Safety helmet intelligent control method based on real-time pressure sensing and damping regulation and control

Abstract

The invention relates to the technical field of multi-dimensional data processing, and particularly discloses a safety helmet intelligent control method based on real-time pressure sensing and damping regulation and control. A pressure sensor, an inertial measurement unit, a camera, a dust sensor, a mask driving mechanism, an oxygen supply assembly, a wireless communication module and a control processor are integrated in a safety helmet worn by an underground worker, and the control processor collects impact pressure signals, posture data and environment data. Image features and dust signals are fused to calculate dust concentration, and when the dust concentration exceeds a threshold value, the mask is automatically closed, and oxygen supply is started; when an impact event is detected, the posture before impact is taken as a reference, and the inclination angle difference after smashing, the posture variation and the image variation degree are combined to recognize violent posture change and long-time static state, so that suspected coma is judged, a mask is linked, oxygen is supplied and an alarm is given, and meanwhile, a damping buffer parameter is adjusted according to the impact strength. Dynamic protection against head impact is achieved, rescue efficiency is improved, and false alarm and missing alarm are reduced.

Description

Technical Field

[0001] This invention relates to the field of multidimensional data processing technology, and more specifically, to an intelligent control method for safety helmets based on real-time pressure sensing and damping regulation. Background Technology

[0002] Underground coal mine operations are subject to various geological conditions and working conditions, resulting in persistent safety risks such as rockfalls, roof collapses, falling objects, and coal dust explosions. To ensure worker safety, existing coal mining enterprises widely equip themselves with protective helmets, respiratory protection devices, and various environmental monitoring systems. Regarding personal protective equipment (PPE), traditional safety helmets primarily rely on the shell and fixed buffer layer for passive protection. They typically lack digital data processing units to collect and process impact pressure, posture changes, and environmental parameters, and therefore lack the ability to analyze multiple sensor signals in real time. Consequently, they lack the ability to perceive and actively control impact intensity in real time, and cannot promptly report whether the wearer is unconscious or disabled. Rescue responses heavily rely on visual observation and experience-based judgment by colleagues.

[0003] With the development of sensor, wireless communication, and embedded processing technologies, smart safety helmets for mining have emerged, integrating positioning modules, accelerometers, and communication modules. These helmets can achieve position tracking, fall detection, and one-button alarms. However, these solutions are mostly based on a single three-axis acceleration signal, using simple threshold or pattern recognition algorithms to determine falls or abnormal movements. The data processing flow is relatively simple, making it difficult to fuse and perform time-series analysis on multi-source information such as pressure, posture sequences, and images. They lack a robust electro-digital data processing and feature extraction mechanism, making it difficult to accurately distinguish between violent movements, normal pitching, and external impacts, leading to false alarms and missed alarms. Furthermore, existing fall detection systems are mostly designed for surface environments, failing to adequately consider the complex posture changes in narrow underground tunnels, prolonged operating postures, and the impact characteristics of falling rocks.

[0004] In terms of environmental risk control, some systems monitor dust concentration in tunnels using dust sensors and combine this with ventilation systems for centralized control. Individual protection relies mainly on wearing dust masks or self-rescue respirators, with activation methods dependent on manual judgment and operation. In cases of sudden, rapid increases in dust concentration or when workers are already stunned, timely protective actions cannot be guaranteed. Existing helmet solutions with built-in face shields or oxygen supply devices are mostly manually triggered or subject to simple time control. They lack a unified digital processing and decision-making logic for dust sensor data and image data, lack quantitative calculations and threshold judgment processes for dust characteristics, and lack intelligent control logic based on a comprehensive assessment of environmental and human conditions.

