Intelligent wearing system based on deep learning and intelligent LED light bar waistcoat

Abstract

The invention relates to the technical field of artificial intelligence, intelligent wearing systems and the like, and provides an intelligent wearing system and an intelligent LED light bar waistcoat based on deep learning. LED light bars are distributed on an intelligent LED light bar waistcoat body, and a visual sensor and a host unit are arranged; the visual sensor is used for collecting an environment image of an environment where a user wearing the intelligent LED light bar waistcoat body is located, the host unit is in communication connection with the LED light bar and the visual sensor, the environment image is analyzed through a convolutional neural network, the environment type of the environment where the user is located is recognized, and the lighting mode of the LED light bar is automatically adjusted according to the environment type. Therefore, specific environment types are identified, the lighting mode is adaptively adjusted, and the flexibility and accuracy of the intelligent LED light bar waistcoat are improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of artificial intelligence, smart wearable systems, and in particular to a smart wearable system and a smart LED light bar vest based on deep learning. Background Art

[0002] Smart LED light bar vests with LED light strips are usually equipped with light sensors. The host unit roughly judges the brightness requirements through the light intensity collected by the light sensor and controls the lighting mode of the LED light strip. Smart LED light bar vests based on light sensors can usually only sense the light intensity and then perform simple lighting mode control. It is difficult to identify specific environment types (such as cities, forests or foggy days), and it is also impossible to adaptively adjust the lighting mode in special environments, making the functions of smart LED light bar vests lack flexibility and accuracy in a variety of usage scenarios.

[0003] To sum up, existing smart LED light bar vests have technical problems such as difficulty in identifying specific environment types, inability to adaptively adjust lighting modes under special environments, and lack of flexibility and accuracy in various usage scenarios. Summary of the invention

[0004] In view of the shortcomings of the above-mentioned prior art, the present invention provides a smart wearable system and a smart LED light bar vest based on deep learning, so as to realize the identification of specific environment types, adaptively adjust the lighting mode, and improve the flexibility and accuracy of the smart LED light bar vest.

[0005] In a first aspect, the present invention provides a deep learning-based smart wearable system, comprising: A smart LED light bar vest body, wherein the smart LED light bar vest body is provided with LED light bars; A visual sensor, which is arranged on the smart LED light bar vest body and is used to collect an environmental image of the environment in which a user wearing the smart LED light bar vest body is located; The host unit is mounted on the smart LED light bar vest body, connected and communicated with the LED light bar and the visual sensor. The host unit analyzes the environmental image through a convolutional neural network, identifies the environment type of the user's environment, and automatically adjusts the lighting mode of the LED light bar according to the environment type.

[0006] In a second aspect, the present invention provides a smart LED light bar vest, which applies the above-mentioned smart wearable system based on deep learning.

[0007] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides an intelligent wearable system and an intelligent LED light bar vest based on deep learning. LED light bars are distributed on the main body of the intelligent LED light bar vest, and a visual sensor and a host unit are set. The visual sensor is used to collect an environmental image of the environment in which a user wearing the intelligent LED light bar vest main body is located. The host unit is connected and communicated with the LED light bar and the visual sensor. The environmental image is analyzed by a convolutional neural network to identify the environmental type of the user's environment. The lighting mode of the LED light bar is automatically adjusted according to the environmental type, thereby realizing the recognition of the specific environmental type, adaptively adjusting the lighting mode, and improving the flexibility and accuracy of the intelligent LED light bar vest. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their description are used to explain the present invention and do not constitute an improper limitation of the present invention. Some specific embodiments of the present invention will be described in detail in an exemplary and non-restrictive manner with reference to the drawings. The same reference numerals in the drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the drawings: Figure 1 It is a schematic diagram of the architecture of a smart wearable system based on deep learning according to an embodiment of the present invention; Figure 2 It is a schematic diagram of an architecture for connecting and communicating between a host unit and a user terminal according to an embodiment of the present invention.

[0009] Figure 3 It is a schematic diagram of an architecture in which an LED light strip is arranged on the main body of a smart LED light strip vest according to an embodiment of the present invention. DETAILED DESCRIPTION

[0010] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention.

