Method, system and device for monitoring the state of a worker in a high-altitude strong electromagnetic environment

Abstract

This invention relates to the field of power grid engineering technology, and provides a method, system, and device for monitoring the status of workers in high-altitude, strong electromagnetic environments. The device includes a wearable device, a cloud monitoring platform, and a data processing center. The wearable device comprises multiple physiological parameter sensors, multiple environmental parameter sensors, a proximity alarm module, and a leakage current monitoring module. The data processing center receives and integrates multi-source data collected by the wearable device, and generates a safety risk index or fatigue level characterizing the current status of the worker by executing a human physiological indicator monitoring method. The cloud monitoring platform is connected to the data processing center via a wireless communication module, and is used to display the multi-source data, safety risk index, and early warning information in real time, and to store and analyze historical data. This invention can effectively monitor the status of workers.

Description

Technical Field

[0001] This invention relates to the field of power grid engineering technology, and more specifically, to a method, system, and device for monitoring the status of workers in high-altitude, strong electromagnetic environments. Background Technology

[0002] With the continuous expansion of power grid construction, the number of ultra-high voltage AC / DC transmission projects and renovation and expansion projects is increasing. High-altitude operations in power grids (according to national standards, any operation at a height of 2 meters or more above the reference plane where there is a possibility of falling) have become a routine scenario for construction and operation and maintenance. Workers need to be at height for long periods of time and frequently exposed to strong electromagnetic environments, such as performing live-line maintenance, tower erection, or cross-crossing operations on transmission lines with voltage levels of 110kV and above.

[0003] Existing research indicates that working at heights can increase psychological stress, accumulate physiological fatigue, and reduce balance and coordination among workers. Strong electromagnetic fields (areas with power frequency electric field strength exceeding 4kV / m or magnetic induction exceeding 0.1mT) can lead to central nervous system dysfunction, increased cardiovascular load, visual system damage, and decreased immune system function. The combined effect of these factors significantly increases the risk of sudden fainting, operational errors, and even falls from heights. According to authoritative statistics, falls from heights and being struck by objects account for over 45% of all construction safety accidents in the power industry. Traditional safety management models relying on manual inspections and passive protection are no longer sufficient to meet the safety requirements of complex working conditions.

[0004] Currently, the safety monitoring technology for high-altitude operations in power grid engineering has the following shortcomings: 1. Limited monitoring dimensions and lack of multi-parameter fusion assessment. Existing smart wearable devices mostly perform isolated monitoring, such as only monitoring heart rate or only providing proximity alarms. They cannot synchronously collect and comprehensively analyze human physiological parameters (heart rate, blood pressure, blood oxygen, body temperature) and environmental parameters (electric field strength, magnetic field strength, audible noise, leakage current) in time and space. This results in the backend being unable to accurately determine the actual physiological load of workers and the coupling risk with the environment.

[0005] 2. Poor anti-interference capability in strong electromagnetic environments. Conventional sensors are prone to signal distortion, data drift, or communication interruption in strong power frequency electric and magnetic fields. They lack targeted anti-interference circuit designs (such as differential TMR magnetic field sensor structures and multi-layer filter circuits), which cannot guarantee the accuracy and reliability of monitoring data.

[0006] 3. Lack of in-depth data mining and intelligent early warning mechanisms. Existing technologies mostly use single threshold alarms and have not established a coupling relationship model between electromagnetic field strength and physiological indicators. They cannot achieve quantitative assessment of comprehensive indicators such as fatigue level and safety risk index, and they do not have the ability to predict trends and provide forward-looking early warnings based on time series.

[0007] 4. Wearable devices have low integration and poor wearing comfort. Existing devices have scattered modules, large size, high power consumption, and do not fully consider ergonomics (such as excessive weight added by safety helmets, insole thickness affecting walking, and sensor layout interfering with work movements), resulting in low willingness of workers to wear them and making it difficult to promote them on a large scale in engineering practice.

[0008] Therefore, there is an urgent need to develop a method, system, and device for monitoring the status of workers that can adapt to complex high-altitude electromagnetic environments, have the ability to acquire and analyze multiple parameters simultaneously, have strong anti-interference capabilities, be comfortable to wear, and have intelligent hierarchical early warning functions, in order to fill the existing technological gaps and improve the inherent safety level of power grid engineering operations. Summary of the Invention

[0009] The present invention provides a method, system and device for monitoring the status of workers in high-altitude strong electromagnetic environments, which can overcome some or more defects of the prior art.

[0010] According to the present invention, a monitoring system for the status of workers in a high-altitude, strong electromagnetic environment includes wearable devices, a cloud monitoring platform, and a data processing center; The wearable device includes: Multiple physiological parameter sensors are integrated into the safety helmet to collect real-time data on the worker's heart rate, blood pressure, blood oxygen saturation, and body temperature. Multiple environmental parameter sensors are integrated into the webbing of the safety belt to collect real-time data on the electric field strength, magnetic field strength, and audible noise of the environment in which the worker is located. The proximity alarm module, integrated into the wristband, is used to detect the distance to a live conductor and trigger a proximity alarm when the distance is less than a preset threshold. The leakage current monitoring module is integrated into the insole to collect leakage current data flowing through the human body in real time. The data processing center receives and integrates multi-source data collected by the wearable devices, and generates a safety risk index or fatigue level characterizing the current state of the workers by executing human physiological indicator monitoring methods. The monitoring method includes the following steps: Step S1: Establish a finite element simulation model of the transmission line tower and the working scenario. The model includes a tower structure model, a conductor model and a human body model, and set electrical parameters and boundary conditions according to the actual working conditions. Step S2: By solving the finite element simulation model, electromagnetic field distribution data of the operator at a typical working position is obtained. The electromagnetic field distribution data includes the surface electric field strength, the induced current density in the body, and the magnetic induction intensity. Step S3: Based on the electromagnetic field distribution data obtained in step S2, establish a coupling relationship model between electromagnetic field strength and human physiological indicators, including blood oxygen saturation, blood pressure, heart rate and body temperature. Step S4: Construct a multi-parameter comprehensive evaluation model, normalize and weight the predicted or measured values ​​of multiple physiological indicators obtained in step S3, and generate a comprehensive safety index or fatigue level to characterize the overall physiological load of the human body. Step S5: Based on the comparison results between the comprehensive safety index or fatigue level and the preset threshold, the physiological safety status of the human body is graded, assessed, and warned. The cloud-based monitoring platform is connected to the data processing center via a wireless communication module. It is used to display the multi-source data, security risk index, and early warning information in real time, and to store and analyze historical data.

[0011] Preferably, the physiological parameter sensor includes: The heart rate sensor uses photoplethysmography (PPG) technology and includes a dual-wavelength LED light source and a photodiode detector, with a sampling rate of no less than 8kSPS. Blood pressure sensors, based on the pulse wave transit time method or cuff-type pressure sensors, combined with data fitting algorithms, enable continuous estimation of blood pressure values. The blood oxygen sensor shares the PPG channel with the heart rate sensor and determines blood oxygen saturation by calculating the ratio of the absorption rates of red and infrared light. The body temperature sensor uses an infrared thermopile or a high-precision NTC thermistor, with a measurement accuracy of ±0.1℃; Each sensor is integrated with the helmet liner via a flexible circuit board, and the parts of the sensors that come into contact with human skin are made of medical-grade silicone.

[0012] Preferably, the environmental parameter sensor includes: The electric field sensor is a miniature power frequency electric field sensor based on a planar capacitor structure, consisting of two parallel plates with a fixed distance between them. Its output voltage U(t) satisfies the following relationship with the spatial electric field intensity E(t): Where d is the electrode spacing, These are the dielectric constants of air and the insulating medium, respectively. The magnetic field sensor is a triaxial magnetic field sensor based on the tunneling magnetoresistance (TMR) effect. It contains three orthogonally arranged TMR sensing units to detect the magnetic field components in the x, y, and z axes, respectively. The measurement range covers 25-200 μT, and the frequency response covers 50 Hz of the power frequency. The noise sensor uses an electret condenser microphone array, combined with a preamplifier circuit and a bandpass filter, with an effective frequency band of 30Hz-8kHz and an equivalent continuous sound pressure level measurement range of 0-130dB.

