Real-time monitoring response method and platform for a fall arrest device

Abstract

The present application relates to the field of anti-falling monitoring, and more particularly to a real-time monitoring response method and platform for an anti-falling device. The method comprises the following steps: continuously collecting information about workers based on the anti-falling device to generate a baseline sequence of working states; calculating a real-time falling rate peak value of the baseline sequence of working states; comparing the real-time falling rate peak value with a preset safety falling threshold value; when the real-time falling rate peak value is greater than a first-level safety falling threshold value, activating a slow locking mechanism of the anti-falling device; and when the real-time falling rate peak value is greater than a second-level safety falling threshold value, activating an emergency locking mechanism of the anti-falling device. The present application actively intervenes in high-altitude workers and provides full-process safety protection, thereby improving the continuity and safety of high-altitude work.

Description

Technical Field

[0001] This invention relates to the field of fall protection monitoring, and more particularly to a real-time monitoring and response method and platform for fall protection devices. Background Technology

[0002] With the rapid development of high-altitude work industries such as construction, power maintenance, and wind power operation and maintenance, the safety risks faced by high-altitude workers are becoming increasingly prominent. Traditional high-altitude work safety measures, such as safety ropes, manual safety locks, and personal protective equipment, can reduce fall accidents to some extent, but they suffer from insufficient real-time monitoring capabilities, delayed risk warnings, and reliance on manual operation. In actual work, the worker's body posture, vertical height changes, and horizontal displacement can undergo complex changes depending on environmental conditions and work actions; even a minor mistake can trigger a serious fall accident. Therefore, how to achieve real-time monitoring and timely risk warnings for high-altitude workers has become a key issue in ensuring workplace safety.

[0003] Existing fall arrest devices mostly rely on mechanical limiters and passive locking mechanisms, typically only locking after a fall has occurred, lacking dynamic recognition and early intervention capabilities for worker behavior. Traditional devices cannot continuously and accurately monitor worker status, making it difficult to effectively identify sudden abnormal behavior, postural imbalances, or rapid descent trends. While some intelligent fall arrest devices incorporate sensor monitoring and warning functions, they largely remain at the basic alarm level, failing to achieve multi-dimensional behavioral feature fusion, risk level assessment, and automated response locking. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a real-time monitoring and response method and platform for fall arrest devices, thereby resolving at least one of the aforementioned technical issues.

[0005] To achieve the above objectives, the present invention provides a real-time monitoring and response method for a fall arrest device, comprising the following steps: Based on fall protection devices, continuous data is collected from workers to generate a baseline sequence of work status. Based on the work status baseline sequence, work behavior is evaluated to obtain work behavior patterns; the work behavior patterns include normal descent work characteristics, parallel movement characteristics, and unexpected abnormal trend characteristics.

[0006] When the operation behavior pattern is an unexpected abnormal trend, calculate the peak value of the real-time descent rate of the operation status baseline sequence; compare the deviation of the peak value of the real-time descent rate according to the preset safety descent threshold; when the peak value of the real-time descent rate is greater than the first-level safety descent threshold, activate the slow locking mechanism of the fall arrest device; when the peak value of the real-time descent rate is greater than the second-level safety descent threshold, activate the emergency locking mechanism of the fall arrest device.

[0007] This specification provides a real-time monitoring and response platform for a fall arrestor, used to execute the real-time monitoring and response method for a fall arrestor as described above, including: The data acquisition unit is used to continuously collect data on workers based on fall protection devices and generate a work status baseline sequence. The evaluation unit is used to evaluate the work behavior based on the work status baseline sequence to obtain the work behavior pattern; the work behavior pattern includes normal descent work characteristics, parallel movement characteristics, and unexpected abnormal trend characteristics.

[0008] The locking control unit is used to: calculate the real-time descent rate peak of the work status baseline sequence when the work behavior mode is an unexpected abnormal trend; compare the real-time descent rate peak with the preset safety descent threshold; activate the slow locking mechanism of the fall arrest device when the real-time descent rate peak is greater than the first-level safety descent threshold; and activate the emergency locking mechanism of the fall arrest device when the real-time descent rate peak is greater than the second-level safety descent threshold.