[0005] Furthermore, the cushioning layer inside traditional safety helmets typically uses materials with fixed thickness and elastic parameters, providing good protection only under designed conditions. In actual working conditions, it's difficult to balance comfort during minor impacts with protection against severe impacts. Moreover, it fails to use pressure sensor data and posture change characteristics as inputs to perform digital calculations in the controller to adaptively adjust the cushioning structure parameters, and it lacks coordinated control with pressure sensing and posture recognition data. Overall, current technology lacks a smart helmet control method that performs electro-digital data processing and fusion analysis on multi-source sensor data such as impact pressure, posture changes, image changes, and dust concentration at the helmet end; automatically identifies suspected unconscious states through feature extraction and threshold judgment; and outputs control commands to link damping regulation, respiratory protection, and wireless alarms. This fails to meet the requirements for intelligent and precise personal protective equipment in the high-risk environment of underground coal mines. Summary of the Invention

[0006] To overcome the aforementioned deficiencies in the prior art, this invention provides an intelligent control method for safety helmets based on real-time pressure sensing and damping regulation. This method uses digital processing of multi-source sensor data to identify dust risks, rockfall impact intensity, and the wearer's unconscious state. It dynamically adjusts the helmet's damping and buffering characteristics and automatically activates the face mask, oxygen supply, and alarm, thereby improving the life safety protection level of coal mine workers and solving the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A smart control method for safety helmets based on real-time pressure sensing and damping regulation is applied to safety helmets worn on the heads of workers. The control processor in the safety helmet collects, preprocesses, and calculates features from multiple digital signals, and performs data processing and judgment based on preset threshold rules, including the following steps:

[0009] Step 1: Collect and cache attitude data and environmental perception data to form a multi-source time series;

[0010] Step 2: Perform numerical calculations and feature extraction on the environmental perception data in the multi-source time series, calculate the dust concentration index, and when the dust concentration index is greater than the dust threshold, control the mask drive mechanism to close the mask and control the oxygen supply component to deliver oxygen into the safety helmet.

[0011] Step 3: When the synthetic acceleration calculated based on the impact pressure signal exceeds the impact threshold, it is determined that a rockfall event has occurred. The attitude data of the moment most recently before the rockfall event occurs is used as the initial attitude and the time of the event is recorded.

[0012] Step 4: Within the first time window after the impact event, determine the worker's drastic posture change based on the tilt angle difference sequence;

[0013] Step 5: Within the second time window after the impact event, determine that the safety helmet is in a stationary state based on the maximum change in the attitude angle time series and the inter-frame difference of the image.

[0014] Step 6: If the worker experiences a drastic change in posture and remains stationary, and the duration of the incident exceeds the unconsciousness time threshold, and no active reset operation by the wearer is detected, the worker's condition is determined to be a suspected unconscious state.

[0015] Preferably, the calculation of the dust concentration index includes: performing contrast, brightness attenuation and fogging analysis on the image captured by the camera to obtain a first dust feature value; normalizing the particulate matter concentration signal output by the dust sensor to obtain a second dust feature value; and weighting and fusing the first dust feature value and the second dust feature value according to a preset weight coefficient to obtain the dust concentration index, wherein the weight coefficient is determined based on historical calibration data.

[0016] Preferably, the determination of a rockfall event includes: calculating the peak value and rising slope of the impact pressure signal and the peak value of the composite acceleration based on the attitude data within a preset sliding time window; when at least one of the peak value of the impact pressure and the peak value of the composite acceleration is greater than the corresponding threshold and the rising slope is greater than the slope threshold, a rockfall event is determined to have occurred; the sampling frequency of the pressure sensor and the inertial measurement unit is in the range of 100Hz to 1000Hz.

