A construction worker's vital signs monitoring bracelet and monitoring method thereof

Abstract

The present invention discloses a construction worker vital signs monitoring bracelet and a monitoring method thereof, which specifically relates to the field of health monitoring technology, including a vital signs sensing module, an environmental monitoring module, a local data processing module, a cloud analysis module, a local early warning module, and a cloud early warning module; the vital signs sensing module monitors vital signs data in real time, and the environmental monitoring module monitors environmental information; the local data processing module performs preliminary health analysis and decides whether to upload data or issue an early warning based on the results; the cloud analysis module performs health risk assessment based on comprehensive environmental factors through a decision tree algorithm, and the cloud early warning module decides whether to issue a positioning early warning based on the analysis results; the local early warning module executes a corresponding early warning strategy; the present invention introduces an environmental factor consideration mechanism, and by combining environmental data with vital signs data, it can more comprehensively assess the health risks of construction workers, dynamically adjust the risk assessment model, and improve the accuracy of health risk early warnings.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring, and more particularly to a construction worker vital sign monitoring wristband and a monitoring method thereof. Background Art

[0002] With the development of modern construction and engineering projects, the working environment of construction workers has become increasingly complex and harsh. Especially in high-temperature environments, the health risks faced by construction workers, such as heatstroke and heat stroke, have increased significantly. Therefore, how to effectively monitor the vital signs of construction workers, promptly detect health anomalies, and issue early warnings has become an urgent problem that needs to be solved. Traditional health monitoring of construction workers usually relies on manual observation and simple vital sign measurements, such as regular temperature monitoring and heart rate monitoring. However, these methods are not only inefficient and difficult to respond to sudden health issues in a timely manner, but also lack comprehensive consideration of environmental factors in special environments such as high temperature and humidity, making it impossible to accurately assess the actual health risks of construction workers.

[0003] In existing technologies, some smart wearable devices can monitor vital signs in real time, but their analytical capabilities are limited. They typically rely on local devices for simple health data processing and struggle to cope with the influence of complex environmental factors. Furthermore, existing health monitoring systems typically only consider vital sign data and fail to combine environmental factors with health data for comprehensive analysis, thereby reducing the accuracy and timeliness of early warnings. Therefore, there is an urgent need for a method that can monitor construction workers' vital signs in real time and, when necessary, conduct comprehensive analysis based on environmental factors to provide accurate and timely health warnings, effectively protecting the health and safety of construction workers in harsh environments. Summary of the Invention

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A construction worker's vital signs monitoring bracelet, comprising a vital signs sensing module, an environmental monitoring module, a local data processing module, a cloud analysis module, a local early warning module, and a cloud early warning module;

[0006] The vital signs sensing module is used to monitor the vital signs of construction workers in real time through a variety of pre-installed vital signs sensors and transmit the information to the local data processing module;

[0007] The environmental monitoring module is used to monitor the construction workers' working environment information in real time through a variety of pre-installed environmental sensors and transmit the information to the cloud analysis module;

[0008] The local data processing module is used to preliminarily process the vital signs of construction workers monitored in real time and conduct preliminary health status analysis locally. Based on the results of the preliminary health status analysis, it then decides whether to transmit the preliminary processing results to the cloud analysis module or send local warning signals of different levels to the local warning module.

[0009] The cloud analysis module determines whether environmental factors need to be considered based on the decision tree algorithm and the construction workers' working environment information, and performs cloud analysis based on the judgment results, and then transmits the cloud analysis results to the cloud warning module;

[0010] The local warning module is used to execute the preset local warning strategy according to the level of the local warning signal;

[0011] The cloud warning module is used to decide whether to perform cloud positioning warning operations based on the cloud analysis results.

[0012] In a preferred embodiment, the initial processing of the vital signs information of the construction workers monitored in real time includes:

[0013] Perform preliminary noise filtering and correction on the collected vital signs data, and then calculate the basic statistics of the preset vital signs data.

[0014] In a preferred embodiment, performing a preliminary health status analysis locally refers to:

[0015] Obtain the average value, maximum value, and minimum value of the basic statistics of each vital sign data, then calculate the difference between the maximum value and the minimum value of the target vital sign data, and compare the average value with the preset standard numerical range of the target vital sign data. If the difference is less than or equal to the preset standard fluctuation threshold of the target vital sign data and the average value is within the preset standard numerical range of the target vital sign data, then the target vital sign data is marked as normal data. If the difference is not less than or equal to the preset standard fluctuation threshold of the target vital sign data and the average value is within the preset standard numerical range of the target vital sign data, then the target vital sign data is marked as abnormal data.

[0016] In a preferred embodiment, determining whether to transmit the preliminary processed results to the cloud analysis module or to send local warning signals of different levels to the local warning module based on the results of the preliminary health status analysis refers to:

[0017] When there is no vital sign data marked as abnormal data, the preliminary processing results are transmitted to the cloud analysis module. When there is vital sign data marked as abnormal data, the total number m of vital sign data types marked as abnormal data and the total number n of vital sign data types marked as normal data are counted, and the proportion p of abnormal data is calculated, p=m / (n+m). A proportion range interval-warning level form is preset, and a local warning signal of the corresponding level is issued to the local warning module according to the proportion range interval into which the proportion p of abnormal data falls.

