Visual monitoring device and method for power transmission line inspection

Abstract

This invention relates to power system monitoring technology and discloses a visual monitoring device and method for transmission line inspection. The method includes: collecting the dominant frequency of conductor vibration, the impact acceleration of tower materials / fittings, and the conductor's tilt angle offset to the ground, and fusing them to generate a mechanical dynamic threat coefficient; collecting radar target distance and distance change rate, and the spatial power frequency electric field intensity change rate, and fusing them to generate an external threat coefficient; combining ambient illuminance and signal strength RSSI with the aforementioned threat coefficient to generate a demand-operating condition coordination coefficient; generating an energy survival constraint coefficient based on battery remaining capacity and ambient temperature; and finally, interpolating the demand-operating condition coordination coefficient and the energy survival constraint coefficient between the lowest and highest frame rates to obtain the target image acquisition frame rate. This invention achieves dynamic response of the monitoring frame rate to multi-source risks, balancing monitoring effectiveness and device endurance.

Description

Technical Field

[0001] This invention pertains to power system monitoring technology, and particularly relates to a visual monitoring device and method for power transmission line inspection. Background Technology

[0002] Visual monitoring devices for power transmission lines are crucial equipment for ensuring the safe operation of the power grid. Their core task is to monitor conductor status, corridor environment, and potential external damage hazards through image acquisition. Currently, monitoring devices primarily employ two image acquisition strategies: fixed frame rate or single threshold triggering. The fixed frame rate mode cannot dynamically adjust the sampling frequency according to the actual risk of the line. In high-risk scenarios, it is prone to losing critical process images due to excessively large sampling intervals. During stable operation, high-frequency acquisition leads to power consumption and data transmission redundancy. The single threshold triggering mode typically relies solely on over-limit signals from a single sensor such as microwave radar or accelerometer to trigger image capture. It lacks the ability to fuse and analyze multi-source monitoring data, resulting in a high probability of false triggering. Frequent startups further exacerbate the energy consumption of the device in field environments. Summary of the Invention

[0003] The purpose of this invention is to provide a visual monitoring device and method for power transmission line inspection, in order to solve the above-mentioned problems.

[0004] This invention is implemented as follows: a visual monitoring method for power transmission line inspection includes the following steps: collecting conductor vibration frequency, tower / fitting impact acceleration, and conductor tilt angle offset reflecting the conductor's operating status, and performing fusion processing to generate a mechanical dynamic threat coefficient characterizing the structural stability of the line itself; collecting radar target distance and distance change rate, as well as spatial power frequency electric field intensity change rate reflecting the monitoring channel environment, and performing fusion processing to generate an external threat coefficient characterizing the risk of external intrusion and the degree of electric field disturbance; collecting ambient light intensity and signal strength RSSI reflecting image acquisition conditions, and combining the mechanical dynamic threat coefficient and the external threat coefficient to generate a demand-condition coordination coefficient characterizing the monitoring requirements and execution feasibility; collecting battery remaining capacity reflecting the device's power supply status and ambient temperature reflecting environmental tolerance, and performing constraint processing to generate an energy survival constraint coefficient characterizing continuous operation capability; and obtaining the target image acquisition frame rate and adjusting the current value based on the demand-condition coordination coefficient and the energy survival constraint coefficient.

[0005] A further technical solution involves calculating the mechanical dynamic threat coefficient as follows: obtaining the conductor vibration dominant frequency, tower / fitting impact acceleration, and conductor tilt angle offset; generating corresponding conductor vibration dominant frequency, tower / fitting impact acceleration, and conductor tilt angle offset indices after dimensionless and amplitude-limiting processing; weightedly fusing the conductor vibration dominant frequency, tower / fitting impact acceleration, and conductor tilt angle offset indices, and then performing saturation characteristic nonlinear mapping on the fusion result to obtain the mechanical dynamic threat coefficient; wherein, the mechanical dynamic threat coefficient monotonically increases with the increase of the fusion result.

