Vital sign monitoring system and method based on multi-sensor helmet

Abstract

The invention discloses a vital sign monitoring system and method based on a multi-sensor helmet, and relates to the technical field of safety helmets.The vital sign monitoring system comprises a data acquisition module, the data acquisition module comprises a sensor unit and a data acquisition unit, and the sensor unit specifically comprises a heart rate sensor used for monitoring the real-time heart rate of a wearer in real time; the body temperature sensor is used for monitoring the body surface temperature of the wearer in real time; the respiratory rate sensor is used for monitoring the respiratory rate of the wearer in real time; according to the latest sensor data, the dynamic base line value is recalculated according to the calculation method, so that the dynamic base line can adapt to the change of the physiological parameters in time, accurate reflection of the current physiological state is always kept, and if the physiological parameters of the wearer change in a short time, the wearer can be prevented from being damaged by the dynamic base line. The changes can be taken into consideration in time, and the dynamic baseline is updated, so that more accurate reference is provided for subsequent physiological status monitoring and analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of safety helmets, and more particularly to a vital sign monitoring system and method based on a multi-sensor helmet. Background Art

[0002] Smart safety helmets mainly focus on the intelligent management of construction sites, including real-name attendance, video surveillance, BIM and smart site integration, etc. These safety helmets usually have basic head protection functions, and integrate some sensors to achieve simple environmental monitoring and wearer status monitoring. Smart safety helmets are used in smart construction sites to improve engineering efficiency, ensure construction safety and optimize resource allocation;

[0003] However, in actual use, some smart safety helmets mainly focus on site management functions, such as attendance and video monitoring, and have limited health monitoring functions for the wearer. They cannot monitor the wearer's vital signs in real time, such as heart rate, body temperature, and respiratory rate. Existing smart safety helmets have limited data processing capabilities and cannot achieve localized data processing. They rely on cloud processing, resulting in high data processing delays and cannot meet real-time requirements.

[0004] Moreover, the working environment on the construction site is complex, and the alarm methods of existing smart safety helmets are relatively simple, usually only with sound and light alarms, which cannot ensure that the wearer receives warning information in a timely manner in various environments. This may result in the wearer being unable to notice the alarm signal in a noisy or low-light environment. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a vital sign monitoring system and method based on a multi-sensor helmet.

[0006] The present invention provides a vital sign monitoring system based on a multi-sensor helmet, comprising a data acquisition module, wherein the data acquisition module comprises a sensor unit and a data acquisition unit, wherein the sensor unit specifically comprises a heart rate sensor: used for real-time monitoring of the wearer's instant heart rate, a body temperature sensor: used for real-time monitoring of the wearer's body surface temperature, a respiratory rate sensor: used for real-time monitoring of the wearer's respiratory rate, an electroencephalogram sensor: used for monitoring the wearer's fatigue state and abnormal brain activity, a blood oxygen saturation sensor: used for real-time monitoring of the wearer's blood oxygen saturation, and a gas sensor, specifically comprising CO, H 2 S, used to detect the concentration of harmful gases in the construction site environment;

[0007] It also includes a temperature and humidity sensor: used to monitor the temperature and humidity of the construction site environment in real time, and an air pressure sensor: used to monitor the air pressure of the construction site environment in real time;

[0008] The data acquisition unit is used to receive parameter data of all sensors in the sensor unit, perform fusion processing on the parameter data of all sensors, obtain processed sensor parameter data, and transmit it;

[0009] A data analysis module, the data analysis module is used to receive the sensor parameter data processed by the data acquisition unit, identify the current state of the wearer according to the processed sensor parameter data, classify the current state of the wearer, obtain the classification result, and transmit it;

[0010] A response module is used to receive the classification result of the data analysis unit and implement different response strategies according to different classification results.

[0011] The data storage module stores the data fragments of the data acquisition module and the data analysis module in multiple nodes, and supports data synchronization and recovery in offline mode.

[0012] Preferably, the specific working steps of the data acquisition unit are as follows:

[0013] The number of installed sensors M and the current reading of each sensor are obtained. Every other cycle, the average value of the M sensor readings is calculated, and the average value is used as the processed sensor parameter data and transmitted.

[0014] Preferably, the data acquisition unit is further used to perform error analysis on the sensor readings of the sensor unit to remove abnormal sensor readings, and the specific steps are as follows:

[0015] For each type of sensor in the sensor unit, according to the formula Calculate and obtain the standard deviation σ of this type of sensor, where S i is the reading of the i-th sensor, μ is the average historical reading of this type of sensor;

[0016] According to the formula The value range [A, B] of each type of sensor in the sensor unit is calculated. Every other cycle, before calculating the average value of the M sensor readings, it is determined whether the M sensor readings are within the value range [A, B]. If so, the average value of the M sensor readings is further calculated. If not, it is determined that the sensor reading is abnormal, and an abnormal signal is generated and transmitted to the wearer's mobile phone terminal.

[0017] Preferably, the data acquisition unit is further used to adjust the average value of the corresponding sensor reading according to the environmental parameters, and the specific steps are as follows:

[0018] After obtaining the average value of the M sensor readings, before transmission, the current wearer's ambient temperature T1, ambient humidity T2 and ambient pressure T3 are obtained every cycle to set an ambient temperature base value t1, ambient humidity base value t2 and ambient pressure base value t3;

[0019] For each sensor reading, according to the formula:

[0020] SX i =S i -[0.5*(T1-t1)+0.3*(T2-t2)+0.2*(T3-t3)]

[0021] The average value of the adjusted sensor readings is calculated and then transmitted to the data analysis module.

