High-altitude operation safety early warning method and system monitored by double-point barometer

Abstract

The invention discloses a high-altitude operation safety early warning method and system monitored by a double-point barometer, and relates to the technical field of safety early warning, and the method comprises the steps: analyzing and deploying a standard pressure point according to a high-altitude operation scene, and building a main-auxiliary linkage relation between the standard pressure point and a safety equipment pressure meter; collecting a vertical barometric gradient in real time, and constructing a barometric value curve and a barometric gradient curve; the biological state sensing layer is connected with the safety equipment and is used for monitoring biological state index characteristics of a wearer; constructing a personnel perception data pair; and carrying out high-altitude operation risk analysis to obtain a risk coefficient, and carrying out personnel safety early warning feedback. According to the invention, the technical problem of low reliability and accuracy of high-altitude operation risk early warning caused by insufficient correlation analysis of the state of operating personnel and environmental change in the prior art is solved, accurate analysis and early warning of the high-altitude operation risk are realized through combination of double-point air pressure monitoring and biological state sensing, and the high-altitude operation risk early warning efficiency is improved. And the safety and reliability of high-altitude operation are improved.

Description

Technical Field

[0001] This invention relates to the field of safety early warning technology, specifically to a method and system for high-altitude operation safety early warning using dual-point barometer monitoring. Background Technology

[0002] In the field of high-altitude operations, sudden incidents such as falls from heights often have serious consequences. Traditional safety warning methods mostly rely on single equipment status monitoring or manual inspections, which suffer from problems such as delayed warnings and insufficient accuracy. For example, judging whether a fall has occurred solely based on changes in the tension of the safety rope is insufficient to distinguish between normal work actions and dangerous fall conditions; relying solely on biosensors to monitor the physiological state of personnel cannot be combined with key information such as changes in the height of the working environment, which easily leads to false alarms or missed alarms. In addition, the environments of different high-altitude work scenarios vary greatly, and the movement states of workers are complex and diverse. Existing warning methods lack effective handling of scenario adaptability and movement interference, resulting in a need to improve the reliability and accuracy of safety warnings.

[0003] Existing technologies suffer from insufficient analysis of the correlation between the status of workers and changes in the environment, resulting in low reliability and accuracy of risk warnings for high-altitude operations. Summary of the Invention

[0004] This application provides a method and system for high-altitude operation safety early warning using dual-point barometer monitoring, which addresses the technical problem of insufficient correlation analysis between the status of workers and environmental changes in existing technologies, resulting in low reliability and accuracy of high-altitude operation risk early warning.

[0005] In view of the above problems, this application provides a method and system for high-altitude operation safety early warning by dual-point barometer monitoring.

[0006] The first aspect of this application provides a method for high-altitude work safety early warning monitoring using a dual-point barometer, the method comprising:

[0007] Based on the high-altitude operation scenario, standard pressure points are analyzed and deployed, and a primary and secondary linkage relationship is established between the standard pressure points and the pressure gauges of the safety equipment. Based on this primary and secondary linkage relationship, the vertical air pressure gradient is collected in real time to construct air pressure value curves and air pressure gradient curves. The biological state perception layer of the safety equipment is connected to monitor the biological state index characteristics of the wearer. The air pressure value curves and air pressure gradient curves are aligned with the biological state index characteristics to construct a personnel perception data pair. Based on the personnel perception data pair, high-altitude operation risk analysis is performed to obtain risk coefficients, and personnel safety early warning feedback is provided according to the risk coefficients.

[0008] A second aspect of this application provides a high-altitude work safety early warning system monitored by a dual-point barometer, the system comprising:

[0009] The system includes a master-slave linkage establishment module, used to analyze and deploy standard pressure points according to high-altitude operation scenarios, and establish a master-slave linkage relationship between the standard pressure points and the pressure gauges of safety equipment; a vertical air pressure gradient acquisition module, used to acquire vertical air pressure gradients in real time based on the master-slave linkage relationship, and construct air pressure value curves and air pressure gradient curves; a biological state index feature monitoring module, used to connect to the biological state perception layer of the safety equipment and monitor the biological state index features of the wearer; a personnel perception data pair construction module, used to align the air pressure value curves and air pressure gradient curves with the biological state index features to construct personnel perception data pairs; and a safety early warning feedback module, used to perform high-altitude operation risk analysis based on the personnel perception data pairs, obtain risk coefficients, and provide personnel safety early warning feedback according to the risk coefficients.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Based on the high-altitude operation scenario, standard pressure points are deployed and a primary / secondary linkage relationship is established between the standard pressure points and the pressure gauges of safety equipment. Vertical air pressure gradients are collected in real time to construct air pressure value curves and air pressure gradient curves. The bio-sensing layer of the safety equipment is connected to monitor the bio-state index characteristics of the wearer. Personnel perception data pairs are constructed. Based on these personnel perception data pairs, high-altitude operation risk analysis is performed to obtain risk coefficients, and personnel safety early warning feedback is provided according to these risk coefficients. This achieves the technical effect of accurately analyzing and warning of high-altitude operation risks through the combination of dual-point air pressure monitoring and bio-state perception, thereby improving the safety and reliability of high-altitude operations. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic flowchart of a high-altitude operation safety early warning method using a dual-point barometer monitoring system provided in an embodiment of this application.

[0014] Figure 2 This is a schematic diagram of the structure of a high-altitude operation safety early warning system with dual-point barometer monitoring provided in an embodiment of this application.

[0015] Figure 3 This is a schematic diagram of the air pressure value curve and air pressure gradient curve in the high-altitude operation safety early warning method monitored by a dual-point barometer provided in the embodiments of this application.

[0016] Figure labeling: Module 10 for establishing main and secondary linkage relationship, Module 20 for vertical air pressure gradient acquisition, Module 30 for monitoring biological state index characteristics, Module 40 for constructing personnel perception data pairs, and Module 50 for safety early warning feedback. Detailed Implementation

[0017] This application provides a method and system for high-altitude operation safety early warning by monitoring with a dual-point barometer, which addresses the technical problem of insufficient correlation analysis between the status of workers and environmental changes in existing technologies, resulting in low reliability and accuracy of high-altitude operation risk early warning.

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] Example 1, as Figure 1 As shown, this application provides a high-altitude operation safety early warning method using dual-point barometer monitoring, the method comprising:

[0020] Step S100: According to the high-altitude operation scenario, analyze and deploy standard pressure points, and establish the main and auxiliary linkage relationship between standard pressure points and safety equipment pressure gauges.