[0018] In a preferred embodiment, the cloud analysis module determines whether environmental factors need to be considered based on the decision tree algorithm and the construction workers' working environment information:

[0019] The environmental monitoring module collects the construction workers' working environment information monitored in real time by a variety of pre-installed environmental sensors to obtain an environmental data set, which consists of multiple environmental data parameters.

[0020] In a preferred embodiment, multiple environmental data parameters are screened to obtain a main parameter and multiple subsidiary parameters;

[0021] Set the root node and branch nodes of the decision tree: the root node uses the main parameter as the judgment condition, and the branch node uses the auxiliary parameter as the judgment condition. Then set the decision conditions of the root node and branch nodes. The decision tree will output whether environmental factors need to be considered as part of vital signs analysis. If the final output result is "yes", environmental factors will be taken into consideration when analyzing vital signs data in the cloud. If the final output result is "no", environmental factors will not be taken into consideration when analyzing vital signs data in the cloud.

[0022] In a preferred embodiment, screening multiple environmental data parameters to obtain a main parameter and multiple subsidiary parameters means:

[0023] The correlation coefficients of all environmental data parameters in the environmental data set with the possibility of environmental heat stress are calculated respectively. According to the absolute value of the correlation coefficient, the environmental data parameters are sorted from high to low. After sorting, the parameter with the strongest correlation with the possibility of environmental heat stress is selected as the main parameter. Then, the correlation coefficients of the remaining environmental data parameters are compared with the preset screening threshold. If the absolute value of the correlation coefficient of the environmental data parameter is less than the preset screening threshold, it is marked as a useless parameter and eliminated. If the absolute value of the correlation coefficient of the environmental data parameter is not less than the preset screening threshold, it is marked as an auxiliary parameter and retained.

[0024] In a preferred embodiment, the correlation coefficient uses the Pearson correlation coefficient or the Spearman rank correlation coefficient.

[0025] In a preferred embodiment, cloud-based analysis refers to:

[0026] When environmental factors are taken into consideration, the basic statistics, main parameters, and auxiliary parameters of all preset vital signs data are summarized to obtain an input vector X, which is then input into the pre-trained neural network model 1, which outputs a risk value of 1.

[0027] When environmental factors are not taken into consideration, the basic statistics of all preset vital signs data are summarized to obtain an input vector P, and then the input vector P is input into the pre-trained neural network model 2, and the neural network model 2 outputs a risk value 2.

[0028] In a preferred embodiment, the cloud warning module is used to decide whether to perform a cloud positioning warning operation based on the cloud analysis results, which means that the cloud warning module receives risk value one or risk value two, compares risk value one or risk value two with the corresponding preset risk warning threshold, and if it exceeds the corresponding preset risk warning threshold, obtains the location positioning information of the monitoring bracelet and issues a cloud warning.

[0029] In a preferred embodiment, a method for monitoring vital signs of construction workers comprises the following steps:

[0030] The construction workers' vital signs are monitored in real time through a variety of pre-installed vital signs sensors, and the construction workers' working environment is monitored in real time through a variety of pre-installed environmental sensors;

[0031] Initially process the vital signs of construction workers monitored in real time and conduct a preliminary health status analysis locally. Based on the results of the preliminary health status analysis, the system will decide whether to transmit the preliminary processing results to the cloud for further analysis or send local warning signals of different levels. Then, the system will execute the preset local warning strategy based on the level of the local warning signal.

[0032] Based on the decision tree algorithm and the construction workers' working environment information, it is determined whether environmental factors need to be considered, and cloud analysis operations are performed based on the judgment results. Then, based on the cloud analysis results, it is decided whether to perform cloud positioning warning operations.

[0033] Technical effects and advantages of the present invention:

[0034] The present invention uses smart wearable devices to monitor the vital signs data of construction workers in real time, including heart rate, blood oxygen saturation, body temperature, etc., and combines it with environmental data collected by environmental sensors, such as temperature, humidity, light intensity, etc. Through the cloud analysis module, the health status of construction workers can be evaluated in real time to ensure the accuracy and timeliness of health monitoring. The present invention specifically introduces a mechanism for considering environmental factors. By combining environmental data with vital signs data, the health risks of construction workers can be more comprehensively evaluated. According to different environmental conditions, the risk assessment model, that is, the neural network model, is dynamically adjusted to improve the accuracy of health risk warnings. In particular, in high temperature or other harsh environments, the system can identify potential health threats earlier.

[0035] The dual risk assessment mechanism of the present invention uses two neural network models for risk assessment: one takes environmental factors into account, and the other does not. By comparing the risk assessment results of the two models, the system can more flexibly respond to health risks under different environmental conditions, thereby providing more personalized and accurate health management services. When it is detected that the health risk of the construction workers exceeds the preset warning threshold, the cloud-based warning module can automatically obtain the real-time location of the construction workers and immediately issue a warning signal. This intelligent early warning mechanism ensures that when health risks occur, managers can obtain the location information of the construction workers in a timely manner, quickly take necessary intervention measures, and reduce the incidence and severity of health problems.

