Full-period safety supervision method for electric power engineering

Abstract

The invention provides an electric power engineering full-period safety supervision method, and relates to the technical field of electric power engineering safety monitoring, and the method comprises the steps: collecting the electromagnetic field intensity and environment humidity data of a hot-line work region, obtaining three-dimensional space coordinates in real time through combining with a positioning tag on a safety helmet of a constructor, and dynamically calculating the three-dimensional boundary of a dangerous region; a comprehensive proximity index is constructed by analyzing the spatial relationship and the moving trend of the constructors and the dangerous boundary, the trend of the constructors approaching the dangerous area is judged in real time, and early warning is given out through the dynamically adjusted vibration frequency. In this way, the problems that in the prior art, dangerous area division is not accurate, monitoring of the movement trend of constructors is insufficient, and early warning response is single can be solved, and the safety monitoring capacity of the hot-line work environment is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power engineering safety monitoring technology, and more specifically, to a full-cycle safety supervision method for power engineering. Background Art

[0002] With the increasing complexity and high risk of power engineering operations, the safety of construction workers in live working environments has become a focus of research and practice in this field. Traditional safety supervision methods mainly rely on manual observation, fixed monitoring equipment and simple alarm devices, which makes it difficult to achieve real-time and dynamic safety status monitoring of construction workers. These methods usually lack comprehensive analysis of key parameters that affect electrical safety and cannot accurately define the real-time boundaries of dangerous areas. In addition, existing monitoring systems can only issue a single alarm signal when construction workers approach dangerous areas, making it difficult to provide multi-dimensional safety prompts and implement differentiated early warning strategies for different levels of danger. Therefore, existing technologies have significant deficiencies in dynamic dangerous area identification, real-time monitoring accuracy, and multi-level early warning mechanisms.

[0003] The development of multi-point sensor networks and high-precision positioning technology has provided new technical means for power engineering safety supervision. For example, methods for demarcating hazardous areas based on electromagnetic field intensity distribution and spatial relationship analysis techniques based on construction worker location data have partially addressed the problem of monitoring hazards in live working areas. However, these technologies still have limitations in terms of real-time performance, accuracy, and comprehensiveness. For example, existing methods often ignore the impact of ambient humidity on electromagnetic field intensity and are unable to dynamically adjust the boundaries of hazardous areas. Analysis of construction worker movement trends often relies on simple linear models, making it difficult to predict potential trends of construction workers approaching hazardous areas. Therefore, a full-cycle safety supervision method that can dynamically calculate hazardous areas, accurately capture construction worker behavior approaching hazardous areas, and provide adaptive early warning capabilities is urgently needed. Summary of the Invention

[0004] To address the aforementioned technical issues, the present invention provides a full-cycle safety supervision method for power projects. This method can, to a certain extent, address the inability to accurately define the danger boundaries of live working areas in real time, the neglect of the effect of ambient humidity on electromagnetic field strength, which leads to inaccurate determination of the danger zone, and the inability to dynamically monitor the tendency of construction workers to approach danger zones, making it difficult to provide graded warnings and multi-dimensional safety alerts.

[0005] According to one aspect of the present invention, a method for full-cycle safety supervision of a power project is provided, comprising:

[0006] Collect electromagnetic field strength data and environmental humidity data in live working areas, and collect construction workers' three-dimensional spatial coordinate data through positioning tags installed on their helmets;

[0007] Calculating the real-time electrical hazard range of the live working area based on the electromagnetic field strength data and the ambient humidity data, and constructing a three-dimensional boundary of the hazard area based on a geometric model of the live equipment;

[0008] Calculating a spatial relationship between the three-dimensional spatial coordinate data and the three-dimensional boundary, and calculating a movement trend vector of the construction worker when it is detected that the shortest distance between the construction worker and the three-dimensional boundary is less than a preset safety distance;

[0009] Based on the movement trend vector and the danger reference vector, a comprehensive proximity index is constructed as the basic judgment basis for the danger trend, the spatial relationship between the construction personnel and the danger area is calculated in real time, and an early warning is issued by dynamically adjusting the vibration frequency.

[0010] Furthermore, the three-dimensional boundary generates a distribution surface through interpolation processing based on the electromagnetic field strength data and the ambient humidity data, dynamically adjusts the electromagnetic field strength in combination with the humidity correction coefficient, and calculates the danger boundary with a 0.5m grid, connecting the points where the intensity exceeds the standard to form a real-time three-dimensional danger range.