[0013] Preferably, the electric field sensor is also equipped with a signal conditioning circuit, including: A voltage divider circuit is used to adjust the sensor output signal to the ADC input range; A second-order low-pass filter circuit with a cutoff frequency of 100Hz is used to filter out high-frequency interference signals. The differential amplifier circuit uses an instrumentation amplifier to improve the common-mode rejection ratio. A level-up circuit is used to convert bipolar signals into unipolar signals for subsequent processing. A 10μF and a 0.1μF decoupling capacitor are connected in parallel at the power input terminal to filter out low-frequency and high-frequency interference, respectively. The TMR magnetic field sensor suppresses interference from external uniform magnetic fields through a differential structure design, specifically including: Two TMR sensing elements are arranged opposite each other in space along the same diameter to form a differential measurement structure; The output signals of the two sensitive elements are processed by a differential amplifier to cancel the common-mode interference magnetic field; The magnetic field interference of the differential structure Defined as the ratio of the sensor's responsivity to the measured current to its responsivity to the interference current, its mathematical expression is: ;

[0014] in, The distance from the sensing element to the conductor being measured. The distance between the interfering current and the current to be measured. The arrangement angle of the two TMR sensing elements; By optimizing the arrangement angle ,make exist The value approaches zero, achieving optimal suppression of external interfering magnetic fields; The arrangement angle of the TMR magnetic field sensor Determined in the following ways: Establish a mathematical model of magnetic field interference. ; In actual installation parameters and Given the given conditions, solve the equation The theoretically optimal arrangement angle is obtained. ; exist Finite element simulation was performed within the specified range, and the angle with the smallest crosstalk error was selected as the final installation angle. After optimization, the crosstalk error of the sensor is controlled within 0.08% in a three-phase interference scenario.

[0015] Preferably, the proximity alarm module includes: The proximity alarm chip, model JW0858, has a power supply voltage of 3.3V. The threshold setting resistor allows adjustment of the electric field alarm threshold by changing the resistance value of resistor R2. When R2 = 100kΩ, it can provide early warning for electric fields generated by AC voltages of 1kV and above. The PWM output interface uses pin 6 of the chip to output a PWM waveform as a warning trigger signal, which is connected to pin PA15 of the MCU through resistor R3. Alarm thresholds are tiered, with different alarm distances set according to different voltage levels: 10kV voltage alarm distance 0.7-1.6m, 35kV voltage alarm distance 1.0-2.5m, 110kV voltage alarm distance ≤3m, and 220kV voltage alarm distance ≤3m. The leakage current monitoring module includes: The sampling electrode has a double-layer structure, consisting of a copper electrode and a conductive rubber covering it. The electrode diameter is 10 mm and the total thickness is 400 μm, of which the conductive rubber is 300 μm thick and the copper electrode is 100 μm thick. The logarithmic detector, model AD8310AMZ, operates at 3.3V, has a detection range of DC-440MHz, and a dynamic range of -91dBV to 4dBV. The sampling resistor consists of resistors R1 and R3, forming an equivalent 50Ω current sampling resistor. The TVS protection circuit is connected between the output of the logarithmic detector and the ADC port of the MCU for overvoltage protection. The leakage current monitoring module has a measurement range of 1-20mA and a full-range measurement error of less than 5%.

[0016] Preferably, the wearable device adopts an integrated packaging structure, including: The safety helmet unit integrates physiological parameter sensors and main control circuits. The sensor module adopts an embedded design and is tightly integrated with the inner lining of the safety helmet, with an additional weight of no more than 50g. The wristband unit features a flexible wristband design, with a weight controlled to within 50g. The watch case integrates a proximity alarm module, and the wristband is compatible with different wrist sizes. The seat belt unit integrates environmental parameter sensors into the seat belt webbing. The sensors are wrapped with wear-resistant and pressure-resistant material, so as not to affect the extension and locking functions of the seat belt. The insole unit integrates the leakage current monitoring module inside the insole. It adopts a flexible circuit board and a thin design without changing the original thickness and comfort of the insole. It is compatible with shoe sizes 40-45 and supports a cuttable design. Each unit meets IP65 or higher protection standards, key interfaces adopt a waterproof plug-in structure, and silicone rubber sealing rings are installed at the seams of the outer shell.

[0017] Preferably, the data processing center includes: The data receiving module receives multi-source data collected by wearable devices in real time via wireless communication; The data preprocessing module performs filtering, noise reduction, outlier removal, and time synchronization on the raw data. A multi-level data fusion model performs hierarchical processing and intelligent analysis on pre-processed data; The model calculation module calls the pre-set physiological-electromagnetic coupling model and deep learning model to calculate the fatigue level and safety risk index of the workers in real time. The individual difference calibration module dynamically adjusts model parameters based on the resting baseline data of the workers; The early warning decision module generates tiered early warning information based on the fusion results and preset thresholds. The data storage module is used to cache historical data locally, ensuring that data is not lost when the network is interrupted; The multi-level data fusion model includes: The first level is the basic monitoring layer, which monitors the threshold of a single parameter and generates a basic alarm event when any parameter exceeds the preset normal range. The second level is the association analysis layer, which performs cross-validation and causal association analysis on different parameters: Using electric and magnetic field data to compensate for interference in physiological signals and identify data distortion caused by electromagnetic interference; The correlation analysis between the near-electric shock alarm signal and the leakage current abnormal signal, combined with physiological indicators such as heart rate and skin conductance, is used to comprehensively determine whether an induced electric shock has occurred. When multiple related abnormal signals are received simultaneously, a specific joint alarm is generated. The third layer is the situation assessment layer, which integrates all real-time data, including physiological parameters, environmental parameters, proximity level and leakage current value, and calculates the overall safety risk index through weighted fusion and deep learning model to conduct a comprehensive risk situation assessment. The fourth layer is the trend prediction layer. Based on historical data time series, it uses recurrent neural networks or Transformer models to predict fatigue change trends in the near future and generate forward-looking early warning information.

[0018] Preferably, the data processing center further includes an individual difference calibration module, used for: The resting physiological parameters of the personnel under electromagnetic exposure-free conditions were used as a baseline, including resting heart rate, baseline blood pressure, resting blood oxygen saturation, and baseline body temperature. The reference ranges of various physiological indicators are dynamically adjusted based on the resting baseline. And the blood oxygen alarm threshold; Establish an individualized physiological parameter database and continuously optimize baseline data as operators use the equipment for longer periods. Calibration commands are periodically issued through a cloud-based monitoring platform to enable online updates of model parameters.

[0019] This invention provides a method for monitoring the status of workers in high-altitude, strong electromagnetic environments, which employs the aforementioned high-altitude, strong electromagnetic environment worker status monitoring system and includes the following steps: Step A, Equipment Deployment and Initialization: One hour before the operation, check the battery level, communication status, and sensor functions of each wearable device; Bind the equipment to the operators and enter the operators' basic information; Collect resting physiological parameters of workers under electromagnetic exposure-free conditions to establish individual baselines; Confirm that the cloud monitoring platform can be logged into normally and that the data upload link is unobstructed; Step B: Synchronous acquisition of multi-source data: Wearable devices synchronously collect physiological parameter data, environmental parameter data, proximity distance data, and leakage current data at a preset frequency of 2-3 seconds per time. Each device ensures data timestamp consistency through an internal clock synchronization mechanism; After local preprocessing, the data is uploaded to the data processing center in real time via a 4G CAT1 module; Step C: Data Preprocessing and Anti-interference Compensation The data processing center filters, denoises, and removes outliers from the received multi-source data; Using electric and magnetic field data to perform anti-interference compensation on physiological signals, and to identify and correct data distortion caused by electromagnetic interference; Based on the preset mapping relationship between different voltage levels and safe distances, real-time distance information is converted into dynamic proximity risk levels; Step D: Multi-level data fusion analysis: Level 1: Threshold monitoring of a single parameter to generate basic alarm events; The second level involves cross-validation and causal correlation analysis of different parameters to generate a targeted joint alarm by integrating multiple abnormal signals. The third level: calculate the security risk index by integrating all real-time data and conduct a comprehensive risk situation assessment; The fourth level: Based on historical time series data, use deep learning models to predict fatigue trends in the near future; Step E: Calculation of Fatigue Level and Safety Risk Index: By invoking a pre-defined physiological-electromagnetic coupling model, and based on the current environmental electromagnetic field strength and physiological parameters, the instantaneous fatigue level of the workers is calculated in real time. ; Output predicted fatigue levels for future moments using a deep learning model. ; The fatigue level is corrected by combining the electromagnetic field strength to generate the final safety risk index; Step F, Tiered Early Warning and Response: when or At that time, a Level 1 emergency alarm was triggered; When a single parameter exceeds the normal range but the overall risk is low, a Level 3 warning is triggered. The cloud-based monitoring platform displays various data visually, automatically issues audible and visual alarms, and notifies the remote management terminal via SMS, APP push, and other means. Management personnel will take corresponding measures based on the warning level, including remotely directing evacuation, dispatching rescue teams, or adjusting operational methods; Step G, Data Storage and Analysis: All collected data and alarm events are stored in a cloud database; Regularly generate health reports for workers and on-site safety situation analysis reports; By utilizing historical data to optimize model parameters, the system can achieve self-learning and continuous improvement.

[0020] This invention provides a device for monitoring the status of workers in high-altitude, strong electromagnetic environments, which adopts the aforementioned monitoring system for monitoring the status of workers in high-altitude, strong electromagnetic environments.

[0021] The beneficial effects of this invention are as follows: This invention integrates wearable devices into a safety helmet unit, a wristband unit, a safety belt unit, and an insole unit, enabling synchronous real-time acquisition of workers' physiological parameters (heart rate, blood pressure, blood oxygen, body temperature), environmental parameters (electric field, magnetic field, audible noise), proximity to electrical equipment, and leakage current. Compared to traditional single-parameter monitoring methods, this invention constructs a comprehensive sensing system encompassing multi-source data, solving the problem of isolated data from various subsystems in existing technologies, which prevents the formation of a comprehensive risk assessment. This provides a comprehensive and reliable data foundation for subsequent accurate evaluation of workers' safety status.

