Prefabricated part construction safety management method based on information coding

Abstract

The invention relates to the technical field of prefabricated part construction, and discloses a prefabricated part construction safety management method based on information coding, and the method comprises the following steps: S1, building a prefabricated part information coding system; s2, performing identity binding on the prefabricated part through a radio frequency identification tag and a narrowband Internet of Things module; s3, deploying a multi-source sensor network; and S4, constructing a dynamic risk assessment model based on the Bayesian network. According to the prefabricated part construction safety management method based on information coding, a layered information coding system is used for accurately identifying prefabricated parts, and identity binding and real-time position data transmission of the prefabricated parts are achieved in combination with a radio frequency identification tag and a narrowband Internet of Things module; then, a multi-source sensor network is adopted to collect data such as component postures and environment, the data are input into the dynamic risk assessment model based on the Bayesian network, influences of various factors on risks such as falling and structural deformation are analyzed in real time, and potential risks can be predicted more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of prefabricated component construction, and in particular to a prefabricated component construction safety management method based on information coding. Background Art

[0002] The construction of prefabricated components is a key link in the construction process of prefabricated buildings. It involves the installation of the main structure or components of the building through processes such as hoisting, positioning, and connection of precast concrete, steel structures, etc. Its construction efficiency and safety directly affect the quality and progress of the project. Current technologies usually use BIM models and IoT sensors to collect component information and construction data respectively, but lack a unified information coding system, resulting in the inability to effectively associate component identity identification with real-time status data, and the lack of fusion analysis capabilities of multi-source sensor data, making it difficult to achieve accurate monitoring and risk warning of the component hoisting process.

[0003] Existing technologies for prefabricated component construction safety management have the following core issues: First, risk assessment relies on manual inspections or single threshold judgments, and cannot couple and analyze in real time the dynamic impact of multiple factors such as component posture, ambient wind speed, and hoist matching on risks such as falling and structural deformation. Second, there is a lack of a dynamic adjustment mechanism based on closed-loop control. When risks occur, it is impossible to automatically optimize the lifting path or adjust the tower crane motion parameters through the PID algorithm, resulting in delayed safety response and insufficient control accuracy. Therefore, we propose a prefabricated component construction safety management method based on information coding. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides a prefabricated component construction safety management method based on information coding. Through layered information coding, Bayesian network dynamic risk assessment and PID closed-loop control algorithm, precise management and real-time response of prefabricated component construction safety are achieved, and the following core problems existing in the existing technology in prefabricated component construction safety management are solved: First, risk assessment relies on manual inspection or single threshold judgment, and cannot couple and analyze in real time the dynamic influence of multiple factors such as component posture, ambient wind speed, and hoist matching on risks such as falling and structural deformation; second, there is a lack of a dynamic adjustment mechanism based on closed-loop control. When a risk occurs, it is impossible to automatically optimize the lifting path or adjust the tower crane motion parameters through the PID algorithm, resulting in delayed safety response and insufficient control accuracy.

[0006] (2) Technical solution

[0007] To achieve the above-mentioned purpose of precise management and real-time response of prefabricated component construction safety through hierarchical information coding, Bayesian network dynamic risk assessment and PID closed-loop control algorithm, the present invention provides the following technical solution: a prefabricated component construction safety management method based on information coding, comprising the following steps:

[0008] S1. Establish a prefabricated component information coding system, which includes:

[0009] Project level code, including project name, project number, and construction stage;

[0010] Component layer coding, including component type, size, material, and hoisting sequence;

[0011] Attribute layer encoding, including production batch, quality inspection status, and spreader adaptation parameters;

[0012] The coding rule adopts a mixed coding method, where the first 3 digits are the binary code of the project layer, the middle 4 digits are the hexadecimal code of the component layer, and the last 5 digits are the ASCII code of the attribute layer;

[0013] S2. Bind the identity of the prefabricated component through an RFID tag and a narrowband Internet of Things module, wherein the RFID tag stores the coded information of step S1, and the narrowband Internet of Things module transmits the component position data to the cloud server at a frequency of once per second;

