Intelligent multifunctional safety helmet

Abstract

The invention discloses an intelligent multifunctional safety helmet, which belongs to the technical field of intelligent safety helmets, integrates a plurality of functional modules such as high-definition audio and video, positioning, alarming and the like, is used for solving the safety problem in a production field operation process, and is used for assisting sensing positioning and correcting a real-time coordinate position by image analysis in a positioning and tracking process. The corrected real-time coordinate positions form continuous track points to generate a personnel dynamic operation track, the positioning accuracy is improved, then the personnel dynamic operation track and a preset standard operation track are subjected to matching early warning analysis, and based on the personnel dynamic operation track, video data and an environment sensing technology are combined, so that the positioning accuracy is improved. The track data, the video data and the environment sensor data are subjected to space-time alignment, track-environment associated risk analysis is realized, the safety of a real-time operation environment is further guaranteed, front-end field operation is more intelligent and safer, and rear-end project management is simpler and more efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent safety helmets, and more particularly to an intelligent multifunctional safety helmet. Background Art

[0002] With the development of society, production safety has received more and more attention. In various work scenarios (such as construction, petroleum and petrochemical, maritime forestry, power inspection, fire inspection, coal mines, etc.), workers wearing safety helmets has become one of the important measures to improve production safety management.

[0003] At present, although functions such as personnel positioning data collection and information transmission have been added to safety helmets, it is still impossible to monitor the operating standards and on-site working environment of operators and ensure the personal safety of operators. Especially for high-risk working scenarios, when operators behave improperly or an abnormality occurs in a certain equipment (such as high temperature, fire source or gas leakage), it is impossible to fully monitor the personal safety of operators and the working environment.

[0004] To this end, we propose an intelligent multifunctional safety helmet, a highly integrated IoT terminal device suitable for high-risk operation scenarios, and combine it with the background management system to build a complete production safety management system. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned practical problems. An intelligent multifunctional safety helmet is provided, which integrates multiple functional modules such as high-definition audio and video, positioning, and alarm, and is used to help solve safety problems in the production site operation process, making front-end field operations smarter and safer, and making back-end project management simpler and more efficient.

[0006] The purpose of the present invention can be achieved by the following technical solutions: an intelligent multifunctional safety helmet, comprising a safety helmet, a flexible monitoring panel embedded in the safety helmet, and a signal of the flexible monitoring panel is connected to a management platform;

[0007] The flexible monitoring panel is integrated with a cap removal monitoring module, a positioning tracking module, a safety supervision module, an environmental risk assessment module, and an alarm module. The management platform is used to divide the work site into different work areas for different operators as target monitoring areas.

[0008] The hat removal monitoring module is used to monitor whether workers are wearing helmets when entering the target monitoring area and whether they take off their hats during operation supervision;

[0009] The positioning and tracking module is used to obtain the motion data and video data of the operator, generate the operator's dynamic operation trajectory based on the motion data and video data, and send the operator's dynamic operation trajectory to the safety supervision module and the environmental risk assessment module;

[0010] After receiving the dynamic operation trajectory of the personnel, the safety supervision module matches the dynamic operation trajectory of the personnel with the preset standard operation trajectory stored in the management platform. Based on the matching results, the operation trajectory of the operator is analyzed for early warning, and a trajectory deviation warning signal and an abnormal behavior warning signal are generated and sent to the alarm module;

[0011] The environmental risk assessment module is based on the dynamic work trajectory of personnel. It obtains video data through the positioning and tracking module and environmental data through the sensor group installed on the mounting cap. It performs early warning analysis on the working environment based on the video data and environmental data, generates environmental abnormality signals and sends them to the alarm module.

[0012] As a further solution of the present invention: the process of obtaining the dynamic operation trajectory of personnel includes:

[0013] The motion data and video data of the workers are collected. The motion data include real-time coordinate position and behavior posture. The video data include monitoring video. The real-time coordinate position is obtained by the Beidou, Bluetooth beacon, and Wi-Fi positioning integrated on the safety helmet (1). The monitoring video is obtained by the high-definition camera integrated on the safety helmet (1).

[0014] As a further solution of the present invention: the monitoring video is segmented and processed to obtain multiple monitoring images that are consistent with the time points of real-time coordinate position acquisition, and the environmental feature points of the work site in the monitoring images are extracted to correct the real-time position. The environmental feature points of the work site are equipment and site identifications pre-stored in the management platform as feature identification. The corrected real-time coordinate position is composed of continuous trajectory points, and the continuous trajectory points are used to generate the dynamic work trajectory of the personnel.

