vibration mode characteristics of the module structure under different orders. By comparing the calculated natural frequency with the typical external excitation frequency, the resonance coupling risk can be identified, so as to evaluate the dynamic response stability of the structure. In specific implementation, the typical external excitation frequency includes but is not limited to the disturbance in the range of 1~10Hz caused by traffic load.

[0043] For design schemes that have modal resonance risk, corresponding penalty terms will be set in subsequent optimization evaluation to reduce their fitness score and avoid being selected into the final solution set.

[0044] In addition, a nonlinear buckling analysis method is used to model the potential instability behavior of the module under various complex load combinations and calculate the critical buckling load value. This buckling load will be used as a boundary constraint condition in the genetic algorithm to screen out pre-deformation design schemes with insufficient stability.

[0045] (3) Structural parameter optimization design: In order to improve the deformation fit degree of the module after installation and further reduce the stress mismatch with the surrounding structure or module, the pre-deformation parameters of the module are iteratively optimized through genetic algorithm. This process is realized by Python, and typical algorithm parameters include: population size 100, iteration number 200, crossover probability 0.8, mutation probability 0.05. During the optimization process, the finite element analysis software is called through script linkage to calculate the fitness value of the individual.

[0046] The optimization objective function is set to minimize the steady-state deformation error after installation, and the optimization variable is the unit length pre-deformation of the module in X, Y and Z directions. The fitness function also includes multiple penalty terms to evaluate the potential structural performance risks caused by each set of pre-deformation parameters, thereby achieving a balance between geometric compatibility and structural safety.

[0047] Specifically, the penalty terms include but are not limited to: displacement residuals during simulation, stress concentration coefficients at key locations, and risk indicators for structural modal frequencies falling into typical resonance frequency bands.

[0048] Specifically, by means of weighted fusion, a comprehensive fitness function is constructed according to these penalty terms, thereby guiding the optimization algorithm to identify pre-deformation schemes that are robust in both mechanical performance and dynamic response.

[0049] Finally, according to the optimal pre-deformation correction amount, an STL format three-dimensional mold design drawing is generated, and a module mold is processed according to the three-dimensional mold design drawing through a numerical control machine tool or a 3D printer.

[0050] 2. Embed NiTi shape memory alloy wire mesh After the completion of the module steel mold, first construct the steel reinforcement framework of C50 strength grade concrete modules. During the steel reinforcement framework forming process, embed the NiTi shape memory alloy wire mesh. In this embodiment, the diameter of the NiTi shape memory alloy wire is 3mm, with a tolerance of ±0.1mm, and is arranged according to a grid spacing of 150mm x 150mm. The NiTi shape memory alloy wire used in this embodiment can produce a phase transition strain of 4%±0.2% at about 60°C, corresponding to an axial shrinkage of about 40mm per meter length.

[0051] At the same time, sensor groups are pre-embedded in key structural areas of the steel reinforcement framework. Key structural areas include but are not limited to joints, corners, ends, and special-shaped nodes. In this embodiment, the sensor group includes a multi-dimensional miniature environmental state sensor (MESM) and a platinum resistance temperature sensor. One set of MESM is configured every 10 square meters to ensure that it covers all key node areas. One temperature sensor is provided every 5 meters along the grid spacing. In specific implementation, the sensor group can be arranged according to the complexity of the module and the monitoring requirements.

[0052] All sensors are communicatively connected to edge computing devices through industrial-grade communication cables. The edge computing devices are used to filter and preliminarily identify sensor data, providing localized data support for state diagnosis and response control of structural operating devices.

[0053] The active adjustment mechanism of the detection module adopts a closed-loop control method: when local stress concentration or deformation anomaly is detected, the corresponding NiTi shape memory alloy wire is powered and heated to activate its phase transition strain behavior, thereby generating an active contraction force at the target site to achieve structural posture fine-tuning, node tensioning or joint stability. After the structure returns to a stable state, the system stops heating and the alloy wire returns to its original shape, forming a dynamic response adjustment closed loop. This mechanism significantly reduces the disturbance risk to the surrounding soil or adjacent structures during assembly, improving overall construction accuracy and safety margin.