[0014] S3. Deploy a multi-source sensor network, including:

[0015] Inertial measurement unit: collects component pitch angle, roll angle, and heading angle at a frequency of 200Hz;

[0016] Total station prism: real-time positioning of component three-dimensional coordinates;

[0017] Vibrating wire strain gauge: monitors the structural stress distribution during component lifting, in N / mm 2 ;

[0018] Environmental sensors: monitor wind speed, temperature, humidity and light intensity;

[0019] S4. Construct a dynamic risk assessment model based on Bayesian network. The input parameters include:

[0020] Sensor data: pitch angle error > 3°, roll angle error > 2°, stress value exceeds 110% of the rated value;

[0021] Coding information: The spreader adaptation parameters do not match the actual spreader;

[0022] Environmental data: wind speed>8m / s or humidity>85%;

[0023] Output risk level: level range is 1-5, level 5 triggers emergency braking;

[0024] S5. Visual management and control is achieved through a BIM model and IoT data fusion platform, which includes:

[0025] Perception layer: Deploy the sensors and RFID / NB-IoT devices from step S3;

[0026] Network layer: Use LoRaWAN protocol to transmit non-real-time data, and 5G network to transmit real-time video stream;

[0027] Platform layer: Build 3D models based on the IFC4 standard and integrate multi-source data into the BIM database;

[0028] Application layer: provides collision detection, lifting path optimization and risk heat map display functions;

[0029] The component entity object of the BIM model includes extended attribute fields: pitch angle, roll angle, heading angle; stress distribution cloud map, unit is N / mm 2 ;The minimum safety distance threshold from surrounding components / personnel is ≤1.5m;

[0030] S6. When the risk level reaches the threshold, the dynamic adjustment mechanism is activated:

[0031] The tower crane luffing speed is adjusted by PID control algorithm, and the PID parameters are set as proportional coefficient Kp = 0.8, integral coefficient Ki = 0.1, differential coefficient Kd = 0.3, and sampling period 50ms;

[0032] Generate a new lifting sequence plan and optimize the path to avoid dangerous areas. The path cost function is f(n) = g(n) + h(n) + 0.5 × risk(n), where risk(n) is the node risk value.

[0033] Trigger the voice and visual alarm system to locate the nearest safe evacuation point, with a response time of ≤200ms.

[0034] Preferably, the hexadecimal field of the component layer code includes: the first two digits represent the component type, the third to fourth digits represent the size specification, and the fifth to sixth digits represent the material strength grade.

[0035] Preferably, the nodes of the Bayesian network include parent nodes and child nodes, wherein:

[0036] The parent node includes component type, hoisting height and ambient wind speed;

[0037] Sub-nodes include falling risk, structural deformation risk, and personnel collision risk;

[0038] The marginal probability is trained using historical accident data, and the parameters are optimized using the EM algorithm with a convergence threshold ≤ 0.01.

[0039] Preferably, the component entity object of the BIM model includes an extended attribute field:

[0040] Real-time pitch angle, roll angle, and heading angle, with an accuracy of ±0.5°;

[0041] Stress distribution cloud map, unit is N / mm 2 , resolution 0.1MPa;

[0042] Safety distance threshold: minimum distance from surrounding components / personnel ≤1.5m.

[0043] Preferably, the output control signal of the PID control algorithm adjusts the motor speed through the tower crane frequency converter, the response time is ≤200ms, and the motor speed adjustment range is 0-60rpm.

[0044] A prefabricated component construction safety management system based on information coding, comprising:

[0045] Coding generation module: generates a unique identifier according to coding rules;

[0046] Data acquisition module: integrated IMU, total station, strain gauge and environmental sensor, sampling frequency ≥ 20Hz;

[0047] Risk Assessment Module: A Bayesian network-based processor with an input layer containing 12 sensor data channels;

[0048] Visual control module: runs on the Unity3D engine and supports real-time rendering of BIM models and overlay of sensor data;

[0049] Dynamic adjustment module: Contains PID controller hardware circuit, parameters Kp = 0.8, Ki = 0.1, Kd = 0.3, and Zigbee communication module, transmission distance ≥ 100m.

