阅读说明：本技术 一种基于多生理信号监测的智能安全帽及其控制方法 (Intelligent safety helmet based on multiple physiological signal monitoring and control method thereof ) 是由 朱孟周 尹康涌 孙志明 梁伟 黄浩声 李虎成 黄哲忱 姚楠 王静君 贾萌萌 张昱 于 2021-01-27 设计创作，主要内容包括：本发明公开了一种基于多生理信号监测的智能安全帽,包括安全帽本体和安装在安全帽本体上的脑血氧采集模块、脑电采集模块、状态检测模块、六轴传感器模块、电场强度传感器模块、电源模块、处理器模块、通信模块、以及警报模块,将采集到的脑血氧、脑电、六轴和电场强度数据上传至管理平台,由脑血氧数据和脑电数据得到佩戴者的生理状态、由六轴数据得到佩戴者的运动状态、以及由电场强度数据得到电磁环境状态,若存在异常则进行警报。本发明采集包含脑电、脑血氧水平、六轴数据的多生理信号以及电场强度数据,综合判断各种异常生理或环境状态,做出提醒或警报。(The invention discloses an intelligent safety helmet based on multi-physiological-signal monitoring, which comprises a safety helmet body, a brain blood oxygen acquisition module, an electroencephalogram acquisition module, a state detection module, a six-axis sensor module, an electric field intensity sensor module, a power supply module, a processor module, a communication module and an alarm module, wherein the brain blood oxygen acquisition module, the electroencephalogram acquisition module, the six-axis sensor module, the electric field intensity sensor module, the power supply module, the processor module, the communication module and the alarm module are arranged on the safety helmet body, the acquired brain blood oxygen, electroencephalogram, six-axis and electric field intensity data are uploaded to a management platform, the physiological state of a wearer is obtained from the brain blood oxygen data and the electroencephalogram data, the motion state of the wearer. The invention collects multiple physiological signals containing electroencephalogram, cerebral blood oxygen level and six-axis data and electric field intensity data, comprehensively judges various abnormal physiological or environmental states and gives out a prompt or an alarm.)

1. An intelligent safety helmet based on multi-physiological signal monitoring is characterized by comprising a safety helmet body, a brain blood oxygen acquisition module (1), an electroencephalogram acquisition module (2), a state detection module (4), a six-axis sensor module (9), an electric field intensity sensor module (10), a power supply module (3), a processor module (7), a communication module (6) and an alarm module (8), wherein the brain blood oxygen acquisition module, the electroencephalogram acquisition module, the state detection module (4), the six-axis sensor module (9) are mounted on the safety helmet body;

the brain blood oxygen acquisition module (1) is used for acquiring brain blood oxygen data of a wearer;

the electroencephalogram acquisition module (2) is used for acquiring electroencephalogram data of a wearer;

the state detection module (4) is used for acquiring the wearing or uncapping state of the safety helmet;

the six-axis sensor module (9) is used for collecting six-axis data of a wearer, wherein the six-axis data comprises acceleration and angular velocity;

the electric field intensity sensor module (10) is used for collecting electric field intensity data of an external environment;

the power supply module (3) is used for providing working power supply for other modules;

the communication module (6) is used for connecting the processor module (7) with the management platform to realize data interaction between the processor module and the management platform;

the alarm module (8) is used for sending alarm information;

the processor module (7) is used for uploading the collected cerebral blood oxygen data, electroencephalogram data, six-axis data and electric field intensity data to the management platform, obtaining the physiological state of a wearer from the cerebral blood oxygen data and the electroencephalogram data, obtaining the motion state of the wearer from the six-axis data, obtaining the electromagnetic environment state from the electric field intensity data, and receiving alarm information sent by the management platform due to the fact that the safety helmet state, the physiological state, the motion state and/or the electromagnetic environment state are abnormal to control the alarm module (8) to give an alarm.