[0011] Embodiment 1 See also Figure 1-Figure 3 , this embodiment provides a smart wearable system based on deep learning, including: A smart LED light bar vest body, wherein the smart LED light bar vest body is provided with LED light bars; A visual sensor, which is arranged on the smart LED light bar vest body and is used to collect an environmental image of the environment in which a user wearing the smart LED light bar vest body is located; The host unit is mounted on the smart LED light bar vest body, connected and communicated with the LED light bar and the visual sensor. The host unit analyzes the environmental image through a convolutional neural network, identifies the environment type of the user's environment, and automatically adjusts the lighting mode of the LED light bar according to the environment type.

[0012] It should be noted that in the prior art, the smart LED light bar vest based on the light sensor can usually only sense the light intensity and then perform a simple lighting mode control, which is difficult to identify the specific environment type (such as city, forest or foggy day), and it is also impossible to adaptively adjust the lighting mode under special environments, making the function of the smart LED light bar vest lack flexibility and accuracy in a variety of usage scenarios. In this embodiment, a visual sensor is integrated on the smart LED light bar vest to collect image information of the surrounding environment. Compared with the traditional light sensor that can only detect the ambient light intensity, the visual sensor can capture the complete visual information of the environment (such as scenery, color, light and shadow, etc.), providing richer data for environmental type identification. The host unit uses a convolutional neural network (CNN) to analyze the image collected by the visual sensor, and can identify different features and patterns in the image, thereby identifying different environmental types (such as city, forest, mountain, foggy day, etc.). Through accurate identification of the environment type, in this embodiment, it can automatically switch to the appropriate LED light bar lighting mode according to the specific environment. For example, a low brightness mode is used in an urban environment, while a high brightness or flashing mode is used in a foggy environment. In addition, the visual sensor can continuously collect environmental images, and the host unit can run CNN in real time for environmental recognition, so as to adjust the lighting mode in real time as the environment changes, so as to achieve fast and automatic response. For example, when the user enters a dim area, the LED light bar immediately switches automatically to a brightness setting that adapts to low light, improving the ability to adapt to sudden changes in the environment. In this embodiment, the smart LED light bar vest is more flexible in a variety of scenarios. Whether it is day or night, foggy or sunny, forest or city, the user's safety, energy saving and ease of use can be ensured by automatically adjusting the lighting effect. Users do not need to worry about the lighting mode being inappropriate in certain scenarios, thus realizing the flexibility of "one smart LED light bar vest, adapting to multiple scenarios".

[0013] In some preferred embodiments, the LED light strips are distributed in key areas of the intelligent LED light strip vest body, including the chest, back and shoulders, and the LED light strips in each key area work independently under the control of the host unit; the lighting modes of the LED light strips in different key areas are the same or different. It should be noted that in this embodiment, the LED light strips are distributed in key areas such as the chest, back and shoulders to ensure that the user's presence can be observed from different angles. Whether it is pedestrians, vehicles, or other people, the LED lights can be clearly seen from the front, back, left and right of the user. This multi-angle visibility can improve the safety of users at night or in low-visibility environments. In different key areas such as the chest, back and shoulders, the LED light strips in different areas adopt the same or different lighting modes. For example, in the chest and shoulder areas of the user, the light strip adopts a low brightness mode to reduce visual interference to the user, while the back area adopts a high brightness to improve visibility from the rear. In this embodiment, by independently controlling the LED light strips in different areas, the lighting mode of each area can be set separately according to the current environment or scene requirements. For example, in an urban environment, the LED light bar in the user's chest area can be set to a soft low-brightness mode, while the back area maintains a slightly higher brightness to improve the visibility of pedestrians at night. In special environments such as foggy days or mountains, the smart LED light bar vest can maintain different lighting modes in different areas. For example, the shoulder light bar can be turned on in a flashing mode to improve the recognition of the left and right sides; the back and chest areas maintain a high-brightness mode to ensure the visibility of the user from the front and back. In some further preferred embodiments, when the user turns his back to the road, the LED light bar in the back area turns on a high-brightness mode or a flashing mode, and the LED light bar in the chest and shoulder areas maintains a low-brightness mode or is turned off, thereby reducing power consumption while ensuring safety and extending the battery life of the device. In some further preferred embodiments, in a dense fog environment, the LED light bars in the shoulder and back areas turn on a high-brightness mode or a flashing mode, and the LED light bar in the chest area maintains a soft brightness mode, thereby increasing visibility to the side and rear and reducing direct exposure to the user.