[0050] Preferably, the code generation module uses blockchain technology to store code records, and each block contains a timestamp accurate to milliseconds, a digital signature of the code generator, and a hash value link to the previous block.

[0051] Preferably, the BIM model of the visualization management and control module supports the following interactive functions:

[0052] Hovering the mouse will display detailed coding information of the component;

[0053] Double-click a component to trigger playback of historical lifting data, supporting timestamp retrieval;

[0054] Risk areas are highlighted, red indicates high risk, yellow indicates medium risk, and the color thresholds are risk levels ≥4 and ≥2, respectively.

[0055] (3) Beneficial effects

[0056] Compared with the prior art, the present invention provides a method for prefabricated component construction safety management based on information coding, which has the following beneficial effects:

[0057] 1. This prefabricated component construction safety management method based on information coding establishes a prefabricated component construction safety management system based on information coding. First, a hierarchical information coding system is used to accurately identify prefabricated components. Then, radio frequency identification tags and narrowband Internet of Things modules are combined to realize the identity binding and real-time location data transmission of prefabricated components. Then, a multi-source sensor network is used to collect component posture, environment and other data, and input them into a dynamic risk assessment model based on Bayesian network. The model analyzes the impact of various factors on risks such as falling and structural deformation in real time, which can more accurately predict potential risks and improve the safety management level of construction sites.

[0058] 2. This prefabricated component construction safety management method based on information coding, when the risk level reaches a set threshold, the system activates a dynamic adjustment mechanism based on the PID control algorithm, responding to risk changes by precisely adjusting the tower crane's boom speed. Specifically, the STM32F4 microcontroller calculates the speed parameters that need to be adjusted based on the risk level, and adjusts the motor speed through the FR-E740 inverter to optimize the lifting path to avoid dangerous areas, while triggering the voice and visual alarm systems to guide personnel evacuation. This closed-loop control process not only speeds up safety response speed, but also ensures control accuracy, effectively avoiding safety accidents caused by response delays, and improving construction safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of the prefabricated component construction safety management method based on information coding of the present invention;

[0060] Figure 2 This is an architecture diagram of the prefabricated component construction safety management system based on information coding of the present invention. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention and the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] See also Figure 1-2, a prefabricated component construction safety management method based on information coding, comprising the following steps:

[0063] S1. Establish a prefabricated component information coding system, which includes:

[0064] Project level code, including project name, project number, and construction stage;

[0065] Component layer coding, including component type, size, material, and hoisting sequence;

[0066] Attribute layer encoding, including production batch, quality inspection status, and spreader adaptation parameters;

[0067] The coding rule adopts a mixed coding method, where the first 3 digits are the binary code of the project layer, the middle 4 digits are the hexadecimal code of the component layer, and the last 5 digits are the ASCII code of the attribute layer;

[0068] S2. Bind the identity of the prefabricated component through an RFID tag and a narrowband Internet of Things module, wherein the RFID tag stores the coded information of step S1, and the narrowband Internet of Things module transmits the component position data to the cloud server at a frequency of once per second;

[0069] S3. Deploy a multi-source sensor network, including:

[0070] Inertial measurement unit: collects component pitch angle, roll angle, and heading angle at a frequency of 200Hz;

[0071] Total station prism: real-time positioning of component three-dimensional coordinates;

[0072] Vibrating wire strain gauge: monitors the structural stress distribution during component lifting, in N / mm 2 ;

[0073] Environmental sensors: monitor wind speed, temperature, humidity and light intensity;

[0074] S4. Construct a dynamic risk assessment model based on Bayesian network. The input parameters include:

[0075] Sensor data: pitch angle error > 3°, roll angle error > 2°, stress value exceeds 110% of the rated value;

[0076] Coding information: The spreader adaptation parameters do not match the actual spreader;

[0077] Environmental data: wind speed>8m / s or humidity>85%;

[0078] Output risk level: level range is 1-5, level 5 triggers emergency braking;

[0079] S5. Visual management and control is achieved through a BIM model and IoT data fusion platform, which includes:

[0080] Perception layer: Deploy the sensors and RFID / NB-IoT devices from step S3;

[0081] Network layer: Use LoRaWAN protocol to transmit non-real-time data, and 5G network to transmit real-time video stream;

[0082] Platform layer: Build 3D models based on the IFC4 standard and integrate multi-source data into the BIM database;

[0083] Application layer: provides collision detection, lifting path optimization and risk heat map display functions;

[0084] The component entity object of the BIM model includes extended attribute fields: pitch angle, roll angle, heading angle; stress distribution cloud map, unit is N / mm 2 ;The minimum safety distance threshold from surrounding components / personnel is ≤1.5m;

[0085] S6. When the risk level reaches the threshold, the dynamic adjustment mechanism is activated:

[0086] The tower crane luffing speed is adjusted by PID control algorithm, and the PID parameters are set as proportional coefficient Kp = 0.8, integral coefficient Ki = 0.1, differential coefficient Kd = 0.3, and sampling period 50ms;

[0087] Generate a new lifting sequence plan and optimize the path to avoid dangerous areas. The path cost function is f(n) = g(n) + h(n) + 0.5 × risk(n), where risk(n) is the node risk value.

[0088] Trigger the voice and visual alarm system to locate the nearest safe evacuation point, with a response time of ≤200ms.

[0089] Preferably, the hexadecimal field of the component layer code includes: the first two digits represent the component type, the third to fourth digits represent the size specification, and the fifth to sixth digits represent the material strength grade.

[0090] Example 1:

[0091] This embodiment implements the full life cycle identity identification of prefabricated components and designs a three-layer coding system:

[0092] First, the project-level coding uses a 3-bit binary code, for example, 001 represents a residential building project, combined with automatic generation rules (YYYYMMDD-serial number) to ensure uniqueness.

[0093] Secondly, there is the component layer code, which uses a 4-digit hexadecimal code. The field is defined as follows: the first two digits indicate the component type, for example, 01 = column, 02 = beam, 03 = shear wall, 04 = staircase; the 3rd and 4th digits indicate the size specification, for example, 12 corresponds to 3m×1.5m×0.2m, and 34 corresponds to 6m×2m×0.3m; the 5th and 6th digits indicate the material strength grade, for example, 34 = C30 concrete, 50 = Q345 steel.

[0094] Finally, there is the attribute layer encoding, which uses 5-bit ASCII code and includes: production batch - represented by the first letter, and quality inspection status. For example, A1234 means that the Q2 batch of 2023 is qualified and the spreader needs to be adapted to the 40t model.

[0095] The hardware deployment and data binding of the layered information coding system include radio frequency identification (RFID) tags, narrowband IoT (NB-IoT) modules, and blockchain extensions. The RFID tags use the NXP UCODE 8 chip, have a storage capacity of 1KB, support the ISO 15693 protocol, and are embedded in pre-embedded components. They have a temperature tolerance range of -20°C to 85°C. The NB-IoT module uses the B515 module with a transmission frequency of 1Hz. Data packets contain: project code, component code, attribute code, latitude and longitude coordinates (accuracy ±1m), and a timestamp (ISO 8601 format). Data encryption uses the AES-128 algorithm, with keys dynamically generated by the cloud and updated hourly. The blockchain extension uses Hyperledger Fabric consortium chain storage, and each block contains:

[0096] Millisecond timestamp, such as 1712800814123;

[0097] Operator signature, SHA-256 hash value, such as:

[0098] 9f86d081884c7d659a2feaa0c55ad015a3bf4f1b2b0b822cd15d6c15b0f00a08;

[0099] The hash pointer of the previous block ensures that the data cannot be tampered with.

[0100] The cloud parsing process of the layered information coding system includes:

[0101] Step 1: Verify the hexadecimal field length and ASCII code format;

[0102] Step 2: Associate the construction phase in the project database through project_code;

[0103] Step 3. Map component_code to the component ID in the Revit model;

[0104] Step 4: Compare the spreader parameters in attribute_code with the on-site spreader ID.