[0021] Specifically, based on the high-altitude operation scenario, the height distribution relationship is first analyzed to determine the highest point, lowest point, and intermediate platform heights to establish a height node distribution. Then, based on the operator's work process and movement path, key identification areas such as the work activity area and hazardous areas are identified. Simultaneously, environmental condition parameters are acquired and their impact on air pressure detection is analyzed. Finally, based on the aforementioned height node distribution, key identification areas, and air pressure detection impact, the platform height with the best monitoring stability for the key identification areas is determined as the low point, i.e., the vertical distribution detection position, which serves as the standard pressure point. On this basis, a standard... The standard pressure point and safety equipment, such as the pressure gauges on safety helmets and safety harnesses, are linked in a primary and secondary manner. For the selection of pressure gauges on safety equipment, the MS5611, BMP280, and SCP1000 barometers are suitable options. The MS5611 barometer is the preferred choice due to its ±1.5 hPa measurement accuracy, fast response, and low power consumption, making it suitable for real-time pressure data acquisition in complex high-altitude environments. The BMP280 barometer offers strong stability and lower cost, while the SCP1000 barometer boasts excellent anti-interference capabilities. The appropriate choice depends on the accuracy requirements of the work scenario and the intensity of environmental interference. The standard pressure point serves as the primary pressure monitoring point, and the pressure gauges on the safety equipment act as secondary pressure monitoring points. These points communicate in real-time via a data transmission link. The primary pressure monitoring point provides a reference pressure for the secondary pressure monitoring point, and the pressure data collected by the secondary monitoring point is compared and linked with the reference data from the primary monitoring point.

[0022] Step S200: Based on the main-supplement linkage relationship, the vertical pressure gradient is collected in real time to construct the pressure value curve and the pressure gradient curve.

[0023] Specifically, based on the established primary and secondary linkage relationship between the standard pressure point (primary pressure monitoring point) and the pressure gauge on the safety equipment (secondary pressure monitoring point), the primary pressure monitoring point provides reference air pressure data through the real-time communication link between the primary and secondary pressure monitoring points, while the secondary pressure monitoring point synchronously collects real-time air pressure data at the location of the operator. The two exchange and synchronize data at preset time intervals, such as 1 second per time, to ensure consistent data collection timing. Based on the air pressure data collected by the main and auxiliary pressure gauges, the pressure difference between the two at the same time point is calculated and divided by the operating height to obtain the vertical pressure gradient data. Then, with time as the horizontal axis and air pressure as the vertical axis, the air pressure data collected by the main and auxiliary pressure gauges are connected sequentially according to the collection time to construct an air pressure curve reflecting the dynamic change of air pressure over time, including the air pressure curves at the main pressure point and the auxiliary pressure point. Simultaneously, with time as the horizontal axis and the vertical pressure gradient as the vertical axis, the calculated vertical pressure gradient data are arranged sequentially and connected to generate a pressure gradient curve, fully presenting the real-time changing trend of air pressure and pressure gradient during high-altitude operations. Figure 3As shown, data interaction and synchronization are achieved through a real-time communication link between the main and auxiliary pressure monitoring points. Data is collected every second at preset time intervals. The main pressure monitoring point provides stable reference air pressure data, which is basically stable at 1013.0±0.2hPa within 0-600 seconds. The auxiliary pressure monitoring point synchronously collects real-time air pressure data at the location of the operator. The air pressure varies with altitude: 1012.0hPa at 0 seconds and 1008.0hPa at 600 seconds and 30 meters. The vertical air pressure gradient is calculated based on the air pressure data at the same time point of both points. The gradient at 0 seconds is 0.1hPa / m, and the gradient at 600 seconds is approximately 0.167hPa / m. Then, with time as the horizontal axis and air pressure value as the vertical axis, the data from the main and auxiliary pressure gauges are connected in time sequence to construct an air pressure value curve containing sub-curves of the main and auxiliary pressure values. With time as the horizontal axis and the vertical air pressure gradient as the vertical axis, the gradient data are connected in time sequence to generate an air pressure gradient curve, which fully presents the real-time change trend of air pressure and gradient during the operation.

[0024] Examples of primary and secondary pressure values ​​and vertical pressure gradient data are shown in Table 1:

[0025] Table 1: Main and Secondary Pressure Values ​​and Vertical Pressure Gradient Data

[0026] Data collection time (seconds) Main pressure value (hPa) Secondary pressure value (hPa) Vertical pressure gradient (hPa / m) 0 1013 1012 0.1 50 1013 1011.8 0.109 100 1013.1 1011.5 0.123 120 1013.1 1011 0.14 150 1013 1011 0.133 180 1012.9 1011 0.127 200 1012.9 1010.8 0.131 250 1013 1010.5 0.147 300 1013 1010 0.15 350 1012.9 1009.8 0.148 400 1013 1009.5 0.152 450 1012.9 1009 0.156 500 1012.9 1008.5 0.163 550 1013 1008.2 0.166 600 1013 1008 0.167

[0027] Step S300: Connect the bio-state perception layer of the safety device to monitor the bio-state index characteristics of the wearer.

[0028] Specifically, the process first connects to the built-in bio-state sensing layer of safety equipment such as helmets and safety harnesses via a data interface. This sensing layer is a collection of sensors integrated into the safety equipment to collect physiological and motion data of the wearer, including both physiological and motion sensors. Specifically, the physiological sensors on the helmet can collect the wearer's heart rate, while the motion sensors can collect motion acceleration and equipment displacement coordinates. Similarly, the physiological sensors on the safety harness can also assist in collecting physiological data such as heart rate, and the motion sensors can also collect motion acceleration and equipment displacement coordinates. Physiological sensors, such as heart rate sensors, can be installed inside the helmet where they contact the skin, or inside the safety harness near the torso or limbs, to accurately acquire physiological data such as heart rate. Motion sensors, such as acceleration and displacement sensors, can be installed on the top of the helmet, or on the buckles or webbing of the safety harness, to stably collect motion acceleration and equipment displacement coordinates. After acquiring the above data, bio-indicator features are constructed based on the relationship of heart rate changes. Motion state and posture-related features are analyzed based on motion acceleration and equipment displacement coordinates. Then, a temporal correspondence between the three is established, ultimately yielding the bio-state index features.