[0036] Through precise risk assessment and multi-level warning threshold settings, the present invention effectively reduces the incidence of false alarms. Different warning thresholds are set according to different scenarios to ensure that warning signals are triggered only when there is a real health risk, avoiding overreaction and improving the credibility and practicality of the warning. The present invention provides a comprehensive and intelligent health monitoring and early warning solution in the construction environment, effectively improving the safety level of construction workers. Especially in extreme weather or harsh working environments, it can play a key role in helping to prevent and promptly respond to health emergencies and reduce health risks on the construction site. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0038] Figure 1 This is a schematic diagram of the construction worker's vital signs monitoring bracelet in the present invention.

[0039] Figure 2 This is a schematic diagram of a method for monitoring vital signs of construction workers in the present invention. DETAILED DESCRIPTION

[0040] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0041] Reference Figure 1 - Figure 2 The following examples were obtained:

[0042] Example 1: A construction worker's vital signs monitoring bracelet, comprising a vital signs sensing module, an environmental monitoring module, a local data processing module, a cloud analysis module, a local early warning module, and a cloud early warning module;

[0043] The vital signs sensing module uses a variety of pre-installed sensors to monitor construction workers' vital signs in real time and transmit this information to the local data processing module. Sensors include heart rate sensors, blood oxygen saturation sensors, and body temperature sensors, which monitor construction workers' vital signs in real time. Data collection: Heart rate, blood oxygen saturation, body temperature, and other information are collected in real time and transmitted to the local data processing module.

[0044] The environmental monitoring module uses a variety of pre-installed environmental sensors to monitor construction workers' working environment in real time and transmit this information to the cloud-based analysis module. The light sensor detects ambient light intensity. The temperature sensor detects the temperature of the construction environment to determine whether it is exposed to high temperatures. The humidity sensor detects air humidity to determine air dryness.

[0045] The local data processing module is used to preliminarily process the vital signs of construction workers monitored in real time and conduct preliminary health status analysis locally. Based on the results of the preliminary health status analysis, it then decides whether to transmit the preliminary processing results to the cloud analysis module or send local warning signals of different levels to the local warning module.

[0046] The cloud analysis module determines whether environmental factors need to be considered based on the decision tree algorithm and the construction workers' working environment information, and performs cloud analysis based on the judgment results, and then transmits the cloud analysis results to the cloud warning module;

[0047] The local warning module is used to execute the preset local warning strategy according to the level of the local warning signal;

[0048] The cloud warning module is used to decide whether to perform cloud positioning warning operations based on the cloud analysis results.

[0049] The initial processing of the vital signs of construction workers monitored in real time refers to:

[0050] Perform preliminary noise filtering and correction on the collected vital signs data, and then calculate the basic statistics of the preset vital signs data.

[0051] Noise filtering: When sensors collect data, they may be affected by factors such as the external environment, equipment vibration, and electromagnetic interference, resulting in noise in the data. For example, in a construction environment, a heart rate sensor may record abnormal heart rate values (such as sudden, abnormally high or low values) due to worker movement or equipment vibration. For example, if a heart rate sensor records a heart rate of 200 bpm at a certain point in time, while the surrounding values are all within the 80-100 bpm range, this may be noise. Eliminating noisy data can improve data accuracy and ensure the reliability of subsequent analysis and decision-making.

[0052] Data correction: Corrects data deviations to ensure that sensor data accurately reflects actual conditions. Sensors may experience inaccuracies due to temperature fluctuations, device aging, or other factors, requiring correction through a calibration model. For example, if a temperature sensor displays a value of 32°C when the actual temperature is 30°C, this value can be corrected to a value closer to 30°C using historical calibration data or a standard calibration curve. Significance: Data correction eliminates systematic errors, ensuring a closer match between data and actual conditions, and improving the accuracy of the overall monitoring system.

[0053] Basic Statistics: Statistics include the mean, maximum, minimum, and standard deviation of vital sign data. These statistics are used to assess the health status of construction workers and identify abnormalities. Mean: For example, the mean heart rate is calculated to assess whether a worker's daily heart rate is within the normal range. If a worker's average heart rate during work is 90 bpm, compared to the normal range of 60-100 bpm, this is considered normal. However, an average heart rate above 120 bpm may indicate excessive stress or physical exertion. Maximum and Minimum: Recording the maximum and minimum body temperature values can determine whether a high fever or hypothermia is present. Standard Deviation: The standard deviation reflects the fluctuation of the data. A very large standard deviation of the heart rate may indicate unstable heart rate and potential health risks. Calculating these statistics can provide a preliminary quantitative assessment of a worker's health status, helping to promptly identify health risks, initiate early warnings, or conduct further analysis.

[0054] Conducting a preliminary local health status analysis means:

[0055] Obtain the average value, maximum value, and minimum value of the basic statistics of each vital sign data, then calculate the difference between the maximum value and the minimum value of the target vital sign data, and compare the average value with the preset standard numerical range of the target vital sign data. If the difference is less than or equal to the preset standard fluctuation threshold of the target vital sign data and the average value is within the preset standard numerical range of the target vital sign data, then the target vital sign data is marked as normal data. If the difference is not less than or equal to the preset standard fluctuation threshold of the target vital sign data and the average value is within the preset standard numerical range of the target vital sign data, then the target vital sign data is marked as abnormal data.