[0009] The specific benefits of this invention are as follows: It accurately characterizes the dynamic behavioral features of workers, making the device more sensitive to minute changes in movement and sudden falls. By forming a standardized, continuous, and stable operational state benchmark sequence, it not only provides a reliable data foundation for subsequent behavioral pattern analysis but also ensures continuity and traceability during the monitoring process. It achieves multi-dimensional behavioral recognition, distinguishing between normal working states and potentially risky behaviors. The identification of normal descent and parallel movement characteristics prevents misjudgments of normal working actions, reduces false alarms, and improves the targeting and efficiency of the system response; while the determination of unexpected abnormal trend characteristics can promptly detect risks in the early stages of vertical acceleration or posture imbalance, providing triggering conditions for safety intervention. By monitoring real-time rate peaks and comparing them with threshold deviations, the severity of falls can be accurately determined, avoiding injuries caused by delayed responses, while minimizing interference with normal operations. This strategy ensures that the high-altitude work fall protection device can provide both gradual intervention for minor anomalies and immediate, rigid protection against highly dangerous falls, achieving dynamic intelligent management of high-altitude work safety. It provides highly reliable protection against sudden and rapid falls, ensuring the safety of workers. At the same time, it uploads key data to the cloud. Through the combined use of these two-level locking mechanisms, the device achieves a dynamic safety protection strategy, balancing timely protection and operational continuity, and improving the overall safety level and intelligent early warning capabilities of high-altitude operations. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of a real-time monitoring and response method for a fall arrest device according to the present invention. Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a flowchart illustrating the detailed implementation steps of step S2. Detailed Implementation

[0011] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0012] This application provides a real-time monitoring and response method and platform for a fall arrestor. The execution entities of the real-time monitoring and response method and platform for the fall arrestor include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of an audio-visual management system, an information management system, and a cloud-based data management system.

[0013] Please see Figures 1 to 3 This invention provides a real-time monitoring and response method for fall arrest devices, comprising the following steps: Based on fall protection devices, continuous data is collected from workers to generate a baseline sequence of work status. Based on the work status baseline sequence, work behavior is evaluated to obtain work behavior patterns; the work behavior patterns include normal descent work characteristics, parallel movement characteristics, and unexpected abnormal trend characteristics.

[0014] When the operation behavior pattern is an unexpected abnormal trend, calculate the peak value of the real-time descent rate of the operation status baseline sequence; compare the deviation of the peak value of the real-time descent rate according to the preset safety descent threshold; when the peak value of the real-time descent rate is greater than the first-level safety descent threshold, activate the slow locking mechanism of the fall arrest device; when the peak value of the real-time descent rate is greater than the second-level safety descent threshold, activate the emergency locking mechanism of the fall arrest device.

[0015] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of a real-time monitoring and response method for a fall arrest device according to the present invention. In this example, the steps of the real-time monitoring and response method for the fall arrest device include: Based on fall protection devices, continuous data is collected from workers to generate a baseline sequence of work status. In this embodiment, during high-altitude operations, the fall arrestor continuously and frequently collects data on the worker's movement status. The sampling frequency is set to 100Hz, meaning 100 sets of multi-dimensional monitoring data are collected per second. Through the device's internal height sensor, inertial measurement unit, and displacement estimation module, the vertical height change of the worker relative to the ground reference, the three-dimensional descent rate per unit time, body posture angle change parameters, and horizontal displacement components are acquired in real time. Using the initial work platform as a reference, the height change of the worker at each sampling point is continuously measured, generating a height change time series. Combining triaxial acceleration and angular velocity data, and using attitude calculation and velocity estimation methods, the three-dimensional descent rate per unit time is obtained, and the amplitude and rate of change of pitch angle, roll angle, and yaw angle are recorded to reflect the worker's posture stability and body balance. Differential calculations are performed on the horizontal displacement to obtain the horizontal displacement component, which is used to describe the worker's lateral movement trajectory on the work surface. After outlier removal and standardization, multidimensional indicators such as height variation variance, peak descent rate, number of attitude angle abrupt changes, and horizontal displacement trajectory curvature are combined in chronological order to form a continuous, stable, and behaviorally applicable operational status baseline sequence. Based on the work status baseline sequence, work behavior is evaluated to obtain work behavior patterns; the work behavior patterns include normal descent work characteristics, parallel movement characteristics, and unexpected abnormal trend characteristics. In this embodiment, after obtaining the baseline sequence of operational status, multidimensional behavioral feature analysis is performed on the data to extract key indicators reflecting operational behavior and determine operational behavior patterns. By analyzing changes in height, descent rate, attitude angle changes, and horizontal displacement curves, the type of operator behavior is determined. Operational behavior patterns mainly include three categories: normal descent characteristics, parallel movement characteristics, and unexpected abnormal trend characteristics. Normal descent characteristics are characterized by a stable descent rate, with an average descent rate between 0.3 and 0.6 meters per second, a descent amplitude fluctuation of less than 0.2 meters, low attitude angle changes, and horizontal displacement changes within 0.5 meters. Parallel movement characteristics are characterized by a descent amplitude of less than 0.2 meters, a relatively large horizontal displacement (0.5 to 1.5 meters), low rate fluctuations, and few abrupt changes in attitude angle. Unexpected abnormal trend characteristics are characterized by a significant acceleration in descent within a short period, with a peak descent rate reaching 1.5 to 3 meters per second, a rapid increase in descent amplitude, and irregular short-term fluctuations in attitude angle changes.

