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CN-122004561-A - Intelligent safety helmet control method based on real-time pressure sensing and damping regulation and control

CN122004561ACN 122004561 ACN122004561 ACN 122004561ACN-122004561-A

Abstract

The invention relates to the technical field of multidimensional data processing, and particularly discloses an intelligent safety helmet control method based on real-time pressure sensing and damping regulation. The safety helmet is internally integrated with a pressure sensor, an inertia measurement unit, a camera, a dust sensor, a mask driving mechanism, an oxygen supply assembly, a wireless communication module and a control processor, wherein the control processor collects impact pressure signals, attitude data and environment data, integrates image features and dust signals to calculate dust concentration, automatically closes the mask and opens oxygen supply when the dust concentration exceeds a threshold value, and when an impact event is detected, the impact event is used as a benchmark, and the difference of an inclination angle after impact, the change of the attitude and the change of the image are combined to identify violent attitude change and long-time stillness, so that suspected coma is judged, the mask, the oxygen supply and the alarm are linked, and meanwhile, the damping buffer parameters are adjusted according to the impact strength, so that the dynamic protection on head impact is realized, the rescue efficiency is improved, and false alarm is reduced.

Inventors

  • ZHANG MING

Assignees

  • 肇庆博涵体育用品有限公司

Dates

Publication Date
20260512
Application Date
20260113

Claims (10)

