High-rise building intelligent construction platform worker attention safety risk monitoring and early warning system

Abstract

The invention discloses a high-rise building intelligent construction platform worker attention safety risk monitoring and early warning system which integrates the modern information technology, the Internet of Things and the artificial intelligence technology and is composed of an attention detection module, a data analysis module and an early warning and response module. The attention detection module monitors the physiological and behavior data of a worker in real time through a wearable device and a fixed sensor; the data analysis module is responsible for safely transmitting the monitoring data to a cloud or a local server, and performing deep analysis by using an artificial intelligence algorithm so as to evaluate the attention state of the worker; and the early warning and response module automatically triggers early warning and executes emergency measures when attention decline or potential safety hazards are detected, so that the construction safety is ensured, and the operation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of building safety management in the process of intelligent construction, and specifically relates to a worker attention safety risk monitoring and early warning system for an intelligent construction platform of a high-rise building. Background Art

[0002] In the complex environment of intelligent construction of high-rise buildings, the attention state of construction workers is a key factor in ensuring the safety and quality of the project. However, traditional construction management methods are powerless in the face of large-scale, high-intensity construction operations. First, the construction site environment is changeable, and adverse factors such as noise, vibration, and high temperature can easily distract workers' attention, increasing the risk of operational errors and safety accidents. Secondly, traditional manual supervision methods are difficult to achieve continuous and uninterrupted attention monitoring of each worker, especially in special situations such as high-altitude operations and night construction. The supervision effect is even more insufficient. With the advancement of science and technology, especially the rapid development of modern information technology, the Internet of Things, and artificial intelligence technology, revolutionary changes have been brought to the safety management of the construction industry. These technologies can not only realize real-time data collection and transmission, but also conduct in-depth analysis of data through algorithm models to dig out hidden risks and laws. Therefore, applying these advanced technologies to the safety monitoring of workers' attention in the intelligent construction platform of high-rise buildings has become an important way to improve construction safety and efficiency. Summary of the invention

[0003] To solve the above problems, the present invention discloses a safety risk monitoring and early warning system for workers' attention on an intelligent construction platform of high-rise buildings. The system realizes real-time monitoring of workers' physiological indicators and behavioral characteristics by integrating Internet of Things technologies such as wearable devices and fixed sensors. With the help of cloud computing and artificial intelligence algorithms, the monitoring data is deeply processed and analyzed to accurately identify the potential risks of attention distraction. And through intelligent early warning and emergency response mechanisms, workers and managers are promptly reminded to take corresponding measures, thereby effectively preventing the occurrence of safety accidents and improving construction management level and work efficiency.

[0004] To achieve the above object, the technical solution of the present invention is as follows: A worker attention safety risk monitoring and early warning system for a high-rise building intelligent construction platform includes an attention detection module, a data analysis module, and an early warning and response module.

[0005] Furthermore, the specific functions of the attention detection module are: a. Real-time monitoring of workers’ status: Through wearable devices (such as smart helmets, smart watches, etc.) and fixed sensors (such as cameras, heart rate monitors, etc.), workers’ physiological indicators (such as heart rate, blood pressure, eye movement, etc.) and behavioral characteristics (such as gait, posture, etc.) are collected in real time; b. Data collection and recording: All monitored data will be collected and recorded in real time in local storage devices to ensure data integrity and traceability, providing a reliable basis for subsequent analysis; c. Data preprocessing: preprocess the collected raw data, including denoising, filtering and standardization, to ensure data quality and improve the accuracy and reliability of subsequent analysis; d. Attention assessment algorithm: Use advanced algorithm models, such as the Random Forest Classifier algorithm model, to perform real-time analysis on pre-processed data, assess workers’ attention status, and identify possible distractions.

