Signal processing method and device, safety helmet, storage medium and program product

Abstract

The invention discloses a signal processing method and device, a safety helmet, a storage medium and a program product, and relates to the technical field of safety management. When the fatigue or distraction of the person wearing the intelligent safety helmet is detected based on the attention concentration degree, and / or the sudden muscle tension of the person wearing the intelligent safety helmet is recognized, the high-risk tool currently used by the person wearing the intelligent safety helmet is automatically locked, and the safety of the person wearing the intelligent safety helmet is improved. And the system is linked with an industrial control system to suspend the current high-risk process of the personnel wearing the intelligent safety helmet. Through the signal processing method of the integrated intelligent safety helmet, unnecessary damage possibly caused by safety protection early warning is avoided, and full-consideration and rigorous safety protection early warning is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of safety management, and in particular to a signal processing method for an integrated smart safety helmet, a signal processing device for an integrated smart safety helmet, an integrated smart safety helmet, a storage medium, and a computer program product. Background Art

[0002] Smart helmets are intelligent wearable devices that integrate multiple advanced technologies. They are widely used in various industries such as construction, power and mining, petrochemicals, railway transportation, municipal engineering, disaster relief, etc. They have various functions such as video and image acquisition, voice interaction, real-time intercom, positioning, electronic fencing, safety protection warning, face recognition and video behavior analysis.

[0003] However, at present, the functional applications of smart helmets in various application scenarios, such as safety protection warnings, can only realize the monitoring and warning of people's movement status, such as falls or falls from heights, and do not consider other factors in the environment where people are located, such as other personnel status factors, additional supporting facilities, etc. The safety protection warning is not considered and rigorous enough, which may cause unnecessary injuries.

[0004] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a signal processing method for an integrated smart helmet, a signal processing device for an integrated smart helmet, an integrated smart helmet, a storage medium and a computer program product, aiming to solve the technical problem that safety protection warnings may cause unnecessary injuries.

[0006] To achieve the above objectives, the present application proposes a signal processing method for an integrated smart helmet, the signal processing method for the integrated smart helmet comprising: The micro EEG sensor integrated into the smart helmet can monitor the concentration of the person wearing the smart helmet; The myoelectric sensor integrated into the smart helmet can identify sudden muscle tension of the wearer; When fatigue or distraction of the person wearing the smart safety helmet is detected based on the concentration level, and / or sudden muscle tension of the person wearing the smart safety helmet is identified, the high-risk tools currently being used by the person wearing the smart safety helmet are automatically locked, and the current high-risk process of the person wearing the smart safety helmet is suspended in conjunction with the industrial control system.

[0007] In one embodiment, after the step of detecting fatigue or distraction of the person wearing the smart helmet based on the concentration level, and / or identifying sudden muscle tension of the person wearing the smart helmet, the step further includes: Through the micro AR projection module embedded inside the goggles of the smart helmet, the equipment parameters and / or operating instructions are superimposed in the field of view of the person wearing the smart helmet; and / or, The smart helmet uses a micro camera built into the brim to recognize the gestures of the person wearing the smart helmet.

[0008] In one embodiment, the method further comprises: The anxiety index of people wearing smart helmets is monitored through the micro EEG sensor integrated into the smart helmets; When it is detected based on the anxiety index that the emotional fluctuation of the person wearing the smart helmet exceeds a threshold, the built-in fragrance device integrated in the smart helmet is automatically triggered to release a calming component, and / or soothing audio is played or sound wave stimulation of a target frequency is emitted through the bone conduction headphones integrated in the smart helmet.

[0009] In one embodiment, the method further comprises: Through the micro mass spectrometer probe integrated in the smart helmet, the harmful gas components in the air can be analyzed in real time, automatically activating the breathing mask of the person wearing the smart helmet and synchronously adjusting the filtering mode of the breathing mask.

[0010] In one embodiment, the method further comprises: When the anxiety index detects that the emotional fluctuation of the person wearing the smart helmet exceeds a threshold and harmful gas components are analyzed in the air, it is determined whether the calming components released by the built-in fragrance device integrated in the smart helmet automatically trigger a reaction with the harmful gas components, and whether the substances generated by the reaction cannot be absorbed by the breathing mask; If a reaction occurs and the substances generated after the reaction cannot be absorbed by the breathing mask, the built-in fragrance device integrated in the smart helmet will be triggered to release calming ingredients and an emergency evacuation alarm will be given.