[0011] Furthermore, the calculation of the shortest distance adopts a weighted minimum distance formula that takes into account the gradient of the electromagnetic field intensity instead of the traditional Euclidean distance method.

[0012] Furthermore, the movement trend vector is obtained by collecting three-dimensional spatial coordinate data of 10 groups of construction workers, fitting the motion trajectory of the construction workers using an improved least squares method that introduces time decay features, and giving the latest data a higher weight through a nonlinear time decay weight function; on this basis, the weighted least squares method is used to calculate the velocity components of the construction workers in three directions respectively, and synthesize them into a three-dimensional movement trend vector.

[0013] Furthermore, the danger reference vector is constructed by combining the line vector between the current position of the construction worker and the nearest point of the three-dimensional boundary and the current electromagnetic field intensity through threshold normalization and weighting of the electromagnetic field influence coefficient.

[0014] Furthermore, the movement trend vector and the danger reference vector are combined to construct a comprehensive proximity index, which is expressed as:

[0015]

[0016] in, Indicates the preset safety distance. Indicates the shortest distance from the construction worker’s current location to the boundary of the dangerous area. is the moving trend vector, is the hazard reference vector, Represents the comprehensive closeness index, when A positive value indicates an approaching trend, while a negative value indicates a moving away trend.

[0017] Furthermore, the danger trend of the construction workers is judged based on the comprehensive proximity index, and a secondary evaluation is performed in combination with the cosine value of the angle between the moving trend vector and the danger reference vector and the moving speed.

[0018] Furthermore, based on the results of the secondary assessment, an adaptive vibration frequency calculation formula integrating multiple factors is constructed for different risk levels, which is expressed as:

[0019]

[0020] in, is the reference vibration frequency, It is the frequency adjustment coefficient, which determines the maximum frequency increment that can be superimposed on the base frequency; is the distance sensitivity coefficient, which determines the sensitivity of vibration frequency to distance changes; is the reference distance, It is a dynamic adjustment coefficient, which is used to adjust the influence of speed item on warning intensity. It is the rate of change of the shortest distance, indicating the speed at which construction workers approach or move away from the danger zone.

[0021] Furthermore, based on the adaptive vibration frequency, a dynamic weighting mechanism is introduced to integrate the directional weight coefficient and the electric field strength weight coefficient to dynamically adjust the influence of different factors in vibration control and construct the final vibration control frequency. The directional weight coefficient is adjusted according to the relative position of the construction personnel and the danger boundary; the electric field strength weight coefficient is determined by the ratio of the current electromagnetic field strength to the threshold.

[0022] Furthermore, different warning levels are divided based on the magnitude of the final vibration control frequency, including:

[0023] When the frequency is between 20Hz and 35Hz, it uses gentle vibration to remind construction workers to pay attention to the environment, and samples and records location information and trends every 100 milliseconds.

[0024] In the 35Hz to 45Hz range, increase the vibration intensity, shorten the sampling time, enable sound and light auxiliary warnings, and record the response behavior;

[0025] In the 45Hz to 55Hz range, the vibration intensity increases significantly, further shortening the sampling time, triggering a voice warning, and pushing an early warning message to management personnel;

[0026] When the frequency reaches the emergency warning range of 55Hz to 60Hz, the vibrator outputs maximum intensity, activates the all-round warning mechanism and coordinated warning, notifies management personnel to intervene and fully records the incident process.

[0027] Compared with the existing technology, the present invention realizes real-time and accurate three-dimensional danger zone construction through the joint data collection of multi-point electromagnetic field intensity sensor array and environmental humidity sensor, combined with the correction coefficient to dynamically adjust the danger zone boundary; uses the time-attenuated weighted least squares method to accurately obtain the three-dimensional spatial coordinates of construction personnel and their movement trends, significantly improving the monitoring and prediction accuracy of behaviors approaching danger zones; and based on the spatial relationship between construction personnel and danger zones, constructs a multi-factor dynamically weighted adaptive vibration warning frequency model, realizing graded response of mild, moderate, high and emergency warnings, significantly improving the safety monitoring capability of live working environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0029] Figure 1 Flowchart of a method for full-cycle safety supervision of a power project according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0031] Example 1

[0032] As mentioned in the above background technology, there are three major outstanding problems in the existing technology: First, the division of dangerous areas is not accurate enough. The traditional method is only based on static parameters and it is difficult to dynamically reflect the changes in electromagnetic field intensity and ambient humidity, resulting in deviations in the boundaries of dangerous areas; second, there is a lack of real-time monitoring of the movement trends of construction personnel. Relying only on fixed positions or simple distance judgments cannot accurately predict the behavior of personnel approaching dangerous areas; third, the early warning method is single. The existing systems are mostly based on sound and light alarms, and cannot provide graded responses for different levels of danger, resulting in poor early warning effects.