[0017] Preferably, determining that a worker has experienced a drastic change in attitude includes: after detecting a rockfall event and recording the event time T0, the control processor continuously samples and analyzes the attitude data output by the inertial measurement unit within a preset first time window, where the first time window is 0 to 3 seconds after the rockfall event. Within the first time window, the control processor sequentially reads the attitude data at each sampling time with a fixed sampling period. The attitude data at the most recent valid sampling time before the rockfall event and within the attitude data sampling period is taken as the initial attitude. Based on the pitch angle, roll angle, and / or the spatial attitude vector calculated by quaternions, the tilt angle difference between the current attitude and the initial attitude is calculated, and a tilt angle difference sequence is constructed in chronological order. Subsequently, the control processor traverses the tilt angle difference sequence, determining whether the tilt angle difference at each sampling moment is greater than a first angle threshold, and counting the number of consecutively exceeding the threshold among adjacent sampling points. When the number of consecutively exceeding the threshold sampling points reaches a preset counting threshold, it is determined that a drastic posture change corresponding to the impact event has occurred within the first time window, and the determination result is used as a prerequisite for subsequent suspected coma identification. If no segment meeting the requirements of consecutively exceeding the threshold and counting threshold is formed within the entire first time window, it is considered that the impact did not cause significant posture rollover or falling, and the subsequent coma determination process based on the impact event is not triggered. The first angle threshold is located in the range of 30 degrees to 60 degrees.

[0018] Preferably, determining that the safety helmet is stationary includes: after the rockfall event is confirmed to have occurred and the time of the event is recorded through the aforementioned steps, the control processor performs joint analysis on the attitude data output by the inertial measurement unit and the image sequence acquired by the camera within a preset second time window, the second time window being 30 to 60 seconds after the rockfall event. Within the second time window, the control processor sequentially reads attitude data at each sampling moment using the same fixed sampling period as the first time window, extracts attitude angles such as pitch and roll angles, calculates the maximum change in the attitude angle time series within the second time window, and uses this maximum change as the attitude change. Simultaneously, the control processor reads image frames within the second time window according to the image acquisition frame rate, performs frame difference calculations, feature point matching, or optical flow estimation on adjacent image frames to obtain an image change index reflecting the strength of overall image changes, and performs cumulative averaging or maximum value statistics on this index throughout the second time window. When the attitude change is less than a preset second angle threshold and the image change index is less than a preset image threshold, the control processor determines that the overall attitude of the safety helmet remains basically unchanged within the second time window and that there is no significant movement or displacement of the surrounding scene, thus defining this state as the helmet being stationary. The second threshold is no greater than 15 degrees.

[0019] Preferably, when the operator is suspected of being unconscious, the control processor controls the mask drive mechanism to close the mask and controls the oxygen supply component to deliver oxygen into the safety helmet, and sends an alarm message containing the wearer's identification and location information to an external terminal via the wireless communication module.

[0020] Preferably, the active reset operation includes at least one of the following: the operator triggers a reset signal by pressing a button located on the outside of the safety helmet shell; the operator issues a preset voice command, and the control processor recognizes the voice command through an audio acquisition module and generates a reset signal.

[0021] Preferably, the alarm message includes the worker's identification, location information, the timestamp of the rockfall event, the peak value of the impact pressure, the tilt angle difference, and the result of the unconsciousness determination. The location information is obtained by the wireless communication module interacting with the underground positioning base station or relay node.

[0022] Preferably, the damping buffer structure includes an adjustable damping module, which includes a buffer cavity filled with a compressible medium and a damping control component for changing the effective volume or the flow resistance of the medium within the buffer cavity. The control processor selects a damping level or a continuous damping coefficient based on the steady-state pressure level collected by the pressure sensor and the peak impact pressure of the rockfall event before and after the rockfall event to limit the peak acceleration transmitted to the worker's head from not exceeding a safety threshold.

[0023] Preferably, the control processor statistically analyzes the peak impact pressure, attitude change, and coma determination results of rockfall events during long-term operation, and adaptively adjusts at least one of the dust threshold, the impact threshold, the first angle threshold, and the second angle threshold, as well as the damping parameters of the adjustable damping module based on the statistical data, in order to improve the accuracy of suspected coma determination and reduce false alarm and false negative rates.

[0024] Preferably, the safety helmet is used in underground coal mine operations. When the dust concentration index is close to the dust threshold and the posture data indicates that the wearer is in a tilted-up or tilted-up posture, the control processor sends an early warning message to the monitoring center through the wireless communication module and increases the damping level of the adjustable damping module in advance.

