An intelligent management method and system for construction safety risks of prefabricated buildings

Abstract

The present invention relates to the field of building construction safety, and in particular to an intelligent management method and system for construction safety risks of prefabricated buildings. The method receives construction personnel data and assembly construction data within a preset range in real time; analyzes the construction type based on the construction personnel data and assembly construction data; obtains current environmental data, and analyzes whether construction conditions are met based on the environmental data and construction type; when the construction conditions are met, analyzes the risk level of the current construction in combination with the environmental data and construction type; obtains construction personnel behavior data, and determines whether the behavior data meets early warning conditions based on the current construction risk level; and sends early warning information to target construction personnel when the behavior data meets the early warning conditions. By collecting construction environment and personnel behavior data in real time, risks are dynamically assessed. This data-driven approach significantly improves the accuracy of early warnings and effectively reduces the problems of missed reports and false alarms caused by traditional static assessments.

Description

Technical Field

[0001] The present application relates to the field of building construction safety technology, and in particular to an intelligent management method and system for construction safety risks of prefabricated buildings. Background Art

[0002] Prefabricated buildings (also known as prefabricated buildings) are a construction model in which the various components of a building (such as walls, floor slabs, columns, etc.) are prefabricated in a factory and then transported to the construction site for assembly. Although this construction method has advantages such as shortening construction time, improving construction quality, and reducing resource waste, there are also some safety risks during the construction process. Such as working at heights, mechanical injuries, falls, etc. Weather changes at the construction site (such as strong winds, heavy rain, lightning, etc.) may affect construction safety, especially when working at heights. Develop emergency plans and adjust construction plans according to weather changes to ensure that operations are carried out under safe climatic conditions.

[0003] Traditional video surveillance and manual inspections rely on humans to monitor and identify safety hazards at construction sites. Due to the complex and rapidly changing construction environment, manual monitoring struggles to provide comprehensive, real-time coverage, making it easy to miss certain danger signals. Monitoring personnel may miss critical data due to fatigue, negligence, or environmental interference. Furthermore, existing safety monitoring systems rely on fixed risk thresholds for early warning. These static thresholds are unable to adapt to the changing construction environment, construction type, and personnel status, resulting in some situations where potential hazards cannot be detected in a timely manner. For example, certain behaviors may be considered normal in high-risk operations, but may lead to excessive warnings in low-risk operations, thus affecting the accuracy of warnings.

[0004] Therefore, the existing technology has defects and needs to be improved. Summary of the Invention

[0005] In order to solve one or several problems in the prior art, the main purpose of this application is to provide an intelligent management method and system for construction safety risks of prefabricated buildings.

[0006] In order to achieve the above-mentioned invention objectives, the present application proposes an intelligent management method for construction safety risks of prefabricated buildings, the method comprising:

[0007] Receive construction personnel data and assembly construction data within the preset range in real time;

[0008] analyzing the construction type based on the construction personnel data and the assembly construction data;

[0009] Obtain current environmental data, and analyze whether construction conditions are met based on the environmental data and construction type;

[0010] When construction conditions are met, analyze the risk level of the current construction by combining environmental data and construction type;

[0011] Obtaining behavioral data of construction personnel, and determining whether the behavioral data meets the warning conditions based on the risk level of the current construction;

[0012] When the behavior data meets the warning conditions, a warning message is sent to the target construction personnel.

[0013] The present application also provides an intelligent management system for construction safety risks of prefabricated buildings, including:

[0014] A receiving module, used to receive construction personnel data and assembly construction data within a preset range in real time;

[0015] A first analysis module is used to analyze the construction type based on the construction personnel data and the assembly construction data;

[0016] A judgment module is used to obtain current environmental data and analyze whether construction conditions are met based on the environmental data and construction type;

[0017] The second analysis module is used to analyze the risk level of the current construction by combining environmental data and construction type when the construction conditions are met;

[0018] An acquisition module is used to acquire the behavior data of the construction personnel and determine whether the behavior data meets the warning conditions according to the risk level of the current construction;

[0019] The sending module is used to send warning information to the target construction personnel when the behavior data meets the warning conditions.

[0020] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0021] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0022] The intelligent management method and system for construction safety risks of prefabricated buildings in the embodiments of the present application dynamically assess risks by collecting construction environment and personnel behavior data in real time. This data-driven approach greatly improves the accuracy of early warnings and effectively reduces the problems of missed reports and false alarms caused by traditional static assessments. Combined with algorithms, the automated early warning system can promptly notify relevant personnel when the risk threshold is triggered, reducing the response time to accidents, thereby effectively reducing the incidence of safety accidents. The intelligent monitoring system reduces dependence on manual inspections, reduces monitoring blind spots caused by human factors, improves the efficiency and reliability of safety management, and ensures that construction personnel work in a safe environment. The system provides real-time data feedback to help managers make decisions quickly. This timely flow of information improves the flexibility of on-site management and can adapt to sudden safety issues more quickly. Through the application of technology, the safety awareness of construction personnel is enhanced, and a self-supervision mechanism for safety responsibilities is formed, thereby improving the safety of the construction site as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flowchart of an intelligent management method for construction safety risks of prefabricated buildings according to an embodiment of the present application is provided;