[0006] A further technical solution involves processing the conductor vibration dominant frequency, tower / fitting impact acceleration, and conductor ground tilt offset as follows: The current conductor vibration dominant frequency and tower / fitting impact acceleration are compared with the upper limit of conductor vibration frequency monitoring and the upper limit of impact acceleration threshold, respectively. After truncating the upper limit of the ratio to 1, the conductor vibration dominant frequency index and the tower / fitting impact acceleration index are obtained. The absolute value of the current conductor ground tilt offset is compared with the maximum allowable conductor ground tilt angle. After truncating the upper limit of the ratio to 1, the conductor ground tilt offset index is obtained.

[0007] A further technical solution involves calculating the external threat coefficient as follows: obtaining the radar target distance and its rate of change, as well as the rate of change of the space power frequency electric field intensity; based on the radar target distance, obtaining the radar target distance index characterizing the proximity of the target; extracting the current radar target distance rate of change to obtain the target approach rate index characterizing the urgency of the target's approach; based on the fluctuation amplitude of the current space power frequency electric field intensity rate of change, obtaining the space power frequency electric field intensity rate of change index characterizing the degree of electric field anomaly; and substituting the radar target distance index, the target approach rate index, and the space power frequency electric field intensity rate of change index into the formula. Obtain the external threat coefficient ,in, This is the radar target range index. To approximate the rate exponent, It is the exponent of the rate of change of the power frequency electric field intensity in space.

[0008] A further technical solution involves processing the radar target distance and its rate of change, as well as the rate of change of the spatial power frequency electric field intensity, as follows: The current radar target distance is compared to the radar protection zone radius threshold to obtain the radar target distance index; the inverse of the current radar target distance rate of change is taken, the non-negative portion is truncated, and then compared with the upper limit of the radar target approach rate saturation to obtain the target approach rate index; the absolute value of the current spatial power frequency electric field intensity rate of change is compared to the power frequency electric field rate of change abrupt change threshold, and the upper limit of the ratio is truncated to 1 to obtain the spatial power frequency electric field intensity rate of change index.

[0009] A further technical solution involves calculating the demand-condition coordination coefficient as follows: obtaining ambient illuminance and signal strength RSSI; generating an ambient illuminance index whose value decreases as illuminance decreases and has a non-zero lower limit based on ambient illuminance; generating a signal strength RSSI index whose value decreases as signal strength weakens and is constrained by amplitude limiting based on signal strength RSSI; weighting and combining the mechanical dynamic threat coefficient and the external threat coefficient to obtain a threat fusion quantity; and multiplicatively coupling the threat fusion quantity with the ambient illuminance index and the signal strength RSSI index to obtain the demand-condition coordination coefficient; wherein the demand-condition coordination coefficient is positively correlated with the threat fusion quantity, the ambient illuminance index, and the signal strength RSSI index.

[0010] A further technical solution involves processing the ambient illuminance and signal strength RSSI as follows: Based on the comparison between the current ambient illuminance and the upper limit of ambient illuminance saturation, an ambient illuminance index is generated to indicate the suitability of ambient lighting. When the ambient illuminance is lower than the upper limit of ambient illuminance saturation, the ambient illuminance index decreases as the ambient illuminance decreases, but does not fall below the lower limit threshold for maintaining operating conditions. The difference between the current signal strength RSSI and the lower limit of weak signal strength is compared with the difference between the upper limit of communication signal strength saturation and the lower limit of weak signal strength. After truncating the ratio to 1 and the lower limit threshold for maintaining signal strength, the signal strength RSSI index is obtained.

[0011] A further technical solution involves calculating the energy survival constraint coefficient as follows: obtaining the remaining battery capacity and ambient temperature; generating an energy sufficiency index characterizing the degree of energy sufficiency based on the comparison between the remaining battery capacity and the critical threshold of the remaining battery charge; when the remaining battery capacity is lower than the critical threshold of the remaining battery charge, the energy sufficiency index decreases as the remaining battery capacity decreases, and the lowest value after the decrease is limited by the minimum operating maintenance threshold; generating a temperature suitability index characterizing the degree of temperature suitability based on the degree of deviation of the ambient temperature from the optimal operating reference temperature; wherein, when the deviation is greater than or equal to half the allowable temperature difference, the temperature suitability index is set as the minimum temperature protection threshold; when the deviation is less than half the allowable temperature difference, the temperature suitability index increases as the degree of deviation decreases until it reaches a maximum value of 1; and multiplying the energy sufficiency index and the temperature suitability index to obtain the energy survival constraint coefficient.

