Building construction safety monitoring system and monitoring method thereof

Abstract

The invention discloses a building construction safety monitoring system and a monitoring method thereof. The building construction safety monitoring system comprises a sensing layer, a transmission layer, a data processing layer and an application layer. The sensing layer is composed of multi-dimensional sensor clusters of environment, structure, personnel and the like and can comprehensively collect various data of a construction site; the transmission layer adopts a wireless and wired combined mode, and ensures stable and safe transmission of data through encryption and redundancy transmission technologies; the data processing layer is deployed on a cloud platform, and performs data preprocessing, model training and risk decision making by using multiple algorithms; the application layer provides a monitoring center client and a mobile terminal APP for a user, and realizes data display, early warning and management functions. The system is high in intelligent degree, achieves the omnibearing, real-time and precise monitoring of the building construction process, improves the accuracy of safety risk assessment and the timeliness of early warning, provides a powerful guarantee for the safety of building construction, and has a higher practical value and a higher popularization prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction safety monitoring, and in particular to a building construction safety monitoring system and a monitoring method thereof. Background Art

[0002] During construction, complex environments, diverse construction techniques, and a large workforce present numerous safety hazards. Accidents such as falls from heights, impacts, collapses, electric shocks, and mechanical injuries are common occurrences. These accidents not only cause casualties but also lead to significant financial losses and delays to construction projects.

[0003] Traditional construction safety monitoring relies primarily on manual inspections, which have numerous limitations. First, the frequency of manual inspections is limited, making real-time monitoring difficult. Many safety hazards can develop between inspections, ultimately leading to accidents. Second, manual inspections are significantly influenced by subjective factors, and different inspectors may have different criteria for judging safety hazards, making it easy for missed inspections to occur. Furthermore, manual inspections are difficult for some concealed projects or dangerous areas such as high-altitude and deep foundation pits, making it difficult to obtain comprehensive and accurate safety information.

[0004] With the continuous advancement of science and technology, some sensor-based monitoring technologies have begun to be applied in the field of construction safety monitoring. However, existing sensor monitoring systems often suffer from problems such as limited functionality, unstable data transmission, and low intelligence. For example, some systems can only monitor a single physical quantity, such as displacement or stress, and are unable to comprehensively analyze the impact of multiple factors on construction safety. The data transmission process is easily interfered with by the complex electromagnetic environment of the construction site, resulting in data loss or errors. Moreover, when faced with large amounts of monitoring data, they lack effective data analysis and processing capabilities, making it impossible to issue timely and accurate safety warnings. Therefore, it is necessary to develop a construction safety monitoring system and monitoring method. Summary of the Invention

[0005] The object of the present invention is to provide a construction safety monitoring system and a monitoring method thereof to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a construction safety monitoring system, comprising:

[0007] The perception layer is composed of a multi-dimensional sensor cluster, including an environmental monitoring subcluster, a structural monitoring subcluster, and a personnel monitoring subcluster;

[0008] The transport layer includes wireless transmission units, wired transmission units, and edge gateways.

[0009] Data processing layer, deployed on the cloud platform;

[0010] The application layer includes: the monitoring center client, which can display real-time monitoring data heat maps, equipment status topology maps and early warning information; the mobile terminal APP, which has data query, voice broadcast warning, and electronic fence out-of-bounds reminder functions. The administrator account can remotely configure sensor collection parameters.

[0011] Preferably, the environmental monitoring subcluster includes temperature and humidity sensors, wind speed and direction sensors, noise sensors, and dust sensors, which are distributed in the construction site work area, material storage area, and office area; the structural monitoring subcluster includes high-precision displacement sensors, strain gauge stress sensors, and inclination sensors, which are respectively pre-buried or fixed in the building foundation pedestal, beam-column nodes, scaffolding upright and horizontal bar connection nodes, tower crane booms, and tower body standard sections; the personnel monitoring subcluster uses an intelligent safety helmet with an integrated UWB positioning chip and a three-axis acceleration sensor, and a wrist-worn bracelet with a built-in optical heart rate sensor and blood pressure sensor.