  1. 1. The intelligent safety helmet control method based on real-time pressure sensing and damping regulation is applied to a safety helmet worn on the head of an operator, and is characterized in that a control processor in the safety helmet performs acquisition, preprocessing and characteristic calculation on multi-source digital signals, and performs data processing judgment based on a preset threshold rule, and the intelligent safety helmet control method comprises the following steps: Step 1, acquiring and caching attitude data and environment perception data to form a multi-source time sequence; step 2, performing numerical operation and feature extraction on the environment sensing data in the multi-source time sequence, calculating a dust concentration index, controlling a mask driving mechanism to close a mask when the dust concentration index is larger than a dust threshold value, and controlling an oxygen supply assembly to convey oxygen into the safety helmet; Step 3, judging that a falling stone impact event occurs when the synthesized acceleration obtained based on the impact pressure signal calculation exceeds an impact threshold value, taking posture data of the latest moment before the falling stone impact event as an initial posture and recording the occurrence time of the impact event; step 4, determining that the operator has severe gesture change based on the inclination angle difference value sequence in a first time window after the impact event; Step 5, determining that the safety helmet is in a static state based on the maximum variation of the attitude angle time sequence and the difference degree between the image frames in a second time window after the impact event; And 6, judging the operator state as a suspected coma state under the condition that the severe posture change is satisfied and is in a static state, the duration time from the occurrence of a knocking event is larger than a coma time threshold value, and the active reset operation of the wearer is not detected.
  2. 2. The intelligent control method for the safety helmet based on real-time pressure sensing and damping regulation and control according to claim 1, wherein the calculating of the dust concentration index comprises the steps of performing contrast, brightness attenuation and atomization analysis based on a digital image processing algorithm on an image acquired by a camera to obtain a first dust characteristic value, performing normalization numerical processing on a particulate matter concentration signal output by a dust sensor to obtain a second dust characteristic value, and performing weighted fusion operation on the first dust characteristic value and the second dust characteristic value according to a preset weight coefficient to obtain the dust concentration index.
  3. 3. The intelligent safety helmet control method based on real-time pressure sensing and damping regulation according to claim 1, wherein the determining that the falling rock hitting event occurs comprises calculating a peak value and a rising edge slope of the impact pressure signal within a sliding time window of a preset length and calculating a peak value of a synthesized acceleration according to gesture data, and determining that the falling rock hitting event occurs when at least one of the impact pressure peak value and the synthesized acceleration peak value is greater than a corresponding threshold value and the rising edge slope is greater than a slope threshold value.
  4. 4. The intelligent safety helmet control method based on real-time pressure sensing and damping regulation and control according to claim 1 is characterized in that in the step 4, determining that a severe gesture change occurs to an operator based on a tilting angle difference sequence comprises the steps that after a falling stone impact event is detected and the occurrence time of the impact event is recorded, the control processor continuously samples and analyzes gesture data output by an inertia measurement unit in a preset first time window, sequentially reads gesture data of each sampling moment in a fixed sampling period in the first time window, takes gesture data of an effective sampling moment which is the nearest to the falling stone impact event in the sampling period of the gesture data before the falling stone impact event as an initial gesture, calculates the obtained spatial gesture vector, obtains the tilting angle difference between the current gesture and the initial gesture, constructs a tilting angle difference sequence in time sequence, counts the number of continuous super-threshold sampling points in adjacent sampling points, and determines that a corresponding severe state change is recognized as a suspected condition after the corresponding gesture change occurs in the first time window when the number of the continuous super-threshold sampling points is detected.
  5. 5. The intelligent safety helmet control method based on real-time pressure sensing and damping regulation and control according to claim 1 is characterized in that in the step 5, the safety helmet is determined to be in a static state based on the maximum variation of the attitude angle time sequence and the difference between image frames, wherein after the occurrence of a falling rock impact event is confirmed and the occurrence time of the impact event is recorded through the steps, the control processor performs joint analysis on the attitude data output by an inertial measurement unit and the image sequence acquired by a camera in a preset second time window, sequentially reads the attitude data of each sampling moment in the same fixed sampling period as the first time window in the second time window, respectively extracts the pitch angle and the roll angle attitude angle, calculates the maximum variation of the attitude angle time sequence in the second time window, takes the maximum variation as the attitude variation, reads the image frames in the second time window according to the image acquisition frame rate sequence, performs frame difference operation, feature point matching or optical flow estimation on the adjacent image frames, performs accumulation or maximum value on the index in the whole second time window, and determines that the attitude data of the helmet is equal to the fixed sampling period in the same as the fixed sampling period as the first time window, and the attitude variation is not defined to be equal to the preset in the overall state of the second time window, and the state is not defined to be equal to the overall motion threshold value, and the state is not defined to be the static state.
  6. 6. The intelligent control method for the safety helmet based on real-time pressure sensing and damping regulation and control according to claim 1, wherein when the operator is judged to be in a suspected coma state, the control processor controls the mask driving mechanism to close the mask and controls the oxygen supply assembly to convey oxygen into the safety helmet, and an alarm message containing the identity and the position information of the wearer is sent to an external terminal through the wireless communication module.
  7. 7. The intelligent safety helmet control method based on real-time pressure sensing and damping regulation and control according to claim 1 is characterized in that the active reset operation comprises at least one step that an operator triggers a reset signal through a key, the key is arranged on the outer side of a safety helmet shell, the operator sends out a preset voice command, and the control processor obtains a digital voice signal through an audio acquisition module, recognizes the voice command through a voice recognition algorithm and generates a reset signal.
  8. 8. The intelligent safety helmet control method based on real-time pressure sensing and damping regulation and control according to claim 1, wherein the alarm message comprises an operator identifier, position information, a time stamp of occurrence of the falling stone impact event, the impact pressure peak value, the inclination angle difference value and a coma state judgment result, the position information is obtained through interaction of a wireless communication module with a downhole positioning base station or a relay node, and the alarm message is transmitted on a wireless communication link after being encoded in a digital data form.
  9. 9. The intelligent safety helmet control method based on real-time pressure sensing and damping regulation according to claim 1, wherein the damping buffer structure comprises an adjustable damping module comprising a buffer cavity filled with a compressible medium and a damping regulation component for changing the effective volume or the flow resistance of the medium in the buffer cavity, and the control processor selects a damping gear or a continuous damping coefficient before and after a falling rock impact event according to the steady-state pressure level acquired by the pressure sensor and the impact pressure peak value of the falling rock impact event so as to limit the peak acceleration transmitted to the head of the operator from exceeding a safety threshold.
  10. 10. The intelligent control method for the safety helmet based on real-time pressure sensing and damping regulation according to claim 1, wherein the control processor counts the impact pressure peak value, the attitude variation and the coma state judgment result of the falling rock hitting event in the long-term operation process, performs digital statistical analysis on the statistical data, and adaptively adjusts at least one of the dust threshold value, the impact threshold value, the first angle threshold value and the second angle threshold value and the damping parameter of the adjustable damping module based on the analysis result.

