Intelligent safety management method and system for constructors and medium

Abstract

The invention provides an intelligent safety management method and system for constructors and a medium, and the method comprises the steps: obtaining original data streams of a plurality of Internet of Things devices, and obtaining a unified data stream; binding each piece of Internet of Things equipment with the identity information of the construction personnel, and creating and activating the digital twinborn body of the construction personnel; positioning data are obtained, real-time calculation is carried out based on a multi-source fusion positioning algorithm, and three-dimensional coordinates of constructors are output; the method comprises the following steps: pre-defining an attendance checking area and an electronic fence area based on a three-dimensional live-action model constructed by a site BIM or oblique photography technology; acquiring historical positioning data, performing trajectory analysis on the historical positioning data, identifying a behavior pattern and abnormal information, and generating early warning information based on the behavior pattern and the abnormal information; the real-time and reliable access and cleaning of massive and heterogeneous Internet of Things data are realized through a professional data receiving and collecting unit, and the field safety level is greatly improved by fusing real-time high-precision position and historical track mining.

Description

Technical Field

[0001] This application relates to the field of industrial internet technology, and more specifically, to an intelligent safety management method, system, and medium for construction workers. Background Technology

[0002] Currently, personnel management practices at construction sites remain largely traditional and heavily reliant on manual operations. Although some technological explorations and applications have emerged within the industry, their adoption and integration depth still need improvement, and a systematic and intelligent management system has not yet been formed. Typical examples of current technology applications include: The application of technology is scattered and limited: Some pilot projects have adopted smart hardware based on the Internet of Things (IoT) (such as wristbands and safety helmets) for personnel positioning or heart rate monitoring, or have deployed independent RFID attendance and video surveillance analysis systems in certain areas. However, these technologies often exist in a "point-like" form, with limited coverage, and are mostly used as auxiliary means for manual inspection and recording, failing to become core management support.

[0003] System isolation and data fragmentation: Various business subsystems deployed on-site (such as attendance, behavior monitoring, and safety education apps) are usually provided by different vendors with varying construction standards and independent functions. This results in personnel data, attendance data, and behavior data being scattered across different "data silos," making it impossible to effectively connect and integrate them, hindering the formation of a unified panoramic view of personnel, and significantly reducing management efficiency.

[0004] Superficial early warning and high degree of human intervention: In the few projects that have introduced positioning technology, the early warning function mostly relies on simple rules such as preset electronic fences, with limited intelligence. After an alarm is generated, it still requires complete manual verification, intervention and handling by security personnel. In essence, it only changes some of the discovery process from "purely manual inspection" to "system prompts + manual handling", without fundamentally reducing the dependence on manpower or improving response efficiency.

[0005] In summary, existing personnel management methods are still dominated by manual labor, and the limited and fragmented application of technology has failed to effectively solve core pain points such as data inefficiency, extensive management, and delayed response. Summary of the Invention

[0006] The purpose of this application is to provide an intelligent safety management method, system and medium for construction workers. By integrating real-time high-precision location and historical trajectory mining, the early warning mechanism becomes more intelligent and proactive, and can identify potential risks and intervene in advance, which greatly improves the on-site safety level.

[0007] This application embodiment also provides an intelligent safety management method for construction personnel, including: acquiring raw data streams from various Internet of Things devices, preprocessing the raw data streams, and obtaining a unified data stream; Bind each IoT device to the construction worker's identity information to create and activate a digital twin of the construction worker; Based on the digital twin of the construction personnel, the location data in the unified data stream is obtained, and the real-time calculation is performed based on the multi-source fusion positioning algorithm to output the three-dimensional coordinates of the construction personnel. A 3D reality model built based on site BIM or oblique photogrammetry technology predefines attendance areas and electronic fence areas with 3D geometric attributes. Acquire historical location data, perform trajectory analysis on the historical location data, identify behavioral patterns and abnormal information, and generate early warning information based on behavioral patterns and abnormal information.

[0008] Optionally, in the intelligent safety management method for construction workers described in this application embodiment, the raw data streams of various IoT devices are acquired, and the raw data streams are preprocessed to obtain a unified data stream, specifically including: Acquire raw data streams, which include raw GNSS global navigation satellite system or NBIoT narrowband Internet of Things positioning data, device unique ID identifiers, vital sign sensor data, and alarm signals; The original data stream is parsed to obtain the parsing results; The parsed results are then formatted to obtain standard format data; Denoising of standard format data is performed using Kalman filtering or low-pass filtering algorithms to obtain noise-free data. By analyzing data duplication information based on noise-free data, duplicate data is removed to obtain a unified data stream.