[0033] Figure 1 FIG. 1 is a system block diagram of a method for full-cycle safety supervision of electric power engineering according to an embodiment of the present invention. Figure 1 As shown in the figure, the full cycle safety supervision method for power projects includes:

[0034] S1: Collect electromagnetic field strength data and environmental humidity data in the live working area, and collect the three-dimensional spatial coordinate data of the construction workers through the positioning tags installed on the construction workers' safety helmets.

[0035] In the live working area, electromagnetic field strength data is collected by deploying a multi-point electromagnetic field strength sensor array. The electromagnetic field strength sensor array includes 12 measuring points evenly arranged along the circumference of the live equipment, and each measuring point is equipped with an industrial frequency electric field strength sensor and an industrial frequency magnetic field strength sensor.

[0036] The measurement range of the power frequency electric field strength sensor is 0.5V / m-100kV / m;

[0037] The measurement range of the power frequency magnetic field strength sensor is 0.1μT-10mT, and the sampling frequency is 10Hz;

[0038] Eight temperature and humidity sensors are installed around the live working area. The humidity measurement range of the temperature and humidity sensors is 5% to 95% RH, the measurement accuracy is ±2% RH, and the sampling frequency is 1 Hz. They are used to collect environmental humidity data.

[0039] A UWB positioning tag is installed on the top of the construction workers' safety helmets. The UWB positioning tag cooperates with the four UWB base stations installed at the four corners of the work area and uses the TDOA positioning algorithm to calculate the construction workers' three-dimensional spatial coordinate data in real time. The positioning accuracy of the three-dimensional spatial coordinate data is better than 10cm, and the positioning frequency is 20Hz.

[0040] The electromagnetic field intensity data, environmental humidity data and three-dimensional space coordinate data are transmitted to the data acquisition server through the on-site wireless network for time synchronization storage.

[0041] S2: Calculate the real-time electrical hazard range of the live working area based on the electromagnetic field strength data and the ambient humidity data, and construct a three-dimensional boundary of the hazard area based on a geometric model of the live equipment.

[0042] Based on the electromagnetic field strength data and ambient humidity data, an adaptive electrical hazard range calculation method is used to determine the real-time electrical hazard range of the live working area, including:

[0043] Firstly, the power frequency electric field intensity data and power frequency magnetic field intensity data collected at 12 measurement points are spatially interpolated to generate electric field intensity distribution surface and magnetic field intensity distribution surface respectively;

[0044] Based on the ambient humidity data collected by eight temperature and humidity sensors, the weighted average method is used to calculate the comprehensive humidity value of the operating area. When the comprehensive humidity value is within the range of 30% RH to 70% RH, the corresponding electric field strength correction coefficient α and magnetic field strength correction coefficient β are selected according to the correction coefficient table, where the correction coefficients in the correction coefficient table vary piecewise linearly with the comprehensive humidity value. Then, the values ​​of the electric field strength distribution surface are multiplied by the electric field strength correction coefficient α, and the values ​​of the magnetic field strength distribution surface are multiplied by the magnetic field strength correction coefficient β to obtain the corrected electromagnetic field strength distribution;

[0045] Discrete sampling points were established on the outer surface of the three-dimensional geometric model of the live equipment with a grid spacing of 0.5m. The corrected electromagnetic field strength value was calculated at each sampling point. Sampling points with electric field strength greater than 5kV / m or magnetic field strength greater than 0.4mT were connected to form a closed three-dimensional boundary. The three-dimensional boundary is the real-time electrical hazard range of the live working area, and the three-dimensional boundary is updated with each new set of electromagnetic field strength data and ambient humidity data at a period of 1 second.