[0011] In one embodiment, the method further comprises: By integrating AR glasses into the smart helmet and combining it with the gas diffusion model, a dynamic concentration gradient thermal map of harmful gas components is generated on the AR interface.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a signal processing device for an integrated smart helmet, the signal processing device for the integrated smart helmet comprising: The first module is used to monitor the concentration of the person wearing the smart helmet through the micro EEG sensor integrated in the smart helmet; The second module is used to identify sudden muscle tension of the person wearing the smart helmet through the myoelectric sensor integrated in the smart helmet; The third module is used to automatically lock the high-risk tools currently being used by the person wearing the smart safety helmet when fatigue or distraction of the person wearing the smart safety helmet is detected based on the concentration level, and / or when sudden muscle tension of the person wearing the smart safety helmet is identified, and to coordinate with the industrial control system to suspend the current high-risk process of the person wearing the smart safety helmet.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes an integrated smart safety helmet, which includes: a micro EEG sensor, an electromyography sensor, a micro AR projection module, a micro camera, a micro mass spectrometer probe and at least one component of AR glasses; the integrated smart safety helmet also includes: a memory, a processor and a computer program stored on the memory and runnable on the processor, and the computer program is configured to implement the steps of the signal processing method of the integrated smart safety helmet as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the signal processing method of the integrated smart safety helmet as described above are implemented.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the signal processing method of the integrated smart helmet as described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: In this application, firstly, the personnel status of the person wearing the smart helmet is fully considered, and their concentration and sudden muscle tension are comprehensively considered; secondly, the additional supporting facilities corresponding to the person wearing the smart helmet are fully considered, and the high-risk tools currently being used by the person wearing the smart helmet and the corresponding high-risk processes in the industrial control system are comprehensively considered. In this way, when fatigue or distraction of the person wearing the smart helmet is detected based on the concentration of attention, and / or when sudden muscle tension of the person wearing the smart helmet is identified, the high-risk tools currently being used by the person wearing the smart helmet are automatically locked, and the high-risk process currently being used by the person wearing the smart helmet is suspended in conjunction with the industrial control system. Through the signal processing method of the integrated smart helmet, unnecessary injuries that may be caused by safety protection warnings are avoided, and a fully considered and rigorous safety protection warning is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 A flowchart of the first embodiment of the signal processing method for the integrated smart helmet of the present application is provided; Figure 2 A flowchart illustrating a second embodiment of a signal processing method for an integrated smart helmet of the present application; Figure 3 An application diagram provided for an embodiment of the signal processing method for an integrated smart helmet of the present application; Figure 4 This is a schematic diagram of the module structure of the signal processing device of the integrated smart helmet according to the embodiment of the present application; Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the signal processing method of the integrated smart helmet in the embodiment of the present application.

[0020] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0022] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0023] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of implementing the above functions, such as an integrated smart helmet. The following uses an integrated smart helmet as an example to illustrate this embodiment and the following embodiments.

[0024] Based on this, the embodiment of the present application provides a signal processing method for an integrated smart helmet, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the signal processing method for the integrated smart helmet of the present application.

[0025] In this embodiment, the signal processing method of the integrated smart helmet includes steps S10 to S30: Step S10, monitoring the concentration of the person wearing the smart helmet through the micro EEG sensor integrated in the smart helmet; The brain produces distinct EEG signal characteristics under different states of attention. When a person is highly focused, specific EEG frequency activity increases in the frontal and parietal regions of the brain, such as beta waves (13-30Hz) and gamma waves (above 30Hz). When attention is distracted, alpha waves (8-13Hz) and theta waves (4-8Hz) may be relatively stronger. Micro EEG sensors can capture these EEG signal changes, providing a basis for monitoring attentional concentration. The micro EEG sensors integrated into the smart helmet use a non-invasive electrode design that contacts the surface of the scalp to detect brain electrical activity. These electrodes sense changes in the electrical signals of brain neurons and convert them into measurable electrical signals. The sensor's high sensitivity and low noise ensure accurate capture of weak EEG signals and stable operation even in complex environmental conditions.

[0026] In one embodiment, referring to Figure 3The EEG signals collected by the micro EEG sensor integrated into the smart helmet first need to be amplified and filtered to improve signal quality. Then, signal processing algorithms such as the Fast Fourier Transform (FFT) are used to convert the time-domain EEG signals into frequency-domain signals, extracting characteristic frequency components related to attention. Based on the extracted EEG signal features, a machine learning or deep learning algorithm is used to construct an attention concentration assessment model. By learning from large amounts of annotated data, these algorithms can establish a nonlinear mapping relationship between EEG signal features and attention concentration, thereby accurately assessing the wearer's attention state. For example, traditional machine learning algorithms such as support vector machines (SVM) and random forests (RF), or deep learning algorithms such as convolutional neural networks (CNN) and recurrent neural networks (RNN) can be used for modeling. Furthermore, the micro EEG sensor, signal processing module, assessment algorithm, and related power management and data storage units are integrated into the smart helmet to form a complete monitoring system. The collected EEG data and assessment results can be transmitted to external devices such as smartphones, tablets, or monitoring platforms via wireless communication technologies such as Bluetooth and Wi-Fi for real-time display and further analysis.