[0009] Optionally, in the intelligent safety management method for construction workers described in the embodiments of this application, each IoT device is bound to the identity information of the construction worker, and a digital twin of the construction worker is created and activated, specifically including: Collect core identity information of construction personnel to generate a standardized personnel information database, which includes: name, gender, age, ID number, job type, work team, entry time, safety education and training records, special operation certificate information, emergency contact person and contact information; Analyze the unique ID identifier of each IoT device deployed on site, analyze the working status of the IoT devices, and screen out qualified devices that can be bound. The binding result is obtained by binding the core identity information of construction personnel with the qualified equipment that can be bound. The identity information is verified based on the binding result, and the verification result is analyzed to determine whether the verification was successful. If the verification is successful, the binding is considered successful, and the construction worker's digital twin is created and activated. If the verification fails, the identity information is re-matched with the qualified equipment.

[0010] Optionally, in the intelligent safety management method for construction workers described in this application embodiment, the method involves obtaining positioning data from a unified data stream based on the construction worker's digital twin, performing real-time calculation based on a multi-source fusion positioning algorithm, and outputting the construction worker's three-dimensional coordinates. Specifically, this includes: Bind each IoT device to the construction worker's identity information, create and activate the construction worker's digital twin, obtain the mapping relationship between IoT devices and construction workers, and obtain the raw location data; The digital twin filters the acquired raw location data based on the characteristics of the personnel and work areas associated with it, eliminating interfering data that does not match the current work scenario. Based on the multi-source fusion positioning algorithm, the multi-source positioning related data synchronized by the digital twin is extracted and processed in real time to generate preliminary three-dimensional coordinates; Based on the historical location trajectory and work habit data of personnel associated with the digital twin, the initial three-dimensional coordinates are corrected for deviation, and the final three-dimensional coordinates that match the real-time location of the construction personnel are output.

[0011] Optionally, the intelligent safety management method for construction workers described in this application embodiment also includes a contactless attendance process, the steps of which are as follows: Obtain location data from the unified data stream; Based on the principles of computational geometry, it can determine in real time whether a three-dimensional coordinate point has entered the preset three-dimensional attendance area; If a person is determined to have entered the 3D attendance area and has stayed there for more than a preset time threshold, the business logic will be automatically triggered to generate an unalterable attendance record containing the person's ID, time, and location, and store it in the business database.

[0012] Optionally, the intelligent safety management method for construction workers described in this application embodiment also includes a personalized content push process, as detailed below: Based on management needs or early warning information, push matching safety education or management instructions to IoT devices bound to specific individuals or groups; Real-time collection of learning records from specific individuals or groups to determine whether the learning progress meets set conditions. If the set conditions are met, online test questions for the corresponding knowledge points will be automatically sent, and the learning effect will be determined based on the test results to see if it meets the standard. If the standard information is met, personalized content will be pushed. If the standard information is not met, a second safety education message will be sent or a make-up exam will be initiated.

[0013] Secondly, embodiments of this application provide an intelligent safety management system for construction workers. The system includes a memory and a processor. The memory includes a program for an intelligent safety management method for construction workers. When the program for the intelligent safety management method for construction workers is executed by the processor, it implements the following steps: Acquire raw data streams from various IoT devices, preprocess the raw data streams, and obtain a unified data stream; Bind each IoT device to the construction worker's identity information to create and activate a digital twin of the construction worker; Based on the digital twin of the construction personnel, the location data in the unified data stream is obtained, and the real-time calculation is performed based on the multi-source fusion positioning algorithm to output the three-dimensional coordinates of the construction personnel. A 3D reality model built based on site BIM or oblique photogrammetry technology predefines attendance areas and electronic fence areas with 3D geometric attributes. Acquire historical location data, perform trajectory analysis on the historical location data, identify behavioral patterns and abnormal information, and generate early warning information based on behavioral patterns and abnormal information.

[0014] Optionally, in the intelligent safety management system for construction workers described in this application embodiment, the raw data streams from various IoT devices are acquired, and the raw data streams are preprocessed to obtain a unified data stream, specifically including: Acquire raw data streams, which include raw GNSS global navigation satellite system or NBIoT narrowband Internet of Things positioning data, device unique ID identifiers, vital sign sensor data, and alarm signals; The original data stream is parsed to obtain the parsing results; The parsed results are then formatted to obtain standard format data; Denoising of standard format data is performed using Kalman filtering or low-pass filtering algorithms to obtain noise-free data. By analyzing data duplication information based on noise-free data, duplicate data is removed to obtain a unified data stream.

[0015] Optionally, in the intelligent safety management system for construction workers described in this application embodiment, each IoT device is bound to the identity information of the construction worker, and a digital twin of the construction worker is created and activated, specifically including: Collect core identity information of construction personnel to generate a standardized personnel information database, which includes: name, gender, age, ID number, job type, work team, entry time, safety education and training records, special operation certificate information, emergency contact person and contact information; Analyze the unique ID identifier of each IoT device deployed on site, analyze the working status of the IoT devices, and screen out qualified devices that can be bound. The binding result is obtained by binding the core identity information of construction personnel with the qualified equipment that can be bound. The identity information is verified based on the binding result, and the verification result is analyzed to determine whether the verification was successful. If the verification is successful, the binding is considered successful, and the construction worker's digital twin is created and activated. If the verification fails, the identity information is re-matched with the qualified equipment.