[0054] In practical applications, the system does not activate all embedded NiTi shape memory alloy wires at the same time, but rather adjusts the structure posture or stress state feedback through regional management to activate specific alloy wire groups at certain locations. This closed-loop control method can flexibly output local stress field changes while maintaining overall layout parameters, achieving macro stress regulation through internal force transmission, meeting the requirements of assembly precision and posture correction.

[0055] 3. Concrete pouring and surface treatment This embodiment uses C50 strength grade concrete with a water-cement ratio of about 0.32 and a fly ash content of about 20%. This ratio takes into account performance indicators such as fluidity, strength, and durability, and is suitable for complex reinforcement framework configurations and multiple embedding requirements. The concrete is mixed by a forced mixer, with a slump of 180±20mm to ensure good formability and fillability under dense reinforcement and sensor network layout.

[0056] During the initial setting stage of the concrete, a laser grooving technique is used to process the microstructure of the module's joint interface, forming a micro-groove array to enhance the interface mechanical interlocking force at the joint site. To balance the bonding performance and interface strength, in this embodiment, the groove parameters are a processing pitch of 0.5mm and a depth of 0.3mm, with a surface roughness controlled at Ra≈1.6μm. It should be noted that the initial setting stage of the concrete is typically within 4~6 hours after pouring.

[0057] To improve the anti-pollution and durability of the module's outer surface, in this embodiment, a low-pressure plasma spraying process is used to set a super-hydrophobic nano-coating on the non-joint area of the module. The thickness of the super-hydrophobic nano-coating is preferably 50~100nm, with a typical water droplet contact angle not less than 150° and a rolling angle not higher than 5°. It should be noted that to avoid interference with the bonding performance of the joint adhesive, the surface of the joint area is not treated with the coating.

[0058] In the joint interface area, a light-sensitive structural adhesive is applied in advance to form a uniform coating about 0.2mm thick, and a layer of PET protective film is applied to maintain interface activity and cleanliness. In this embodiment, the light-sensitive structural adhesive is of UV-328 type.

[0059] Second stage: staggered peak fast installation 1. Staggered peak construction start This embodiment starts construction during the night traffic off-peak period (23:00~5:00) to reduce the disturbance risk to ground traffic and public operation systems. The on-site operation is performed by a car crane equipped with a high-precision positioning system and an attitude sensing unit.

[0060] 2. Module positioning and fine adjustment Module lifting is performed by a multi-degree-of-freedom high-precision mechanical arm. During the lifting process, the system constructs a stable spatial reference coordinate system through the ground laser positioning marker system, combines the attitude information collected by the MESM sensor, and the real-time displacement feedback of the laser ranging device during the lifting process, to realize the positioning and installation of the module.

[0061] 3. Interface solidification and alloy wire activation After the module is positioned, the PET protective film on the interface pre-coated structural adhesive is manually or automatically removed by a mechanical hand to ensure that the interface is clean and has photoactive activity. Then, a UVLED array device with a central wavelength of 365nm and an irradiation power of about 10W / cm² is used to irradiate the interface adhesive layer, with an irradiation time controlled within 30 seconds to 2 minutes, to realize the solidification of the photosensitive structural adhesive.

[0062] After solidification is completed, initial stress adjustment is performed: the alloy wire is controlled to heat to the phase transition temperature, exciting it to generate 4%±0.2% axial strain, generating active shrinkage force. The stress is transmitted to the structural node area through the internal steel framework of the module, realizing self-adaptive adjustment of the joint residual displacement and assembly error, and to a certain extent, releasing the local stress concentration caused by lifting.

[0063] 4. Traffic recovery and cover plate installation After the module assembly and interface reinforcement are completed, the site realizes the temporary traffic function recovery of the construction section by installing customized cover plates.