[0029] Step S400: Align the air pressure curve and air pressure gradient curve with the biological state index features to construct a personnel perception data pair.

[0030] Specifically, the air pressure curve and air pressure gradient curve are first fused to construct an air pressure time-series change curve. Then, using the timestamp of the data collection by the safety equipment as a benchmark, a time-series alignment relationship is established between the air pressure time-series change curve and the biological state index characteristics. Based on this, the mapping data pairs between air pressure changes and biological state characteristic changes are extracted. In the process, continuous change window features of motion state characteristics, air pressure change characteristics, and biological indicator characteristics are established through experimental sample data. Based on this feature, the air pressure time-series change curve and biological indicator characteristics are matched to identify the motion influence feature window and correct the air pressure value and biological indicator. Finally, the mapping data pairs are extracted using the corrected time-series alignment relationship, that is, the personnel perception data pairs are constructed.

[0031] Step S500: Based on the personnel perception data, perform high-altitude operation risk analysis to obtain risk coefficients, and provide personnel safety early warning feedback according to the risk coefficients.

[0032] Specifically, based on the constructed personnel perception data pairs, an amplitude fluctuation relationship between air pressure gradient and biological indicator characteristics and motion state characteristics is established according to the temporal change relationship. The high-altitude fluctuation coefficient is determined according to this amplitude fluctuation relationship, and the fluctuation difference value is obtained by comparing it with the target coefficient threshold. The risk coefficient is then calculated by the ratio of the fluctuation difference value to the target coefficient threshold. At the same time, an air pressure gradient descent threshold is set by combining the standard air pressure difference of the main and secondary linkage relationship and the air pressure difference constrained by the change of the operation mode. When the air pressure gradient change rate reaches the threshold and the duration reaches the time threshold, a high-risk warning is generated. Finally, based on the risk coefficient and the above warning conditions, corresponding safety warning feedback is given to high-altitude workers.

[0033] In one possible implementation, step S100 further includes:

[0034] Step S110: The safety equipment includes: a safety helmet and a safety rope.

[0035] Specifically, the safety equipment includes a safety helmet and a safety rope, both of which integrate built-in sensors related to a pressure gauge and a bio-state sensing layer. The pressure gauge works in conjunction with a standard pressure point to achieve primary and secondary linkage, participating in the real-time acquisition of vertical air pressure gradients. The built-in sensors include, but are not limited to, physiological sensors on the safety helmet that can collect the wearer's heart rate, and motion sensors that can collect motion acceleration and equipment displacement coordinates. Physiological sensors, such as heart rate sensors, can be installed inside the safety helmet where they contact the skin, or inside the safety rope near the torso or limbs, to accurately acquire physiological data such as heart rate. Motion sensors, such as acceleration sensors and displacement sensors, can be installed on the top of the safety helmet, or on the buckles or webbing of the safety rope, to stably collect motion acceleration and equipment displacement coordinates, serving as an important hardware carrier for high-altitude work safety monitoring and early warning.

[0036] In one possible implementation, step S100 further includes:

[0037] Step S120: Analyze the height distribution relationship of the high-altitude operation scenario to determine the vertical distribution detection position, which is the relative detection position that meets the sensing requirements and the height difference of the operation.

[0038] Step S130: Use the low point as the standard pressure point based on the vertical distribution detection position.

[0039] Specifically, the process begins by collecting complete height distribution information for high-altitude operations, including the minimum baseline height, maximum operating height, critical platform heights, and frequency of operations within each height range. Examples include platform distribution at 10m, 20m, and 30m in building exterior wall work, and maintenance locations at 15m and 25m on power poles during inspections. This spatial distribution relationship at each height level is then analyzed through on-site mapping. Next, based on the sensing range of the sensors (e.g., the effective detection radius of barometric pressure sensors, signal transmission distance, and accuracy requirements, such as a height detection error ≤0.5m), and in conjunction with… In industrial scenarios, key height differences that need to be monitored include, for example, a 10m height interval between adjacent work platforms and a 5m warning height difference in hazardous work areas. Locations that can cover the entire working height range and meet the requirements for effective sensor data collection are selected. Finally, vertically distributed detection positions are determined. For example, starting from the working reference plane, detection positions are set at key heights such as 10m, 20m, and 30m at intervals of 5m or 10m. At the same time, it is ensured that the height difference between adjacent detection positions meets the requirements for continuous sensing, and that each detection position can accurately correspond to the actual height area of ​​the operator's operation.

[0040] Among the established vertically distributed detection positions, based on the height distribution characteristics and detection requirements of high-altitude work scenarios, points at relatively low positions are selected and designated as standard pressure points. These low points must meet the sensing conditions required by the vertically distributed detection positions and form an effective working height difference with other detection positions, serving as a reference point for air pressure monitoring. This provides a stable reference benchmark for subsequently establishing a primary and secondary linkage with the pressure gauges of safety equipment and for real-time acquisition of vertical air pressure gradients.

[0041] In one possible implementation, step S120 further includes:

[0042] Step S121: Determine the highest and lowest points of the high-altitude operation scenario, as well as the platform heights involved in between, and establish a height node distribution.

[0043] Step S122: Identify the work activity area and hazardous area based on the operator's work process and movement path, and determine the key identification area.

[0044] Step S123: Obtain the operating environment condition parameters and analyze their impact on air pressure detection.

[0045] Step S124: Based on the height node distribution, key identification area, and air pressure detection influence, determine the platform height with the best monitoring stability for the key identification area as the low point position, and obtain the vertical distribution detection position.

[0046] Specifically, the first step is to identify the specific high-altitude operation scenario, such as building construction, power tower maintenance, and bridge maintenance. Different scenarios differ due to environmental characteristics and operational requirements. Based on this, an on-site investigation is conducted for each scenario to determine the highest point within the work area, such as the rooftop in building construction and the top of the power tower in power tower maintenance; the lowest point, such as the ground in building construction and the base of the power tower in power tower maintenance; and the height of intermediate platforms where personnel may stay or pass during the operation, such as floor platforms in building construction and intermediate maintenance platforms in power tower maintenance. This height information system is then integrated to establish a complete height node distribution, providing basic data support for the subsequent determination of vertical distribution detection positions.