[0006] Furthermore, the specific functions of the data analysis module are: a. Data encryption and transmission: Use advanced encryption technologies, such as AES (Advanced Encryption Standard), to encrypt the collected data and securely transmit the data to the cloud or local server via wireless or wired networks to ensure data security and privacy protection; b. Cloud storage and management: Establish a big data storage platform in the cloud to classify, store and manage the transmitted data to facilitate subsequent data analysis and query; c. Data analysis and modeling: Using cloud computing and artificial intelligence technologies, we conduct in-depth analysis and modeling of stored data, dig out potential risks and patterns of distraction, and form scientific analysis reports; d. Visualization and report generation: Through data visualization technology, the analysis results are intuitively displayed to users in the form of charts, reports, etc., and detailed attention monitoring reports are generated to help managers quickly understand the workers' attention status.

[0007] Furthermore, the specific functions of the early warning and response module are: a. Real-time warning system: Once it detects that workers’ concentration has dropped significantly or there is a safety hazard, the system will immediately send out a warning signal (such as sound alarm, vibration prompt, etc.) to alert workers and managers; b. Automatic emergency response: The system automatically executes corresponding emergency response measures according to the warning level, such as prompting workers to suspend work, reminding workers to rest, reallocating work tasks, etc., to ensure the safety of the construction site; c. Manager notification: The system will promptly notify on-site managers of warning information and emergency response measures through intercom systems and voice robot telephone systems, so that managers can intervene in time and improve management efficiency; d. Recording and feedback: Keep detailed records of each warning and response to form historical data for subsequent analysis and improvement by management personnel; at the same time, continuously optimize and improve the system's warning and response strategies through the feedback mechanism.

[0008] The operating method of the building intelligent construction platform worker attention safety risk monitoring and early warning system of the present invention comprises the following steps: (1) The system first initializes the system through wearable devices (such as smart helmets, smart watches, etc.) and fixed sensors (such as cameras, heart rate monitors, etc.) installed on the workers, and begins to monitor the workers' physiological indicators and behavioral characteristics in real time; these devices will collect the workers' heart rate, eye movement, gait, posture and other data in real time, and record them in local storage devices to ensure the integrity and traceability of the data; then, the collected raw data is denoised, filtered and standardized to improve data quality and ensure the accuracy and reliability of the pre-processed data. Finally, the system uses a random forest classifier to analyze the pre-processed data in real time, evaluate the workers' attention state, and identify possible distractions, thereby achieving accurate attention state assessment; (2) The system encrypts the assessment results and monitoring data, and securely transmits the data to the cloud or local server through a wireless or wired network for storage and management. In the cloud, a big data storage platform is established to classify, store and manage the transmitted data to ensure the security of data storage and facilitate subsequent data analysis and query. Subsequently, cloud computing and artificial intelligence technologies are used to conduct in-depth analysis and modeling of the stored data to uncover potential risks and patterns of attention distraction and form a scientific analysis report. Through data visualization technology, the analysis results are intuitively displayed to users in the form of charts, reports, etc., and a detailed attention monitoring report is generated to help managers quickly understand the workers' attention status. (3) When the system detects that workers' attention has dropped significantly or there are safety hazards, the system immediately sends out warning signals (such as sound alarms, vibration prompts, etc.) to remind workers and managers of potential risks; the system automatically executes corresponding emergency response measures according to the warning level, such as prompting workers to suspend work, reminding workers to rest, and reallocating work tasks, etc., to ensure the safety of the construction site; at the same time, the system will promptly notify on-site managers of warning information and emergency response measures through intercom systems and voice robot telephone systems, so that they can intervene in time and improve management efficiency. Each warning and response situation is recorded in detail to form historical data for subsequent analysis and improvement by managers, and the system's warning and response strategies are continuously optimized and improved through feedback mechanisms.

[0009] The beneficial effects of the present invention are: 1. By real-time monitoring of workers' physiological indicators and behavioral characteristics, workers' attention status can be accurately assessed to effectively improve safety during the construction process.

[0010] 2. Use advanced artificial intelligence algorithms and data analysis technologies to quickly identify potential risks of worker distraction and take timely countermeasures.

[0011] 3. Ensure data security and privacy protection through data encryption and transmission technology.

[0012] 4. The early warning and emergency response mechanism is flexible and effective, which can promptly remind workers and managers and improve construction management efficiency.