[0012] Preferably, the wireless transmission unit is composed of a 5G industrial module and a LoRa gateway. The 5G module is connected to the high-frequency acquisition sensor in the structure monitoring subcluster, and the LoRa gateway is connected to the environment monitoring subcluster and the personnel monitoring subcluster. The wired transmission unit uses a single-mode optical fiber to connect the displacement sensor. The edge gateway synchronizes the sensor data with the timestamp and converts the protocol, and then transmits it to the cloud server through a VPN tunnel.

[0013] Preferably, the data processing layer includes: a data preprocessing module, which uses the Kalman filter algorithm to eliminate noise data and fills in missing values ​​based on the Lagrange interpolation method; a model training module, which constructs a structural health assessment model that integrates LSTM neural network and finite element analysis, and a personnel behavior recognition model based on the YOLOv8 algorithm; a risk decision module, which has a built-in dynamic threshold database and calculates the risk level through a fuzzy comprehensive evaluation method.

[0014] Preferably, the smart helmet is also integrated with: an infrared pyroelectric sensor for detecting the wearing status of the helmet; a Bluetooth 4.2 module for realizing close-range data interaction with the wristband; and an audible and visual alarm unit for triggering an alarm when a person is detected entering a dangerous area.

[0015] Preferably, a method for using a construction safety monitoring system comprises the following steps:

[0016] S1. Dynamic data collection configuration: The system automatically adjusts data collection parameters based on the construction phase. Environmental sensors sample at a 5-minute interval during foundation construction, shortening this to 1 minute during main construction. Structural sensors trigger a 1kHz high-frequency data collection during concrete pouring, reducing this to 10Hz during static periods. Human sensors continuously collect positioning data at 10Hz, with heart rate data recorded every 30 seconds.

[0017] S2. Encrypted transmission process: Sensor data is encrypted with AES-256 and sent to the gateway. The gateway establishes a connection with the cloud using RSA asymmetric encryption. The transmitted data packet contains: device ID, acquisition time, and checksum.

[0018] S3. Stratified Data Analysis:

[0019] Environmental data: A sliding window algorithm is used to calculate the duration of dust concentration exceeding the standard. When three consecutive windows exceed the standard, it is marked as continuous pollution;

[0020] Structural data: Input strain sensor data into the finite element model, calculate the structural stress distribution cloud map, and combine it with the LSTM model to predict the stress change trend within 24 hours;

[0021] Personnel data: Through the overlay analysis of UWB positioning trajectory and BIM model, violations of walking routes that do not follow the prescribed route can be identified;

[0022] S4. Multi-level warning response:

[0023] Level 1 Yellow Warning: A single parameter exceeds the threshold by less than 10%, triggering an app push notification and a pop-up window in the monitoring center.

[0024] Level 2 Orange Warning: If a single parameter exceeds the threshold by 10%-30% or two parameters are slightly exceeded, an SMS notification will be sent to the project leader and the smart helmet will issue an intermittent alarm.

[0025] Level 3 Red Alert: If a parameter exceeds the threshold by more than 30%, all terminals will trigger audible and visual alarms, automatically cutting off the power supply to the relevant area, and simultaneously sending an alert message to the supervision unit and the housing and construction department's supervision platform;

[0026] S5. Closed-loop decision execution: The system generates a report including risk location, impact scope, and disposal suggestions. After the management personnel confirm the disposal measures through the APP, the system tracks the rectification time and automatically upgrades the warning if the preset time limit is exceeded.

[0027] Preferably, the specific process of structural data prediction in step S3 is:

[0028] S31. Normalize the historical stress data;

[0029] S32. Divide the training set, validation set, and test set into the LSTM model.

[0030] S33. Iterate the training using the Adam optimizer and stop when the validation set loss function is less than 0.01.

[0031] S34. Perform weighted fusion of the prediction results and the finite element analysis results to output the final stress prediction value.

[0032] Preferably, step S3 also includes digital twin modeling analysis:

[0033] Based on real-time collected structural displacement and stress data, a digital twin of the construction site is constructed in the cloud;

[0034] The temperature, humidity, and wind speed data collected by environmental sensors are input into the digital twin model to simulate the coupling effect of structural deformation under different environmental conditions;

[0035] Through digital twins, virtual rehearsals of key construction processes such as deep foundation pit excavation and tower crane installation are carried out, and possible stress concentration points are predicted one hour in advance. The rehearsal results are used as a supplementary basis for risk assessment. When the probability of the stress in a certain part exceeding the limit in the virtual rehearsal is greater than 70%, an early warning is triggered.