[0046] Furthermore, based on the physical characteristics of the influence of ambient humidity on electromagnetic field strength, a correction coefficient table was preliminarily established for segmented correction. Specifically, when the comprehensive humidity value is in the range of 30%RH to 45%RH, the electric field strength correction coefficient α is calculated according to α=1+0.02×(comprehensive humidity value-30), and the magnetic field strength correction coefficient β is calculated according to β=1+0.015×(comprehensive humidity value-30); when the comprehensive humidity value is in the range of 45%RH to 55%RH, the electric field strength correction coefficient α and the magnetic field strength correction coefficient β are both maintained at 1.3; when the comprehensive humidity value is in the range of 55%RH to 70%RH, the electric field strength correction coefficient α is calculated according to α=1.3+0.025×(comprehensive humidity value-55), and the magnetic field strength correction coefficient β is calculated according to β=1.3+0.02×(comprehensive humidity value-55);

[0047] If the comprehensive humidity value is lower than 30%RH, the electric field strength correction coefficient α and the magnetic field strength correction coefficient β are both 1; if the comprehensive humidity value is higher than 70%RH, the electric field strength correction coefficient α is 1.7, and the magnetic field strength correction coefficient β is 1.6;

[0048] The calculated correction coefficients are multiplied by the values ​​at each sampling point on the electric and magnetic field intensity distribution surfaces to produce a corrected electromagnetic field intensity distribution that accounts for humidity. The electric field intensity distribution surface is generated using cubic spline interpolation, while the magnetic field intensity distribution surface is generated using inverse distance weighted interpolation. To balance accuracy and real-time performance, the spatial resolution of the interpolation calculation is set to 0.1m. Combined with the discrete grid spacing of the three-dimensional boundary (0.5m), high-resolution interpolation captures detailed changes in the hazardous area boundary. The corrected electromagnetic field intensity value at each sampling point serves as the input for the subsequent construction of the three-dimensional boundary of the hazardous area.

[0049] S3: Calculating the spatial relationship between the three-dimensional spatial coordinate data and the three-dimensional boundary, and calculating the movement trend vector of the construction worker when it is detected that the shortest distance between the construction worker and the three-dimensional boundary is less than a preset safety distance.

[0050] Based on the real-time acquired 3D spatial coordinate data of the construction personnel, the spatial relationship with the 3D boundary of the live working area is dynamically calculated. Specifically, first, the 3D spatial coordinate point P( , , ) and all mesh nodes on the 3D boundary surface ( , , ) is used to calculate the Euclidean distance. Considering the uneven distribution of electromagnetic field intensity in the charged area, the traditional simple Euclidean distance calculation method cannot reflect the actual degree of danger. Therefore, the weighted minimum distance calculation formula considering the electromagnetic field intensity gradient is adopted, which is expressed as:

[0051]

[0052] in, Represents the electromagnetic field intensity gradient at node i, which is obtained by performing central difference calculation on the electromagnetic field intensity values ​​of adjacent measuring points. It represents the maximum electromagnetic field intensity gradient in the field area and is used for normalization processing. k is the weight coefficient.

[0053] when When the distance is less than the preset 3-meter safety distance, in order to accurately predict the movement trend of the construction workers, 10 sets of 3D spatial coordinate data of the construction workers are continuously collected at a sampling frequency of 50Hz within a 0.5-second time window. , j=1,2,...,10.

[0054] Considering the continuity of personnel movement and the fact that the latest position data is more valuable for predicting movement trends, based on these 10 sets of coordinate data, an improved least squares method with time decay characteristics is used to fit the movement trajectory of construction workers, and a nonlinear time decay weight function is constructed, which is expressed as:

[0055]

[0056] in, is the time attenuation coefficient, For the current moment, is the moment of the jth sampling point.

[0057] Furthermore, on this basis, the speed components of the construction workers in three directions are calculated by weighted least squares method, which is expressed as:

[0058]

[0059]

[0060]

[0061] in, The jth sampling point is The coordinate value of the axis direction, The jth sampling point is The coordinate value of the axis direction, The jth sampling point is Coordinate values ​​in the axis direction; The j-1th sampling point is The coordinate value of the axis direction, The j-1th sampling point is The coordinate value of the axis direction, The j-1th sampling point is Coordinate values ​​in the axis direction.

[0062] In the formula, the denominator is in the form of the sum of weighted time intervals, which can effectively eliminate the impact of uneven sampling time on speed calculation;

[0063] The three velocity components are further combined into the movement trend vector of the construction workers , expressed as:

[0064]

[0065] Based on the calculated construction personnel movement trend vector , represents the speed and direction of the construction workers in three-dimensional space. This vector reflects the immediate motion state of the construction workers.