[0016] Thirdly, embodiments of this application also provide a computer-readable storage medium, which includes a program for an intelligent safety management method for construction workers. When the program for the intelligent safety management method for construction workers is executed by a processor, it implements the steps of the intelligent safety management method for construction workers as described in any of the preceding claims.

[0017] As can be seen from the above, the intelligent safety management method, system, and medium for construction personnel provided in this application acquires raw data streams from various IoT devices, preprocesses the raw data streams to obtain a unified data stream; binds each IoT device to the identity information of the construction personnel, creating and activating a digital twin of the construction personnel; acquires positioning data from the unified data stream based on the construction personnel's digital twin, performs real-time calculation based on a multi-source fusion positioning algorithm, and outputs the three-dimensional coordinates of the construction personnel; predefines attendance areas and electronic fence areas with three-dimensional geometric attributes based on a three-dimensional real-scene model constructed using site BIM or oblique photography technology; acquires historical positioning data, performs trajectory analysis on the historical positioning data, identifies behavioral patterns and abnormal information, and generates early warning information based on behavioral patterns and abnormal information; through a professional data receiving and acquisition unit, it achieves real-time and reliable access and cleaning of massive and heterogeneous IoT data, providing a high-quality and trustworthy data foundation for the system, completely breaking down data silos; by integrating real-time high-precision location and historical trajectory mining, the early warning mechanism becomes more intelligent and proactive, capable of identifying potential risks and intervening in advance, greatly improving the on-site safety level. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating an intelligent safety management method for construction workers provided in an embodiment of this application; Figure 2 A block diagram of an intelligent safety management system for construction workers provided in this application embodiment; Figure 2 clearly shows the logical relationship and data flow between the data receiving and acquisition unit, equipment management unit, precise positioning unit, 3D electronic map unit, automatic attendance logic unit, trajectory analysis unit, proactive early warning unit, and learning content push unit; Figure 3 This is a flowchart of a preferred embodiment of an intelligent safety management method for construction workers provided in this application. Figure 3 The document sequentially presents the complete sequence of steps from data aggregation, device binding, location calculation, spatial modeling, contactless attendance, intelligent early warning, to precise push notifications. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] Please refer to Figure 1 , Figure 1This is a flowchart of an intelligent safety management method for construction workers according to some embodiments of this application. The intelligent safety management method for construction workers is used in a terminal device and includes the following steps: S101: Obtain raw data streams from various IoT devices, preprocess the raw data streams, and obtain a unified data stream; S102, bind each IoT device to the construction worker's identity information, and create and activate the construction worker's digital twin; S103: Based on the digital twin of the construction personnel, the positioning data in the unified data stream is obtained, and the multi-source fusion positioning algorithm is used to perform real-time calculation to output the three-dimensional coordinates of the construction personnel. S104 is a 3D real-world model built based on site BIM or oblique photogrammetry technology, which predefines attendance areas and electronic fence areas with 3D geometric attributes. S105: Acquire historical positioning data, perform trajectory analysis on the historical positioning data, identify behavioral patterns and abnormal information, and generate early warning information based on behavioral patterns and abnormal information.

[0023] According to an embodiment of the present invention, the raw data streams of various IoT devices are acquired, and the raw data streams are preprocessed to obtain a unified data stream, specifically including: Acquire raw data streams, which include raw GNSS global navigation satellite system or NBIoT narrowband Internet of Things positioning data, device unique ID identifiers, vital sign sensor data, and alarm signals; The original data stream is parsed to obtain the parsing results; The parsed results are then formatted to obtain standard format data; Denoising of standard format data is performed using Kalman filtering or low-pass filtering algorithms to obtain noise-free data. By analyzing data duplication information based on noise-free data, duplicate data is removed to obtain a unified data stream.

[0024] According to an embodiment of the present invention, binding each IoT device with the identity information of construction workers to create and activate a digital twin of the construction workers specifically includes: Collect core identity information of construction personnel to generate a standardized personnel information database. The standardized personnel information database includes: name, gender, age, ID number, job type, work team, entry time, safety education and training records, special operation certificate information, emergency contact person and contact information; Analyze the unique ID identifier of each IoT device deployed on site, analyze the working status of the IoT devices, and screen out qualified devices that can be bound. The binding result is obtained by binding the core identity information of construction personnel with the qualified equipment that can be bound. The identity information is verified based on the binding result, and the verification result is analyzed to determine whether the verification was successful. If the verification is successful, the binding is considered successful, and the construction worker's digital twin is created and activated. If the verification fails, the identity information is re-matched with the qualified equipment.