[0036] Preferably, the linkage control logic of the three-level warning in step S4 is:

[0037] When the tower crane's inclination angle exceeds the threshold, the edge gateway sends an emergency stop command to the tower crane control cabinet;

[0038] When a third-level warning of a person breaking into a deep foundation pit area occurs, the surrounding sound and light alarm devices will be automatically activated, and the intrusion location and person information will be highlighted in the BIM model.

[0039] Beneficial effects:

[0040] (1) The present invention has a high degree of intelligence and realizes all-round, real-time and accurate monitoring of the construction process, which improves the accuracy of safety risk assessment and the timeliness of early warning, provides a strong guarantee for construction safety, and has high practical value and promotion prospects.

[0041] (2) The present invention comprehensively covers the environment, structure and personnel of the construction site through various types of sensors, thereby realizing all-round real-time monitoring of the construction process and timely discovering various potential safety hazards; using advanced data analysis algorithms, in-depth analysis of a large amount of monitoring data is carried out, and accurate risk assessment is carried out in combination with safety thresholds, which greatly improves the accuracy and reliability of risk assessment; once it is detected that the safety risk exceeds the threshold, the system can issue early warning information in a timely manner through various means, so that relevant personnel can take quick measures to effectively prevent accidents.

[0042] The above description is only an overview of the technical solutions of the embodiments of the present application. In order to more clearly understand the technical means of the embodiments of the present application, they can be implemented according to the contents of the specification. In addition, in order to make the above and other purposes, features and advantages of the embodiments of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Fig. 1 This is a system architecture diagram of the present invention;

[0044] Fig. 2 This is a principle block diagram of the present invention;

[0045] Fig. 3 It is a flowchart of the present invention;

[0046] In the figure: perception layer 1, environmental monitoring subcluster 2, structure monitoring subcluster 3, personnel monitoring subcluster 4, transmission layer 5, wireless transmission unit 6, wired transmission unit 7, edge gateway 8, data processing layer 9, application layer 10, monitoring center client 11, mobile terminal APP 12, temperature and humidity sensor 13, wind speed and direction sensor 14, noise sensor 15, dust sensor 16, displacement sensor 17, strain gauge stress sensor 18, tilt sensor 19, smart helmet 20, wristband 21, 5G industrial module 22, LoRa gateway 23, cloud server 24, data preprocessing module 25, model training module 26, risk decision module 27. DETAILED DESCRIPTION

[0047] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification, claims and drawings of this application are intended to cover non-exclusive inclusions.

[0049] References to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase "embodiment" in various places in the specification does not necessarily refer to the same embodiment, nor does it necessarily refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0051] See also Figs. 1-3 The present invention discloses a construction safety monitoring system, comprising:

[0052] Perception layer 1, consisting of a multi-dimensional sensor cluster, including environmental monitoring sub-cluster 2, structure monitoring sub-cluster 3, and personnel monitoring sub-cluster 4;

[0053] The transport layer 5 includes a wireless transmission unit 6, a wired transmission unit 7, and an edge gateway 8;

[0054] Data processing layer 9, deployed on the cloud platform;

[0055] The application layer 10 includes: a monitoring center client 11, which can display real-time monitoring data heat maps, equipment status topology maps and warning information; a mobile terminal APP 12, which has data query, voice broadcast warning, and electronic fence crossing reminder functions. The administrator account can remotely configure sensor collection parameters.

[0056] Among them, the environmental monitoring sub-cluster 2 includes temperature and humidity sensors 13, wind speed and direction sensors 14, noise sensors 15, and dust sensors 16, which are all distributed in the construction site work area, material storage area, and office area; the structural monitoring sub-cluster 3 includes high-precision displacement sensors 17, strain gauge stress sensors 18, and inclination sensors 19, which are respectively pre-buried or fixed in the building foundation pedestal, beam-column nodes, scaffolding vertical pole and horizontal pole connection nodes, tower crane boom, and tower body standard sections; the personnel monitoring sub-cluster 4 uses an intelligent safety helmet 20 with an integrated UWB positioning chip and a three-axis acceleration sensor, and a wrist-worn bracelet 21 with a built-in optical heart rate sensor and blood pressure sensor.

