A Smart Safety Helmet IoT Data Acquisition and Analysis Method for High-Altitude Operations

Abstract

This invention belongs to the field of safety protection technology and discloses an IoT data acquisition and analysis method for smart safety helmets used in high-altitude operations. Through a fuzzy adaptive control algorithm, combined with communication links, acquired data, and device power consumption status, the method dynamically adjusts the acquisition frequency and link parameters. Under normal operating conditions, it can achieve low-power operation, and when a risk signal is detected, it can quickly switch to a high-specification acquisition and transmission mode. Simultaneously, a transmission error compensation mechanism is set up to avoid the problems of invalid data accumulation and omission of key risk data, improving the device's endurance and ensuring the reliability and real-time performance of data transmission, adapting to the dynamic scene characteristics of high-altitude operations. Based on safety reinforcement learning, it achieves differentiated preprocessing of data anomalies, ensuring data validity through repair and fault isolation. Furthermore, a PID adaptive weight control algorithm is embedded in a Bayesian network to achieve scenario-based dynamic fusion of multi-source data.

Description

Technical Field

[0001] This invention belongs to the field of safety protection technology, specifically a smart safety helmet IoT data collection and analysis method for high-altitude operations. Background Technology

[0002] Construction work, power line maintenance, bridge maintenance, and other high-altitude operations fall under the category of high-risk work. The working environments are complex and changeable, posing multiple safety hazards such as falls from heights, falling objects, sudden environmental changes, and physiological abnormalities in workers. Smart safety helmets, as core protective equipment for high-altitude operations, have incorporated IoT and sensor technologies to achieve operational data collection and safety monitoring. However, current technology still faces the following technical challenges: Most existing data collection and analysis methods for smart safety helmets adopt a fixed frequency collection mode, which cannot adaptively adjust the collection strategy according to the dynamic scenarios of high-altitude operations such as changes in working posture, fluctuations in environmental parameters, and switching of signal strength. This leads to the accumulation of invalid data, the omission of key risk data, and increased power consumption of the device, which in turn affects its battery life. Data transmission often uses a single link. In high-altitude operation scenarios, signal obstruction and interference are severe, which can easily lead to data packet loss and delay, making it impossible to guarantee data real-time performance. Data preprocessing only performs simple filtering and lacks the ability to identify anomalies based on learning systems, making it difficult to distinguish between sensor malfunctions, data distortion and real risk anomalies. The isolated analysis of multi-source data, including physiological, environmental, equipment, and posture data, without the establishment of a correlation and fusion model, results in low accuracy in risk assessment and an inability to adapt to the differences in risk thresholds across different work scenarios. Furthermore, the analysis model lacks a closed-loop optimization mechanism and cannot iterate autonomously based on historical data and work feedback. It is also difficult to quickly transform the analysis results into safety control instructions, thus failing to fundamentally improve the safety control efficiency of high-altitude operations. Summary of the Invention

[0003] The purpose of this invention is to provide an IoT data acquisition and analysis method for smart safety helmets used in high-altitude operations, in order to solve one or more of the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a smart safety helmet IoT data acquisition and analysis method for high-altitude operations, comprising the following specific steps: Preferably, the initialization phase integrates a multi-source sensor module inside the smart safety helmet, and integrates it with an IoT multi-mode communication module, an audible and visual warning module, and an adaptive control module. An initialization control model is constructed based on reinforcement learning and PID adaptive adjustment algorithms. Through pre-collected scene sample data, the initial acquisition frequency, data priority weight, and acquisition reference parameters of each sensor are automatically calibrated. At the same time, multi-objective optimization control criteria for risk level, data validity, and device power consumption are dynamically generated. The multi-objective optimization control criteria are implemented in a hierarchical manner based on priority, with the priorities from highest to lowest being risk level, data validity, and equipment power consumption. When a risk signal is detected, priority is given to high-frequency acquisition and high-speed transmission of risk data, without considering power consumption. When there is no risk signal, equipment power consumption is minimized while ensuring the validity of core data (valid data percentage ≥ 99%). The criteria are embedded into the adaptive control module, which can adjust the weight coefficients of the three objectives—risk level, data validity, and equipment power consumption—according to scenario configuration instructions issued from the cloud. Priority is based on a hierarchical ranking, and the weight coefficients issued from the cloud only fine-tune the importance of each objective without changing the core priority level, with risk level always being the highest priority.

[0005] The adaptive control module collects initial environmental and physiological state data of workers in real time, automatically sets dynamic initial thresholds for posture tilt angle, environmental parameters, and physiological parameters, establishes a linkage control relationship between thresholds and acquisition strategies, and realizes the initial adaptation of acquisition strategies and work scenarios. The sensor self-test and control accuracy calibration module is embedded in the initialization control model, and ensures the validity of the initial acquisition data through real-time calibration of deviations and fault self-test alarms. This invention ensures control accuracy and scenario adaptability during the data acquisition initialization phase through dynamic thresholds and adaptive calibration mechanisms, meeting the initial control requirements of different high-altitude operation scenarios.

[0006] Based on the above multi-objective optimization control criteria, this invention sets dynamic initial thresholds for core monitoring parameters of high-altitude operations differently according to the operation scenario. For high-altitude operations in building construction, the tilt danger threshold is set to 30° and the warning threshold is set to 20°; for power maintenance operations, the tilt danger threshold is set to 25° and the warning threshold is set to 15°. Among environmental parameters, the wind speed warning threshold is set to 6 m / s and the danger threshold is set to 10 m / s; the ambient temperature warning threshold is set to 35℃ and the danger threshold is set to 40℃. Among physiological parameters, the heart rate warning threshold is set to 120 beats / minute and the danger threshold is set to 140 beats / minute; the blood oxygen warning threshold is set to 90% and the danger threshold is set to 85%. The thresholds for each scenario can be remotely fine-tuned by the cloud according to the actual situation at the operation site.