[0025] According to an embodiment of the present invention, positioning data is obtained from a unified data stream based on the digital twin of the construction worker, and real-time calculation is performed based on a multi-source fusion positioning algorithm to output the three-dimensional coordinates of the construction worker, specifically including: Bind each IoT device to the construction worker's identity information, create and activate the construction worker's digital twin, obtain the mapping relationship between IoT devices and construction workers, and obtain the raw location data; The digital twin filters the acquired raw location data based on the characteristics of the personnel and work areas associated with it, eliminating interfering data that does not match the current work scenario. Based on the multi-source fusion positioning algorithm, the multi-source positioning related data synchronized by the digital twin is extracted and processed in real time to generate preliminary three-dimensional coordinates; Based on the historical location trajectory and work habit data of personnel associated with the digital twin, the initial three-dimensional coordinates are corrected for deviation, and the final three-dimensional coordinates that match the real-time location of the construction personnel are output.

[0026] According to an embodiment of the present invention, a contactless attendance process is also included, the steps of which are as follows: Obtain location data from the unified data stream; Based on the principles of computational geometry, it can determine in real time whether a three-dimensional coordinate point has entered the preset three-dimensional attendance area; If a person is determined to have entered the 3D attendance area and has stayed there for more than a preset time threshold, the business logic will be automatically triggered to generate an unalterable attendance record containing the person's ID, time, and location, and store it in the business database.

[0027] According to embodiments of the present invention, a personalized content push process is also included, as detailed below: Based on management needs or early warning information, push matching safety education or management instructions to IoT devices bound to specific individuals or groups; Real-time collection of learning records from specific individuals or groups to determine whether the learning progress meets set conditions. If the set conditions are met, online test questions for the corresponding knowledge points will be automatically sent, and the learning effect will be determined based on the test results to see if it meets the standard. If the standard information is met, personalized content will be pushed. If the standard information is not met, a second safety education message will be sent or a make-up exam will be initiated.

[0028] like Figure 3 As shown, in one specific embodiment of the present invention, the following steps are included: Data aggregation and preprocessing steps: Through the data receiving and acquisition unit, raw data streams from various IoT devices are received in parallel, and real-time cleaning, noise reduction and format standardization are performed to form a unified data stream.

[0029] Device identity binding and digitization steps: Through the device management unit, each IoT device is bound to the corresponding construction personnel's identity information, and its digital twin is created and activated in the system.

[0030] High-precision positioning calculation steps: Through the precise positioning unit, the positioning data in the unified data stream is continuously consumed, and the multi-source fusion positioning algorithm is used for real-time calculation to output the optimized high-precision three-dimensional coordinates.

[0031] Three-dimensional geospatial modeling steps: In the three-dimensional electronic map unit, based on the site BIM (Building Information Modeling), predefine attendance areas and electronic fence areas with three-dimensional geometric attributes.

[0032] Seamless automatic attendance process: The automatic attendance logic unit continuously compares the high-precision three-dimensional coordinates of the personnel with the three-dimensional attendance area. When the spatial and temporal conditions are met, attendance events are automatically generated and recorded.

[0033] Intelligent analysis and proactive early warning steps: The trajectory analysis unit mines historical location data to identify behavioral patterns and anomalies.

[0034] The proactive early warning unit integrates real-time high-precision location and behavior analysis results, and triggers multi-level, proactive safety warnings based on a pre-set intelligent rule base.

[0035] Personalized content delivery steps: The learning content delivery unit automatically or manually pushes targeted safety education or management instructions to IoT devices bound to specific personnel or groups based on management needs or system events (such as triggering an alert), thus completing the information loop.

[0036] In summary, the present invention has the following beneficial effects: A high-performance data pipeline was built to unlock the value of data: Through professional data receiving and acquisition units, real-time and reliable access and cleaning of massive and heterogeneous IoT data were achieved, providing the system with a high-quality and trustworthy data foundation and completely breaking down data silos.

[0037] It achieves hardware and software decoupling and flexible system expansion: the modular unit design enables the system to flexibly adapt to various IoT devices, and the addition of new functions or the upgrading of old functions will not affect the overall architecture, greatly improving the system's life cycle and adaptability.

[0038] By leveraging algorithms, the bottleneck of hardware positioning accuracy is broken: Independent precision positioning units utilize advanced fusion algorithms to enhance the limited hardware positioning capabilities to a high level of precision that is usable in business operations. In particular, they enable refined management in the vertical dimension, meeting the rigid requirements of modern three-dimensional construction.

[0039] It has realized a paradigm shift from post-event tracing to pre-event prevention: by integrating real-time high-precision location and historical trajectory mining, the early warning mechanism has become more intelligent and proactive, capable of identifying potential risks and intervening in advance, greatly improving the level of on-site safety.