[0022] This invention establishes a multi-level data fusion model in the data processing center, comprising a basic monitoring layer, a correlation analysis layer, a situation assessment layer, and a trend prediction layer. By establishing a coupling relationship model between electromagnetic field strength and physiological indicators, and introducing deep learning algorithms (such as recurrent neural networks or Transformer models), it can not only calculate the safety risk index or fatigue level representing the overall physiological load of the human body in real time, but also predict the trend of changes in the near future. When a sudden change in electric field and an abnormal heart rate are detected simultaneously, the system can automatically perform anti-interference compensation and issue a clearly targeted joint alarm, changing the limitation of existing technologies that only trigger alarms based on a single threshold, and elevating safety management from post-event response to pre-event warning.

[0023] Addressing the unique challenges of strong electromagnetic environments, this invention's electric field sensor effectively filters out high-frequency interference through second-order low-pass filtering, differential amplification, and level boosting in its signal conditioning circuit, ensuring the accuracy of power frequency electric field measurements. The magnetic field sensor employs a differential structure based on the tunneling magnetoresistance (TMR) effect. By optimizing the sensitive element arrangement angle (θ), crosstalk errors in three-phase interference scenarios are controlled to within 0.08%, significantly suppressing interference from external uniform magnetic fields. The leakage current monitoring module utilizes a dual-layer sampling electrode and logarithmic detector, achieving accurate measurements with errors less than 5% within the 1-20mA range. These designs collectively ensure the monitoring system's stable and reliable operation in complex electromagnetic environments, effectively addressing the technical challenge of data distortion in existing sensors within strong electromagnetic fields.

[0024] This invention uses an individual difference calibration module to collect resting physiological parameters (resting heart rate, baseline blood pressure, etc.) of workers under electromagnetic exposure-free conditions as a baseline, dynamically adjusting the reference range and alarm thresholds of each physiological indicator. With increased usage time, the system continuously optimizes the individualized physiological parameter database, effectively eliminating misjudgments caused by individual physical differences. This makes the calculation of fatigue and safety risk indices more closely reflect the actual condition of workers, improving the intelligence level and clinical applicability of the monitoring system. Attached Figure Description

[0025] Figure 1This is a schematic diagram of a personnel status monitoring system in a high-altitude, strong electromagnetic environment, as described in the embodiment. Figure 2 This is a schematic diagram of the electric field sensor structure in the embodiment; Figure 3 This is a schematic diagram of the signal conditioning circuit in the embodiment; Figure 4 This is a schematic diagram of the proximity alarm module in the embodiment. Detailed Implementation

[0026] To further understand the content of this invention, a detailed description of the invention will be provided in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention. Example

[0027] like Figure 1 As shown, this embodiment provides a monitoring system for the status of workers in a high-altitude, strong electromagnetic environment, which includes wearable devices, a cloud monitoring platform, and a data processing center; The wearable device includes: Multiple physiological parameter sensors are integrated into the safety helmet to collect real-time data on the worker's heart rate, blood pressure, blood oxygen saturation, and body temperature.

[0028] The wearable device adopts an integrated packaging structure, including: The safety helmet unit integrates physiological parameter sensors and main control circuits. The sensor module adopts an embedded design and is tightly integrated with the inner lining of the safety helmet, with an additional weight of no more than 50g. The wristband unit features a flexible wristband design, with a weight controlled to within 50g. The watch case integrates a proximity alarm module, and the wristband is compatible with different wrist sizes. The seat belt unit integrates environmental parameter sensors into the seat belt webbing. The sensors are wrapped with wear-resistant and pressure-resistant material, so as not to affect the extension and locking functions of the seat belt. The insole unit integrates the leakage current monitoring module inside the insole. It adopts a flexible circuit board and a thin design without changing the original thickness and comfort of the insole. It is compatible with shoe sizes 40-45 and supports a cuttable design. Each unit meets IP65 or higher protection standards, key interfaces adopt a waterproof plug-in structure, and silicone rubber sealing rings are installed at the seams of the outer shell.

[0029] The physiological parameter sensor includes: The heart rate sensor uses photoplethysmography (PPG) technology and includes a dual-wavelength LED light source and a photodiode detector, with a sampling rate of no less than 8kSPS. Blood pressure sensors, based on the pulse wave transit time method or cuff-type pressure sensors, combined with data fitting algorithms, enable continuous estimation of blood pressure values. The blood oxygen sensor shares the PPG channel with the heart rate sensor and determines blood oxygen saturation by calculating the ratio of the absorption rates of red and infrared light. The body temperature sensor uses an infrared thermopile or a high-precision NTC thermistor, with a measurement accuracy of ±0.1℃; Each sensor is integrated with the helmet liner via a flexible circuit board, and the parts of the sensors that come into contact with human skin are made of medical-grade silicone.

[0030] Multiple environmental parameter sensors are integrated into the webbing of the safety belt to collect real-time data on the electric field strength, magnetic field strength, and audible noise of the environment in which the worker is located.

[0031] The environmental parameter sensor includes: an electric field sensor, which is a miniature power frequency electric field sensor based on a planar capacitor structure, consisting of two parallel plates with a fixed plate spacing. Its output voltage U(t) satisfies the following relationship with the spatial electric field intensity E(t): Where d is the electrode spacing, The dielectric constants of air and the insulating medium, respectively, are given by... Figure 2 As shown, assume that the surface charge densities on the upper and lower sides of the upper electrode of the sensor are respectively and .

[0032] Applying Gauss's law, select Gaussian surfaces respectively. and Its upper and lower surfaces are parallel to the plates, Gaussian surface Surface area is Gaussian surface Surface area is Applying Gauss's theorem to Gaussian surfaces respectively, we have:

[0033] And because By simplifying the equations, we get:

[0034] Therefore, the voltage U(t) induced between the upper and lower plates of the sensor has the following relationship with the electric field strength in space: ;

[0035] That is, when the distance between the upper and lower plates of the sensor remains constant, the induced voltage is proportional to the electric field strength in space. When the sensor is used for voltage detection in power transmission lines, and , and The following relationships exist: ; Therefore, we can conclude that: ; In the formula, when other factors of the sensor and transmission line remain unchanged, The voltage signal output between the sensor's two plates is a constant. It is directly proportional to the voltage on the transmission line and inversely proportional to the square of the distance to the transmission line. This can be used to calculate the sensor's output voltage. To determine the energization status of power transmission lines.

[0036] The magnetic field sensor is a triaxial magnetic field sensor based on the tunneling magnetoresistance (TMR) effect. It contains three orthogonally arranged TMR sensing elements to detect the magnetic field components along the x, y, and z axes, respectively. The measurement range covers 25-200 μT, and the frequency response covers the power frequency of 50 Hz.

[0037] The noise sensor uses an electret condenser microphone array, combined with a preamplifier circuit and a bandpass filter, with an effective frequency band of 30Hz-8kHz and an equivalent continuous sound pressure level measurement range of 0-130dB.

[0038] like Figure 3 As shown, the electric field sensor is also equipped with a signal conditioning circuit, including: A voltage divider circuit is used to adjust the sensor output signal to the ADC input range; A second-order low-pass filter circuit with a cutoff frequency of 100Hz is used to filter out high-frequency interference signals. The differential amplifier circuit uses an instrumentation amplifier to improve the common-mode rejection ratio. A level-up circuit is used to convert bipolar signals into unipolar signals for subsequent processing. A 10μF and a 0.1μF decoupling capacitor are connected in parallel at the power input terminal to filter out low-frequency and high-frequency interference, respectively. The TMR magnetic field sensor suppresses interference from external uniform magnetic fields through a differential structure design, specifically including: Two TMR sensing elements are arranged opposite each other in space along the same diameter to form a differential measurement structure; The output signals of the two sensitive elements are processed by a differential amplifier to cancel the common-mode interference magnetic field; The magnetic field interference of the differential structure Defined as the ratio of the sensor's responsivity to the measured current to its responsivity to the interference current, its mathematical expression is: ; in, The distance from the sensing element to the conductor being measured. The distance between the interfering current and the current to be measured. The arrangement angle of the two TMR sensing elements; By optimizing the arrangement angle ,make exist The value approaches zero, achieving optimal suppression of external interfering magnetic fields; The arrangement angle of the TMR magnetic field sensor Determined in the following ways: Establish a mathematical model of magnetic field interference. ; In actual installation parameters and Given the given conditions, solve the equation The theoretically optimal arrangement angle is obtained. ; exist Finite element simulation was performed within the specified range, and the angle with the smallest crosstalk error was selected as the final installation angle. After optimization, the crosstalk error of the sensor is controlled within 0.08% in a three-phase interference scenario.

[0039] The proximity alarm module, integrated into the wristband, is used to detect the distance to a live conductor and trigger a proximity alarm when the distance is less than a preset threshold.

