Intelligent construction site management and control platform based on Internet of Things and application method

Abstract

The invention discloses an intelligent construction site management and control platform based on the Internet of Things and an application method, and relates to the technical field of intelligent construction sites. A front-end sensing management and control execution module is built, a position tracking terminal is embedded in the safety helmet, and the position information of constructors is updated at the precision of no more than 0.5 m every 10 seconds by combining a high-precision satellite positioning technology with an advanced indoor positioning algorithm. Multi-source data of personnel positions, equipment parameters, environment information and video images are acquired in a high-precision manner from the front-end sensing management and control execution module to the data transmission link module to ensure safe and stable transmission of the data, and then to the core data processing center module, intelligent analysis and efficient storage are performed by applying an advanced algorithm. And a complete data processing chain is formed. And particularly, the CNN is combined with the transfer learning technology in video image analysis, so that the illegal behavior recognition efficiency is greatly improved, accurate data support is provided for construction site supervision, and all-around and real-time efficient supervision of the construction site is realized.

Description

Technical Field

[0001] The present invention relates to the field of smart construction site technology, and in particular to a smart construction site management and control platform based on the Internet of Things and an application method. Background Art

[0002] Against the backdrop of the rapid development of the construction industry, the need for intelligent and information-based construction site management is becoming increasingly urgent. While the gradual application of IoT technology in construction site management has enabled the automated collection and transmission of some data, it still faces numerous technical bottlenecks. In terms of data collection, existing construction site personnel positioning technology lacks accuracy in indoor environments, and satellite positioning signals are easily obstructed by building structures, making it difficult to accurately monitor personnel movements in real time. Construction equipment monitoring data is collected in a single dimension, failing to fully reflect equipment operating conditions. The frequency and quality of environmental perception and video surveillance data collection also fall short of meeting the supervision requirements of complex construction site scenarios. During data transmission, network stability is poor, making data transmission prone to interruption and loss in areas with weak signals or strong interference. Furthermore, data encryption methods are insecure, posing information leakage risks. At the data processing level, the ability to clean, integrate, and analyze massive amounts of heterogeneous data is limited, making it difficult for traditional algorithms to quickly identify abnormal behavior and potential risks. Furthermore, data storage methods are unable to effectively address the storage and retrieval needs of large-scale video and image data. In terms of human-computer interaction and management, existing construction site supervision platforms have complex user interfaces, low levels of visualization, and a lack of personalized management features, making it difficult to meet the diverse needs of users with different roles. User permission management is crude, posing data security risks. Furthermore, data backup and recovery mechanisms are imperfect, and data loss or corruption could impact the normal operation of construction site operations. Therefore, there is an urgent need for a method for building a construction site supervision platform based on Internet of Things technology that can overcome the above-mentioned defects and achieve precise positioning, efficient data processing, safe and reliable transmission, intelligent management and comprehensive protection, so as to improve the intelligence level of construction site management and overall operational efficiency. Summary of the Invention

[0003] The purpose of the present invention is to provide a smart construction site management and control platform and application method based on the Internet of Things, which solves the technical problems raised in the background technology.

[0004] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: an intelligent construction site management and control platform based on the Internet of Things, including a front-end perception management and control execution module, a data transmission link module, a core data processing hub module, a human-computer interaction application module, a user authority management system and a data backup and recovery mechanism.

[0005] Preferably, the front-end perception control execution module includes: The helmet positioning terminal uses Beidou / GPS satellite positioning and Bluetooth / Wi-Fi indoor positioning technology to dynamically adjust positioning weights through an intelligent weight distribution algorithm, updating the personnel position with an accuracy of ≤0.5 meters every 10 seconds; The equipment operation monitoring unit is equipped with a Hall-type speed sensor, a thermocouple temperature sensor, etc., to collect parameters such as equipment speed and temperature in real time; The data transmission link module sets a wireless-wired network switching mechanism within 5 seconds when the signal is ≤-80dBm, using AES-256 encryption combined with blockchain key management; The core data processing module uses CNN+transfer learning technology to analyze video images, with an accuracy rate of ≥95% for identifying violations; The environmental perception nodes of the front-end perception control execution module are deployed at 50-100 square meters, collecting environmental parameters such as temperature and humidity every minute, and the video acquisition device uses a 1920×1080 pixel, 25 frame / second camera.

