Intelligent safety belt monitoring method and system based on double air pressure sensors

Abstract

The invention discloses an intelligent safety belt monitoring method and system based on double air pressure sensors, and belongs to the technical field of industrial safety monitoring. The technical problems that an existing mechanical hook sensor is complex in structure and prone to loss, and a traditional air pressure monitoring scheme cannot recognize the irregular hooking state of a hook are solved. The method comprises the following steps: acquiring air pressure in real time and calculating a relative height difference through monitoring modules worn on a safety helmet and a hook; extracting a motion correlation characteristic value of the relative height difference in the time window; when the relative motion state of the hook and the human body is judged to be a high-correlation following motion state, the alarm triggering threshold value is tightened; and when it is determined that the state is a low-correlation independent hooking state, the alarm triggering threshold value is relaxed. According to the invention, the signal processing technology is utilized, the accurate recognition of the hooking state is realized on the premise of not changing the mechanical structure of the hook, and the environment adaptability and the measurement stability of the system are improved by combining the common-mode rejection and dynamic calibration technology.

Description

Technical Field

[0001] This invention relates to the field of industrial safety monitoring technology, specifically to a method and system for monitoring and identifying fall prevention during high-altitude operations using a microelectromechanical system (MEMS) barometric pressure sensor. Background Technology

[0002] In high-altitude work scenarios, proper suspension of the safety belt is crucial to ensuring the safety of workers. Current safety regulations require workers to "suspend the belt high and use it low." However, in actual operations, it is common for the belt to be improperly suspended or suspended too low.

[0003] Existing intelligent seatbelt monitoring technologies are mainly divided into two categories:

[0004] The first type is the contact detection solution, which involves installing a microswitch or Hall sensor at the hook latch. While this solution can detect whether the hook is closed, it requires mechanical modification to the hook, compromising its structural integrity. Furthermore, the sensor is highly susceptible to damage from impacts and wear in harsh construction environments, leading to high maintenance costs.

[0005] The second category is height detection solutions based on air pressure. Among them, the dual-air pressure solution (helmet + hook) determines the suspension position by measuring the relative height. However, existing technologies have significant drawbacks: first, they cannot identify "non-independent attachment" behavior (commonly known as "false attachment"), such as when a worker holds the hook to their chest or hangs it on clothing. In this case, the relative height difference may meet the requirements, but the hook is not actually attached to a sturdy object; second, air pressure sensors are easily affected by ambient temperature drift and sudden changes in air pressure (such as gusts of wind or opening and closing doors), resulting in a high false alarm rate.

[0006] Therefore, there is an urgent need for a monitoring system that does not rely on easily damaged mechanical sensors, can effectively identify non-independent connection states, and has strong anti-interference capabilities. Summary of the Invention

[0007] The main objective of this invention is to provide an intelligent seat belt monitoring method and system based on dual air pressure sensors, in order to solve the technical problems of short lifespan and high cost of mechanical sensors in the prior art, as well as the inability of traditional air pressure monitoring schemes to identify irregular attachment states.

[0008] To achieve the above objectives, this invention provides an intelligent seatbelt monitoring method based on dual barometric pressure sensors. This method is based on the principles of physical kinematics, utilizing the differences in the correlation between the hook and the human body's movement under different attachment states for state identification.

[0009] The specific technical solution includes: the system collects air pressure data between the helmet end and the hook end in real time and calculates the relative height difference. Furthermore, the system analyzes the statistical fluctuation characteristics (such as variance or standard deviation) of this relative height difference within a time window.

[0010] When a worker holds the hook in their hand or hangs it on their body, the hook and the body move in sync, resulting in minimal fluctuations in their relative height difference. In response to this, the system automatically adjusts its logic, tightening alarm thresholds (e.g., requiring the hook height to be at or above head height) to prevent improper hooking.

[0011] When workers attach the hook to the fixed crossbeam, the worker moves while the hook remains relatively stationary, resulting in an "independent attachment state" and significant fluctuations in the relative height difference. To address this, the system relaxes the alarm threshold (e.g., allowing the hook to be above waist level) to accommodate normal work activities.