[0040] An integrated digital operation command center was built, which integrates the scattered management functions into one, and enables the various units to work together to achieve full-process, visualized and intelligent management from personnel entry, on-the-job operation to safe exit, which greatly improves management efficiency and scientific decision-making.

[0041] like Figure 2 As shown, in a second aspect, embodiments of this application provide an intelligent safety management system for construction workers. The system includes a memory and a processor. The memory includes a program for an intelligent safety management method for construction workers. When the program for the intelligent safety management method for construction workers is executed by the processor, it implements the following steps: Acquire raw data streams from various IoT devices, preprocess the raw data streams, and obtain a unified data stream; Bind each IoT device to the construction worker's identity information to create and activate a digital twin of the construction worker; Based on the digital twin of the construction personnel, the location data in the unified data stream is obtained, and the real-time calculation is performed based on the multi-source fusion positioning algorithm to output the three-dimensional coordinates of the construction personnel. A 3D reality model built based on site BIM or oblique photogrammetry technology predefines attendance areas and electronic fence areas with 3D geometric attributes. Acquire historical location data, perform trajectory analysis on the historical location data, identify behavioral patterns and abnormal information, and generate early warning information based on behavioral patterns and abnormal information.

[0042] It should be noted that the system includes the following logical units that interact with data and instructions via a system bus: Data receiving and acquisition unit: This unit serves as the system's data entry point and is configured to establish concurrent connections with a large number of heterogeneous IoT devices (such as smartwatches, UWB tags, smart helmets, etc.) deployed on-site through various communication protocols (including but not limited to MQTT (Message Queuing Telemetry Transmission), HTTP (Hypertext Transfer Protocol), TCP (Transmission Control Protocol).

[0043] Its core function is to receive raw data streams from these devices in real time. These data streams include at least: raw GNSS global navigation satellite system / NBIoT (narrowband Internet of Things) positioning data, unique device ID identifiers, vital sign sensor data (heart rate, blood oxygen, body temperature), and various alarm signals (SOS emergency call, fall detection).

[0044] This unit has an embedded data cleaning and preprocessing engine. Its execution steps include: data parsing (parsing data packets of different protocols according to a preset rule base), invalid data removal (such as signals with too weak strength), data denoising (smoothing sensor data using Kalman filtering or low-pass filtering algorithms), and format standardization (converting all data into the system's internally defined JSON (JavaScript object notation) or Protobuf (protocol buffer) format, and finally publishing high-quality structured data in real time to the system's message middleware (such as Kafka) for other units to consume.

[0045] Equipment Management Unit: This unit is connected to the data receiving and acquisition unit and is the core of the system for assetizing equipment and digitizing personnel.

[0046] It provides RESTful (API representing state transition application programming interface) and a graphical user interface, supporting two device binding modes: Manual entry: Administrators directly enter the device ID identifier and bind it with personnel identity information (name, job type, work group, etc.).

[0047] Automatic scanning and discovery: Utilizing BLE (Bluetooth Low Energy) or UWB (Ultra Wideband) signals, it automatically discovers unregistered devices at close range and guides administrators to complete a quick pairing process.

[0048] This unit maintains a dynamic device-person mapping table and tracks device status (online, offline, low battery, fault).

[0049] 3D Electronic Map Unit: This unit serves as the foundation for the system's visualization and spatial management. It is configured to load and render with high precision a 3D reality model of the construction site based on BIM (Building Information Modeling) or oblique photogrammetry technology.

[0050] It offers a wealth of graphical editing tools, allowing administrators to intuitively view 3D models: Define a three-dimensional attendance area: for example, designate a section of a floor or the entire building as an attendance area.

[0051] Define three-dimensional electronic fence areas: for example, define a cylindrical space surrounding a tower crane, or a cube representing a hazardous area near the edge of a specific floor. These areas all have three-dimensional spatial coordinates and boundaries.

[0052] Precise positioning unit: This unit is the "spatial perception brain" of the system, and is connected to the data receiving and acquisition unit and the three-dimensional electronic map unit.

[0053] It is configured to perform the following core tasks: a) Subscribe to cleaned raw location data streams from the message middleware.

[0054] (b) The built-in multi-source fusion positioning algorithm (using extended Kalman filtering or factor graph optimization algorithm) is invoked to fuse GNSS (Global Navigation Satellite System), UWB (Ultra-Wideband), IMU (Inertial Measurement Unit), and barometer data to perform collaborative calculation on the raw positioning data. This process effectively suppresses multipath effects, compensates for signal blind spots, and ultimately outputs optimized positioning data with meter-level (or even sub-meter-level) horizontal accuracy and sub-meter-level (within 0.5 meters) vertical accuracy.