[0057] In the present invention, the wireless transmission unit 6 consists of a 5G industrial module 22 and a LoRa gateway 23. The 5G module 22 connects to the high-frequency data acquisition sensors in the structure monitoring subcluster 3, while the LoRa gateway 23 connects to the environmental monitoring subcluster 2 and the personnel monitoring subcluster 4. The wired transmission unit 7 uses single-mode optical fiber to connect to the displacement sensor 17. The edge gateway 8 synchronizes the sensor data with timestamps and performs protocol conversion before transmitting it to the cloud server 24 via a VPN tunnel. For sensors that are close to the gateway and have large data transmission volumes, such as those on tower cranes, a 5G industrial module is used to transmit data to the gateway. For sensors located in remote areas with smaller data transmission volumes, such as temperature and humidity sensors installed in corners of construction sites, a LoRa wireless transmission module is used for data transmission. Furthermore, for sensors with extremely high requirements for data transmission stability, such as high-precision displacement sensors installed in the core structure of a building, optical fiber is laid for wired transmission.

[0058] In this invention, the data processing layer 9 includes: a data preprocessing module 25, which uses the Kalman filter algorithm to remove noisy data and Lagrange interpolation to fill missing values; a model training module 26, which constructs a structural health assessment model that integrates LSTM neural networks and finite element analysis, and a human behavior recognition model based on the YOLOv8 algorithm; and a risk decision module 27, which has a built-in dynamic threshold database and calculates risk levels using a fuzzy comprehensive evaluation method. For example, the structural health monitoring model can be trained using historical monitoring data to accurately predict the safety status of a building structure at different construction stages.

[0059] In the present invention, the smart helmet 20 also integrates: an infrared pyroelectric sensor for detecting the wearing status of the helmet; a Bluetooth 4.2 module for realizing close-range data interaction with the wristband; and an audible and visual alarm unit for triggering an alarm when a person is detected entering a dangerous area.

[0060] Working principle: A method for using a construction safety monitoring system includes the following steps:

[0061] S1. Dynamic data collection configuration: The system automatically adjusts data collection parameters based on the construction phase. Environmental sensors sample at a 5-minute interval during foundation construction, shortening this to 1 minute during main construction. Structural sensors trigger a 1kHz high-frequency data collection during concrete pouring, reducing this to 10Hz during static periods. Human sensors continuously collect positioning data at 10Hz, with heart rate data recorded every 30 seconds.

[0062] S2. Encrypted transmission process: Sensor data is encrypted with AES-256 and sent to the gateway. The gateway establishes a connection with the cloud using RSA asymmetric encryption. The transmitted data packet contains: device ID, acquisition time, and checksum.

[0063] S3. Stratified Data Analysis:

[0064] Environmental data: A sliding window algorithm is used to calculate the duration of dust concentration exceeding the standard. When three consecutive windows exceed the standard, it is marked as continuous pollution;

[0065] Structural data: Input strain sensor data into the finite element model, calculate the structural stress distribution cloud map, and combine it with the LSTM model to predict the stress change trend within 24 hours;

[0066] Personnel data: Through the overlay analysis of UWB positioning trajectory and BIM model, violations of walking routes that do not follow the prescribed route can be identified;

[0067] S4. Multi-level warning response:

[0068] Level 1 Yellow Warning: A single parameter exceeds the threshold by less than 10%, triggering an app push notification and a pop-up window in the monitoring center.

[0069] Level 2 Orange Warning: If a single parameter exceeds the threshold by 10%-30% or two parameters are slightly exceeded, an SMS notification will be sent to the project leader and the smart helmet will issue an intermittent alarm.

[0070] Level 3 Red Alert: If a parameter exceeds the threshold by more than 30%, all terminals will trigger audible and visual alarms, automatically cutting off the power supply to the relevant area, and simultaneously sending an alert message to the supervision unit and the housing and construction department's supervision platform;

[0071] S5. Closed-loop decision execution: The system generates a report including risk location, impact scope, and disposal suggestions. After the management personnel confirm the disposal measures through the APP, the system tracks the rectification time and automatically upgrades the warning if the preset time limit is exceeded.