[0012] Furthermore, this invention combines dynamic zero-point calibration technology based on gait frequency analysis with environmental interference suppression technology based on common-mode signal characteristics, further improving the robustness of the system. Beneficial effects

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] 1. Improved monitoring accuracy and anti-counterfeiting capabilities: By introducing relative motion correlation analysis, abnormal states of "following motion" (i.e., handheld or hanging) were effectively identified, filling the technical blind spot of non-contact monitoring.

[0015] 2. Reduced hardware costs and maintenance difficulty: This invention completely eliminates the easily damaged mechanical switch structure, relying solely on a robust and durable MEMS barometric pressure sensor. Through algorithm upgrades, it achieves the sensing of the connection status, significantly extending the device's lifespan. This invention trades computing resources for hardware resources, demonstrating significant economic benefits in the current technological context where computing power is abundant but sensors and mechanical structures are expensive.

[0016] 3. Enhanced environmental adaptability: Through common-mode signal suppression and dynamic zero-point calibration, the false alarm problem of barometric pressure sensors in complex airflow environments and the temperature drift problem during long-term operation are effectively solved, thus improving the user experience. Attached Figure Description

[0017] Figure 1 This is the main flowchart of the method provided in the embodiments of the present invention;

[0018] Figure 2 This is a schematic diagram comparing the signal characteristics of the following motion state and the independent attachment state in an embodiment of the present invention;

[0019] Figure 3 This is a system structure block diagram provided in the embodiments of the present invention;

[0020] Figure 4 This is a schematic diagram illustrating the principle of environmental interference suppression logic in an embodiment of the present invention. Detailed Implementation

[0021] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0022] Example 1

[0023] like Figure 3 As shown, this embodiment provides an intelligent seat belt monitoring system, including a first air pressure monitoring module, a second air pressure monitoring module, and a data processing unit.

[0024] The first barometric pressure monitoring module is installed on the safety helmet and is used to collect first barometric pressure data that characterizes the height of the human head.

[0025] The second air pressure monitoring module is installed in a non-functional area (such as the base) of the seatbelt hook to collect second air pressure data characterizing the hook's height. This module has a fully enclosed structure and does not contain sensors for detecting hook mechanical deformation or the state of the latch.

[0026] The data processing unit is built into the second air pressure monitoring module and is used to execute the status recognition algorithm.

[0027] Example 2

[0028] like Figure 1 As shown, the monitoring method in this embodiment includes the following core steps:

[0029] Step 1: Common-mode interference elimination. The system monitors the rate of change of air pressure values ​​of the two modules in real time. When a jump in the same direction with an amplitude exceeding a preset threshold (e.g., 15Pa) is detected within a very short time (e.g., 100ms), it is determined to be a sudden change in ambient airflow. The system maintains the state output of the previous moment and pauses the judgment logic until the air pressure stabilizes.

[0030] Step Two: Dynamic Zero-Point Calibration. The system performs frequency domain analysis on the air pressure data. When a periodic signal is detected in the frequency range of 1.2Hz to 2.5Hz, it is determined that the operator is in a moving state. In this state, based on statistical regularity (the hook is at the waist when moving), the system calculates the deviation between the current measurement value and the theoretical reference value, and dynamically adjusts the zero-point offset in small steps to eliminate the cumulative error caused by temperature drift.

[0031] Step 3: Motion State Recognition and Threshold Adjustment. When in a non-moving working state, the system calculates the standard deviation σ of the relative height difference over the last 3 seconds.

[0032] If σ is less than the preset first characteristic threshold (e.g., 5cm), it indicates that the hook is synchronized with the height of human movement and is judged as "following movement state". At this time, the system determines that the operator may not have hung the hook on a fixed object, so the alarm threshold is adjusted to the first height threshold (e.g., 0cm, which requires the height to be the same as the top of the head).

[0033] If σ is greater than the preset second characteristic threshold (e.g., 10cm), it indicates that the hook has separated from the human body and is determined to be in an "independent hooking state". At this time, the system determines that the hook has been hooked to a fixed object and adjusts the alarm threshold to the second height threshold (e.g., 75cm, which means that the hook is allowed to be higher than the waist).

[0034] Step 4: Integral Alarm Control. The system uses an integral pool mechanism to filter the alarm signal. When the relative height difference continuously exceeds the currently set alarm threshold, the integral value increases; conversely, it decreases. An audible and visual alarm is triggered only when the integral value exceeds the alarm upper limit.