[0025] Compared to existing technologies, the beneficial effects of this application are as follows:

[0026] This invention integrates multi-source sensor data (including impact pressure, attitude change, image analysis, and dust concentration) to construct a unified time-series data set in the control processor. It then sequentially executes a series of electro-digital data processing steps, including denoising, zero-drift correction, attitude calculation, image feature extraction, numerical normalization, sliding window peak detection, and threshold decision. This enables accurate identification and intelligent response to rockfall impacts, dust risks, and worker unconsciousness in high-risk environments like underground coal mines. The identification results further serve as the basis for digital control decisions, driving damping parameter adjustments, mask opening and closing, and oxygen supply activation and deactivation. It can dynamically adjust the helmet's damping and cushioning characteristics to balance comfort and impact resistance, and automatically trigger mask closure, oxygen supply activation, and wireless alarm functions. This effectively addresses the shortcomings of traditional safety helmets in active protection, status assessment, and rescue response, while also improving the automation level of sensor data processing and status assessment. This demonstrates the application value of electro-digital data processing in intelligent safety helmet control, enhancing the intelligence level of individual protection and the timeliness of emergency rescue. Attached Figure Description

[0027] Figure 1 This is a flowchart of an intelligent control method for a safety helmet based on real-time pressure sensing and damping regulation according to the present invention.

[0028] Figure 2 This is a schematic diagram of the dust concentration index calculation and feature fusion structure of the present invention.

[0029] Figure 3 This is a schematic diagram showing the arrangement of the external buttons and voice acquisition module on the safety helmet of the present invention.

[0030] Figure 4 This is a schematic diagram of multiple structural schemes for the adjustable damping module of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Example 1

[0033] like Figure 1 As shown, a smart control method for safety helmets based on real-time pressure sensing and damping regulation is applied to safety helmets worn on the heads of workers. The control processor in the safety helmet collects, preprocesses, and performs feature calculations on multi-source digital signals output from pressure sensors, inertial measurement units, cameras, and dust sensors, and performs data processing and judgment based on preset threshold rules, including the following steps:

[0034] Step 1: Acquire and cache the impact pressure signal output by the pressure sensor, the attitude data output by the inertial measurement unit, and the environmental perception data output by the camera and dust sensor at a preset sampling period, and perform time synchronization and alignment on the data with different sampling rates to form a multi-source time series.

[0035] Step 2: Perform numerical calculations and feature extraction on the environmental perception data in the multi-source time series, calculate the dust concentration index, and when the dust concentration index is greater than the dust threshold, control the mask drive mechanism to close the mask and control the oxygen supply component to deliver oxygen into the safety helmet.

[0036] Step 3: When the synthetic acceleration calculated based on the impact pressure signal exceeds the impact threshold, it is determined that a rockfall event has occurred. The attitude data of the moment most recently before the rockfall event occurs is used as the initial attitude and the time of the event is recorded.

[0037] Step 4: Within the first time window after the impact event, determine the worker's drastic posture change based on the tilt angle difference sequence;

[0038] Step 5: Within the second time window after the impact event, determine that the safety helmet is in a stationary state based on the maximum change in the attitude angle time series and the inter-frame difference of the image.

[0039] Step 6: If the worker experiences a drastic change in posture and remains stationary, and the duration of the incident exceeds the unconsciousness time threshold, and no active reset operation by the wearer is detected, the worker's condition is determined to be a suspected unconscious state.