[0066] S4: Based on the movement trend vector and the danger reference vector, a comprehensive proximity index is constructed as a basic judgment basis for the danger trend, the spatial relationship between the construction personnel and the danger area is calculated in real time, and an early warning is issued by dynamically adjusting the vibration frequency.

[0067] However, relying solely on the moving trend vector It is impossible to fully assess whether construction workers have a tendency to approach hazardous areas, because the relative position relationship between construction workers and hazardous areas and the local electromagnetic field intensity distribution characteristics must also be considered. Therefore, a hazard reference vector that considers the influence of electromagnetic field intensity is introduced. , expressed as:

[0068]

[0069] in, is the line vector connecting the construction worker’s current position and the nearest point on the three-dimensional boundary, is the electromagnetic field strength at the current location, which is obtained through real-time measurement. is the electromagnetic field intensity threshold, is the electromagnetic field influence coefficient.

[0070] Move the trend vector Danger reference vector Combined, a comprehensive proximity index is constructed, which is expressed as:

[0071]

[0072] in, Indicates the preset safety distance. Indicates the shortest distance from the construction worker’s current location to the boundary of the dangerous area. Represents the comprehensive closeness index, when A positive value indicates an approaching trend, while a negative value indicates a moving away trend.

[0073] Based on the comprehensive proximity index obtained as the basic basis for judging the danger trend, the first item is the moving trend vector Danger reference vector The cosine value of the angle between them is initially set as the comprehensive closeness index When the calculated value is greater than 0.6, it indicates that the construction worker may be approaching a dangerous area. This triggers the secondary judgment phase, which extracts the cosine value of the angle between the movement trend vector and the dangerous reference vector, as well as the construction worker's current movement speed for evaluation. If the cosine value of the angle is greater than 0.5 and the movement speed is detected to be greater than 0.3 meters per second, the construction worker is determined to be moving toward the dangerous area.

[0074] Then, on this basis, an adaptive vibration frequency calculation formula integrating the influence of multiple factors is constructed for different hazard levels, which is expressed as:

[0075]

[0076] in, is the reference vibration frequency, It is the frequency adjustment coefficient, which determines the maximum frequency increment that can be superimposed on the base frequency; is the distance sensitivity coefficient, which determines the sensitivity of vibration frequency to distance changes; is the reference distance, It is a dynamic adjustment coefficient, which is used to adjust the influence of speed item on warning intensity. It is the rate of change of the shortest distance, indicating the speed at which construction workers approach or move away from the danger zone.

[0077] Furthermore, in order to achieve precise control of the warning intensity, a dynamic weighting mechanism considering the influence of multiple factors is constructed, which is expressed as:

[0078]

[0079]

[0080] in, is the direction weight coefficient, is the electric field strength weight coefficient. The direction weight coefficient is dynamically adjusted according to the relative position of the construction personnel and the danger boundary, and the initial value is set to 0.7. The electric field strength weight coefficient depends on the ratio of the current electromagnetic field strength to the threshold, and the initial value is set to 0.6.

[0081] The final vibration control frequency calculation formula is:

[0082]

[0083] Calculate the final vibration control frequency Finally, different warning levels are divided according to the frequency value, including:

[0084] When the frequency is in the mild warning range of 20Hz to 35Hz, it indicates that the construction workers are at the edge of a potential danger zone. At this time, the vibrator will produce a mild reminder vibration to remind the construction workers to pay attention to the surrounding environment. At the same time, the system will encrypt and record the construction workers' location information and movement trends, maintain a normal data sampling frequency of once every 100 milliseconds, and ensure the continuity of basic monitoring data.

[0085] When the frequency rises to the moderate warning range of 35Hz to 45Hz, it indicates that the construction workers have entered an area of ​​increased danger. The vibration intensity is significantly increased to attract the full attention of the construction workers. At the same time, the data sampling interval is shortened to 50 milliseconds, and the sound and light auxiliary warning module of the safety helmet is activated. The warning effect is enhanced through multi-sensory warning methods, and the construction workers' response behavior to the warning is recorded in detail to evaluate the effectiveness of the warning.