[0027] Step S20, identifying sudden muscle tension of the person wearing the smart helmet through the myoelectric sensor integrated in the smart helmet; Muscle tension generates weak electrical signals, which can be detected by myoelectric sensors. When a wearer's muscles tense suddenly, such as due to fear, surprise, or sudden physical stress, the electrical activity of the muscles changes significantly. The myoelectric sensors detect this change and can determine the state of muscle tension.

[0028] In one embodiment, referring to Figure 3 First, the myoelectric sensors integrated into the smart helmet continuously collect electrical signals from the wearer's head and neck muscles. These signals contain information about muscle activity. The collected myoelectric signals are then analyzed to extract features such as signal amplitude, frequency, and phase. When muscles are tense, the signal amplitude increases and the frequency may also change. The extracted features are then compared with preset muscle tension patterns. Through machine learning or deep learning algorithms, the signal features under different muscle tension states are learned, enabling accurate identification. At the same time, a threshold is set. When the myoelectric signal features exceed this threshold, it is determined to be a sudden muscle tension event. The threshold can be adjusted, optimized, and set based on a large amount of experimental data and actual application scenarios.

[0029] Step S30: When fatigue or distraction of the person wearing the smart helmet is detected based on the concentration level, and / or sudden muscle tension of the person wearing the smart helmet is identified, the high-risk tools currently being used by the person wearing the smart helmet are automatically locked, and the high-risk process currently being used by the person wearing the smart helmet is suspended in conjunction with the industrial control system.

[0030] High-risk tools are equipped with smart locks, which can be electronic and receive lock signals from the smart helmet's central control system. These tool locks can communicate with the central control system via wired (such as USB ports for charging and data transmission) or wireless (such as low-energy Bluetooth). The industrial control system is responsible for controlling and managing the entire process. It connects to the smart helmet's central control system and / or high-risk tools via a network, which can be a local area network (such as Ethernet within the workshop) or a dedicated industrial wireless network (such as ZigBee, a low-power, short-range wireless network designed for industrial environments).

[0031] In one embodiment, micro EEG sensors and electromyography sensors integrated into the smart helmet continuously collect corresponding data. For example, the micro EEG sensor collects brain electrical activity data several times per second, while the electromyography sensor collects muscle electrical activity data. This data first undergoes preliminary preprocessing within the smart helmet, such as filtering and amplification, to improve data quality.

[0032] Then, the state determination algorithm runs in the industrial control system or the smart helmet's local central control system processor (if sufficient computing power is available). In addition to the attention concentration assessment model mentioned above, to adapt to the computing power and power consumption requirements of the smart helmet, a model of normal attention states can also be established, such as a database of normal EEG patterns. When the collected data differs significantly from the normal pattern (for example, when the power variation in a specific EEG frequency band exceeds a threshold), fatigue or distraction is determined. Similarly, in addition to the muscle tension identification model based on machine learning or deep learning algorithms mentioned above, to adapt to the computing power and power consumption requirements of the smart helmet, a model of normal muscle electrical activity can also be established. When characteristics such as the amplitude and frequency of the EEG signal suddenly exceed the normal range, a sudden muscle tension state is determined.

[0033] Then, once it is determined to be a state of fatigue, distraction or sudden muscle tension, the linkage decision-making algorithm in the central control system will generate an instruction to lock the high-risk tools based on the information of the high-risk tools currently being used by the person wearing the smart safety helmet (this information can be pre-configured in the central control system, such as each worker's corresponding tool usage permissions and the tools corresponding to the current work task), and at the same time generate an instruction to suspend the current high-risk process.

[0034] Regarding tool locking, the central control system sends a locking instruction to the smart lock of the corresponding high-risk tool. After receiving the instruction, the smart lock immediately activates the locking mechanism, such as cutting off the power supply of the tool or mechanically locking the operating parts of the tool, so that the tool can no longer be used. Regarding process suspension, the central control system sends a pause instruction to the industrial control system for the equipment responsible for the current high-risk process, and the industrial control system forwards it to the equipment responsible for the current high-risk process. These equipment can be large-scale mechanical processing equipment, hazardous chemical processing equipment, etc. After receiving the pause instruction, the equipment stops the current operation according to the predetermined safety procedures, such as gradually reducing the operating speed, closing relevant valves, etc., to ensure the safety of the working environment.

[0035] In this way, if fatigue or distraction is detected based on the wearer's concentration, and / or if sudden muscle tension is detected, the smart helmet automatically locks the high-risk tool currently being used by the wearer and, in conjunction with the industrial control system, suspends the wearer's current high-risk process. This integrated smart helmet signal processing method avoids unnecessary harm caused by safety warnings, achieving a fully considered and rigorous safety warning.