[0105] The error handling mechanism process of the layered information coding system includes:

[0106] If the spreader does not match, for example, 40t is required but the actual spreader is 30t, the voice module will be triggered to broadcast: "Component 031234 spreader does not match, please replace it with HT-40T series", and a lock command will be sent to the on-site controller through the narrowband Internet of Things module. The response time is ≤200ms.

[0107] Example 2:

[0108] This embodiment realizes the construction and application of a Bayesian network dynamic risk assessment model.

[0109] The construction and parameter training of the Bayesian network dynamic risk assessment model includes the following steps:

[0110] Step 1: Node definition: This includes parent nodes and child nodes. The parent node includes the component type, which is parsed through the coding layer, such as 03 for shear wall; the hoisting height, which is obtained through the total station prism with an accuracy of ±2mm; the ambient wind speed, which is collected through an anemometer, with a threshold of >8m / s triggering a high risk;

[0111] Subnodes include fall risk, whose triggering condition is pitch angle error > 3° or stress > 110% of rated value; structural deformation risk, whose triggering condition is roll angle error > 2° or strain mutation > 5%; and personnel collision risk, whose triggering condition is the minimum safety distance calculated by the BIM model < 1.5m.

[0112] Step 2: Parameter training: This includes historical data cleaning and EM algorithm optimization. Historical data cleaning removes extreme data with wind speeds > 20 m / s, retaining 120 valid fall cases. EM algorithm optimization includes initializing edge probabilities, such as setting fall risk | wind speed > 8 m / s to 0.3, and iterating 15 times for convergence, ensuring an error of ≤ 0.01.

[0113] The real-time data collection and risk calculation of the Bayesian network dynamic risk assessment model includes the following steps:

[0114] Step 1: Sensor data:

[0115] IMU (MPU-9250): 200Hz sampling, attitude angle accuracy ±0.5°, denoised by Kalman filtering (Q=0.1, R=0.05);

[0116] Strain gauge: resolution 0.1MPa, sampling interval 5ms;

[0117] Anemometer: accuracy ±0.5m / s, trigger threshold>8m / s;

[0118] Step 2: Calculate the risk value:

[0119] Risk value = (fall risk × 0.4) + (structural deformation × 0.3) + (personnel collision × 0.3);

[0120] Risk Level Mapping:

[0121] Level 1: risk value <0.2;

[0122] Level 5: Risk value ≥ 0.8 (triggering emergency braking).

[0123] Example 3:

[0124] This embodiment realizes the hardware implementation and dynamic adjustment of the PID closed-loop control algorithm.

[0125] The PID parameters were determined through multiple rounds of experimental optimization: the proportional coefficient Kp was set to 0.8 to balance system response speed and stability; the integral coefficient Ki was set to 0.1 to suppress steady-state errors and prevent long-term cumulative deviations in the control signal; the differential coefficient Kd was set to 0.3 to suppress excessive oscillations in the control signal by eliminating high-frequency noise; and the sampling period was set to 50 milliseconds to synchronize with the data acquisition frequency of the IMU sensor, ensuring real-time alignment of control commands and sensor inputs.

[0126] The hardware core of the control system using the PID closed-loop control algorithm is an STM32F4 microcontroller with a main frequency of 168MHz. It supports an interrupt response time of less than 1 microsecond, meeting the requirements of high-frequency control. The execution end adopts the FR-E740 inverter, which supports stepless speed regulation from 0-60rpm and a torque response delay of ≤50 milliseconds, ensuring precise adjustment of the motor speed. The communication module uses the CAN bus, which transmits control signals at a baud rate of 500kbps, achieving millisecond-level response and no data packet loss, ensuring the reliability of the closed-loop control link.

[0127] The control logic and protection mechanism of the PID closed-loop control algorithm include input and output, and hardware protection. In terms of input and output:

[0128] Control is triggered when the risk level is ≥ 4, such as when the component stress reaches 115% of the rated value;

[0129] The target speed calculation formula is as follows:

[0130] V target = Base speed × (1-risk weight) (e.g. base speed 2 m / s, weight 0.2 → V target =1.6m / s)

[0131] Output PWM signal to the inverter to adjust the motor speed;

[0132] Hardware protection includes overload protection and response time verification. Overload protection detects motor current >150% of the rated value, such as triggering the 30A threshold when rated at 20A. Response time verification uses the STM32 timer interrupt accuracy of ±1μs, ensuring that the time from risk triggering to braking execution is ≤200ms.