[0006] Preferably, the read and write speed of the SSD cache area of the data transmission link module is ≥3000MB / s, and the data packet loss rate during the switching process is ≤0.1%.

[0007] Preferably, the core data processing hub module reduces storage space by 80% through video key frame extraction technology and combines Ceph distributed file system to store data.

[0008] Preferably, the human-computer interaction application module builds a three-dimensional map based on Three.js and BIM models, supports custom report generation and export to Excel / PDF.

[0009] An application method of a smart construction site management and control platform based on the Internet of Things, applied to the smart construction site management and control platform based on the Internet of Things according to any one of claims 1 to 5, comprises the following steps: Step S100: Build a front-end perception control execution module, embed a location tracking terminal inside the hard hat, and use high-precision satellite positioning technology combined with advanced indoor positioning algorithms to update the construction worker's location information every 10 seconds with an accuracy of no more than 0.5 meters; equip various types of construction machinery and equipment with operating parameter monitoring units, equipped with a variety of high-precision sensors to collect equipment speed, temperature, vibration frequency, and fuel consumption parameters in real time; evenly deploy environmental perception nodes in various areas of the construction site, collecting environmental parameters once a minute; install video acquisition devices in key areas of the construction site, and use high-definition cameras to record on-site images at a rate of 25 frames per second and a resolution of no less than 1920×1080 pixels; Step S200: Build a data transmission link module and set up an intelligent network switching mechanism, giving priority to wireless data transmission. When the wireless signal strength is lower than -80dBm, it will automatically switch to the wired network within 5 seconds. Use advanced symmetric encryption algorithms to encrypt the transmitted data to ensure data security. Step S300: Establish a core data processing hub module and use the data pre-processing unit to clean, convert the format, and integrate the front-end data; use the big data analysis algorithm and deep learning model in the deep analysis engine to analyze the personnel location, equipment operation, environment, and video image data respectively to determine abnormal situations; use a distributed storage architecture to build a massive data repository to store various types of data; build an intelligent early warning system, trigger warnings based on the analysis results, and notify relevant personnel through various means; Step S400: Create a human-computer interaction application module, design an intuitive and convenient operation interface, support multi-terminal login; realize visual display, display construction site information with charts, maps, and videos; provide remote management and operation functions, including video monitoring, equipment viewing, and personnel scheduling; have statistical report generation functions, and can generate various management reports; Step S500: Establish a user rights management system. This system implements refined rights division for different user roles to meet the diverse needs of construction site management. Administrators have the highest management authority on the platform and can create user accounts, assign permissions, and set system parameters. Ordinary employees can only view information related to their own work, while safety supervisors have special authority to inspect safety hazards and handle violations. This hierarchical authority management effectively ensures platform security and data privacy, and avoids risks caused by unauthorized operations. Step S600, set up a data backup and recovery mechanism, which fully considers the importance of construction site data and business continuity requirements, allowing users to flexibly set daily, weekly or monthly backup cycles according to the frequency and importance of data updates. Data backup adopts a combination of full backup and incremental backup. Full backup retains a complete copy of the data to deal with major data disasters; incremental backup only records data that has changed since the last backup, greatly shortening the backup time and reducing storage space occupancy. During the data recovery process, data consistency verification technology is introduced to verify the integrity and accuracy of the recovered data through hash verification, checksum comparison, to ensure that the recovered data is completely consistent with the original data, thereby ensuring the stable operation of the platform business.

[0010] Preferably, in step S100, indoor positioning is achieved through Bluetooth beacon and Wi-Fi fingerprint matching algorithm, and is integrated with satellite positioning through Kalman filtering.

[0011] Preferably, in step S300, video key frame extraction is combined with parallel reading and writing of a distributed file system to ensure high data availability.

[0012] Preferably, in step S500, the safety supervisor has special authority to handle violations, and ordinary employees can only access related data.

[0013] Preferably, in step S600, a full backup is automatically triggered when the data change amount is ≥10GB, and data integrity is ensured by hash verification during recovery.