[0014] In some further preferred embodiments, in group activities, different environmental types are associated with corresponding risk factors. When the host unit of the intelligent LED light bar vest worn by the leader identifies the current environmental type, the associated risk factors corresponding to the current environmental type are automatically matched. According to the associated risk factors, the lighting mode of the LED light bar in the shoulder area and the back area of ​​the intelligent LED light bar vest worn by the leader is automatically adjusted to the risk warning mode corresponding to the associated risk factors to perform group warning. It should be noted that in group activities, the leader is usually in front of the team and is exposed to different environments and potential risks earlier. Through the host unit of the leader vest, the current environmental type is identified and matched with associated risk factors, and the leader's LED light bar can be automatically adjusted to the corresponding warning mode, thereby providing warnings for the entire team. Other members of the team can learn about the risks ahead in advance by observing the leader's lighting mode, which helps to take precautions in dangerous environments as soon as possible. Moreover, the leader's intelligent LED vest conveys environmental risks with the lighting mode of the shoulder and back areas, which can form a unified visual warning signal in the group, so that all members can quickly understand the current potential risks, centrally transmit warning information, and avoid confusion. In addition, different types of environments may involve specific risk factors. For example, mountain environments may be accompanied by the risk of slippery or steep slopes, while forest environments may have low light or dense forest obstacles. By associating different types of environments with risk factors, the host unit can more accurately identify potential risks in the environment. For example, in slippery or steep slope environments, the host unit can directly adjust the LED light bars in the shoulder and back areas to red flashing mode to remind group members to slow down and pay attention to their feet. In addition, the leader's vest can automatically match the risk factors after identifying the environment type, and automatically adjust the lighting mode without manual operation, ensuring the immediacy of the warning response, so that the team can be informed of the dangerous situation ahead in a timely manner. In fast-changing scenes such as outdoor hiking or exploration, this automatic adjustment helps all members respond in the shortest time. In group activities, the leader usually needs to concentrate on guiding and observing the environment. After setting the warning mode to automatic adjustment, the leader does not need to frequently manually control the lighting mode, and can focus more on leading the team, reducing the leader's operating burden, ensuring the warning effect while improving the safety management efficiency of the activity. In addition, the shoulder and back areas are easily observed by other members, especially when traveling, and the lighting patterns of these areas can be intuitively conveyed to other members of the team. For example, when the LED light bar in the shoulder and back areas flashes red, the entire team can clearly see the warning signal and immediately raise vigilance. Moreover, the lighting patterns in the shoulder and back areas can distinguish different risk levels through colors and dynamic effects (such as flashing or continuous lighting). For example, flashing yellow light can indicate a slight slope risk, while flashing red light indicates a high-risk slippery or steep slope environment. This distinction allows team members to quickly understand the current environmental conditions and make corresponding adjustments.It is understandable that traditional verbal reminders are prone to information transmission delays or even ineffective communication in outdoor or noisy environments. In this embodiment, through the automatic warning of the LED light bar, the team leader can directly remind team members with the light mode without verbal communication, thereby avoiding the risk of untimely information transmission.