2. The intelligent safety helmet based on multiple physiological signal monitoring as claimed in claim 1, wherein the brain blood oxygen acquisition module (1) is installed right in front of a head support in a helmet liner on a safety helmet body; the electroencephalogram acquisition module (2) comprises 4-lead electroencephalogram electrodes which are distributed beside the cerebral blood oxygen acquisition module (1); the state detection module (4) comprises a 1-lead electroencephalogram electrode and is arranged in front of the right side of the head support.

3. The intelligent safety helmet based on multi-physiological signal monitoring as claimed in claim 1, wherein the six-axis sensor module (9) is mounted at a position on the front end of the top of the safety helmet body, which is right at the middle and outer surfaces; the electric field intensity sensor module (10) is installed at the position of the middle and outer surfaces of the rear end of the top of the helmet body.

4. The intelligent safety helmet based on multiple physiological signal monitoring as claimed in claim 1, wherein the power supply module (3) is installed on the outer side of the safety helmet body and above the inclined front visor; the communication module (6) is positioned at the outer edge of the left side of the helmet body; the alarm module (8) is arranged at the position below the right side of the visor and above the corresponding right eye.

5. The intelligent safety helmet based on multi-physiological signal monitoring of claim 1, further comprising a speaker (11) and a microphone (5), wherein the speaker (11) is installed right above the communication module (6) outside the safety helmet body; the microphone (5) is arranged below the right side of the visor.

6. The intelligent safety helmet based on multi-physiological-signal monitoring as claimed in claim 1, wherein when the state detection module (4) collects that the safety helmet is in a wearing state, the processor module (7) activates the cerebral blood oxygen collection module (1), the electroencephalogram collection module (2), the six-axis sensor module (9) and the electric field intensity sensor module (10) to collect data.

7. The intelligent safety helmet based on multi-physiological-signal monitoring as claimed in claim 1, wherein when the state detection module (4) collects that the safety helmet is in an uncapped state, the processor module (7) sleeps the cerebral blood oxygen collection module (1), the electroencephalogram collection module (2), the six-axis sensor module (9) and the electric field intensity sensor module (10).

8. The intelligent safety helmet based on multiple physiological signal monitoring of claim 1, wherein when the power module (3) is powered off, the processor module (7) uploads a power-off state to a management platform.

9. The method for controlling the intelligent safety helmet based on multiple physiological signal monitoring is characterized by comprising the following processes of:

acquiring the acquired state of the safety helmet;

if the helmet state is a wearing state, acquiring cerebral blood oxygen data, electroencephalogram data, six-axis data and electric field intensity data, uploading the acquired data to a management platform, and analyzing the physiological state of a wearer by the cerebral blood oxygen data and the electroencephalogram data, judging the motion state of the wearer by the six-axis data and judging the electromagnetic environment state by the electric field intensity data;

and if alarm information issued by the management platform due to the abnormal safety helmet state, physiological state, motion state and/or electromagnetic environment state is received, sending out corresponding alarm.

10. The intelligent safety helmet control method based on multi-physiological-signal monitoring as claimed in claim 9, wherein if the safety helmet state is an uncapped state, the safety helmet state is periodically and cyclically acquired.

Technical Field

The invention belongs to the technical field of electric power safety equipment, and particularly relates to an intelligent safety helmet based on multi-physiological-signal monitoring, and a control method of the intelligent safety helmet based on multi-physiological-signal monitoring.

Background

The electric power high-altitude operation workers are high-risk workers, need to keep attention concentrated and full physical strength of the workers, need real-time communication conditions during working, and cannot monitor physiological states and potentially dangerous environments of the workers due to the fact that the traditional safety helmet only has the function of collision alleviation.

At present, related research and achievement of the intelligent safety helmet mainly comprises ground workers or construction workers in the aspect of application, a corresponding physiological sensor is integrated on a liner or a helmet belt of the helmet in the aspect of physiological signals, one or more physiological signals such as electroencephalogram, body temperature, an accelerometer, blood pressure, blood oxygen saturation, electrocardio and the like are directly or indirectly measured, effective features are extracted based on the acquired signals, and normal and abnormal states are distinguished.