[0016] When the operation behavior pattern is an unexpected abnormal trend, calculate the peak value of the real-time descent rate of the operation status baseline sequence; compare the deviation of the peak value of the real-time descent rate according to the preset safety descent threshold; when the peak value of the real-time descent rate is greater than the first-level safety descent threshold, activate the slow locking mechanism of the fall arrest device; when the peak value of the real-time descent rate is greater than the second-level safety descent threshold, activate the emergency locking mechanism of the fall arrest device.

[0017] In this embodiment, when the work behavior pattern is determined to have unexpected abnormal trend characteristics, the peak value of the real-time descent rate is calculated on the vertical motion data of the work status baseline sequence to determine the maximum value of the worker's current fall speed. The analysis adopts the short-time sliding window method, performing local rate calculation on the height change data within a continuous period of 50 to 100 milliseconds, with the peak value typically between 1 and 3 meters per second. Subsequently, the peak value of the real-time descent rate is compared with a preset safe descent threshold, which is set as a graded mechanism: the first-level safe descent threshold is typically 1.5 to 2 meters per second, used to judge mild abnormalities or initial fall trends; the second-level safe descent threshold is typically 2.5 to 3 meters per second, used to judge highly dangerous fall states. If the peak value exceeds the first-level safe descent threshold but is lower than the second-level threshold, the slow locking mechanism of the fall arrestor is immediately activated, gradually braking the rope to slow the displacement until it stabilizes. During the locking process, information such as descent rate, rope displacement, and locking force are collected and uploaded to the cloud platform for recording. If the peak real-time descent rate exceeds the level 2 safety descent threshold, the emergency locking mechanism is immediately activated to rigidly brake the rope, achieve rapid stopping, generate a safety warning signal and emergency fall response data, and upload them to the cloud platform to record the entire event process, providing immediate protection for workers and triggering subsequent rescue or alarm procedures.

[0018] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: A sampling frequency of 100Hz is set, and continuous data is collected from workers based on the fall arrest device to extract worker operation monitoring data. The worker operation monitoring data includes the vertical height change of the worker relative to the ground reference, the three-dimensional spatial descent rate per unit time, body posture angle change parameters, and horizontal displacement components.

[0019] The worker operation monitoring data is subjected to data quality verification, and abnormal quality data points are marked; the abnormal quality data points include sensor failure, signal loss or abnormal data jumps. The abnormal quality data points are removed to obtain standardized data; Based on standardized data, the variance of height variation, peak rate, number of attitude angle abrupt changes, and curvature of horizontal displacement trajectory are calculated to generate a baseline sequence of operational status.

[0020] In this embodiment, during the operation of the intelligent fall arrestor for high-altitude operations, the data sampling frequency is set to 100Hz to continuously collect the dynamic behavior of the workers. This sampling frequency can fully capture the subtle posture changes and sudden fall characteristics of the workers in the high-altitude environment while ensuring real-time response requirements. Specifically, through height sensing units, inertial measurement units, and displacement sensing units deployed inside the fall arrestor, multi-dimensional motion parameters of the workers during the operation are acquired synchronously. Using the ground or the initial position of the operation as a height reference, the vertical height change of the workers relative to this reference is continuously measured to form a height change time series. Combining triaxial acceleration and angular velocity information, attitude calculation and velocity estimation methods are used to obtain the descent rate of the workers in three-dimensional space per unit time, which is used to characterize their vertical and composite motion states. Based on this, parameters of the worker's body posture angle changes are extracted, including the amplitude and rate of change of pitch, roll, and yaw angles, to reflect their balance and stability. For horizontal movement, the horizontal displacement component is obtained by differential processing of continuous position estimation results to describe the worker's lateral movement under the constraints of the work surface or safety rope. Multi-dimensional judgment rules are established to check the rationality, continuity, and physical consistency of data at each sampling point, and data points that do not meet the requirements are marked as abnormal. Specifically, based on sensor technical specifications and physical motion constraints, reasonable ranges for height, rate, and posture angle changes are defined; when a sampling point exceeds these ranges, it is judged as abnormal data. For sensor malfunctions, identification is based on whether the data remains unchanged within a certain time or exhibits obvious drift characteristics. For example, if height or posture angle parameters do not change effectively within multiple consecutive sampling periods and are inconsistent with other motion parameters, the data point can be considered abnormal. To address the signal loss problem, the sampling time interval is continuously monitored. When the interval between adjacent sampling points deviates significantly from the normal time interval under 100Hz sampling conditions, it is marked as abnormal data.