[0015] As a further solution of the present invention, the process of performing early warning analysis on the operation trajectory of the operator includes:

[0016] The personnel's dynamic work trajectory is matched with the preset standard work trajectory stored in the management platform. The Hausdorff distance between the personnel's dynamic work trajectory and the preset standard work trajectory is calculated and marked as the offline distance. The offline distance is compared with the set centrifugal distance threshold. When the offline distance is greater than the set centrifugal distance threshold, it is determined that the operator's current work trajectory has deviated, and a trajectory deviation warning signal is generated.

[0017] As a further solution of the present invention: obtain the real-time coordinate position corresponding to the offline distance being greater than the set offline distance threshold, mark it as the actual deviation point, obtain the monitoring image of the actual deviation point and the behavior posture of the operator, and judge whether the operator has abnormal behavior based on the monitoring image and behavior posture analysis.

[0018] As a further solution of the present invention: the walking posture of the operator is monitored and identified by the accelerometer, gyroscope and height sensor integrated in the helmet. By extracting abnormal motion characteristics (sudden change speed, static time, height from the ground), the abnormal motion characteristics are compared with the abnormal operation characteristic threshold. When it is greater than the abnormal operation characteristic threshold, it is judged that the operator has abnormal behavior and a behavior abnormality warning signal is generated.

[0019] As a further solution of the present invention, the process of performing early warning analysis on the operating environment is as follows:

[0020] The monitoring image is obtained through the positioning and tracking module, and the monitoring image is processed and abnormal conditions in the monitoring image are extracted through image processing technology. Abnormal conditions include a wet working surface, debris piled on the working surface, and fire on the equipment. The sensor group obtains environmental data. The environmental data includes the temperature, humidity, equipment noise and harmful gas leakage of the working area based on the dynamic working trajectory of the personnel. When an abnormal state is monitored or the environmental data exceeds the environmental data threshold, the corresponding real-time coordinate position is marked as an abnormal area, and an environmental abnormality signal is generated.

[0021] As a further solution of the present invention: the safety helmet is also integrated with a laser light, a warning light, an alarm button, a display screen, a power button, a recording button, an intercom button and a lighting button.

[0022] Compared with the prior art, the advantages of the present invention are:

[0023] 1. This solution integrates multiple functional modules such as high-definition audio and video, positioning, and alarms to help solve safety issues during production site operations. During the positioning and tracking process, sensor positioning and image analysis are integrated. For complex working environments, video image positioning is used as a supplement to correct the real-time coordinate position. The corrected real-time coordinate position is formed into continuous trajectory points. The continuous trajectory points are used to generate the dynamic operation trajectory of the personnel, improving positioning accuracy. The dynamic operation trajectory of the personnel is then matched and analyzed with the preset standard operation trajectory stored in the management platform, and early warning analysis is performed on the operation trajectory of the operator;

[0024] 2. Based on the above content, based on the dynamic work trajectory of personnel, combined with video data and environmental perception technology, the trajectory data, video data, and environmental sensor data are aligned in time and space to realize "trajectory-environment" correlation analysis. It analyzes whether there are risks in the environment and equipment at a certain point in time and location, further ensuring the safety of the real-time working environment, making front-end on-site operations smarter and safer, and making back-end project management simpler and more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a structural schematic diagram of the present invention;

[0026] Figure 2 A structural schematic diagram of another perspective of the present invention;

[0027] Figure 3 It is a schematic diagram of the side structure of the present invention;

[0028] Figure 4 A schematic diagram of the side structure of the present invention from another perspective;

[0029] Figure 5 A bottom view of the present invention;

[0030] Figure 6 This is a block diagram of the module principle of the present invention.

[0031] Description of the numbers in the figure:

[0032] 1. Safety helmet; 2. Laser light; 3. HD camera; 4. Warning light; 5. Alarm button; 6. Display screen; 7. Power button; 8. Recording button; 9. Intercom button; 10. Lighting button. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work shall fall within the scope of protection of the present invention.