[0036] In a feasible embodiment, after the step of detecting fatigue or distraction of the person wearing the smart helmet based on the concentration level, and / or identifying sudden muscle tension of the person wearing the smart helmet, step S30 further includes: Through the micro AR projection module embedded inside the goggles of the smart helmet, the equipment parameters and / or operating instructions are superimposed in the field of view of the person wearing the smart helmet; and / or, The smart helmet uses a micro camera built into the brim to recognize the gestures of the person wearing the smart helmet.

[0037] When it is confirmed that the person wearing the smart safety helmet is tired or distracted and / or has sudden muscle tension, in addition to automatically locking the high-risk tools currently being used by the person wearing the smart safety helmet and linking with the industrial control system to suspend the current high-risk process of the person wearing the smart safety helmet, the smart safety helmet can also use the micro AR projection module embedded in the inside of the goggles to superimpose equipment parameters and / or operation instructions in the field of view of the person wearing the smart safety helmet; and / or, the micro camera integrated into the brim of the smart safety helmet can recognize the gesture commands of the person wearing the smart safety helmet, further guiding the person wearing the smart safety helmet to perform self-protection operations in high-risk situations.

[0038] The micro AR projection module features high brightness and resolution, ensuring clear projection in all lighting conditions. It is embedded inside the goggles, and optical design optimizes the projection effect, seamlessly integrating it with the wearer's field of view. Furthermore, the smart helmet incorporates multiple sensors, such as accelerometers, gyroscopes, and ambient light sensors, to detect the wearer's movements, posture, and changes in the surrounding environment, providing data support for AR projection.

[0039] When generating AR projection content, the corresponding AR projection content includes device parameters, operating steps, warning messages, etc. The position and angle of the projected content are further dynamically adjusted based on the wearer's line of sight, head posture, and other information to ensure that it is always within the wearer's field of view and does not affect the wearer's normal line of sight. Key device parameters such as device operating status, fault information, and remaining battery life are projected in real time into the wearer's field of view, allowing the wearer to promptly understand the device's status and prepare countermeasures in advance. When specific operations are required, such as device troubleshooting and emergency response, the projection module projects detailed operating steps and instructions into the wearer's field of view to guide the wearer through the corresponding operations. In high-risk situations, such as equipment failure or environmental hazards, the projection module immediately projects eye-catching warning messages and self-protection operation prompts, such as the location of the emergency stop button and safe evacuation routes, to guide the wearer to quickly take self-protection measures.

[0040] Regarding the micro camera, selecting a suitable micro camera to be integrated into the brim of the smart helmet requires high resolution, good imaging capabilities in low-light environments, and a wide-angle field of view so that the wearer's hand movements can be clearly captured. For example, clear images can be obtained in a dimly lit environment by automatically adjusting parameters. Similar to the micro AR projection module, in order to more accurately recognize gestures and judge the wearer's status, other sensors such as accelerometers and gyroscopes can be combined to sense the motion state of the smart helmet and the wearer's head movements, providing more reference information for gesture recognition.

[0041] When identifying gestures from people wearing smart helmets, deep learning methods, such as convolutional neural networks (CNNs), can be used to extract features and classify hand images captured by the camera. First, a large amount of gesture sample data is collected, including images of various gestures from different angles and lighting conditions. This data is then annotated and used to train the deep learning model. In practice, the hand images captured in real time by the camera are fed into the trained model, which then outputs the corresponding gesture category.

[0042] When identifying gesture commands of people wearing smart helmets, it is also possible to pre-process the hand image based on traditional computer vision methods, such as filtering, binarization, edge detection, etc., to extract hand contours, key points and other features, and then match these features with preset gesture templates to determine the category of the gesture.

[0043] In one embodiment, upon recognizing a specific gesture command such as an "OK" gesture command or a gesture command of frequent or regular waving, the smart helmet can further determine that the person wearing the smart helmet is in a state of fatigue or distraction and / or sudden muscle tension, and can superimpose device parameters and / or operating instructions in the field of view of the person wearing the smart helmet through a micro AR projection module embedded inside the goggles of the smart helmet.

[0044] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , Figure 2 This is a flow chart of the second embodiment of the signal processing method for the integrated smart helmet of the present application.

[0045] In this embodiment, the method further includes steps T10 to T20: Step T10: Monitor the anxiety index of the person wearing the smart helmet through the micro EEG sensor integrated in the smart helmet; For the introduction of the micro EEG sensor integrated in the smart helmet, please refer to the above-mentioned embodiment 1, which will not be repeated here.

[0046] Similar to monitoring the concentration of people wearing smart helmets, micro EEG sensors can collect weak electrical signals generated by the brain in real time. These signals reflect neural activity. Different brain states, such as anxiety and relaxation, produce different electrical signal patterns. After pre-processing such as amplification and filtering, the collected EEG signals are extracted to identify anxiety-related features, such as EEG energy in specific frequency bands and EEG signal complexity. Based on these extracted features, a model is built using machine learning or deep learning algorithms to calculate the wearer's anxiety index.