[0021] Different processing strategies are adopted based on the type and duration of abnormal quality data points. For isolated anomalies or slightly abrupt data points that occur within a short period, neighborhood interpolation or smoothing substitution is used for correction. This involves using the mean or trend of several normal sampling points before and after the anomaly to reasonably replace the anomaly value, thereby maintaining the continuity of the time series. In practical applications, 5 to 10 sampling points before and after the anomaly are usually selected as a reference interval. For abnormal data segments that last for a long time or are clearly caused by sensor failure, communication interruption, etc., they are directly removed as a whole, and empty segments are marked in the data series to avoid introducing false motion information. After removing abnormal data, the remaining valid data is standardized, including unifying the physical dimensions, normalizing the numerical range, and aligning the time axis. For example, the change in height is uniformly converted into a change in value relative to the initial height, and the descent rate and attitude angle changes are mapped to a preset standard interval to reduce the impact of differences in the body size of different workers or differences in equipment installation. The variance of height change is calculated based on the height change time series to measure the vertical position fluctuation of the worker within the time window. The larger the variance value, the worse the vertical stability. Secondly, peak value analysis is performed on the three-dimensional descent rate data per unit time to extract the peak rate index, which reflects whether the worker is experiencing a rapid descent trend. Simultaneously, the posture angle change data is statistically analyzed to calculate the number of posture angle abrupt changes, i.e., the frequency of events where the posture angle change exceeds a preset threshold within the analysis window, used to characterize whether there is a severe imbalance in body posture. For horizontal displacement data, geometric analysis is performed on the motion trajectory to calculate the curvature of the horizontal displacement trajectory, describing the smoothness of the worker's movement path. When the curvature changes significantly within a short period, it often indicates a risk of slippage or misstepping.

[0022] In this embodiment, see Figure 3 The specific steps for evaluating work behavior based on the work status baseline sequence to obtain the work behavior pattern are as follows: The descent rate and magnitude of workers within a cycle are calculated based on the work status baseline sequence. Based on the stated rate of decline and magnitude of decline, a downward trend assessment is performed to obtain the characteristics of the downward trend fluctuation. The operational behavior is evaluated based on the downward trend fluctuation characteristics to obtain the operational behavior pattern; the operational behavior pattern includes normal downward operation characteristics, parallel movement characteristics, and unexpected abnormal trend characteristics.

[0023] In this embodiment, based on the baseline sequence of work status, the vertical motion characteristics of the worker's work within each analysis cycle are quantified. A cycle length of 2 to 5 seconds is selected, and continuous height change data is acquired at a sampling frequency of 100Hz within each cycle, recording 100 sampling points per second to ensure that minute displacements and sudden descents of the worker during the work process can be captured. By differentiating the height changes of the continuous sampling points, the instantaneous descent rate can be obtained, typically ranging from 0 to 3 meters per second. The descent amplitude, i.e., the cumulative vertical displacement within the cycle, ranges from 0 to 2 meters. During processing, to ensure data continuity and reliability, abnormal data points marked in the previous stage are removed or smoothed, so that the descent rate and amplitude sequence can truly reflect the vertical movement trend of the worker.

[0024] The average and peak descent rates are calculated for the rate sequence. The average descent rate is typically between 0.2 and 0.6 meters per second, while the peak rate can reach 2 to 3 meters per second. The rate of change in descent amplitude is between 0 and 1.5 meters per second. Local trend analysis of rate and amplitude changes using the sliding window method can identify whether the descent behavior is stable, gradually increasing, or suddenly accelerating. Further analysis extracts the characteristics of the descent trend fluctuations, including the range of amplitude fluctuations, the frequency of extreme values, and the duration of fluctuations. The duration of fluctuations is generally 0.2 to 1 second, and the frequency of extreme values ​​is typically 1 to 5 times per cycle. Comprehensive analysis of these indicators can identify whether workers are exhibiting unexpected descent behavior. For example, when the descent amplitude exceeds 0.5 meters within a short period and the peak rate frequently reaches 2 meters per second or more, an abnormal trend is observed. A stable descent amplitude and rate fluctuation within 0.2 to 0.5 meters per second indicates normal descent behavior.