[0013] 5. Help managers continuously improve construction management methods and improve overall construction quality by keeping detailed records and generating monitoring reports. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is the overall architecture diagram of the present invention.

[0015] Figure 2 It is the technical roadmap of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0017] As shown in the figure, a high-rise building intelligent construction platform worker attention safety risk monitoring and early warning system according to the present invention includes: 1) Attention Detection Module The main task of the attention detection module is to perceive the environment of the construction site and the status of the workers. It is similar to the "sensory organ" of the system and is responsible for comprehensively collecting and monitoring the on-site environmental information and the physiological and behavioral characteristics of the workers.

[0018] Specific functions of the attention detection module: a. Real-time monitoring of workers’ status: Through wearable devices (such as smart helmets, smart watches, etc.) and fixed sensors (such as cameras, heart rate monitors, etc.), workers’ physiological indicators (such as heart rate, blood pressure, eye movements, etc.) and behavioral characteristics (such as gait, posture, etc.) are collected in real time.

[0019] b. Data collection and recording: All monitored data will be collected and recorded in real time in local storage devices to ensure data integrity and traceability, providing a reliable basis for subsequent analysis.

[0020] c. Data preprocessing: Preprocess the collected raw data, including denoising, filtering and standardization, to ensure data quality and improve the accuracy and reliability of subsequent analysis.

[0021] d. Attention assessment algorithm: Use advanced algorithm models, such as the Random Forest Classifier algorithm model, to perform real-time analysis on pre-processed data, assess workers’ attention status, and identify possible distractions.

[0022] 2) Data analysis module The main task of the data analysis module is to ensure the secure transmission and efficient processing of data, and to achieve in-depth analysis and management of collected data through advanced encryption technology and cloud computing capabilities. The specific functions are as follows: a. Data encryption and transmission: Use advanced encryption technologies, such as AES (Advanced Encryption Standard), to encrypt the collected data and securely transmit the data to the cloud or local server via wireless or wired networks to ensure data security and privacy protection.

[0023] b. Cloud storage and management: Establish a big data storage platform in the cloud to classify, store and manage the transmitted data to facilitate subsequent data analysis and query.

[0024] c. Data analysis and modeling: Using cloud computing and artificial intelligence technologies, we conduct in-depth analysis and modeling of stored data, uncover potential risks and patterns of distraction, and form scientific analysis reports.

[0025] d. Visualization and report generation: Through data visualization technology, the analysis results are intuitively displayed to users in the form of charts, reports, etc., and detailed attention monitoring reports are generated to help managers quickly understand the workers' attention status.

[0026] 3) Early warning and response module The main task of the early warning and response module is to monitor the workers' attention status in real time, issue early warning signals in time, and implement corresponding emergency response measures to ensure the safety of the construction site. The specific functions are as follows: a. Real-time warning system: Once it detects that workers’ concentration has dropped significantly or there is a safety hazard, the system will immediately send out a warning signal (such as sound alarm, vibration prompt, etc.) to alert workers and managers.

[0027] b. Automatic emergency response: The system automatically executes corresponding emergency response measures according to the warning level, such as prompting workers to suspend work, reminding workers to rest, reallocating work tasks, etc., to ensure the safety of the construction site.

[0028] c. Manager notification: The system will promptly notify on-site managers of warning information and emergency response measures through intercom systems and voice robot telephone systems, so that managers can intervene in time and improve management efficiency.

[0029] d. Recording and feedback: Record each warning and response in detail to form historical data for subsequent analysis and improvement by management personnel. At the same time, through the feedback mechanism, continuously optimize and improve the system's warning and response strategies.

[0030] The technical architecture of the high-rise building intelligent construction platform worker attention safety risk monitoring and early warning system described in the present invention is as follows: Attention detection module: wearable devices and fixed sensor technologies.