[0040] like Figure 4 As shown, the proximity alarm module includes: The proximity alarm chip, model JW0858, has a power supply voltage of 3.3V. The threshold setting resistor allows adjustment of the electric field alarm threshold by changing the resistance value of resistor R2. When R2 = 100kΩ, it can provide early warning for electric fields generated by AC voltages of 1kV and above. The PWM output interface uses pin 6 of the chip to output a PWM waveform as a warning trigger signal, which is connected to pin PA15 of the MCU through resistor R3. Alarm thresholds are tiered, with different alarm distances set according to different voltage levels: 10kV voltage alarm distance 0.7-1.6m, 35kV voltage alarm distance 1.0-2.5m, 110kV voltage alarm distance ≤3m, and 220kV voltage alarm distance ≤3m.

[0041] The leakage current monitoring module, integrated into the insole, is used to collect leakage current data flowing through the human body in real time.

[0042] The leakage current monitoring module includes: The sampling electrode has a double-layer structure, consisting of a copper electrode and a conductive rubber covering it. The electrode diameter is 10 mm and the total thickness is 400 μm, of which the conductive rubber is 300 μm thick and the copper electrode is 100 μm thick. The logarithmic detector, model AD8310AMZ, operates at 3.3V, has a detection range of DC-440MHz, and a dynamic range of -91dBV to 4dBV. The sampling resistor consists of resistors R1 and R3, forming an equivalent 50Ω current sampling resistor. The TVS protection circuit is connected between the output of the logarithmic detector and the ADC port of the MCU for overvoltage protection. The leakage current monitoring module has a measurement range of 1-20mA and a full-range measurement error of less than 5%.

[0043] The data processing center receives and integrates multi-source data collected by the wearable devices, and generates a safety risk index or fatigue level characterizing the current state of the workers by executing human physiological indicator monitoring methods. The monitoring method includes the following steps: Step S1: Establish a finite element simulation model of the transmission line tower and the working scenario. The model includes a tower structure model, a conductor model, and a human body model. Set electrical parameters and boundary conditions according to the actual working conditions.

[0044] The human body model established in step S1 is a simplified geometric model, using a homogeneous medium approximation, with its conductivity set to 0.1 S / m and relative permittivity set to 10. 6 Furthermore, during finite element analysis, the mesh is refined for the human body surface and areas with large curvature changes.

[0045] The electrical parameters and boundary conditions set in step S1 include: 1.1) Conductor voltage levels, including one or more of 10kV, 110kV, 220kV, 500kV, ±500kV, ±800kV and 1000kV; 1.2) Effective value of conductor current; 1.3) Conductor height above ground, phase spacing, and structural parameters of split conductors; 1.4) Boundary conditions include: ground potential is set to 0, conductor surface potential is set to operating voltage, and artificial boundary potential is set to nominal voltage.

[0046] Step S2: By solving the finite element simulation model, electromagnetic field distribution data of the operator at a typical working position is obtained. The electromagnetic field distribution data includes the surface electric field strength, the induced current density in the body, and the magnetic induction intensity.

[0047] In step S2, the typical work location includes at least: 2.11) Ground location beneath the tower; 2.12) Position of the crossarm on the upper part of the tower; 2.13) Equipotential working positions on conductors; 2.14) Location of foundation construction equipment near the tower; 2.15) Work locations crossing or traversing under railway lines; Step S2 also includes considering the influence of different meteorological factors on the electromagnetic field distribution, wherein the meteorological factors include at least: 2.21) Relative humidity, ranging from 30% to 80%; 2.22) Ambient temperature, ranging from -20℃ to 40℃; 2.23) Atmospheric pressure, ranging from 50 kPa to 101.325 kPa; 2.24) Wind speed and wind direction.

[0048] Step S3: Based on the electromagnetic field distribution data obtained in step S2, establish a coupling relationship model between electromagnetic field strength and human physiological indicators, including blood oxygen saturation, blood pressure, heart rate, and body temperature.

[0049] In step S3, blood oxygen saturation is established. The mathematical model specifically includes: Establish an alveolar-arterial oxygen dynamics model: ; In the formula: It is the partial pressure of oxygen in arterial blood; This refers to the partial pressure of oxygen in the alveoli. For time; This is due to the frequent occurrence of pulmonary gas exchange; For the blood volume involved in rapid gas exchange; This refers to the rate of total body oxygen consumption. alveolar oxygen partial pressure Affected by altitude: ; In the formula: The oxygen fraction inhaled. Atmospheric pressure decreases with altitude. It is the vapor pressure of water. This refers to the partial pressure of carbon dioxide in the alveoli. For respiratory quotient; Explicitly incorporate height effects and electromagnetic disturbances into the parameterization terms: ; ; In the formula: h For altitude, This is the atmospheric pressure decay constant with altitude; This refers to the basal oxygen consumption rate at rest. Operating power; It senses electrical charges within and on the body surface. Indicators of psychological stress; The contribution coefficient of workload to oxygen consumption rate; The contribution coefficient of electromagnetic field induced current to oxygen consumption rate; The contribution coefficient of psychological stress to oxygen consumption rate; As an indicator of psychological stress; Blood oxygen saturation is expressed using the Hill equation: ; in To achieve an arterial oxygen partial pressure with 50% Hb saturation, n is the Hill coefficient.

[0050] In step S3, a mathematical model for blood pressure (BP) is established, which specifically includes: Mean arterial pressure Dynamic model representation: ;

[0051] For cardiac output; Total peripheral resistance; cardiac output is determined by heart rate. With stroke volume Decide: ; The sympathetic-parasympathetic regulation and external disturbances are modeled as state equations: ; ; In the formula: This refers to the baseline heart rate at rest. The time constant for heart rate regulation. For pressure reflection gain, The setpoint blood pressure value for pressure reflex. This is the direct driving term of the electromagnetic field on heart rate. Environmental factors drive heart rate; Basic peripheral resistance; The coefficient representing the effect of low oxygen on peripheral resistance; This is a hypoxia indicator function; The coefficient representing the influence of electromagnetic field-induced current on peripheral resistance; This refers to the induced current density or the surface potential difference. The coefficient representing the influence of psychological stress on peripheral resistance; The linear sensitivity to short-time disturbances is approximated as follows: ; The change in blood pressure The change in cardiac output. Based on the core output volume This represents the change in total peripheral resistance; thus, the contribution of elevation gain and EM to BP can be estimated.

[0052] In step S3, a risk assessment model for heart rate variability (HRV) is established, including: Let the instantaneous trigger rate λ(t) be used to describe the instantaneous risk of severe cardiac arrhythmias: ; Based on the basic cardiac rhythm event trigger rate, The contribution coefficient of hypoxia to the risk of cardiac arrhythmias. This represents the contribution coefficient of electromagnetic fields to the risk of cardiac arrhythmias. g ( ) is a nonlinear mapping function for the risk of induced electrical events; The contribution coefficient of psychological stress to the risk of cardiac arrhythmic events is given within the observation window. Internal event occurrence probability : ; For integration variables; Using time-domain and frequency-domain HRV indices to Alternatively, dynamic calibration can be performed using baroreflex sensitivity.

[0053] In step S3, a mathematical model of body temperature T is established, and the Pennes biological heat conduction equation is used to describe the local tissue temperature change: ; in, For tissue density, To organize specific heat capacity, Let r be the tissue temperature at time t. To improve the thermal conductivity of the tissue, For the Laplace operator, Blood density, For the specific heat capacity of blood, For blood perfusion rate, Arterial blood temperature, Metabolic heat production rate, The rate of heat generation from electromagnetic field energy absorption; Instantaneous power density This is used to quantify the thermal effects of electromagnetic field energy absorption on local tissues. Let r be the tissue conductivity at position r. Let be the electric field strength at position r at time t; Simplified full-body box model: ; In the formula: For total body heat capacity, For the core temperature, For heat dissipation, related to ambient temperature It is related to wind speed and insulating clothing. Metabolic power, This refers to electromagnetic absorption power.

[0054] Step S4: Construct a multi-parameter comprehensive evaluation model. Normalize and weight the predicted or measured values ​​of multiple physiological indicators obtained in step S3 to generate a comprehensive safety index or fatigue level that characterizes the overall physiological load of the human body.

[0055] In step S4, the constructed multi-parameter comprehensive evaluation model includes the following sub-steps: Step S41: Normalize the physiological indicators: Heart rate normalization function : ; This represents the lower limit of the normal heart rate range. This represents the upper limit of the normal heart rate range. Blood pressure normalization function : ; To measure systolic blood pressure, To measure diastolic blood pressure, This represents the upper limit of the normal range for systolic blood pressure. This represents the lower limit of the normal range for diastolic blood pressure. Blood oxygen normalization function : ; Body temperature normalization function : ; Step S42, weighted fusion to generate instantaneous fatigue value :

[0056] Among them, the weighting coefficient satisfy Furthermore, adjustments are made dynamically based on the type of work and individual differences; Step S43, establish the electromagnetic field correction factor: ;

[0057] in, The fatigue value after electromagnetic field correction. For the normalized electric and magnetic field strengths, This is a correction factor.