[0022] Preferably, the specific working steps of the data analysis module are as follows:

[0023] First, every other cycle, the average value of each sensor reading is obtained, and the average value of each sensor reading is used as the reading of the sensor in this cycle;

[0024] Obtain the reading Q1 of the heart rate sensor, the reading Q2 of the body temperature sensor, the reading Q3 of the respiratory rate sensor, and the reading Q4 of the blood oxygen saturation sensor;

[0025] According to the formula;

[0026]

[0027] Calculate the wearer's dynamic baseline value G i ,in represents the reading of the i-th sensor at time t, W L is the first time interval, specifically 7, W S is the second time interval, specifically 8, and the baseline value G is recalculated every certain period of time. i ,i ranges from 1 to 4, there are four types of sensors in total, and the baseline value G corresponding to each parameter of heart rate, body temperature, respiratory rate, and blood oxygen saturation is calculated i , where the corresponding G 1 is the heart rate parameter, G 2 is the body temperature parameter, G 3 is the respiratory rate parameter, G 4 is the blood oxygen saturation parameter;

[0028] Then according to the formula:

[0029]

[0030] The compensation coefficients are calculated when i is equal to 1, 2, and 4. When i is equal to 3, the compensation coefficient is 1. i =K i *G i *[1±10%], get the threshold range N for each parameter i ; It should be noted that, AGE is the age of the wearer, BMI is the body mass index of the wearer, and SM is the number of cigarettes smoked by the wearer in a single day divided by five and then rounded up to an integer;

[0031] When the ratio of the wearer's heart rate to respiratory rate reaches 2.5, the body temperature threshold is multiplied by the corresponding compensation coefficient and then multiplied by [1±15%] as the body temperature threshold range;

[0032] When the wearer's blood oxygen saturation parameter is less than 92% and the body temperature is higher than the critical value, firstly according to the baseline value G corresponding to the blood oxygen saturation i Multiply by the corresponding compensation coefficient, then multiply by [1±10%] to get the threshold range of blood oxygen saturation, and then subtract Get the corrected blood oxygen saturation threshold range, where G 2 It is the wearer's body temperature parameter.

[0033] Preferably, if the wearer's heart rate, body temperature, respiratory rate, and blood oxygen saturation parameters are all within the corresponding threshold ranges, the wearer is judged to be in a normal state;

[0034] If only the heart rate of the wearer is not within the corresponding threshold range, the reading of the wearer's brain wave sensor is further judged whether the θ wave energy rises by more than 20%. If so, the wearer is judged to be in a state of mild fatigue. If not, the wearer is judged to be in a normal state.

[0035] If any two or three of the wearer's heart rate, respiratory rate and blood oxygen saturation are not within the corresponding threshold range, the wearer's brain wave sensor reading is further judged to see whether the θ wave energy rises by more than 20%. If so, the wearer is judged to be in a state of severe fatigue;

[0036] If not, it is further determined whether the air pressure of the wearer's environment is greater than 1000hpa. If so, it is determined that the wearer is in a hypoxia warning state. If not, it is determined that the wearer is in a normal state.

[0037] If the wearer's body temperature and heart rate are both outside the corresponding threshold range, it is further determined whether the wearer's ambient temperature is greater than 32°C. If so, it indicates that the wearer is at risk of heat stroke. If not, it is determined that the wearer is in a normal state.

[0038] If the wearer's heart rate, body temperature, respiratory rate, and blood oxygen saturation parameters are not within the corresponding threshold range, the wearer is judged to be in a sudden abnormal state;

[0039] The above judgment result is used as the classification result and transmitted to the response module.

[0040] Preferably, the specific steps of the data analysis module also include the following:

[0041] Get the wearer's working hours;

[0042] Obtain the wearer's historical average working time V and historical standard deviation σ;

[0043] According to the formula V1=V+1.05*σ, the critical working time V1 of the wearer is calculated;

[0044] Obtain the wearer's current heart rate, body temperature, respiratory rate, blood oxygen saturation parameters and the corresponding wearer's dynamic baseline value G i The difference is calculated by normalizing all the differences, adding them up and dividing by 4 to get the total difference value J;

[0045] According to the formula V2 = V1-0.1*J, the wearer's personal critical working time V2 is calculated

[0046] If the current working time of the wearer is greater than V2, it is judged that the wearer is in a state of mild fatigue;

[0047] The time and duration of fatigue will be automatically recorded according to the formula. If the cumulative number of mild fatigue states reaches 3 times within 4 hours, the judgment result will be changed to severe fatigue state even if the current classification result is still mild fatigue state;

[0048] If any of the wearer's heart rate, body temperature, respiratory rate, or blood oxygen saturation parameters fluctuates by more than 20% within 5 minutes, and the classification result is mild fatigue;

[0049] In the next 10 minutes, continue to monitor the data of the abnormal item. If the fluctuation range of the abnormal data continues to increase or other abnormal physiological data appears, immediately upgrade the classification result to a sudden abnormal state.

[0050] Preferably, the specific working steps of the response module are as follows:

[0051] If the classification result is normal, no alarm will be triggered;

[0052] If the classification result is a mild fatigue state, a voice prompt will be issued through the helmet's built-in speakers or headphones;

[0053] If the classification result is severe fatigue, a vibration alarm will be issued through the helmet's built-in vibration motor;

[0054] If the classification result is a hypoxia warning state, a vibration alarm is issued through the helmet's built-in vibration motor, and the current state data and location information are sent to the monitoring center for further measures;

[0055] If the classification result is a heat stroke risk state, a vibration alarm is issued through the helmet's built-in vibration motor, and the current state data and location information are sent to the monitoring center for further measures;

[0056] If the classification result is a sudden abnormal state, a vibration alarm will be issued through the helmet's built-in vibration motor, and the current state data and location information will be sent to the monitoring center for further measures to be taken. The emergency rescue number will be automatically dialed and the location information and current state data will be sent.

[0057] Preferably, the specific steps of the data storage module are:

[0058] Divide the data of the data acquisition module and the data analysis module into multiple data subsets according to the preset sharding strategy, and store these data subsets on different nodes to complete the storage;

[0059] Each helmet has a built-in RFID reader and memory card, which stores the physiological parameters of the data acquisition unit;

[0060] The memory card data of each helmet is connected via the Internet of Things;

[0061] The wearer wears an anti-tamper electronic work badge with an integrated passive RFID tag. The helmets are stored uniformly. The wearer collects the helmet through the anti-tamper electronic work badge and puts it back to its original position at the end of use. When the wearer wears the helmet, the helmet reads the badge RFID, obtains the basic identity code, obtains the threshold range of each parameter corresponding to the wearer, and then monitors it.