[0056] The significance of performing preliminary local health status analysis lies in assessing whether construction workers' vital signs are within safe ranges through simple and rapid calculations, thereby promptly identifying potential health issues. This step helps the system react quickly, for example, triggering further cloud-based analysis or issuing direct alerts when abnormal fluctuations in vital sign data occur. This local analysis approach reduces the computing burden on the cloud, improves the overall system response speed, and ensures timely warnings.

[0057] Specific examples: Example 1: Heart rate data analysis: Assume that the basic statistics of a construction worker's heart rate data over a period of time are as follows: average value 85bpm; maximum value 100bpm; minimum value 70bpm; the preset standard value range is 60-100bpm, and the standard fluctuation threshold is 40bpm.

[0058] Difference calculation: Maximum minus minimum; the difference is 100 - 70 = 30 bpm. Comparative analysis: The difference of 30 bpm is less than the standard fluctuation threshold of 40 bpm. The average value of 85 bpm falls within the preset standard range of 60-100 bpm. Result: This heart rate data is marked as normal.

[0059] Example 2: Temperature Data Analysis: Suppose another construction worker's temperature data has the following statistics: average 38.5°C; maximum 39.2°C; minimum 37.8°C. The preset standard range is 36.5-37.5°C, with a standard fluctuation threshold of 1°C. Difference calculation: Subtract the minimum from the maximum; the difference is 39.2-37.8 = 1.4°C. This 1.4°C difference exceeds the standard fluctuation threshold of 1°C. The average value of 38.5°C is outside the standard range of 36.5-37.5°C. Result: This temperature data is marked as abnormal.

[0060] Further expansion: In the preliminary health status analysis, the difference between the maximum value and the minimum value can also be replaced by the standard deviation in basic statistics. The following is an explanation of the applicable scenarios of the two:

[0061] The difference between the maximum and minimum values is useful for quickly assessing data fluctuations, especially when data collection is infrequent or observation time is short. This simple and direct method can quickly identify significant fluctuation anomalies. Suitable parameters: When data has clear upper and lower limits (such as heart rate or body temperature) and a significant range of fluctuation, the difference between the maximum and minimum values can effectively reflect the degree of data dispersion.

[0062] Standard Deviation: This metric is useful for assessing data volatility and dispersion, particularly when data is collected frequently or observed over long periods of time. It provides a more precise measure of volatility. Suitable for use with data that fluctuates frequently and exhibits subtle variations (such as continuously monitored heart rate or blood oxygen saturation), the standard deviation better captures these variations and provides a more reliable assessment of volatility.

[0063] When performing a preliminary health status analysis, the choice between the maximum minus minimum difference or the standard deviation depends on the characteristics of the data and the actual application scenario. For simple fluctuations within a short period of time, the maximum-minimum difference is a quick and effective indicator. For scenarios requiring more sophisticated analysis, the standard deviation provides a more comprehensive measure of volatility. Since the monitoring time is generally short, the present invention uses the maximum-minimum difference as a single indicator. If the monitoring time is extended in actual use, the standard deviation, a more comprehensive measure of volatility, can be used.

[0064] Based on the results of the preliminary health status analysis, it is decided to transmit the preliminary processing results to the cloud analysis module or send different levels of local warning signals to the local warning module.

[0065] When there is no vital sign data marked as abnormal data, the preliminary processing results are transmitted to the cloud analysis module. When there is vital sign data marked as abnormal data, the total number m of vital sign data types marked as abnormal data and the total number n of vital sign data types marked as normal data are counted, and the proportion p of abnormal data is calculated, p=m / (n+m). A proportion range interval-warning level form is preset, and a local warning signal of the corresponding level is issued to the local warning module according to the proportion range interval into which the proportion p of abnormal data falls.

[0066] Through local pre-processing, construction workers' health status is promptly assessed, and when potential health risks arise, corresponding warning signals are quickly issued. By calculating the proportion p of abnormal data, the warning level can be dynamically adjusted to ensure that health risks receive appropriate attention and treatment. This graded warning mechanism can reduce false alarms while improving warning accuracy, ensuring that construction workers do not overreact to relatively minor health anomalies and can respond promptly to more serious situations.

[0067] A specific example: monitoring vital signs of construction workers: Suppose a construction worker wears a wristband that monitors the following three vital signs: heart rate, blood oxygen saturation, and body temperature. Heart rate is marked as normal; blood oxygen saturation is marked as abnormal; and body temperature is marked as abnormal. In this case, the total number of vital sign data types marked as abnormal is m = 2 (blood oxygen saturation and body temperature). The total number of vital sign data types marked as normal is n = 1 (heart rate). The proportion of abnormal data, p, is calculated to be 0.67. Assume the following warning level table: 0.0 ≤ p < 0.3: low warning; 0.3 ≤ p < 0.6: medium warning; 0.6 ≤ p ≤ 1.0: high warning. In this example, the proportion p falls within the range of 0.6 ≤ p ≤ 1.0, and a high warning signal is issued to the local warning module.