The prior art realizes that a physiological monitoring device and an environmental monitoring device are added on a worker safety helmet, but the integrated multifunctional intelligent monitoring safety helmet is lack of more accurate and comprehensive physiological state monitoring on workers working aloft aiming at the characteristics that the workers working aloft are close to electricity and high in altitude and need high concentration attention.

Disclosure of Invention

The invention aims to overcome the defects in the prior art and provides an intelligent safety helmet based on multi-physiological-signal monitoring and a control method thereof.

In order to solve the technical problems, the invention provides an intelligent safety helmet based on multi-physiological-signal monitoring, which comprises a safety helmet body, and a brain blood oxygen acquisition module, an electroencephalogram acquisition module, a state detection module, a six-axis sensor module, an electric field intensity sensor module, a power supply module, a processor module, a communication module and an alarm module which are arranged on the safety helmet body;

the brain blood oxygen acquisition module is used for acquiring brain blood oxygen data of a wearer;

the electroencephalogram acquisition module is used for acquiring electroencephalogram data of a wearer;

the state detection module is used for acquiring the wearing or uncapping state of the safety helmet;

the six-axis sensor module is used for acquiring six-axis data of a wearer, wherein the six-axis data comprises acceleration and angular velocity;

the electric field intensity sensor module is used for acquiring electric field intensity data of an external environment;

the power supply module is used for providing working power supply for other modules;

the communication module is used for realizing data interaction between the processor module and the management platform;

the alarm module is used for sending alarm information;

the processor module is used for uploading the collected data to the management platform, obtaining the physiological state of the wearer from the cerebral blood oxygen data and the electroencephalogram data, obtaining the motion state of the wearer from the six-axis data, obtaining the electromagnetic environment state from the electric field intensity data, and receiving alarm information sent by the management platform due to the fact that the safety helmet state, the physiological state, the motion state and/or the electromagnetic environment state are abnormal to control the alarm module to give an alarm.

Further, the cerebral blood oxygen collection module is arranged right in front of a head support in the upper cap of the safety cap body; the electroencephalogram acquisition module comprises 4-lead electroencephalogram electrodes which are distributed beside the cerebral blood oxygen acquisition module; the state detection module comprises a 1-lead electroencephalogram electrode and is arranged in front of the right side of the head support.

Further, the six-axis sensor module is arranged at the position of the middle and outer surfaces of the front end of the top of the safety helmet body; the electric field intensity sensor module is arranged at the position of the middle and outer surfaces of the rear end of the top of the helmet body.

Further, the power supply module is arranged above the visor obliquely in front of the outer side of the safety helmet body; the communication module is positioned at the outer edge of the left side of the safety helmet body; the alarm module is arranged at the position below the right side of the visor and above the corresponding right eye.

Furthermore, the helmet also comprises a loudspeaker and a microphone, wherein the loudspeaker is arranged right above the communication module on the outer side of the helmet body; the microphone is arranged below the right side of the visor.

Further, when the state detection module acquires that the safety helmet is in a wearing state, the processor module activates the brain blood oxygen acquisition module, the electroencephalogram acquisition module, the six-axis sensor module and the electric field intensity sensor module to acquire data.

Further, when the state detection module acquires that the safety helmet state is an uncapped state, the processor module sleeps the cerebral blood oxygen acquisition module, the electroencephalogram acquisition module, the six-axis sensor module and the electric field intensity sensor module.

Further, if the power module is powered off, the processor module uploads a power-off state to the management platform.

Based on the intelligent safety helmet based on multi-physiological-signal monitoring, the invention also provides a control method of the intelligent safety helmet system based on multi-physiological-signal monitoring, which comprises the following processes:

acquiring the acquired state of the safety helmet;

if the helmet state is a wearing state, acquiring cerebral blood oxygen data, electroencephalogram data, six-axis data and electric field intensity data, uploading the acquired data to a management platform, and analyzing the physiological state of a wearer by the cerebral blood oxygen data and the electroencephalogram data, judging the motion state of the wearer by the six-axis data and judging the electromagnetic environment state by the electric field intensity data;

and if receiving alarm information issued by the management platform due to the fact that the safety helmet state, the physiological state, the motion state and/or the electromagnetic environment state are abnormal, sending out corresponding alarms.