[0062] The present invention also proposes a method for monitoring vital signs based on a multi-sensor helmet, comprising the following steps:

[0063] Step 1: Multiple sensors are installed at specific locations on the safety helmet to monitor the wearer’s physiological parameters and construction site environmental data in real time;

[0064] Step 2: receiving the processed sensor parameter data, identifying the current state of the wearer through analysis, classifying the state, and obtaining a classification result;

[0065] Step 3: Adopt different response strategies based on the classification results.

[0066] Beneficial effect: According to the latest sensor data, the dynamic baseline value is recalculated according to the above calculation method. The purpose of this is to enable the dynamic baseline to adapt to the changes in physiological parameters in a timely manner and always maintain an accurate reflection of the current physiological state. If the wearer's physiological parameters change in a short period of time, these changes can be taken into account in a timely manner and the dynamic baseline can be updated, thereby providing a more accurate reference for subsequent physiological state monitoring and analysis;

[0067] In high-risk working environments, such as construction sites and mines, real-time monitoring of the wearer's physiological state can promptly detect risks such as fatigue, heat stroke, and hypoxia, thereby improving the safety of workers. By promptly detecting and dealing with fatigue, it can avoid the decline in work efficiency due to fatigue and improve overall work efficiency. By long-term monitoring of the wearer's physiological state, it can provide data support for health management and promptly detect potential health problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a flow chart of the system of the present invention;

[0069] Figure 2 It is a diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0070] Application scenarios: In actual use, some smart safety helmets mainly focus on site management functions, such as attendance and video monitoring, and have limited health monitoring functions for the wearer. They cannot monitor the wearer's vital signs in real time, such as heart rate, body temperature, and respiratory rate. Existing smart safety helmets have limited data processing capabilities and cannot achieve localized data processing. They rely on cloud processing, resulting in high data processing delays and cannot meet real-time requirements.

[0071] like Figure 1 As shown: A vital sign monitoring system based on a multi-sensor helmet includes a data acquisition module, the data acquisition module includes a sensor unit and a data acquisition unit, the sensor unit specifically includes a heart rate sensor: used to monitor the wearer's instant heart rate in real time, a body temperature sensor: used to monitor the wearer's body surface temperature in real time, a respiratory rate sensor: used to monitor the wearer's respiratory rate in real time, an electroencephalogram sensor: used to monitor the wearer's fatigue state and abnormal brain activity, a blood oxygen saturation sensor: used to monitor the wearer's blood oxygen saturation in real time, and a gas sensor, specifically including CO, H 2S, used to detect the concentration of harmful gases in the construction site environment; it should be noted that the heart rate sensor: can be installed on the lining of the safety helmet, close to the wearer's forehead or temple, so as to accurately capture the heart rate signal, the body temperature sensor: can be distributed in multiple parts where the lining of the safety helmet contacts the head skin, such as the forehead, temple, etc., to improve the accuracy of the measurement, the breathing rate sensor: can be installed on the mandible or neck of the safety helmet, and monitor the breathing rate by detecting the breathing movements of the wearer, the brain wave sensor: can be installed on the lining of the safety helmet, close to the wearer's forehead or top of the head, so as to accurately capture the brain wave signal, the blood oxygen saturation sensor: can be installed on the lining of the safety helmet, close to the wearer's earlobe or finger position, so as to accurately measure the blood oxygen saturation, the gas sensor: can be installed on the top or front of the safety helmet, so as to detect the concentration of harmful gases in the construction site environment in real time;

[0072] There are at least two sensors of each type, and the specific number is adjusted according to the actual situation and installed in the above positions;

[0073] It also includes a temperature and humidity sensor: used to monitor the temperature and humidity of the construction site environment in real time, and an air pressure sensor: used to monitor the air pressure of the construction site environment in real time; it should be noted that the temperature and humidity sensor: can be installed on the top or front of the safety helmet to accurately capture the changes in environmental temperature and humidity, and the air pressure sensor: can be installed on the top or side of the safety helmet to accurately measure the changes in environmental air pressure;

[0074] The data acquisition unit is used to receive parameter data of all sensors in the sensor unit, perform fusion processing on the parameter data of all sensors, obtain processed sensor parameter data, and transmit it;

[0075] A data analysis module, the data analysis module is used to receive the sensor parameter data processed by the data acquisition unit, identify the current state of the wearer according to the processed sensor parameter data, classify the current state of the wearer, obtain the classification result, and transmit it;

[0076] A response module is used to receive the classification result of the data analysis unit and implement different response strategies according to different classification results.

[0077] The data storage module stores the data fragments of the data acquisition module and the data analysis module in multiple nodes, and supports data synchronization and recovery in offline mode.

[0078] As an optional embodiment: the specific working steps of the data acquisition unit are as follows:

[0079] Obtain the number of installed sensors M of each type and the current reading of each sensor. Calculate the average of the M sensor readings every cycle, use the average as the processed sensor parameter data, and transmit it. It should be noted that the information obtained by different sensors is complementary. A single sensor may be interfered by various factors during the measurement process, resulting in measurement errors. Multi-sensor data fusion can reduce the error of a single sensor and improve the reliability of the measurement results by comprehensively analyzing the data of multiple sensors. When one of the sensors fails or the data is abnormal, the data of other sensors can be used as a supplement to ensure the normal operation of the system. This redundancy can improve the reliability and stability of the system and reduce the risk of system failure due to a single sensor failure.

[0080] It should also be noted that the period is a fixed time interval set in advance, which is 2 seconds in this embodiment.

[0081] As an optional embodiment: the data acquisition unit is further used to perform error analysis on the sensor readings of the sensor unit to remove abnormal sensor readings, and the specific steps are as follows:

[0082] For each type of sensor in the sensor unit, according to the formula Calculate and obtain the standard deviation σ of this type of sensor, where S i is the reading of the ith sensor, μ is the average historical reading of this type of sensor;

[0083] According to the formula Calculate and obtain the value range [A, B] of each type of sensor of the sensor unit. Before calculating the average value of the M sensor readings every cycle, determine whether the M sensor readings are within the value range [A, B]. If so, further calculate the average value of the M sensor readings. If not, determine that the sensor reading is abnormal, and generate an abnormal signal to be transmitted to the wearer's mobile phone terminal.