[0133] When the pitch angle error reaches 4°, the PID controller calculates the error:

[0134] error=1.6m / s-current speed 2.0m / s=-0.4m / s

[0135] The output control signal drives the inverter to decelerate to 1.6m / s, and the response time is ≤200ms.

[0136] In summary, the prefabricated component construction safety management method based on information coding establishes a prefabricated component construction safety management system based on information coding. First, a hierarchical information coding system is used to accurately identify prefabricated components, and radio frequency identification tags and narrowband Internet of Things modules are combined to realize the identity binding and real-time location data transmission of prefabricated components; then, a multi-source sensor network is used to collect component posture, environment and other data, and input them into a dynamic risk assessment model based on a Bayesian network. The impact of various factors on risks such as falling and structural deformation is analyzed in real time, which can more accurately predict potential risks and improve the safety management level of the construction site.

[0137] Furthermore, the prefabricated component construction safety management method based on information coding initiates a dynamic adjustment mechanism based on a PID control algorithm when the risk level reaches a set threshold, responding to risk changes by precisely adjusting the tower crane's boom speed. Specifically, the STM32F4 microcontroller calculates the speed parameters that need to be adjusted based on the risk level and adjusts the motor speed via the FR-E740 inverter to optimize the lifting path to avoid dangerous areas. It also triggers voice and visual alarm systems to guide personnel evacuation. This closed-loop control process not only speeds up safety response but also ensures control accuracy, effectively avoiding safety accidents caused by delayed response, improving construction safety, and resolving the following core issues with existing technologies in prefabricated component construction safety management: First, risk assessment relies on manual inspections or single threshold judgments, failing to couple and analyze in real time the dynamic impact of multiple factors such as component posture, ambient wind speed, and hoist matching on risks such as falls and structural deformation. Second, the lack of a dynamic adjustment mechanism based on closed-loop control prevents automatic optimization of the lifting path or adjustment of the tower crane's motion parameters via the PID algorithm when risks occur, resulting in delayed safety response and insufficient control accuracy.

[0138] The relevant modules involved in this system are all hardware system modules or functional modules that combine computer software programs or protocols with hardware in the existing technology. The computer software programs or protocols involved in the functional modules are themselves technologies that are well known to those skilled in the art and are not improvements to this system. The improvements to this system are the interaction or connection relationships between the modules, that is, improvements to the overall structure of the system to solve the corresponding technical problems to be solved by this system.