[0055] c) Associate the optimized positioning data with the corresponding device ID identifier and timestamp, and push it to the 3D electronic map unit in real time for visualization display, while storing it in the time series database for subsequent analysis.

[0056] Automatic attendance logic unit: This unit is connected to the precise positioning unit and the 3D electronic map unit to achieve "seamless attendance".

[0057] The workflow of the automatic attendance logic unit is as follows: a) Continuously monitor the optimized location data stream of a specific device.

[0058] b) Based on the principles of computational geometry, determine in real time whether the three-dimensional coordinate points of the device have entered the preset three-dimensional attendance area (i.e., perform inclusion detection between points and three-dimensional polyhedra).

[0059] c) When an entry is detected and the person stays in the area for more than a preset threshold (e.g., 5 minutes), the business logic is automatically triggered to generate an unalterable attendance record containing the person's ID, time, and location, and write it to the business database.

[0060] Trajectory Analysis Unit: This unit is connected to the Precision Positioning Unit and is the system's behavioral intelligence analysis engine.

[0061] The trajectory analysis unit is configured as follows: a) Extract historical optimized positioning data sequences of specific devices within a set time period from the time-series database to form high-precision motion trajectories.

[0062] b) Run data mining algorithms: Cluster analysis (DBSCAN density clustering algorithm): Identify frequently visited areas and work hotspots for workers.

[0063] Anomaly detection (based on a Recurrent Neural Network (RNN) model): Identify abnormal movements that deviate from the usual route (such as entering idle areas or lingering near dangerous areas for extended periods).

[0064] Active early warning unit: This unit is the safety decision center of the system and is connected to the precise positioning unit, the three-dimensional electronic map unit and the trajectory analysis unit.

[0065] The active warning unit is configured to trigger two types of advanced warnings: a) Real-time spatial boundary crossing warning: Based on optimized positioning data, when it is determined that a device has entered or approached (based on three-dimensional spatial distance calculation) a preset three-dimensional electronic fence area without authorization, an early warning is immediately triggered. The warning rules can be tiered (e.g., reminder, warning, alarm).

[0066] b) Abnormal Behavior Warning: Based on the abnormal movement pattern results output by the trajectory analysis unit, an early warning is triggered. For example, if the system detects that a person is wandering irregularly for a long time in the area under a crane, an "abnormal loitering in a high-risk area" warning is triggered.

[0067] Learning Content Push Unit: This unit serves as the system's information distribution portal, configured to establish a multimedia knowledge base to store and manage safety education materials such as text, images, and short videos. Administrators can use this unit to select target audiences based on multiple filtering criteria (such as employee type, work group, real-time geographical location, and historical violation records), and accurately distribute customized learning content to their linked IoT devices via a downstream channel, while also tracking learning completion status.

[0068] In addition to supporting MQTT (Message Queuing Telemetry Transmission) and HTTP (Hypertext Transfer Protocol), the data receiving and acquisition unit can also be adapted to IoT protocols suitable for low-power devices, such as CoAP (Restricted Application Protocol) or LoRaWAN (Long Distance Wide Area Network).

[0069] In addition to Kalman filtering and factor graph optimization, the core algorithms of the precise positioning unit can also employ particle filtering or machine learning models (such as positioning correction algorithms based on LSTM long short-term memory networks) for data fusion and accuracy improvement.

[0070] In addition to manually drawing on BIM (Building Information Model), three-dimensional electronic fence areas can also be defined by analyzing on-site monitoring videos through AI image recognition, automatically identifying and marking dangerous areas, and thus generating dynamic electronic fences.

[0071] In addition to recurrent neural networks, anomaly detection in trajectory analysis units can also employ unsupervised learning algorithms such as One-Class SVM or Autoencoder.

[0072] The entire system can be deployed in the central cloud for centralized management, or it can adopt an edge-cloud collaborative architecture to offload latency-sensitive tasks such as data preprocessing and real-time early warning to edge computing nodes to reduce network bandwidth pressure and improve response speed.

[0073] According to an embodiment of the present invention, the raw data streams of various IoT devices are acquired, and the raw data streams are preprocessed to obtain a unified data stream, specifically including: Acquire raw data streams, which include raw GNSS global navigation satellite system or NBIoT narrowband Internet of Things positioning data, device unique ID identifiers, vital sign sensor data, and alarm signals; The original data stream is parsed to obtain the parsing results; The parsed results are then formatted to obtain standard format data; Denoising of standard format data is performed using Kalman filtering or low-pass filtering algorithms to obtain noise-free data. By analyzing data duplication information based on noise-free data, duplicate data is removed to obtain a unified data stream.