Further, if the safety helmet state is the uncapping state, the safety helmet state is periodically and circularly acquired.

Compared with the prior art, the invention has the following beneficial effects: the invention collects multiple physiological signals containing electroencephalogram, cerebral blood oxygen level and six-axis data and electric field intensity data, transmits the collected data to an information platform through a wireless communication module, comprehensively judges various abnormal physiological or environmental states such as uncapping, fatigue, pressure, brain load grade, strong electromagnetic dangerous environment and the like, and reminds or alarms through an alarm device.

Drawings

FIG. 1 illustrates various modules of an intelligent safety helmet;

FIG. 2 is a block diagram of the intelligent headgear system module connections;

fig. 3 is a flow chart of the working process of the intelligent safety helmet.

Reference numerals:

1. the brain blood oxygen collection module comprises a brain blood oxygen collection module 2, an electroencephalogram collection module 3, a power supply module 4, a state detection module 5, a microphone 6, a communication module 7, a processor module 8, an alarm module 9, a six-axis sensor module 10, an electric field intensity sensor module 11 and a loudspeaker.

Detailed Description

The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.

The invention is based on physiological signal acquisition and carries out physiological monitoring and alarming on the high-altitude electric power operation workers. The utility model provides a contain brain electricity, brain blood oxygen level, many physiological signal detection safety helmet system of six sensor module (accelerometer and gyroscope), and combine high altitude electric power operation characteristics, integrated electric field intensity sensor module, with detect peripheral electric power environment, transmit data to management platform through communication module, judge construction worker and its current environmental condition and make corresponding treatment, wherein communication module includes the speech communication module, use in combination with microphone and speaker, in order to deal with complicated construction environment, guarantee the construction team in time to communicate and command the dispatch.

Example 1

The invention discloses an intelligent safety helmet based on multi-physiological-signal monitoring, which is shown in figure 1 and comprises a safety helmet body, a cerebral blood oxygen collection module 1, an electroencephalogram collection module 2, a state detection module 4, a six-axis sensor module 9, an electric field intensity sensor module 10, a power supply module 3, a processor module 7, a communication module 6, a loudspeaker 11, a microphone 5 and an alarm module 8.

The brain blood oxygen acquisition module 1 is arranged right in front of a head support of a lining of a helmet on a helmet body (namely, the position corresponding to the forehead center of a constructor) and is used for acquiring brain blood oxygen data of a wearer;

the brain electricity acquisition module 2 comprises 4-lead brain electricity electrodes which are distributed beside the brain blood oxygen acquisition module 1 and used for acquiring brain electricity data of a wearer;

the state detection module 4 comprises a 1-lead electroencephalogram electrode (also used as a reference electrode of the electroencephalogram acquisition module) which is arranged in front of the right side of the head support and is used for acquiring the state of the safety helmet, including wearing and uncapping;

the six-axis sensor module 9 is arranged at the position of the front middle and outer surfaces of the top of the helmet body and used for collecting the acceleration and angular velocity data of a wearer;

the electric field intensity sensor module 10 is arranged at the position of the front, middle and outer surfaces of the rear end of the top of the helmet body and is used for collecting electric field intensity data of an external environment;

the power supply module 3 is arranged above the visor obliquely in front of the outer side of the safety helmet body and provides working power supply for other modules;

the communication module 6 is positioned at the outer edge of the left side of the safety helmet body (namely the right side outside the safety helmet) and is used for realizing data interaction between the processor module and the management platform;

the processor module 7 is arranged at the left oblique rear position outside the safety helmet body, is a core control unit and analyzes the acquired data to obtain the states of the safety helmet and a wearer;

the loudspeaker 11 is arranged right above the communication module 6 at the outer side of the cap body; the microphone 5 is arranged below the right side of the visor;

the alarm module 8 is arranged at the position below the right side of the visor and above the corresponding right eye and used for sending alarm information.