[0015] In some further preferred embodiments, a PPG sensor is arranged in the front chest area of ​​the smart LED light bar vest body near the left chest position, and a fall detection sensor is arranged in the shoulder area or the back area of ​​the smart LED light bar vest body, and the host unit is connected and communicated with the PPG sensor and the fall detection sensor; the PPG sensor monitors the user's heart rate data in real time, and the host unit obtains the heart rate data and compares it with the user's baseline heart rate range to obtain the user's sudden heart rate change, and the sudden heart rate change indicates that the user's heart rate suddenly rises or falls, and there is a high risk problem; the fall detection sensor The user's motion state is monitored in real time, and the host unit obtains the motion state and compares it with the preset speed change and posture change range to obtain the user's fall event, which is manifested as the user's speed change and posture change exceeding the preset speed change and posture change range; when the fall event is obtained and there is no sudden heart rate change, the host unit controls the LED light bar in the shoulder area to enter a high-brightness flashing mode; when the fall event is obtained and there is a sudden heart rate change, the host unit controls the LED light bars in the back area, shoulder area and chest area to enter a high-brightness, fast-flashing SOS mode. It should be noted that the left side of the chest is close to the heart area and has rich blood flow signals. The PPG sensor can detect pulse waves and heart rate changes more accurately at this position. The blood flow in this area is strong, which can provide stable and clear pulse data, which is conducive to real-time monitoring of the heart rate state. Moreover, the chest position is relatively stable when worn, and is not easily disturbed by the arm or other movements, and is suitable for long-term and accurate heart rate monitoring. This position design helps to improve the accuracy and continuity of heart rate data. In addition, the shoulder and back areas are key areas for user posture changes and movement direction changes. The shoulder sensor can keenly capture the user's tilt angle, while the back area is close to the center of gravity of the human body, which is suitable for detecting significant changes in acceleration and gravity direction. Fall events are usually accompanied by drastic changes in acceleration and posture. Sensors in the shoulder or back area can accurately detect these characteristic signals, such as rapid pitch, rotation or rollover, thereby improving the accuracy of fall detection. Moreover, the shoulder and back areas are relatively stable during normal movement (such as walking and running), so they can effectively reduce false alarms, increase the reliability of fall detection, and improve user experience. In addition, the host unit can comprehensively analyze the changes of the PPG sensor's heart rate data and the fall sensor's motion state data by obtaining the heart rate data of the PPG sensor and the motion state data of the fall sensor. When a fall event is detected without a sharp change in heart rate, only a slight reminder is required, and when a sharp change in heart rate and a fall event occur at the same time, it is judged as an emergency. When the user falls but the heart rate does not change sharply, it is judged as a general fall event. At this time, the LED light bar in the shoulder area is controlled to enter a high-brightness flashing mode, which can attract the attention of nearby people so as to obtain necessary attention and help.At the same time, turning on the high-brightness flashing mode only in the shoulder area can not only serve as a warning, but also reduce unnecessary energy consumption. Moreover, the flashing at the shoulder position is easy to be discovered by others, which is conducive to obtaining quick help. When the user falls and is accompanied by a sharp change in heart rate, it is identified as a serious emergency state, and the user may have cardiovascular problems or loss of consciousness. At this time, starting the high-brightness, fast-flashing SOS mode of the whole body can send out an obvious distress signal. The synchronous high-brightness fast flashing of the chest, back and shoulders can attract the attention of people around from all directions. In particular, the fast-flashing SOS mode can convey a signal for help, making it easier for others to identify and help.

[0016] In some further preferred embodiments, the host unit records the environment type of the user's environment when the fall event and the rapid heart rate change occur, and learns the dangerous environment type in which the user is prone to fall and rapid heart rate change through a recurrent neural network. When the host unit recognizes that the user enters a dangerous environment adapted to the dangerous environment type again, the LED light bar in the chest area is controlled to enter a red light flashing mode to remind the user that the user is currently in a dangerous environment. It should be noted that each user may have different physical conditions and environmental sensitivities. For example, some users may be more prone to falls or heart rate changes on slippery floors, steep slopes, or dimly lit stairs. By recording and learning the user's falls and abnormal heart rates in these environments, the recurrent neural network (RNN) can summarize the specific "dangerous environment type" of each user, thereby providing a more personalized safety reminder. In addition, by identifying the type of environment the user enters, the host unit can provide an early warning when the user enters a dangerous environment, and by controlling the LED light bar in the chest area to flash red, the user is reminded that the user is currently in a potential dangerous environment, so that the user can be more vigilant and reduce the risk of falling and rapid changes in heart rate. Moreover, setting the red flashing reminder in the chest area is easy for users to see and is conducive to quick reminders. At the same time, the chest area is within the user's field of vision, relatively hidden, and will not interfere too much with people around. In addition, RNN can learn complex patterns of user behavior and environment through long-term memory of historical environmental data. As user data accumulates, RNN will gradually optimize and can continuously improve the accuracy of identifying dangerous environments for users. For example, RNN can learn and optimize based on multiple data samples (such as frequent falls on slippery roads or steep slopes) to help users get more accurate safety reminders in different environments.

[0017] In some preferred embodiments, the lighting mode includes a color mode, a brightness mode, and a dynamic lighting effect mode. It should be noted that color is an intuitive visual signal, and different colors can represent different safety states or reminder information. For example, red usually indicates emergency or danger, green indicates normal state, and blue may be used to indicate the activation of a specific function or location guidance. Through color distinction, users or people around can recognize the current state at a glance and react quickly. In addition, in foggy days, at night, or in complex light environments, different color effects have different penetration. For example, red and warm colors have higher penetration effects in low visibility environments (such as foggy days), while cold colors are easier to identify at night. Color modes help smart LED light bar vests provide optimal visibility in a variety of environments. In addition, dynamic lighting effects (such as flashing, gradient, SOS flashing) are particularly effective in emergency situations. Flashing or fast flashing lighting effect modes can enhance the user's sense of presence and make it easier for people around to identify their location and status. For example, when a fall or a sharp change in heart rate is detected, activating the SOS fast flashing mode can effectively send a signal for help.