[0025] After obtaining the characteristics of the downward trend fluctuations, worker behavior is categorized to form work behavior patterns. These patterns include normal descent characteristics, parallel movement characteristics, and unexpected abnormal trend characteristics. Normal descent characteristics are characterized by a stable descent rate, typically between 0.3 and 0.6 meters per second, with fluctuations in the descent amplitude not exceeding 0.2 meters, indicating that the worker is descending slowly along a planned trajectory. Parallel movement characteristics are characterized by a descent amplitude of less than 0.2 meters, while the horizontal displacement is between 0.5 and 1.5 meters, with low rate fluctuations, indicating that the worker is primarily moving horizontally while maintaining vertical stability. Unexpected abnormal trend characteristics are characterized by a rapid increase in the descent amplitude within a short period (0.5 to 1 meter within 0.2 seconds), with peak rates reaching 2 to 3 meters per second. Frequent descent trend fluctuations exceeding preset thresholds indicate a risk of fall or instability. By analyzing and judging the downward trend fluctuation characteristics of each cycle, work behavior patterns can be identified in real time.

[0026] In this embodiment, when the work behavior mode exhibits unexpected abnormal trend characteristics; the real-time descent rate peak of the work status baseline sequence is calculated, and the deviation of the real-time descent rate peak is compared with a preset safe descent threshold; when the real-time descent rate peak exceeds the first-level safe descent threshold, the specific steps for activating the slow locking mechanism of the fall arrestor are as follows: When an unexpected abnormal trend is detected in the operation behavior pattern, the peak value of the real-time decline rate of the operation status baseline sequence is calculated. The deviation of the peak real-time descent rate is compared according to a preset safety descent threshold. When the security degradation threshold includes a primary security degradation threshold and a secondary security degradation threshold; When the peak real-time descent rate exceeds the first-level safety descent threshold, it is determined to be a suspected fall. Activate the slow locking mechanism of the fall arrestor to gradually brake the rope displacement and record real-time locking information; The real-time locking information is fed back to the cloud platform for recording.

[0027] In this embodiment, when the work behavior pattern is determined to have unexpected abnormal trend characteristics, the peak descent rate of the vertical motion data in the work status baseline sequence is calculated in real time to quantify the worker's maximum fall speed at that point in time. The peak descent rate is usually measured in meters per second. During the calculation, a short-time sliding analysis is performed using data points 50 to 100 milliseconds before and after the peak to ensure that the peak accurately reflects the sudden descent behavior and filters out errors caused by minor local vibrations. Subsequently, the obtained peak descent rate is compared with a preset safe descent threshold. This safety threshold is set in a graded mechanism, including a first-level safe descent threshold and a second-level safe descent threshold. The first-level safe descent threshold is usually 1.5 to 2 meters per second, indicating a slight abnormality or initial fall trend; the second-level safe descent threshold is usually set at 2.5 to 3 meters per second, indicating a highly dangerous fall state. Through real-time comparison, if the peak exceeds the first-level safe descent threshold but is lower than the second-level threshold, it is determined to be a suspected fall state. At this time, a slow locking response is triggered, and the rope is gradually braked to slow down the fall speed and reduce the impact force without affecting the worker's normal operation, ensuring that the safety protection function takes effect in a timely manner.

[0028] During the activation of the slow locking mechanism, rope displacement is continuously monitored by built-in sensors, with displacement change information collected every 10 milliseconds. The locking force is controlled by a gradual increase in force or a progressive braking device, gradually slowing the rope displacement until it stabilizes, thus ensuring that the worker's body is not injured by a sudden stop. During this process, real-time locking information, including parameters such as peak descent rate, rope locking displacement, locking start time, and duration, is recorded in a structured form and stored locally. Subsequently, this real-time locking information is fed back to the cloud platform via a wireless communication channel, enabling remote recording and historical trajectory archiving, facilitating subsequent safety analysis, behavior pattern optimization, and risk statistics. Through this process, the device can respond quickly to unexpected abnormal descent behavior by workers, assess the fall risk level in real time, and achieve a closed-loop safety protection system of progressive locking and data feedback while ensuring personnel safety.

[0029] In this embodiment, the specific steps for feeding back the real-time locking information to the cloud platform for recording are as follows: The real-time locking information is fed back to the cloud platform for recording, and a safety status request signal is sent to the operators simultaneously. Once the operator confirms safety, the slow locking mechanism is released, restoring the normal locking state.