The output ends of the cerebral blood oxygen collection module 1, the electroencephalogram collection module 2, the state detection module 4, the six-axis sensor module 9 and the electric field intensity sensor module 10 are connected with the input end of the processor module 7 so as to upload collected data to the processor module 7;

the output end of the processor module 7 is respectively connected with the loudspeaker 4, the microphone 5 and the alarm module 8;

the processor module 7 is connected with the management platform through the communication module 6 so as to realize data interaction between the processor module 7 and the management platform.

The functional block diagram of the safety helmet system of the present invention is shown in fig. 2, and the functions of the above modules are described in detail:

1) a management platform:

the management platform is responsible for monitoring the state of each safety helmet, receiving the collected data (including safety helmet state, physiology, operation and electric field strength) sent by each safety helmet, performing comprehensive analysis on the received physiological data to evaluate the physiological state of a wearer (worker), and analyzing electric field strength signals to judge the environmental state of the worker.

The management platform determines the different states according to the classification results of the pre-trained classifiers, which are the prior art. Wherein the state of strong electromagnetic hazard, mild fatigue, severe fatigue, heavy stress, and brain load is abnormal. When the analysis result shows that a certain worker is in an abnormal state, the management platform sends a corresponding alarm instruction to the safety helmet of the worker, and the processor module in the safety helmet makes a corresponding response according to the instruction.

Furthermore, when the helmet is powered on, the management platform can also communicate with the worker by voice directly through the microphone 5 and the speaker 11.

2) The processor module:

the processor module 7 is the core part of the whole safety helmet and is responsible for controlling each module on the safety helmet. The processor module in the present embodiment is implemented by using STM32F series microprocessor manufactured by ST corporation. When detecting that the safety helmet is about to be powered off, the processor module 7 sends information of the power off of the safety helmet to the management platform through the communication module 6 before the power off.

The processor module 7 receives the state detection information sent by the state detection module 4 every other minute:

when the safety helmet is in a state that the power supply is turned on and the safety helmet is worn, the data acquisition modules (including the cerebral blood oxygen acquisition module 1, the electroencephalogram acquisition module 2, the state detection module 4, the six-axis sensor module 9 and the electric field intensity sensor module 10) are turned on and acquire data according to a certain sampling frequency, the processor module 7 receives information acquired by the data acquisition modules in parallel, integrates and packages the data, and then sends the information to the management platform through the communication module 6 at intervals.

When detecting that the state is changed to a power-on but not worn state, the processor module 7 sets each data acquisition module to sleep and sends this state information to the management platform via the communication module 6. If the state is detected to be changed into the worn state later, the microprocessor module activates the data acquisition module and sends the state to the management platform through the communication module 6.

3) Data acquisition module

3.1) a brain blood oxygen collection module:

the concentration of the blood oxygen saturation SPO2 is calculated, and the detected part is irradiated by red light and infrared light. In this embodiment, the brain blood oxygen collecting module 1 adopts an analog front-end chip AFE4400 manufactured by TI corporation to realize the collection of the blood oxygen saturation of the wearer.

The cerebral blood oxygen collection module is arranged at the right middle position of the head support of the safety helmet and used for measuring the blood oxygen saturation of a wearer. The brain blood oxygen acquisition module 1 is connected to the processor module 7 so as to upload the acquired blood oxygen saturation data of the wearer to the processor module for subsequent processing.

3.2) an electroencephalogram acquisition module:

the traditional electroencephalogram acquisition mode generally adopts a wet electrode to improve the quality of acquired signals, but the intelligent safety helmet adopts a dry electrode mode in consideration of portability and practicability, and the electroencephalogram acquisition module in the embodiment adopts an ADS1299 chip produced by TI company. Considering that the forehead part is in close contact with the head support, no hair is shielded, and the brain forehead leaves can better reflect the mental state of the brain, the brain electricity acquisition module is arranged at the front position of the head support, comprises 4-lead brain electricity electrodes and is distributed beside the brain blood oxygen acquisition module (1) to avoid the large superficial veins in the middle of the forehead.

The electroencephalogram acquisition module 2 is connected to the processor module 7 so as to upload acquired electroencephalogram data of a wearer to the processor module for subsequent processing.