[0007] Preferably, the link adjustment stage adopts a fuzzy adaptive control algorithm, which takes the communication link status, the characteristics of the collected data, and the power consumption status of the device as the collaborative control input, establishes a correlation control logic model of the three, clarifies the correspondence between each input and output, and the collaborative control module dynamically adjusts the acquisition frequency and communication link parameters in real time. When the operator's posture is stable, the environment is normal, and the signal is good, the acquisition frequency is adaptively reduced, and the communication link is adjusted to a low-power mode to achieve coordinated control of power consumption and data validity. In the low-power mode, the device power consumption can be reduced by more than 30% compared with the conventional acquisition and communication mode. When risk signals such as posture tilt, sudden increase in wind speed, or abnormal heart rate are detected, a coordinated control command is triggered to synchronously encrypt the acquisition frequency, switch to a high-speed communication link, and increase the link transmission bandwidth. Meanwhile, the link transmission error adaptive compensation algorithm runs synchronously to control and correct data deviations in real time during transmission, realizing the coordinated linkage of data acquisition and communication. Various types of data are efficiently transmitted to the cloud platform through the optimized communication link.

[0008] The cloud platform supports parallel reception of data from multiple terminals and of multiple types. It adopts a distributed storage architecture to classify and store collected data, preprocessed data, fused data, and risk assessment data. The storage period can be set as needed from 1 to 12 months. The cloud is equipped with a real-time data processing engine to perform second-level analysis on real-time data transmitted to the cloud, while retaining historical data to provide data support for risk trend prediction and model iteration. The data analysis results in the cloud are cross-validated with the analysis results of the local adaptive control module to ensure the accuracy of risk judgment.

[0009] Preferably, the data preprocessing stage is based on an adaptive preprocessing control module using security reinforcement learning. The module takes the data anomaly type and anomaly degree as control inputs, dynamically adjusts the preprocessing strategy and acquisition feedback instructions, uses an adaptive interpolation repair algorithm for slight data distortion, dynamically adjusts the repair weight according to the anomaly degree, and feeds back to the acquisition end to fine-tune the corresponding sensor acquisition frequency. When a sensor malfunction occurs, the fault type is identified and fed back to the adaptive control module, triggering fault isolation control, stopping the data acquisition of the faulty sensor, and synchronously encrypting the acquisition frequency of associated sensors; in the event of severe data packet loss, the feedback is sent to the link adjustment stage, triggering the coordinated adjustment of the communication link and the acquisition frequency, and re-acquiring key data. The preprocessing accuracy adaptive optimization algorithm adjusts the anomaly detection threshold and repair parameters in real time based on historical preprocessing data and risk event feedback. It improves the reliability of data preprocessing through a micro closed-loop control mechanism and outputs the resulting multi-source effective data to the data association stage.

[0010] Preferably, in the data association stage, the PID adaptive adjustment algorithm of the acquisition initialization stage is optimized into a PID adaptive weight control algorithm, and a Bayesian network is embedded to realize the dynamic adjustment of the weights of multiple data dimensions. The real-time characteristics of multiple data, the type of operation scenario, and historical risk data are used as the weight control input to dynamically adjust the association weights of each data dimension and realize the scenario-based dynamic adaptation of the association weights. In high-temperature environments, the system automatically increases the correlation weight of ambient temperature data and decreases the weight of irrelevant data. During high-altitude climbing operations, the system automatically increases the correlation weight of posture tilt and equipment tightness data to achieve scenario-based adaptation of multi-source data. The weight control stability detection module monitors the rationality of weight adjustments in real time and sets the stability threshold to ±0.05. The collaborative analysis and fusion of multi-source data is achieved through adaptive weight control. The data redundancy adaptive control algorithm runs synchronously, automatically identifying and eliminating redundant related data with a correlation of less than 0.3, reducing the computational complexity of the model. After the multi-source data is fused, the risk correlation of each data dimension is clarified.

[0011] Preferably, the risk assessment stage combines preprocessed multi-source correlation data and historical risk event data, and uses a model prediction and control algorithm to construct a risk trend prediction model to predict the risk development trend in advance and accurately control the prediction error within ±10%. The adaptive risk threshold control module dynamically adjusts the assessment thresholds for each risk level in real time based on the type of work scenario and historical risk data. It generates differentiated and executable adaptive control instructions based on the predicted risk trends and real-time assessment results. For high-risk trends, immediately trigger emergency control orders; for medium-risk trends, trigger advance intervention control orders; for low-risk trends, maintain normal monitoring, while adjusting the sensitivity of the assessment threshold, and output the control decision orders generated by the risk assessment to the closed-loop feedback stage.

[0012] Preferably, the closed-loop feedback stage uses the control decision instructions from the risk assessment stage and the feedback information from each terminal as the closed-loop control input. A closed-loop control optimization module is constructed through a reinforcement learning adaptive control algorithm. An iteration cycle of 10 minutes is set. The data on the execution of decision instructions are collected in real time and the effectiveness is summarized and analyzed periodically. The analysis results are fed back to the aforementioned stages to dynamically optimize the control parameters. Feedback is provided to the data acquisition initialization stage to adjust the initial threshold of the sensor and the data acquisition baseline parameters; feedback is provided to the link adjustment stage to optimize the collaborative control strategy and link switching threshold; feedback is provided to the data association stage to adjust the association weight of the Bayesian network; and feedback is provided to the risk assessment stage to optimize the risk prediction model and assessment threshold. The closed-loop control delay adaptive adjustment algorithm controls the feedback delay in real time, ensuring that instruction execution and parameter optimization are completed within 50ms.

[0013] Preferably, in the model iteration stage, a control parameter transfer model is constructed based on the transfer reinforcement learning algorithm. The core control parameters of similar high-altitude operation scenarios are used as transfer samples. The adaptive transfer control module adaptively transfers the control parameters of the mature scenario to the new scenario. At the same time, it automatically adjusts the adaptability of the transfer parameters according to the initial data of the new scenario. The model control performance monitoring module monitors the system's operating performance in real time, sets performance index thresholds, and automatically triggers model iteration instructions when the performance index is lower than the preset control threshold. It then adaptively optimizes the control algorithms and parameters at each stage by combining newly added work data and feedback information. The iterative stability control algorithm in the model iteration stage ensures the stability of the system control after iteration, enabling the system to adapt to the dynamic changes and complex working conditions of high-altitude operation scenarios.

[0014] The smart safety helmet has a built-in local data caching module. When the communication link is interrupted and a network outage occurs, the local caching mechanism is automatically triggered to cache key collected data and risk data in an orderly manner according to timestamps to the local storage unit. The cache capacity supports the storage of core data for 4 consecutive hours. When the communication link is restored, the system automatically triggers the data resume transmission command to prioritize uploading the locally cached historical data to the cloud platform. During the resume transmission, the collection and transmission of real-time data are not affected, ensuring that no data is lost during network outages.