[0072] The specific process of structural data prediction in step S3 is as follows:

[0073] S31. Normalize the historical stress data;

[0074] S32. Divide the training set, validation set, and test set into the LSTM model.

[0075] S33. Iterate the training using the Adam optimizer and stop when the validation set loss function is less than 0.01.

[0076] S34. Perform weighted fusion of the prediction results and the finite element analysis results to output the final stress prediction value.

[0077] Step S3 also includes digital twin modeling analysis:

[0078] Based on real-time collected structural displacement and stress data, a digital twin of the construction site is constructed in the cloud;

[0079] The temperature, humidity, and wind speed data collected by environmental sensors are input into the digital twin model to simulate the coupling effect of structural deformation under different environmental conditions;

[0080] Through digital twins, virtual rehearsals of key construction processes such as deep foundation pit excavation and tower crane installation are carried out, and possible stress concentration points are predicted one hour in advance. The rehearsal results are used as a supplementary basis for risk assessment. When the probability of the stress in a certain part exceeding the limit in the virtual rehearsal is greater than 70%, an early warning is triggered.

[0081] The linkage control logic of the three-level warning in step S4 is:

[0082] When the tower crane's inclination angle exceeds the threshold, the edge gateway sends an emergency stop command to the tower crane control cabinet;

[0083] When a third-level warning of a person breaking into a deep foundation pit area occurs, the surrounding sound and light alarm devices will be automatically activated, and the intrusion location and person information will be highlighted in the BIM model.

[0084] Example:

[0085] Data collection phase

[0086] Environmental sensor: The default collection frequency is 1 minute / time, which automatically switches to 10 seconds / time when the wind speed is greater than 10m / s;

[0087] Structural sensors: Deep foundation pit and high formwork sensors collect data every 5 minutes during non-construction periods and every 1 second during concrete pouring. Tower crane sensors collect data at 10Hz during lifting operations and 1Hz when stationary.

[0088] Personnel sensor: Positioning data is uploaded in real time at 10Hz, heart rate data is collected every 30 seconds, and 1Hz high-frequency collection is triggered when a person running (acceleration > 1.5g) is detected.

[0089] Data transmission process

[0090] The sensor data is encrypted locally and then sent to the gateway, which aggregates the data and adds the gateway ID and verification code.

[0091] Dual-channel transmission is adopted: key structural data is transmitted simultaneously through the 5G main channel and the optical fiber backup channel, and automatically switches when the main channel delay is greater than 300ms; environmental and personnel data are transmitted through LoRa, using frequency hopping technology to resist interference; a VPN tunnel is established between the gateway and the cloud, and the data transmission delay is controlled within 500ms, with a packet loss rate of less than 1%.

[0092] Data analysis and processing

[0093] Preprocessing: Kalman filtering is used to reduce noise on deep foundation pit displacement data to remove construction vibration interference; trajectory smoothing is performed on personnel positioning data;

[0094] Structural analysis: Input the tower crane strain data into the finite element model to calculate the boom bending moment distribution, and use the LSTM model to predict the stress change trend within 1 hour;

[0095] Personnel behavior analysis: The YOLOv5 algorithm is used to identify the helmet wearing status and UWB trajectory is used to determine whether the person has entered an unauthorized area.

[0096] Risk assessment and early warning

[0097] Threshold setting: Referring to GB50202-2018 "Standard for Acceptance of Construction Quality of Building Foundation Engineering", the foundation pit horizontal displacement warning value is set to 30mm (level 1), 40mm (level 2), and 50mm (level 3); the tower crane inclination angle threshold is 1.0° (level 1) and 1.5° (level 2);

[0098] Warning trigger:

[0099] Level 1 Yellow Warning: A single parameter exceeds the threshold but lasts for less than 5 minutes, such as "a point in the foundation pit has displaced 32mm." This warning is pushed to the safety officer via the app.

[0100] Level 2 Orange Warning: If a parameter exceeds the threshold and persists for more than 5 minutes, or two related parameters exceed the limit, the monitoring center will trigger an audible and visual alarm, and the project leader will be notified via SMS.