[0047] In one embodiment, the wearer first wears a smart helmet in different anxiety states (e.g., simulating work stress scenarios, conducting anxiety-inducing experiments, etc.) to collect EEG data. The collected EEG data is then divided into training and test sets. The training set is used to train a machine learning model (e.g., support vector machine, random forest, etc.) or a deep learning model (e.g., convolutional neural network, recurrent neural network, etc.), adjusting the model parameters to enable the model to accurately predict the anxiety index based on EEG features. Secondly, the trained model is evaluated using the test set, and the model is optimized based on the evaluation results to improve its accuracy and generalization ability. Finally, the trained model is deployed in the computing unit of the smart helmet's central control system to enable real-time monitoring of the wearer's anxiety index.

[0048] Furthermore, the EEG signal characteristics (such as frequency and amplitude) can be analyzed based on machine learning models (such as LSTM neural networks), combined with multimodal data such as heart rate variability (HRV) or galvanic skin response (GSR), to identify the anxiety index of people wearing smart helmets, thereby improving recognition accuracy.

[0049] Step T20: When it is detected based on the anxiety index that the emotional fluctuation of the person wearing the smart helmet exceeds a threshold, the built-in fragrance device integrated in the smart helmet is automatically triggered to release a calming component, and / or, soothing audio is played or sound wave stimulation of a target frequency is emitted through the bone conduction headphones integrated in the smart helmet.

[0050] Reference Figure 3 Regarding the aroma device, when the smart helmet's central control system determines that the anxiety index exceeds a threshold, it sends a signal to the built-in aroma device. The aroma device can contain a small reservoir containing a calming essential oil (such as lavender). The signal from the smart helmet's central control system triggers a micropump, which draws the essential oil from the reservoir at a set dosage and releases it into the space inside the smart helmet through a tiny atomizing nozzle, allowing the wearer to smell it and achieve a calming effect.

[0051] Reference Figure 3Regarding bone conduction headphones, a series of soothing audio tracks, such as natural sounds (rain, waves, etc.) and gentle music (such as slow movements from classical music), are pre-stored in the smart helmet's memory unit. When the anxiety index exceeds a threshold, the smart helmet's central control system selects the audio track to play based on pre-set rules. For example, different audio tracks can be selected based on different anxiety index ranges: soft piano music for slightly higher anxiety indexes, and natural rain sounds for even higher anxiety indexes. The smart helmet's central control system transmits the selected audio signal to the bone conduction headphones' driver circuit, which amplifies the audio signal and converts it into an electrical signal suitable for playback, enabling the bone conduction headphones to produce the soothing audio. Alternatively, a specific frequency range can be determined (such as the 4-8Hz theta wave frequency range, which is considered associated with a state of relaxation). When the anxiety index exceeds the threshold, an electrical signal of the target frequency is generated and transmitted to the bone conduction headphones. The headphones convert the electrical signal into mechanical vibrations, generating sound waves of the target frequency, stimulating the wearer's auditory system and achieving emotional relief.

[0052] In a feasible embodiment, the method further includes: Through the micro mass spectrometer probe integrated in the smart helmet, the harmful gas components in the air can be analyzed in real time, automatically activating the breathing mask of the person wearing the smart helmet and synchronously adjusting the filtering mode of the breathing mask.

[0053] The micro mass spectrometer probe can analyze gas molecules, ionize gas molecules in the air, and determine the type and concentration of the gas based on the mass-to-charge ratio and abundance of the ions. Figure 3 The miniature mass spectrometer on the smart helmet monitors harmful gases in the air in real time, such as carbon monoxide, hydrogen sulfide, and volatile organic compounds, and transmits the results to the smart helmet's central control system. When the concentration of a harmful gas exceeds a set safety threshold, the smart helmet's central control system automatically sends an activation signal to the breathing mask, initiating its operation. The smart helmet's central control system also automatically adjusts the mask's filtration mode based on the type and concentration of the detected harmful gas to ensure optimal protection.

[0054] In another feasible embodiment, the method further includes: When the anxiety index detects that the emotional fluctuation of the person wearing the smart helmet exceeds a threshold and harmful gas components are analyzed in the air, it is determined whether the calming components released by the built-in fragrance device integrated in the smart helmet automatically trigger a reaction with the harmful gas components, and whether the substances generated by the reaction cannot be absorbed by the breathing mask; If a reaction occurs and the substances generated after the reaction cannot be absorbed by the breathing mask, the built-in fragrance device integrated in the smart helmet will be triggered to release calming ingredients and an emergency evacuation alarm will be given.