[0015] The beneficial effects of this invention are as follows: 1. This invention uses a fuzzy adaptive control algorithm to dynamically adjust the acquisition frequency and link parameters in combination with the communication link, acquired data, and device power consumption status. Under normal operating conditions, it can achieve low power consumption operation. When a risk signal is detected, it can quickly switch to a high-specification acquisition and transmission mode. At the same time, a transmission error compensation mechanism is set up to avoid the problems of invalid data accumulation and omission of key risk data, improve the device's endurance, ensure the reliability and real-time performance of data transmission, and adapt to the dynamic scene characteristics of high-altitude operations.

[0016] 2. This invention achieves differentiated preprocessing of data anomalies based on safety reinforcement learning, ensuring data validity through repair and fault isolation. At the same time, it embeds the PID adaptive weight control algorithm into a Bayesian network to achieve scenario-based dynamic fusion of multi-source data. It can adjust the weights of each data dimension according to the operation scenario, remove redundant data, clarify the risk correlation between data, and improve the accuracy of risk judgment.

[0017] 3. This invention constructs a closed-loop control optimization module through reinforcement learning, which applies risk assessment decisions and terminal feedback information in reverse to each stage, dynamically optimizing control parameters and ensuring the effectiveness of control instructions. At the same time, it achieves adaptive migration of core parameters from mature scenarios to new scenarios based on transfer reinforcement learning, and combines system performance monitoring to trigger model iteration, continuously optimizing algorithms and parameters, enabling the system to flexibly adapt to the dynamic changes of high-altitude operation scenarios and improving the safety control efficiency of high-altitude operations. Attached Figure Description

[0018] Figure 1 This is an overall flowchart of the method of the present invention; Figure 2This is a flowchart of the data acquisition initialization and link coordination control process of the present invention; Figure 3 This is a flowchart of the multi-source data preprocessing and correlation fusion process of the present invention; Figure 4 This is a flowchart of the risk assessment and closed-loop feedback optimization process of this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figures 1 to 4 As shown, this embodiment of the invention provides a method for IoT data acquisition and analysis of smart safety helmets for high-altitude operations, including the following specific steps: The initialization phase of data acquisition integrates a multi-source sensor module inside the smart safety helmet, including sensors for collecting human physiological data such as heart rate, blood oxygen, and body temperature; gyroscopes and accelerometers for collecting work posture data; wind speed, temperature, humidity, and air pressure sensors for collecting high-altitude environmental data; and positioning and wearing tightness sensors for collecting equipment status data. The sensor ranges and accuracies are adapted to the monitoring needs of high-altitude operations: Heart rate sensor: range 30-220 beats / minute, accuracy ±1 beat / minute; Blood oxygen sensor: range 70%-100%, accuracy ±1%; Body temperature sensor: range 35℃-42℃, accuracy ±0.1℃; Gyroscope: measurement range ±2000° / s; Accelerometer: range ±16g, all high-precision industrial-grade monitoring specifications. Wind speed sensor: range 0-30m / s, accuracy ±0.2m / s; Temperature and humidity sensor: temperature range -20℃-60℃, humidity range 0-100%RH; Barometric pressure sensor: range 300-1100hPa; Positioning sensor: positioning accuracy ≤5m; Wearing tightness sensor: detects 0-100% wearing fit, trigger threshold set at 80%. The helmet's fit tightness sensor uses a pressure-sensing detection method. It collects the fit pressure value through multiple pressure sensing points inside the helmet and converts the pressure value into a fit tightness of 0-100%. When the tightness is <80%, a local audible and visual alarm is immediately triggered, and the tightness data is uploaded to the cloud according to the highest data priority. If the tightness is <60%, a high-risk alarm is triggered, and the data collection frequency is increased and a reminder instruction is sent to the on-site monitoring terminal until the fit tightness is restored to above 80%, at which point both the audible and visual alarm and the high-risk alarm are deactivated. If the tightness is restored to 60%-80%, the high-risk alarm is deactivated, but the basic audible and visual alarm remains in effect.

[0021] The aforementioned multi-source sensor module is integrated with the IoT multi-mode communication module (4G / 5G / LoRa), the audible and visual early warning module, and the adaptive control module. Data interaction between these modules is achieved via serial communication. The adaptive control module, as the core control module, transmits data bidirectionally with the multi-source sensor module, receiving sensor data and issuing commands for sampling frequency and parameter calibration. It also transmits data bidirectionally with the IoT multi-mode communication module, issuing link switching and bandwidth adjustment commands and receiving link status feedback data. Furthermore, it transmits control commands unidirectionally with the audible and visual early warning module, receiving control commands during the risk assessment phase and triggering corresponding early warning actions. All module interaction data is cached in real-time with a 128MB cache capacity, capable of storing at least two hours of core module interaction data to prevent data loss due to short-term data transmission interruptions. An initial control model is constructed based on reinforcement learning and PID adaptive adjustment algorithm. By pre-collecting a small amount of scenario sample data, such as typical environment and attitude data of different high-altitude operation scenarios such as building construction, power maintenance, and bridge maintenance, the initial acquisition frequency, data priority weight and acquisition reference parameters of each sensor are automatically calibrated. At the same time, multi-objective optimization control criteria such as risk level, data validity and equipment power consumption are dynamically generated. The adaptive control module collects initial environmental data such as wind speed and signal strength, as well as the initial physiological state data of the workers in real time. It automatically sets dynamic initial thresholds for posture tilt angle, environmental parameters, and physiological parameters, and establishes a linkage control relationship between the thresholds and the acquisition strategy to achieve initial adaptation of the acquisition strategy to the work scenario. The sensor self-test and control accuracy calibration module is embedded in the initialization control model to ensure the validity of the initial acquisition data through real-time calibration of deviations and fault self-test alarms. This invention ensures control accuracy and scenario adaptability during the data acquisition initialization phase through dynamic thresholds and adaptive calibration mechanisms, adapting to the initial control requirements of different high-altitude operation scenarios.

