Intelligent safety helmet for health monitoring
By introducing dual early warning and dual monitoring modules into smart safety helmets, combined with BP neural network and special backup batteries, the problems of failure of traditional smart safety helmets cannot alarm and limited functions are solved, achieving more efficient health monitoring and significant early warning effects.
Patent Information
- Application Number
- CN202411454626.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-17
- Publication Date
- 2025-03-04
AI Technical Summary
Traditional smart safety helmets cannot make emergency alarms when the equipment fails, and have limited functions and can only monitor body temperature, heart rate and blood pressure, and the health monitoring effect is insufficient.
A smart safety helmet for health monitoring is designed, using a dual early warning module and a dual monitoring module, combined with a special backup battery, passive and active early warning is achieved, and the calculation of vital sign parameters is optimized through the BP neural network, and a two-color LED light and a buzzer are used for early warning.
It effectively improves the safety and functionality of the smart safety helmet, realizes comprehensive monitoring and accurate calculation of user vital signs and environmental parameters, and has significant early warning effect.
Smart Images
Figure CN119547949A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of engineering construction production safety prevention, and in particular to an intelligent safety helmet for health monitoring. Background Art
[0002] In the field of engineering construction, production safety has always been a top priority. The construction site environment is complex and changeable, and there are many potential safety risks, such as falling from heights, mechanical injuries, electric shock accidents, etc. Traditional safety management methods mainly rely on manual inspections and rules and regulations, but these methods are difficult to monitor the safety status of each worker in real time, especially in large construction sites, where there are a large number of workers and supervision is more difficult. Therefore, providing each worker with a helmet has become a top priority. The main function of traditional helmets is physical protection, such as anti-collision and anti-puncture, but they lack intelligent functions and cannot monitor the physiological state and environmental conditions of workers in real time. Once a worker becomes unwell or has an emergency, the traditional helmet cannot issue an alarm in time, which may lead to serious safety accidents.
[0003] With the rapid development of the Internet of Things, sensor technology and wireless communication technology, smart helmets have gradually become a new trend in construction site safety management. At present, there are two types of smart helmets that are commonly used: First, multiple sensors are set in the helmet to measure the user's body temperature, heart rate, blood pressure and other data. When the data is abnormal, the helmet will directly flash and vibrate to remind the user and the people around him; second, a variety of sensors are used to obtain the user's various physical parameters, and then remote communication monitoring is achieved through wireless transmission devices. The remote terminal is used to determine whether the user has a problem. When a problem occurs, the alarm in the user's helmet is directly controlled remotely to sound an alarm.
[0004] However, the above-mentioned commonly used smart helmets have at least the following two problems: 1) Lack of emergency measures to deal with equipment failures. For example, when a user is in danger, the helmet fails to work and there is no remote communication to send an alarm; 2) It has few functions and poor monitoring effect. It only detects the user's body temperature, heart rate and blood pressure data, and the health monitoring effect is insufficient. Summary of the invention
[0005] In view of the above two problems, the purpose of the present invention is to provide a smart safety helmet for health monitoring. By utilizing a dual warning module, it can provide passive warning to protect the user, and active warning to enable the user to remind other people around him to pay attention to the danger. Combined with a special backup battery, it can provide warning with the help of other users, effectively improving the safety and functionality of the smart safety helmet. A dual monitoring module is also provided to simultaneously monitor the user's vital signs, health and environmental parameters, effectively improving the effect of health monitoring. At the same time, the calculation method of environmental parameters is optimized and the BP neural network is used to calculate the vital signs parameters, effectively improving the calculation accuracy. In addition, the warning method is also improved, using two-color LED light and a buzzer for warning, and the warning effect is more significant.
[0006] The specific technical solutions are as follows: A health monitoring smart helmet comprises a helmet body, a central control chip, a remote communication module, a dual warning module, a dual monitoring module and a power supply module; the central control chip and each module are mounted on the helmet body, and the central control chip is connected to each module respectively to control the operation of each module; wherein the dual monitoring module comprises a vital sign monitoring unit and an environmental monitoring unit, the vital sign monitoring unit is used to obtain multiple vital sign parameters of the user and transmit them to the central control chip, and the environmental monitoring unit is used to obtain multiple environmental parameters and transmit them to the central control chip; the remote communication module is used to support data interaction between the central control chip and a remote terminal; the central control chip determines the health status of the user based on multiple vital sign parameters, determines the environmental health level based on multiple environmental parameters, and determines the health status of the user based on multiple vital sign parameters. The user's health status and environmental health level control whether the dual warning module issues an alert, and at the same time transmits the data generated when each module interacts to the remote terminal through the remote communication module; the dual warning module includes a passive warning unit and an active warning unit. The passive warning unit is used to automatically alarm with dual-color LED light and sound waves when the user's health status is abnormal, and the active warning unit is used for the user to actively apply for the use of dual-color LED light and sound waves to warn the surroundings when their own health is safe; the power supply module includes a rechargeable battery and a special backup battery. The rechargeable battery is used to power the central control chip and other modules, and the special backup battery is used to send a warning signal to other special backup batteries in other nearby smart helmets via Bluetooth when the rechargeable battery is damaged or the remote communication module is damaged.