[0034] Example 1: The present invention discloses a smart multifunctional helmet, please refer to Figure 1-Figure 5 , including a safety helmet 1, on which are integrated a laser light 2, a high-definition camera 3, a warning light 4, an alarm button 5, a display screen 6, a power button 7, a recording button 8, an intercom button 9, and a lighting button 10. The high-definition camera 3 is located in front of the safety helmet 1, and the laser light 2 is located on the side of the high-definition camera 3 to assist in positioning and shooting. The warning light 4 is located behind the safety helmet 1 and flashes alternately in red and blue.

[0035] A flexible monitoring panel is embedded in the helmet 1. Figure 6 The flexible monitoring panel signal is connected to the management platform. The flexible monitoring panel is integrated with a cap removal monitoring module, a positioning tracking module, a safety supervision module, an environmental risk assessment module, and an alarm module. The management platform is used to divide the work site into different work areas for different workers as target monitoring areas.

[0036] The signal of the hat-off monitoring module is connected to a light distance sensor, which detects the distance from the head (the distance is fixed when the hat is worn normally, and the distance increases sharply when the hat is taken off). It is used to monitor whether the operator is wearing a safety helmet 1 when entering the target monitoring area and whether there is any hat-off behavior during the operation supervision, so as to prevent the operator from not wearing the safety helmet normally during the operation.

[0037] The positioning and tracking module is used to obtain the motion data and video data of the operator and generate the operator's dynamic operation trajectory based on the motion data and video data. The process of obtaining the operator's dynamic operation trajectory includes:

[0038] Collect motion data and video data of workers. Motion data includes real-time coordinates and behavior. Video data includes surveillance video. Real-time coordinates are obtained by the Beidou / GPS, Bluetooth beacon, and Wi-Fi positioning system integrated on the helmet 1. Surveillance video is obtained by the high-definition camera 3 integrated on the helmet 1.

[0039] The acquired surveillance video is segmented and processed. During the processing, a unified timestamp (such as UTC time) is added to the real-time coordinate location data and video frames to ensure time synchronization. Multiple surveillance images are obtained at the same time as the real-time coordinate location acquisition point. The environmental feature points of the work site in the surveillance images are extracted to correct the real-time location. The environmental feature points of the work site are the equipment and site identifiers pre-stored in the management platform for feature recognition.

[0040] Through SLAM (Simultaneous Localization and Mapping) technology, the feature points captured by the camera are used to correct the coordinate position information. It is particularly suitable for indoor environments (such as tunnels and factories) or complex terrains (such as mining areas) with weak GPS signals. Video positioning is used to assist in correction to improve positioning accuracy. The corrected real-time coordinate positions are constructed into continuous trajectory points, and the dynamic work trajectory of personnel is generated with continuous trajectory points. The dynamic work trajectory of personnel is then sent to the safety supervision module and the environmental risk assessment module.

[0041] After receiving the dynamic operation trajectory of the personnel, the safety supervision module matches the dynamic operation trajectory of the personnel with the preset standard operation trajectory stored in the management platform. Different preset standard operation trajectories are stored for different target monitoring areas. The preset standard operation trajectory is a standard path for a specific target monitoring area, such as from point A to point B and then to point C. Based on the matching results of the two, the operation trajectory of the operator is analyzed for early warning. The specific early warning analysis process is as follows:

[0042] Match the personnel's dynamic operation trajectory with the preset standard operation trajectory stored in the management platform, calculate the Hausdorff distance between the personnel's dynamic operation trajectory and the preset standard operation trajectory, and mark it as the offline distance. Compare the offline distance with the set centrifugal distance threshold. When the offline distance is greater than the set centrifugal distance threshold, it is determined that the operator's current operation trajectory has deviated, and a trajectory deviation warning signal is generated. The larger the offline distance, the more the personnel's dynamic operation trajectory deviates from the preset standard operation trajectory, such as not acting according to the standard operation trajectory or illegally climbing over or entering a dangerous area.

[0043] Obtain the real-time coordinate position corresponding to the offline distance greater than the set offline distance threshold, mark it as the actual deviation point, and when the trajectory is found to be inconsistent, find the inconsistent position and call the corresponding surveillance image to obtain the surveillance image of the actual deviation point and the operator's behavior posture. Based on the surveillance image and behavior posture analysis, determine whether the operator has abnormal behavior;

[0044] The worker's walking posture is monitored and identified by the accelerometer, gyroscope, and altitude sensor integrated into the helmet. By extracting abnormal motion characteristics (sudden change speed, duration of rest, height above the ground), it is determined whether the rescuer is stationary, walking, running, climbing stairs, or falling. The abnormal motion characteristics are compared with the abnormal operation characteristic threshold;

[0045] When the value is greater than the abnormal operation characteristic threshold, it is judged that the operator has abnormal behavior, and a behavior abnormality warning signal is generated. The generated trajectory deviation warning signal and behavior abnormality warning signal are sent to the alarm module. After receiving the trajectory deviation warning signal and behavior abnormality warning signal, the alarm module promptly notifies the relevant personnel of the management platform. The back-end management personnel can monitor the construction site in real time on the large screen, capture abnormal behavior through trajectory playback, and conduct remote command and dispatch to achieve true visual management.