[0040] In this embodiment, the safety helmet includes a shell, a damping buffer structure disposed inside the shell, a pressure sensor, an inertial measurement unit, a camera, a dust sensor, a mask drive mechanism, an oxygen supply component, a wireless communication module, and a control processor. When the worker wears the safety helmet while working underground, the control processor collects the impact pressure signal from the pressure sensor, the attitude data from the inertial measurement unit, the image sequence from the camera, and the particulate matter concentration signal from the dust sensor according to a preset sampling period. The processor preprocesses, extracts features, and determines the state of the data, and controls the damping parameters of the damping buffer structure, the opening and closing state of the mask drive mechanism, the working state of the oxygen supply component, and the data transmission of the wireless communication module based on the determination results. The damping buffer structure includes an adjustable damping module. The control processor outputs a damping control signal to the adjustable damping module to change the effective volume of the buffer cavity, the internal medium flow resistance, or the equivalent stiffness of the supporting structure, thereby adjusting the helmet's impact energy absorption characteristics.

[0041] The control processor sets the sampling frequency of the pressure sensor and inertial measurement unit to within the range of 100Hz to 1000Hz, the frame rate of the camera to no less than 10 frames per second, and the sampling period of the dust sensor to 1 to 5 seconds. The control processor performs noise reduction, zero drift correction, and time synchronization on the data from each channel, aligning data with different sampling rates to a unified time axis to form a multi-source time series.

[0042] For attitude data, the control processor calculates pitch angle, roll angle and yaw angle based on the acceleration and angular velocity output by the inertial measurement unit through attitude calculation algorithm, and calculates the composite acceleration amplitude at the same time; for impact pressure signals, the control processor calculates instantaneous pressure peak and rise slope using a sliding window; for image data, the control processor calculates indicators such as image contrast, brightness statistics and edge strength.

[0043] The control processor calculates the average brightness, contrast, and haze factor for each image, mapping these indicators to a first dust characteristic value; simultaneously, it reads the particulate matter concentration signal from the dust sensor, and obtains a second dust characteristic value after normalization. For example... Figure 2 As shown, the control processor weights and fuses the first and second dust characteristic values ​​according to the weighting coefficients obtained from historical calibration to obtain the dust concentration index. The dust threshold can be pre-configured or dynamically adjusted according to different roadways and working conditions. When the dust concentration index is greater than the dust threshold, the control processor outputs a closing signal to the mask drive mechanism to close the mask, blocking underground dust from entering the breathing channel. At the same time, it outputs a start signal to the oxygen supply component to deliver breathing gas into the safety helmet. When the dust concentration index is lower than the dust threshold and the person is not in a suspected unconscious state, the mask drive mechanism can be controlled to open the mask and close the oxygen supply component to reduce energy consumption and improve comfort.

[0044] The control processor calculates the peak value of the impact pressure signal and the rising slope obtained from the pressure rise segment within a sliding time window, and simultaneously calculates the synthetic acceleration amplitude based on the attitude data. When the peak value exceeds the pressure threshold or the synthetic acceleration amplitude exceeds the acceleration threshold and the rising slope exceeds the slope threshold, the control processor determines that an impact event has occurred, records the current time as the impact time T0, and stores the attitude data from the most recent sampling moment before the impact as the initial attitude. The control processor selects the damping level of the damping buffer structure according to the peak impact duration. For example, a lower damping level is selected when the peak value is within the first range, a medium damping level is selected when the peak value is within the second range, and a high damping level is selected when the peak value exceeds the upper limit of the second range to limit the peak synthetic acceleration transmitted to the wearer's head from exceeding the safety threshold. The correspondence between the damping level and the pressure range can be obtained through experimental calibration. The first range can be set to 10 kPa ≤ peak value < 50 kPa; the second range can be set to 50 kPa ≤ peak value < 150 kPa.