The physiological state judged according to the electroencephalogram data and the cerebral blood oxygen data can be divided into: waking, slight fatigue, severe fatigue, excessive pressure, and brain overload. The physiological state signals are judged through a trained classification model, the training process comprises the steps of feature extraction, feature selection, classification algorithm selection, model training, classification result evaluation and optimal classification model selection, the trained model is written into a processor module, effective features are extracted in real time, and state judgment is carried out. The feature set changes differently in different physiological states, such as changes in electroencephalogram signals: the energy and power of theta and alpha frequency bands are obviously increased in a fatigue state, the asymmetry of the alpha wave band is obviously improved in a pressure state, and the energy and power of beta waves of a brain in a relatively calm state are larger due to forced response in a high brain load state.

3.3) six-axis sensor module:

the six-axis sensor module 9 comprises a three-axis accelerometer and a three-axis gyroscope and is used for acquiring the acceleration of workers and judging behavior characteristics. The combination of the three-axis accelerometer and the three-axis gyroscope can calculate the motion state (such as falling down) of a worker and know the operation track of the worker. Unexpected behaviors such as falling can be monitored in time, and whether workers are far away from or close to the environment can be judged in a strong electromagnetic environment.

A three-axis accelerometer: when the six-axis sensor module is opened by the processor module, the module collects three-axis accelerometer data according to a certain sampling frequency and transmits the data to the processor module through the interface. When the six-axis sensor module is turned off by the processor, the module enters a sleep state.

A three-axis gyroscope: when the six-axis sensor module is opened by the processor module, the module collects three-axis angular velocity data according to a certain sampling frequency and transmits the data to the processor module through the interface. When the six-axis sensor module is turned off by the processor, the module enters a sleep state.

In this embodiment, the six-axis sensor module is an MPU6050 module in the prior art, and the module integrates a three-axis accelerometer and a three-axis gyroscope.

When the six-axis sensor module detects that the person falls down, the voice module is directly accessed for communication and processing measures are determined.

3.4) electric field intensity sensor module:

when power failure occurs, the surrounding electromagnetic environment changes, and the strong electromagnetic environment can cause harm to human physiology, so that the surrounding electromagnetic environment needs to be monitored. The electric field intensity sensor module 10 can monitor the electric field intensity of the surrounding electromagnetic environment in real time, upload the acquired data to the processor module 7 and upload the processing result to the management platform.

When the safety helmet is powered on and worn by workers, the module starts to work to monitor the electric field strength of a peripheral electromagnetic environment in real time, the processor module receives the electric field strength to judge, when the electric field strength is detected to be greater than 3kV/m and less than 4kV/m, an abnormal electromagnetic field is considered, the work stay time is reduced or effective protection measures are taken in the environment, the processor module 7 triggers the alarm module 8 to prompt, when the electric field strength is detected to be greater than 4kV/m, the abnormal strong electromagnetic field is considered, effective and sufficient protection is required in the environment, the processor module 7 triggers the alarm module 8 to warn, and the alarm has the highest priority in a wearing state.

4) A state detection module:

the state detection module adopts a 1-lead electroencephalogram electrode and also serves as a reference electrode of the electroencephalogram acquisition module, and when the resistance of the electrode is reduced from infinity to a specific value, the process from uncapping to wearing is corresponded, and otherwise, the process from uncapping to wearing is corresponded. That is to say, when the state detection module detects that the reference electrode is a fixed value, it is determined that the helmet is in a worn state, and conversely (the resistance of the reference electrode is infinite), it is determined that the helmet is in an uncapped state.

The reference electrode continues to work as long as the device is powered, and is not affected by instructions for sleep and activation of the processor module 7.

5) A communication module:

the communication module consists of a voice communication module and a data transmission module. The voice communication module is responsible for the contact, dispatching and distribution of the construction individuals, other individuals and the management platform, and is combined with the loudspeaker 11 and the microphone 5 to carry out voice communication; the data transmission module is responsible for transmitting the physiological and environmental information of the construction individuals to the management platform for judging and defining the physiological and environmental conditions, and sending the state information once every minute. When the management platform detects that the worker state is abnormal, a corresponding instruction can be sent to the safety helmet through the data transmission module.