[0047] Based on the work processes of personnel in high-altitude work scenarios, such as installation and maintenance steps in construction, or inspection and operation links and actual movement paths in maintenance, such as routes to and from different work platforms, the frequency and trajectory of personnel activities are analyzed to identify areas with frequent personnel activity during the operation. At the same time, dangerous areas with safety risks are identified by combining the characteristics of the scenario, such as windy locations with strong winds and platform edges, which are prone to fall risks. Finally, these areas with frequent personnel activity, dangerous areas, and other areas that require key monitoring are integrated to determine the key identification areas.

[0048] By deploying environmental sensors in high-altitude work scenarios, real-time environmental condition parameters are acquired. These parameters cover various environmental factors that may affect air pressure detection. Based on the air pressure detection principle and historical data, the accuracy requirements of air pressure sensing under the dual-point barometer monitoring system are first referenced to meet the error threshold for synchronous data acquisition by the main and auxiliary linkage pressure gauges. Influence weights are then assigned to key environmental parameters such as wind force, temperature, and humidity. For example, wind force is weighted at 0.4 because it easily causes instantaneous fluctuations in local air pressure; temperature affects the characteristics of the barometer sensing element and is weighted at 0.3; humidity has a long-term impact on sensor stability and is weighted at 0.2. The remaining 0.1 weight is allocated to other secondary factors. A quantitative assessment model is then constructed, comparing the real-time measured values ​​of each environmental parameter with the baseline values ​​under standard environmental conditions (temperature 25℃, wind speed ≤1m / s, humidity 40%~60%). The parameter deviation rate is calculated, and a weighted sum is performed based on the corresponding weights to obtain the quantitative value of the impact of each environmental condition on air pressure detection. For example, when the wind speed reaches 6m / s, the deviation rate is 30%, and the corresponding quantitative value of the impact is 0.12; when the temperature deviation is 5℃, the deviation rate is 20%, and the corresponding quantitative value of the impact is 0.06. This clarifies the specific numerical values ​​of the impact of different environmental factors on air pressure detection, providing a basis for ensuring the accuracy of the data collected by the dual-point barometer main and auxiliary linkage.

[0049] Based on the established height node distribution, the identified key identification areas, and the analyzed impact of air pressure detection, the platform height with the best monitoring stability for the key identification areas is selected from the height nodes as the low point, thus obtaining the vertical distribution detection positions. Under normal circumstances, the standard position is set on the ground. However, when the working scene is at a high altitude, has obstructions, or has poor vertical relationship, the middle position can be selected as the standard position to ensure the sensitivity and monitoring effect of air pressure difference. This ensures that the determined vertical distribution detection positions can stably and accurately support subsequent air pressure monitoring work.

[0050] In one possible implementation, step S300 further includes:

[0051] Step S310: Connect the built-in sensor of the safety device to obtain the wearer's heart rate, motion acceleration, and device displacement coordinates.

[0052] Step S320: Construct biometric features based on the heart rate changes of the wearer.

[0053] Step S330: Based on the motion acceleration and equipment displacement coordinates, analyze the worker's motion state and related body characteristics.

[0054] Step S340: Establish the temporal correspondence between the biological indicator features, movement state features and body posture-related features to obtain the biological state index features.

[0055] Specifically, by connecting to the sensors built into safety equipment such as helmets and safety harnesses via data connection, the system can acquire real-time data on the wearer's heart rate, acceleration, and device displacement coordinates. This data will serve as the foundation for constructing biometric features and analyzing movement and posture-related characteristics, providing raw information support for comprehensive monitoring of the wearer's condition.

[0056] The heart rate data of the wearer, acquired through the built-in sensors of the safety device, is continuously tracked and analyzed, with a focus on the dynamic relationship of heart rate changes over time, including the fluctuation range, frequency of change, timing and duration of peak and trough values ​​in different work stages. By extracting key characteristic parameters of these heart rate changes, such as the baseline value of resting heart rate, the average increase in heart rate during work, and the magnitude of sudden heart rate changes, a biometric feature that can objectively reflect the changes in the physiological state of the wearer is constructed.

[0057] The motion acceleration data is filtered to remove environmental interference factors such as airflow disturbances and slight equipment vibrations in high-altitude work scenarios, ensuring data accuracy and eliminating environmental interference. The motion intensity parameter is obtained by calculating the amplitude change of the acceleration vector, and the motion direction is determined by combining the component changes of acceleration in the three-dimensional coordinate system. This allows for the analysis of the worker's motion state, such as stationary, uniform movement, or accelerated movement. Simultaneously, parameters such as curvature and slope of the movement trajectory are calculated based on the temporal changes of the equipment displacement coordinates. Combined with the abrupt changes in motion acceleration, indicators such as body tilt angle and center of gravity shift are identified. This allows for the analysis of posture-related features, such as whether a stable standing posture is maintained and whether there is a tendency to lose balance. Ultimately, this achieves accurate extraction of the worker's motion state and posture characteristics.

[0058] Using the timestamp of the data collected by the safety device as a unified benchmark, the biometric features constructed through heart rate changes, the motion state features based on motion acceleration analysis, and the body posture-related features obtained by combining the device displacement coordinates are time-series aligned to establish a correspondence between the three on the same time dimension. That is, the biometric features at a certain moment are associated and matched with the motion state features and body posture-related features at that moment. By integrating these features with time-series correspondence, a biometric state index feature that can comprehensively reflect the physiological state, movement status, and body posture of the wearer at different time points is formed.

[0059] In one possible implementation, step S400 further includes:

[0060] Step S410: Perform feature fusion on the pressure value curve and the pressure gradient curve to construct a pressure time series change curve.

[0061] Step S420: Based on the data collection timestamp of the security device, establish the time-series alignment relationship between the air pressure time-series change curve and the biological state index characteristics.

[0062] Step S430: Based on the time alignment relationship, extract the mapping data pairs of air pressure change-biological state characteristic change.

[0063] Specifically, the air pressure curve is aligned with the air pressure gradient curve using timestamps. A sliding time window is used to extract the common trend of change between the two curves within the window, and key feature points are identified and their corresponding time coordinates are marked simultaneously. Then, the correlation parameter of the rate of change of the two curves is calculated using the Pearson correlation coefficient, and effective data segments with a correlation ≥ 0.8 are selected to eliminate abnormal fluctuations. Subsequently, a weighted feature superposition algorithm is used, with the average of the primary and secondary pressure values ​​as the base term of the air pressure value and a weight of 0.6. The gradient change rate is calculated by the ratio of the gradient difference to the time interval and standardized to the range of air pressure values. The weight of the standardized gradient change rate is set to 0.4. The two are superimposed to form a fused feature value. Finally, the fused feature values ​​of each window are concatenated in chronological order to generate a continuous air pressure time series change curve.