[0045] In this embodiment, after detecting a rockfall event and recording the event occurrence time T0, the control processor continuously samples and analyzes the attitude data output by the inertial measurement unit within a preset first time window, which is 0 to 3 seconds after the rockfall event. Within the first time window, the control processor sequentially reads the attitude data at each sampling time with a fixed sampling period. The attitude data at the most recent valid sampling time before the rockfall event and within the attitude data sampling period is taken as the initial attitude. Based on the pitch angle, roll angle, and / or the spatial attitude vector calculated by quaternions, the tilt angle difference between the current attitude and the initial attitude is calculated, and a tilt angle difference sequence is constructed in chronological order. Subsequently, the control processor traverses the tilt angle difference sequence, determines whether the tilt angle difference at each sampling moment is greater than the first angle threshold, and counts the number of consecutive points exceeding the threshold among adjacent sampling points. When the number of consecutively exceeding the threshold sampling points reaches the preset counting threshold, it is determined that a violent posture change corresponding to the impact event has occurred within the first time window, and the determination result is used as a prerequisite for subsequent identification of suspected unconscious state. If no segment that meets the requirements of consecutive exceeding the threshold and counting threshold is formed within the entire first time window, it is considered that the impact did not cause significant posture flipping or falling, and the subsequent unconsciousness determination process based on the impact event is not triggered. The first angle threshold is within the range of 30 degrees to 60 degrees.

[0046] After the rockfall incident is confirmed and its occurrence time is recorded through the aforementioned steps, the control processor performs joint analysis on the attitude data output by the inertial measurement unit and the image sequence acquired by the camera within a preset second time window, which is 30 to 60 seconds after the rockfall incident. Within the second time window, the control processor sequentially reads the attitude data at each sampling moment using the same fixed sampling period as the first time window, extracting attitude angles such as pitch and roll angles, and calculating the maximum change in the attitude angle time series within the second time window. This maximum change is taken as the attitude change. Simultaneously, the control processor reads the image frames within the second time window according to the image acquisition frame rate, performing frame difference calculations, feature point matching, or optical flow estimation on adjacent image frames to obtain an image change index reflecting the strength of overall image changes. This index is then accumulated and averaged or its maximum value is statistically analyzed throughout the second time window. When the attitude change is less than a preset second angle threshold and the image change is less than a preset image threshold, the control processor determines that the overall attitude of the safety helmet remains essentially unchanged within the second time window and that there is no significant movement or displacement of the surrounding scene, thus defining this state as the helmet being stationary. The second threshold is no greater than 15 degrees.

[0047] Simultaneously, the control processor continuously monitors for any active reset operation after the rockfall incident. Active reset operations can include: the operator pressing a one-button confirmation button located on the outside of the casing; the operator issuing a preset voice command, which the audio acquisition module detects and recognizes. If no active reset operation is detected and the aforementioned conditions for drastic posture change and static state are simultaneously met, and the duration from the time of the rockfall incident exceeds the unconsciousness time threshold, the control processor determines the wearer's state as a suspected unconscious state. Figure 3 As shown, a one-button confirmation button is set on the outside of the safety helmet shell, and an audio acquisition module is arranged near the wearer's mouth to facilitate active reset operation.

[0048] In a suspected state of unconsciousness, the control processor reconfirms that the mask is closed and activates the oxygen supply system. Simultaneously, it sends an alarm message via wireless communication to the terminal devices worn by nearby workers and the underground relay node. The alarm message includes the wearer's identification, location information, timestamp of the impact event, peak impact pressure, posture change characteristics, and the result of the unconsciousness assessment. Upon receiving the alarm message, the monitoring center highlights the alarm area on the monitoring interface and generates a voice prompt, guiding nearby workers and rescue personnel to quickly reach the scene for rescue.

[0049] In this embodiment, the control processor records the peak impact pressure, posture change, unconsciousness determination result, and whether a false alarm was manually confirmed during each impact event during long-term operation. The control processor periodically performs statistical analysis on the data to evaluate the false alarm rate and missed alarm rate under the current dust threshold, impact threshold, and angle threshold settings. When a certain threshold setting leads to an excessively high false alarm rate or missed alarm rate, the control processor fine-tunes the corresponding threshold according to a preset adjustment strategy to achieve a balance between system safety and stability. Similarly, the control processor can statistically analyze the actual deceleration level of the helmet's buffer layer under different impact intensities and update the mapping relationship between damping levels and pressure ranges to improve the targeting and protective effect of damping control.