[0058] Step S4 also includes a step of predicting fatigue using a deep learning model: 4.1) Construct a recurrent neural network or Transformer model. Input features include: historical physiological index time series, environmental electromagnetic parameter time series, work load information, and meteorological parameters. 4.2) Model output is the future Predicted fatigue level at any time : ; For time window The input feature sequence within; 4.3) Adopt a hybrid evaluation strategy: when or When this happens, an alert is triggered.

[0059] To more accurately describe the nonlinear memory characteristics and cumulative load effects of the human physiological system under complex electromagnetic environments, the fatigue mathematical model constructed in step S4 introduces fractional calculus theory to replace the traditional integer-order model. Specifically, it defines the cumulative fatigue state of workers in high-altitude, strong electromagnetic environments. Satisfy the following fractional differential equations: ; in, Let be the cumulative fatigue state value at time t, ranging from 0 to 100%. It is a fractional order used to characterize the memory effect of fatigue accumulation in physiological systems. The smaller the value, the greater the impact of historical conditions on current fatigue. for A fractional-order differential operator, where t is the time variable. Let be the normalized heart rate deviation function at time t, reflecting the degree to which the heart rate deviates from the normal range. Let be the normalized blood pressure deviation function at time t, reflecting the degree to which blood pressure deviates from the normal range. Let be the normalized blood oxygen deviation function at time t, reflecting the degree to which blood oxygen saturation deviates from the normal range. Let be the normalized body temperature deviation function at time t, reflecting the degree to which body temperature deviates from the normal range. a, b, c, and d are coupling coefficients, corresponding to the weights of the contributions of heart rate, blood pressure, blood oxygen, and body temperature changes to fatigue, respectively. The recovery coefficient characterizes the rate of fatigue recovery for workers during rest.

[0060] The fractional model is discretized and solved using the Grunwald-Letnikov numerical approximation method: ; Where T is the sampling period, and n is the truncation order, representing the number of historical states considered. The coefficients are the generalized binomial coefficients. Before time t The function value in units of time; Compared to integer-order models, this model can more accurately characterize the nonlinear accumulation process of fatigue and the recovery delay effect, improving prediction accuracy by about 20%. It is especially suitable for the non-stationary evolution of the physiological state of workers in high-altitude, strong electromagnetic environments.

[0061] Step S5: Based on the comparison results between the comprehensive safety index or fatigue level and the preset threshold, the physiological safety status of the human body is graded, assessed, and warned.

[0062] In step S5, the grading assessment includes: Level I: Indicates safety; overall safety index. or fatigue The human body is in a state of physiological homeostasis; Level II: Indicates mild risk. The physiological compensation mechanism has been activated; it is recommended to strengthen monitoring. Level III: Indicates danger. When physiological regulation is out of balance, an audible and visual alarm is immediately triggered, and the warning information is uploaded to the cloud monitoring platform.

[0063] Following step S5, there is also an individual difference calibration step: I. The resting physiological parameters of the personnel under electromagnetic exposure-free conditions were used as the baseline; II. Dynamically adjust the reference range of each physiological indicator based on the resting baseline. and individual differences in the model ; III. Regularly update model parameters to adapt to long-term changes in personnel's physiological state.

[0064] The method also includes model validation and correction steps: i. Compare the electromagnetic field data and physiological parameters measured on-site with the model predictions; ii. Calculate the error rate. If the error exceeds the preset threshold (e.g., 5%), then correct the model parameters using the least squares method or Bayesian optimization. iii. The corrected model parameters are sent to the wearable device via the wireless communication module to achieve online model updates.

[0065] The method also includes an adaptive adjustment step for the early warning threshold: Based on the real-time fatigue level of the workers Based on historical data, the alarm thresholds for environmental parameters such as electric field, magnetic field, and leakage current are dynamically adjusted. when When the levels are high, appropriately lower the alarm threshold for environmental parameters to improve the sensitivity of the early warning. when When the alarm level is low, restore the standard alarm threshold to reduce false alarms.

[0066] The above method obtains electromagnetic field distribution data by establishing a finite element simulation model, constructs an electromagnetic-physiological coupling model and a multi-parameter comprehensive evaluation model, generates a comprehensive safety index or fatigue level, and realizes graded assessment and early warning of human physiological safety status.

[0067] The data processing center includes: The data receiving module receives multi-source data collected by wearable devices in real time via wireless communication; The data preprocessing module performs filtering, noise reduction, outlier removal, and time synchronization on the raw data. A multi-level data fusion model performs hierarchical processing and intelligent analysis on pre-processed data; The model calculation module calls the pre-set physiological-electromagnetic coupling model and deep learning model to calculate the fatigue level and safety risk index of the workers in real time. The individual difference calibration module dynamically adjusts model parameters based on the resting baseline data of the workers; The early warning decision module generates tiered early warning information based on the fusion results and preset thresholds. The data storage module is used to cache historical data locally, ensuring that data is not lost when the network is interrupted.

[0068] The multi-level data fusion model includes: The first level is the basic monitoring layer, which monitors the threshold of a single parameter and generates a basic alarm event when any parameter exceeds the preset normal range. The second level is the association analysis layer, which performs cross-validation and causal association analysis on different parameters: Using electric and magnetic field data to compensate for interference in physiological signals and identify data distortion caused by electromagnetic interference; The correlation analysis between the near-electric shock alarm signal and the leakage current abnormal signal, combined with physiological indicators such as heart rate and skin conductance, is used to comprehensively determine whether an induced electric shock has occurred. When multiple related abnormal signals are received simultaneously, a specific joint alarm is generated. The third layer is the situation assessment layer, which integrates all real-time data, including physiological parameters, environmental parameters, proximity level and leakage current value, and calculates the overall safety risk index through weighted fusion and deep learning model to conduct a comprehensive risk situation assessment. The fourth layer is the trend prediction layer. Based on historical data time series, it uses recurrent neural networks or Transformer models to predict fatigue change trends in the near future and generate forward-looking early warning information.

[0069] To achieve holistic situational awareness of the complex coupling risks among workers, the work environment, and live equipment, the multi-level data fusion model introduces a risk propagation model based on a spatiotemporal graph neural network in its third level (situation assessment layer). This model, derived from knowledge graph and complex network theory, is used to capture the dynamic relationships and evolution paths between various risk factors (such as personnel physiological state, proximity to electrical equipment, electromagnetic field distribution, and meteorological conditions) in high-altitude work scenarios. The specific construction method is as follows: 1) Graph structure definition. Construct a heterogeneous graph. ,in, It is a set of nodes, including worker nodes, equipment nodes (such as towers and conductors), and environmental nodes (such as electric field regions and magnetic field regions). An edge set represents the physical association or risk propagation path between nodes. For example, the "person-conductor" edge represents near-electric risk, and the "person-environment" edge represents electromagnetic exposure.

[0070] 2) Node Feature and Edge Weight Update. At each time step, the feature vector of each node... Updated based on corresponding real-time monitoring data (such as fatigue level and electric field strength). The node state is updated by aggregating information from neighboring nodes using a graph convolutional network. ; in, for Time Node The feature vector contains the real-time monitoring data corresponding to that node. for Time Node The updated feature vector, For non-linear activation functions, such as ReLU or Sigmoid, This is a self-connection weight matrix used to preserve the state information of each node. A weight matrix is ​​aggregated for neighbors, used to fuse the state information of neighbor nodes. For nodes The set of neighboring nodes, The edge weight function calculates the node weights based on factors such as physical distance and shielding efficiency. With nodes The strength of the correlation between them for Time Neighbor Nodes eigenvectors.

[0071] 3) Overall Risk Index Calculation. An attention mechanism is used to weight and pool the updated states of each node to generate an overall risk situation index for the operational scenario. : ; in, This is a risk situation index for the overall scenario, with a value range of... A higher value indicates a higher overall risk. The query matrix is ​​obtained by linear transformation of the updated states of each node and is used to represent the risk type currently of concern. The key matrix is ​​obtained by linear transformation of the updated states of each node and is used to represent the risk information carried by each node. The value matrix is ​​obtained by linear transformation of the updated states of each node and is used to represent the contribution weight of each node to the overall risk. The dimension of the key vector is used to scale the dot product result and prevent gradient vanishing. This graph neural network model, through end-to-end learning, can uncover complex risk propagation paths that are difficult to identify using traditional methods (such as increased wind speed, conductor galloping, fluctuations in proximity to power lines, and intensified stress responses by personnel), achieving a leap from single-parameter early warning to full-scenario situational awareness.

[0072] The cloud-based monitoring platform is connected to the data processing center via a wireless communication module. It is used to display the multi-source data, security risk index, and early warning information in real time, and to store and analyze historical data.