[0084] It should be noted that by comparing the data of multiple sensors, sensor failures or abnormalities can be discovered in a timely manner. If the data of a certain sensor is significantly different from that of other sensors, it indicates that there is a problem with the sensor and maintenance or replacement is required. This fault detection capability can improve the system's self-diagnosis and self-repair capabilities and further enhance the system's reliability.

[0085] It should also be noted that the purpose of generating an abnormal signal and transmitting it to the wearer's mobile phone terminal is to inform the wearer that the sensor is abnormal, so that the wearer can perform subsequent maintenance or replacement of the sensor.

[0086] As an optional embodiment: the data acquisition unit is further used to adjust the average value of the corresponding sensor reading according to the environmental parameters, and the specific steps are as follows:

[0087] After obtaining the average value of the M sensor readings, before transmission, the current wearer's ambient temperature T1, ambient humidity T2 and ambient pressure T3 are obtained every cycle to set an ambient temperature base value t1, ambient humidity base value t2 and ambient pressure base value t3;

[0088] For each sensor reading, according to the formula:

[0089] SX i =S i -[0.5*(T1-t1)+0.3*(T2-t2)+0.2*(T3-t3)]

[0090] The average value of the adjusted sensor readings is calculated and then transmitted to the data analysis module.

[0091] It should be noted that the base values ​​of ambient temperature, ambient humidity and ambient air pressure are 25°C, 50% and 1000hpa respectively. It should also be noted that the environmental sensor data is used to calibrate physiological signals in real time to reduce environmental interference. For example, the body temperature sensor dynamically adjusts the baseline value according to the ambient temperature to avoid false alarms. The self-calibration function can significantly improve the accuracy of physiological signals and reduce errors caused by environmental interference. It can adapt to the wearer in various complex environments such as high temperature, high humidity and high altitude. The self-calibration function can ensure the stability and reliability of the data.

[0092] As an optional embodiment: the specific working steps of the data analysis module are as follows:

[0093] First, every other cycle, the average value of each sensor reading is obtained, and the average value of each sensor reading is used as the reading of the sensor in this cycle;

[0094] Obtain the reading Q1 of the heart rate sensor, the reading Q2 of the body temperature sensor, the reading Q3 of the respiratory rate sensor, and the reading Q4 of the blood oxygen saturation sensor;

[0095] According to the formula;

[0096]

[0097] Calculate the wearer's dynamic baseline value G i ,in represents the reading of the i-th sensor at time t, W L is the first time interval, specifically 7, W Sis the second time interval, specifically 8, and the baseline value G is recalculated every certain period of time. i ,i ranges from 1 to 4, there are four types of sensors in total, and the baseline value G corresponding to each parameter of heart rate, body temperature, respiratory rate, and blood oxygen saturation is calculated i , where the corresponding G 1 is the heart rate parameter, G 2 is the body temperature parameter, G 3 is the respiratory rate parameter, G 4 is the blood oxygen saturation parameter;

[0098] It should be noted that the basic value of this time period in this embodiment is 4 hours. The first time interval is used to collect the average value of the wearer's physiological parameters one week before the current time point, so the value is 7 days. The second time interval is used to collect the average value of the wearer's physiological parameters one day before the current time period during actual use. Because the wearer's daily state is different, in actual use, not only the wearer's historical data of the previous week but also the wearer's data of the day should be considered. The dynamic baseline value G of the wearer is obtained by combining the two sets of data. i , where t1 is the average parameter of the i-th sensor in the t1 time period, t2 is the average parameter of the i-th sensor in the t2 time period, the unit of t1 is day, and the unit of t2 is hour;

[0099] Then according to the formula:

[0100]

[0101] The compensation coefficients are calculated when i is equal to 1, 2, and 4. When i is equal to 3, the compensation coefficient is 1. i =K i *G i *[1±10%], get the threshold range N for each parameter i ; It should be noted that AGE is the age of the wearer, BMI is the body mass index of the wearer, and SM is the number of cigarettes smoked by the wearer in a single day divided by five and then rounded up to an integer;;

[0102] For wearers who wear the device for the first time or for no more than seven days, obtain the average heart rate q1, average body temperature q2, average respiratory rate q3, and average blood oxygen saturation q4 of all wearers that have been recorded;

[0103] The wearer's heart rate, body temperature, respiratory rate, and blood oxygen saturation parameters are all within the range of ±10% of the corresponding historical average values ​​as the threshold range. Since there is no way to identify the particularity of the wearer's personal physiological parameters for the first time, the average value of other wearers' records is used as a reference to obtain the threshold range;

[0104] It should be noted that during use, the personal situation of each wearer is different. Since the helmet is mainly used on construction sites and the wearers are mainly workers, there are a large number of wearers. Each wearer's own conditions and current physiological parameters are different. If a fixed threshold range is used for judgment, errors will occur in the judgment of demerits. This technical solution combines the periodic law of the wearer's parameters, and means that the dynamic baseline value is not fixed, but is continuously updated over time. Every 4 hours, the dynamic baseline value will be recalculated according to the above calculation method based on the latest sensor data. The purpose of this is to enable the dynamic baseline to adapt to changes in physiological parameters in a timely manner and always maintain an accurate reflection of the current physiological state. If the wearer's physiological parameters change in a short period of time (such as physiological fluctuations caused by exercise, sleep, eating, etc.), by recalculating the baseline value every 4 hours, these changes can be taken into account in a timely manner, and the dynamic baseline can be updated, thereby providing a more accurate reference for subsequent physiological status monitoring and analysis;

[0105] When the ratio of the wearer's heart rate to respiratory rate reaches 2.5, the body temperature threshold is multiplied by the corresponding compensation coefficient and then multiplied by [1±15%] as the body temperature threshold range; It should be noted that under normal circumstances, there is a certain proportional relationship between heart rate and respiratory rate. When this ratio exceeds 2.5, it means that there is an abnormality in the relationship between the two, which may be caused by certain physiological or pathological factors;

[0106] There is a certain physiological correlation between heart rate and respiratory rate. When the ratio of the two is abnormal, it may be due to certain specific physiological or pathological conditions. At this time, relaxing the temperature threshold can avoid misjudging slight fluctuations in body temperature as abnormal conditions and more accurately reflect the true state of the body. For example, after strenuous exercise, both heart rate and respiratory rate will increase. At this time, the heart rate / respiration ratio may temporarily exceed the normal range. However, if the body temperature is also slightly elevated at this time, relaxing the temperature threshold can avoid misjudging this normal physiological reaction as abnormal conditions such as fever.