[0074] According to an embodiment of the present invention, binding each IoT device with the identity information of construction workers to create and activate a digital twin of the construction workers specifically includes: Collect core identity information of construction personnel to generate a standardized personnel information database. The standardized personnel information database includes: name, gender, age, ID number, job type, work team, entry time, safety education and training records, special operation certificate information, emergency contact person and contact information; Analyze the unique ID identifier of each IoT device deployed on site, analyze the working status of the IoT devices, and screen out qualified devices that can be bound. The binding result is obtained by binding the core identity information of construction personnel with the qualified equipment that can be bound. The identity information is verified based on the binding result, and the verification result is analyzed to determine whether the verification was successful. If the verification is successful, the binding is considered successful, and the construction worker's digital twin is created and activated. If the verification fails, the identity information is re-matched with the qualified equipment.

[0075] This application constructs a powerful data intelligence processing hub to achieve unified access, high-quality cleaning and standardization of multi-source heterogeneous IoT data, providing reliable data fuel for upper-layer applications.

[0076] Design a modular, highly cohesive, and loosely coupled system architecture to achieve seamless collaboration and flexible expansion of various management functional units.

[0077] By enhancing positioning accuracy through software algorithms, especially improving horizontal and vertical positioning accuracy in complex environments, a technological foundation is provided for three-dimensional safety management.

[0078] Establish a proactive early warning mechanism based on data mining and multi-source information fusion to achieve a shift from post-event alarms to pre-event prediction and in-event intervention.

[0079] To create a unified operation platform that integrates management, service, early warning, and training, and to achieve a precise and efficient closed loop of information flow.

[0080] A third aspect of the present invention provides a computer-readable storage medium including a program for an intelligent safety management method for construction workers, wherein when the program is executed by a processor, it implements the steps of the intelligent safety management method for construction workers as described in any of the above claims.

[0081] This invention discloses an intelligent safety management method, system, and medium for construction workers. It acquires raw data streams from various IoT devices, preprocesses them to obtain a unified data stream, binds each IoT device to the construction worker's identity information, and creates and activates a digital twin of the worker. Based on the construction worker's digital twin, it acquires location data from the unified data stream, performs real-time calculations using a multi-source fusion positioning algorithm, and outputs the worker's three-dimensional coordinates. A three-dimensional real-scene model is constructed based on site BIM or oblique photogrammetry technology, predefining attendance areas and electronic fence areas with three-dimensional geometric attributes. Historical location data is acquired, and trajectory analysis is performed to identify behavioral patterns and abnormal information, generating early warning information based on these patterns and information. Through a professional data receiving and acquisition unit, it achieves real-time, reliable access and cleaning of massive, heterogeneous IoT data, providing a high-quality, trustworthy data foundation for the system, completely breaking down data silos. By integrating real-time high-precision location with historical trajectory mining, the early warning mechanism becomes more intelligent and proactive, capable of identifying potential risks and intervening in advance, greatly improving on-site safety levels.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0083] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0084] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0085] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] Alternatively, if the integrated units of the present invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. An intelligent safety management method for construction workers, characterized in that, include: Acquire raw data streams from various IoT devices, preprocess the raw data streams, and obtain a unified data stream; Bind each IoT device to the construction worker's identity information to create and activate a digital twin of the construction worker; Based on the digital twin of the construction personnel, the location data in the unified data stream is obtained, and the real-time calculation is performed based on the multi-source fusion positioning algorithm to output the three-dimensional coordinates of the construction personnel. A 3D reality model built based on site BIM or oblique photogrammetry technology predefines attendance areas and electronic fence areas with 3D geometric attributes. Acquire historical location data, perform trajectory analysis on the historical location data, identify behavioral patterns and abnormal information, and generate early warning information based on behavioral patterns and abnormal information.

2. The intelligent safety management method for construction workers according to claim 1, characterized in that, Acquire raw data streams from various IoT devices, preprocess the raw data streams to obtain a unified data stream, specifically including: Acquire raw data streams, which include raw GNSS global navigation satellite system or NBIoT narrowband Internet of Things positioning data, device unique ID identifiers, vital sign sensor data, and alarm signals; The original data stream is parsed to obtain the parsing results; The parsed results are then formatted to obtain standard format data; Denoising of standard format data is performed using Kalman filtering or low-pass filtering algorithms to obtain noise-free data. By analyzing data duplication information based on noise-free data, duplicate data is removed to obtain a unified data stream.

3. The intelligent safety management method for construction workers according to claim 2, characterized in that, Bind each IoT device to the construction worker's identity information to create and activate a digital twin of the construction worker, specifically including: Collect core identity information of construction personnel to generate a standardized personnel information database, which includes: name, gender, age, ID number, job type, work team, entry time, safety education and training records, special operation certificate information, emergency contact person and contact information; Analyze the unique ID identifier of each IoT device deployed on site, analyze the working status of the IoT devices, and screen out qualified devices that can be bound. The binding result is obtained by binding the core identity information of construction personnel with the qualified equipment that can be bound. The identity information is verified based on the binding result to obtain the verification result, and the verification result is analyzed to determine whether the verification was successful. If the verification is successful, the binding is considered successful, and the construction worker's digital twin is created and activated. If the verification fails, the identity information is re-matched with the qualified equipment.