[0073] The cloud-based monitoring platform connects to the data processing center via a wireless communication module to display multi-source data, safety risk indices, and early warning information (such as heart rate waveforms and electric field strength) in real time, and to store and analyze historical data. The platform features a visual interface that displays the real-time location of workers, physiological parameter curves, changes in environmental parameters, and a list of alarm events. Upon receiving a Level 1 emergency alarm, the platform automatically issues an audible and visual alarm and notifies the remote management terminal via SMS and app push notifications. Management personnel can take appropriate measures based on the warning level, including remotely directing evacuation, dispatching rescue teams, or adjusting work methods.

[0074] The cloud-based monitoring platform also includes a portable on-site monitoring terminal, which is a handheld explosion-proof tablet with a high-brightness display and physical buttons, suitable for outdoor environments with strong light. It can connect directly to wearable devices via Bluetooth or Wi-Fi to achieve local monitoring in environments without a network. It has an on-site map display function, which can display the location and status of workers in real time. It supports a one-click emergency call function (workers trigger an emergency signal by pressing the emergency button on their wristband) and an on-site command and dispatch function (managers send voice commands or evacuation signals to workers through the terminal).

[0075] The data processing center also includes an individual difference calibration module, used for: The resting physiological parameters of the personnel under electromagnetic exposure-free conditions were used as a baseline, including resting heart rate, baseline blood pressure, resting blood oxygen saturation, and baseline body temperature. The reference ranges of various physiological indicators are dynamically adjusted based on the resting baseline. and blood oxygen alarm threshold; Establish an individualized physiological parameter database and continuously optimize baseline data as operators use the equipment for longer periods. Calibration commands are periodically issued through a cloud-based monitoring platform to enable online updates of model parameters.

[0076] In this embodiment, the conductive rubber is prepared by the following method: Using ethylene-ethyl acrylate copolymer (EEA) as the matrix material; Carbon black (CB) was used as a conductive additive, with a doping mass fraction of 30%. 0.7% by mass of 3(1,4)-bis(tert-butylperoxyisopropyl)benzene (BIPB) was used as a crosslinking agent; Mix thoroughly in an internal mixer at 115℃ for 20 minutes; The mixed material is placed in a mold and hot-pressed using a flat vulcanizing machine. The vulcanization temperature is 120℃, the vulcanization time is 30min, and the pressure is 20MPa. The final product is a conductive rubber sheet with a diameter of 10 mm and a thickness of 400 μm.

[0077] In this embodiment, the wireless communication module adopts 4G CAT1 communication mode, follows the MQTT transmission protocol, and periodically sends custom messages containing collected information to the cloud monitoring platform; Data is reported every 2-3 seconds. The communication module integrates an anti-static protection circuit, and a TVS diode is used to suppress surge interference. It supports network reconnection and data resume functions, automatically re-uploading data that was offline after the network is restored; The communication protocol supports both data reporting and command issuance, and is used for remote configuration and firmware upgrades.

[0078] In this embodiment, the power management unit includes: Lithium battery, battery voltage 3.7V, capacity 80mAh, dimensions 20mm×10mm×3mm; The charging chip, model BQ24072RGTR, has a maximum charging current of 1.5A and a charging voltage of 4.2V. It features USB interface connection detection, charging indication, and charging completion indication functions. The voltage regulator chip, model LD3985M33R, steps down the battery voltage to 3.3V DC, with a maximum output current of 550mA. The monostable switch control circuit uses button S1 in conjunction with the PB7 pin of the MCU to control the system's power on / off state. LED status indicators are used to indicate various states such as USB connection, charging complete, and system power-on. The low-power management strategy employs an RTC intermittent wake-up mechanism, switching the system to sleep mode during non-data acquisition periods. The operating current is ≤4mA, the sleep current is ≤10μA, and the battery life is ≥72 hours.

[0079] In this embodiment, the audible and visual alarm unit includes: The light-emitting alarm circuit includes a transistor, an optocoupler chip TLP185, and a light-emitting diode, which are used to drive the light alarm. The audible alarm circuit includes a transistor, an optocoupler chip TLP185, and a buzzer, used to drive the audible alarm. The optocoupler chip is used to achieve signal isolation and enhance anti-interference capability; Alarm methods include: on-site audible and visual alarms, wristband vibration alarms, cloud platform pop-up alarms, and SMS push alarms; The alarm levels include: Level 3 warning (indicative), Level 2 alarm (warning), and Level 1 emergency alarm (immediate evacuation).

[0080] In this embodiment, the system also includes a portable on-site monitoring terminal, the portable terminal comprising: This handheld explosion-proof tablet features a high-brightness display and physical buttons, making it suitable for outdoor environments with strong sunlight. The local data receiving module connects directly to wearable devices via Bluetooth or Wi-Fi, enabling local monitoring in environments without a network. The on-site map display function shows the real-time location distribution and status of the workers; The one-button emergency call function allows workers to trigger an emergency signal via the emergency button on their wristband; The on-site command and dispatch function allows managers to send voice commands or evacuation signals to workers via terminals.

[0081] This embodiment provides a method for monitoring the status of workers in a high-altitude, strong electromagnetic environment. It employs the aforementioned high-altitude, strong electromagnetic environment worker status monitoring system and includes the following steps: Step A, Equipment Deployment and Initialization: One hour before the operation, check the battery level, communication status, and sensor functions of each wearable device; Bind the equipment to the operators and enter the operators' basic information; Collect resting physiological parameters of workers under electromagnetic exposure-free conditions to establish individual baselines; Confirm that the cloud monitoring platform can be logged into normally and that the data upload link is working smoothly.

[0082] Step B: Synchronous acquisition of multi-source data: Wearable devices synchronously collect physiological parameter data, environmental parameter data, proximity distance data, and leakage current data at a preset frequency of 2-3 seconds per time. Each device ensures data timestamp consistency through an internal clock synchronization mechanism; After local preprocessing, the data is uploaded to the data processing center in real time via a 4G CAT1 module.

[0083] Step C: Data Preprocessing and Anti-interference Compensation The data processing center filters, denoises, and removes outliers from the received multi-source data; Using electric and magnetic field data to perform anti-interference compensation on physiological signals, and to identify and correct data distortion caused by electromagnetic interference; Based on the preset mapping relationship between different voltage levels and safe distances, real-time distance information is converted into a dynamic proximity risk level.

[0084] In this embodiment, to address the nonlinearity, non-stationarity, and strong interference characteristics of multi-source sensor signals under high-altitude strong electromagnetic environments, data preprocessing includes a hybrid noise reduction algorithm based on adaptive Kalman filtering and variational mode decomposition. This algorithm originates from advanced signal processing and adaptive control theory and specifically includes the following steps: The first step is to construct a state-space model. The collected raw physiological signals (such as heart rate and blood pressure) are used as observations. The system state vector is defined as the real physiological parameters and their first derivatives. State transition equations and observation equations that include electromagnetic interference as process noise are established.

[0085] The second step is to perform variational mode decomposition. The original signal is preprocessed and decomposed into several eigenmode functions with sparse characteristics. Based on the center frequency, the effective physiological signal frequency band and the electromagnetic interference frequency band are distinguished, and high-frequency noise modes are removed.

[0086] The third step is adaptive Kalman filtering. The reconstructed signal after variational mode decomposition is used as the observation input to the Kalman filter. Mahalanobis distance is introduced as an outlier detection metric. The statistical characteristics of the innovation sequence are calculated in real time, and the process noise covariance matrix of the filter is dynamically adjusted to achieve adaptive updating of the filter gain. This algorithm can be described as follows: State estimation formula: ; Kalman gain formula: ; in, The state estimate at time k (the filtered physiological parameters, such as the true values ​​of heart rate and blood pressure). The state value at time k is predicted based on time k-1. The Kalman gain matrix at time k controls the fusion weights of the predicted and observed values. The value at time k is the observation (the raw physiological signal collected by the sensor). The observation matrix maps the state vector to the observation space. Let be the prediction error covariance matrix at time k. Let be the observation noise covariance matrix at time k, which characterizes the statistical properties of the sensor measurement noise; Formula for calculating Mahalanobis distance: ; in, Mahalanobis distance is used to determine whether the current observation is an outlier. Let T be the information covariance matrix, which represents the statistical properties of the difference between the predicted and observed values. T is the matrix transpose symbol. In the above algorithm, According to Mahalanobis distance The real-time calculation results are dynamically adjusted: when When the value exceeds a preset threshold, it indicates that the observed value may be subject to strong electromagnetic interference, and the system will automatically increase the threshold. This reduces the weight of the observation in the state estimation. This design effectively suppresses periodic noise and pulse interference caused by power frequency electromagnetic fields, improving the signal-to-noise ratio by approximately 15 dB compared to traditional low-pass filtering while ensuring real-time signal performance.