[0068] The cloud analysis module uses the decision tree algorithm and the construction workers' working environment information to determine whether environmental factors need to be considered.

[0069] The environmental monitoring module collects the construction workers' working environment information monitored in real time by a variety of pre-installed environmental sensors to obtain an environmental data set, which consists of multiple environmental data parameters.

[0070] Screening multiple environmental data parameters to obtain a main parameter and multiple subsidiary parameters;

[0071] Set the root node and branch nodes of the decision tree: the root node uses the main parameter as the judgment condition, and the branch node uses the auxiliary parameter as the judgment condition. Then set the decision conditions of the root node and branch nodes. The decision tree will output whether environmental factors need to be considered as part of vital signs analysis. If the final output result is "yes", environmental factors will be taken into consideration when analyzing vital signs data in the cloud. If the final output result is "no", environmental factors will not be taken into consideration when analyzing vital signs data in the cloud.

[0072] Through appropriate environmental parameter screening and the application of decision trees, it is dynamically determined whether environmental factors should be considered in the health analysis of construction workers. Various factors in the construction environment, such as temperature, humidity, and light intensity, can affect the vital signs of construction workers. Through this process, the system can intelligently determine whether to incorporate these environmental factors into the health analysis, thereby improving the accuracy of the analysis and the effectiveness of early warnings. This helps to more accurately identify potential health risks in complex environments and provide timely warnings or interventions.

[0073] For example, suppose the environmental monitoring module at a construction site monitors the following environmental parameters: temperature, humidity, and light intensity. Data screening and parameter classification: Based on the significance and relevance of each parameter to the health of construction workers, temperature is first selected as the primary parameter, as it has the greatest impact on health. Humidity and light intensity are then classified as secondary parameters.

[0074] Decision tree settings: Root node: Set temperature as the judgment condition. For example, if the temperature exceeds 35 degrees, it is considered that the ambient temperature has a significant impact on health and other environmental factors need to be further considered.

[0075] Branch node: Set humidity and light intensity as judgment conditions. For example, if the humidity exceeds 75% and the light intensity is also high, the risk of heat stress may be increased, so these factors need to be considered comprehensively.

[0076] Decision tree output: If the temperature exceeds 35 degrees Celsius and the humidity or light intensity also reaches a certain level, the decision tree outputs "yes," indicating that environmental factors need to be considered when analyzing vital sign data. Conversely, if the temperature is not high or other ancillary parameters are within normal range, the decision tree outputs "no," indicating that environmental factors can be ignored and the vital sign data can be directly analyzed.

[0077] Example 1: On a particular day, the construction environment temperature is 36 degrees Celsius, the humidity is 80 percent, and the light intensity is high. According to the decision tree, the temperature has exceeded the root node threshold of 35 degrees Celsius, and the humidity and light intensity are also high. The decision tree output is "yes," indicating that environmental factors must be considered when analyzing the construction workers' vital signs.

[0078] Example 2: On another construction day, the temperature was 32 degrees Celsius, the humidity was 65 percent, and the light intensity was moderate. Although the temperature was high, it did not reach the 35-degree threshold at the root node. The decision tree output was "No," indicating that the impact of environmental factors was minor and could be ignored. Therefore, environmental factors did not need to be specifically considered when analyzing vital sign data.

[0079] Screening multiple environmental data parameters to obtain a main parameter and multiple subsidiary parameters means:

[0080] The correlation coefficients of all environmental data parameters in the environmental data set with the possibility of environmental heat stress are calculated respectively. According to the absolute value of the correlation coefficient, the environmental data parameters are sorted from high to low. After sorting, the parameter with the strongest correlation with the possibility of environmental heat stress is selected as the main parameter. Then, the correlation coefficients of the remaining environmental data parameters are compared with the preset screening threshold. If the absolute value of the correlation coefficient of the environmental data parameter is less than the preset screening threshold, it is marked as a useless parameter and eliminated. If the absolute value of the correlation coefficient of the environmental data parameter is not less than the preset screening threshold, it is marked as an auxiliary parameter and retained.

[0081] By analyzing the correlation of environmental data, the decision tree structure for health risk assessment is optimized. By filtering and classifying environmental data parameters, the system focuses on those factors that have the greatest impact on environmental heat stress risk, thereby improving decision-making accuracy and efficiency. Eliminating unnecessary parameters reduces the system's computational complexity, making health analysis more streamlined and efficient, while retaining key factors with significant health impacts, ensuring the effectiveness of early warnings.

[0082] The benefits of setting branch nodes in order of ranking: Improved decision-making efficiency: By setting the most relevant parameter as the root node, you can quickly determine whether to consider other factors, thereby reducing unnecessary calculations and judgments. Enhanced warning accuracy: After sorting, setting branch nodes in order ensures that each decision is based on the most influential factors, ensuring the accuracy of warning signals. Simplified decision tree structure: After filtering out useless parameters, the decision tree structure is more concise, reducing unnecessary complexity and making the system more flexible and efficient in practical applications.