[0055] In one embodiment, a local chemical substance reaction database can be constructed (storing the components and known reaction formulas of sedative ingredients and 3000+ harmful gases), and a respiratory mask material property database can be established (including the effective ingredients and concentrations of respiratory masks, etc.).

[0056] If the two conditions of emotional fluctuation exceeding the threshold and harmful gas existing are met, then Figure 3 The smart helmet's built-in fragrance device is shown in the figure. The calming components and harmful gas components released by the device are analyzed. This can be done by querying the local chemical reaction database and the respiratory mask material property database, and referring to information such as their respective chemical formulas. For example, let's assume the calming component is A and the harmful gas component is B. It is necessary to determine whether A and B will react. If a reaction occurs, the resulting substance is assumed to be C. Based on the local chemical reaction database, the characteristics of the respiratory mask's absorption material and the physicochemical properties of substance C (such as molecular size and polarity) are further used to determine whether C cannot be absorbed by the respiratory mask. If a reaction is confirmed and the resulting substance cannot be absorbed by the respiratory mask, the release of the calming component from the built-in fragrance device is suspended, and an emergency evacuation alarm is issued.

[0057] Furthermore, it is also possible to confirm whether the calming component released by the built-in fragrance device integrated in the smart safety helmet reacts with the effective component of the breathing mask, and whether the substance generated during the reaction cannot be absorbed by the breathing mask; similarly, if a reaction occurs and the substance generated after the reaction cannot be absorbed by the breathing mask, the built-in fragrance device integrated in the smart safety helmet will be temporarily triggered to release the calming component, and an emergency evacuation alarm will be given.

[0058] Thus, it is prevented that the fragrance device releases calming components and harmful gas components or reacts with the breathing mask to produce new substances that cannot be absorbed by the breathing mask, thereby causing new secondary chemical damage to the person wearing the smart helmet.

[0059] In another feasible embodiment, the method further includes: By integrating AR glasses into the smart helmet and combining it with the gas diffusion model, a dynamic concentration gradient thermal map of harmful gas components is generated on the AR interface.

[0060] Reference Figure 3A gas diffusion model is used to predict the diffusion behavior of harmful gases in the environment. This mathematical model takes into account the gas's physical and chemical properties, environmental conditions (such as wind speed, direction, temperature, and humidity), and emission source characteristics (such as emission rate and height). The model then calculates the gas concentration distribution at different locations and times. The AR glasses 2 integrated into the smart helmet 1 can obtain the wearer's location and viewing direction in real time and also provide a display function. By interacting with the calculated results of the gas diffusion model, the concentration of harmful gases is intuitively displayed on the AR interface as a heat map.

[0061] In one embodiment, a gas sensor network, such as a gas composition detection sensor network composed of multiple miniature mass spectrometer probes or a gas temperature and humidity detection sensor network composed of multiple temperature and humidity sensors, is deployed on the smart helmet to monitor real-time data such as hazardous gas concentration, temperature, humidity, wind speed, and wind direction, and transmit this data to the smart helmet's central control system. Simultaneously, the smart helmet's positioning system acquires the wearer's location information and also transmits it to the smart helmet's central control system. Based on the received gas monitoring data and environmental parameters, the smart helmet's central control system uses a gas diffusion model to calculate the concentration distribution of hazardous gases throughout the entire area. Furthermore, the calculated hazardous gas concentration distribution data is matched with the position information and viewing angle of the AR glasses mounted on the smart helmet, generating a dynamic concentration gradient heat map on the AR interface. The color of the heat map can intuitively indicate the concentration of hazardous gases, for example, red indicates high concentration and green indicates low concentration. Thus, based on the dynamic concentration gradient heat map of hazardous gas components generated on the AR interface, the wearer of the smart helmet can be guided to locate the source of hazardous gas leaks or develop an efficient and safe escape route.

[0062] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the signal processing method of the integrated smart helmet of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0063] This application also provides a signal processing device for an integrated smart helmet, please refer to Figure 4 , the signal processing device of the integrated intelligent helmet includes: The first module 10 is used to monitor the concentration of the person wearing the smart helmet through the micro EEG sensor integrated in the smart helmet; The second module 20 is used to identify sudden muscle tension of the person wearing the smart helmet through the myoelectric sensor integrated in the smart helmet; The third module 30 is used to automatically lock the high-risk tools currently being used by the person wearing the smart safety helmet when fatigue or distraction of the person wearing the smart safety helmet is detected based on the person's concentration, and / or when sudden muscle tension of the person wearing the smart safety helmet is identified, and to coordinate with the industrial control system to suspend the current high-risk process of the person wearing the smart safety helmet.

[0064] In one embodiment, the third module 30 is further configured to: Through the micro AR projection module embedded inside the goggles of the smart helmet, the equipment parameters and / or operating instructions are superimposed in the field of view of the person wearing the smart helmet; and / or, The smart helmet uses a micro camera built into the brim to recognize the gestures of the person wearing the smart helmet.