[0064] Extract the collection timestamps generated by safety equipment, such as safety helmets and safety harnesses, when collecting biological state-related data such as heart rate, acceleration, and equipment displacement coordinates, and use these timestamps as a time reference. Simultaneously, obtain the timestamps corresponding to the barometric pressure curves and barometric pressure gradient curves during the construction of the barometric pressure time-series change curve. Use a timestamp matching algorithm to accurately associate the timestamps of the barometric pressure time-series change curve with the collection timestamps of the safety equipment, ensuring that the barometric pressure time-series change data and the biological state index feature data at the same time point form a one-to-one mapping relationship. This establishes a synchronous alignment relationship between the two in the time dimension, laying the foundation for time-series consistency for subsequent extraction of mapping data pairs.

[0065] The time-aligned dataset is retrieved, which has precisely linked each fused feature value of the air pressure time-series change curve (e.g., 1012.58 hPa at 0 seconds, 1012.47 hPa at 100 seconds) to the corresponding biological state characteristic data at that time point, namely, the worker's heart rate, blood oxygen saturation, and respiratory rate. Then, data is extracted point-by-point at fixed time intervals, consistent with the air pressure data acquisition interval (1 second / extraction). Using the single-time-point air pressure fused feature value - corresponding biological state characteristic parameter group as the core structure, data without missing values ​​or... The effective time points for abnormal fluctuations are as follows: at 0 seconds, the mapping relationship between the barometric pressure fusion feature value 1012.58 hPa and the heart rate 72 beats / min, blood oxygen saturation 98%, and respiratory rate 16 breaths / min is extracted; at 100 seconds, the mapping relationship between the barometric pressure fusion feature value 1012.47 hPa and the heart rate 75 beats / min, blood oxygen saturation 97%, and respiratory rate 17 breaths / min is extracted; and so on, to complete the extraction of data for the entire cycle from 0 to 600 seconds, ultimately forming a complete set of mapped data pairs containing timestamps, barometric pressure change data, and biological state characteristic change data.

[0066] In one possible implementation, step S430 further includes:

[0067] Step S431: Using experimental sample data, establish a continuous change window feature of motion state characteristics, air pressure change characteristics, and biological indicator characteristics, which is used to characterize the temporal relationship of air pressure change characteristics and biological indicator characteristics changes within the continuous motion state window period.

[0068] Step S432: Based on the continuous change window feature, perform feature change matching on the air pressure time series change curve and biological indicator feature, and combine the mapped motion state feature and body posture related feature to identify the motion influence feature window in the air pressure time series change curve and biological indicator feature.

[0069] Step S433: Correct the air pressure value and biological indicators within the motion influence feature window for motion influence, and extract the mapping data pair by using the corrected air pressure time series change curve and the biological state index feature time series alignment relationship.

[0070] Specifically, experimental sample data from high-altitude work scenarios are collected. This data covers the movement characteristics of workers in different motion states, such as stationary, moving, bending, and jumping, as well as corresponding air pressure change characteristics, such as rises and falls in air pressure values ​​and fluctuations in gradients, and biological indicators, such as changes in heart rate. Based on these experimental sample data, continuous time windows are divided, and the dynamic change patterns of movement state characteristics, air pressure change characteristics, and biological indicator characteristics within each window are extracted. The correlation pattern of the three within the continuous movement state window period is established to form a continuous change window feature. This clearly represents the temporal relationship between air pressure change characteristics and biological indicator characteristics as movement state changes within the window period, providing a basis for subsequent feature matching and interference identification.

[0071] By matching the time-series change curve of air pressure with the characteristics of biological indicators, and comparing the correlation between the two in terms of change trend, amplitude fluctuation and time node, the characteristic range that may be affected by motion can be preliminarily located. Simultaneously, by combining motion state characteristics analyzed from motion acceleration and equipment displacement coordinates, such as motion intensity, speed, and posture-related characteristics, such as body tilt angle and center of gravity position, the above intervals are precisely verified to identify feature windows affected by motion in the air pressure time-series change curve and biometric characteristics. The specific identification process is as follows: Since changes in the worker's posture and motion state (such as jumping, bending over, etc.) directly cause changes in air pressure, as detected by the barometer, the identification process needs to specifically incorporate characteristic parameters of typical non-falling actions: For rapid bending / squatting actions, by detecting the characteristic of a rapid but short-duration increase in Δh (height change), combined with data such as the accelerometer feedback of the body's center of gravity accelerating downwards with an acceleration >0g (not reaching weightlessness), and the controllable pitch angle change displayed by the gyroscope, the motion impact feature window corresponding to this action can be identified. Because this window lacks continuous weightlessness and a rapid overall height decrease, it is determined that no warning will be triggered; for jumping actions, by capturing the take-off... The system identifies a sequence of motion impact characteristics, including instantaneous vertical acceleration >> 1g (overweight) with a brief decrease in Δh (body curling up), acceleration ≈ 0g during the airborne phase (brief weightlessness) with a rapid increase in Δh, and acceleration >> 1g during the landing phase (high-g impact) with a rapid decrease in Δh. It also verifies key parameters such as the entire motion duration < 1 second, the weightlessness phase < 0.3 seconds conforming to a fixed "overweight-weightlessness-overweight" pattern, and the landing impact time matching expectations. The corresponding motion impact characteristic window is identified. Because the weightlessness time is too short and conforms to a jump pattern, it is determined that no warning will be triggered. For falls (without suspension), by monitoring changes in Δh and acceleration impact signals, combined with data such as the lack of a continuous weightlessness phase due to the body not completely leaving the support surface, and complex changes in vertical acceleration due to sliding or collision (non-free fall characteristics), the corresponding motion impact characteristic window is identified. This window may trigger or partially trigger a warning, requiring algorithm adjustments based on the specific scenario. In cases of severe falls, even without suspension, alarm judgment should be included. Through the above multi-dimensional feature matching and motion parameter verification, accurate identification of motion-affecting feature windows can be achieved.