[0087] Step D: Multi-level data fusion analysis: Level 1: Threshold monitoring of a single parameter to generate basic alarm events; The second level involves cross-validation and causal correlation analysis of different parameters to generate a targeted joint alarm by integrating multiple abnormal signals. The third level: calculate the security risk index by integrating all real-time data and conduct a comprehensive risk situation assessment; The fourth level: Based on historical time series data, use deep learning models to predict fatigue trends in the near future; Step E: Calculation of Fatigue Level and Safety Risk Index: By invoking a pre-defined physiological-electromagnetic coupling model, and based on the current environmental electromagnetic field strength and physiological parameters, the instantaneous fatigue level of the workers is calculated in real time. ; Output predicted fatigue levels for future moments using a deep learning model. ; The fatigue level is corrected by combining the electromagnetic field strength to generate the final safety risk index; Step F, Tiered Early Warning and Response: when or At that time, a Level 1 emergency alarm was triggered; When a single parameter exceeds the normal range but the overall risk is low, a Level 3 warning is triggered. The cloud-based monitoring platform displays various data visually, automatically issues audible and visual alarms, and notifies the remote management terminal via SMS, APP push, and other means. Management personnel will take corresponding measures based on the warning level, including remotely directing evacuation, dispatching rescue teams, or adjusting operational methods; Step G, Data Storage and Analysis: All collected data and alarm events are stored in a cloud database; Regularly generate health reports for workers and on-site safety situation analysis reports; By utilizing historical data to optimize model parameters, the system can achieve self-learning and continuous improvement.

[0088] This embodiment provides a device for monitoring the status of workers in high-altitude, strong electromagnetic environments, which adopts the aforementioned monitoring system for monitoring the status of workers in high-altitude, strong electromagnetic environments.

[0089] To verify the effectiveness and reliability of the system in this embodiment, finite element simulation analysis and field engineering demonstration applications under multiple voltage levels were carried out.

[0090] 1. Finite element simulation verification Based on the COMSOL Multiphysics simulation platform, finite element models of 10kV, 110kV, 220kV, and 500kV AC transmission towers and ±500kV, ±800kV, and 1000kV DC transmission lines were established, and a human body model (height 1.7m, conductivity 0.1S / m, relative permittivity 10) was imported into the simulation. 6 The simulation results are as follows: 10kV maintenance conditions: When B-phase is disconnected during maintenance, the electric field strength at the operator's hand reaches 922.6kV / m, the surrounding magnetic induction intensity reaches 117.1μT, and the induced current density is several orders of magnitude higher than that under normal conditions.

[0091] Working conditions on a 110kV tower: When the worker is located on the crossarm, the peak electric field strength above the head reaches 20kV / m (twice the occupational exposure limit of 10kV / m), and the magnetic induction intensity above the head reaches 50μT.

[0092] Working conditions on a 500kV tower: The peak electric field strength above the head reaches 100kV / m (10 times the national limit), the magnetic induction intensity above the head reaches 200μT, and the induced current density reaches 7.05×10 -8 A / m².

[0093] ±800kV maintenance conditions: When maintenance personnel are on the crossarm of the straight tower, the maximum electric field strength on their body surface reaches 1596.65kV / m. After wearing a shielding suit with a shielding efficiency of 40dB, the electric field strength inside their body can be reduced to a safe range (<15kV / m).

[0094] Simulation results show that the electromagnetic-human coupling model established in this embodiment can accurately reflect the electromagnetic exposure level under different working conditions, providing a theoretical basis for setting the early warning threshold.

[0095] 2. Engineering Demonstration Application At a 220kV transmission line tower construction site in Tongling, five workers were selected to wear the wearable device of this invention for a week-long engineering demonstration application. During the application period, the system accumulated 120 hours of operation, collected over 200,000 data entries, and successfully triggered 15 early warning events (including 8 near-field warnings, 4 abnormal heart rate warnings, and 3 fatigue warnings). On-site comparative tests showed that: The electric field sensor has a measurement error of <5%, and the deviation from the measurement value of the standard field strength meter (Narda EHP-50F) is within the allowable range.

[0096] Synchronous comparison of heart rate monitoring values ​​with those of a medical-grade ECG monitor (Mindray iMEC8) showed an average error of <2 bpm.

[0097] In simulated electric shock tests, the leakage current monitoring module achieved 100% accuracy in identifying currents above 5mA.

[0098] During the application, the system successfully issued a warning once due to the worker's fatigue level exceeding the standard (F=85%) caused by prolonged high-altitude work. The back-end administrator promptly issued a rest instruction, avoiding potential safety risks. The worker reported that the wearable equipment was lightweight and comfortable and did not interfere with normal work.

[0099] The present invention and its embodiments have been described above illustratively. This description is not restrictive, and the figures shown are only one embodiment of the present invention; the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, such designs should fall within the protection scope of the present invention.

Claims

1. A personnel status monitoring system for high-altitude, high-electromagnetic environments, characterized in that: This includes wearable devices, cloud monitoring platforms, and data processing centers; The wearable device includes: Multiple physiological parameter sensors are integrated into the safety helmet to collect real-time data on the worker's heart rate, blood pressure, blood oxygen saturation, and body temperature. Multiple environmental parameter sensors are integrated into the webbing of the safety belt to collect real-time data on the electric field strength, magnetic field strength, and audible noise of the environment in which the worker is located. The proximity alarm module, integrated into the wristband, is used to detect the distance to a live conductor and trigger a proximity alarm when the distance is less than a preset threshold. The leakage current monitoring module is integrated into the insole to collect leakage current data flowing through the human body in real time. The data processing center receives and integrates multi-source data collected by the wearable devices, and generates a safety risk index or fatigue level characterizing the current state of the workers by executing human physiological indicator monitoring methods. The monitoring method includes the following steps: Step S1: Establish a finite element simulation model of the transmission line tower and the working scenario. The model includes a tower structure model, a conductor model and a human body model, and set electrical parameters and boundary conditions according to the actual working conditions. Step S2: By solving the finite element simulation model, electromagnetic field distribution data of the operator at a typical working position is obtained. The electromagnetic field distribution data includes the surface electric field strength, the induced current density in the body, and the magnetic induction intensity. Step S3: Based on the electromagnetic field distribution data obtained in step S2, establish a coupling relationship model between electromagnetic field strength and human physiological indicators, including blood oxygen saturation, blood pressure, heart rate and body temperature. Step S4: Construct a multi-parameter comprehensive evaluation model, normalize and weight the predicted or measured values ​​of multiple physiological indicators obtained in step S3, and generate a comprehensive safety index or fatigue level to characterize the overall physiological load of the human body. Step S5: Based on the comparison results between the comprehensive safety index or fatigue level and the preset threshold, the physiological safety status of the human body is graded, assessed, and warned. The cloud-based monitoring platform connects to the data processing center via a wireless communication module to display the multi-source data, security risk index, and early warning information in real time, and to store and analyze historical data.

2. The personnel status monitoring system under high-altitude strong electromagnetic environment according to claim 1, characterized in that: The physiological parameter sensor includes: The heart rate sensor uses photoplethysmography (PPG) technology and includes a dual-wavelength LED light source and a photodiode detector, with a sampling rate of no less than 8kSPS. Blood pressure sensors, based on the pulse wave transit time method or cuff-type pressure sensors, combined with data fitting algorithms, enable continuous estimation of blood pressure values. The blood oxygen sensor shares the PPG channel with the heart rate sensor and determines blood oxygen saturation by calculating the ratio of the absorption rates of red and infrared light. The body temperature sensor uses an infrared thermopile or a high-precision NTC thermistor, with a measurement accuracy of ±0.1℃; Each sensor is integrated with the helmet liner via a flexible circuit board, and the parts of the sensors that come into contact with human skin are made of medical-grade silicone.

3. The personnel status monitoring system under high-altitude strong electromagnetic environment according to claim 2, characterized in that: The environmental parameter sensors include: The electric field sensor is a miniature power frequency electric field sensor based on a planar capacitor structure, consisting of two parallel plates with a fixed plate spacing. Its output voltage U(t) satisfies the following relationship with the spatial electric field intensity E(t): Where d is the electrode spacing, These are the dielectric constants of air and the insulating medium, respectively. The magnetic field sensor is a triaxial magnetic field sensor based on the tunneling magnetoresistance (TMR) effect. It contains three orthogonally arranged TMR sensing units to detect the magnetic field components in the x, y, and z axes, respectively. The measurement range covers 25-200 μT, and the frequency response covers 50 Hz of the power frequency. The noise sensor uses an electret condenser microphone array, combined with a preamplifier circuit and a bandpass filter, with an effective frequency band of 30Hz-8kHz and an equivalent continuous sound pressure level measurement range of 0-130dB.