[0030] In this embodiment, during the gradual braking of the worker's vertical descent by the activation of the slow locking mechanism, real-time locking information must be fed back to the cloud platform immediately for remote recording and safety status monitoring. The collected real-time locking information includes peak descent rate, rope displacement, locking start time, locking duration, and locking force level. Each parameter is measured using a high-precision sensor, with a sampling interval typically between 10 and 20 milliseconds. This data is uploaded to the cloud platform in the form of structured data packets via a wireless communication module, and is timestamped and logged on the platform to ensure complete historical data archiving for each locking event, providing a basis for subsequent safety analysis, behavior pattern evaluation, and device performance optimization. A safety status request signal is sent to the worker's mobile terminal upon information feedback. This signal includes the locking status, current descent rate, and operational risk level. The worker must perform a safety confirmation operation within 5 to 10 seconds of receiving the signal, such as button confirmation or touch confirmation, to confirm that they are in a safe state.

[0031] After the safety of the worker is confirmed, the slow locking mechanism initiates the release process, gradually reducing the rope locking force to normal operating conditions. Specific operations include controlling the gradual release of the locking mechanism to restore the rope displacement to its normal limit range, and gradually reducing the locking force to its original safe load state, ensuring no impact or secondary fall risk occurs during release. The release process is typically completed within 2 to 3 seconds to smoothly transition back to the normal locking state. The rope displacement, locking force recovery curve, and release start and end times are continuously recorded and uploaded to the cloud platform for archiving. This closed-loop process ensures timely safety protection for workers after unexpected abnormal descents and enables intelligent recovery of the slow locking mechanism and worker status confirmation, allowing the fall arrest device for high-altitude operations to operate safely, efficiently, and traceably in real-time risk monitoring and emergency response.

[0032] In this embodiment, the specific steps for activating the emergency locking mechanism of the fall arrestor when the peak real-time descent rate exceeds the secondary safety descent threshold are as follows: When the peak real-time descent rate exceeds the secondary safety descent threshold, the descent trend fluctuation characteristics and the number of attitude angle abrupt changes are extracted for fall confirmation analysis. When the descent trend fluctuation characteristics are linear accelerated descent and the number of attitude angle abrupt changes are short-term irregular changes, it is determined to be an abnormal fall state. Activate the emergency locking mechanism of the fall arrestor to rigidly brake the rope displacement and generate a safety warning signal; Safety warning signals are uploaded to the cloud platform to generate incident logs.

[0033] In this embodiment, when the peak real-time descent rate exceeds the secondary safety descent threshold, a rapid fall confirmation analysis of the worker's vertical motion is required to determine whether an abnormal fall has occurred. The descent trend fluctuation characteristics and the number of attitude angle abrupt changes are extracted. The descent trend fluctuation characteristics are obtained by analyzing a sequence of height changes over a continuous period of 50 to 100 milliseconds, including the rate of change of descent amplitude, acceleration trend, and local linear fitting. The number of attitude angle abrupt changes is determined by collecting the rates of change of pitch, roll, and yaw angles, and counting the number of abrupt events exceeding a preset threshold within the same time window. If the descent trend fluctuation exhibits an approximately linear accelerated descent, i.e., the vertical displacement increases almost linearly with time and the acceleration remains stable between 1.5 and 3 meters per second², and the number of attitude angle abrupt changes is irregular, low-frequency, or intermittent in the short term (usually less than 5 times per second), then the worker is determined to be in an abnormal fall state. This determination process uses multi-dimensional feature fusion for redundancy verification to reduce the risk of misjudgment and completes the analysis within 5 to 10 milliseconds, ensuring that the device can respond promptly during an instantaneous fall.

[0034] Upon confirmation of an abnormal fall, the emergency locking mechanism of the fall arrestor is immediately activated, rigidly braking the rope for rapid cessation. The emergency locking instantly tightens the rope, reducing displacement to zero within 0.05 to 0.1 seconds, generating a high-rigidity locking force to prevent further fall. During braking, the rope's built-in displacement, force, and locking status sensors simultaneously collect data, recording the locking start time, locking completion time, peak locking force, and displacement change curve. The sampling interval is controlled between 5 and 10 milliseconds to ensure traceability of the locking process. The emergency locking triggers a safety warning signal, which can be transmitted to the worker and remote monitoring platform via sound, optical, or wireless means, indicating that an abnormal fall has occurred and initiating a safety response. Through these steps, the device can quickly identify abnormal fall behavior when the secondary safety threshold is triggered, implement rigid braking, and generate a warning signal, providing immediate protection for workers at height and ensuring complete and reliable data recording of fall events for subsequent safety analysis and accident assessment.

[0035] In this embodiment, the specific steps for uploading the safety warning signal to the cloud platform and generating an accident log are as follows: Upload safety warning signals to the cloud platform to generate emergency fall response data; Simultaneously send a safety status request signal to the operators; Set the security response time length; If the operator fails to respond within the safety response time, it is considered a serious safety incident. Send an automatic distress signal to the cloud platform; When the cloud platform receives an automatic distress signal, it triggers a telephone alarm for help; Extract the emergency fall response data and mark it with a timestamp to generate an accident log.