[0064] After installation is completed, the site uses conventional equipment to perform basic detection on the cover plate bearing condition, and after confirming that it has the traffic conditions, opens the vehicle traffic.

[0065] Third stage: dynamic maintenance during operation 1. Real-time data collection of sensor network During operation, the multi-modal sensor network pre-buried in the first stage is continuously relied on to realize real-time data collection and edge processing of the structure state.

[0066] 2. Intelligent judgment and fine adjustment control In this embodiment, the closed-loop control mechanism is based on the integration of NiTi shape memory alloy wire mesh and multi-modal sensor group, combined with structural mechanics model to set stress and temperature threshold, to realize automatic state recognition and local displacement compensation control.

[0067] In specific implementation, the stress control range is preferably 10-15 MPa, and the early warning threshold is set to 110% of the upper limit of stress, which is set to 16.5 MPa in this embodiment, to balance response sensitivity and safety margin.

[0068] The temperature threshold is set according to the shape memory performance of the NiTi shape memory alloy wire: the phase change activation temperature is about 60℃, and the temperature threshold is not more than 70℃, to avoid aging or phase change degradation of the NiTi shape memory alloy wire.

[0069] The PID current control unit performs dynamic adjustment through the incremental PID algorithm, and the parameters are set to Kp=1.2, Ki=0.3, and Kd=0.1.

[0070] When the monitoring value exceeds the set threshold, the system realizes adaptive heating of the NiTi shape memory alloy wire through the PID current control unit, heats the temperature of the corresponding NiTi shape memory alloy wire to 60±2℃, and makes the NiTi shape memory alloy wire produce an axial strain of about 4%±0.2%.

[0071] 3. Emergency power supply In order to ensure the continuous and stable operation of the system during operation, dual redundant power supply paths are configured in this embodiment. The main power supply accesses the tunnel operation power network, and a backup lithium battery module is configured for each edge node to ensure uninterrupted operation of the system in the area for more than 6 hours when the main power supply is interrupted, and a UPS power management module is provided to ensure uninterrupted operation of the system in the event of power failure or extreme environment.

[0072] 4. Vehicle guidance and risk prompt In this embodiment, a visual interface area is reserved at the top of the module.

[0073] The visual interface area displays the module operation state and risk prompt information in real time through LED status indicators and / or low-power electronic ink screens. In this embodiment, when the edge control unit detects that the local stress exceeds the early warning value, the structural posture deviates, the temperature is abnormal, or the power supply fails, the risk is prompted through indicator lights, voice prompts, and graphic prompt lights. For example: when the local stress exceeds the early warning value, a red light flashes with a voice prompt. When the structural posture deviates, a yellow indicator light flashes with a speed limit prompt icon. When the temperature is abnormal or the power supply fails, the display screen displays a graphic prompt and performs background pushing.

[0074] Further, the state information can also be uploaded to a central control platform or a cloud monitoring system through a low-power wireless mesh network for data archiving, trend prediction and maintenance scheduling.

[0075] The above detailed description is used to explain and illustrate the present application, rather than limiting the present application, any modification and change made to the present application within the spirit and protection scope of the claims of the present application shall fall into the protection scope of the present application.

[0076] The above described is only the preferred embodiment of the present application, therefore, any equivalent change or modification made to the structure, features and principles described in the scope of the present application shall fall into the scope of the present application.