[0014] Compared with related technologies, the construction site supervision platform and method based on Internet of Things technology provided by the present invention have the following beneficial effects: 1. The present invention provides an IoT-based smart construction site management and control platform and application method. This platform integrates high-precision data collection from multiple sources, including personnel locations, equipment parameters, environmental information, and video images, from the front-end sensing and control execution module. This platform then ensures secure and stable data transmission through the data transmission link module. Finally, the core data processing module utilizes advanced algorithms for intelligent analysis and efficient storage, forming a complete data processing chain. In particular, the use of CNN combined with transfer learning technology in video image analysis significantly improves the efficiency of identifying violations, provides accurate data support for construction site supervision, and enables comprehensive, real-time, and efficient supervision of construction sites.

[0015] 2. This invention provides an IoT-based intelligent construction site management and control platform and application method. Through a human-computer interaction application module, it provides a convenient user interface, three-dimensional visualization, customizable reporting, and other functions. It supports multi-terminal login and remote management, meeting the diverse needs of different users and making construction site management more intuitive and flexible. Furthermore, the intelligent early warning system customizes warnings based on personnel roles, facilitating rapid response and resolution of issues, effectively improving construction site operational management efficiency and optimizing resource allocation and personnel scheduling.

[0016] 3. This invention provides an IoT-based smart construction site management and control platform and application method. Through data transmission encryption, hierarchical user authority management, multi-factor identity authentication, and a combined full and incremental data backup and recovery mechanism, a multi-layered security system covering data transmission, user access, and data storage is established. This prevents data leakage and illegal operations while ensuring data integrity and business continuity, providing a solid security barrier for the stable operation of the construction site supervision platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the present invention; Figure 2 This is an extended flow chart of the front-end perception control execution module of the present invention; Figure 3 This is a structural diagram of the data transmission link module of the present invention; Figure 4 This is a structural diagram of the core data processing hub module of the present invention; Figure 5 It is a structural diagram of the human-computer interaction application module of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Example

[0019] See also Figure 1-Figure 5 , the present invention provides a technical solution: a construction site supervision platform based on Internet of Things technology, including a front-end perception control execution module, a data transmission link module, a core data processing hub module, a human-computer interaction application module, a user authority management system and a data backup and recovery mechanism; The front-end perception control execution module includes: The helmet positioning terminal uses Beidou / GPS satellite positioning and Bluetooth / Wi-Fi indoor positioning technology to dynamically adjust positioning weights through an intelligent weight distribution algorithm, updating the personnel position with an accuracy of ≤0.5 meters every 10 seconds; The equipment operation monitoring unit is equipped with a Hall-type speed sensor, a thermocouple temperature sensor, etc., to collect parameters such as equipment speed and temperature in real time; The data transmission link module sets a wireless-wired network switching mechanism within 5 seconds when the signal is ≤-80dBm, using AES-256 encryption combined with blockchain key management; The core data processing module uses CNN+transfer learning technology to analyze video images, with an accuracy rate of ≥95% for identifying violations; The environmental sensing nodes of the front-end perception control execution module are deployed in 50-100 square meters, collecting environmental parameters such as temperature and humidity every minute. The video acquisition device uses a 1920×1080 pixel, 25 frame / second camera; The read and write speed of the SSD cache area of the data transmission link module is ≥3000MB / s, and the data packet loss rate during the switching process is ≤0.1%; The core data processing hub module uses video keyframe extraction technology to reduce storage space by 80% and combines it with the Ceph distributed file system to store data; The human-computer interaction application module builds 3D maps based on Three.js and BIM models, supports custom report generation and export to Excel / PDF.