[0007] The dual warning module can be used to protect users through passive warnings, or actively warn users to alert others around them of danger. Combined with a special backup battery, it can also provide warnings with the help of other users, effectively improving the safety and functionality of the smart helmet. In addition, a dual monitoring module is also set up to monitor the user's vital signs, health and environmental parameters at the same time, effectively improving the effect of health monitoring.
[0008] Preferably, in the vital sign monitoring unit, multiple optical sensors are used to obtain multiple heart rate parameters of the user, multiple optical sensors are used to obtain multiple blood oxygen parameters of the user, and multiple temperature sensors are used to obtain multiple body temperature parameters of the user; in the environment monitoring unit, multiple air quality sensors are used to obtain multiple air quality parameters of the current environment, multiple microphones are used to obtain multiple noise parameters of the user's current environment, multiple light sensors are used to obtain multiple light intensity parameters of the user's current environment, and multiple temperature and humidity sensors are used to obtain multiple temperature and humidity parameters of the user's current environment. Using sensors of corresponding types to obtain multiple data of each parameter respectively facilitates data processing and analysis based on multiple data, thereby improving the accuracy of monitoring.
[0009] Preferably, after the central control chip obtains multiple vital sign parameters, the multiple vital sign parameters are divided into types, data cleaning is performed under each type, and the cleaned multiple vital sign parameters are used as basic input data, and input into the BP neural network for calculation to obtain the corresponding user health status. The structure of the BP neural network is an nm-1 structure, where n is a positive integer not less than 3, 2n≤m≤3n, and m is a positive integer. The BP neural network can effectively calculate the user's health status and improve the monitoring effect. At the same time, the BP neural network is convenient for adjusting the structure, that is, adjusting the resources required by itself, so that it can be used by the central control chip.
[0010] Preferably, after the central control chip obtains multiple environmental parameters, it divides the multiple environmental parameters into environmental types, performs data screening and data supplementation under each environmental type, obtains each correction data under each environmental type, and determines the corresponding environmental health level based on each correction data. The environmental health level is obtained based on the environmental parameters, and then the impact of the environment on the user's health can be judged, so that the user can be warned in time when the environmental pollution is serious.
[0011] Preferably, the passive warning unit includes parallel red warning LED, blue warning LED and buzzer. The use of two-color LED for warning, combined with the buzzer, has a more obvious warning effect, which can not only remind the user himself, but also better remind other people around the user, which meets the needs of actual scenes such as construction sites with heavy dust and impaired vision.
[0012] Preferably, the active warning unit is built with a button trigger for receiving a user's press to generate an active alarm signal and transmit it to the central control chip. The active warning unit can be triggered by the user, so that the user can get an early warning when he finds danger but the danger has not been triggered immediately.
[0013] Preferably, the remote communication module uses WiFi or LoRa or NB-loT communication protocol to connect to the remote terminal, and the remote communication module is also provided with an encryption unit, which uses RSA and AES hybrid encryption to encrypt the data transmission of the remote communication module. Using the communication protocol for remote data transmission, combined with hybrid encryption, is convenient for protecting the user's privacy data.
[0014] Preferably, a positioning module is also included, and the positioning module is used to transmit the user's location information to the central control chip and the remote terminal. The positioning module can effectively obtain the user's location information, so that others can find the user when health problems or safety risks occur.
[0015] The beneficial effects of the present invention compared with the prior art are: The present invention utilizes a dual early warning module, which can not only provide passive early warning to protect users, but also actively provide early warning to enable users to remind other people around them of danger. Combined with a special backup battery, it can also provide early warning with the help of other users, effectively improving the safety and functionality of the smart helmet; a dual monitoring module is also provided to simultaneously monitor the user's vital signs, health and environmental parameters, effectively improving the effect of health monitoring; at the same time, the calculation method of environmental parameters is optimized and the BP neural network is used to calculate the vital signs parameters, effectively improving the calculation accuracy; in addition, the early warning method is also improved, using two-color LED light and a buzzer for early warning, and the early warning effect is more significant. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 A schematic diagram of the main structure of a smart helmet for health monitoring connected to a remote terminal; Figure 2 This is a schematic diagram of the structure of a 3-6-1 BP neural network. DETAILED DESCRIPTION
[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.