[0024] Figure 2 A flowchart of an intelligent management method for construction safety risks of prefabricated buildings according to an embodiment of the present application is provided;

[0025] Figure 3 This is a schematic block diagram of the structure of an intelligent management system for construction safety risks of prefabricated buildings according to one embodiment of the present application;

[0026] Figure 4 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0027] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0029] Reference Figure 1 In an embodiment of the present application, a method for intelligent management of construction safety risks of prefabricated buildings is provided, the method comprising:

[0030] S1. Receive construction personnel data and assembly construction data within a preset range in real time;

[0031] S2. Analyzing the construction type based on the construction personnel data and the assembly construction data;

[0032] S3. Obtain current environmental data and analyze whether construction conditions are met based on the environmental data and construction type;

[0033] S4. When construction conditions are met, analyze the risk level of the current construction based on environmental data and construction type;

[0034] S5. Obtaining the behavior data of the construction personnel and determining whether the behavior data meets the warning conditions based on the risk level of the current construction;

[0035] S6. When the behavior data meets the warning conditions, a warning message is sent to the target construction personnel.

[0036] As described in steps S1-S2 above, the system uses sensors, RFID, GPS, and other technologies to collect real-time data such as construction worker identity, location, and status, as well as data from the assembly and construction process (such as equipment status and material usage). Based on the project's scale and characteristics, the system sets a reasonable preset range to monitor construction activities within that area. This allows for real-time understanding of dynamic changes at the construction site, ensuring that managers can keep abreast of construction progress and personnel status. Timely access to construction data allows for the timely identification of potential safety hazards, improving overall safety assurance. Construction site environments are complex and ever-changing, and the collection of real-time data helps improve the timeliness and accuracy of decision-making. Data-driven management approaches enable more precise resource allocation and safety management. The system uses algorithms to analyze collected construction worker and construction data to identify specific construction types (e.g., frame structures, concrete pouring, etc.). The system categorizes different types of construction processes and identifies corresponding risk characteristics. This system accurately identifies the current construction type, providing a foundation for subsequent risk analysis. Based on the characteristics of each construction type, more customized safety management strategies can be provided. Different types of construction have different risk characteristics, necessitating accurate analysis of the construction type to formulate appropriate safety management measures. Able to dynamically adjust the allocation of resources and personnel according to the type of construction.

[0037] As described in steps S3-S4 above, the system uses environmental monitoring equipment (such as sensors for temperature, humidity, wind speed, and noise) to collect real-time environmental data from the construction site. Combining the construction type with the environmental data, the system analyzes whether current construction conditions are met using pre-set standards and rules. This ensures that construction is carried out under suitable environmental conditions, thereby reducing the impact of environmental factors on construction safety. Prompt alerts are issued in the event of unsuitable conditions, avoiding safety risks associated with construction. Changes in the construction environment have a direct impact on construction safety. Timely acquisition and analysis of environmental data can prevent accidents caused by these changes. This ensures that the construction process complies with relevant safety regulations and standards. Based on the identified construction type and current environmental data, the system applies a risk assessment model to calculate the risk level of the current construction project (e.g., low, medium, or high). The risk level can be dynamically adjusted as the environment and construction conditions change. Managers can formulate appropriate safety measures and response strategies based on the real-time risk assessment results. High-risk construction projects can be prioritized for more safety resources and personnel to ensure construction safety. Scientific risk assessment can reduce the influence of subjective factors on decision-making, improving the scientific and effective nature of decision-making. Dynamic risk assessment can help managers adjust strategies in real time and enhance responsiveness.

[0038] As described in steps S5-S6 above, the system monitors construction worker behavior (such as operating procedures and movement patterns) in real time and collects relevant data. Based on the current construction risk level and pre-set behavioral standards, it determines whether the construction worker's behavior meets safety requirements and whether there are potential hazards. By monitoring behavioral data, construction workers can be reminded of safety concerns in real time, enhancing their safety awareness. Promptly identifying behaviors that do not meet safety requirements allows intervention measures to reduce the incidence of safety accidents. Construction worker behavior is a significant factor affecting construction safety, and monitoring and analysis can effectively improve safety management. Establishing a behavior monitoring mechanism provides immediate feedback on worker behavior, enhancing the real-time nature of safety management. When the system detects that a construction worker's behavior does not meet safety standards and the current risk level meets the warning criteria, it automatically generates a warning message and sends it to the relevant personnel via SMS, app push, or other means. The system can set different warning levels, sending different types of warning messages tailored to different risk levels. Construction workers receive timely warnings and can take necessary safety measures. This immediate warning mechanism effectively reduces the likelihood of accidents. Timely warnings encourage construction workers to take immediate action, reducing their exposure to risk.