[0031] The attention detection module collects workers' physiological indicators (such as heart rate, blood pressure, eye movement) and behavioral characteristics (such as gait, posture) in real time by integrating wearable devices (such as smart helmets, smart watches) and fixed sensors (such as cameras, heart rate monitors). These devices are deployed at key locations and on workers at the construction site to ensure comprehensive monitoring. The collected data is instantly recorded in the local storage device through a wireless or wired network to ensure the integrity and traceability of the data. Subsequently, the collected raw data is preprocessed, including denoising, filtering and standardization, to improve data quality and the accuracy of subsequent analysis. The preprocessed data is analyzed in real time through advanced algorithm models. The system can accurately evaluate the workers' attention status and identify potential distractions, thereby achieving continuous monitoring and management of workers' attention and improving construction safety and efficiency. Data analysis module: The data analysis module uses encryption technology and cloud computing technology.

[0032] The data analysis module first encrypts all collected data to ensure the security and privacy of the data during transmission. The encrypted data is securely transmitted to the cloud or local server through a highly reliable wireless network or wired network. The cloud storage platform efficiently classifies, stores and manages the transmitted data to facilitate subsequent data analysis and query. Using cloud computing and artificial intelligence technology, the system conducts in-depth analysis and modeling of the stored data, digs out the potential risks and patterns of workers' distraction, and generates scientific analysis reports. The data processing results generate intuitive charts and detailed attention monitoring reports through data visualization technology, which helps managers quickly understand the workers' attention status, provide a reliable basis for decision-making, and further optimize construction management. Early Warning and Response Module: The Early Warning and Response Module uses real-time monitoring and automated emergency response technology.

[0033] The early warning and response module monitors the workers' attention status in real time. Once the system detects that the workers' attention has dropped significantly or there are safety hazards, it will immediately send out early warning signals (such as sound alarms and vibration prompts) to remind workers and managers of potential risks. According to the level of the early warning signal, the system will automatically execute corresponding emergency response measures, such as prompting workers to suspend work, reminding workers to rest, and reallocating work tasks to ensure the safety of the construction site. Early warning information and emergency response measures are promptly notified to on-site managers through intercom systems and voice robot telephone systems, so that they can quickly intervene and improve management efficiency. The system will record each early warning and response in detail to form historical data for managers to conduct subsequent analysis and improvement. Through the feedback mechanism, the system continuously optimizes and improves the early warning and response strategies to ensure safety and efficiency during the construction process. System physical architecture: Attention detection module: The physical architecture of the attention detection module includes wearable devices such as smart helmets (such as X-Sense ProHelmet) and smart watches (such as Fitbit Charge 5), as well as fixed sensors such as cameras (such as Hikvision DS-2CD2085FWD-I) and heart rate monitors (such as Polar H10). These devices collect workers' physiological indicators and behavioral characteristics in real time, and the data is recorded and pre-processed in real time through local storage devices (such as Synology DS220+) to ensure the integrity and traceability of the data, providing a reliable basis for subsequent analysis.

[0034] Data analysis module: The physical architecture of the data analysis module includes wireless network modules (such as TP-Link ArcherAX50) and wired network modules (such as Cisco SG350-28) for secure transmission of encrypted data. Data is transmitted to cloud computing platforms (such as AWS EC2) for efficient storage and management in the cloud. Data analysis and modeling software (such as TensorFlow) conducts in-depth analysis and modeling of data to generate intuitive charts and detailed attention monitoring reports.