[0018] like Figure 1 As shown, it is a schematic diagram of the main structure of a health monitoring smart helmet connected to a remote terminal. The smart helmet mainly includes a helmet body, a central control chip, a remote communication module, a dual warning module, a dual monitoring module and a power supply module. The central control chip and each module are installed on the helmet body. The central control chip is connected to each module respectively to control the operation of each module. The power supply module is used to supply power to the central control chip and other modules, thereby ensuring the normal operation of the smart helmet.
[0019] In this embodiment, the dual monitoring module includes a parallel vital sign monitoring unit and an environmental monitoring unit. The vital sign monitoring unit is used to obtain multiple vital sign parameters of the user and transmit them to the central control chip, and the environmental monitoring unit is used to obtain multiple environmental parameters and transmit them to the central control chip. In the vital sign monitoring unit, multiple optical sensors are used to obtain multiple heart rate parameters of the user, multiple optical sensors are used to obtain multiple blood oxygen parameters of the user, and multiple temperature sensors are used to obtain multiple body temperature parameters of the user. In the environmental monitoring unit, multiple air quality sensors are used to obtain multiple air quality parameters of the current environment, multiple microphones are used to obtain multiple noise parameters of the user's current environment, multiple light sensors are used to obtain multiple light intensity parameters of the user's current environment, and multiple temperature and humidity sensors are used to obtain multiple temperature and humidity parameters of the user's current environment. Using sensors of corresponding types to obtain multiple data of each parameter respectively facilitates data processing and analysis based on multiple data, thereby improving the accuracy of monitoring.
[0020] In this embodiment, the remote communication module is used to support the data interaction between the central control chip and the remote terminal. The remote communication module can use WiFi or LoRa or NB-loT communication protocol to connect the remote terminal, thereby realizing the interaction between the central control chip and the remote terminal. The remote communication module is also provided with an encryption unit, which uses RSA and AES mixed encryption to encrypt the data transmission of the remote communication module, thereby protecting the user's privacy data and avoiding data leakage from causing harm to the user. The process of encrypted transmission is as follows: the sender generates an RSA public-private key pair and discloses the public key to the receiver; the sender generates a random AES key and encrypts the AES key with the RSA public key; the sender uses the AES key to encrypt the data to be transmitted; the sender sends the encrypted AES key and the encrypted data to the receiver together; the receiver uses its own RSA private key to decrypt the received AES key; the receiver uses the decrypted AES key to decrypt the received data to obtain plaintext data.
[0021] In this embodiment, the central control chip can adopt the STM32 platform. The central control chip determines the user's health status based on multiple vital sign parameters and determines the environmental health level based on multiple environmental parameters. The central control chip controls whether the dual warning module issues an alert based on the user's health status and the environmental health level, and transmits the data generated when each module interacts to the remote terminal through the remote communication module.
[0022] Specifically, after the central control chip obtains multiple vital sign parameters, it will classify the multiple vital sign parameters into types, perform data cleaning under each type, and use the cleaned multiple vital sign parameters as basic input data, input them into the BP neural network for calculation, and obtain the corresponding user health status. The cleaning here is mainly to eliminate the interference of erroneous data. For example, there are five parameters of body temperature type, the unit is Celsius, and their values are 36.5, 36.5, 36.6, 37.8 and 36.6 in chronological order. At this time, it is necessary to clean the erroneous value of 37.8, and then only use the remaining four parameters for calculation, or calculate the average of the two values closest to 37.8, and replace 37.8 with the average for calculation. The structure of the BP neural network is nm-1 structure, where n is a positive integer not less than 3, 2n≤m≤3n, and m is a positive integer. The BP neural network can effectively calculate the user's health status and improve the monitoring effect. At the same time, the BP neural network is convenient for adjusting the structure, that is, adjusting the resources required by itself, so that it can be used by the central control chip.