[0086] As the frequency further increases to the high-alert range of 45Hz to 55Hz, it indicates that the construction workers are approaching a high-risk area. At this time, the vibration intensity will increase significantly to produce a clear warning effect, and the data sampling interval will be further shortened to 20 milliseconds to obtain more detailed motion feature data. At the same time, the voice prompt function of the safety helmet will be triggered to issue a clear voice warning, and early warning information will be pushed to on-site managers in real time. The complete approach process data of the construction workers will be recorded to prepare for possible emergency response.

[0087] When the frequency reaches the emergency warning range of 55Hz to 60Hz, it indicates that the construction workers are in an extremely dangerous state. The vibrator is immediately adjusted to the maximum intensity output to issue a warning. At the same time, a full range of warning mechanisms including vibration, sound, light, and voice are activated, and a coordinated warning is issued to surrounding construction workers to prevent chain accidents. On-site safety management personnel are required to intervene immediately and start the abnormal recording mode to comprehensively record and save the complete process data of the incident, providing detailed data support for subsequent event analysis and safety management optimization.

[0088] It should be noted that when the vibration control frequency changes, there may be false alarms, and appropriate treatment measures must be taken, including:

[0089] If the vibration control frequency remains persistently high, meaning a construction worker continuously works near the boundary of the danger zone (within 2-3 meters), causing the vibration control frequency to remain above 45Hz for more than five minutes, an abnormal state is identified. The system first verifies whether the location is a planned fixed work point. If so, the worker is asked to confirm the work type and estimated duration using a handheld terminal. Based on this confirmation, the warning strategy is adjusted, reducing the vibration frequency to a comfortable 35Hz level while maintaining a strong warning every 10 minutes (briefly increasing to 50Hz) to ensure continued vigilance. If the worker is not working at a fixed location, the warning frequency is maintained at a higher level, with a warning message broadcast every three minutes via the helmet voice module. Furthermore, the system monitors the worker's movements in real time, and immediately reverts to strong warning mode if any movement toward the danger zone is detected.

[0090] If the vibration control frequency exhibits repeated fluctuations—that is, construction workers repeatedly moving near the boundary of the danger zone, causing the vibration control frequency to fluctuate frequently between 30Hz and 60Hz—and if peaks occur more than five times within 10 minutes, an abnormal state is identified. The system then analyzes the worker's movements and identifies their work characteristics. If a normal inspection route is confirmed, the system optimizes the warning triggering rules to minimize unnecessary warning interference while ensuring safety. If a worker deviates from the expected trajectory or exhibits an abnormal movement pattern, the system immediately reverts to strong warning mode.

[0091] If multiple construction workers simultaneously trigger a high-frequency alert—that is, three or more workers working within the same hazardous area (within a 5-meter radius) and their vibration control frequencies exceed 40Hz—this is considered a collective abnormality. Work tickets are then reviewed to determine whether this is a planned, multi-person collaborative operation. If so, the on-site supervisor is required to confirm the start of the work using a handheld terminal. A temporary collaborative work area is established, and alert parameters are adjusted, with a focus on monitoring movement within the area boundary. Relative positional relationships between workers are also monitored. If any worker exhibits abnormal movement, a coordinated alert is issued to all involved. If the gathering is unplanned, an evacuation message is issued via the helmet voice module, and on-site safety management personnel are immediately notified for action.

[0092] All exception handling processes are fully documented, including exception type, handling measures, personnel response, and final outcome. When similar exception patterns recur, they can be quickly identified and the appropriate handling mechanism activated.

[0093] In summary, a full-cycle safety supervision method for power engineering projects based on an embodiment of the present invention is explained. It realizes real-time and accurate three-dimensional dangerous area construction through joint data collection of a multi-point electromagnetic field intensity sensor array and an environmental humidity sensor, combined with a correction coefficient to dynamically adjust the boundaries of the dangerous area; uses the time-attenuated weighted least squares method to accurately obtain the three-dimensional spatial coordinates of construction personnel and their movement trends, significantly improving the monitoring and prediction accuracy of behaviors approaching dangerous areas; and based on the spatial relationship between construction personnel and dangerous areas, a multi-factor dynamically weighted adaptive vibration warning frequency model is constructed, realizing a graded response of mild, moderate, high and emergency warnings, significantly improving the safety monitoring capability of the live working environment.