[0039] As mentioned above, real-time data on the construction environment and personnel behavior is collected to dynamically assess risks. This data-driven approach significantly improves the accuracy of early warnings and effectively reduces the problems of missed alerts and false alarms associated with traditional static assessments. Combined with algorithms, the automated early warning system promptly notifies relevant personnel when risk thresholds are triggered, reducing response time to incidents and thereby effectively reducing the incidence of safety incidents. The intelligent monitoring system reduces reliance on manual inspections, eliminates blind spots caused by human factors, improves the efficiency and reliability of safety management, and ensures that construction workers work in a safe environment. The system provides real-time data feedback, enabling managers to make quick decisions. This timely flow of information increases the flexibility of on-site management and enables faster adaptation to unexpected safety issues. Through the application of technology, construction workers' safety awareness is enhanced, and a self-monitoring mechanism for safety responsibilities is established, thereby improving overall construction site safety.

[0040] Reference Figure 2 In one embodiment, the method of obtaining current environmental data and analyzing the risk level of the current construction in combination with the environmental data and the construction type includes:

[0041] S41. Acquire current environmental data, where the environmental data includes meteorological parameters, lighting parameters, and noise parameters;

[0042] S42. Analyze standard construction parameters of the current construction type according to the construction type;

[0043] S43: Construct an impact analysis model, input the environmental data and construction standard parameters into the impact analysis model, and output the impact coefficient of each parameter in the environmental data on the construction standard parameters through the impact analysis model;

[0044] S44. Determine a weight coefficient for each parameter in the environmental data based on the output result;

[0045] S45, analyzing the difference between each parameter in the environmental data and the construction standard parameter;

[0046] S46. Based on the gap value and the weight coefficient, perform weighted summation on the gap value of each environmental factor to obtain a comprehensive impact coefficient;

[0047] S47. According to the comprehensive impact coefficient, risk levels are divided, and the risk levels include low risk, medium risk and high risk.

[0048] As described in the previous steps, meteorological monitoring equipment collects information such as temperature, humidity, and wind speed. Light sensors measure light intensity at the construction site. Noise monitors collect ambient noise data. This environmental data provides foundational information for subsequent analysis, helping to fully understand the external conditions at the construction site. This is the first step in safety risk analysis, ensuring that the system can assess the actual environment. Based on the nature of the construction project (e.g., building, road, etc.), appropriate construction standards are determined. For example, safety distances and allowable noise levels are required. These determined construction standard parameters provide a benchmark for subsequent risk analysis, making the analysis results more targeted and actionable. Statistical or machine learning methods can be used to construct a model to quantify the impact of environmental data on construction standard parameters. The model uses the acquired environmental data and construction standard parameters as input, and is trained based on historical data to output impact coefficients. By quantifying the influencing factors, the degree of impact of each environmental parameter on construction safety can be clearly determined. The impact coefficients output by the impact analysis model are used to determine the relative importance of each environmental parameter. The impact coefficients can be normalized for comparison and weighting. Determining the weighting coefficients better reflects the combined impact of different environmental factors on construction safety, ensuring that the assessment results are more consistent with the actual risk situation. The gap between each environmental parameter and its corresponding construction standard parameter is calculated to reflect the degree of deviation between current environmental conditions and safety standards. This gap value can intuitively reflect safety hazards at the construction site, helping managers quickly identify risk factors that require attention. The gap value of each environmental parameter is multiplied by its weight coefficient and summed to obtain a comprehensive impact coefficient. This weighted summation comprehensively considers the impact of various environmental factors to form a holistic risk assessment indicator, allowing managers to clearly understand the current risk situation. Based on the value of the comprehensive impact coefficient, risks are classified into three levels: low risk, medium risk, and high risk, and corresponding response measures are formulated. This risk classification allows managers to quickly implement targeted measures and effectively prevent safety accidents. This hierarchical management system can improve the level of safety management at construction sites.

[0049] In one embodiment, after the step of analyzing the risk level of the current construction in combination with the environmental data and the construction type, the method includes:

[0050] When the risk level is low risk or medium risk, the behavioral data and physiological data of the construction workers are acquired in real time;

[0051] Setting status feedback data according to different risk levels, building a verification model based on the status feedback data, inputting the behavioral data, physiological data, and risk level into the verification model, analyzing whether the status feedback of the construction workers matches the current risk level through the verification model, and outputting the analysis results;

[0052] If the result of the analysis is that the status feedback of the construction personnel does not match the risk level, identifying the risk level corresponding to the status feedback of the construction personnel;

[0053] If the risk level corresponding to the status feedback is a low level, it is determined that the warning condition is met, and a warning message is sent to the construction personnel based on the result of the determination.