Description

Intelligent safety helmet control method based on real-time pressure sensing and damping regulation and control Technical Field The invention relates to the technical field of multidimensional data processing, in particular to an intelligent safety helmet control method based on real-time pressure sensing and damping regulation. Background The underground coal mine working environment is influenced by geological conditions and working conditions, and safety risks such as falling rocks, roof collapse, equipment falling objects, coal dust explosion and the like exist for a long time. In order to ensure the safety of operators, the existing coal mine enterprises are widely provided with protective caps, respiratory protection devices and various environment monitoring systems. In the aspect of individual protection, the traditional safety helmet mainly relies on a shell and a fixed buffer layer to provide passive protection, generally, an electric digital data processing unit for collecting and calculating impact pressure, attitude change and environmental parameters is not configured, and the real-time analysis capability of various sensing signals is lacking, so that the real-time sensing and active regulation capability of impact strength is lacking, whether a wearer is in a coma or disabled state cannot be fed back in time, and the rescue response is seriously dependent on visual discovery and experience judgment of a companion. With the development of sensors, wireless communication and embedded processing technologies, the mining intelligent safety helmet integrating a positioning module, an acceleration sensor and a communication module can realize position tracking, falling detection and one-key alarm, but the schemes are mostly based on single triaxial acceleration signals, falling or abnormal actions are judged through a simple threshold value or a pattern recognition algorithm, the data processing flow is simple, fusion operation and time sequence analysis are difficult to be carried out on multi-source information such as pressure quantity, gesture sequences and images, and a perfect electric digital data processing and feature extraction mechanism is lacked, so that severe actions, normal pitching and external force impact are difficult to be accurately distinguished, and false alarm and missing alarm are easy to generate. In addition, the existing fall detection is designed aiming at the ground environment, and the complicated posture change, the long-time operation posture and the falling stone impact characteristics in the underground narrow roadway are not considered enough. In the aspect of environment risk control, some systems monitor roadway dust concentration through dust sensors and combine ventilation systems to carry out centralized treatment, individual protection is mostly dependent on wearing a dust mask or a self-rescue respirator, a starting mode is dependent on manual judgment and manual operation, and protection actions cannot be timely executed under the condition that the sudden dust concentration is rapidly increased or operators are smashed and dizziness occurs. The scheme of the existing helmet built-in mask or oxygen supply device is mostly manually triggered or simply controlled in time, unified digital processing and decision logic is not established for dust sensor data and image data, quantitative calculation and threshold decision processes of dust characteristics are lacked, and intelligent control logic based on comprehensive judgment of environment and human body states is also lacked. In addition, the buffer layer in the traditional safety helmet generally adopts a material structure with fixed thickness and elastic parameters, so that a better protection effect can be exerted only under a design working condition, the comfort during light impact and the protection capability during heavy impact are difficult to be considered in an actual working condition, the data of the pressure sensor and the gesture change characteristic are not used as input, the digital calculation is performed in the controller to adaptively adjust the buffer structure parameters, and the cooperative regulation and control with the pressure sensing and gesture recognition data are not realized. In general, in the prior art, an intelligent control method for a safety helmet for linkage damping regulation and control, respiratory protection and wireless alarm is not capable of meeting the requirements of intellectualization and refinement of individual protection in a high-risk underground coal mine environment by performing electric digital data processing and fusion analysis on multi-source sensing data such as impact pressure, attitude change, image change, dust concentration and the like at the helmet end, automatically identifying suspected coma and the like through feature extraction and threshold judgment and outputting a control instruction. Disclosure of Invention In order to overco