[0094] Example 2

[0095] To verify the effectiveness of a real-time safety monitoring and early warning system for live working areas, an experiment was conducted in the live maintenance environment of a 500kV high-voltage transmission line. The experiment was conducted under both experimental and control conditions. The experimental group employed a real-time dynamic monitoring and early warning system to monitor and warn construction workers of their electromagnetic exposure risks and their approach to hazardous areas. The control group employed a traditional monitoring solution based on fixed hazardous areas, without real-time updates or dynamic adjustments.

[0096] Twelve power-frequency electromagnetic field intensity sensors and eight humidity sensors were deployed in the experimental area to collect electromagnetic field intensity and ambient humidity data. The sensor array was evenly spaced around the equipment, with a sampling frequency of 10 Hz (electromagnetic field sensors) and 1 Hz (humidity sensors). Construction workers wore smart hard hats equipped with UWB positioning tags. These, combined with four UWB base stations located at the four corners of the work area, acquired three-dimensional spatial coordinates in real time, achieving positioning accuracy better than 10 cm. In the experimental group, the three-dimensional boundaries of the danger zone were dynamically calculated, and the spatial relationship between the construction workers and the danger zone was updated in real time. Graded warnings were triggered based on proximity indicators. The control group used a fixed danger zone (a 3-meter sphere) for monitoring and alarm triggering.

[0097] The experiment involved various scenarios, including simulations of construction workers approaching hazardous areas under varying humidity conditions, and a group warning test in a collaborative work environment. The experiment lasted three hours, with data recorded every second to generate indicators such as hazardous area boundaries, construction worker locations, and alarm status. To enhance the scientific nature and objectivity of the experiment, each set of tests was repeated three times, with the average value used as the final result.

[0098] Experimental data demonstrates that the real-time dynamic monitoring and warning system significantly outperforms solutions based on fixed danger zones in several key indicators. First, at 40% relative humidity, the experimental group was able to dynamically calculate the three-dimensional boundaries of the danger zone based on real-time electromagnetic field strength and ambient humidity data. The results showed that the danger boundary was reduced from a fixed 3-meter spherical area to 2.7 meters, demonstrating a more accurate reflection of the actual electromagnetic field distribution, reducing unnecessary safety buffer zones and thus improving operational efficiency.

[0099] In terms of alarm response time, the experimental group's average response time was only 0.7 seconds, while the control group took 3.2 seconds, a significant difference of 2.5 seconds, resulting in an improvement rate of 78%. This is because the experimental group calculated a comprehensive proximity index based on the construction workers' real-time location and movement trends, enabling rapid risk assessment and triggering of alarms, while the control group relied on a fixed-area trigger mechanism and was unable to respond promptly to the dynamic behavior of workers.

[0100] In terms of false alarm rates, the experimental group triggered only two false alarms per hour, while the control group experienced 11, a reduction of 82%. This was attributed to the experimental group's calculation of the weighted spatial relationship between construction workers and hazardous areas, as well as modeling the effect of humidity correction on electromagnetic field intensity, which effectively reduced false alarms caused by inaccurate parameter settings.

[0101] The duration of exposure to high-risk areas is a key indicator of construction workers' exposure risk. The experimental group's exposure time was 1.5 minutes, while the control group's was 6.8 minutes, representing a significant 77% reduction in high-risk exposure time.

[0102] In addition, in the multi-person collaborative work scenario, the number of collective abnormal responses in the experimental group was 4 times, while that in the control group was 9 times, reducing the occurrence of collective false alarms.

[0103] Comprehensively judging, the experimental group outperformed the control group in terms of dynamic modeling of dangerous areas, prediction of construction personnel behavior, alarm trigger response and false alarm rate control. The dynamic monitoring and early warning system not only improved the accuracy of safety monitoring, but also reduced construction interference, providing more efficient and reliable safety protection for live operations.

Claims

1. A full-cycle safety supervision method for power engineering, characterized in that: include: Collect electromagnetic field strength data and environmental humidity data in live working areas, and collect construction workers' three-dimensional spatial coordinate data through positioning tags installed on their helmets; Calculating the real-time electrical hazard range of the live working area based on the electromagnetic field strength data and the ambient humidity data, and constructing a three-dimensional boundary of the hazard area based on a geometric model of the live equipment; Calculating a spatial relationship between the three-dimensional spatial coordinate data and the three-dimensional boundary, and calculating a movement trend vector of the construction worker when it is detected that the shortest distance between the construction worker and the three-dimensional boundary is less than a preset safety distance; Based on the movement trend vector and the danger reference vector, a comprehensive proximity index is constructed as the basic judgment basis for the danger trend, the spatial relationship between the construction personnel and the danger area is calculated in real time, and an early warning is issued by dynamically adjusting the vibration frequency.