[0054] As mentioned above, when the construction site's risk level is assessed as low or medium, the system collects construction workers' behavioral data (such as movement trajectory and work posture) and physiological data (such as heart rate and body temperature) in real time. This real-time monitoring allows for timely detection of abnormal conditions in construction workers. For example, if a worker's heart rate increases significantly and their behavioral data indicates signs of fatigue or discomfort, the system can quickly identify potential risks. The system then sets corresponding status feedback data based on the risk level. For example, in a low-risk situation, the status feedback might be "normal work," while in a medium-risk situation, the status feedback might be "increased vigilance required." This setting helps management quickly understand the relationship between the current construction status and risk. For example, when the construction type involves aerial work, even though the risk level is assessed as medium, the status feedback still requires workers to remain vigilant. The collected behavioral data, physiological data, and risk level are input into a validation model for analysis to determine whether the worker's status feedback matches the current risk level. The validation model can identify any potential risks. For example, if a worker's physiological data (such as heart rate) is abnormally high in a low-risk situation, the model will identify this mismatch and issue a corresponding warning. If the analysis results indicate a mismatch between a worker's status feedback and their risk level, the system will identify the risk level corresponding to their actual status feedback. This step ensures that any potential risks are promptly identified. For example, if a worker exhibits restless behavior during normal work, the system can identify the risk level corresponding to their status feedback and respond accordingly. If the risk level corresponding to their status feedback is determined to be low, the system will send an early warning message to the worker, reminding them to be vigilant. This early warning message allows workers to adjust their behavior promptly to avoid accidents. For example, if the system determines that a worker's status feedback indicates fatigue despite being in a low-risk situation, the early warning message can remind them to rest, reducing the likelihood of an accident. Through real-time monitoring and data analysis, construction site safety is significantly improved, enabling rapid identification and response to potential risks, protecting workers. An effective early warning system helps workers adjust their work pace appropriately, thereby improving overall construction efficiency. The system provides scientific decision-making support through data analysis, allowing managers to make more informed decisions based on the analysis results and reduce the likelihood of accidents.

[0055] Consider a construction site where a risk assessment indicates a medium risk level. The system monitors a construction worker's elevated heart rate in real time, while behavioral data indicates the worker is frequently checking their phone, exhibiting signs of fatigue and distraction. Validation model analysis reveals that the worker's status feedback does not meet the requirements for a medium risk level. Therefore, the system identifies the risk level corresponding to this feedback as high and sends a warning message, advising the worker to take a break.

[0056] In one specific embodiment, behavioral data includes movement speed: the deviation of a construction worker's movement speed (e.g., walking, running) from a normal speed. Work posture: posture recognition technology is used to detect whether a construction worker's work posture meets safety standards (e.g., whether they are bending over, lifting heavy objects, etc.). Concentration: behavioral recognition is used to determine whether a worker is focused on the current task (e.g., frequently checking their phone). Physiological data includes heart rate: real-time monitoring of changes in the worker's heart rate. Typically, a worker's heart rate should be within a normal range (e.g., 60-100 bpm). Body temperature: monitoring changes in body temperature. The normal range is 36.1°C to 37.2°C. Respiratory rate: monitoring respiratory rate. The normal range is 12-20 breaths / minute. Scores are set based on behavioral and physiological data: Movement speed: normal (score 10); Work posture: poor (score 4); Concentration: low (score 5); Heart rate: 95 bpm (score 8); Body temperature: 37.5°C (score 6); Respiratory rate: 22 breaths / minute (score 4). Based on the comprehensive scoring results, a scoring standard is set: a comprehensive score of 0-10 indicates low risk; a comprehensive score of 11-20 indicates medium risk; and a comprehensive score of 21-30 indicates high risk. Based on the above analysis, a comprehensive score is assigned based on multiple parameters (behavioral data and physiological data) to determine the status feedback of construction workers.

[0057] In one embodiment, after the step of analyzing the status feedback of the construction workers using the verification model to determine whether it matches the current risk level and outputting the analysis results, the method further includes:

[0058] If the result of the analysis is that the status feedback of the construction personnel matches the current risk level;

[0059] Determining, based on the behavioral data and physiological data, a time-series-based behavioral change trend and a physiological data change trend of the construction personnel;

[0060] Constructing a prediction model, inputting the behavior change trend, physiological data change trend and construction type into the prediction model, predicting the behavior data and physiological data of construction workers within a preset time through the prediction model, and outputting the prediction results;

[0061] According to the prediction result, determining whether the prediction result meets the warning condition;

[0062] When the predicted result meets the early warning condition, a response strategy is generated according to the predicted result and sent to the target construction personnel.