[0018] In some preferred embodiments, the environment types include urban environments, forest or mountain environments, night or dim environments, foggy or rainy days, and tunnels or dark corridors. When the environment type is an urban environment, the lighting mode is automatically adjusted to a low brightness mode or a soft gradient mode; when the environment type is a forest or mountain environment, the lighting mode is automatically adjusted to a high brightness continuous mode or a breathing light mode; when the environment type is a night or dim environment, the lighting mode is automatically adjusted to a high brightness mode or a flashing mode; when the environment type is a foggy or rainy day, the lighting mode is automatically adjusted to a high brightness and warm light flashing mode; when the environment type is a tunnel or a dark corridor, the lighting mode is automatically adjusted to a high brightness mode. It should be noted that in an urban environment, there are usually sufficient street lights and lighting, and excessive brightness may cause light pollution to people around. Therefore, the low brightness mode can provide basic visibility and adapt to the bright environment of the city without causing interference to others. The soft gradient mode provides users with a comfortable visual effect in an urban environment, and will not affect the user experience due to abrupt brightness changes. Gradient mode allows the LED light bar to light up in a soft way, maintaining visibility while creating a lighting effect that blends in with the urban environment. In addition, forest or mountain environments usually have low light levels, especially in shaded or shadowed areas. High brightness continuous mode can enhance user visibility, making it easier to be found in low-light conditions. Battery life is particularly important in outdoor activities in forests or mountains. Breathing light mode not only enhances visibility through gradual changes in brightness and darkness, but also saves power, making it suitable for long-term outdoor activities. In addition, visibility is poor at night or in dim environments, and high brightness mode allows users to be clearly visible at a distance, helping to improve user safety. Flashing mode is particularly conspicuous in night environments, helping to quickly attract the attention of vehicles or pedestrians and avoid accidents. The dynamic flashing effect can improve recognition at night, especially suitable for outdoor sports, running or cycling at night. In addition, in foggy or rainy days, water droplets in the air will weaken the penetration of light. Warm lights (such as yellow or orange) are more penetrating in haze and rainy days, helping to improve visibility in low visibility conditions. The flashing mode can attract more attention in foggy and rainy days through dynamic lighting effects, reminding other pedestrians and vehicles of their presence, thereby increasing user safety. In addition, the light in tunnels and dark corridors is usually lower than the outside brightness, and there will be sudden changes in light when entering and exiting the tunnel. The high brightness mode can help users maintain clear visibility in this environment, adapt to the rapid changes in ambient light, and avoid visual blind spots.

[0019] In some preferred embodiments, the visual sensor includes a plurality of small visual sensors using a wide-angle lens or a fisheye lens; the plurality of small visual sensors are distributed at the two shoulder positions of the smart LED light bar vest body. It should be noted that the wide-angle lens and the fisheye lens have a large viewing angle range, which can significantly expand the visual range of the visual sensor. In addition, placing the visual sensors at the two shoulder positions respectively can achieve all-round environmental perception on both sides. In some complex environments (such as forests and mountains), there are many lights, terrains and obstacles around the user. The multi-sensor layout can obtain environmental information from different angles, helping the system to more accurately identify these complex scenes, so as to adjust the lighting mode in time to ensure that users receive appropriate safety reminders in a variety of environments.

[0020] In some preferred embodiments, the smart wearable system based on deep learning includes a manual lighting adjustment module, which runs on the user end, and the user end is connected to the host unit for communication; the lighting adjustment of the manual lighting adjustment module is superior to the automatic adjustment of the lighting mode of the LED light bar. It should be noted that the needs and preferences of each user may be different. For example, some users may prefer low brightness or specific color lighting effects in specific environments. The manual lighting adjustment module allows users to manually set the lighting mode according to their personal preferences to meet personalized needs. In this embodiment, the user is allowed to manually control the lighting mode first, improve the operability of the device, and enable the user to flexibly control the lighting effect of the smart LED light bar vest according to their own needs. Even when the automatic adjustment does not meet personal preferences, the user can quickly adjust it to avoid inconvenience caused by automatic adjustment of the system. It is understandable that the user end can be a smart phone terminal. The manual lighting adjustment module can be set through an APP or a mini program.