[0101] Level 3 Red Alert: If the parameter exceeds the threshold by more than 30%, the power supply of the tower crane will be cut off immediately, the APP will push it to all management personnel, and the emergency number will be called at the same time.

[0102] Decision support cases

[0103] When a horizontal displacement of 35mm is detected three times in a row in a certain area of ​​a deep foundation pit, the system automatically retrieves the historical data of the area and generates treatment suggestions based on the geological survey report:

[0104] Suspend stacking around the area;

[0105] Increase monitoring frequency to 10 seconds / time;

[0106] Arrange personnel to conduct on-site inspections, focusing on checking cracks in retaining piles;

[0107] The information is pushed to the safety officer through the APP. After the handling is completed, the rectification photos are uploaded to the system. The system automatically tracks the rectification time. If the loop is not closed within 2 hours, the warning will be upgraded.

[0108] According to third-party testing, the structural stress measurement error is less than 2%, the personnel positioning error is less than 0.5 meters, and the early warning response time is less than 3 seconds. During the three-month trial operation, hidden dangers such as tower crane overload 8 times, personnel not wearing safety helmets 15 times, and abnormal foundation pit displacement 3 times were successfully identified, and no safety accidents occurred.

[0109] To sum up, the present invention has a high degree of intelligence, realizes all-round, real-time and accurate monitoring of the construction process, improves the accuracy of safety risk assessment and the timeliness of early warning, provides a strong guarantee for construction safety, and has high practical value and promotion prospects.

[0110] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the above embodiments, the general skilled in the art will

[0111] It should be understood by those skilled in the art that the technical solutions described in the above embodiments can still be applied to

[0112] Modify or replace some of the technical features with equivalent ones; and these modifications or

[0113] The replacement does not deviate the essence of the corresponding technical solution from the essence of the technical solution of each embodiment of this application.

[0114] God and scope.

Claims

1. A construction safety monitoring system, characterized by: include: The perception layer (1) consists of a multi-dimensional sensor cluster, including the environmental monitoring sub-cluster (2), the structural monitoring sub-cluster (3), and the personnel monitoring sub-cluster (4); The transport layer (5) includes a wireless transmission unit (6), a wired transmission unit (7) and an edge gateway (8); Data processing layer (9), deployed on the cloud platform; The application layer (10) includes: a monitoring center client (11) that can display real-time monitoring data heat maps, equipment status topology maps and early warning information; The mobile terminal APP (12) has the functions of data query, voice broadcast warning, and electronic fence crossing reminder. The administrator account can remotely configure the sensor collection parameters.

2. A construction safety monitoring system according to claim 1, characterized in that: The environmental monitoring sub-cluster (2) includes temperature and humidity sensors (13), wind speed and direction sensors (14), noise sensors (15), and dust sensors (16), which are all distributed in the construction site operation area, material storage area, and office area; the structural monitoring sub-cluster (3) includes high-precision displacement sensors (17), strain gauge stress sensors (18), and tilt sensors (19), which are respectively pre-buried or fixed in the building foundation pedestal, beam-column nodes, scaffolding vertical pole and horizontal pole connection nodes, tower crane boom, and tower body standard sections; the personnel monitoring sub-cluster (4) uses an intelligent safety helmet (20) with an integrated UWB positioning chip and a three-axis acceleration sensor, and a wrist-worn bracelet (21) with a built-in optical heart rate sensor and blood pressure sensor.

3. A construction safety monitoring system according to claim 1, characterized in that: The wireless transmission unit (6) is composed of a 5G industrial module (22) and a LoRa gateway (23), wherein the 5G module (22) is connected to the high-frequency acquisition sensor of the structure monitoring subcluster (3), and the LoRa gateway (23) is connected to the environment monitoring subcluster (2) and the personnel monitoring subcluster (4); the wired transmission unit (7) uses a single-mode optical fiber to connect to the displacement sensor (17); the edge gateway (8) performs timestamp synchronization and protocol conversion on the sensor data, and then transmits the data to the cloud server (24) through a VPN tunnel.

4. A construction safety monitoring system according to claim 1, characterized in that: The data processing layer (9) includes: a data preprocessing module (25), which uses the Kalman filter algorithm to eliminate noise data and fills missing values ​​based on the Lagrange interpolation method; a model training module (26), which constructs a structural health assessment model that integrates LSTM neural network and finite element analysis, and a personnel behavior recognition model based on the YOLOv8 algorithm; a risk decision module (27), which has a built-in dynamic threshold database and calculates the risk level through a fuzzy comprehensive evaluation method.