[0022] The initialization and training of the control model consists of two steps: offline pre-training and online fine-tuning. ① Offline pre-training: Using sample data from typical high-altitude operation scenarios such as building construction, power maintenance, and bridge maintenance as the training set, the training set and validation set are divided in an 8:2 ratio. The mini-batch gradient descent method is used for training, with a batch size of 32, an initial learning rate of 0.01, and 200 training epochs. The loss function is optimized based on the validity of the collected data, the power consumption of the equipment, and the accuracy of risk identification. The basic parameters of the model are trained. ② Online fine-tuning: Using the initial environmental and physiological data collected in real time at the work site as incremental samples, an online fine-tuning strategy is adopted to reduce the learning rate to 0.9 times the original value every 50 iterations. The connection relationship of each module of the model is as follows: sensor data acquisition module → PID adaptive adjustment sub-module → reinforcement learning decision sub-module → parameter output module, so as to realize the scenario-based adaptation output of the acquisition strategy.

[0023] Sensor self-testing and accuracy calibration are performed according to a fixed procedure. After the equipment is powered on, the adaptive control module first triggers a full sensor self-test, sending test commands to each sensor. If there is no feedback or the feedback signal is abnormal, it is determined to be a fault and triggers a local audible and visual alarm. At the same time, the fault information is uploaded to the cloud. Accuracy calibration adopts the benchmark comparison method, comparing the initial data collected by each sensor in real time with the industry standard benchmark value. When the deviation exceeds the accuracy range of the sensor itself, a calibration compensation value is automatically generated and sent to the sensor to complete the real-time calibration. The calibration process is completely automated, and the total time for a single self-test and calibration is controlled within 3 seconds.

[0024] The link adjustment phase employs a fuzzy adaptive control algorithm, using communication link status, collected data characteristics, and device power consumption status as collaborative control inputs. A correlation control logic model is established among these three, clarifying the correspondence between each input and output. The collaborative control module dynamically adjusts the acquisition frequency and communication link parameters in real time, with a response time controlled within 50ms. In the communication link status, the signal strength threshold is 0-100dBm, where ≥60dBm indicates a good signal, <30dBm indicates a poor signal, and 30dBm≤signal strength<60dBm indicates a moderate signal. In this phase, the signal is temporarily switched to a 4G link while maintaining the regular acquisition frequency. The transmission delay is 0-500ms, <100ms is considered normal, and the packet loss rate is 0-100%, <5% is considered normal. Collected data characteristics include data priority and risk correlation. When the signal strength is ≥60dBm, the transmission delay is <100ms, and the packet loss rate is <5%, the LoRa low-power link is used. When any one of the indicators is not met and no risk signal is detected, the link is temporarily switched to 4G and the sampling frequency is increased to 1 time / 5s. When a risk signal is detected, regardless of the link status, the link is forcibly switched to 5G high-speed link and the transmission bandwidth is locked at 10Mbps until the risk signal is eliminated.

[0025] The collaborative control module adopts a lightweight embedded program architecture, eliminating redundant calculation code, and is equipped with a dedicated high-speed data processing chip to perform parallel detection of link, acquisition, and power consumption status data instead of serial detection, which greatly shortens the data processing and command issuance time and ensures that the entire process response from status perception to acquisition frequency / link parameter adjustment is completed within 50ms.

[0026] Data priority is divided into three levels according to monitoring dimensions. Level 1 is core risk data, including posture tilt, heart rate, wearing tightness, and wind speed; Level 2 is important environmental / physiological data, including body temperature, blood oxygen, and temperature and humidity; Level 3 is auxiliary data, including air pressure and positioning. The risk correlation is divided into 0.1~1.0 according to the degree of correlation between the data and typical risks of high-altitude operations. Among them, the risk correlation between posture tilt and fall from height, and between abnormal heart rate and physiological fatigue is 1.0, and the risk correlation of positioning data is 0.1. During the link adjustment phase, the transmission bandwidth will be allocated according to data priority and risk correlation. Level 1 data will occupy ≥70% of the transmission bandwidth, Level 2 data will occupy 20%~25% of the transmission bandwidth, and Level 3 data will occupy ≤5% of the transmission bandwidth, with a total bandwidth allocation ratio of 100%.

[0027] When the operator's posture is stable, the environment is normal, and the signal is good, the acquisition frequency is adaptively reduced to 1 time / 10s, and the communication link is adjusted to low power mode, using LoRa link for low bandwidth transmission, to achieve coordinated control of power consumption and data validity. In low power mode, the device power consumption can be reduced by more than 30% compared with the conventional acquisition and communication mode. The device's power consumption is controlled by monitoring two core indicators in real time: operating current and remaining power. The smart safety helmet has a built-in 1000mAh rechargeable lithium battery. In the normal acquisition and communication mode, the operating current is 80mA. In the LoRa low power mode, the operating current drops to below 50mA. When the remaining power is ≤20%, a deep low power mode is automatically triggered. Based on the LoRa link, the acquisition frequency is further reduced to once / 30s, and the operating current is reduced to below 30mA. Only the core data acquisition of heart rate and posture tilt is retained until the device is charging or the operation is completed.

[0028] When risk signals such as tilting, sudden increase in wind speed, or abnormal heart rate are detected, a collaborative control command is triggered to simultaneously encrypt the data collection frequency to once every 0.5 seconds, switch to a 4G / 5G high-speed communication link, and increase the link transmission bandwidth to ensure that no critical risk data is missed and that it is transmitted in real time. Simultaneously, the link transmission error adaptive compensation algorithm runs synchronously, controlling and correcting data deviations in real time during transmission. The compensation accuracy is controlled within ±5%, ensuring data transmission accuracy, realizing coordinated linkage between data acquisition and communication, improving the reliability and real-time performance of data transmission, and efficiently transmitting various types of data to the cloud platform through the optimized communication link.

[0029] The adaptive compensation for transmission errors is performed according to a fixed procedure. First, a checksum is added to each frame of transmitted data. After receiving the data, the cloud performs checksum matching to determine if there is any deviation in the data. If a deviation is detected, the feature dimension of the deviation data is extracted and compared with the valid data of the same dimension of the previous 3 frames cached locally to calculate the deviation value. Finally, the distorted data is corrected in real time according to the deviation value. The corrected data is matched with the checksum again. If the verification passes, it is stored in the cloud database. If the verification fails, it is fed back to the acquisition end to retransmit the frame of data.