The voice conversation adopts a digital wireless voice talkback module, wherein the voice coding and decoding adopts an AP280 module in the prior art, the data sending and receiving adopts a radio frequency module SX278, and the module adopts a LORA communication mode.

6) An alarm module:

the state detection module can be divided into uncapping and wearing states according to equipment, and gives out light alarm and buzzing alarm in the uncapping state within normal working time so as to remind workers of paying attention to wearing the safety helmet, and the environmental state and the physiological state can be further divided in the wearing state.

The environmental state judged according to the electric field intensity collected by the electric field intensity sensor module can be divided into: normal environment, normal electromagnetic abnormality and strong electromagnetic hazard abnormality. The electric field intensity is directly judged through a threshold, the environment is normal when the electric field intensity is below 3kV/m, the common electromagnetic abnormality is when the electric field intensity is 3kV/m-4kV/m, and the strong electromagnetic hazard abnormality is when the electric field intensity is more than 4 kV/m. The alarm module makes corresponding prompt, warning or warning according to different states, including: normal state is recovered after buzzing prompt is carried out on ordinary electromagnetic anomaly, and rapid light flashing alarm is carried out under strong electromagnetic hazard to prompt workers to pay attention to abnormal environment.

The physiological state judged according to the electroencephalogram data and the cerebral blood oxygen data can be divided into: waking, slight fatigue, severe fatigue, excessive pressure, and brain overload. The alarm module makes corresponding prompt, warning or warning according to different states, including: recovering to normal after normal bright prompt in a mild fatigue state; carrying out rapid light flashing alarm and buzzing prompt in a severe fatigue state to remind workers of paying attention to self state and having a rest; carrying out rapid light flashing alarm under the state of overlarge pressure, accessing a voice module for communication and determining a processing measure; and performing normally bright and buzzing alarm under the condition of excessive brain load, accessing a voice module for communication and determining a treatment measure.

The work flow of the intelligent safety helmet system of the invention, as shown in figure 3, comprises:

when the power of safety helmet was in the on state (the power is normally supplied power, processor module will supply power information to send management platform through communication module under the state of power supply), the workman worn the safety helmet more than a minute after, state detection module 4's reference electrode fully contacts with workman's forehead, reference electrode resistance is reduced to certain particular value by the infinity, and send the signal to processor module, processor module 7 judges this moment for wearing the state according to the resistance value of this reference electrode, processor module 7 activates all data acquisition modules, specifically include: the brain blood oxygen collection module 1, the brain electricity collection module 2, the six-axis sensor module 9 and the electric field intensity sensor module 10 send information that the data collection module is activated to the management platform through the communication module 6, and data can be collected through normal work.

The brain blood oxygen collecting module 1, the brain electricity collecting module 2, the six-axis sensor module 9 and the electric field intensity module 10 start to collect data, respectively collect the brain blood oxygen data, the brain electricity data, the six-axis data and the electric field intensity data, and transmit the collected data to the processor module 7. The received data are packed and compressed in the processor module 7, and after integration, compressed data of all collected signals are obtained and then sent to the management platform via the communication module 6 at intervals.

The management platform decompresses the received data, respectively takes out electroencephalogram data, cerebral blood oxygen data, six-axis data and electric field intensity data, then carries out feature extraction and feature fusion on the data, sends the fused features into a trained classifier, and judges different states of workers. The specific judgment process is as follows:

and directly judging the environmental state through a threshold according to the electric field intensity data: when the electric field intensity is below 3kV/m, the environment is normal, when the electric field intensity is 3kV/m-4kV/m, the normal electromagnetic abnormality is caused, and when the electric field intensity is more than 4kV/m, the strong electromagnetic hazard abnormality is caused.