[0107] If the judgment is strictly based on a fixed temperature threshold, in the case of an abnormal heart rate / respiration ratio, unnecessary intervention may be required due to a slight increase in body temperature, such as the use of antipyretics. By relaxing the temperature threshold, such excessive intervention can be reduced, avoiding unnecessary harm and burden on the wearer.

[0108] There are certain differences in physiological parameters such as heart rate, respiratory rate and body temperature among different individuals. Fixed thresholds may not be applicable to all individuals. By dynamically adjusting the body temperature threshold according to abnormalities in the heart rate / respiration ratio, it can better adapt to individual differences and provide more personalized diagnosis and treatment plans for each wearer.

[0109] When the wearer's blood oxygen saturation parameter is less than 92% and the body temperature is higher than the critical value, firstly according to the baseline value G corresponding to the blood oxygen saturation i Multiply by the corresponding compensation coefficient, then multiply by [1±10%] to get the threshold range of blood oxygen saturation, and then subtract Get the corrected blood oxygen saturation threshold range, where G 2 The wearer's body temperature parameter; The threshold is the normal body temperature, which is 37°C in this embodiment. The critical value is the critical value of the wearer's body temperature above the normal range, which is 37.5°C in this embodiment. Based on the final blood oxygen threshold, the blood oxygen threshold is lowered according to the degree to which the body temperature exceeds 37.0°C, which means that when the body temperature rises by 1°C, the blood oxygen threshold is correspondingly reduced by 0.5 percentage points. The reason is that considering that the increase in body temperature may affect the measurement and actual demand of blood oxygen, by lowering the blood oxygen threshold, the blood oxygen status in the case of fever can be more accurately evaluated, avoiding the misjudgment of severe hypoxia due to the relative decrease in blood oxygen caused by fever.

[0110] It should be noted that when blood oxygen is low and body temperature is high, it means that the body may be in a more serious pathological state, such as infection, inflammation, etc. By urgently correcting the blood oxygen threshold, this potential risk can be discovered more promptly;

[0111] If the wearer's heart rate, body temperature, respiratory rate, and blood oxygen saturation parameters are all within the corresponding threshold range, the wearer is judged to be in a normal state;

[0112] If only the heart rate of the wearer is not within the corresponding threshold range, the reading of the wearer's brain wave sensor is further judged whether the θ wave energy rises by more than 20%. If so, the wearer is judged to be in a state of mild fatigue. If not, the wearer is judged to be in a normal state.

[0113] If any two or three of the wearer's heart rate, respiratory rate and blood oxygen saturation are not within the corresponding threshold range, the wearer's brain wave sensor reading is further judged to see whether the θ wave energy rises by more than 20%. If so, the wearer is judged to be in a state of severe fatigue;

[0114] If not, it is further determined whether the air pressure of the wearer's environment is greater than 1000hpa. If so, it is determined that the wearer is in a hypoxia warning state. If not, it is determined that the wearer is in a normal state.

[0115] If the wearer's body temperature and heart rate are both outside the corresponding threshold range, it is further determined whether the wearer's ambient temperature is greater than 32°C. If so, it indicates that the wearer is at risk of heat stroke. If not, it is determined that the wearer is in a normal state.

[0116] If the parameters of the wearer's heart rate, body temperature, respiratory rate, and blood oxygen saturation are not within the corresponding threshold ranges, it is determined that the wearer is in a sudden abnormal state;

[0117] Use the above determination result as the classification result and transmit it to the response module;

[0118] If the readings of the gas sensor indicate that the CO concentration is between 30 - 50 ppm and the H 2 S concentration is between 5 - 10 ppm, it is determined as a first-level dangerous environment;

[0119] If the CO concentration is between 50 - 100 ppm and the H2S concentration is between 10 - 20 ppm, it is determined as a second-level dangerous environment;

[0120] If the CO concentration is greater than 100 ppm and the H2S concentration is greater than 20 ppm, it is determined as a third-level dangerous environment, and the determination result is also transmitted as the classification result. It should be noted that in high-risk working environments such as construction sites and mines, real-time monitoring of the wearer's physiological state can promptly detect risks such as fatigue, heatstroke, and hypoxia, improving the safety of workers. By promptly detecting and dealing with the fatigue state, the decline in work efficiency caused by fatigue can be avoided, enhancing the overall work efficiency. By long-term monitoring of the wearer's physiological state, data support can be provided for health management, and potential health problems can be detected in a timely manner.

[0121] As an optional embodiment: The specific steps of the data analysis module further include the following:

[0122] Obtain the working time of the wearer; The specific way to obtain the working time is that the helmet automatically times when the wearer wears it until the current time;

[0123] Obtain the historical average working duration V and historical standard deviation σ of the wearer; It should be noted that if it is the first time wearing, the average data of the construction site is used; By adjusting the personalized fatigue threshold according to the difference between the current physiological parameters and the dynamic baseline, it helps to more precisely manage the fatigue level of the subject;

[0124] According to the formula V1 = V + 1.05 * σ, calculate and obtain the critical working duration V1 of the wearer; It should be noted that through the historical average working duration and historical standard deviation, the historical working duration data of the wearer can be approximately obtained, and the initial critical working duration V1 can be obtained through the historical working duration data of the wearer;

[0125] Obtain the difference between the current heart rate, body temperature, respiratory rate, blood oxygen saturation parameters of the wearer and the corresponding dynamic baseline value G i of the wearer, normalize and sum all the differences and then divide by 4 to obtain the difference total value J;

[0126] According to the formula V2 = V1-0.1*J, the wearer's personal critical working time V2 is calculated

[0127] If the current working time of the wearer is greater than V2, it is judged that the wearer is in a state of mild fatigue;

[0128] The time and duration of fatigue will be automatically recorded according to the formula. If the cumulative number of mild fatigue states reaches 3 times within 4 hours, the judgment result will be changed to severe fatigue state even if the current classification result is still mild fatigue state;

[0129] If any of the wearer's heart rate, body temperature, respiratory rate, or blood oxygen saturation parameters fluctuates by more than 20% within 5 minutes, and the classification result is mild fatigue;

[0130] In the next 10 minutes, continue to monitor the data of the abnormal item. If the fluctuation range of the abnormal data continues to increase or other abnormal physiological data appears, immediately upgrade the classification result to a sudden abnormal state.