[0023] like Figure 2 As shown in FIG. 1 , it is a schematic diagram of the structure of a BP neural network with a 3-6-1 structure. For the BP neural network, taking the vital signs parameters of blood oxygen, body temperature and heart rate as an example, the BP neural network can adopt a 3-6-1 structure, that is, 3 input layers, 6 hidden layers and 1 output layer. The 3 input layers correspond to the three parameters of blood oxygen, body temperature and heart rate, the 6 hidden layers are 6 intermediate layer neurons responsible for calculation, and the 1 output layer is the output health status. Since the resources inside the central control chip are fixed, when setting the BP neural network, the central control chip must be able to meet the resource requirements of the BP neural network. Therefore, as a part that occupies more resources, the number of neurons used in the hidden layer must be strictly controlled. If the number is too low, the calculation may be inaccurate, and if the number is too high, it will exceed the use resources of the central control chip. Therefore, when there are 3 input layers, selecting a hidden layer with 6 neurons can be compatible with the calculation accuracy and the required resources. When the BP neural network is calculating, the hidden layer can select the Sigmoid function and the output layer can use the softmax activation function for calculation. The specific calculation process of using these two functions has been made public and will not be repeated here.
[0024] In this embodiment, after the central control chip obtains multiple environmental parameters, it divides the multiple environmental parameters into environmental types, performs data screening and data supplementation under each environmental type, and obtains each corrected data under each environmental type. Data screening is to select the data with errors, which is the same operation process as the above-mentioned data cleaning; while data supplementation is to supplement the erroneous data with appropriate data for replacement. Here, the data supplementation method, when there are fewer missing data, can use a method similar to the above-mentioned averaging method to replace the erroneous data with the mean; when there are more missing data, for example, there are 20 data in total, of which 9-12 are missing, at this time, the original data of 5-8 can be migrated to replace the original data of 9-12, or the original data of 13-16 can be migrated to replace the original data of 9-12. Then, based on each corrected data, the corresponding environmental health level is determined. For example, if the air quality is excellent, the light intensity is high, and the temperature and humidity are appropriate, the environmental health level is defined as level one; if the air quality is excellent, the light intensity is too high, and the temperature and humidity are very dry, the environmental health level is defined as level two; if the air quality is poor, the light intensity is ordinary, and the temperature and humidity are very humid, the environmental health level is also defined as level two; if the air quality is poor, the light intensity is too high, and the temperature and humidity are very dry, the environmental health level is defined as level three. At this time, the central control chip will control the passive warning unit to issue an early warning. The specific division of environmental health levels and which level to issue an early warning can be set by the user according to the actual usage scenario. This is just a reference example. Based on the environmental parameters, the environmental health level can be obtained, and then the impact of the environment on the user's health can be judged, so that the user can be warned in time when the environmental pollution is serious.
[0025] In this embodiment, the dual warning module includes a passive warning unit and an active warning unit. The passive warning unit is used to automatically alarm with dual-color LED light and sound waves when the user's health condition is abnormal, and the active warning unit is used for the user to actively apply for the use of dual-color LED light and sound waves to warn the surroundings when they are healthy and safe. The passive warning unit includes parallel red light warning LED, blue light warning LED and buzzer. The buzzer is responsible for sounding the alarm, and the red light warning LED and the blue light warning LED are responsible for flashing red and blue light at the same time for warning, thereby improving the warning effect and facilitating detection by others. Since one of the common scenes of smart safety helmets is complex scenes such as construction sites or mountains, there are often obstructed sight lines and excessive noise in these scenes. If only the conventional single-color light and sound warning method is used, it may only remind the user himself, but may not remind others around him enough. Therefore, the dual-color light can effectively improve the conspicuousness and improve the warning effect. The use of dual-color LED for early warning, combined with a buzzer, makes the early warning effect more obvious. It can not only remind the user himself, but also better remind other people around the user. It meets the needs of actual scenes such as construction sites with heavy dust and impaired vision.
[0026] In this embodiment, the active warning unit is built with a button trigger, which is used to receive the user's press to generate an active alarm signal and transmit it to the central control chip. The active warning unit can be triggered by the user, so that the user can issue an early warning when he finds danger but the danger has not been triggered immediately, thereby reminding people around him to pay attention to safety.
[0027] The power supply module includes a rechargeable battery and a special backup battery. The rechargeable battery is used to power the central control chip and other modules. The special backup battery is connected to a Bluetooth unit for interaction between different smart helmets at close range. It is mainly used to send early warning signals to other special backup batteries in other nearby smart helmets via Bluetooth when the rechargeable battery is damaged or the remote communication module is damaged and the main part of the smart helmet cannot work. After other smart helmets detect the early warning signal, they can also transmit it to the remote terminal to determine the location of the damaged smart helmet, thereby forming an auxiliary alarm protection function.
[0028] This embodiment also includes a positioning module, such as Beidou positioning or GPS positioning, which is used to transmit the user's location information to the central control chip and the remote terminal. The positioning module can effectively obtain the user's location information, so that others can find the user when health problems or safety risks occur.