Claims

1. A smart precast concrete module construction system, characterized by, The application relates to a precast concrete module and a digital pre-deformation design method thereof. The digital pre-deformation design module comprises a three-dimensional laser scanning unit, a finite element analysis unit and an optimization unit; the three-dimensional laser scanning unit is used for collecting three-dimensional geological point cloud data of a construction area; the optimization unit is coupled with the finite element analysis unit and is used for generating an optimal three-dimensional geometric model of a precast concrete module; the precast concrete module is internally embedded with a shape memory alloy wire mesh, a distributed MEMS sensor and a distributed temperature sensor, the surface of a non-splicing area is coated with a super-hydrophobic nano coating, and the surface of a splicing area is provided with a multi-scale bionic connecting interface structure; the shape memory alloy wire mesh is used for generating controllable deformation of the precast concrete module after being heated by power supply. The monitoring module is used for monitoring the precast concrete module and controlling the power supply heating of the shape memory alloy wire according to real-time information collected by the distributed MEMS sensor and the distributed temperature sensor. The shape memory alloy wire is a millimeter-level NiTi alloy wire and has a reversible phase transition temperature range of 55-65 DEG C. In the digital pre-deformation design module, the finite element analysis unit loads the structural self-weight, surrounding rock pressure and vehicle dynamic load working conditions into the constructed three-dimensional geological model, carries out mechanical response simulation and outputs a steady-state deformation error after installation; 2. The intelligent precast concrete module construction system of claim 1, wherein: The optimization unit adopts a genetic algorithm, takes unit length pre-deformation amounts of the precast concrete module in X, Y and Z directions as to-be-optimized variables, takes the minimum steady-state deformation error after installation as an optimization target, obtains a pre-deformation correction amount of the precast concrete module and generates an optimal three-dimensional geometric model according to the pre-deformation correction amount.

3. The smart precast concrete module construction system according to claim 1, characterized in that: The intelligent monitoring platform comprises an edge computing unit, a PID current control unit and a double-redundancy power supply unit; The edge computing unit receives real-time information collected by the distributed MEMS sensor and the distributed temperature sensor, processes the real-time information through an extended Kalman filtering method and a state space model, so that data filtering, abnormality detection and state prediction are realized; 4. The intelligent precast concrete modular construction system according to claim 1, wherein: The PID current control unit receives instructions from the edge computing unit, controls the input current of the shape memory alloy wire according to a target temperature and a real-time temperature of the shape memory alloy wire and controls the power supply heating of the shape memory alloy wire. The double-redundancy power supply unit is electrically connected with and supplies power to the shape memory alloy wire mesh, the edge computing unit and the PID current control unit and comprises a main power supply, a standby power supply and a UPS power management. The multi-scale bionic connecting interface structure comprises a mechanical interlocking structure, a groove array and a photosensitive adhesive; the width of the groove is millimeter-level. The method comprises the following steps:

5. The intelligent precast concrete modular construction system according to claim 1, wherein: S1) generating an optimal three-dimensional geometric model of a precast concrete module and manufacturing the precast concrete module through pouring; 6. A method for construction and maintenance of the construction system of prefabricated concrete modules according to any one of claims 1 to 5, characterized in that, S2) installing the precast concrete module; S3) realizing post-installation maintenance. The step S1 comprises: S1.1) generating an optimal three-dimensional geometric model through three-dimensional scanning and mechanical simulation; 7. The method of claim 6, wherein: S1.2) embedding an alloy wire mesh and a sensor group in a mold, performing laser grooving on the surface of the obtained precast concrete module after pouring concrete, and forming grooves; S1.3) coating a super-hydrophobic nano coating on the surface of a non-splicing area of the precast concrete module and pre-coating a photosensitive adhesive on the surface of a splicing area and covering a protective film. ​ ​ 8. The method of claim 6, wherein: The step S2 comprises: S2.1) hoisting the prefabricated concrete module, installing the prefabricated concrete module in the corresponding position by positioning according to real-time MEMS data; S2.2) stripping the interface protective film, and curing the photosensitive adhesive; S2.3) energizing and heating the shape memory alloy wire mesh to trigger the shape memory effect to complete the initial stress adjustment.

9. The method of claim 6, wherein: The step S3 comprises: starting the PID current control unit according to the stress threshold value and the temperature threshold value through the sensor data collected in real time by the distributed MEMS sensor and the distributed temperature sensor, and controlling the input current of the shape memory alloy wire according to the target temperature and the real-time temperature of the shape memory alloy wire by using the PID current control unit.

10. The method of claim 9, wherein: The stress threshold value is 110% of the design upper limit value, and the temperature threshold value is 70 DEG C.

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