[0012] A further technical solution involves calculating the target image acquisition frame rate as follows: obtaining the minimum allowed frame rate, the maximum allowed frame rate, the demand-operating condition coordination coefficient, and the energy survival constraint coefficient; and interpolating between the minimum allowed frame rate and the maximum allowed frame rate based on the product of the demand-operating condition coordination coefficient and the energy survival constraint coefficient to obtain the target image acquisition frame rate; wherein, when the product approaches 0, the target image acquisition frame rate approaches the minimum allowed frame rate; and when the product approaches 1, the target image acquisition frame rate approaches the maximum allowed frame rate.

[0013] A visual monitoring device for power transmission line inspection includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned visual monitoring method for power transmission line inspection.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0015] 1. This invention generates a mechanical dynamic threat coefficient by fusing multiple mechanical state variables such as conductor vibration, impact, and tilt angle; it generates an external threat coefficient by fusing radar target motion information and electric field disturbance; and it combines these with ambient light intensity and RSSI signal strength in a multiplicative coupling manner to form a demand-condition coordination coefficient. This allows the adjustment of the image acquisition frame rate to not only respond sensitively to changes in various types of risks, but also to be reasonably constrained when lighting or communication conditions are poor, significantly reducing false triggering and invalid acquisition, and improving the accuracy of monitoring and the efficiency of resource utilization.

[0016] 2. This invention generates an energy survival constraint coefficient by using the remaining battery capacity and ambient temperature, and together with the demand-operating condition coordination coefficient, determines the target image acquisition frame rate. Under low power or extreme temperature conditions, the frame rate is automatically reduced to protect the continuous operation capability of the device, overcoming the shortcomings of traditional fixed frame rate or single threshold triggering modes that are prone to energy depletion or monitoring interruption under harsh outdoor conditions. Attached Figure Description

[0017] Figure 1 This invention provides a flowchart of a visual monitoring method for power transmission line inspection. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0020] like Figure 1 As shown, an embodiment of the present invention provides a visual monitoring method for power transmission line inspection, comprising the following steps:

[0021] The system collects data reflecting the conductor's operational status, including the dominant vibration frequency, tower / fitting impact acceleration, and conductor tilt offset. These data are then fused to generate a mechanical dynamic threat coefficient characterizing the line's structural stability. The dominant vibration frequency refers to the frequency at which energy is most concentrated during the vibration of the transmission line conductor caused by factors such as wind and ice shedding. This parameter directly reflects the conductor's dynamic stress state and is a crucial indicator for assessing the line's mechanical stability. Tower / fitting impact acceleration refers to the instantaneous acceleration experienced by the structural components of the transmission line tower structure or connecting hardware when subjected to external impacts (such as foreign object impacts or ice shedding). This parameter is used to monitor the risk of sudden mechanical damage to the line structure. The conductor tilt offset refers to the degree of deviation of the transmission line conductor from its normal ground tilt baseline. This offset may be caused by conductor icing, galloping, sag changes, or tower tilting, reflecting abnormal conductor geometry and potential safety hazards.

[0022] The system collects radar target distance and its rate of change, as well as the rate of change of spatial power frequency electric field intensity, reflecting the environment of the monitoring channel. These data are then fused to generate an external threat coefficient characterizing the risk of external intrusion and the degree of electric field disturbance. Radar target distance and its rate of change refer to the spatial distance between the monitoring device and external objects detected by radar sensors, and the rate at which this distance changes over time. These parameters are used to assess the intrusion risk of external objects (such as drones, flocks of birds, construction machinery, etc.) to transmission lines. The rate of change of spatial power frequency electric field intensity refers to the rate at which the power frequency electric field intensity generated by alternating current changes over time in the space surrounding the transmission line. Drastic changes in electric field intensity may indicate abnormal line insulation, discharge phenomena, or abnormal disturbances in the external environment, and are an important basis for assessing the safety of the line's operating environment.