[0063] As described above, by monitoring construction workers' behavioral and physiological data in real time, the system determines whether their status meets safety requirements based on set thresholds and standards. The system assesses the current risk level (e.g., low, medium, or high) based on the construction environment and worker status. This ensures that the actual status of construction workers matches the environmental risk, improving construction safety. Promptly detect abnormal conditions and prevent safety accidents. Verifying this compatibility helps identify risks in advance, allowing timely measures to be taken to reduce the probability of accidents. By collecting behavioral and physiological data over a specific time period, time series analysis techniques (such as moving averages and ARIMA models) are used to extract trends. The system analyzes changes in construction workers' activity patterns (e.g., work intensity and frequency) over time. It monitors fluctuations and changes in physiological parameters (e.g., heart rate and body temperature) over time. This system can identify long-term behavioral patterns and physiological responses of construction workers and predict potential health risks. Based on trend changes, it predicts whether workers are at high risk. Trend analysis provides a more comprehensive and dynamic status assessment, facilitating timely adjustments to safety measures and management strategies. The extracted behavioral and physiological data trends and construction type serve as input. Predictive models can be built using a variety of machine learning or deep learning algorithms (such as regression analysis, random forests, and LSTM). These predict the behavioral and physiological data of construction workers within a specific timeframe. This provides a foresight of their future status, helping management take timely action. This provides data support for safety management and optimizes construction plans. Predictive models enable data-driven management, proactively identifying potential issues and enhancing construction site safety. Based on historical data and industry standards, specific warning thresholds (such as elevated heart rate and excessive work intensity) can be set. The behavioral and physiological data output by the predictive model are compared with the warning criteria to determine whether they meet the thresholds. This ensures real-time monitoring of potential risks during construction and allows for rapid response. This improves the accuracy of risk identification and reduces false alarms. A timely warning mechanism can effectively reduce the risk of accidents and ensure the safety of construction workers. Based on the prediction results and warning criteria, appropriate response strategies (such as rest, hydration, and adjusting work intensity) can be developed. These strategies are disseminated to relevant construction workers through appropriate channels (such as mobile apps and text messages). Targeted guidance is provided to help workers take appropriate measures to mitigate risks. Strengthen the safety awareness and self-protection ability of construction workers.

[0064] In one embodiment, after the steps of obtaining current environmental data and analyzing the risk level of the current construction in combination with the environmental data and the construction type, the method further includes:

[0065] When the risk level is high, the construction personnel's behavior data is acquired in real time, and whether the construction personnel have implemented risk avoidance measures is determined based on the behavior data;

[0066] When the construction personnel do not perform risk avoidance, it is determined that the behavior data meets the warning condition;

[0067] Based on the judgment results, early warning information is sent to the target construction personnel.

[0068] As mentioned above, sensors are installed on construction sites or mobile devices (such as smartphones) are used to monitor construction worker activities in real time. These devices can track construction workers' locations, tools used, and work methods, collecting behavioral data. Real-time data collection provides management with a comprehensive overview of the construction site, helping to identify whether construction workers' behavior complies with safety regulations. In high-risk environments, timely access to behavioral data is crucial. This data enables management to quickly determine whether construction workers are operating in potentially hazardous situations, providing a foundation for subsequent risk assessments and early warnings. Real-time behavioral data is compared with pre-defined safety standards to determine whether construction workers are taking necessary safety measures (such as wearing safety equipment and following safe operating procedures). This assessment allows management to identify construction workers who are not taking appropriate safety measures in high-risk conditions. If construction workers fail to implement risk avoidance measures, this may indicate a lack of risk awareness or insufficient attention to potential hazards. Once this is identified, management can implement targeted interventions and training to improve safety awareness. The collected behavioral data is analyzed based on pre-defined warning criteria (such as not wearing safety equipment or remaining in a hazardous area for an extended period of time) to determine whether the warning conditions are met. Once the data meets the early warning conditions, the system promptly identifies potential safety hazards. It can proactively identify risks and issue alerts. An effective early warning mechanism ensures that construction workers are promptly informed of potential dangers and can take necessary measures to avoid accidents. When the system determines that construction workers have not implemented risk avoidance measures and that the early warning conditions have been met, it automatically sends a warning message (such as a text message, app notification, or alarm tone) to the relevant personnel. Construction workers can quickly receive early warning information and take timely action to avoid entering a high-risk situation. Timely early warning information is key to ensuring construction safety. By alerting construction workers to potential dangers, their risk awareness can be increased, prompting them to take necessary safety measures, thereby reducing the risk of accidents.

[0069] In one embodiment, the method further comprises:

[0070] monitoring the behavior data and assembly construction data of the construction personnel in real time, and performing abnormality monitoring on the behavior data and assembly construction data;

[0071] When the behavior data and / or assembly construction data meet abnormal conditions, abnormal information is sent to the target construction personnel and the management terminal.