4. The personnel status monitoring system under high-altitude strong electromagnetic environment according to claim 3, characterized in that: The electric field sensor is also equipped with a signal conditioning circuit, including: A voltage divider circuit is used to adjust the sensor output signal to the ADC input range; A second-order low-pass filter circuit with a cutoff frequency of 100Hz is used to filter out high-frequency interference signals. The differential amplifier circuit uses an instrumentation amplifier to improve the common-mode rejection ratio. A level-up circuit is used to convert bipolar signals into unipolar signals for subsequent processing. A 10μF and a 0.1μF decoupling capacitor are connected in parallel at the power input terminal to filter out low-frequency and high-frequency interference, respectively. The TMR magnetic field sensor suppresses interference from external uniform magnetic fields through a differential structure design, specifically including: Two TMR sensing elements are arranged opposite each other in space along the same diameter to form a differential measurement structure; The output signals of the two sensitive elements are processed by a differential amplifier to cancel the common-mode interference magnetic field; The magnetic field interference of the differential structure Defined as the ratio of the sensor's responsivity to the measured current to its responsivity to the interference current, its mathematical expression is: ； in, The distance from the sensing element to the conductor being measured. The distance between the interfering current and the current to be measured. The arrangement angle of the two TMR sensing elements; By optimizing the arrangement angle ,make exist The value approaches zero, achieving optimal suppression of external interfering magnetic fields; The arrangement angle of the TMR magnetic field sensor Determined in the following ways: Establish a mathematical model of magnetic field interference. ; In actual installation parameters and Given the given conditions, solve the equation The theoretically optimal arrangement angle is obtained. ; exist Finite element simulation was performed within the specified range, and the angle with the smallest crosstalk error was selected as the final installation angle. After optimization, the crosstalk error of the sensor is controlled within 0.08% in a three-phase interference scenario.

5. The personnel status monitoring system under high-altitude strong electromagnetic environment according to claim 4, characterized in that: The proximity alarm module includes: The proximity alarm chip, model JW0858, has a power supply voltage of 3.3V. The threshold setting resistor allows adjustment of the electric field alarm threshold by changing the resistance value of resistor R2. When R2 = 100kΩ, it can provide early warning for electric fields generated by AC voltages of 1kV and above. The PWM output interface uses pin 6 of the chip to output a PWM waveform as a warning trigger signal, which is connected to pin PA15 of the MCU through resistor R3. Alarm thresholds are tiered, with different alarm distances set according to different voltage levels: 10kV voltage alarm distance 0.7-1.6m, 35kV voltage alarm distance 1.0-2.5m, 110kV voltage alarm distance ≤3m, and 220kV voltage alarm distance ≤3m. The leakage current monitoring module includes: The sampling electrode has a double-layer structure, consisting of a copper electrode and a conductive rubber covering it. The electrode diameter is 10 mm and the total thickness is 400 μm, of which the conductive rubber is 300 μm thick and the copper electrode is 100 μm thick. The logarithmic detector, model AD8310AMZ, operates at 3.3V, has a detection range of DC-440MHz, and a dynamic range of -91dBV to 4dBV. The sampling resistor consists of resistors R1 and R3, forming an equivalent 50Ω current sampling resistor. The TVS protection circuit is connected between the output of the logarithmic detector and the ADC port of the MCU for overvoltage protection. The leakage current monitoring module has a measurement range of 1-20mA and a full-range measurement error of less than 5%.

6. The personnel status monitoring system under high-altitude strong electromagnetic environment according to claim 5, characterized in that: The wearable device adopts an integrated packaging structure, including: The safety helmet unit integrates physiological parameter sensors and main control circuits. The sensor module adopts an embedded design and is tightly integrated with the inner lining of the safety helmet, with an additional weight of no more than 50g. The wristband unit features a flexible wristband design, with a weight controlled to within 50g. The watch case integrates a proximity alarm module, and the wristband is compatible with different wrist sizes. The seat belt unit integrates environmental parameter sensors into the seat belt webbing. The sensors are wrapped with wear-resistant and pressure-resistant material, so as not to affect the extension and locking functions of the seat belt. The insole unit integrates the leakage current monitoring module inside the insole. It adopts a flexible circuit board and a thin design without changing the original thickness and comfort of the insole. It is compatible with shoe sizes 40-45 and supports a cuttable design. Each unit meets IP65 or higher protection standards, key interfaces adopt a waterproof plug-in structure, and silicone rubber sealing rings are installed at the seams of the outer shell.

7. The personnel status monitoring system for high-altitude strong electromagnetic environment according to claim 6, characterized in that: The data processing center includes: The data receiving module receives multi-source data collected by wearable devices in real time via wireless communication; The data preprocessing module performs filtering, noise reduction, outlier removal, and time synchronization on the raw data. A multi-level data fusion model performs hierarchical processing and intelligent analysis on pre-processed data; The model calculation module calls the pre-set physiological-electromagnetic coupling model and deep learning model to calculate the fatigue level and safety risk index of the workers in real time. The individual difference calibration module dynamically adjusts model parameters based on the resting baseline data of the workers; The early warning decision module generates tiered early warning information based on the fusion results and preset thresholds. The data storage module is used to cache historical data locally, ensuring that data is not lost when the network is interrupted; The multi-level data fusion model includes: The first level is the basic monitoring layer, which monitors the threshold of a single parameter and generates a basic alarm event when any parameter exceeds the preset normal range. The second level is the association analysis layer, which performs cross-validation and causal association analysis on different parameters: Using electric and magnetic field data to compensate for interference in physiological signals and identify data distortion caused by electromagnetic interference; The correlation analysis between the near-electric shock alarm signal and the leakage current abnormal signal, combined with physiological indicators such as heart rate and skin conductance, is used to comprehensively determine whether an induced electric shock has occurred. When multiple related abnormal signals are received simultaneously, a specific joint alarm is generated. The third layer is the situation assessment layer, which integrates all real-time data, including physiological parameters, environmental parameters, proximity level and leakage current value, and calculates the overall safety risk index through weighted fusion and deep learning model to conduct a comprehensive risk situation assessment. The fourth layer is the trend prediction layer. Based on historical data time series, it uses recurrent neural networks or Transformer models to predict fatigue change trends in the near future and generate forward-looking early warning information.

8. The personnel status monitoring system for high-altitude strong electromagnetic environment according to claim 7, characterized in that: The data processing center also includes an individual difference calibration module, used for: The resting physiological parameters of the personnel under electromagnetic exposure-free conditions were used as a baseline, including resting heart rate, baseline blood pressure, resting blood oxygen saturation, and baseline body temperature. The reference ranges of various physiological indicators are dynamically adjusted based on the resting baseline. and blood oxygen alarm threshold; Establish an individualized physiological parameter database and continuously optimize baseline data as operators use the equipment for longer periods. Calibration commands are periodically issued through a cloud-based monitoring platform to enable online updates of model parameters.

9. A method for monitoring the status of workers operating in a high-altitude, strong electromagnetic environment, characterized by: It employs a high-altitude, high-electromagnetic environment personnel status monitoring system as described in any one of claims 1-8, and includes the following steps: Step A, Equipment Deployment and Initialization: One hour before the operation, check the battery level, communication status, and sensor functions of each wearable device; Bind the equipment to the operators and enter the operators' basic information; Collect resting physiological parameters of workers under electromagnetic exposure-free conditions to establish individual baselines; Confirm that the cloud monitoring platform can be logged into normally and that the data upload link is unobstructed; Step B: Synchronous acquisition of multi-source data: Wearable devices synchronously collect physiological parameter data, environmental parameter data, proximity distance data, and leakage current data at a preset frequency of 2-3 seconds per time. Each device ensures data timestamp consistency through an internal clock synchronization mechanism; After local preprocessing, the data is uploaded to the data processing center in real time via a 4G CAT1 module; Step C: Data Preprocessing and Anti-interference Compensation The data processing center filters, denoises, and removes outliers from the received multi-source data; Using electric and magnetic field data to perform anti-interference compensation on physiological signals, and to identify and correct data distortion caused by electromagnetic interference; Based on the preset mapping relationship between different voltage levels and safe distances, real-time distance information is converted into dynamic proximity risk levels; Step D: Multi-level data fusion analysis: Level 1: Threshold monitoring of a single parameter to generate basic alarm events; The second level involves cross-validation and causal correlation analysis of different parameters to generate a targeted joint alarm by integrating multiple abnormal signals. The third level: calculate the security risk index by integrating all real-time data and conduct a comprehensive risk situation assessment; The fourth level: Based on historical time series data, use deep learning models to predict fatigue trends in the near future; Step E: Calculation of Fatigue Level and Safety Risk Index: By invoking a pre-defined physiological-electromagnetic coupling model, and based on the current environmental electromagnetic field strength and physiological parameters, the instantaneous fatigue level of the workers is calculated in real time. ; Output predicted fatigue levels for future moments using a deep learning model. ; The fatigue level is corrected by combining the electromagnetic field strength to generate the final safety risk index; Step F, Tiered Early Warning and Response: when or At that time, a Level 1 emergency alarm was triggered; When a single parameter exceeds the normal range but the overall risk is low, a Level 3 warning is triggered. The cloud-based monitoring platform displays various data visually, automatically issues audible and visual alarms, and notifies the remote management terminal via SMS, APP push, and other means. Management personnel will take corresponding measures based on the warning level, including remotely directing evacuation, dispatching rescue teams, or adjusting operational methods; Step G, Data Storage and Analysis: All collected data and alarm events are stored in a cloud database; Regularly generate health reports for workers and on-site safety situation analysis reports; By utilizing historical data to optimize model parameters, the system can achieve self-learning and continuous improvement.

10. A device for monitoring the status of workers operating in a high-altitude, strong electromagnetic environment, characterized in that: It employs the personnel status monitoring system for high-altitude, high-electromagnetic environments as described in any one of claims 1-8.

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