[0030] Training steps for the safety reinforcement learning adaptive preprocessing control module: ① Construct an abnormal data sample library, which includes three types of abnormal data: sensor failure, data distortion, and packet loss, and label the abnormality type and degree. ②The reward function is the accuracy of data repair / timeliness of fault isolation, and the penalty function is the proportion of invalid data output. The DQN algorithm is used for training. The initial exploration rate is set to 0.8, which decreases linearly to 0.1 with training iterations. The discount factor is set to 0.95. ③ Training is stopped when the anomaly handling accuracy on the validation set is ≥99%. The module connection relationship is: anomaly data identification module → reinforcement learning decision submodule → preprocessing strategy execution submodule → acquisition end feedback module, realizing dynamic output of preprocessing strategy and reverse fine-tuning of acquisition parameters.

[0031] The data preprocessing stage is based on an adaptive preprocessing control module using security reinforcement learning. The module takes the data anomaly type and degree as control inputs. Data anomaly types include sensor failure, data distortion, and packet loss. The module dynamically adjusts the preprocessing strategy and acquisition feedback instructions. For minor data distortion, an adaptive interpolation repair algorithm is used. The repair weight is dynamically adjusted according to the degree of anomaly. At the same time, the module feeds back to the acquisition end to fine-tune the acquisition frequency of the corresponding sensor, ensuring that the data error after repair is controlled within a reasonable range. Data anomalies are categorized into three levels—minor, moderate, and severe—based on quantitative indicators. A deviation of less than ±5% is considered minor distortion, ±5% to ±10% is moderate distortion, and more than ±10% is severe distortion. A single-frame data packet loss rate of less than 5% is considered minor packet loss, 5% to 20% is moderate packet loss, and more than 20% is severe packet loss. No sensor data output is considered a severe fault, abnormal data fluctuation is considered a moderate fault, and slight deviation in data acquisition accuracy is considered a minor fault. Different levels of anomalies correspond to different preprocessing strategies.

[0032] The specific operation process of adaptive interpolation repair is as follows: First, locate the specific data frame and feature dimension with slight data distortion and remove invalid values. Second, extract the valid values ​​of the two frames before and after the distorted data frame in that dimension as interpolation reference data. Then, assign different repair weights to the reference data before and after the frame according to the degree of data anomaly. The lower the degree of anomaly, the higher the weight of the closer frame data. The higher the degree of anomaly, the more even the weights of the data before and after the frame data tend to be. Finally, calculate the values ​​to fill in the distorted position according to the weights to generate a complete valid data frame. After the repair is completed, the validity of the data frame is verified. After the verification is passed, it is included in the subsequent data fusion analysis.

[0033] When a sensor malfunction occurs, the fault type is immediately identified and fed back to the adaptive control module, triggering fault isolation control, stopping the data acquisition of the faulty sensor, and synchronously encrypting the acquisition frequency of associated sensors. For example, when the heart rate sensor malfunctions, the acquisition frequency of the blood oxygen and body temperature sensors is encrypted to avoid the accumulation of invalid data occupying storage resources. In case of severe data packet loss, the feedback is sent to the link adjustment stage to trigger the coordinated adjustment of the communication link and the acquisition frequency, and re-acquiring key data. The standardized execution process for sensor fault isolation is as follows: First, the fault type and fault level are determined by the sensor self-test module, and the device code of the faulty sensor is marked; second, the data acquisition and transmission of the faulty sensor are immediately stopped, and the fault status is reported to the cloud and the on-site monitoring terminal in real time; finally, according to the monitoring dimension of the faulty sensor, the acquisition frequency of related sensors of the same type is adjusted with a basic adjustment range of 2 times the original frequency. If the faulty sensor is a core monitoring dimension, such as heart rate or attitude gyroscope, the adjustment range is 4 times the original frequency to ensure that the monitoring data of the faulty dimension is not lost.

[0034] The preprocessing accuracy adaptive optimization algorithm adjusts the anomaly detection threshold (such as the data distortion deviation threshold and the packet loss rate anomaly threshold) and repair parameters in real time based on historical preprocessing data and risk event feedback, ensuring the dynamic improvement of preprocessing accuracy. The reliability of data preprocessing is improved through a micro closed-loop control mechanism, and the obtained multi-source effective data is output to the data association stage.

[0035] The micro-closed-loop control mechanism for data preprocessing is a closed-loop operation logic of preprocessing execution, effect detection, and parameter optimization. First, a targeted preprocessing strategy is executed on abnormal data. Then, the data validity detection module determines whether the accuracy of the processed data meets the standard. If it does, the processed data is output to the data association stage, and the parameters of the preprocessing strategy are recorded. If it does not meet the standard, the detection result is fed back to the preprocessing strategy generation module, which adjusts parameters such as repair weight and fault isolation range in real time and re-executes the preprocessing. The processing cycle of a single micro-closed loop is controlled within 10ms to ensure the high efficiency of preprocessing.

[0036] In the data association stage, the PID adaptive weight control algorithm is embedded in a Bayesian network. Real-time features of multi-source data such as physiology, posture, environment, and equipment, as well as operation scenario type and historical risk data, are used as weight control inputs to dynamically adjust the association weights of each data dimension. The association weights of each data dimension are 0 to 1, and the weight adjustment step size is controlled between 0.01 and 0.1. Moreover, the weight adjustment process does not require manual intervention, thus realizing the scenario-based dynamic adaptation of association weights. The weight adjustment step size is dynamically adapted according to the risk level of the operation scenario. For low-risk and stable operation scenarios, a small step size of 0.01~0.03 is used to ensure the stability of weight adjustment; for medium-risk early warning scenarios, a medium step size of 0.03~0.07 is used to achieve rapid weight adaptation; and for high-risk emergency scenarios, a large step size of 0.07~0.1 is used to rapidly increase the weight of core risk data and meet the real-time requirements of risk analysis.

[0037] In high-temperature environments exceeding 35℃, the association weight of ambient temperature data is automatically increased to 0.3~0.4, while the weight of irrelevant data is reduced to below 0.05. During high-altitude climbing operations, the association weight of posture tilt and equipment tightness data is automatically increased and adjusted to approximately 0.35 and 0.25 respectively, achieving scenario-based adaptation of multi-source data. The weight control stability detection module monitors the rationality of weight adjustments in real time and sets a stability threshold of ±0.05 to avoid fusion errors caused by excessive weight fluctuations. The collaborative analysis and fusion of multi-source data is achieved through adaptive weight control. The data redundancy adaptive control algorithm and the redundant data removal mechanism operate synchronously, automatically identifying and removing redundant related data with a correlation of less than 0.3. Compared with the case where redundant data is not removed, the computational complexity of the model can be reduced by more than 30%, improving the efficiency of data fusion and risk analysis, ensuring the accuracy and real-time nature of risk analysis results, and clarifying the risk correlation relationships of each data dimension after the multi-source data is fused.