4. The intelligent safety management method for construction workers according to claim 3, characterized in that, Location data is obtained from a unified data stream based on digital twins of construction workers, and real-time calculation is performed using a multi-source fusion positioning algorithm to output the three-dimensional coordinates of the construction workers, specifically including: Bind each IoT device to the construction worker's identity information, create and activate the construction worker's digital twin, obtain the mapping relationship between IoT devices and construction workers, and obtain the raw location data; The digital twin filters the acquired raw location data based on the characteristics of the employees and work areas associated with it, eliminating interfering data that does not match the current work scenario. Based on the multi-source fusion positioning algorithm, the multi-source positioning related data synchronized by the digital twin is extracted and processed in real time to generate preliminary three-dimensional coordinates; Based on the historical location trajectory and work habit data of personnel associated with the digital twin, the initial three-dimensional coordinates are corrected for deviation, and the final three-dimensional coordinates that match the real-time location of the construction personnel are output.

5. The intelligent safety management method for construction workers according to claim 4, characterized in that, It also includes a seamless attendance process, with the following steps: Obtain location data from the unified data stream; Based on the principles of computational geometry, it can determine in real time whether a three-dimensional coordinate point has entered the preset three-dimensional attendance area; If it is determined that someone has entered the 3D attendance area and has stayed in the area for more than a preset time threshold, the business logic will be automatically triggered to generate an unalterable attendance record containing the person's ID, time, and location, and store it in the business database.

6. The intelligent safety management method for construction workers according to claim 5, characterized in that, It also includes a personalized content delivery process, as detailed below: Based on management needs or early warning information, push matching safety education or management instructions to IoT devices bound to specific individuals or groups; Real-time collection of learning records from specific individuals or groups to determine whether the learning progress meets set conditions. If the set conditions are met, online test questions for the corresponding knowledge points will be automatically sent, and the learning effect will be determined based on the test results to see if it meets the standard. If the standard information is met, personalized content will be pushed. If the standard information is not met, a second push of security education will be triggered or a make-up exam will be initiated.

7. An intelligent safety management system for construction workers, characterized in that, The system includes: a memory and a processor. The memory includes a program for an intelligent safety management method for construction workers. When the program for the intelligent safety management method for construction workers is executed by the processor, it performs the following steps: Acquire raw data streams from various IoT devices, preprocess the raw data streams, and obtain a unified data stream; Bind each IoT device to the construction worker's identity information to create and activate a digital twin of the construction worker; Based on the digital twin of the construction personnel, the location data in the unified data stream is obtained, and the real-time calculation is performed based on the multi-source fusion positioning algorithm to output the three-dimensional coordinates of the construction personnel. A 3D reality model built based on site BIM or oblique photogrammetry technology predefines attendance areas and electronic fence areas with 3D geometric attributes. Acquire historical location data, perform trajectory analysis on the historical location data, identify behavioral patterns and abnormal information, and generate early warning information based on behavioral patterns and abnormal information.

8. The intelligent safety management system for construction workers according to claim 7, characterized in that, Acquire raw data streams from various IoT devices, preprocess the raw data streams to obtain a unified data stream, specifically including: Acquire raw data streams, which include raw GNSS global navigation satellite system or NBIoT narrowband Internet of Things positioning data, device unique ID identifiers, vital sign sensor data, and alarm signals; The original data stream is parsed to obtain the parsing results; The parsed results are then formatted to obtain standard format data; Denoising of standard format data is performed using Kalman filtering or low-pass filtering algorithms to obtain noise-free data. By analyzing data duplication information based on noise-free data, duplicate data is removed to obtain a unified data stream.

9. The intelligent safety management system for construction workers according to claim 8, characterized in that, Bind each IoT device to the construction worker's identity information to create and activate a digital twin of the construction worker, specifically including: Collect core identity information of construction personnel to generate a standardized personnel information database, which includes: name, gender, age, ID number, job type, work team, entry time, safety education and training records, special operation certificate information, emergency contact person and contact information; Analyze the unique ID identifier of each IoT device deployed on site, analyze the working status of the IoT devices, and screen out qualified devices that can be bound. The binding result is obtained by binding the core identity information of construction personnel with the qualified equipment that can be bound. The identity information is verified based on the binding result to obtain the verification result, and the verification result is analyzed to determine whether the verification was successful. If the verification is successful, the binding is considered successful, and the construction worker's digital twin is created and activated. If the verification fails, the identity information is re-matched with the qualified equipment.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for an intelligent safety management method for construction workers, which, when executed by a processor, implements the steps of the intelligent safety management method for construction workers as described in any one of claims 1 to 6.

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