2. The method for full-cycle safety supervision of electric power engineering according to claim 1, characterized in that: The three-dimensional boundary generates a distribution surface through interpolation processing based on the electromagnetic field strength data and the ambient humidity data, dynamically adjusts the electromagnetic field strength in combination with the humidity correction coefficient, and calculates the danger boundary with a 0.5m grid, connecting the points where the intensity exceeds the standard to form a real-time three-dimensional danger range.

3. The method for full-cycle safety supervision of electric power engineering according to claim 1, characterized in that: The calculation of the shortest distance adopts a weighted minimum distance formula that takes into account the gradient of the electromagnetic field intensity instead of the traditional Euclidean distance method.

4. The method for full-cycle safety supervision of electric power engineering according to claim 1, characterized in that: The movement trend vector is obtained by collecting 10 sets of three-dimensional spatial coordinate data of construction workers, using the improved least squares method with time decay feature to fit the movement trajectory of the construction workers, and giving the latest data a higher weight through the nonlinear time decay weight function; On this basis, the weighted least squares method is used to calculate the construction workers' velocity components in three directions respectively, and then synthesize them into a three-dimensional movement trend vector.

5. The method for full-cycle safety supervision of electric power engineering according to claim 1, characterized in that: The danger reference vector is constructed by combining the line vector between the current position of the construction worker and the nearest point of the three-dimensional boundary and the current electromagnetic field strength through threshold normalization and weighting of the electromagnetic field influence coefficient.

6. The method for full-cycle safety supervision of electric power engineering according to claim 5, characterized in that: The movement trend vector and the danger reference vector are combined to construct a comprehensive proximity index, which is expressed as: in, Indicates the preset safety distance. Indicates the shortest distance from the construction worker’s current location to the boundary of the dangerous area. is the moving trend vector, is the hazard reference vector, Represents the comprehensive closeness index, when A positive value indicates an approaching trend, while a negative value indicates a moving away trend.

7. The method for full-cycle safety supervision of electric power engineering according to claim 6, characterized in that: The danger trend of the construction workers is judged based on the comprehensive proximity index, and a secondary evaluation is performed based on the cosine value of the angle between the moving trend vector and the danger reference vector and the moving speed.

8. The method for full-cycle safety supervision of electric power engineering according to claim 7, characterized in that: Based on the results of the secondary assessment, an adaptive vibration frequency calculation formula integrating multiple factors is constructed for different risk levels, which is expressed as: in, is the reference vibration frequency, It is the frequency adjustment coefficient, which determines the maximum frequency increment that can be superimposed on the base frequency; is the distance sensitivity coefficient, which determines the sensitivity of vibration frequency to distance changes; is the reference distance, It is a dynamic adjustment coefficient, which is used to adjust the influence of speed item on warning intensity. It is the rate of change of the shortest distance, indicating the speed at which construction workers approach or move away from the danger zone.

9. The method for full-cycle safety supervision of electric power engineering according to claim 8, characterized in that: Based on the adaptive vibration frequency, a dynamic weighting mechanism is introduced to integrate the directional weight coefficient and the electric field strength weight coefficient to dynamically adjust the influence of different factors in vibration control and construct the final vibration control frequency. The directional weight coefficient is adjusted according to the relative position of the construction personnel and the danger boundary; the electric field strength weight coefficient is determined by the ratio of the current electromagnetic field strength to the threshold.

10. The method for full-cycle safety supervision of electric power engineering according to claim 9, characterized in that: Different warning levels are divided based on the magnitude of the final vibration control frequency, including: When the frequency is between 20Hz and 35Hz, it uses gentle vibration to remind construction workers to pay attention to the environment, and samples and records location information and trends every 100 milliseconds. In the 35Hz to 45Hz range, increase the vibration intensity, shorten the sampling time, enable sound and light auxiliary warnings, and record the response behavior; In the 45Hz to 55Hz range, the vibration intensity increases significantly, further shortening the sampling time, triggering a voice warning, and pushing an early warning message to management personnel; When the frequency reaches the emergency warning range of 55Hz to 60Hz, the vibrator outputs maximum intensity, activates the all-round warning mechanism and coordinated warning, notifies management personnel to intervene and fully records the incident process.

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