[0139] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for prefabricated component construction safety management based on information coding, characterized in that: The following steps are involved: S1. Establish a prefabricated component information coding system, which includes: Project level code, including project name, project number, and construction stage; Component layer coding, including component type, size, material, and hoisting sequence; Attribute layer encoding, including production batch, quality inspection status, and spreader adaptation parameters; The coding rule adopts a mixed coding method, where the first 3 digits are the binary code of the project layer, the middle 4 digits are the hexadecimal code of the component layer, and the last 5 digits are the ASCII code of the attribute layer; S2. Bind the identity of the prefabricated component through an RFID tag and a narrowband Internet of Things module, wherein the RFID tag stores the coded information of step S1, and the narrowband Internet of Things module transmits the component position data to the cloud server at a frequency of once per second; S3. Deploy a multi-source sensor network, including: Inertial measurement unit: collects component pitch angle, roll angle, and heading angle at a frequency of 200Hz; Total station prism: real-time positioning of component three-dimensional coordinates; Vibrating wire strain gauge: monitors the structural stress distribution during component lifting, in N / mm 2 ; Environmental sensors: monitor wind speed, temperature, humidity and light intensity; S4. Construct a dynamic risk assessment model based on Bayesian network. The input parameters include: Sensor data: pitch angle error > 3°, roll angle error > 2°, stress value exceeds 110% of the rated value; Coding information: The spreader adaptation parameters do not match the actual spreader; Environmental data: wind speed>8m / s or humidity>85%; Output risk level: level range is 1-5, level 5 triggers emergency braking; S5. Visual management and control is achieved through a BIM model and IoT data fusion platform, which includes: Perception layer: Deploy the sensors and RFID / NB-IoT devices from step S3; Network layer: Use LoRaWAN protocol to transmit non-real-time data, and 5G network to transmit real-time video stream; Platform layer: Build 3D models based on the IFC4 standard and integrate multi-source data into the BIM database; Application layer: provides collision detection, lifting path optimization and risk heat map display functions; The component entity object of the BIM model includes extended attribute fields: pitch angle, roll angle, heading angle; stress distribution cloud map, unit is N / mm 2 ;The minimum safety distance threshold from surrounding components / personnel is ≤1.5m; S6. When the risk level reaches the threshold, the dynamic adjustment mechanism is activated: The tower crane luffing speed is adjusted by PID control algorithm, and the PID parameters are set as proportional coefficient Kp = 0.8, integral coefficient Ki = 0.1, differential coefficient Kd = 0.3, and sampling period 50ms; Generate a new lifting sequence plan and optimize the path to avoid dangerous areas. The path cost function is f(n) = g(n) + h(n) + 0.5 × risk(n), where risk(n) is the node risk value. Trigger the voice and visual alarm system to locate the nearest safe evacuation point, with a response time of ≤200ms.

2. A method for prefabricated component construction safety management based on information coding according to claim 1, characterized in that: The hexadecimal field of the component layer code includes: the first two digits represent the component type, the third to fourth digits represent the size specification, and the fifth to sixth digits represent the material strength grade.

3. A method for prefabricated component construction safety management based on information coding according to claim 1, characterized in that: The nodes of the Bayesian network include parent nodes and child nodes, wherein: The parent node includes component type, hoisting height and ambient wind speed; Sub-nodes include falling risk, structural deformation risk, and personnel collision risk; The marginal probability is trained using historical accident data, and the parameters are optimized using the EM algorithm with a convergence threshold ≤ 0.

01.

4. A method for prefabricated component construction safety management based on information coding according to claim 1, characterized in that: The component entity object of the BIM model includes extended attribute fields: Real-time pitch angle, roll angle, and heading angle, with an accuracy of ±0.5°; Stress distribution cloud map, unit is N / mm 2 , resolution 0.1MPa; Safety distance threshold: minimum distance from surrounding components / personnel ≤1.5m.

5. The method for prefabricated component construction safety management based on information coding according to claim 1, characterized in that: The output control signal of the PID control algorithm adjusts the motor speed through the tower crane frequency converter, with a response time of ≤200ms and a motor speed adjustment range of 0-60rpm.

6. A prefabricated component construction safety management system based on information coding, characterized in that: include: Coding generation module: generates a unique identifier according to coding rules; Data acquisition module: integrated IMU, total station, strain gauge and environmental sensor, sampling frequency ≥ 20Hz; Risk Assessment Module: A Bayesian network-based processor with an input layer containing 12 sensor data channels; Visual control module: runs on the Unity3D engine and supports real-time rendering of BIM models and overlay of sensor data; Dynamic adjustment module: Contains PID controller hardware circuit, parameters Kp = 0.8, Ki = 0.1, Kd = 0.3, and Zigbee communication module, transmission distance ≥ 100m.

7. The prefabricated component construction safety management system based on information coding according to claim 1 is characterized in that: The code generation module uses blockchain technology to store code records. Each block contains a timestamp accurate to milliseconds, the digital signature of the code generator, and a hash value linking the previous block.

8. The prefabricated component construction safety management system based on information coding according to claim 1 is characterized in that: The BIM model of the visualization management and control module supports the following interactive functions: Hovering the mouse will display detailed coding information of the component; Double-click a component to trigger playback of historical lifting data, supporting timestamp retrieval; Risk areas are highlighted, red indicates high risk, yellow indicates medium risk, and the color thresholds are risk levels ≥4 and ≥2, respectively.

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