[0046] In addition, the helmet 1 is also integrated with a proximity sensor and a gravity sensor, supporting proximity alarm and impact alarm.

[0047] The smart helmet 1 integrates multiple functional modules such as high-definition audio and video, positioning, and alarm, and realizes functions such as real-time positioning, trajectory playback, photo recording, audio and video calls, group intercom, electronic fence, SOS alarm, hat removal alarm, and near-electric alarm.

[0048] Example 2: Environmental risk assessment module based on personnel dynamic operation trajectory, please refer to Figure 6 , video data is obtained through the positioning and tracking module, and environmental data is obtained through the sensor group installed on the mounting cap 1. Early warning analysis of the working environment is performed based on the video data and environmental data. The specific process is as follows:

[0049] The monitoring image is acquired through the positioning and tracking module. The image processing technology is used to process the monitoring image and extract abnormal conditions in the monitoring image. Abnormal conditions include moisture on the working surface, debris accumulated on the working surface, and fire on the equipment. The sensor group acquires environmental data, where the sensor group includes temperature sensors, humidity sensors, noise sensors, and gas sensors;

[0050] Environmental data includes the temperature, humidity, equipment noise, and harmful gas leakage of the work area based on the dynamic work trajectory of the personnel. When an abnormal state is detected or the environmental data exceeds the environmental data threshold, the corresponding real-time coordinate position is marked as an abnormal area, an environmental abnormality signal is generated, and sent to the alarm module;

[0051] After receiving the environmental abnormality signal, the alarm module promptly notifies the operator. The operator activates the alarm button, which simultaneously triggers the sound and light alarm and pushes the message to the management platform. The relevant personnel of the management platform take corresponding measures according to the type of environmental abnormality, or automatically trigger the emergency procedure.

[0052] In summary, it integrates multiple functional modules such as high-definition audio and video, positioning, and alarm to help solve safety issues during production site operations. During the positioning and tracking process, it integrates sensor positioning and image analysis. For complex working environments, it uses video positioning as a supplement to correct the real-time coordinate position. The corrected real-time coordinate position is formed into continuous trajectory points, and the continuous trajectory points are used to generate the dynamic operation trajectory of the personnel to improve positioning accuracy. The dynamic operation trajectory of the personnel is then matched and analyzed with the preset standard operation trajectory to conduct early warning analysis of the operation trajectory of the operator.

[0053] Based on the dynamic work trajectory of personnel, combined with video data and environmental perception technology, the trajectory data, video data, and environmental sensor data are aligned in time and space to realize "trajectory-environment" correlation analysis, and analyze whether there are risks in the environment and equipment at a certain point in time and location, further ensuring the safety of the real-time working environment, making front-end on-site operations smarter and safer, and making back-end project management simpler and more efficient.

[0054] In addition, this solution involves multiple parameter thresholds. It should be noted that the thresholds or preset values, preset ranges, etc. are set for result comparison and analysis in order to determine whether they are good or bad. The value of each threshold is set based on a combination of large-scale model analysis of sample data and manual experience to enter and store data. Appropriate adjustments can also be made based on seasonal or common-sense influencing conditions.

[0055] The above description is only a preferred specific embodiment of the present invention; however, the protection scope of the present invention is not limited thereto; any technician familiar with the technical field within the technical scope disclosed by the present invention; any equivalent replacement or change based on the technical solution and improved conception of the present invention shall be covered within the protection scope of the present invention.