[0038] The identification and removal of redundant data is carried out according to the following process: First, the multi-source fusion data is classified into four dimensions: physiology, posture, environment, and equipment, and the correlation coefficients of data within and between each dimension are calculated; Second, data with a correlation coefficient < 0.3 are marked as redundant data, and core risk-related data (such as posture tilt and fall from height, abnormal heart rate and physiological fatigue) are screened out and protected, and not included in the removal scope; Finally, the marked redundant data are removed in batches, and only the valid core data is retained after removal. This data is then organized into a standardized data format and input into the risk assessment stage. At the same time, the removal record is reported to the cloud to facilitate subsequent model iteration analysis.

[0039] The technical connection relationship of the PID adaptive weight control algorithm embedded in the Bayesian network is as follows: each node of the Bayesian network is a multi-source data dimension, including physiological, posture, environmental, and device dimensions. The PID adaptive weight sub-module serves as the node weight update module of the Bayesian network, outputting the correlation weights of each node in real time and feeding them back to the Bayesian network inference layer. The training of the fusion model aims to optimize the risk correlation identification accuracy of multi-source data fusion. The weight update rate during training is controlled by the proportional coefficient (KP=0.6), integral coefficient (KI=0.2), and derivative coefficient (KD=0.1) of the PID. The inference threshold of the Bayesian network is set to 0.5. After training, the model supports dynamic weight adjustment without human intervention.

[0040] In the risk assessment stage, the preprocessed multi-source correlation data and historical risk event data are combined with a model prediction control algorithm to construct a risk trend prediction model. Based on real-time collected multi-source correlation continuous data, the risk development trend is predicted 5 to 10 seconds in advance, such as the risk evolution of a continuous increase in heart rate and a gradual tilt in posture. The prediction error is precisely controlled within ±10% to ensure the reliability of the prediction results. The adaptive risk threshold control module dynamically adjusts the assessment thresholds for each risk level in real time based on the type of work scenario and historical risk data. For example, the wind speed risk threshold for tower crane operations is set to 8 m / s, and the wind speed risk threshold for high-altitude maintenance operations is set to 10 m / s, replacing fixed thresholds to achieve dynamic assessment of risk levels. Based on the predicted risk trends and real-time assessment results, it generates differentiated and executable adaptive control instructions. Risk trends are classified into three levels based on quantitative indicators. A low-risk trend is defined as a single parameter reaching the warning threshold without a continued deterioration trend, or multiple parameters simultaneously reaching the upper limit of the normal threshold. A medium-risk trend is defined as a single parameter reaching the danger threshold or multiple parameters reaching the warning threshold with a slight deterioration trend. A high-risk trend is defined as a single parameter exceeding the danger threshold and continuously deteriorating, or multiple parameters simultaneously reaching the danger threshold. The results of the trend determination are synchronized to the on-site monitoring terminal and the cloud platform in real time.

[0041] For example, if a high-risk trend such as impending loss of control is predicted, an emergency control command is immediately triggered, including a safety helmet audible and visual vibration warning, a work stoppage command from the on-site monitoring terminal, and an emergency rescue dispatch command. For medium-risk trends, an early intervention control command is triggered, reminding users to rest and adjust their work posture in advance. For low-risk trends, normal monitoring is maintained, and the false alarm rate within a single work process is reduced to below 5% by dynamically adjusting the sensitivity of the assessment threshold. The control decision command generated by the risk assessment is output to the closed-loop feedback stage.

[0042] The smart safety helmet's sound, light, and vibration warning module triggers differentiated actions based on risk level. In cases of high risk, it triggers a red flashing light (2 times / second), a high-decibel buzzer alarm (85dB), and continuous vibration (5 times / second), all three actions occurring simultaneously until the risk is eliminated. In cases of medium risk, it triggers a constant yellow light, an intermittent buzzer alarm (once every 3 seconds), and intermittent vibration (once every 3 seconds) to remind workers to adjust their work status. In cases of low risk, it only triggers a slow green flashing light (once every 5 seconds), without buzzer or vibration, to avoid excessive interference with workers' normal operations.

[0043] The training of the risk trend prediction model consists of two steps: model building and iterative optimization. ① Model construction: Using preprocessed multi-source associated data and historical risk event data as training sets, a time series prediction model based on model prediction and control is constructed. The input layer is the feature dimension of multi-source data, with the number of dimensions set to 12. The hidden layer is set to 2 layers, with the number of neurons being 64 and 32 respectively. The output layer is the risk trend probability value. ② Iterative optimization: The Adam optimizer is used for training with a learning rate of 0.001. Training stops when the prediction error is ≤ ±10%. The model module connection relationship is as follows: fusion data input module → time series feature extraction submodule → model prediction control submodule → risk trend output + threshold adjustment module, realizing dynamic linkage between risk trend prediction and evaluation threshold.

[0044] The closed-loop feedback phase uses the control decision instructions from the risk assessment phase and feedback information from each terminal as the input to the closed-loop control. A closed-loop control optimization module is constructed through a reinforcement learning adaptive control algorithm. An iteration cycle of 10 minutes is set, and the execution data of the decision instructions is collected in real time and the effectiveness is summarized and analyzed periodically. The analysis includes the causes of false alarms and the efficiency of the alarm response. The analysis results are fed back to the aforementioned phases to dynamically optimize the control parameters and ensure the rationality and real-time nature of the parameter adjustments. The terminal feedback information includes the confirmation of false alarms by operators, the efficiency of the alarm response, the results of emergency response, and the operational feedback from on-site supervisors. The feedback information collection terminals include the smart safety helmet terminal, the handheld terminal of the on-site supervisor, and the cloud platform terminal. The workers can complete the false alarm confirmation feedback through the physical button on the safety helmet. The handheld terminal and the cloud platform can enter the feedback information of the alarm response and emergency handling in the form of text / command. The feedback information of all terminals is uploaded to the cloud in real time at a frequency of 1 second / time. After the cloud completes the data summary, it is synchronized to the closed-loop control optimization module as the basis for parameter optimization.