[0072] First, based on the identified motion-affected feature window, the range of air pressure and biometric data within the window affected by worker movement is defined. For air pressure, considering the worker's motion characteristics within the window, such as exercise intensity and direction, and postural characteristics like body tilt angle and center of gravity shift, the deviation caused by motion in air pressure detection is calculated by referring to the air pressure interference patterns corresponding to the same type of motion in historical experimental samples. This deviation is then subtracted from the original air pressure value within the window to correct for the motion-induced impact on air pressure. For biometrics, based on the correlation between motion characteristics and changes in biometrics within the window, such as the normal fluctuation range of heart rate at a specific exercise intensity, abnormal biometric data caused by motion are removed to obtain the corrected biometrics. After the correction is completed, the time-series alignment relationship between the corrected air pressure time-series change curve and the biological state index characteristics is reconfirmed based on the data collection timestamp of the safety equipment. This ensures that the air pressure data and biological state data at the same time point are accurately matched. Then, from the aligned dataset, the air pressure change characteristics and biological state characteristic change data corresponding to each time point are extracted one by one to form multiple sets of air pressure change-biological state characteristic change mapping data pairs.

[0073] In one possible implementation, step S500 further includes:

[0074] Step S510: Based on the personnel perception data pair, establish the amplitude fluctuation relationship between air pressure gradient and biological indicator characteristics and motion state characteristics according to the time sequence change relationship.

[0075] Step S520: Determine the high-altitude fluctuation coefficient based on the amplitude fluctuation relationship, and compare the high-altitude fluctuation coefficient with the target coefficient threshold to obtain the fluctuation difference value.

[0076] Step S530: Obtain the risk coefficient based on the ratio of the volatility difference value to the target coefficient threshold.

[0077] Specifically, based on personnel-perceived data pairs—that is, time-series aligned mapping data pairs of air pressure changes and biological state characteristic changes—the amplitude change parameters of the air pressure gradient in the data pairs are extracted in chronological order. These parameters include the gradient value at each moment, the gradient difference between adjacent moments, and the gradient fluctuation amplitude per unit time. Simultaneously, the amplitude fluctuation data of biological indicators under the same time series are extracted, namely, the real-time value of heart rate, the amplitude of heart rate rise and fall, the frequency of heart rate fluctuations, and the amplitude parameters of motion state characteristics, namely, the magnitude of motion acceleration, the rate of acceleration change, and the amplitude of displacement coordinate changes. By arranging these parameters along the time axis, the synchronicity, consistency of change trends, and numerical correlation of air pressure gradient amplitude fluctuations with the amplitude fluctuations of biological indicators and motion state characteristics at time points are analyzed. This establishes the amplitude fluctuation relationship among the three in time-series changes. For example, first extract parameters of pressure gradient amplitude change, such as 0.02 hPa / m in the 1st second, 0.03 hPa / m in the 2nd second, and 0.01 hPa / m in the 3rd second, as well as the difference between adjacent time points (the 2nd second is +0.01 hPa / m from the 1st second) and the fluctuation amplitude per unit time (0.02 hPa / m in the first 3 seconds); then extract simultaneous biological indicators, taking heart rate as an example: 75 beats / minute in the 1st second, 82 beats / minute in the 2nd second, and 78 beats / minute in the 3rd second, as well as the rise and fall amplitude (the 2nd second is +7 beats / minute from the 1st second) and the fluctuation frequency (2 times in the first 3 seconds); and also extract motion status, taking acceleration as an example: 0.2 m / s² in the 1st second, 0.8 m / s² in the 2nd second, and 0.4 m / s² in the 3rd second, as well as the acceleration / deceleration value (the 2nd second is +0.6 m / s² from the 1st second) and displacement change (0.5 m horizontal / 0.3 m vertical). Arranged along the time axis, it can be seen that when the air pressure gradient increases sharply in the 2nd second, the heart rate and acceleration increase in the same step; when the air pressure gradient decreases in the 3rd second, the heart rate and acceleration decrease synchronously. This establishes the amplitude fluctuation relationship among the three, clearly showing the correlation pattern between air pressure gradient and biological and kinematic characteristics in dynamic changes.

[0078] Based on the established relationship between the amplitude fluctuations of pressure gradients and biological indicators and motion characteristics, three types of parameters are first extracted: the rate of change of the amplitude of pressure gradient fluctuations, the peak deviation of the fluctuations of biological indicators, and the frequency of the fluctuations of motion characteristics. Then, according to the degree of influence of different parameters on the safety risks of high-altitude operations, corresponding weights are assigned to the three types of parameters. The rate of change of the amplitude of pressure gradient fluctuations has the highest weight, followed by the peak deviation of the fluctuations of biological indicators, and the frequency of the fluctuations of motion characteristics has the lowest weight. By multiplying each parameter by its corresponding weight and summing the results, the high-altitude fluctuation coefficient, which comprehensively characterizes the stability of high-altitude operation status, is calculated. Then, the preset target coefficient threshold is invoked. This threshold is determined in combination with the high-altitude operation safety regulations and historical safety case data. For example, for high-altitude climbing operation scenarios, the preset target coefficient threshold is 1.2 for low risk, 2.5 for medium risk, and 4.0 for high risk. The calculated high-altitude fluctuation coefficient is compared with the corresponding target coefficient threshold. The difference between the two is obtained by subtracting the target coefficient threshold from the high-altitude fluctuation coefficient. This is used to quantify the degree of deviation between the actual operation fluctuation and the safety standard fluctuation.

[0079] The risk coefficient is calculated by using the difference between the obtained fluctuation difference value (i.e., the difference between the high-altitude fluctuation coefficient and the target coefficient threshold) as the numerator and the target coefficient threshold as the denominator. The ratio of these two values ​​quantifies the relative degree of deviation of actual operational fluctuations from safety standards. The magnitude of the risk coefficient directly reflects the risk level of high-altitude operations, which can be divided into three categories: low, medium, and high. Low risk corresponds to a risk coefficient < 0.3, where the actual operational fluctuations deviate slightly from safety standards, the operational status is stable, and only continuous data monitoring is required. Medium risk corresponds to 0.3 ≤ risk coefficient ≤ 0.8, where actual operational fluctuations exceed the slight deviation range, posing potential risks. A yellow alert needs to be activated, and operators should be reminded to check equipment and adjust the operational rhythm. High risk corresponds to a risk coefficient > 0.8, where actual operational fluctuations severely deviate from safety standards, posing significant risks and hazards. A red alert needs to be activated immediately, and operations should only resume after hazard investigation. All levels are determined based on the risk coefficient (the ratio of the fluctuation difference value to the target coefficient threshold).