[0072] As mentioned above, sensors (such as wearable devices, smartphones, or other monitoring tools) can be used to collect real-time data on construction worker behavior. This data can include location, operating status, and tool and equipment usage. This data is transmitted to a central management system via wireless networks (such as Wi-Fi or mobile networks) for analysis. This real-time monitoring provides managers with dynamic information on the construction site, helping to identify potential problems promptly. Real-time monitoring can quickly identify construction workers' work status and ensure they are operating within safe limits. If deviations from safety standards (such as not wearing safety equipment or entering hazardous areas) are detected, management can take immediate action. This proactive management helps improve safety and reduce the likelihood of accidents. In sync with construction worker behavior data, real-time monitoring of assembly construction data can be used. This data may include information on material usage, equipment operating status, and construction progress. Integrating this data into a single monitoring system creates a comprehensive construction dynamic. By monitoring assembly construction data, management can assess the efficiency and safety of the construction process. The importance of real-time monitoring of assembly construction data lies in its ability to provide immediate feedback on construction quality. If substandard materials are used or delays are detected in any part of the construction process, management can intervene quickly to prevent further problems. This improves both construction efficiency and safety. Collected behavioral and assembly data are compared against pre-set standards, and data analysis techniques (such as machine learning or statistical analysis) are used to identify anomalies. Anomalies can occur in behavioral data (e.g., non-compliance with safety regulations) or in assembly data (e.g., use of non-compliant materials). This anomaly monitoring proactively identifies potential safety hazards and construction issues, ensuring the construction site remains under control. The purpose of anomaly monitoring is to promptly detect and respond to problems. In high-risk environments, any minor construction issue could escalate into a major incident. By monitoring anomalies, management can take action before problems escalate into accidents, ensuring construction safety. Once anomaly conditions are detected in behavioral and / or assembly data, the system automatically generates an alert and sends an anomaly notification to relevant personnel via SMS, mobile app notification, or other means. Construction personnel and management are promptly notified of the anomaly and can take necessary action swiftly. Timely alert notification is crucial for ensuring construction safety. Upon receiving anomaly information, construction personnel can immediately adjust their work schedule to avoid potential hazards. Management can also respond quickly and coordinate on-site response measures.

[0073] In one embodiment, the warning information includes risk warning, behavior warning and warning reason.

[0074] In one embodiment, the method of analyzing the gap value between each parameter in the environmental data and the construction standard parameter; and performing weighted summation of the gap values ​​of various environmental factors based on the gap value and a weight coefficient to obtain a comprehensive influence coefficient includes:

[0075] Comparing each parameter in the environmental data with the construction standard parameter;

[0076] According to the comparison results, the value of each environmental parameter exceeding the construction standard parameter is obtained, and the value of each environmental parameter exceeding the construction standard parameter is quantified to obtain the gap value;

[0077] Multiply the gap value of each environmental parameter by the corresponding weight coefficient to obtain the weighted gap value of each environmental parameter;

[0078] The weighted gap values ​​of each environmental parameter are summed to obtain the comprehensive impact coefficient.

[0079] As described above, by comparing real-time environmental data with pre-set construction standard parameters for the current construction type, we determine whether each environmental parameter is within the safe range. We identify which environmental parameters exceed the safety standard, providing basic data for subsequent risk assessment. We quantify the difference between the actual value of each environmental parameter and the standard value to obtain a gap value. This quantifies the impact of environmental parameters on construction safety, making risk assessment more objective and quantifiable. Based on historical data, we assign a weighting factor to each environmental parameter to reflect its importance to construction safety. This ensures that important environmental parameters receive greater attention in the calculation of the comprehensive impact factor. The gap value of each environmental parameter is multiplied by its corresponding weighting factor, and then all weighted gap values ​​are summed. This yields a comprehensive impact factor, which reflects the combined impact of all environmental parameters on construction safety. Based on the size of the comprehensive impact factor, it is compared with the preset risk threshold to determine the construction risk level. This provides construction managers with a clear risk assessment result.

[0080] Assume that: the current construction type corresponds to the construction standard parameters, the temperature standard is: 20℃ to 25℃, the humidity standard is: 40% to 60%, the wind speed standard is: 0 to 5 m / s

[0081] Actual environmental data: temperature: 28°C, humidity: 70%, wind speed: 7 m / s

[0082] Weight coefficient: Temperature weight: 0.4, Humidity weight: 0.3, Wind speed weight: 0.3

[0083] Comparison of environmental data and construction standard parameters: Temperature difference: 28℃-25℃=3℃

[0084] Humidity difference: 70%-60%=10%

[0085] Wind speed difference: 7 m / s - 5 m / s = 2 m / s

[0086] Quantify the gap value:

[0087] Temperature difference: 3℃

[0088] Humidity gap value: 10%

[0089] Wind speed difference: 2 m / s

[0090] Calculate the weighted gap value:

[0091] Temperature weighted difference value: 3℃*0.4=1.2℃

[0092] Humidity weighted difference value: 10%*0.3=3%

[0093] Wind speed weighted difference value: 2 m / s * 0.3 = 0.6 m / s

[0094] The sum is the comprehensive influence coefficient: Comprehensive influence coefficient = 1.2 ° C + 3% + 0.6 m / s = 4.8

[0095] Assuming that the comprehensive impact coefficient is lower than 5, it can be concluded that the current risk is low risk.