Claims

1. An intelligent multifunctional safety helmet, comprising a safety helmet (1), characterized in that: A flexible monitoring panel is installed inside the safety helmet (1), and a signal of the flexible monitoring panel is connected to a management platform; The flexible monitoring panel is integrated with a cap removal monitoring module, a positioning tracking module, a safety supervision module, an environmental risk assessment module, and an alarm module. The management platform is used to divide the work site into different work areas for different operators as target monitoring areas. The hat removal monitoring module is used to monitor whether the operator wears a safety helmet when entering the target monitoring area (1) and whether there is any hat removal behavior during the operation supervision; The positioning and tracking module is used to obtain the motion data and video data of the operator, generate the operator's dynamic operation trajectory based on the motion data and video data, and send the operator's dynamic operation trajectory to the safety supervision module and the environmental risk assessment module; After receiving the dynamic operation trajectory of the personnel, the safety supervision module matches the dynamic operation trajectory of the personnel with the preset standard operation trajectory stored in the management platform. Based on the matching results, the operation trajectory of the operator is analyzed for early warning, and a trajectory deviation warning signal and an abnormal behavior warning signal are generated and sent to the alarm module; The environmental risk assessment module is based on the dynamic working trajectory of personnel, obtains video data through the positioning tracking module, obtains environmental data through the sensor group installed on the installation cap (1), performs early warning analysis on the working environment based on the video data and environmental data, generates an environmental abnormality signal and sends it to the alarm module.

2. The intelligent multifunctional helmet according to claim 1, characterized in that: The process of obtaining personnel dynamic operation trajectories includes: The motion data and video data of the workers are collected. The motion data include real-time coordinate position and behavior posture. The video data include monitoring video. The real-time coordinate position is obtained by the Beidou, Bluetooth beacon, and Wi-Fi positioning integrated on the safety helmet (1). The monitoring video is obtained by the high-definition camera (3) integrated on the safety helmet (1).

3. The intelligent multifunctional helmet according to claim 2, characterized in that: The surveillance video is segmented and processed to obtain multiple surveillance images that are consistent with the time points of real-time coordinate position acquisition. The environmental feature points of the work site in the surveillance images are extracted to correct the real-time position. The environmental feature points of the work site are the equipment and site identifications pre-stored in the management platform for feature recognition. The corrected real-time coordinate position is composed of continuous trajectory points, and the continuous trajectory points are used to generate the dynamic work trajectory of the personnel.

4. The intelligent multifunctional helmet according to claim 3, characterized in that: The process of early warning analysis of the operator's operation trajectory includes: The personnel's dynamic work trajectory is matched with the preset standard work trajectory stored in the management platform. The Hausdorff distance between the personnel's dynamic work trajectory and the preset standard work trajectory is calculated and marked as the offline distance. The offline distance is compared with the set centrifugal distance threshold. When the offline distance is greater than the set centrifugal distance threshold, it is determined that the operator's current work trajectory has deviated, and a trajectory deviation warning signal is generated.

5. The intelligent multifunctional helmet according to claim 4, characterized in that: Obtain the real-time coordinate position corresponding to the offline distance greater than the set offline distance threshold, mark it as the actual deviation point, obtain the monitoring image of the actual deviation point and the operator's behavior posture, and judge whether the operator has abnormal behavior based on the monitoring image and behavior posture analysis.

6. The intelligent multifunctional helmet according to claim 5, characterized in that: The walking posture of the operator is monitored and identified through the accelerometer, gyroscope and altitude sensor integrated in the helmet. By extracting abnormal motion characteristics (sudden change speed, static time, height from the ground), the abnormal motion characteristics are compared with the abnormal operation characteristic threshold. When it is greater than the abnormal operation characteristic threshold, it is judged that the operator has abnormal behavior and a behavior abnormality warning signal is generated.

7. The intelligent multifunctional helmet according to claim 6, characterized in that: The process of early warning analysis of the operating environment is as follows: The monitoring image is obtained through the positioning and tracking module, and the monitoring image is processed and abnormal conditions in the monitoring image are extracted through image processing technology. Abnormal conditions include a wet working surface, debris piled on the working surface, and fire on the equipment. The sensor group obtains environmental data. The environmental data includes the temperature, humidity, equipment noise and harmful gas leakage of the working area based on the dynamic working trajectory of the personnel. When an abnormal state is monitored or the environmental data exceeds the environmental data threshold, the corresponding real-time coordinate position is marked as an abnormal area, and an environmental abnormality signal is generated.

8. The intelligent multifunctional helmet according to claim 1, characterized in that: The helmet (1) is also integrated with a laser light (2), a warning light (4), an alarm button (5), a display screen (6), a power button (7), a recording button (8), an intercom button (9) and an illumination button (10).

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