[0045] Feedback is provided to the data acquisition initialization stage to adjust the initial sensor thresholds and data acquisition baseline parameters, thereby improving the adaptability of the initial data acquisition; feedback is provided to the link adjustment stage to optimize the collaborative control strategy and link switching thresholds, thereby reducing link transmission latency and packet loss rate; feedback is provided to the data association stage to adjust the Bayesian network association weights, thereby improving the data fusion accuracy; and feedback is provided to the risk assessment stage to optimize the risk prediction model and assessment thresholds, thereby reducing the false alarm rate. The closed-loop control delay adaptive adjustment algorithm controls the feedback delay in real time, ensuring that the instruction execution and parameter optimization are completed within 50ms, forming a full-process, two-way linkage adaptive closed-loop control system, improving the system's adaptive control capability, and realizing continuous optimization and upgrading of safety control; 10 minutes is the iterative analysis cycle for overall optimization of control parameters, and 50ms is the maximum delay threshold for a single risk instruction execution and parameter fine-tuning. The two are time control indicators of different dimensions.

[0046] In the model iteration phase, a control parameter transfer model is constructed based on a transfer reinforcement learning algorithm. The core control parameters of similar high-altitude operation scenarios are used as transfer samples. The adaptive transfer control module adaptively transfers the control parameters of the mature scenario to the new scenario. At the same time, it automatically adjusts the adaptability of the transfer parameters according to the initial data of the new scenario. The parameter adaptation deviation is controlled within ±0.08. The entire new scenario adaptation process does not require retraining the model, which greatly reduces the sample size and time cost of new scenario adaptation. The core control parameters include acquisition frequency control parameters, link coordination control parameters, Bayesian network weight control parameters, and risk assessment threshold parameters. The core control parameters are categorized into cloud-based parameter databases based on the type of work scenario. An independent parameter sub-database is established for each typical high-altitude work scenario, storing the optimal control parameter set for that scenario. When the system retrieves parameters, it first identifies the current work scenario type, matches the corresponding parameter sub-database from the cloud-based parameter database, and then fine-tunes the parameters based on the initial on-site data. The entire process of parameter retrieval and fine-tuning is completed in the local adaptive control module without the need for full cloud intervention, ensuring that the system can still operate normally when the network is down.

[0047] The local adaptive control module has a built-in independent computing and storage unit, which can independently perform the entire process of data collection, analysis, early warning and control operations in the absence of network power, without relying on cloud computing power. The data types of the local core cache include the optimal control parameter set for each scenario, historical risk data of the past 7 days, and calibration parameters of core sensors. The cached data provides data support for local computing, and all real-time collected data, risk judgment results and early warning records during the network outage are cached in an orderly manner according to timestamps. After the network is restored, they are completely uploaded to the cloud to achieve data synchronization between local and cloud.

[0048] The model control performance monitoring module monitors the system's operational performance in real time, including the validity of data acquisition, the accuracy of risk assessment, and the efficiency of early warning response. It sets performance indicator thresholds, with the preset thresholds being a risk assessment accuracy of no less than 90% and an early warning response efficiency of no less than 95%. When the performance indicators fall below the preset control thresholds, it automatically triggers model iteration instructions. Combining newly added operational data and feedback information, it adaptively optimizes the control algorithms and parameters at each stage to achieve continuous improvement in model control performance. The model control performance monitoring adopts three core judgment indicators: data collection effectiveness ≥98%, risk judgment accuracy ≥90%, and early warning response efficiency ≥95%. All three indicators must be met simultaneously. If any one indicator is lower than the preset threshold for three consecutive operating cycles, the model iteration will be triggered immediately. After the iteration is triggered, the newly added job data, terminal feedback information and risk event records are extracted first to build an incremental training sample library. Then, the control algorithm of each stage is fine-tuned locally, only optimizing the parameter dimensions related to the failure of the indicators. Finally, the fine-tuned model parameters are put into trial operation. The performance indicators are continuously monitored during the trial operation. If the indicators recover to above the preset threshold, the iteration is completed. If they do not recover, the sample library data is added for a second fine-tuning until the indicators meet the standards.

[0049] When the number of high / medium risk events determined by the model is greater than 0, the risk judgment accuracy is calculated as (actual risk events / number of risk events determined by the model) × 100%. The statistical period is one work process. Only risk events that were determined to be high / medium risk by the model and actually occurred are counted, and low-risk judgment results are excluded. The early warning response efficiency is calculated as (number of early warnings with effective responses / total number of early warnings) × 100%. An effective response means that within 30 seconds after the early warning is triggered, the operator or supervisor completes the corresponding handling / confirmation action. The statistical data of the two indicators are automatically summarized by the cloud and synchronized to the model performance monitoring module in real time.

[0050] The iterative stability control algorithm avoids system disturbances caused by large fluctuations in control parameters during model iteration, ensures system control stability after iteration, realizes long-term adaptive iteration of the system, enables the system to adapt to the dynamic changes and complex working conditions of high-altitude operation scenarios, and ensures the safety of high-altitude operations.