[0096] The intelligent management method for construction safety risks of prefabricated buildings in this application is a system that dynamically assesses risks by collecting construction environment and personnel behavior data in real time. This data-driven method greatly improves the accuracy of early warning and effectively reduces the problems of missed reports and false alarms caused by traditional static assessments. Combined with algorithms, the automated early warning system can promptly notify relevant personnel when the risk threshold is triggered, reducing the response time to accidents, thereby effectively reducing the incidence of safety accidents. The intelligent monitoring system reduces dependence on manual inspections, reduces monitoring blind spots caused by human factors, improves the efficiency and reliability of safety management, and ensures that construction personnel work in a safe environment. The system provides real-time data feedback to help managers make decisions quickly. This timely flow of information improves the flexibility of on-site management and can adapt to sudden safety issues more quickly. Through the application of technology, the safety awareness of construction personnel is enhanced, and a self-supervision mechanism for safety responsibilities is formed, thereby improving the safety of the construction site as a whole.

[0097] Reference Figure 3 In an embodiment of the present application, a construction safety risk intelligent management system for prefabricated buildings is also provided, including:

[0098] Receiving module 1, used for receiving construction personnel data and assembly construction data within a preset range in real time;

[0099] A first analysis module 2, configured to analyze the construction type based on the construction personnel data and the assembly construction data;

[0100] Judgment module 3, used to obtain current environmental data and analyze whether construction conditions are met based on the environmental data and construction type;

[0101] The second analysis module 4 is used to analyze the risk level of the current construction in combination with environmental data and construction type when the construction conditions are met;

[0102] Acquisition module 5, for acquiring behavior data of construction personnel, and determining whether the behavior data meets the warning conditions according to the risk level of the current construction;

[0103] The sending module 6 is configured to send a warning message to the target construction personnel when the behavior data meets the warning condition.

[0104] As described above, it can be understood that the various components of the intelligent management system for construction safety risks of prefabricated buildings proposed in this application can realize the functions of any of the intelligent management methods for construction safety risks of prefabricated buildings described above, and the specific structure will not be repeated.

[0105] Reference Figure 4 In the embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as monitoring data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it realizes an intelligent management method for construction safety risks of prefabricated buildings.

[0106] The above-mentioned processor executes the above-mentioned intelligent management method for construction safety risks of prefabricated buildings, including: receiving construction personnel data and assembly construction data within a preset range in real time; analyzing the construction type based on the construction personnel data and assembly construction data; obtaining current environmental data, and analyzing whether the construction conditions are met based on the environmental data and construction type; when the construction conditions are met, analyzing the risk level of the current construction in combination with the environmental data and construction type; obtaining the behavior data of the construction personnel, and judging whether the behavior data meets the warning conditions based on the risk level of the current construction; when the behavior data meets the warning conditions, sending a warning message to the target construction personnel.

[0107] The aforementioned intelligent management method for construction safety risks in prefabricated buildings uses a system that dynamically assesses risks by collecting real-time data on the construction environment and personnel behavior. This data-driven approach significantly improves the accuracy of early warnings and effectively reduces the problems of missed and false alarms associated with traditional static assessments. Combined with algorithms, the automated early warning system promptly notifies relevant personnel when risk thresholds are triggered, reducing response time and thus effectively reducing the incidence of safety incidents. The intelligent monitoring system reduces reliance on manual inspections and eliminates blind spots caused by human error, improving the efficiency and reliability of safety management and ensuring a safe working environment for construction workers. The system provides real-time data feedback, enabling managers to make swift decisions. This timely flow of information enhances the flexibility of on-site management and enables faster adaptation to unexpected safety issues. Through the application of technology, construction workers' safety awareness is enhanced, a self-monitoring mechanism for safety responsibilities is established, and overall construction site safety is improved.

[0108] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, an intelligent management method for construction safety risks of prefabricated buildings is implemented, comprising the following steps: receiving construction personnel data and assembly construction data within a preset range in real time; analyzing the construction type based on the construction personnel data and assembly construction data; obtaining current environmental data, and analyzing whether the construction conditions are met based on the environmental data and the construction type; when the construction conditions are met, analyzing the risk level of the current construction in combination with the environmental data and the construction type; obtaining the behavior data of the construction personnel, and judging whether the behavior data meets the warning conditions based on the risk level of the current construction; and sending a warning message to the target construction personnel when the behavior data meets the warning conditions.

[0109] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM), etc.