[0023] The system collects ambient light intensity and RSSI (Resonance Signal Strength Index) to reflect image acquisition conditions, and combines these with mechanical dynamic threat coefficients and external threat coefficients to generate a demand-condition synergy coefficient characterizing the monitoring requirements and feasibility of execution. Ambient light intensity refers to the ambient brightness at the location of the monitoring device. This parameter directly affects the quality and clarity of image acquisition and is an important environmental factor for assessing the feasibility of image acquisition. RSSI refers to the strength of the wireless communication signal received by the monitoring device. This parameter reflects the connection quality between the device and the communication network and is an important indicator for assessing the feasibility of image data transmission. The demand-condition synergy coefficient is a comprehensive indicator generated by combining parameters such as mechanical dynamic threat coefficient, external threat coefficient, ambient light intensity, and RSSI. This coefficient is used to characterize the degree of synergy between the urgency of the current monitoring task (demand) and the actual conditions (operating conditions) of image acquisition and data transmission, guiding the dynamic adjustment of the image acquisition frame rate.

[0024] The system collects data on the remaining battery capacity, reflecting the device's power supply status, and the ambient temperature, reflecting its environmental tolerance. After constraint processing, an energy survival constraint coefficient is generated, characterizing the device's continuous operating capability. Remaining battery capacity refers to the percentage of electricity currently stored in the battery pack within the monitoring device, relative to its total capacity. This parameter directly reflects the device's power supply status and is a key indicator for evaluating its continuous operating capability. Ambient temperature refers to the temperature of the environment in which the monitoring device is located. Extreme temperature conditions can affect the normal operation of the device and battery performance, and are an important factor in evaluating the device's environmental tolerance and continuous operating capability.

[0025] Based on the demand-operating condition coordination coefficient and energy survival constraint coefficient, the target image acquisition frame rate is obtained and the current value is adjusted. The target image acquisition frame rate refers to the image acquisition frequency intelligently calculated and set by the monitoring device based on comprehensive factors such as the current line operating status, external environment, image acquisition conditions, and device power supply status. This dynamic adjustment of the frame rate aims to balance monitoring effectiveness and energy consumption, achieving efficient and intelligent inspection.

[0026] This application achieves a refined assessment of the structural stability of the power line by collecting the dominant frequency of conductor vibration, the impact acceleration of tower materials / fittings, and the conductor's tilt angle relative to the ground, and fusing these data to generate a mechanical dynamic threat coefficient. This avoids the coarseness of traditional methods in judging the line's condition. Furthermore, by collecting radar target distance and its rate of change, as well as the rate of change of the power frequency electric field intensity in space, and fusing these data to generate an external threat coefficient, this application can comprehensively perceive the risk of external intrusion and the degree of electric field disturbance. Compared with a single threshold triggering mode that relies solely on a single sensor signal for judgment, this significantly reduces the probability of false triggering and improves the accuracy and reliability of monitoring. For example, in the above example, if only radar distance threshold triggering is relied upon, the risk caused by abnormal electric field fluctuations may not be identified; conversely, if only the rate of change of the electric field is relied upon, rapidly approaching external objects may not be detected in time. The fusion processing mechanism of this application effectively compensates for these shortcomings.

[0027] Furthermore, this application introduces operating condition and energy parameters such as ambient light intensity, signal strength RSSI, remaining battery capacity, and ambient temperature, and generates demand-operating condition coordination coefficients and energy survival constraint coefficients respectively. The introduction of these coefficients ensures that the adjustment of the target image acquisition frame rate not only considers monitoring needs but also the actual feasibility of image acquisition and the continuous operation capability of the device. For example, in the above example, when the illuminance is low or the signal is weak, even if the threat coefficient is high, the frame rate will be appropriately adjusted to avoid acquiring low-quality images or being unable to transmit data; when the battery power is low or the ambient temperature is extreme, the frame rate will be further reduced to extend the device's operating life. This multi-dimensional, collaboratively optimized frame rate adjustment strategy effectively solves the problems of high false triggering and energy waste caused by the lack of multi-source monitoring data fusion and analysis capabilities in traditional solutions, achieving a balance between monitoring effectiveness and energy efficiency, and significantly improving the intelligence level and operational efficiency of transmission line visualization monitoring.