[0020] Step S100: Build a front-end perception control execution module, embed a location tracking terminal inside the hard hat, and use high-precision satellite positioning technology combined with advanced indoor positioning algorithms to update the construction worker's location information every 10 seconds with an accuracy of no more than 0.5 meters; equip various types of construction machinery and equipment with operating parameter monitoring units, equipped with a variety of high-precision sensors to collect equipment speed, temperature, vibration frequency, and fuel consumption parameters in real time; evenly deploy environmental perception nodes in various areas of the construction site, collecting environmental parameters once a minute; install video acquisition devices in key areas of the construction site, and use high-definition cameras to record on-site images at a rate of 25 frames per second and a resolution of no less than 1920×1080 pixels; In step S100, indoor positioning is achieved through Bluetooth beacons (10-15 meter intervals) and Wi-Fi fingerprint matching algorithms, and is integrated with satellite positioning through Kalman filtering. The fusion solution of high-precision satellite positioning technology and advanced indoor positioning algorithms used by the location tracking terminal is carefully designed based on the characteristics of the complex construction site environment. Through Bluetooth positioning technology, low-power Bluetooth beacons are used to build a location reference point network in the indoor environment. Its signal coverage is stable and can achieve sub-meter positioning accuracy; Wi-Fi positioning technology is based on the signal strength fingerprint matching algorithm, combined with the W preset at the construction site. The i-Fi signal fingerprint library calculates personnel locations based on received signal strength indicators. These two technologies work in conjunction with satellite positioning technology. Through an intelligent weight distribution algorithm, the weights of each positioning technology are dynamically adjusted according to environmental conditions. This effectively overcomes indoor signal obstruction and multipath effects, significantly improving indoor positioning accuracy and stability. This ensures that construction personnel's position information update accuracy remains within 0.5 meters even in construction sites with dense steel bars and complex floors. When positioning data is fused, the state estimate is updated using the Kalman filter equation, with the state transfer matrix F=1 and the observation matrix H=1, enabling dynamic correction of positioning errors. In this implementation plan, personnel location tracking: a low-power, high-precision positioning chip is embedded in a hard hat as a location tracking terminal. Outdoors, the construction worker's location is located using centimeter-level positioning accuracy by accessing the Beidou and GPS global satellite navigation systems. Indoors, Bluetooth beacons and Wi-Fi access points are deployed. Bluetooth beacons are evenly distributed at intervals of 10-15 meters, and Wi-Fi access points ensure full coverage of the construction site. The positioning terminal integrates satellite positioning, Bluetooth positioning, and Wi-Fi positioning data, and uses the Kalman filter algorithm for data fusion. It updates the construction worker's location information every 10 seconds with an accuracy of no more than 0.5 meters, and sends the data to subsequent modules through the built-in communication module. Equipment operating parameter monitoring: An operating parameter monitoring unit integrating multiple sensors is installed on various types of construction machinery and equipment, such as tower cranes and excavators. The speed sensor uses a Hall-type speed sensor, the temperature sensor uses a thermocouple temperature sensor, the vibration frequency sensor uses a piezoelectric acceleration sensor, and the fuel consumption monitoring uses a flow sensor combined with a high-precision metering chip. Each sensor collects the corresponding equipment parameters in real time. The monitoring unit converts the collected data into analog-to-digital data and performs preliminary processing before sending it to the data transmission link module via an industrial-grade wireless communication module. Environmental parameter collection: Environmental sensing nodes are evenly deployed at intervals of 50-100 square meters in various areas of the construction site. The nodes are integrated with temperature and humidity sensors, PM2.5 / PM10 sensors, noise sensors, and harmful gas sensors. They collect environmental parameters once a minute and aggregate the data to the regional gateway via the ZigBee wireless communication network. The gateway then transmits the data to subsequent modules. Video image acquisition: Install high-definition network cameras in key areas of the construction site. Select cameras with a resolution of no less than 1920×1080 pixels and a frame rate of 25 frames per second. The cameras are equipped with infrared night vision function to meet night shooting needs. The cameras are connected to the construction site LAN via a wired network or a wireless bridge to record on-site images in real time and transmit video data to the core data processing hub module.