[0036] In this embodiment, after activating the emergency locking mechanism and generating a safety warning signal, the safety warning information is uploaded to the cloud platform to generate emergency fall response data. The uploaded data includes key parameters such as the real-time peak descent rate, rope displacement curve, peak locking force, locking start and end times, and number of attitude angle abrupt changes. Each data point has a high-precision timestamp, and the sampling interval is controlled between 5 and 10 milliseconds to ensure data integrity and accuracy. Once the emergency fall response data is uploaded, the cloud platform can uniformly record and analyze the event, triggering the sending of a safety status request signal to the operator's terminal, prompting them to confirm whether they are in a safe state. The safety status request signal is typically sent via touch confirmation or button response on the mobile terminal, with a signal transmission frequency of 1 to 2 times per second to ensure that the operator receives the safety alert within a short time.

[0037] A safety response time is set, typically 10 to 20 seconds, during which personnel must confirm the safety status. If no valid confirmation is received from the personnel within the specified time, the device will determine it as a serious safety incident and immediately send an automatic distress signal to the cloud platform, marking the event as high-risk. The automatic distress signal includes key parameters such as the incident time, worker location information, locking force curve, and peak descent rate, and is sent in the form of a structured data packet to ensure rapid and reliable emergency information transmission. Upon receiving the distress signal, the cloud platform immediately triggers telephone alarms or other remote assistance mechanisms, sending the incident information to the safety management center or relevant emergency rescue departments to ensure timely rescue of personnel.

[0038] During this process, emergency fall response data is timestamped to generate an accident log, recording the precise time of the accident, event sequence, lockout response curve, and worker confirmation status. The accident log can be used for subsequent safety analysis and risk assessment, as well as as a basis for tracing responsibility for safety in high-altitude operations. The entire process ensures that in the event of an abnormal fall, the device can achieve a closed-loop operation of data upload, worker confirmation, automatic determination of serious accidents, and remote assistance, realizing full-process safety protection and data traceability from event detection to remote rescue.

[0039] In this embodiment, a real-time monitoring and response platform for a fall arrestor is provided, for executing the real-time monitoring and response method for a fall arrestor as described above, including: The data acquisition unit is used to continuously collect data on workers based on fall protection devices and generate a work status baseline sequence. The evaluation unit is used to evaluate the work behavior based on the work status baseline sequence to obtain the work behavior pattern; the work behavior pattern includes normal descent work characteristics, parallel movement characteristics, and unexpected abnormal trend characteristics.

[0040] The locking control unit is used to: calculate the real-time descent rate peak of the work status baseline sequence when the work behavior mode is an unexpected abnormal trend; compare the real-time descent rate peak with the preset safety descent threshold; activate the slow locking mechanism of the fall arrest device when the real-time descent rate peak is greater than the first-level safety descent threshold; and activate the emergency locking mechanism of the fall arrest device when the real-time descent rate peak is greater than the second-level safety descent threshold.

[0041] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0042] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A real-time monitoring and response method for a fall arrest device, characterized in that, The fall arrestor has a built-in high-precision attitude measurement unit and a BeiDou differential positioning unit, and includes the following steps: Based on fall protection devices, continuous data is collected from workers to generate a baseline sequence of work status. Based on the work status baseline sequence, work behavior is evaluated to obtain work behavior patterns; the work behavior patterns include normal descent work characteristics, parallel movement characteristics, and unexpected abnormal trend characteristics. When the operation behavior pattern is an unexpected abnormal trend, calculate the peak value of the real-time descent rate of the operation status baseline sequence; compare the deviation of the peak value of the real-time descent rate according to the preset safety descent threshold; when the peak value of the real-time descent rate is greater than the first-level safety descent threshold, activate the slow locking mechanism of the fall arrest device; when the peak value of the real-time descent rate is greater than the second-level safety descent threshold, activate the emergency locking mechanism of the fall arrest device.

2. The real-time monitoring and response method for the fall arrestor according to claim 1, characterized in that, The specific steps for continuously collecting data on workers based on fall protection devices to generate a work status baseline sequence are as follows: A sampling frequency of 100Hz was set, and continuous data collection was performed on workers based on fall protection devices to extract worker operation monitoring data; The worker operation monitoring data is subjected to data quality verification, and abnormal quality data points are marked; the abnormal quality data points include sensor failure, signal loss or abnormal data jumps. The abnormal quality data points are removed to obtain standardized data; Based on standardized data, the variance of height variation, peak rate, number of attitude angle abrupt changes, and curvature of horizontal displacement trajectory are calculated to generate a baseline sequence of operational status.