[0080] In one possible implementation, step S500 further includes:

[0081] Step S540: Based on the standard pressure difference of the main and auxiliary linkage relationship and the pressure difference constrained by the change of the operation mode, set the pressure gradient descent threshold.

[0082] Step S550: When the monitored rate of change of air pressure gradient reaches the air pressure gradient descent threshold and the duration reaches the time threshold, a high-risk warning is generated, wherein the time threshold is determined by the duration of air pressure change through the body movement during operation.

[0083] Specifically, the standard air pressure difference is first calculated through the primary and secondary linkage relationship and used as a benchmark reference value. Then, for different work modes, air pressure difference data generated by various typical actions in that mode, such as walking upright, bending over, squatting, lying down, and jumping, are collected in advance. For work modes that mainly involve walking upright, the variable constraint air pressure difference is set to a small value because the range of motion is small. If the air pressure difference exceeds this range, such as when squatting, it is considered a potential risk. For work modes that require large movements such as bending over and squatting, the maximum constraint threshold is determined based on the collected maximum air pressure difference data. Finally, by combining the standard air pressure difference and the variable constraint air pressure difference corresponding to the work mode, a corresponding air pressure gradient descent threshold is set through weighted calculation to ensure that the threshold can distinguish between normal work actions and abnormal states, and can also adapt to the needs of different work modes.

[0084] The system tracks the rate of change of the air pressure gradient in real time and continuously compares it with a preset air pressure gradient descent threshold. When the rate of change of the air pressure gradient reaches this threshold, a timing mechanism is immediately activated to record the duration of this rate of change. The time threshold is predetermined based on the patterns of air pressure change duration corresponding to various body movements during operations, such as bending over, squatting, jumping, and potential falls. If the duration of the air pressure gradient rate of change reaching the threshold exceeds this time threshold, a high-risk situation such as a potential fall from height is identified, and a high-risk warning signal is generated to promptly notify relevant personnel to take emergency measures.

[0085] Example 2, based on the same inventive concept as the high-altitude operation safety early warning method monitored by the dual-point barometer in the previous examples, such as... Figure 2 As shown, this application provides a high-altitude operation safety early warning system monitored by a dual-point barometer. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0086] The main-supplement linkage relationship establishment module 10 is used to analyze and deploy standard pressure points according to high-altitude operation scenarios, and establish the main-supplement linkage relationship between standard pressure points and safety equipment pressure gauges.

[0087] The vertical pressure gradient acquisition module 20 is used to acquire the vertical pressure gradient in real time based on the main-supplement linkage relationship, and to construct the pressure value curve and the pressure gradient curve.

[0088] The bio-state index feature monitoring module 30 is used to connect to the bio-state perception layer of the safety device and monitor the bio-state index features of the wearer.

[0089] The personnel perception data pair construction module 40 is used to align the air pressure value curve and air pressure gradient curve with the biological state index characteristics to construct personnel perception data pairs.

[0090] The safety early warning feedback module 50 is used to perform high-altitude operation risk analysis based on the personnel perception data, obtain risk coefficients, and provide personnel safety early warning feedback according to the risk coefficients.

[0091] Furthermore, the system is also used to implement the following functions:

[0092] The safety equipment includes: a safety helmet and a safety rope.

[0093] Furthermore, the system is also used to implement the following functions:

[0094] Based on the height distribution relationship of the high-altitude operation scenario, the vertical distribution detection positions are determined. The vertical distribution detection positions are relative detection positions that meet the sensing requirements and the height difference of the operation. The low point is used as the standard pressure point according to the vertical distribution detection positions.

[0095] Furthermore, the system is also used to implement the following functions:

[0096] The highest and lowest points of the high-altitude operation scenario, as well as the platform heights involved in between, are determined to establish a height node distribution. Based on the operator's work process and movement path, the operation activity area and danger zone are identified, and key identification areas are determined. Operation environment condition parameters are obtained, and their impact on air pressure detection is analyzed. Based on the height node distribution, key identification areas, and air pressure detection impact, the platform height with the best monitoring stability for key identification areas is determined as the low point, thus obtaining the vertical distribution detection position.

[0097] Furthermore, the system is also used to implement the following functions:

[0098] The built-in sensors of the safety device are connected to acquire the wearer's heart rate, motion acceleration, and device displacement coordinates; based on the heart rate changes of the wearer, biometric features are constructed; based on the motion acceleration and device displacement coordinates, the worker's motion state and body posture-related features are analyzed; a temporal correspondence between the biometric features, motion state features, and body posture-related features is established to obtain the biometric state index features.

[0099] Furthermore, the system is also used to implement the following functions:

[0100] The pressure value curve and pressure gradient curve are fused to construct a pressure time series change curve; based on the data collection timestamp of the safety equipment, a time series alignment relationship between the pressure time series change curve and the biological state index features is established; based on the time series alignment relationship, the mapping data pairs of pressure change and biological state feature change are extracted.

[0101] Furthermore, the system is also used to implement the following functions:

[0102] Using experimental sample data, a continuous change window feature is established to represent the temporal relationship between the characteristics of motion state, air pressure change, and biological indicators. This feature characterizes the temporal relationship between air pressure change and biological indicator changes within a continuous motion state window. Based on the continuous change window feature, feature change matching is performed on the temporal change curve of air pressure and biological indicators. Combining the mapped motion state features and body posture-related features, motion influence feature windows are identified in the temporal change curve of air pressure and biological indicators. The motion influence of air pressure values ​​and biological indicators within the motion influence feature window is corrected. The mapped data pairs are extracted using the temporal alignment relationship between the corrected temporal change curve of air pressure and biological state index features.

[0103] Furthermore, the system is also used to implement the following functions:

[0104] Based on the personnel perception data pairs, an amplitude fluctuation relationship between air pressure gradient and biological indicator characteristics and motion state characteristics is established according to the temporal change relationship; according to the amplitude fluctuation relationship, the upper-level fluctuation coefficient is determined, and the fluctuation difference value is obtained by comparing the upper-level fluctuation coefficient with the target coefficient threshold; according to the ratio of the fluctuation difference value to the target coefficient threshold, the risk coefficient is obtained.

[0105] Furthermore, the system is also used to implement the following functions:

[0106] Based on the standard pressure difference of the main and auxiliary linkage relationship and the pressure difference constrained by the change of the operation mode, a pressure gradient descent threshold is set; when the monitored pressure gradient change rate reaches the pressure gradient descent threshold and the duration reaches the time threshold, a high-risk warning is generated, wherein the time threshold is determined by the duration of pressure change through the body movement during operation.