[0028] This application further proposes a method for calculating the mechanical dynamic threat factor as follows:

[0029] The system acquires the dominant frequency of conductor vibration, the impact acceleration of tower materials / fittings, and the conductor's tilt angle offset to ground. The dominant frequency of conductor vibration refers to the frequency component with the most concentrated energy in the vibration of the transmission line conductor during operation, caused by factors such as wind, icing, and damper failure. Its function is to reflect the severity and type of conductor vibration. This dominant frequency can be extracted in real-time by acquiring vibration signals from vibration sensors installed on the conductor and then using spectral analysis methods such as Fast Fourier Transform (FFT); or it can be identified and extracted from the raw vibration data using machine learning-based signal processing algorithms. The impact acceleration of tower materials / fittings refers to the instantaneous acceleration generated by the transmission line tower materials or fittings when subjected to external impact. Its function is to reflect abnormal stress conditions on the line structural components. This impact acceleration can be monitored and recorded in real-time by installing high-sensitivity triaxial accelerometers on key stress points of the tower materials or fittings; or by using a piezoelectric sensor array to capture the impact waveform and calculate the peak acceleration using a distributed sensing network. The conductor's tilt angle offset to ground refers to the degree of deviation of the transmission line conductor from its normal tilt angle to ground. Its function is to assess changes in the safe distance between the conductor and the ground or other obstacles, as well as the stability of the conductor's own shape. This offset can be obtained indirectly by measuring the conductor's attitude angle in real time using an inclination sensor installed on the conductor and comparing it with a preset reference inclination angle; or by using a vision sensor combined with image processing technology to monitor and calculate the conductor's sag in real time.

[0030] After dimensionless and amplitude-limiting processing of the conductor vibration dominant frequency, tower / fitting impact acceleration, and conductor tilt angle offset, corresponding conductor vibration dominant frequency index, tower / fitting impact acceleration index, and conductor tilt angle offset index are generated; the processing methods for the conductor vibration dominant frequency, tower / fitting impact acceleration, and conductor tilt angle offset are as follows:

[0031] The current conductor vibration frequency and tower / fitting impact acceleration are compared with the upper limits of conductor vibration frequency monitoring and impact acceleration, respectively. A min function is then used to truncate the ratios to a maximum of 1, yielding the conductor vibration frequency index and the tower / fitting impact acceleration index. This step aims to convert physical quantities with different dimensions into dimensionless relative proportions, thereby standardizing the data and facilitating subsequent fusion calculations. The processor can perform floating-point division, dividing the real-time acquired conductor vibration frequency and tower / fitting impact acceleration values ​​by their corresponding monitoring upper limits and thresholds, or mapping the raw data to ratios between 0 and 1 by looking up a preset mapping table or using a piecewise linear function. The min function is used to truncate the ratios to a maximum of 1 before obtaining the conductor vibration frequency index and the tower / fitting impact acceleration index to ensure that the index values ​​after ratio processing do not exceed 1. In the processor, the calculated ratio is processed using the `min(ratio, 1)` operation. If the ratio is greater than 1, it is set to 1; otherwise, the original value is retained. Alternatively, a conditional statement can be used to achieve the same truncation effect. The upper limit for conductor vibration frequency monitoring can be set according to standards such as the "DL / T1578-2016 Technical Specification for Vibration Monitoring Devices for Overhead Transmission Lines," combined with the effective frequency response range of the accelerometer used in the device. For example, for common wind-induced conductor vibration (frequency range approximately 3Hz-150Hz), if the upper limit of the sensor's effective frequency response is 200Hz, the upper limit for conductor vibration frequency monitoring can be set to 150Hz, covering the main dangerous vibration frequency bands. The upper limit threshold for impact acceleration can be calculated based on the yield strength of the tower materials and fittings, and determined in conjunction with the range of the impact sensor used. For example, if finite element analysis shows that key fittings will enter the plastic deformation stage when subjected to an impact exceeding 10g, the upper limit threshold for impact acceleration can be set to 10g.