[0050] Example 2

[0051] In another embodiment, the camera can employ a wide-angle low-light imaging device to improve image quality under low-light conditions; the dust sensor can employ a laser scattering dust measurement unit to improve the sensitivity of dust concentration detection. The wireless communication module can adopt different wireless communication protocols according to the network conditions in the mining area, such as ad hoc network communication, low-power wide area network communication, or mining-specific wireless communication protocols, to ensure reliable transmission of alarm messages in complex tunnel environments.

[0052] like Figure 4 As shown, the adjustable damping module can employ a multi-layered air cavity and elastic support structure, achieving damping adjustment by changing the connectivity between the air cavities; alternatively, it can use a buffer cavity filled with a viscous medium, changing the damping characteristics by controlling the cross-section of the medium flow channel. Those skilled in the art can select different forms of adjustable damping modules as equivalent alternatives based on the control requirements and specific structural design of the method according to this invention. As long as dynamic adjustment of the helmet's buffering characteristics can be achieved under the control of the control processor, it falls within the scope of protection of this invention.

[0053] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0054] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart control method for safety helmets based on real-time pressure sensing and damping adjustment, applied to safety helmets worn on the heads of workers, characterized in that, The control processor in the safety helmet acquires, preprocesses, and calculates features from multiple digital signals, and performs data processing and judgment based on preset threshold rules, including the following steps: Step 1: Collect and cache attitude data and environmental perception data to form a multi-source time series; Step 2: Perform numerical calculations and feature extraction on the environmental perception data in the multi-source time series, calculate the dust concentration index, and when the dust concentration index is greater than the dust threshold, control the mask drive mechanism to close the mask and control the oxygen supply component to deliver oxygen into the safety helmet. Step 3: When the synthetic acceleration calculated based on the impact pressure signal exceeds the impact threshold, it is determined that a rockfall event has occurred. The attitude data of the moment most recently before the rockfall event occurs is used as the initial attitude and the time of the event is recorded. Step 4: Within the first time window after the impact event, determine the worker's drastic posture change based on the tilt angle difference sequence; Step 5: Within the second time window after the impact event, determine that the safety helmet is in a stationary state based on the maximum change in the attitude angle time series and the inter-frame difference of the image. Step 6: If the worker experiences a drastic change in posture and remains stationary, and the duration of the incident exceeds the unconsciousness time threshold, and no active reset operation by the wearer is detected, the worker's condition is determined to be a suspected unconscious state.

2. The intelligent control method for a safety helmet based on real-time pressure sensing and damping regulation according to claim 1, characterized in that... The calculation of the dust concentration index includes: performing contrast, brightness attenuation, and haze analysis on the image captured by the camera based on digital image processing algorithms to obtain a first dust feature value; performing normalized numerical processing on the particulate matter concentration signal output by the dust sensor to obtain a second dust feature value; and performing a weighted fusion operation on the first dust feature value and the second dust feature value according to a preset weight coefficient to obtain the dust concentration index.

3. The intelligent control method for safety helmets based on real-time pressure sensing and damping regulation according to claim 1, characterized in that... The determination of a rockfall event includes: calculating the peak value and rising slope of the impact pressure signal and the peak value of the composite acceleration based on the attitude data within a preset sliding time window; when at least one of the peak value of the impact pressure and the peak value of the composite acceleration is greater than the corresponding threshold and the rising slope is greater than the slope threshold, a rockfall event is determined to have occurred.