[0051] The training and execution connection of the control parameter transfer model is as follows: mature scene parameter library → transfer reinforcement learning submodule → new scene data adaptation submodule → parameter output module → model performance monitoring module; the model training adopts the fine-tuning strategy of transfer learning, freezing the feature extraction layer of the mature scene model and training only the adaptation layer, with a learning rate of 0.005, and training is completed when the parameter adaptation deviation is ≤ ±0.08; after the model iteration is triggered, the incremental learning method is used to incorporate new data, and the iteration batch size is set to 16 to ensure the stability of the system control parameters after iteration without large fluctuations.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0053] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for IoT data acquisition and analysis of smart safety helmets for high-altitude operations, characterized in that, The specific steps include the following: Data acquisition and initialization phase: Multi-source sensors, IoT multi-mode communication, sound and light warning and adaptive control modules are integrated into the smart safety helmet. An initial control model is built based on reinforcement learning and PID adaptive adjustment algorithm to complete parameter calibration, dynamic threshold setting and equipment self-test. Link adjustment phase: A fuzzy adaptive control algorithm is adopted, with communication link status, data acquisition characteristics and device power consumption as collaborative control inputs, to construct an associated control logic model, and dynamically adjust the acquisition frequency and communication link parameters in real time. Through link switching, acquisition frequency encryption and adaptive compensation for transmission errors, the collaborative linkage between acquisition and communication is realized. Data preprocessing stage: An adaptive preprocessing control module is built based on security reinforcement learning. The preprocessing strategy is dynamically adjusted according to the type and degree of data anomalies. Data acquisition accuracy is optimized through data repair, fault isolation and packet loss handling. Data association stage: The PID adaptive weight control algorithm is embedded into a Bayesian network. With multi-source data features, operation scenarios and historical risk data as input, the association weights of each data dimension are dynamically adjusted. Combined with the weight control stability detection and redundant data removal mechanism, the multi-source data scenario fusion is realized. Risk assessment phase: Combining multi-source correlated data and historical risk data, a risk trend prediction model is constructed using model predictive control algorithms, risk assessment thresholds are dynamically adjusted, and differentiated adaptive control instructions are generated; Closed-loop feedback stage: Taking risk assessment decision instructions and feedback information from each terminal as input, a closed-loop control optimization module is constructed based on reinforcement learning. The effectiveness of the instructions is analyzed according to a set cycle and fed back to the aforementioned stages to dynamically optimize control parameters and construct a full-process bidirectional linkage adaptive closed-loop control system. Model iteration phase: Based on transfer reinforcement learning, a control parameter transfer model is constructed to achieve adaptive transfer of core control parameters from mature scenarios to new scenarios; Model iteration is triggered by monitoring the performance of the control system to optimize the control algorithm and parameters.

2. The method for IoT data acquisition and analysis of smart safety helmets for high-altitude operations according to claim 1, characterized in that, In the initialization phase of data acquisition, the initialization control model is constructed based on reinforcement learning and PID adaptive adjustment algorithm. Through pre-acquired scene sample data, the initial acquisition frequency, data priority weight and acquisition reference parameters of each sensor are automatically calibrated to generate multi-objective optimization control criteria. The adaptive control module collects initial environmental data and physiological status data of workers in real time, automatically sets dynamic initial thresholds for various parameters, and establishes a linkage between thresholds and acquisition strategies. The sensor self-test and control accuracy calibration module is embedded in the initial control model to ensure the validity of the initial acquired data and achieve the adaptation of acquisition strategies to the work scenario.

3. The method for IoT data acquisition and analysis of smart safety helmets for high-altitude operations according to claim 2, characterized in that, During the link adjustment phase, the associated control logic model clarifies the correspondence between the input and output quantities of the collaborative control. The collaborative control module dynamically adjusts the working mode according to the work scenario. When the work status is stable, the environment is normal, and the signal is good, the acquisition frequency is reduced and the mode is switched to low-power communication. When a risk signal is detected, the acquisition frequency is encrypted, the link is switched to high-speed communication, and the transmission bandwidth is increased. The transmission error adaptive compensation algorithm runs synchronously to correct the deviation of the transmission data in real time.

4. The method for IoT data acquisition and analysis of smart safety helmets for high-altitude operations according to claim 3, characterized in that, In the data preprocessing stage, the adaptive preprocessing control module uses the data anomaly type and degree as control inputs to adjust the preprocessing strategy accordingly: for slight data distortion, adaptive interpolation is used to repair it and feedback is given to the acquisition end to fine-tune the corresponding sensor parameters; when a sensor fails, fault isolation is triggered and the acquisition frequency of associated sensors is encrypted; when there is severe packet loss, feedback is given to the link adjustment stage to reacquire key data. The preprocessing accuracy is continuously optimized through a micro-closed-loop control mechanism in the data preprocessing stage, and the processed effective data is output to the data association stage.

5. The method for IoT data acquisition and analysis of smart safety helmets for high-altitude operations according to claim 4, characterized in that, In the data association stage, the Bayesian network uses a PID adaptive weight control algorithm to achieve scenario-based dynamic adaptation of association weights, adjusting the priority of weights for each data dimension according to differences in the work scenario; the weight control stability detection module monitors the rationality of weight adjustment in real time, and the redundant data removal mechanism automatically identifies and removes low-relevance redundant data, reducing the computational complexity of the model and completing the fusion of multi-source data and confirmation of risk association relationships.

6. The method for IoT data acquisition and analysis of smart safety helmets for high-altitude operations according to claim 5, characterized in that, In the risk assessment phase, the risk trend prediction model is built based on the model prediction control algorithm, which can predict the risk development trend in advance. The adaptive risk threshold control module in the risk assessment phase adjusts the risk assessment threshold in real time according to the operation scenario and historical data, generates corresponding differentiated adaptive control instructions for different risk trends, and outputs the control decision instructions to the closed-loop feedback phase.

7. The method for IoT data acquisition and analysis of smart safety helmets for high-altitude operations according to claim 6, characterized in that, In the closed-loop feedback stage, the closed-loop control optimization module is constructed based on reinforcement learning, analyzes the effectiveness of decision commands according to a set iteration cycle, and feeds back the analysis results to the aforementioned stages to optimize the control parameters of the corresponding stages respectively; the closed-loop control delay adaptive adjustment algorithm ensures the real-time nature of the feedback and guarantees the timeliness of command execution and parameter optimization.

8. The method for IoT data acquisition and analysis of smart safety helmets for high-altitude operations according to claim 7, characterized in that, During the model iteration phase, the control parameter transfer model is constructed based on transfer reinforcement learning to achieve adaptive transfer of core parameters from mature scenarios to new scenarios, and adjusts the parameter fit according to the initial data of the new scenario. The model control performance monitoring module monitors the system operation status in real time. When the core performance indicators of the system are lower than the preset control threshold, the model iteration is triggered. The algorithm and parameters of each stage are optimized in combination with the new data to ensure that the system adapts to the dynamic changes of the scenario.

9. The method for IoT data acquisition and analysis of smart safety helmets for high-altitude operations according to claim 8, characterized in that, The smart safety helmet has a built-in local data caching module. When the communication link is interrupted, it caches core collected data and risk data. After the link is restored, it automatically prioritizes the transmission of cached data without affecting real-time data collection and transmission.

10. The method for IoT data acquisition and analysis of smart safety helmets for high-altitude operations according to claim 9, characterized in that, The smart safety helmet is equipped with a fitting tightness sensor, which triggers a graded sound and light warning based on the detected fitting tightness value. The warning cancellation condition is linked to the fitting tightness recovery threshold.

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