[0083] For example: Suppose that at a construction site, the following environmental data is acquired through the environmental monitoring module: temperature, humidity, wind speed, and light intensity. First, the correlation coefficient between each environmental parameter and the potential for environmental heat stress is calculated to determine the strength of each parameter's relationship with heat stress. Sorting: After sorting by the absolute value of the correlation coefficient, temperature has the highest correlation, followed by humidity and light intensity, and wind speed has the lowest. Primary parameter determination: Because temperature has the strongest correlation with the potential for environmental heat stress, it is selected as the primary parameter. Filtering out useless parameters: Filter the remaining humidity, light intensity, and wind speed. Assuming the filtering threshold is set to a certain value, such as 0.2, and the absolute value of the correlation coefficient for wind speed is found to be less than the threshold, wind speed is marked as a useless parameter and removed. Subsidiary parameter determination: If the absolute value of the correlation coefficient for humidity and light intensity is not less than the filtering threshold, the system retains these two parameters as supplementary parameters.

[0084] Decision tree settings: Root node: Temperature is set as the root node of the decision tree, which is used to first determine whether environmental factors need to be considered. Branch nodes: Humidity and light intensity are used as branch nodes in turn to further determine whether to strengthen the warning.

[0085] For example, at a construction site, if the temperature is high, the humidity is high, and the light intensity is strong, the decision tree will determine that all parameters are at high risk, and the environmental factors will be included in the health analysis. Conversely, if the temperature is high but the humidity and light intensity are within normal ranges, the environmental factors will not be considered.

[0086] The correlation coefficient uses either the Pearson correlation coefficient or the Spearman rank correlation coefficient. The Pearson correlation coefficient is used when the relationship between environmental data and the likelihood of heat stress is approximately linear. It is suitable for scenarios where the data follows a normal distribution. The Pearson correlation coefficient works best when the data are continuous and do not contain significant outliers. Meaning: The Pearson correlation coefficient measures the linear correlation between two variables—that is, the extent to which a change in one variable leads to a linear change in the other. Its value ranges from -1 to 1, with values closer to 1 or -1 indicating a stronger linear correlation, and a value of 0 indicating no linear relationship. The Spearman rank correlation coefficient is used when the relationship between environmental data and the likelihood of heat stress is nonlinear or when the data do not follow a normal distribution. It is suitable for scenarios where the data are ordinal or contain outliers. The Spearman rank correlation coefficient is a good choice when the data exhibit a monotonic relationship (i.e., when one variable increases or decreases, the other also increases or decreases monotonically). Significance: The Spearman rank correlation coefficient measures the monotonic relationship between two variables. Whether the relationship is linear or nonlinear, as long as it is monotonic, the Spearman rank correlation coefficient can be used to measure it. Its value also ranges between -1 and 1. The closer it is to 1 or -1, the stronger the monotonic correlation.

[0087] The Pearson correlation coefficient is suitable for situations with linear relationships and ideal data distributions (such as a normal distribution), providing a more accurate measure of linear correlation. The Spearman rank correlation coefficient is suitable for situations with nonlinear relationships or data containing outliers or non-normal distributions, providing a more robust measure of monotonicity. In construction environment monitoring, if the relationship between the data is known to be linear and the data quality is good, the Pearson correlation coefficient is an appropriate choice. If the data has nonlinear relationships or other complexities, the Spearman rank correlation coefficient is more appropriate.

[0088] Cloud analytics refers to:

[0089] When environmental factors are taken into consideration, the basic statistics, main parameters, and auxiliary parameters of all preset vital signs data are summarized to obtain an input vector X, which is then input into the pre-trained neural network model 1, which outputs a risk value of 1.

[0090] When environmental factors are not taken into consideration, the basic statistics of all preset vital signs data are summarized to obtain an input vector P, and then the input vector P is input into the pre-trained neural network model 2, and the neural network model 2 outputs a risk value 2.

[0091] By incorporating or excluding environmental factors into the analysis, the health risk assessment for construction workers under different environmental conditions can be better adapted. Both Neural Network Models 1 and 2 utilize convolutional neural networks. Neural Network Models 1 and 2 calculate risk values with and without environmental factors, respectively. This dual assessment mechanism helps accurately predict health risks for construction workers, especially in high temperatures or other adverse environments. Furthermore, the introduction of neural networks enables the system to handle complex nonlinear relationships, improving the accuracy of risk assessment.

[0092] For example, scenario 1 considers environmental factors: Construction workers are working in a hot environment. The environmental monitoring system records high parameters such as temperature, humidity, and light intensity. At the same time, vital signs such as heart rate and body temperature also fluctuate. Basic statistics of these environmental data (such as temperature, humidity, and light intensity) are aggregated with vital sign data (such as heart rate, blood oxygen saturation, and body temperature) to generate an input vector X. This input vector X is fed into pre-trained neural network model 1, which outputs a high risk value of 1, indicating that the construction worker faces a high health risk in the current environment.

[0093] Scenario 2: Environmental factors are not considered: The construction worker works in a relatively suitable environment, with environmental parameters such as temperature and humidity within normal ranges. Only basic statistics of vital sign data are used to generate the input vector P. This input vector P is fed into the pre-trained neural network model 2, which outputs a low risk value 2, indicating that the construction worker's health risk is low.