[0029] To sum up, the present invention utilizes a dual warning module, which can not only provide passive warning to protect users, but also actively warn users to remind other people around them of danger. Combined with a special backup battery, it can use other users for warning, effectively improving the safety and functionality of the smart helmet. It is also provided with a dual monitoring module to monitor the user's vital signs, health and environmental parameters at the same time, effectively improving the effect of health monitoring. At the same time, it optimizes the calculation method of environmental parameters and uses a BP neural network to calculate vital signs parameters, effectively improving the calculation accuracy. In addition, the warning method is improved, using a two-color LED light and a buzzer for warning, and the warning effect is more significant, with significant progress.
[0030] The above embodiments are only for illustrating the technical idea of the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.
Claims
1. A smart helmet for health monitoring, characterized in that: It includes a helmet body, a central control chip, a remote communication module, a dual warning module, a dual monitoring module and a power supply module; the central control chip and each module are installed on the helmet body, and the central control chip is connected to each module respectively to control the operation of each module; The dual monitoring module includes a vital sign monitoring unit and an environmental monitoring unit. The vital sign monitoring unit is used to obtain multiple vital sign parameters of the user and transmit them to the central control chip, and the environmental monitoring unit is used to obtain multiple environmental parameters and transmit them to the central control chip. The remote communication module is used to support data interaction between the central control chip and the remote terminal; The central control chip determines the user's health status based on multiple vital sign parameters and the environmental health level based on multiple environmental parameters. The central control chip controls whether the dual warning module issues an early warning based on the user's health status and the environmental health level, and transmits the data generated by the interaction of each module to the remote terminal through the remote communication module. The dual warning module includes a passive warning unit and an active warning unit. The passive warning unit is used to automatically warn the user with dual-color LED light and sound waves when the user's health condition is abnormal. The active warning unit is used for the user to actively apply for dual-color LED light and sound waves to warn the surrounding area when the user is healthy and safe. The power supply module includes a rechargeable battery and a special backup battery. The rechargeable battery is used to power the central control chip and other modules. The special backup battery is used to send an early warning signal to other special backup batteries in other nearby smart helmets via Bluetooth when the rechargeable battery is damaged or the remote communication module is damaged.
2. The smart helmet for health monitoring according to claim 1, characterized in that ,In the vital sign monitoring unit, multiple heart rate parameters of the user are obtained by multiple optical sensors, multiple blood oxygen parameters of the user are obtained by multiple optical sensors, and multiple body temperature parameters of the user are obtained by multiple temperature sensors; In the environmental monitoring unit, multiple air quality sensors are used to obtain multiple air quality parameters of the current environment, multiple microphones are used to obtain multiple noise parameters of the user's current environment, multiple light sensors are used to obtain multiple light intensity parameters of the user's current environment, and multiple temperature and humidity sensors are used to obtain multiple temperature and humidity parameters of the user's current environment.
3. The smart helmet for health monitoring according to claim 1, characterized in that: After the central control chip obtains multiple vital sign parameters, it divides the multiple vital sign parameters into types, performs data cleaning under each type, and uses the cleaned multiple vital sign parameters as basic input data, which are input into the BP neural network for calculation to obtain the corresponding user health status. The structure of the BP neural network is an nm-1 structure, where n is a positive integer not less than 3, 2n≤m≤3n, and m is a positive integer.
4. The smart helmet for health monitoring according to claim 1, characterized in that: After the central control chip obtains multiple environmental parameters, it divides the multiple environmental parameters into environmental types, performs data screening and data supplementation under each environmental type, obtains each corrected data under each environmental type, and determines the corresponding environmental health level based on each corrected data.
5. The smart helmet for health monitoring according to claim 1, characterized in that: The passive warning unit includes a parallel red warning LED, a blue warning LED and a buzzer.
6. The smart helmet for health monitoring according to claim 1, characterized in that: The active warning unit has a built-in button trigger, which is used to receive the user's press to generate an active alarm signal and transmit it to the central control chip.
7. The smart helmet for health monitoring according to claim 1, characterized in that: The remote communication module adopts WiFi or LoRa or NB-loT communication protocol to connect to the remote terminal. The remote communication module is also provided with an encryption unit, which adopts RSA and AES hybrid encryption method to encrypt the data transmission of the remote communication module.
8. The smart helmet for health monitoring according to claim 1, characterized in that: It also includes a positioning module, which is used to transmit the user's location information to the central control chip and the remote terminal.