[0035] Early warning and response module: The physical architecture of the early warning and response module includes sound alarm devices (such as HoneywellXS100-EN) and vibration reminder devices (such as PAVLOK 2) for real-time early warning signals. In addition, the module also includes an emergency response management system (such as Incident Response Module) to automatically execute corresponding emergency response measures and promptly notify on-site managers through SMS notification systems (such as Twilio SMS) and email notification systems (such as SendGrid) to improve construction management efficiency. Operating platform: Since the implementation of the system involves the integration and processing of data from multiple parties, and the implementation and execution of a large number of algorithms require a stable and efficient platform support, in order to solve compatibility issues and ensure the efficient operation of the system, the system is implemented on a unified operating platform. Specifically, the system uses a server platform based on the Linux operating system, which has high reliability and good compatibility, and can support a variety of programming languages ​​​​and software frameworks, such as Python, TensorFlow, Docker, etc. The following is a simple attention assessment algorithm code example, implemented using Python and some common libraries. This code is based on hypothetical physiological indicators and behavioral feature data, including heart rate, eye movement, etc. The random forest classifier uses this data to assess the worker's attention state. The random forest classifier is an integrated machine learning algorithm that builds multiple decision trees and combines their predictions to improve classification accuracy and robustness. The algorithm reduces the risk of overfitting and is able to assess the importance of features by randomly selecting features and samples during training. # Example dataset (heart rate, eye movement frequency, posture stability, etc.) data = { 'heart_rate': [75, 85, 95, 70, 90, 100, 60, 80, 110], 'eye_movement_frequency': [30, 40, 45, 20, 35, 50, 10, 25, 55], 'posture_stability': [80, 60, 70, 90, 50, 40, 95, 85, 30], 'attention_status': [1, 0, 0, 1, 0, 0, 1, 1, 0] # 1 means focused, 0 means distracted } # Convert to DataFrame df = pd.DataFrame(data) # Features and labels X = df[['heart_rate', 'eye_movement_frequency', 'posture_stability']] y = df['attention_status'] # Data Standardization scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # Random Forest Classifier clf = RandomForestClassifier(n_estimators=100, random_state=42) clf.fit(X_scaled, y) # New physiological and behavioral data input new_data = { 'heart_rate':

[85] , 'eye_movement_frequency':

[42] , 'posture_stability':

[65] } # Convert to DataFrame and normalize new_df = pd.DataFrame(new_data) new_X_scaled = scaler.transform(new_df) # Predict attention state predicted_attention_status = clf.predict(new_X_scaled) # Output prediction results if predicted_attention_status[0] == 1: print("Focus") else: print("Distraction")

[0036] 1. Data preparation: The sample dataset includes heart rate, eye movement frequency, and posture stability, as well as the corresponding attention state labels.

[0037] 2. Data standardization: Use Standard Scaler to standardize the data to improve the training effect of the model.

[0038] 3. Model training: Use Random Forest Classifier to train the model to predict the worker’s attention state based on physiological and behavioral data.

[0039] 4. New data prediction: Input new physiological and behavioral data into the model to predict the worker’s attention state.

[0040] It should be noted that the above content merely illustrates the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications all fall within the protection scope of the claims of the present invention.

Claims

1. High-rise building intelligent construction platform worker attention safety risk monitoring and early warning system, characterized by: It includes attention detection module, data analysis module and early warning and response module.

2. The high-rise building intelligent construction platform worker attention safety risk monitoring and early warning system according to claim 1 is characterized by: The attention detection module, data analysis module and early warning and response module are seamlessly connected through wireless and wired networks to ensure that the entire process from data collection, transmission, processing to early warning and response is carried out efficiently, safely and in real time; The attention detection module collects workers' physiological and behavioral data in real time, encrypts and analyzes them through the data analysis module, and finally the early warning and response module issues early warning signals and emergency responses.

3. The high-rise building intelligent construction platform worker attention safety risk monitoring and early warning system according to claim 2 is characterized by: Its physical structure includes integrated smart helmets, smart watches, cameras, heart rate monitors and other wearable devices and fixed sensors, as well as local storage devices for data storage and transmission, wireless and wired network modules, and cloud computing platforms.

4. The high-rise building intelligent construction platform worker attention safety risk monitoring and early warning system according to claim 2 is characterized by: The specific functions of the attention detection module are as follows: a. Real-time monitoring of workers’ status: through wearable devices and fixed sensors, workers’ physiological indicators and behavioral characteristics are collected in real time; b. Data collection and recording: All monitored data will be collected and recorded in real time in local storage devices to ensure data integrity and traceability, providing a reliable basis for subsequent analysis; c. Data preprocessing: preprocess the collected raw data, including denoising, filtering and standardization, to ensure data quality and improve the accuracy and reliability of subsequent analysis; d. Attention assessment algorithm: Use advanced algorithm models to perform real-time analysis on pre-processed data, assess workers’ attention status, and identify possible distractions.