5. A construction safety monitoring system according to claim 1, characterized in that: The smart helmet (20) is further integrated with: an infrared pyroelectric sensor for detecting the wearing status of the helmet; a Bluetooth 4.2 module for realizing close-range data interaction with the wristband; and an audible and visual alarm unit for triggering an alarm when a person is detected entering a dangerous area.

6. A method for using the construction safety monitoring system according to claim 1, characterized in that: The method of use includes the following steps: S1. Dynamic data collection configuration: The system automatically adjusts data collection parameters based on the construction phase. Environmental sensors sample at a 5-minute interval during foundation construction, shortening this to 1 minute during main construction. Structural sensors trigger a 1kHz high-frequency data collection during concrete pouring, reducing this to 10Hz during static periods. Human sensors continuously collect positioning data at 10Hz, with heart rate data recorded every 30 seconds. S2. Encrypted transmission process: Sensor data is encrypted with AES-256 and sent to the gateway. The gateway establishes a connection with the cloud using RSA asymmetric encryption. The transmitted data packet contains: device ID, acquisition time, and checksum. S3. Stratified Data Analysis: Environmental data: A sliding window algorithm is used to calculate the duration of dust concentration exceeding the standard. When three consecutive windows exceed the standard, it is marked as continuous pollution; Structural data: Input strain sensor data into the finite element model, calculate the structural stress distribution cloud map, and combine it with the LSTM model to predict the stress change trend within 24 hours; Personnel data: Through the overlay analysis of UWB positioning trajectory and BIM model, violations of walking routes that do not follow the prescribed route can be identified; S4. Multi-level warning response: Level 1 Yellow Warning: A single parameter exceeds the threshold by less than 10%, triggering an app push notification and a pop-up window in the monitoring center. Level 2 Orange Warning: If a single parameter exceeds the threshold by 10%-30% or two parameters are slightly exceeded, an SMS notification will be sent to the project leader and the smart helmet will issue an intermittent alarm. Level 3 Red Alert: If a parameter exceeds the threshold by more than 30%, all terminals will trigger audible and visual alarms, automatically cutting off the power supply to the relevant area, and simultaneously sending an alert message to the supervision unit and the housing and construction department's supervision platform; S5. Closed-loop decision execution: The system generates a report including risk location, impact scope, and disposal suggestions. After the management personnel confirm the disposal measures through the APP, the system tracks the rectification time and automatically upgrades the warning if the preset time limit is exceeded.

7. The method for using a construction safety monitoring system according to claim 6, characterized in that: The specific process of structural data prediction in step S3 is: S31. Normalize the historical stress data; S32. Divide the training set, validation set, and test set into the LSTM model. S33. Iterate the training using the Adam optimizer and stop when the validation set loss function is less than 0.

01. S34. Perform weighted fusion of the prediction results and the finite element analysis results to output the final stress prediction value.

8. The method for using a construction safety monitoring system according to claim 6, characterized in that: Step S3 also includes digital twin modeling analysis: Based on real-time collected structural displacement and stress data, a digital twin of the construction site is constructed in the cloud; The temperature, humidity, and wind speed data collected by environmental sensors are input into the digital twin model to simulate the coupling effect of structural deformation under different environmental conditions; Through digital twins, virtual rehearsals of key construction processes such as deep foundation pit excavation and tower crane installation are carried out, and possible stress concentration points are predicted one hour in advance. The rehearsal results are used as a supplementary basis for risk assessment. When the probability of the stress in a certain part exceeding the limit in the virtual rehearsal is greater than 70%, an early warning is triggered.

9. The method for using a construction safety monitoring system according to claim 6, characterized in that: The linkage control logic of the three-level warning in step S4 is: When the tower crane's inclination angle exceeds the threshold, the edge gateway sends an emergency stop command to the tower crane control cabinet; When a third-level warning of a person breaking into a deep foundation pit area occurs, the surrounding sound and light alarm devices will be automatically activated, and the intrusion location and person information will be highlighted in the BIM model.

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