[0035] Summary statement

[0036] The method provided by this invention achieves intelligent sensing of seat belt engagement status without increasing mechanical complexity by deeply exploring the dynamic statistical characteristics of air pressure signals. It resolves the contradiction between cost, lifespan and accuracy in traditional solutions, and has significant technological progress and application value.

Claims

1. A smart seatbelt monitoring method based on dual barometric pressure sensors, characterized in that, Includes the following steps: Step S1: Obtain first air pressure data through the first air pressure monitoring module and second air pressure data through the second air pressure monitoring module. The first air pressure monitoring module is configured at the head position of the worker, and the second air pressure monitoring module is configured at the safety belt hook position. Step S2: Process the first air pressure data and the second air pressure data to calculate the relative height difference between them; Step S3: Based on a preset time window, calculate the time series statistical feature value of the relative height difference. The time series statistical feature value is used to characterize the degree of motion coupling between the second air pressure monitoring module and the first air pressure monitoring module. Step S4: Compare the time series statistical feature values ​​with the preset state determination benchmark to determine the current connection state mode; If the time series statistical feature value is in the first numerical range, it is determined to be in a following motion state, and the alarm judgment threshold is set to the first height threshold. If the time series statistical feature value is in the second numerical range, it is determined to be an independent connection state, and the alarm judgment threshold is set to the second height threshold. Step S5: Monitor the relative height difference according to the set alarm judgment threshold. When the relative height difference exceeds the allowable safety range, generate an alarm control signal. The safe allowable range corresponding to the second height threshold is wider than the safe allowable range corresponding to the first height threshold.

2. The method according to claim 1, characterized in that, The time series statistical feature value includes at least one of the standard deviation, variance, or cross-correlation coefficient of the relative height difference; The following motion state indicates that the safety belt hook and the worker's body remain relatively stationary or move synchronously. The independent hooking state indicates that the safety belt hook is fixed to an external object while the worker's body is in a state of relative motion. The first height threshold is configured to correspond to a vertical height difference less than or equal to zero; the second height threshold is configured to correspond to a vertical height difference less than or equal to a preset relaxation distance.

3. The method according to claim 1, characterized in that, Following step S1, an environmental interference suppression step is also included: Calculate the first rate of change of the first air pressure data and the second rate of change of the second air pressure data; Determine whether the first rate of change and the second rate of change simultaneously exceed a preset common-mode mutation threshold within a preset synchronous determination period; If so, it is determined to be a sudden change in ambient air pressure. The alarm judgment threshold and alarm status of the previous moment are maintained, and the lockout time preset for steps S3 to S5 is suspended.

4. The method according to claim 1, characterized in that, It also includes a dynamic zero-point calibration step: Perform frequency domain feature analysis on the first or second air pressure data; When a periodic component within a preset frequency range is detected in the data, it is determined to enter the mobile mode; In the movement mode, the current average relative height difference is calculated, and the deviation between the average relative height difference and the preset reference height is calculated; The calculation benchmark for the relative height difference is compensated and corrected based on the deviation.

5. The method according to claim 4, characterized in that, The preset frequency range is 1.2Hz to 2.5Hz, and the preset reference height is configured to correspond to the vertical height difference of the human waist position.

6. The method according to claim 1, characterized in that, In step S5, the alarm control signal is generated using an integral decision-making strategy: Establish a state integration unit; When the relative height difference exceeds the alarm determination threshold, the value of the state integration unit is controlled to change towards the alarm trigger value at a first rate. When the relative height difference does not exceed the alarm judgment threshold, the value of the state integration unit is controlled to change towards the reset value at a second rate; When the value of the state integration unit reaches the alarm trigger value, an alarm activation signal is output.

7. An intelligent seatbelt monitoring system based on dual air pressure sensors, characterized in that, include: The first air pressure monitoring module is suitable for wearing on the head of the operator to collect first air pressure data; The second air pressure monitoring module is suitable for installation on the seat belt hook and is used to collect second air pressure data; The processor is connected to the first air pressure monitoring module and the second air pressure monitoring module; Memory, which stores computer programs; An alarm device used to issue warning signals; When the processor executes the computer program, it implements the method as described in any one of claims 1 to 6.

8. The system according to claim 7, characterized in that, The second air pressure monitoring module adopts a fully enclosed packaging structure and does not contain electromechanical switching components for detecting mechanical contact status.

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