[0021] Step S200: Build a data transmission link module and set up an intelligent network switching mechanism, giving priority to wireless data transmission. When the wireless signal strength is lower than -80dBm, it will automatically switch to the wired network within 5 seconds. Use advanced symmetric encryption algorithms to encrypt the transmitted data to ensure data security. In step S200, the intelligent network switching mechanism demonstrates excellent performance in ensuring data transmission continuity. When the wireless signal strength drops to -80dBm, the system's built-in real-time signal monitoring module quickly triggers the switching program. Within a very short time of 5 seconds, the data packet being transmitted is temporarily stored in the high-speed cache area. The cache area uses solid-state hard drive technology and has millisecond-level read and write speeds to ensure efficient temporary data storage. After the network switch is completed, the cached data is accurately continued according to the orderly queue mechanism of the transmission protocol to avoid data loss and transmission interruption. At the same time, the AES-256 algorithm used for data encryption has a 256-bit key length to build a strong encryption barrier. In conjunction with the distributed key management system based on blockchain, the keys are dispersed and stored in multiple nodes. The consensus mechanism is used to achieve secure key distribution and update, effectively resisting hacker attacks and data theft, and building a solid security line for data transmission. In this implementation plan, intelligent network switching is implemented: 5G base stations, Wi-Fi 6 wireless access points, and industrial Ethernet switches are deployed on the construction site. Each data acquisition terminal prioritizes wireless data transmission via 5G or Wi-Fi 6. Signal strength monitoring modules are installed on front-end devices and transmission nodes to monitor wireless signal strength in real time. When the signal strength is lower than -80dBm, the intelligent network switching mechanism is triggered. The data being transmitted is first cached in the device's built-in high-speed flash memory, and then automatically switched to the industrial Ethernet wired network within 5 seconds. After the switch is completed, the cached data continues to be transmitted to ensure uninterrupted data transmission. Data encryption transmission: The AES-256 symmetric encryption algorithm is used to encrypt the transmitted data. The data is encrypted using a key at the data sending end. The encryption key is generated and distributed through a distributed key management system based on blockchain. Each data collection terminal and receiving node is equipped with a blockchain node client. The consensus algorithm is used to achieve secure sharing and updating of keys, ensuring the security of data during transmission.

[0022] Step S300: Establish a core data processing hub module and use the data pre-processing unit to clean, convert the format, and integrate the front-end data; use the big data analysis algorithm and deep learning model in the deep analysis engine to analyze the personnel location, equipment operation, environment, and video image data respectively to determine abnormal situations; use a distributed storage architecture to build a massive data repository to store various types of data; build an intelligent early warning system, trigger warnings based on the analysis results, and notify relevant personnel through various means; In step S300, the data cleaning process of the data pre-processing unit is highly scientific. When processing multi-dimensional data on personnel locations and equipment operation, the density-based spatial clustering algorithm automatically identifies and marks outliers by setting appropriate neighborhood radius and density threshold. Taking equipment vibration frequency data as an example, when the vibration frequency value at a certain moment deviates from the density range of other data points in its neighborhood, it is determined to be an outlier and removed, effectively improving the reliability and analytical value of the data. In video image data processing, the deep analysis engine uses a target detection algorithm based on convolutional neural networks, combined with transfer learning technology, to pre-train the basic model on a large-scale public image dataset, and then fine-tune the data on construction workers' illegal behaviors in construction site scenes, so that the model can quickly and accurately identify behaviors such as not wearing safety helmets and illegally operating machinery. Compared with traditional algorithms, the recognition speed is increased by 40% and the accuracy rate is increased to more than 95%. In step S300, video key frame extraction is combined with the parallel reading and writing of the distributed file system (throughput ≥ 500MB / s) to ensure high data availability. The massive data repository adopts an innovative strategy for video image data storage. The video key frame extraction technology analyzes the motion vectors and content differences between video frames and intelligently selects representative key frames. Compared with full-frame storage, it can reduce more than 80% of storage space. Combined with the distributed file system, the key frame data is stored in multiple storage nodes in a dispersed manner. By utilizing its powerful parallel reading and writing capabilities and self-repair mechanism, it not only improves data storage efficiency but also ensures high data availability and reliability. The intelligent early warning system fully considers user needs in the transmission of early warning information. According to the role and authority of the receiving personnel, a personalized early warning content template library is constructed. The TensorFlow framework is used, with an initial learning rate of 0.001, a batch size of 32, and a training round of 50. After pre-training on the MSCOCO dataset, it is fine-tuned for construction site violation data. In this implementation plan, data preprocessing involves building a data preprocessing server cluster and using the Apache Flink real-time computing framework to clean, convert, and integrate data transmitted from the front end. During the data cleaning phase, the DBSCAN algorithm is used to process data such as personnel location and equipment operation, setting appropriate neighborhood radius and density thresholds to automatically identify and remove outliers. Sensor data and video data in different formats are unified into a standard format using a data format conversion tool, and are then integrated and associated based on information such as timestamps and device numbers to form structured data. Data analysis and early warning: Deploy a deep analysis engine server, using Hadoop and Spark big data processing platforms, combined with the TensorFlow deep learning framework to implement big data analysis algorithms and deep learning models. For personnel location data, trajectory analysis algorithms are used to determine whether personnel have entered dangerous areas. Equipment operation data uses anomaly detection algorithms, combined with historical equipment operation data to establish a normal operation model, and determine in real time whether the equipment has potential fault hazards. Environmental data uses threshold judgment and trend analysis to warn of environmental pollution and safety risks. In terms of video image analysis, a CNN-based target detection algorithm is combined with transfer learning technology to identify violations by construction workers. When the analysis results trigger the preset warning conditions, the intelligent warning system notifies relevant personnel through various methods such as SMS, email, and APP push, and customizes the warning content according to the role and authority of the recipient. Data storage: The HBase distributed database and Ceph distributed file system are used to build a massive data repository. Structured personnel, equipment, and environmental data are stored in HBase, and its columnar storage and high-concurrency read and write features are used to improve data storage and query efficiency. Video image data is extracted using video key frame extraction technology, and the key frames and video metadata are stored in Ceph to reduce storage space usage. At the same time, Ceph's multiple replicas and erasure coding technologies are used to ensure data reliability and availability.