The physiological state judged according to the electroencephalogram data and the cerebral blood oxygen data can be divided into: five states of clear-headed, mild fatigue, severe fatigue, overlarge pressure and overlarge brain load; the physiological state signals are judged through a trained classification model, the training process comprises the steps of feature extraction, feature selection, classification algorithm selection, model training, classification result evaluation and optimal classification model selection, the trained model is written into a processor module, effective features are extracted in real time, and state judgment is carried out. The feature set changes differently in different physiological states, such as changes in electroencephalogram signals: the energy and power of theta and alpha frequency bands are obviously increased in a fatigue state, the asymmetry of the alpha wave band is obviously improved in a pressure state, and the energy and power of beta waves of a brain in a relatively calm state are larger due to forced response in a high brain load state.

And judging the motion state of the worker according to the six-axis data, and knowing the running track of the worker. When the worker is detected to fall (fall), it is considered to be an abnormal behavior.

According to the states, the electric field intensity data, the electroencephalogram data, the cerebral blood oxygen data and the six-axis motion data, if the analysis result shows that the worker is in an abnormal state at the moment, the abnormal state comprises the following steps: the management platform can send corresponding instructions to the safety helmet in the physiological states of uncapping, light fatigue, severe fatigue, overlarge pressure, overlarge brain load, abnormal falling behaviors and strong electromagnetic hazard environments. The communication module on the safety helmet receives the corresponding instruction and then transmits the instruction to the processor module, and the processor module 7 enables the alarm module 8 to make corresponding reaction according to the instruction. And then detecting the state of the safety helmet every minute, and sending the state of the safety helmet to the management platform.

When the power supply of the safety helmet is in an on state, if a worker takes the safety helmet down for more than one minute, the resistance of the reference electrode of the state acquisition module on the helmet tends to be infinite, data cannot be normally acquired, the processor module judges that the safety helmet is in an unworn state, the processor module sets each data acquisition module to be in a dormant state, and the processor module sends information that the data acquisition module is dormant to the management platform through the communication module. And then detecting the state of the safety helmet every minute, and sending the state of the safety helmet to the management platform.

When a worker presses a power switch to turn off the power supply of the safety helmet, a delay is generated from the preparation of power failure to the complete power failure of the safety helmet, and the processor module of the safety helmet sends the state of the equipment about to be powered off to the management platform before the complete power failure. When a worker presses the switch to turn on the power supply of the safety helmet, the processor module on the safety helmet sends information that the safety helmet is in an on state to the management platform.

When the power supply of the safety helmet is turned on, the safety helmet and the management platform can perform voice communication at any time through the microphone and the loudspeaker.

Example 2

Based on the intelligent safety helmet based on multi-physiological-signal monitoring, the invention discloses a control method of an intelligent safety helmet system based on multi-physiological-signal monitoring, which comprises the following processes:

acquiring the acquired state of the safety helmet;

if the helmet state is a wearing state, acquiring the acquired cerebral blood oxygen data, electroencephalogram data, six-axis data and electric field intensity data, uploading the acquired data to a management platform, and analyzing the physiological state of a wearer by the cerebral blood oxygen data and the electroencephalogram data, judging the motion state of the wearer by the six-axis data and judging the electromagnetic environment state by the electric field intensity data;

and if receiving alarm information issued by the management platform due to the fact that the safety helmet state, the physiological state, the motion state and/or the electromagnetic environment state are abnormal, sending out corresponding alarms.

Further, if the safety helmet state is the uncapping state, the safety helmet state is periodically and circularly acquired.

The invention has the beneficial effects that:

1) the physiological state judged by the single physiological signal has one-sidedness, and the completeness of describing various negative physiological states in the working process is not possessed, so that the physiological state of a human body can be accurately and comprehensively reflected by multiple physiological signals including electroencephalogram.

2) The device has pertinence and applicability to electric power aloft work workers, and comprehensively judges whether the workers can work normally or not from two aspects of electroencephalogram, cerebral blood oxygen, physiological states of an accelerometer and a gyroscope and an electromagnetic intensity environment state.

3) The alarm signal contains optical signal and acoustic signal, carries out different state multilevel warning, and optical signal and acoustic signal send out the police dispatch newspaper simultaneously under comparatively serious or critical condition to make the early warning to the staff, and switch on voice module carries out emergency treatment in necessary condition.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.