[0131] As an optional embodiment: the specific steps of the data analysis module also include the following

[0132] Each helmet has a built-in RFID reader and memory card, which stores the physiological parameters of the data acquisition unit;

[0133] The memory card data of each helmet is connected via the Internet of Things;

[0134] The wearer wears an anti-disassembly electronic badge with an integrated passive RFID tag. The helmet is stored uniformly. The wearer uses the anti-disassembly electronic badge to collect the helmet and puts it back to its original position at the end of use. When the wearer wears the helmet, the helmet reads the badge RFID, obtains the basic identity code, and obtains the threshold range of each parameter corresponding to the wearer, and then monitors. It should be noted that each wearer and the helmet are bound. If the wearer wears someone else's helmet, the data is not shared. This technical solution allows the helmet to identify through the tag, so that even if the wearer wears someone else's helmet, it can identify its own historical physiological parameters.

[0135] As an optional embodiment: the specific working steps of the response module are as follows:

[0136] If the classification result is normal, no alarm will be triggered;

[0137] If the classification result is a mild fatigue state, a voice prompt will be issued through the helmet's built-in speakers or headphones;

[0138] If the classification result is severe fatigue, the helmet's built-in vibration motor will send out a vibration alarm, and detect whether any of the wearer's heart rate, body temperature, respiratory rate, and blood oxygen saturation parameters are within the corresponding average value ±10%. If so, the current status data and location information will be sent to the monitoring center for further measures. If not, the judgment result will be upgraded to a sudden abnormal state.

[0139] If the classification result is a hypoxia warning state, a vibration alarm is issued through the helmet's built-in vibration motor, and the current state data and location information are sent to the monitoring center for further measures;

[0140] If the classification result is a heat stroke risk state, a vibration alarm is issued through the helmet's built-in vibration motor, and the current state data and location information are sent to the monitoring center for further measures;

[0141] If the classification result is a sudden abnormal state, the helmet's built-in vibration motor will send a vibration alarm, send the current state data and location information to the monitoring center, take further measures, and automatically call the emergency rescue number to send the location information and current state data. It should be noted that the monitoring center is the control room of the construction site, responsible for receiving and processing the physiological state data and alarm information from each wearer. According to different classification results, the monitoring center can take the following further measures:

[0142] In the case of mild fatigue, the wearer's direct supervisor or safety manager will be notified and advised to take a proper rest. The current status data will be recorded in the health log and marked as "mild fatigue". The wearer's status will be checked regularly to ensure that it returns to normal;

[0143] In the case of severe fatigue, the wearer's direct supervisor or safety manager will be notified, and the wearer will be advised to rest immediately and other personnel will be arranged to take over his / her work. The current status data will be recorded in the health log and marked as "severe fatigue". The wearer's status will be checked regularly to ensure that he / she has returned to normal and to assess whether further medical examination is needed.

[0144] In the case of hypoxia warning, the wearer's direct supervisor or safety manager will be notified and advised to leave the current environment immediately. The wearer will be provided with necessary oxygen supply, and the current status data will be recorded in the health log, marked as "hypoxia warning". The wearer's status will be checked regularly to ensure that it has returned to normal and to assess whether further medical examination is needed.

[0145] In case of heat stroke risk, the wearer’s direct supervisor or safety manager will be notified and advised to stop working immediately and take cooling measures, such as drinking water or taking a rest. The current status data will be recorded in the health log and marked as “heat stroke risk”. The wearer’s status will be checked regularly to ensure that they have returned to normal and to assess whether further medical examination is needed.

[0146] In case of sudden abnormality, the emergency rescue team will be notified immediately, the wearer's specific location and current status data will be provided, the emergency rescue number will be automatically dialed, the location information and current status data will be sent, the current status data will be recorded in the health log, marked as "sudden abnormality", and the emergency rescue team will be kept in touch to ensure that the wearer receives timely medical assistance and the entire rescue process will be recorded:

[0147] If the classification result is a level 1 hazardous environment, a vibration alarm will be issued through the helmet's built-in vibration motor, and the current environmental data and location information will be sent to the monitoring center for further measures;

[0148] If the classification result is a level 2 hazardous environment, a vibration alarm will be issued through the helmet's built-in vibration motor, and the current environmental data and location information will be sent to the monitoring center for further measures;

[0149] If the classification result is a level three hazardous environment, a vibration alarm will be issued through the helmet's built-in vibration motor, and the current environmental data and location information will be sent to the monitoring center for further measures to be taken.

[0150] It should be noted that for the first-level hazardous environment notification, the wearer's direct supervisor or safety manager is advised to pay attention to ventilation, avoid prolonged exposure, record the current status data in the health log, mark it as "first-level hazardous environment", and regularly check the wearer's status to ensure his or her safety.

[0151] For a Level 2 hazardous environment: notify the wearer’s direct supervisor or safety manager, advise the wearer to evacuate the current environment immediately, record the current status data in the health log, mark it as a Level 2 hazardous environment, and regularly check the wearer’s status to ensure their safety;

[0152] For level 3 dangerous environments, immediately notify the emergency rescue team, provide the wearer's specific location and current status data, automatically dial the emergency rescue number, send location information and current status data, record the current status data in the health log, mark it as "level 3 dangerous environment", keep in touch with the emergency rescue team, ensure that the wearer receives timely medical assistance, and record the entire rescue process.

[0153] As an optional embodiment: the specific steps of the data storage module are:

[0154] The data of the data acquisition module and the data analysis module are divided into multiple data subsets according to the preset sharding strategy, and these data subsets are stored on different nodes to complete the storage.