[0021] In some preferred embodiments, the manual lighting adjustment module records the lighting preference mode of the user manually adjusting the lighting mode; the host unit collects the lighting preference mode, learns the user's lighting mode preference for the LED light bar through a recurrent neural network, and automatically adjusts the lighting mode of the LED light bar according to the lighting mode preference. It should be noted that each user may have different preferences for the lighting mode, and the user's preferences may change with the environment, situation, mood or usage habits. By recording the user's lighting preference mode, the system can automatically adapt the lighting effect that best suits the user's personal preferences according to the user's actual habits and needs, making the lighting mode more personalized. Moreover, RNN can upgrade the system from passive response to active adaptation by learning the user's historical lighting preference mode. After recording and learning, the system can independently judge the user's preferences, thereby automatically adjusting the lighting mode without the user's manual operation, providing the user with a smarter wearing experience.

[0022] Embodiment 2 See also Figure 1-Figure 3 The present embodiment provides a smart LED light bar vest, which applies the deep learning-based smart wearable system described in any of the above embodiments, by distributing LED light strips on the smart LED light bar vest body, setting a visual sensor and a host unit, the visual sensor is used to collect environmental images of the environment in which the user wearing the smart LED light bar vest body is located, the host unit is connected and communicated with the LED light strip and the visual sensor, and the environmental image is analyzed through a convolutional neural network to identify the environmental type of the user's environment, and the lighting mode of the LED light strip is automatically adjusted according to the environmental type, thereby realizing the recognition of the specific environmental type, adaptively adjusting the lighting mode, and improving the flexibility and accuracy of the smart LED light bar vest.

[0023] It should be pointed out that the above embodiments are only preferred specific implementation modes of the present invention, and the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A smart wearable system based on deep learning, characterized in that: include: A smart LED light bar vest body, wherein the smart LED light bar vest body is provided with LED light bars; A visual sensor, which is arranged on the smart LED light bar vest body and is used to collect an environmental image of the environment in which a user wearing the smart LED light bar vest body is located; The host unit is mounted on the smart LED light bar vest body, connected and communicated with the LED light bar and the visual sensor. The host unit analyzes the environmental image through a convolutional neural network, identifies the environment type of the user's environment, and automatically adjusts the lighting mode of the LED light bar according to the environment type.

2. The deep learning-based smart wearable system according to claim 1, characterized in that: The LED light strips are distributed in key areas of the smart LED light strip vest body, including the front chest, back and shoulders. The LED light strips in each key area work independently under the control of the host unit.

3. The deep learning-based smart wearable system according to claim 2, characterized in that: The lighting modes of the LED light strips in different areas of the chest, the back and the shoulders are the same or different.

4. The deep learning-based smart wearable system according to claim 1, characterized in that: The lighting modes include color mode, brightness mode and dynamic lighting effect mode.

5. The deep learning-based smart wearable system according to claim 1, characterized in that: The environment types include urban environment, forest or mountain environment, night or dark environment, foggy or rainy day, and tunnel or dark passage.

6. The deep learning-based smart wearable system according to claim 5, characterized in that: When the environment type is an urban environment, the lighting mode is automatically adjusted to a low brightness mode or a soft gradient mode; when the environment type is a forest or mountain environment, the lighting mode is automatically adjusted to a high brightness continuous mode or a breathing light mode; when the environment type is a nighttime or dim environment, the lighting mode is automatically adjusted to a high brightness mode or a flashing mode; when the environment type is foggy or rainy, the lighting mode is automatically adjusted to a high brightness and warm light flashing mode; when the environment type is a tunnel or a dark corridor, the lighting mode is automatically adjusted to a high brightness mode.

7. The deep learning-based smart wearable system according to claim 1, characterized in that: The visual sensor includes a plurality of small visual sensors using a wide-angle lens or a fisheye lens; the plurality of small visual sensors are distributed at two shoulder positions of the smart LED light bar vest body.

8. The deep learning-based smart wearable system according to any one of claims 1 to 7, characterized in that: It comprises a manual lighting adjustment module, which runs on a user end, and the user end is connected and communicated with the host unit; the lighting adjustment of the manual lighting adjustment module is superior to the automatic adjustment of the lighting mode of the LED light bar.

9. The deep learning-based smart wearable system according to claim 7, characterized in that: The manual lighting adjustment module records the lighting preference mode of the user manually adjusting the lighting mode; the host unit collects the lighting preference mode, learns the user's lighting mode preference for the LED light bar through a recurrent neural network, and automatically adjusts the lighting mode of the LED light bar according to the lighting mode preference.

10. A smart LED light bar vest, characterized in that: The smart LED light bar vest is applied to the deep learning-based smart wearable system as described in any one of claims 1-9.

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