[0110] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0111] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. An intelligent management method for construction safety risks of prefabricated buildings, characterized in that: The method comprises: Receive construction personnel data and assembly construction data within the preset range in real time; analyzing the construction type based on the construction personnel data and the assembly construction data; Obtain current environmental data, and analyze whether construction conditions are met based on the environmental data and construction type; When construction conditions are met, analyze the risk level of the current construction by combining environmental data and construction type; Obtaining behavioral data of construction personnel, and determining whether the behavioral data meets the warning conditions based on the risk level of the current construction; When the behavior data meets the warning conditions, a warning message is sent to the target construction personnel; The method of analyzing the risk level of the current construction in combination with the environmental data and the construction type includes: Acquiring current environmental data, including meteorological parameters, lighting parameters, and noise parameters; Analyze the construction standard parameters of the current construction type according to the construction type; Constructing an impact analysis model, inputting the environmental data and construction standard parameters into the impact analysis model, and outputting the influence coefficient of each parameter in the environmental data on the construction standard parameters through the impact analysis model; Based on the output results, determine the weight coefficient of each parameter in the environmental data; Analyzing the difference between each parameter in the environmental data and the construction standard parameter; Based on the gap value and the weight coefficient, the gap value of each environmental factor is weighted and summed to obtain a comprehensive impact coefficient; According to the comprehensive impact coefficient, risk levels are divided, and the risk levels include low risk, medium risk and high risk.

2. The intelligent management method for construction safety risks of prefabricated buildings according to claim 1 is characterized in that: After the step of analyzing the risk level of the current construction in combination with the environmental data and the construction type, the method includes: When the risk level is low risk or medium risk, the behavioral data and physiological data of the construction workers are acquired in real time; Setting status feedback data according to different risk levels, building a verification model based on the status feedback data, inputting the behavioral data, physiological data, and risk level into the verification model, analyzing whether the status feedback of the construction workers matches the current risk level through the verification model, and outputting the analysis results; If the result of the analysis is that the status feedback of the construction personnel does not match the risk level, identifying the risk level corresponding to the status feedback of the construction personnel; Based on the recognition results, status information and warning information are sent to construction personnel.

3. The intelligent management method for construction safety risks of prefabricated buildings according to claim 2 is characterized in that: After the step of analyzing whether the status feedback of the construction personnel matches the current risk level through the verification model and outputting the analysis result, the method further includes: If the result of the analysis is that the status feedback of the construction personnel matches the current risk level; Determining, based on the behavioral data and physiological data, a time-series-based behavioral change trend and a physiological data change trend of the construction personnel; Constructing a prediction model, inputting the behavior change trend, physiological data change trend and construction type into the prediction model, predicting the behavior data and physiological data of construction workers within a preset time through the prediction model, and outputting the prediction results; According to the prediction result, determining whether the prediction result meets the warning condition; When the predicted result meets the early warning condition, a response strategy is generated according to the predicted result and sent to the target construction personnel.

4. The intelligent management method for construction safety risks of prefabricated buildings according to claim 2, characterized in that: After the steps of obtaining current environmental data and analyzing the risk level of the current construction in combination with the environmental data and the construction type, the method further includes: When the risk level is high, the construction personnel's behavior data is acquired in real time, and whether the construction personnel have implemented risk avoidance measures is determined based on the behavior data; When the construction personnel do not perform risk avoidance, it is determined that the behavior data meets the warning condition; Based on the judgment results, early warning information is sent to the target construction personnel.

5. The intelligent management method for construction safety risks of prefabricated buildings according to claim 1 is characterized in that: The method further comprises: monitoring the behavior data and assembly construction data of the construction personnel in real time, and performing abnormality monitoring on the behavior data and assembly construction data; When the behavior data and / or assembly construction data meet abnormal conditions, abnormal information is sent to the target construction personnel and the management terminal.

6. The intelligent management method for construction safety risks of prefabricated buildings according to claim 1, characterized in that: said analyzing the gap value between each parameter in said environmental data and said construction standard parameter; Based on the gap value and the weight coefficient, the gap values ​​of various environmental factors are weighted and summed to obtain a comprehensive impact coefficient. The method includes: Comparing each parameter in the environmental data with the construction standard parameter; According to the comparison results, the value of each environmental parameter exceeding the construction standard parameter is obtained, and the value of each environmental parameter exceeding the construction standard parameter is quantified to obtain the gap value; Multiply the gap value of each environmental parameter by the corresponding weight coefficient to obtain the weighted gap value of each environmental parameter; The weighted gap values ​​of each environmental parameter are summed to obtain the comprehensive impact coefficient.

7. An intelligent management system for construction safety risks of prefabricated buildings, used in the method according to any one of claims 1 to 6, characterized in that: include: A receiving module, used to receive construction personnel data and assembly construction data within a preset range in real time; A first analysis module is used to analyze the construction type based on the construction personnel data and the assembly construction data; A judgment module is used to obtain current environmental data and analyze whether construction conditions are met based on the environmental data and construction type; The second analysis module is used to analyze the risk level of the current construction by combining environmental data and construction type when the construction conditions are met; An acquisition module is used to acquire the behavior data of the construction personnel and determine whether the behavior data meets the warning conditions according to the risk level of the current construction; The sending module is used to send warning information to the target construction personnel when the behavior data meets the warning conditions.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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