[0065] In one embodiment, the signal processing device of the integrated smart helmet further includes a fourth module for: The anxiety index of people wearing smart helmets is monitored through the micro EEG sensor integrated into the smart helmets; When it is detected based on the anxiety index that the emotional fluctuation of the person wearing the smart helmet exceeds a threshold, the built-in fragrance device integrated in the smart helmet is automatically triggered to release a calming component, and / or soothing audio is played or sound wave stimulation of a target frequency is emitted through the bone conduction headphones integrated in the smart helmet.

[0066] In one embodiment, the signal processing device of the integrated smart helmet further includes a fifth module for: Through the micro mass spectrometer probe integrated in the smart helmet, the harmful gas components in the air can be analyzed in real time, automatically activating the breathing mask of the person wearing the smart helmet and synchronously adjusting the filtering mode of the breathing mask.

[0067] In one embodiment, the signal processing device of the integrated smart helmet further includes a sixth module for: When the anxiety index detects that the emotional fluctuation of the person wearing the smart helmet exceeds a threshold and harmful gas components are analyzed in the air, it is determined whether the calming components released by the built-in fragrance device integrated in the smart helmet automatically trigger a reaction with the harmful gas components, and whether the substances generated by the reaction cannot be absorbed by the breathing mask; If a reaction occurs and the substances generated after the reaction cannot be absorbed by the breathing mask, the built-in fragrance device integrated in the smart helmet will be triggered to release calming ingredients and an emergency evacuation alarm will be given.

[0068] In one embodiment, the signal processing device of the integrated smart helmet further includes a seventh module for: By integrating AR glasses into the smart helmet and combining it with the gas diffusion model, a dynamic concentration gradient thermal map of harmful gas components is generated on the AR interface.

[0069] The integrated smart helmet signal processing device provided in this application utilizes the integrated smart helmet signal processing method described in the aforementioned embodiments, addressing the technical issue of safety warnings potentially causing unnecessary harm. Compared to the prior art, the integrated smart helmet signal processing device provided in this application achieves the same beneficial effects as the integrated smart helmet signal processing method described in the aforementioned embodiments. Other technical features of the integrated smart helmet signal processing device are the same as those disclosed in the aforementioned embodiments and are not further elaborated upon here.

[0070] The present application provides an integrated smart safety helmet, which includes: a micro EEG sensor, an electromyography sensor, a micro AR projection module, a micro camera, a micro mass spectrometer probe and at least one component of AR glasses; the integrated smart safety helmet also includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the signal processing method of the integrated smart safety helmet in the above-mentioned embodiment one.

[0071] Reference below Figure 5 , which shows a structural schematic diagram of an integrated smart safety helmet suitable for implementing an embodiment of the present application. Figure 5 The integrated smart helmet shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0072] like Figure 5As shown, the integrated smart hard hat may include a processing device 1001 (e.g., a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in read-only memory 1002 or programs loaded from storage device 1003 into random access memory 1004. Random access memory 1004 also stores various programs and data required for the operation of the integrated smart hard hat. The processing device 1001, read-only memory 1002, and random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems may be connected to the input / output interface 1006: input devices 1007, such as a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008, such as a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003, such as a magnetic tape or hard disk; and communication device 1009. The communication device 1009 can allow the integrated smart hard hat to communicate with other devices wirelessly or wired to exchange data. Although the figure shows an integrated smart hard hat with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems can be implemented or have alternatively.

[0073] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0074] The integrated smart helmet provided in this application utilizes the signal processing method for the integrated smart helmet in the aforementioned embodiment, addressing the technical issue of safety warnings potentially causing unnecessary harm. Compared to the prior art, the integrated smart helmet provided in this application achieves the same beneficial effects as the signal processing method for the integrated smart helmet in the aforementioned embodiment. Other technical features of the integrated smart helmet are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0075] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0076] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0077] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the signal processing method of the integrated smart helmet in the above-mentioned embodiment.

[0078] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0079] The computer-readable storage medium may be included in the integrated smart helmet, or may exist independently without being assembled into the integrated smart helmet.

[0080] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the integrated smart safety helmet, the integrated smart safety helmet: monitors the concentration of the person wearing the smart safety helmet through the micro EEG sensor integrated on the smart safety helmet; identifies sudden muscle tension of the person wearing the smart safety helmet through the myoelectric sensor integrated on the smart safety helmet; when fatigue or distraction of the person wearing the smart safety helmet is detected based on the concentration of attention, and / or, when sudden muscle tension of the person wearing the smart safety helmet is identified, automatically locks the high-risk tools currently being used by the person wearing the smart safety helmet, and cooperates with the industrial control system to suspend the current high-risk process of the person wearing the smart safety helmet.