[0155] It should be noted that when selecting the hash sharding algorithm, the data ID is used as the sharding key, the hash value is calculated, and the data is distributed to the corresponding nodes according to the calculated hash value.

[0156] For example, node 0 stores data with a hash value of 0, node 1 stores data with a hash value of 1, and so on; configure storage resources and access interfaces for each node;

[0157] It should be noted that by distributing data to multiple nodes through data sharding storage, the efficiency and scalability of data storage are improved.

[0158] The present invention also proposes a method for monitoring vital signs based on a multi-sensor helmet, comprising the following steps:

[0159] Step 1: Multiple sensors are installed at specific locations on the safety helmet to monitor the wearer’s physiological parameters and construction site environmental data in real time;

[0160] Step 2: receiving the processed sensor parameter data, identifying the current state of the wearer through analysis, classifying the state, and obtaining a classification result;

[0161] Step 3: Adopt different response strategies based on the classification results.

[0162] Working principle:

[0163] The data acquisition module includes a sensor unit and a data acquisition unit. The sensor unit specifically includes a heart rate sensor: used to monitor the wearer's instant heart rate in real time, a body temperature sensor: used to monitor the wearer's body surface temperature in real time, a respiratory rate sensor: used to monitor the wearer's respiratory rate in real time, an electroencephalogram sensor: used to monitor the wearer's fatigue state and abnormal brain activity, a blood oxygen saturation sensor: used to monitor the wearer's blood oxygen saturation in real time, and a gas sensor, specifically including CO, H 2 S, used to detect the concentration of harmful gases in the construction site environment;

[0164] Used to monitor the temperature and humidity of the construction site environment in real time. Air pressure sensor: used to monitor the air pressure of the construction site environment in real time;

[0165] The data acquisition unit is used to receive parameter data of all sensors in the sensor unit, perform fusion processing on the parameter data of all sensors, obtain processed sensor parameter data, and transmit it;

[0166] The data analysis module is used to receive the sensor parameter data processed by the data acquisition unit, identify the current state of the wearer according to the processed sensor parameter data, classify the current state of the wearer, obtain the classification result, and transmit it;

[0167] The response module is used to receive the classification result of the data analysis unit and implement different response strategies according to different classification results.

[0168] The above are only preferred implementations of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical staff in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of this template.

Claims

1. A vital sign monitoring system based on a multi-sensor helmet, characterized in that: It includes a data acquisition module, the data acquisition module includes a sensor unit and a data acquisition unit, the sensor unit specifically includes a heart rate sensor: used to monitor the wearer's instant heart rate in real time, a body temperature sensor: used to monitor the wearer's body surface temperature in real time, a respiratory rate sensor: used to monitor the wearer's respiratory rate in real time, an electroencephalogram sensor: used to monitor the wearer's fatigue state and abnormal brain activity, a blood oxygen saturation sensor: used to monitor the wearer's blood oxygen saturation in real time, and a gas sensor; It also includes a temperature and humidity sensor: used to monitor the temperature and humidity of the construction site environment in real time, and an air pressure sensor: used to monitor the air pressure of the construction site environment in real time; The data acquisition unit is used to receive parameter data of all sensors in the sensor unit, perform fusion processing on the parameter data of all sensors, obtain processed sensor parameter data, and transmit it; A data analysis module, the data analysis module is used to receive the sensor parameter data processed by the data acquisition unit, identify the current state of the wearer according to the processed sensor parameter data, classify the current state of the wearer, obtain the classification result, and transmit it; A response module is used to receive the classification result of the data analysis unit and implement different response strategies according to different classification results. The data storage module stores the data fragments of the data acquisition module and the data analysis module in multiple nodes, and supports data synchronization and recovery in offline mode.

2. The multi-sensor helmet-based vital sign monitoring system according to claim 1, characterized in that: The specific working steps of the data acquisition unit are as follows: The number of installed sensors M and the current reading of each sensor are obtained. Every other cycle, the average value of the M sensor readings is calculated, and the average value is used as the processed sensor parameter data and transmitted.

3. The multi-sensor helmet-based vital sign monitoring system according to claim 1, characterized in that: The data acquisition unit is also used to perform error analysis on the sensor readings of the sensor unit to remove abnormal sensor readings. The specific steps are as follows: For each type of sensor in the sensor unit, according to the formula Calculate and obtain the standard deviation σ of this type of sensor, where S i is the reading of the ith sensor, μ is the average historical reading of this type of sensor; According to the formula The value range [A, B] of each type of sensor in the sensor unit is calculated. Every other cycle, before calculating the average value of the M sensor readings, it is determined whether the M sensor readings are within the value range [A, B]. If so, the average value of the M sensor readings is further calculated. If not, it is determined that the sensor reading is abnormal, and an abnormal signal is generated and transmitted to the wearer's mobile phone terminal.

4. The multi-sensor helmet-based vital sign monitoring system according to claim 3, characterized in that: The data acquisition unit is also used to adjust the average value of the corresponding sensor reading according to the environmental parameters, and the specific steps are as follows: After obtaining the average value of the M sensor readings, before transmission, the current wearer's ambient temperature T1, ambient humidity T2 and ambient pressure T3 are obtained every cycle to set an ambient temperature base value t1, ambient humidity base value t2 and ambient pressure base value t3; For each sensor reading, according to the formula: <h2 style=";text-align:left;direction:ltr">SX<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> =S<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> -[0.5*(T1-t1)+0.3*(T2-t2)+0.2*(T3-t3)] The average value of the adjusted sensor readings is calculated and then transmitted to the data analysis module.