5. The high-rise building intelligent construction platform worker attention safety risk monitoring and early warning system according to claim 1 is characterized by: The data analysis module receives the data transmitted by the attention detection module and performs further processing.

6. The high-rise building intelligent construction platform worker attention safety risk monitoring and early warning system according to claim 5 is characterized by: The data analysis module includes a data encryption and transmission unit, a cloud storage and management unit, a data analysis and modeling unit, and a visualization and report generation unit.

7. The high-rise building intelligent construction platform worker attention safety risk monitoring and early warning system according to claim 5 is characterized by: Specific functions of the data analysis module: a. Data encryption and transmission: Use advanced encryption technology to encrypt the collected data and securely transmit the data to the cloud or local server via wireless or wired networks to ensure data security and privacy protection; b. Cloud storage and management: Establish a big data storage platform in the cloud to classify, store and manage the transmitted data to facilitate subsequent data analysis and query; c. Data analysis and modeling: Using cloud computing and artificial intelligence technologies, we conduct in-depth analysis and modeling of stored data, dig out potential risks and patterns of distraction, and form scientific analysis reports; d. Visualization and report generation: Through data visualization technology, the analysis results are intuitively displayed to users in the form of charts and reports, and detailed attention monitoring reports are generated to help managers quickly understand the workers' attention status.

8. The high-rise building intelligent construction platform worker attention safety risk monitoring and early warning system according to claim 1 is characterized by: Specific functions of the early warning and response module: a. Real-time warning system: Once it detects that workers’ concentration has dropped significantly or there are safety hazards, the system will immediately send out a warning signal to alert workers and managers; b. Automatic emergency response: The system automatically executes corresponding emergency response measures according to the warning level to ensure the safety of the construction site; c. Manager notification: The system will promptly notify on-site managers of warning information and emergency response measures through the intercom system and voice robot telephone system, so that managers can intervene in time and improve management efficiency; d. Recording and feedback: Keep detailed records of each warning and response to form historical data for subsequent analysis and improvement by management personnel; at the same time, continuously optimize and improve the system's warning and response strategies through the feedback mechanism.

9. The high-rise building intelligent construction platform worker attention safety risk monitoring and early warning system according to claim 1 is characterized by: The following steps are involved: (1) System initialization and data collection: The system is initialized through wearable devices and fixed sensors installed on workers, and begins to monitor the workers' physiological indicators and behavioral characteristics in real time; These devices will collect workers' heart rate, eye movement, gait, and posture data in real time and record them in local storage devices to ensure data integrity and traceability; the collected raw data will be denoised, filtered, and standardized to improve data quality and ensure the accuracy and reliability of pre-processed data; the system uses advanced artificial intelligence algorithms to analyze pre-processed data in real time, evaluate workers' attention status, and identify possible distractions, thereby achieving accurate attention status assessment; (2) Data transmission and processing: Encrypt the assessment results and monitoring data, and securely transmit the data to the cloud or local server via wireless or wired networks for storage and management; exist In the cloud, a big data storage platform is established to classify, store and manage the transmitted data to ensure the security of data storage and facilitate subsequent data analysis and query; cloud computing and artificial intelligence technologies are used to conduct in-depth analysis and modeling of stored data, to discover potential risks and patterns of distraction, and to form scientific analysis reports; Through data visualization technology, the analysis results are intuitively presented to users in the form of charts and reports, and detailed attention monitoring reports are generated to help managers quickly understand the workers' attention status; (3) Early warning and emergency response: When the system detects that workers' concentration has significantly decreased or there are safety hazards, the system will immediately send out an early warning signal to remind workers and managers of potential risks. The system will automatically execute corresponding emergency response measures according to the early warning level to ensure the safety of the construction site. At the same time, the system will promptly notify on-site managers of early warning information and emergency response measures through the intercom system and voice robot telephone system, so that they can intervene in time and improve management efficiency. Each early warning and response situation will be recorded in detail to form historical data for managers to conduct subsequent analysis and improvement. The system's early warning and response strategies will be continuously optimized and improved through the feedback mechanism to ensure safety and efficiency during the construction process.

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