[0081] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0082] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0083] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0084] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned signal processing method for an integrated smart helmet. This computer-readable storage medium can address the technical issue of safety warnings potentially causing unnecessary harm. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the signal processing method for an integrated smart helmet provided in the aforementioned embodiments, and are not further elaborated here.

[0085] The present application also provides a computer program product, comprising a computer program, which implements the steps of the signal processing method for the integrated smart helmet when executed by a processor.

[0086] The computer program product provided in this application can address the technical issue of safety warnings potentially causing unnecessary harm. Compared to the prior art, the beneficial effects of the computer program product provided in this application are similar to those of the signal processing method for the integrated smart helmet provided in the aforementioned embodiment, and are not further elaborated here.

[0087] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A signal processing method for an integrated smart helmet, characterized in that: The signal processing method of the integrated intelligent helmet includes: The micro EEG sensor integrated into the smart helmet can monitor the concentration of the person wearing the smart helmet; The myoelectric sensor integrated into the smart helmet can identify sudden muscle tension of the wearer; When fatigue or distraction of the person wearing the smart safety helmet is detected based on the concentration level, and / or sudden muscle tension of the person wearing the smart safety helmet is identified, the high-risk tools currently being used by the person wearing the smart safety helmet are automatically locked, and the current high-risk process of the person wearing the smart safety helmet is suspended in conjunction with the industrial control system.

2. The signal processing method of the integrated intelligent helmet according to claim 1, characterized in that: After the step of detecting fatigue or distraction of the person wearing the smart helmet based on the concentration level, and / or identifying sudden muscle tension of the person wearing the smart helmet, the method further includes: Through the micro AR projection module embedded inside the goggles of the smart helmet, the equipment parameters and / or operating instructions are superimposed in the field of view of the person wearing the smart helmet; and / or, The smart helmet uses a micro camera built into the brim to recognize the gestures of the person wearing the smart helmet.

3. The signal processing method of the integrated intelligent helmet according to claim 1, characterized in that: The method further comprises: The anxiety index of people wearing smart helmets is monitored through the micro EEG sensor integrated into the smart helmets; When it is detected based on the anxiety index that the emotional fluctuation of the person wearing the smart helmet exceeds a threshold, the built-in fragrance device integrated in the smart helmet is automatically triggered to release a calming component, and / or soothing audio is played or sound wave stimulation of a target frequency is emitted through the bone conduction headphones integrated in the smart helmet.

4. The signal processing method of the integrated intelligent helmet according to claim 3, characterized in that: The method further comprises: Through the micro mass spectrometer probe integrated in the smart helmet, the harmful gas components in the air can be analyzed in real time, automatically activating the breathing mask of the person wearing the smart helmet and synchronously adjusting the filtering mode of the breathing mask.

5. The signal processing method of the integrated intelligent helmet according to claim 4, characterized in that: The method further comprises: When the anxiety index detects that the emotional fluctuation of the person wearing the smart helmet exceeds a threshold and harmful gas components are analyzed in the air, it is determined whether the calming components released by the built-in fragrance device integrated in the smart helmet automatically trigger a reaction with the harmful gas components, and whether the substances generated by the reaction cannot be absorbed by the breathing mask; If a reaction occurs and the substances generated after the reaction cannot be absorbed by the breathing mask, the built-in fragrance device integrated in the smart helmet will be triggered to release calming ingredients and an emergency evacuation alarm will be given.

6. The signal processing method of the integrated intelligent helmet according to claim 4, characterized in that: The method further comprises: By integrating AR glasses into the smart helmet and combining it with the gas diffusion model, a dynamic concentration gradient thermal map of harmful gas components is generated on the AR interface.

7. A signal processing device for an integrated smart helmet, characterized in that: The signal processing device of the integrated intelligent helmet includes: The first module is used to monitor the concentration of the person wearing the smart helmet through the micro EEG sensor integrated in the smart helmet; The second module is used to identify sudden muscle tension of the person wearing the smart helmet through the myoelectric sensor integrated in the smart helmet; The third module is used to automatically lock the high-risk tools currently being used by the person wearing the smart safety helmet when fatigue or distraction of the person wearing the smart safety helmet is detected based on the concentration level, and / or when sudden muscle tension of the person wearing the smart safety helmet is identified, and to coordinate with the industrial control system to suspend the current high-risk process of the person wearing the smart safety helmet.

8. An integrated smart helmet, characterized in that: The integrated smart helmet includes: a micro EEG sensor, an electromyography sensor, a micro AR projection module, a micro camera, a micro mass spectrometer probe and at least one component of AR glasses; the integrated smart helmet also includes: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the signal processing method for the integrated smart helmet as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the signal processing method of the integrated smart helmet according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the signal processing method of the integrated smart helmet according to any one of claims 1 to 6 are implemented.

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