5. The multi-sensor helmet-based vital sign monitoring system according to claim 4, characterized in that: The specific working steps of the data analysis module are as follows: First, every other cycle, the average value of each sensor reading is obtained, and the average value of each sensor reading is used as the reading of the sensor in this cycle; Get the reading Q1 of the heart rate sensor, the reading Q2 of the body temperature sensor, the reading Q3 of the respiratory rate sensor, and the reading Q4 of the blood oxygen saturation sensor; According to the formula; Calculate the wearer's dynamic baseline value G i ,in represents the reading of the i-th sensor at time t, W L is the first time interval, W S is the second time interval, and the baseline value G is recalculated every certain period of time. i ,i ranges from 1 to 4, there are four types of sensors in total, and the baseline value G corresponding to each parameter of heart rate, body temperature, respiratory rate, and blood oxygen saturation is calculated i , where the corresponding G1 is the heart rate parameter, G2 is the body temperature parameter, G3 is the respiratory rate parameter, and G4 is the blood oxygen saturation parameter; Then according to the formula: The compensation coefficients are calculated when i is equal to 1, 2, and 4. When i is equal to 3, the compensation coefficient is 1. i =K i *G i *[1±10%], get the threshold range N for each parameter i ; When the ratio of the wearer's heart rate to respiratory rate reaches 2.5, the body temperature threshold is multiplied by the corresponding compensation coefficient and then multiplied by [1±15%] as the body temperature threshold range; When the wearer's blood oxygen saturation parameter is less than 92% and the body temperature is higher than the critical value, firstly according to the baseline value G corresponding to the blood oxygen saturation i Multiply by the corresponding compensation coefficient, then multiply by [1±10%] to get the threshold range of blood oxygen saturation, and then subtract The corrected blood oxygen saturation threshold range is obtained, where G2 is the wearer's body temperature parameter.

6. The multi-sensor helmet-based vital sign monitoring system according to claim 5, characterized in that: If the wearer's heart rate, body temperature, respiratory rate, and blood oxygen saturation parameters are all within the corresponding threshold range, the wearer is judged to be in a normal state; If only the heart rate of the wearer is not within the corresponding threshold range, the reading of the wearer's brain wave sensor is further judged whether the θ wave energy rises by more than 20%. If so, the wearer is judged to be in a state of mild fatigue. If not, the wearer is judged to be in a normal state. If any two or three of the wearer's heart rate, respiratory rate and blood oxygen saturation are not within the corresponding threshold range, the wearer's brain wave sensor reading is further judged to see whether the θ wave energy rises by more than 20%. If so, the wearer is judged to be in a state of severe fatigue; If not, it is further determined whether the air pressure of the wearer's environment is greater than 1000hpa. If so, it is determined that the wearer is in a hypoxia warning state. If not, it is determined that the wearer is in a normal state. If the wearer's body temperature and heart rate are both outside the corresponding threshold range, it is further determined whether the wearer's ambient temperature is greater than 32°C. If so, it indicates that the wearer is at risk of heat stroke. If not, it is determined that the wearer is in a normal state. If the wearer's heart rate, body temperature, respiratory rate, and blood oxygen saturation parameters are not within the corresponding threshold range, the wearer is judged to be in a sudden abnormal state; The above judgment result is used as the classification result and transmitted to the response module.

7. The multi-sensor helmet-based vital sign monitoring system according to claim 5, characterized in that: The specific steps of the data analysis module also include the following: Get the wearer's working hours; Obtain the wearer's historical average working time V and historical standard deviation σ; According to the formula V1=V+1.05*σ, the critical working time V1 of the wearer is calculated; Obtain the wearer's current heart rate, body temperature, respiratory rate, blood oxygen saturation parameters and the corresponding wearer's dynamic baseline value G i The difference is calculated by normalizing all the differences, adding them up and dividing by 4 to get the total difference value J; According to the formula V2 = V1-0.1*J, the wearer's personal critical working time V2 is calculated If the current working time of the wearer is greater than V2, it is judged that the wearer is in a state of mild fatigue; The time and duration of fatigue will be automatically recorded according to the formula. If the cumulative number of mild fatigue states reaches 3 times within 4 hours, the judgment result will be changed to severe fatigue state even if the current classification result is still mild fatigue state; If any of the wearer's heart rate, body temperature, respiratory rate, or blood oxygen saturation parameters fluctuates by more than 20% within 5 minutes, and the classification result is a mild fatigue state; In the next 10 minutes, continue to monitor the data of the abnormal item. If the fluctuation range of the abnormal data continues to increase or other abnormal physiological data appears, immediately upgrade the classification result to a sudden abnormal state.

8. The multi-sensor helmet-based vital sign monitoring system according to claim 5, characterized in that: The specific working steps of the response module are as follows: If the classification result is normal, no alarm will be triggered; If the classification result is a mild fatigue state, a voice prompt will be issued through the helmet's built-in speakers or headphones; If the classification result is a severe fatigue state, a vibration alarm will be issued through the helmet's built-in vibration motor; If the classification result is a hypoxia warning state, a vibration alarm is issued through the helmet's built-in vibration motor, and the current state data and location information are sent to the monitoring center for further measures; If the classification result is a heat stroke risk state, a vibration alarm is issued through the helmet's built-in vibration motor, and the current state data and location information are sent to the monitoring center for further measures; If the classification result is a sudden abnormal state, a vibration alarm will be issued through the helmet's built-in vibration motor, and the current state data and location information will be sent to the monitoring center for further measures to be taken. The emergency rescue number will be automatically dialed and the location information and current state data will be sent.

9. The multi-sensor helmet-based vital sign monitoring system according to claim 1, characterized in that: The specific steps of the data storage module are: Divide the data of the data acquisition module and the data analysis module into multiple data subsets according to the preset sharding strategy, and store these data subsets on different nodes to complete the storage; Each helmet has a built-in RFID reader and memory card, which stores the physiological parameters of the data acquisition unit; The memory card data of each helmet is connected via the Internet of Things; The wearer wears an anti-tamper electronic work badge with an integrated passive RFID tag. The helmets are stored uniformly. The wearer collects the helmet through the anti-tamper electronic work badge and puts it back to its original position at the end of use. When the wearer wears the helmet, the helmet reads the badge RFID, obtains the basic identity code, obtains the threshold range of each parameter corresponding to the wearer, and then monitors it.

10. A method for monitoring vital signs based on a multi-sensor helmet, applicable to a method for monitoring vital signs based on a multi-sensor helmet as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Multiple sensors are installed at specific locations on the safety helmet to monitor the wearer’s physiological parameters and construction site environmental data in real time; Step 2: receiving the processed sensor parameter data, identifying the current state of the wearer through analysis, classifying the state, and obtaining a classification result; Step 3: Adopt different response strategies based on the classification results.

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