[0107] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0108] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0109] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A high-altitude work safety early warning method using dual-point barometer monitoring, characterized in that, include: Based on the high-altitude operation scenario, analyze the deployment of standard pressure points and establish the main and auxiliary linkage relationship between standard pressure points and safety equipment pressure gauges; Based on the aforementioned primary-secondary linkage relationship, the vertical pressure gradient is collected in real time to construct pressure value curves and pressure gradient curves. A bio-state sensing layer connected to security devices monitors the bio-state index characteristics of the wearer. Align the air pressure curve and air pressure gradient curve with the biological state index characteristics to construct a human perception data pair; Based on the personnel perception data, a risk analysis of high-altitude operations is conducted to obtain a risk coefficient, and personnel safety early warning feedback is provided according to the risk coefficient.

2. The high-altitude operation safety early warning method based on dual-point barometer monitoring according to claim 1, characterized in that, The safety equipment includes: a safety helmet and a safety rope.

3. The high-altitude operation safety early warning method based on dual-point barometer monitoring according to claim 1, characterized in that, The analysis and deployment of standard pressure points according to high-altitude operation scenarios includes: Based on the height distribution relationship of the high-altitude operation scenario, the vertical distribution detection positions are determined. The vertical distribution detection positions are relative detection positions that meet the sensing requirements and the height difference of the operation. Based on the vertical distribution of detection points, the lowest point is used as the standard pressure point.

4. The high-altitude operation safety early warning method based on dual-point barometer monitoring according to claim 2, characterized in that, Based on the height distribution relationship of the high-altitude operation scenario, the vertical distribution detection positions are determined, including: Determine the highest and lowest points of the high-altitude operation scenario, as well as the platform heights involved in between, and establish a height node distribution; Based on the workers' work processes and movement paths, identify the work activity areas and hazardous areas, and determine the key identification areas; Obtain operational environmental condition parameters and analyze their impact on air pressure detection. Based on the height node distribution, key identification area, and air pressure detection influence, the platform height with the best monitoring stability for the key identification area is determined as the low point, thus obtaining the vertical distribution detection position.

5. The high-altitude operation safety early warning method based on dual-point barometer monitoring according to claim 2, characterized in that, The biometric sensing layer connected to the security device monitors the wearer's biometric characteristics, including: Connect the built-in sensors of the safety device to obtain the wearer's heart rate, motion acceleration, and device displacement coordinates; Based on the heart rate changes of the wearers, biometric features were constructed. Based on the motion acceleration and equipment displacement coordinates, analyze the worker's motion state and related body characteristics; Establish the temporal correspondence between the aforementioned biological indicator features, movement state features, and body posture-related features to obtain the aforementioned biological state index features.

6. The high-altitude operation safety early warning method based on dual-point barometer monitoring according to claim 5, characterized in that, Align the air pressure curve and air pressure gradient curve with the biological state index characteristics to construct a human perception data pair, including: The pressure value curve and the pressure gradient curve are fused to construct a pressure time series variation curve. Based on the data collection timestamps from the security equipment, a time-series alignment relationship is established between the air pressure time-series change curve and the biological state index characteristics; Based on the aforementioned temporal alignment relationship, a mapping data pair of air pressure change and biological state characteristic change is extracted.

7. The high-altitude operation safety early warning method based on dual-point barometer monitoring according to claim 6, characterized in that, Extracting mapping data pairs between changes in air pressure and changes in biological state characteristics also includes: Using experimental sample data, a continuous change window feature of motion state characteristics, air pressure change characteristics, and biological indicator characteristics was established to characterize the temporal relationship of air pressure change characteristics and biological indicator characteristics changes within a continuous motion state window period. Based on the continuous change window feature, feature change matching is performed on the air pressure time series change curve and biological indicator feature. Combined with the mapped motion state feature and body posture related feature, motion influence feature window in air pressure time series change curve and biological indicator feature is identified. The air pressure value and biological indicators within the motion influence feature window are corrected for motion influence. The mapping data pair is extracted by using the alignment relationship between the corrected air pressure time series change curve and the biological state index feature time series.

8. The high-altitude operation safety early warning method based on dual-point barometer monitoring according to claim 1, characterized in that, Based on the aforementioned personnel perception data, a risk analysis of high-altitude operations is conducted to obtain risk coefficients, including: Based on the aforementioned personnel perception data pairs, an amplitude fluctuation relationship between air pressure gradient and biological indicator characteristics and motion state characteristics is established according to the temporal change relationship. Based on the amplitude fluctuation relationship, the high-altitude fluctuation coefficient is determined, and the fluctuation difference value is obtained by comparing the high-altitude fluctuation coefficient with the target coefficient threshold. The risk coefficient is obtained based on the ratio of the volatility difference value to the target coefficient threshold.

9. The high-altitude operation safety early warning method based on dual-point barometer monitoring according to claim 1, characterized in that, Also includes: Based on the standard pressure difference of the main and auxiliary linkage relationship and the pressure difference constrained by the change of the operation mode, a pressure gradient descent threshold is set. When the monitored rate of change of air pressure gradient reaches the air pressure gradient descent threshold and the duration reaches the time threshold, a high-risk warning is generated. The time threshold is determined by the duration of air pressure change through the body's movement during the operation.

10. A high-altitude work safety early warning system monitored by a dual-point barometer, characterized in that, The system is used to implement the high-altitude operation safety early warning method based on dual-point barometer monitoring according to any one of claims 1-9, the system comprising: The module for establishing the primary and secondary linkage relationship is used to analyze and deploy standard pressure points according to high-altitude operation scenarios, and establish the primary and secondary linkage relationship between the standard pressure points and the pressure gauges of safety equipment. The vertical pressure gradient acquisition module is used to acquire the vertical pressure gradient in real time based on the main-supplement linkage relationship, and to construct the pressure value curve and the pressure gradient curve. The bio-state index feature monitoring module is used to connect to the bio-state perception layer of the safety device and monitor the bio-state index features of the wearer. The personnel perception data pair construction module is used to align the air pressure value curve and air pressure gradient curve with the biological state index characteristics to construct personnel perception data pairs. The safety early warning feedback module is used to perform high-altitude operation risk analysis based on the personnel perception data, obtain risk coefficients, and provide personnel safety early warning feedback according to the risk coefficients.

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