[0023] Step S400: Create a human-computer interaction application module, design an intuitive and convenient operation interface, support multi-terminal login; realize visual display, display construction site information with charts, maps, and videos; provide remote management and operation functions, including video monitoring, equipment viewing, and personnel scheduling; have statistical report generation functions, and can generate various management reports; In step S400, the visualization display function of the human-computer interaction application module brings a new experience. The three-dimensional electronic map, based on oblique photogrammetry technology and building information model data, truly restores the site topography, building structure and construction progress. Users can view the site situation in all directions through zooming, rotating and roaming operations. The statistical report generation function allows users to freely drag and drop data fields, set filter conditions, select chart types, and customize report templates through the visual interface. Users can quickly generate various reports on personnel attendance statistics, equipment operation status analysis, and safety hazard distribution according to management needs, greatly improving the flexibility and work efficiency of data processing. In this implementation plan, the operation interface design: Based on Web technology, a multi-terminal adaptive operation interface is developed. The Vue.js front-end framework and the Element-UI component library are used to design a simple and intuitive interactive interface. It supports multi-terminal login on PCs, tablets, and smartphones. Users log in to the platform through a unified identity authentication system, and the corresponding functional modules are displayed according to the user role; Visualization: Utilize the Three.js 3D visualization library to build a 3D electronic map, combining BIM models and oblique photography data to realistically restore the site topography and building structure. Personnel locations, equipment status, and environmental parameter information are annotated on the map in real time, with different colors and icons used to distinguish between normal and abnormal states. The ECharts chart library displays various statistical data in the form of line charts, bar charts, and pie charts, allowing users to view detailed information through interactive operations. Remote management and operation: Users can remotely view video surveillance images, control the PTZ and adjust the focus of the camera on the operation interface; view the operating parameters of construction equipment in real time, remotely start and stop the equipment, and set parameters; and use the personnel dispatch function to view the real-time location and work status of personnel, and perform task allocation and personnel deployment. Statistical report generation: A report designer tool is provided. Users can customize report templates by dragging and dropping data fields, setting filter conditions, and selecting chart types. The system supports the generation of various management reports such as personnel attendance reports, equipment operation statistics reports, and safety hazard analysis reports. Reports can also be exported to Excel and PDF formats.

[0024] The system also includes step S500, which establishes a user rights management system. This system implements refined rights division for different user roles to meet the diverse needs of construction site management. Administrators have the highest management rights on the platform and can create user accounts, assign rights, and set system parameters. Ordinary employees can only view information related to their own work, while safety supervisors have special rights to inspect safety hazards and handle violations. This hierarchical rights management effectively protects platform security and data privacy, and avoids risks caused by unauthorized operations. In step S500, the security supervisor has the special authority to handle violations, and ordinary employees can only access related data. The user authority management system adopts a role-based access control model. This model simplifies the authority management process by binding authority to roles. Combined with multi-factor identity authentication technology, users need to verify fingerprint information, dynamic password and account password at the same time when logging in. The multiple verification mechanism effectively prevents account theft. The system also has an abnormal login behavior monitoring module. When abnormal situations such as remote login and frequent incorrect login are detected, the account lock mechanism is automatically triggered and a security prompt is sent to the user to further enhance the security of user login and operation; Permission model construction: Adopting a role-based access control model, the system defines the user roles of administrator, general employee, and security supervisor. Different functional permissions and data permissions are assigned to each role, and the association between roles and permissions is achieved through the permission configuration table. Identity authentication: Combined with multi-factor identity authentication technology, users need to enter their account password and perform fingerprint recognition or enter a dynamic password when logging in. The system verifies the user's identity information through the identity authentication server. After the verification is passed, an access token is generated. The user's subsequent operations are verified by carrying the token for permission verification.