3. The real-time monitoring and response method for the fall arrestor according to claim 2, characterized in that, The worker's operational detection data includes the worker's vertical height change relative to the ground reference, the three-dimensional spatial descent rate per unit time, body posture angle change parameters, and horizontal displacement components.

4. The real-time monitoring and response method for the fall arrestor according to claim 3, characterized in that, The specific steps for evaluating work behavior based on the work status baseline sequence to obtain the work behavior pattern are as follows: The descent rate and magnitude of workers within a cycle are calculated based on the work status baseline sequence. Based on the stated rate of decline and magnitude of decline, a downward trend assessment is performed to obtain the characteristics of the downward trend fluctuation. The operational behavior is evaluated based on the downward trend fluctuation characteristics to obtain the operational behavior pattern; the operational behavior pattern includes normal downward operation characteristics, parallel movement characteristics, and unexpected abnormal trend characteristics.

5. The real-time monitoring and response method for the fall arrestor according to claim 4, characterized in that, The operation behavior pattern is characterized by unexpected abnormal trends; Calculate the peak value of the real-time descent rate of the work status baseline sequence, and compare the deviation of the peak value of the real-time descent rate according to the preset safe descent threshold; When the peak real-time descent rate exceeds the first-level safety descent threshold, the specific steps for activating the slow locking mechanism of the fall arrestor are as follows: When an unexpected abnormal trend is detected in the operation behavior pattern, the peak value of the real-time decline rate of the operation status baseline sequence is calculated. The deviation of the peak real-time descent rate is compared according to a preset safety descent threshold. When the security degradation threshold includes a primary security degradation threshold and a secondary security degradation threshold; When the peak real-time descent rate exceeds the first-level safety descent threshold, it is determined to be a suspected fall. Activate the slow locking mechanism of the fall arrestor to gradually brake the rope displacement and record real-time locking information; The real-time locking information is fed back to the cloud platform for recording.

6. The real-time monitoring and response method for the fall arrestor according to claim 5, characterized in that, The specific steps for feeding back the real-time locking information to the cloud platform for recording are as follows: The real-time locking information is fed back to the cloud platform for recording, and a safety status request signal is sent to the operators simultaneously. Once the operator confirms safety, the slow locking mechanism is released, restoring the normal locking state.

7. The real-time monitoring and response method for the fall arrestor according to claim 6, characterized in that, The specific steps for activating the emergency locking mechanism of the fall arrestor when the peak real-time descent rate exceeds the secondary safety descent threshold are as follows: When the peak real-time descent rate exceeds the secondary safety descent threshold, the descent trend fluctuation characteristics and the number of attitude angle abrupt changes are extracted for fall confirmation analysis. When the descent trend fluctuation characteristics are linear accelerated descent and the number of attitude angle abrupt changes are short-term irregular changes, it is determined to be an abnormal fall state. Activate the emergency locking mechanism of the fall arrestor to rigidly brake the rope displacement and generate a safety warning signal; Safety warning signals are uploaded to the cloud platform to generate incident logs.

8. The real-time monitoring and response method for the fall arrestor according to claim 7, characterized in that, The specific steps for uploading the safety warning signal to the cloud platform and generating an incident log are as follows: Upload safety warning signals to the cloud platform to generate emergency fall response data; Simultaneously send a safety status request signal to the operators; Set the security response time length; If the operator fails to respond within the safety response time, it is considered a serious safety incident. Send an automatic distress signal to the cloud platform; When the cloud platform receives an automatic distress signal, it triggers a telephone alarm for help; Extract the emergency fall response data and mark it with a timestamp to generate an accident log.

9. A real-time monitoring and response platform for a fall arrest device, characterized in that, A method for performing a real-time monitoring and response of a fall arrestor as described in claim 1, comprising: The data acquisition unit is used to continuously collect data on workers based on fall protection devices and generate a work status baseline sequence. The evaluation unit is used to evaluate the work behavior based on the work status baseline sequence to obtain the work behavior pattern; the work behavior pattern includes normal descent work characteristics, parallel movement characteristics, and unexpected abnormal trend characteristics. The locking control unit is used to: calculate the real-time descent rate peak of the work status baseline sequence when the work behavior mode is an unexpected abnormal trend; compare the real-time descent rate peak with the preset safety descent threshold; activate the slow locking mechanism of the fall arrest device when the real-time descent rate peak is greater than the first-level safety descent threshold; and activate the emergency locking mechanism of the fall arrest device when the real-time descent rate peak is greater than the second-level safety descent threshold.

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