4. The intelligent control method for safety helmets based on real-time pressure sensing and damping regulation according to claim 1, characterized in that, In step 4, determining the drastic posture change of the worker based on the tilt angle difference sequence includes: after detecting a rockfall event and recording the time of the event, the control processor continuously samples and analyzes the posture data output by the inertial measurement unit within a preset first time window; within the first time window, the control processor reads the posture data at each sampling time sequentially at a fixed sampling period, takes the posture data of the most recent valid sampling time before the rockfall event and within the posture data sampling period as the initial posture, calculates the spatial posture vector, obtains the tilt angle difference between the current posture and the initial posture, and constructs a tilt angle difference sequence in chronological order; the control processor traverses the tilt angle difference sequence, counts the number of consecutively exceeding threshold points among adjacent sampling points, and when the number of consecutively exceeding threshold sampling points reaches a preset counting threshold, it is determined that a drastic posture change corresponding to the rockfall event has occurred within the first time window, and the determination result is used as a prerequisite for subsequent identification of suspected unconsciousness.

5. The intelligent control method for a safety helmet based on real-time pressure sensing and damping regulation according to claim 1, characterized in that, In step 5, determining that the safety helmet is stationary based on the maximum change in the attitude angle time series and the inter-frame difference of the image includes: after the rockfall event is confirmed to have occurred and the time of the event is recorded as described in the previous steps, the control processor performs joint analysis on the attitude data output by the inertial measurement unit and the image sequence acquired by the camera within a preset second time window; within the second time window, the control processor reads the attitude data at each sampling time sequentially with the same fixed sampling period as the first time window, extracts the pitch angle and roll angle attitude angles respectively, calculates the maximum change in the attitude angle time series within the second time window, and uses the maximum change as the attitude change; the control processor reads the image frames within the second time window in the order of the image acquisition frame rate, performs frame difference calculation, feature point matching or optical flow estimation on adjacent image frames, and performs cumulative average or maximum value statistics on the index throughout the second time window; when the attitude change is less than a preset second angle threshold and the image change is less than a preset image threshold, the control processor determines that the overall attitude of the safety helmet remains basically unchanged within the second time window and the surrounding scene does not undergo significant movement or displacement, and defines this state as the helmet being stationary.

6. The intelligent control method for a safety helmet based on real-time pressure sensing and damping regulation according to claim 1, characterized in that, When it is determined that the worker is suspected of being unconscious, the control processor controls the mask drive mechanism to close the mask and controls the oxygen supply component to deliver oxygen into the safety helmet. It also sends an alarm message containing the wearer's identification and location information to an external terminal via the wireless communication module.

7. The intelligent control method for a safety helmet based on real-time pressure sensing and damping regulation according to claim 1, characterized in that, The active reset operation includes at least one of the following: the operator triggers a reset signal by pressing a button located on the outside of the safety helmet shell; the operator issues a preset voice command, and the control processor acquires the digital voice signal through the audio acquisition module and recognizes the voice command through a voice recognition algorithm to generate a reset signal.

8. The intelligent control method for a safety helmet based on real-time pressure sensing and damping regulation according to claim 1, characterized in that, The alarm message includes the worker's identification, location information, the timestamp of the rockfall event, the peak value of the impact pressure, the tilt angle difference, and the result of the unconsciousness determination. The location information is obtained by interacting with the underground positioning base station or relay node through the wireless communication module. The alarm message is encoded in digital data and transmitted on the wireless communication link.

9. The intelligent control method for a safety helmet based on real-time pressure sensing and damping regulation according to claim 1, characterized in that, The damping buffer structure includes an adjustable damping module, which includes a buffer cavity filled with a compressible medium and a damping control component for changing the effective volume or the flow resistance of the medium within the buffer cavity. The control processor selects a damping level or a continuous damping coefficient based on the steady-state pressure level collected by the pressure sensor and the peak impact pressure of the rockfall event before and after the rockfall event to limit the peak acceleration transmitted to the worker's head from exceeding a safety threshold.

10. The intelligent control method for a safety helmet based on real-time pressure sensing and damping regulation according to claim 1, characterized in that, During long-term operation, the control processor statistically analyzes the peak impact pressure, attitude change, and coma determination results of rockfall events, performs digital statistical analysis on the statistical data, and adaptively adjusts at least one of the dust threshold, the impact threshold, the first angle threshold, and the second angle threshold, as well as the damping parameters of the adjustable damping module based on the analysis results.

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