[0025] The system also includes step S600, which sets a data backup and recovery mechanism. This mechanism fully considers the importance of construction site data and business continuity requirements, allowing users to flexibly set daily, weekly, or monthly backup cycles based on the frequency and importance of data updates. Data backup adopts a combination of full backup and incremental backup. Full backup retains a complete copy of the data to cope with major data disasters; incremental backup only records data that has changed since the last backup, significantly shortening backup time and reducing storage space usage. During the data recovery process, data consistency verification technology is introduced to verify the integrity and accuracy of the recovered data through hash verification, checksum comparison, and ensure that the recovered data is completely consistent with the original data, thereby ensuring the stable operation of the platform business. In step S600, when the data change is ≥10GB, a full backup is automatically triggered. During recovery, hash verification is used to ensure data integrity. The data backup process adopts an intelligent scheduling strategy, and uses task queues and priority algorithms to automatically execute backup tasks during periods of low system load to avoid affecting the normal operation of the platform. The switch between full backup and incremental backup is dynamically adjusted based on the data change. When the data change exceeds the preset threshold, a full backup is automatically started to ensure the timeliness and effectiveness of the data backup. During data recovery, parallel recovery technology is used to quickly transfer data blocks from off-site storage devices and restore them to local storage. Combined with the pipeline processing of data consistency verification, the recovery time is greatly shortened. In the event of data loss or damage, the platform data can be restored in the shortest time to ensure the continuity of construction site supervision business. Data backup: We build an off-site data center, configure a dedicated storage server, and use a combination of full and incremental backups. We perform incremental backups during low-peak hours each day and full backups weekly. Backup tasks are executed through automated scripts, using the rsync data synchronization tool to transfer data from the local repository to the off-site storage device, and record backup logs. Data recovery: When data loss or damage is detected, the recovery process is initiated through the data recovery management interface. The system selects appropriate backup data based on the backup log and uses parallel transmission technology to quickly transfer the data to the local computer. During the recovery process, hash check data consistency verification technology is used to verify the recovered data to ensure the accuracy and completeness of data recovery.

[0026] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0027] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that modifications or equivalent replacements of the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A smart construction site management and control platform based on the Internet of Things, characterized by: It includes front-end perception control execution module, data transmission link module, core data processing center module, human-computer interaction application module, user authority management system and data backup and recovery mechanism.

2. The IoT-based smart construction site management and control platform according to claim 1, characterized in that: The front-end perception control execution module includes: The helmet positioning terminal uses Beidou / GPS satellite positioning and Bluetooth / Wi-Fi indoor positioning technology to dynamically adjust positioning weights through an intelligent weight distribution algorithm, updating the personnel position with an accuracy of ≤0.5 meters every 10 seconds; The equipment operation monitoring unit is equipped with a Hall-type speed sensor, a thermocouple temperature sensor, etc., to collect parameters such as equipment speed and temperature in real time; The data transmission link module sets a wireless-wired network switching mechanism within 5 seconds when the signal is ≤-80dBm, using AES-256 encryption combined with blockchain key management; The core data processing module uses CNN+transfer learning technology to analyze video images, with an accuracy rate of ≥95% for identifying violations; The environmental perception nodes of the front-end perception control execution module are deployed at 50-100 square meters, collecting environmental parameters such as temperature and humidity every minute, and the video acquisition device uses a 1920×1080 pixel, 25 frame / second camera.

3. The smart construction site management and control platform based on the Internet of Things according to claim 1 is characterized by: The SSD cache area read and write speed of the data transmission link module is ≥3000MB / s, and the data packet loss rate during the switching process is ≤0.1%.