[0032] The absolute value of the current conductor's tilt angle offset is compared with the maximum allowable tilt angle. A min function is then used to truncate the ratio to a maximum of 1 to obtain the conductor tilt angle offset index. Taking the absolute value of the tilt angle offset eliminates its directional influence, focusing only on the magnitude of the offset, as large upward or downward offsets can indicate line abnormalities. The processor first calculates the absolute value of the current conductor tilt angle offset, then divides it by the maximum allowable tilt angle. Alternatively, the absolute value of the offset can be directly output from the sensor, or the absolute value can be converted in the data processing module before the ratio calculation. The min function is used to truncate the ratio to a maximum of 1 to obtain the conductor tilt angle offset index. This ensures the index value is between 0 and 1, preventing extreme offsets from exceeding the limit.

[0033] The upper limit for conductor vibration frequency monitoring, the upper limit threshold for impact acceleration, and the maximum allowable conductor tilt angle to ground are benchmark values ​​used for standardizing raw monitoring data. These parameters define the maximum permissible range of each physical quantity under normal or abnormal conditions and are key reference points for judging the line status. These upper limits can be preset and stored in the monitoring device's memory according to the transmission line design specifications, operating experience, historical data analysis, or industry standards. They can also be dynamically adjusted through remote configuration or manual input to adapt to the needs of different line types, geographical environments, or seasonal changes.

[0034] The conductor vibration dominant frequency index, tower / fitting impact acceleration index, and conductor ground tilt offset index are weighted and fused, and the fused result is then subjected to a saturation characteristic nonlinear mapping to obtain a mechanical dynamic threat coefficient. The mechanical dynamic threat coefficient monotonically increases with the increase of the fused result. The specific saturation characteristic nonlinear mapping formula is as follows:

[0035]

[0036] in, For mechanical dynamic threat coefficient, A value close to 0 indicates that the circuit's mechanical condition is stable, and no additional frame rate increase is needed. A value close to 1 indicates the presence of severe vibration, impact, or serious sag abnormalities, requiring extremely urgent monitoring. The dominant frequency index of conductor vibration. The impact acceleration index for tower materials / fittings. This is the index of the conductor's tilt angle relative to the ground. , and All are mechanical dynamic threat weights ranging from 0 to 1, and Mechanical dynamic threat weight , and It is used to quantify the relative importance of different mechanical dynamic parameters in assessing the degree of mechanical threat to a power line. Its function is to allow the system to flexibly adjust the contribution of each factor to the final threat coefficient based on actual line characteristics, historical data, or expert experience. These weights can be manually set and pre-configured by maintenance personnel based on experience or line type; or they can be dynamically adjusted through adaptive learning and optimization based on historical fault data and machine learning algorithms.

[0037] The aforementioned mathematical model aims to comprehensively quantify the mechanical and dynamic stability of transmission lines, providing a unified and quantifiable indicator for assessing potential risks. This calculation can be performed directly on embedded processors or edge computing devices; alternatively, the various indices and weights can be sent to a cloud server for centralized calculation and result return. The formula employs an exponential function, non-linearly mapping the weighted sum of various threat indices to the 0-1 range, making threat perception more sensitive, especially with slow changes at low threat levels and rapid changes at high threat levels, better meeting the needs of practical risk assessment.