[0094] Training process: Data collection: Collect a large amount of historical data on construction workers under different environmental conditions, including vital signs (basic statistics such as heart rate, blood oxygen saturation, and body temperature) and environmental data (such as temperature, humidity, and light intensity). This data is labeled and corresponding health risk labels (such as low risk, medium risk, and high risk) are generated.

[0095] Data preprocessing: The collected raw data is cleaned, denoised, missing values are processed, and normalized to ensure that the data has the same scale before being input into the neural network. For datasets that consider environmental factors, an input vector X is generated that contains both vital sign data and environmental data; for datasets that do not consider environmental factors, an input vector P is generated that only contains vital sign data.

[0096] Model Training: Neural Network Model 1 is trained using the input vector X containing environmental factors and the corresponding health risk labels. Through multiple iterations of optimization, the model gradually learns how to combine vital sign data with environmental factors to predict health risks. Neural Network Model 2 is trained using the input vector P excluding environmental factors and the corresponding health risk labels. This model focuses on the impact of vital sign data on health risks.

[0097] Model Validation and Adjustment: Models 1 and 2 were validated using independent datasets to assess their accuracy and robustness. Based on the validation results, model parameters were adjusted to optimize performance and ensure accurate prediction of health risks in real-world applications.

[0098] Model deployment: Model 1 and Model 2, after verification and optimization, are deployed in the cloud. When the cloud receives real-time data, it can quickly generate risk values to assist in the health management of construction workers.

[0099] The cloud warning module is used to decide whether to perform a cloud positioning warning operation based on the cloud analysis results. It means that the cloud warning module receives risk value one or risk value two, compares risk value one or risk value two with the corresponding preset risk warning threshold, and if it exceeds the corresponding preset risk warning threshold, the location information of the monitoring bracelet is obtained and a cloud warning is performed to ensure that when the health risk of the construction workers reaches a certain level, an early warning can be issued in time and the construction workers can be located for rapid intervention. By comparing the risk value with the preset risk warning threshold, the present invention can automatically determine whether the current health risk requires immediate action. Different warning threshold settings can be distinguished according to whether environmental factors are taken into account to ensure that the warning is more accurate and effective, thereby reducing false alarms or missed alarms.

[0100] Preset risk warning thresholds: Setting different thresholds: Since Risk Value 1 (taking environmental factors into account) and Risk Value 2 (not taking environmental factors into account) are derived from different analysis models, their risk warning thresholds should also be different. Risk Value 1 threshold: Usually a lower threshold is set. This is because when environmental factors are taken into account, the risk assessment is more comprehensive. Therefore, even a slightly higher risk value may mean a higher actual risk, requiring a more timely warning. Risk Value 2 threshold: A slightly higher threshold is set. Because environmental factors are not taken into account, the model's risk assessment may be more conservative, and warnings will only be triggered when vital sign data clearly show abnormalities.

[0101] For example, in scenario 1, considering environmental factors, the calculated risk value is 70, which exceeds the preset risk warning threshold of 50. The cloud-based warning module recognizes this situation, immediately obtains the location of the construction worker's wristband, and issues a cloud-based warning signal to notify management, providing the construction worker's real-time location so that emergency measures can be taken.

[0102] Scenario 2: Without considering environmental factors: The calculated risk value 2 is 85, but the preset risk warning threshold is 90. After comparing, the cloud-based warning module finds that the risk value 2 does not exceed the threshold and therefore does not issue a warning signal, deeming the current risk level to be within the acceptable range.

[0103] Example 2: A method for monitoring vital signs of construction workers, comprising the following steps:

[0104] The construction workers' vital signs are monitored in real time through a variety of pre-installed vital signs sensors, and the construction workers' working environment is monitored in real time through a variety of pre-installed environmental sensors;

[0105] Initially process the vital signs of construction workers monitored in real time and conduct a preliminary health status analysis locally. Based on the results of the preliminary health status analysis, the system will decide whether to transmit the preliminary processing results to the cloud for further analysis or send local warning signals of different levels. Then, the system will execute the preset local warning strategy based on the level of the local warning signal.

[0106] Based on the decision tree algorithm and the construction workers' working environment information, it is determined whether environmental factors need to be considered, and cloud analysis operations are performed based on the judgment results. Then, based on the cloud analysis results, it is decided whether to perform cloud positioning warning operations.