4. The IoT-based smart construction site management and control platform according to claim 1, characterized in that: The core data processing hub module reduces storage space by 80% through video key frame extraction technology and combines with the Ceph distributed file system to store data.

5. The smart construction site management and control platform based on the Internet of Things according to claim 1 is characterized by: The human-computer interaction application module builds a 3D map based on Three.js and BIM models, supports custom report generation and export to Excel / PDF.

6. An application method of a smart construction site management and control platform based on the Internet of Things, characterized in that: An IoT-based smart construction site management and control platform applied to any one of claims 1 to 5 comprises the following steps: Step S100: Build a front-end perception control execution module, embed a location tracking terminal inside the hard hat, and use high-precision satellite positioning technology combined with advanced indoor positioning algorithms to update the construction worker's location information every 10 seconds with an accuracy of no more than 0.5 meters; equip various types of construction machinery and equipment with operating parameter monitoring units, equipped with a variety of high-precision sensors to collect equipment speed, temperature, vibration frequency, and fuel consumption parameters in real time; evenly deploy environmental perception nodes in various areas of the construction site, collecting environmental parameters once a minute; install video acquisition devices in key areas of the construction site, and use high-definition cameras to record on-site images at a rate of 25 frames per second and a resolution of no less than 1920×1080 pixels; Step S200: Build a data transmission link module and set up an intelligent network switching mechanism, giving priority to wireless data transmission. When the wireless signal strength is lower than -80dBm, it will automatically switch to the wired network within 5 seconds. Use advanced symmetric encryption algorithms to encrypt the transmitted data to ensure data security. Step S300: Establish a core data processing hub module and use the data pre-processing unit to clean, convert the format, and integrate the front-end data; use the big data analysis algorithm and deep learning model in the deep analysis engine to analyze the personnel location, equipment operation, environment, and video image data respectively to determine abnormal situations; use a distributed storage architecture to build a massive data repository to store various types of data; build an intelligent early warning system, trigger warnings based on the analysis results, and notify relevant personnel through various means; Step S400: Create a human-computer interaction application module, design an intuitive and convenient operation interface, support multi-terminal login; realize visual display, display construction site information with charts, maps, and videos; provide remote management and operation functions, including video monitoring, equipment viewing, and personnel scheduling; have statistical report generation functions, and can generate various management reports; Step S500: Establish a user rights management system. This system implements refined rights division for different user roles to meet the diverse needs of construction site management. Administrators have the highest management authority on the platform and can create user accounts, assign permissions, and set system parameters. Ordinary employees can only view information related to their own work, while safety supervisors have special authority to inspect safety hazards and handle violations. This hierarchical authority management effectively ensures platform security and data privacy, and avoids risks caused by unauthorized operations. Step S600, set up a data backup and recovery mechanism, which fully considers the importance of construction site data and business continuity requirements, allowing users to flexibly set daily, weekly or monthly backup cycles according to the frequency and importance of data updates. Data backup adopts a combination of full backup and incremental backup. Full backup retains a complete copy of the data to deal with major data disasters; incremental backup only records data that has changed since the last backup, greatly shortening the backup time and reducing storage space occupancy. During the data recovery process, data consistency verification technology is introduced to verify the integrity and accuracy of the recovered data through hash verification, checksum comparison, to ensure that the recovered data is completely consistent with the original data, thereby ensuring the stable operation of the platform business.

7. The method for applying the smart construction site management and control platform based on the Internet of Things according to claim 6 is characterized by: In step S100, indoor positioning is achieved through Bluetooth beacon and Wi-Fi fingerprint matching algorithm, and is integrated with satellite positioning through Kalman filtering.

8. The method for applying the smart construction site management and control platform based on the Internet of Things according to claim 6, characterized in that: In step S300, video key frame extraction is combined with parallel reading and writing of the distributed file system to ensure high data availability.

9. The method for applying the smart construction site management and control platform based on the Internet of Things according to claim 6, characterized in that: In step S500, the safety supervisor has special authority to handle violations, and ordinary employees can only access related data.

10. The method for applying the smart construction site management and control platform based on the Internet of Things according to claim 6, characterized in that: In step S600, a full backup is automatically triggered when the data change is ≥10GB, and data integrity is ensured by hash verification during recovery.

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