[0107] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0108] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0109] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0110] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0111] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A construction worker's vital signs monitoring bracelet, characterized by: Including vital signs sensing module, environmental monitoring module, local data processing module, cloud analysis module, local warning module, cloud warning module; The vital signs sensing module is used to monitor the vital signs of construction workers in real time through a variety of pre-installed vital signs sensors and transmit the information to the local data processing module; The environmental monitoring module is used to monitor the construction workers' working environment information in real time through a variety of pre-installed environmental sensors and transmit the information to the cloud analysis module; The local data processing module is used to preliminarily process the vital signs of construction workers monitored in real time and conduct preliminary health status analysis locally. Based on the results of the preliminary health status analysis, it then decides whether to transmit the preliminary processing results to the cloud analysis module or send local warning signals of different levels to the local warning module. The cloud analysis module determines whether environmental factors need to be considered based on the decision tree algorithm and the construction workers' working environment information, and performs cloud analysis based on the judgment results, and then transmits the cloud analysis results to the cloud warning module; The local warning module is used to execute the preset local warning strategy according to the level of the local warning signal; The cloud warning module is used to decide whether to perform cloud positioning warning operations based on cloud analysis results; The cloud analysis module uses the decision tree algorithm and the construction workers' working environment information to determine whether environmental factors need to be considered. The environmental monitoring module collects the construction workers' working environment information monitored in real time by a variety of pre-installed environmental sensors to obtain an environmental data set, which consists of multiple environmental data parameters. Screening multiple environmental data parameters to obtain a main parameter and multiple subsidiary parameters; Set the root node and branch nodes of the decision tree: The root node uses the main parameter as the judgment condition, and the branch nodes use the auxiliary parameters as the judgment condition. Then set the decision conditions of the root node and branch nodes. The decision tree will output whether environmental factors should be considered as part of vital sign analysis. If the final output result is "yes", environmental factors will be taken into consideration when analyzing vital sign data on the cloud. If the final output result is "no", environmental factors will not be taken into consideration when analyzing vital sign data on the cloud. Cloud analytics refers to: When environmental factors are taken into consideration, the basic statistics, main parameters, and auxiliary parameters of all preset vital signs data are summarized to obtain an input vector X, which is then input into the pre-trained neural network model 1, which outputs a risk value of 1. When environmental factors are not taken into consideration, the basic statistics of all preset vital signs data are summarized to obtain an input vector P, and then the input vector P is input into the pre-trained neural network model 2, and the neural network model 2 outputs a risk value 2.

2. A construction worker vital signs monitoring bracelet according to claim 1, characterized in that: The initial processing of the vital signs of construction workers monitored in real time refers to: Perform preliminary noise filtering and correction on the collected vital signs data, and then calculate the basic statistics of the preset vital signs data.

3. A construction worker vital signs monitoring bracelet according to claim 2, characterized in that: Conducting a preliminary local health status analysis means: Obtain the average value, maximum value, and minimum value of the basic statistics of each vital sign data, then calculate the difference between the maximum value and the minimum value of the target vital sign data, and compare the average value with the preset standard numerical range of the target vital sign data. If the difference is less than or equal to the preset standard fluctuation threshold of the target vital sign data and the average value is within the preset standard numerical range of the target vital sign data, then the target vital sign data is marked as normal data. If the difference is not less than or equal to the preset standard fluctuation threshold of the target vital sign data and the average value is within the preset standard numerical range of the target vital sign data, then the target vital sign data is marked as abnormal data.

4. A construction worker vital signs monitoring bracelet according to claim 3, characterized in that: Screening multiple environmental data parameters to obtain a main parameter and multiple subsidiary parameters means: The correlation coefficients of all environmental data parameters in the environmental data set with the possibility of environmental heat stress are calculated respectively. According to the absolute value of the correlation coefficient, the environmental data parameters are sorted from high to low. After sorting, the parameter with the strongest correlation with the possibility of environmental heat stress is selected as the main parameter. Then, the correlation coefficients of the remaining environmental data parameters are compared with the preset screening threshold. If the absolute value of the correlation coefficient of the environmental data parameter is less than the preset screening threshold, it is marked as a useless parameter and eliminated. If the absolute value of the correlation coefficient of the environmental data parameter is not less than the preset screening threshold, it is marked as an auxiliary parameter and retained.

5. A construction worker vital signs monitoring wristband according to claim 4, characterized in that: The correlation coefficient used was the Pearson correlation coefficient or the Spearman rank correlation coefficient.

6. A construction worker vital signs monitoring wristband according to claim 5, characterized in that: The cloud warning module is used to decide whether to perform a cloud positioning warning operation based on the cloud analysis results. This means that the cloud warning module receives risk value one or risk value two, compares risk value one or risk value two with the corresponding preset risk warning threshold, and if it exceeds the corresponding preset risk warning threshold, obtains the location information of the monitoring bracelet and issues a cloud warning.

7. A method for monitoring vital signs of construction workers, based on a construction worker vital signs monitoring bracelet according to any one of claims 1 to 6, characterized in that: The following steps are involved: The construction workers' vital signs are monitored in real time through a variety of pre-installed vital signs sensors, and the construction workers' working environment is monitored in real time through a variety of pre-installed environmental sensors; Initially process the vital signs of construction workers monitored in real time and conduct a preliminary health status analysis locally. Based on the results of the preliminary health status analysis, the system will decide whether to transmit the preliminary processing results to the cloud for further analysis or send local warning signals of different levels. Then, the system will execute the preset local warning strategy based on the level of the local warning signal. Based on the decision tree algorithm and the construction workers' working environment information, it is determined whether environmental factors need to be considered, and cloud analysis operations are performed based on the judgment results. Then, based on the cloud analysis results, it is decided whether to perform cloud positioning warning operations.

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