[0038] This application addresses the inaccuracy of traditional methods by specifying the calculation method for the mechanical dynamic threat coefficient, thereby ensuring that the coefficient accurately quantifies the degree of mechanical threat to transmission lines. First, the system acquires three core physical quantities: the dominant frequency of conductor vibration, the impact acceleration of tower materials / fittings, and the conductor's tilt angle relative to the ground. These reflect the conductor's vibration state, the abnormal stress on structural components, and the stability of the conductor's morphology, respectively. Simultaneously, the system also acquires the mechanical dynamic threat weight. , and These weights allow for adjustments to the contribution of different physical quantities to the overall threat level based on actual conditions. Subsequently, to eliminate dimensional differences between different physical quantities and limit the influence of outliers, the system performs dimensionless and amplitude-limiting processing on the conductor vibration dominant frequency, tower material / fitting impact acceleration, and conductor tilt angle offset to the ground, thereby generating the corresponding conductor vibration dominant frequency index. Impact acceleration index of tower materials / fittings and the index of the conductor's tilt angle relative to the ground These indices transform the raw physical measurements into standardized metrics within a uniform range of 0 to 1, enabling fair comparison and weighting in subsequent fusion calculations. Finally, these standardized indices, along with pre-defined mechanical dynamic threat weights, are substituted into a specific exponential function formula. The mechanical dynamic threat coefficient was calculated. This formula cleverly utilizes the properties of exponential functions, ensuring that when the mechanical state of the line is stable (all exponents approach 0). A value close to 0 indicates that no additional frame rate increase is needed; however, when there is severe vibration, impact, or serious sag abnormality (all indices approach 1). A value approaching 1 indicates extremely high monitoring urgency, requiring an immediate increase in the frame rate. This non-linear mapping relationship can more sensitively reflect changes in threat level, especially as the threat level transitions from low to high, the coefficient increases more rapidly, thus triggering a more proactive monitoring response in a timely manner. In this way, the mechanical dynamic threat coefficient... It can accurately quantify the mechanical state of the line, providing a specific and accurate calculation basis for generating the mechanical dynamic threat coefficient that characterizes the structural stability of the line in the above-mentioned visualization monitoring method, thereby improving the accuracy of the entire monitoring system's assessment of the structural stability of the line and the level of intelligence in frame rate adjustment.

[0039] As a specific implementation, the visualization monitoring device for power transmission line inspection can be equipped with various sensors to acquire the required mechanical dynamic data. For example, the dominant frequency of conductor vibration can be acquired by a MEMS accelerometer array mounted on the conductor. This array can monitor minute vibrations of the conductor in real time and transmit the raw data to a local processing unit. The local processing unit can run embedded signal processing algorithms, such as using Fast Fourier Transform (FFT) to perform spectral analysis on the acquired acceleration signal to extract the dominant vibration frequency. The impact acceleration of tower materials / fittings can be acquired using piezoelectric impact sensors mounted on key nodes of the tower materials or fittings. When an impact event occurs, these sensors generate a charge signal proportional to the impact intensity. After amplification and filtering, the processing unit samples and calculates the peak value of the impact acceleration. The conductor's tilt angle offset can be measured by a high-precision three-axis gyroscope and accelerometer fusion module mounted on the conductor. This module can sense the conductor's attitude changes in real time and calculate the real-time tilt angle of the conductor by fusing data from the gyroscope and accelerometer using algorithms such as Kalman filtering. For example, the device's processor can preset the upper limit for conductor vibration frequency monitoring to 50Hz, the upper limit threshold for impact acceleration to 10g (gravitational acceleration), and the maximum allowable value for conductor tilt angle to ground to 5 degrees. When the sensor collects the current conductor vibration frequency of 30Hz in real time, the processor will ratio it to 50Hz to obtain 0.6. If the collected frequency is 60Hz, the ratio will be 1.2, and the processor will truncate it to 1 using a min function to obtain the conductor vibration frequency index. Similarly, when the tower / fitting impact acceleration sensor detects an impact acceleration of 8g, the processor will ratio it to 10g to obtain 0.8. If a 12g impact is detected, the ratio will be 1.2, which will also be truncate to 1 using a min function to obtain the tower / fitting impact acceleration index. For the conductor tilt angle to ground offset, assume the maximum allowable value is 5 degrees. When the tilt offset is -3 degrees (representing a 3-degree offset in a certain direction), the processor first takes its absolute value to obtain 3 degrees, then compares it with 5 degrees to get 0.6. If the offset is 6 degrees, the absolute value is also 6 degrees, and the ratio is 1.2, which will also be truncated to 1 by the min function, thus obtaining the conductor tilt offset exponent. Mechanical dynamic threat weight. , and It can be pre-stored in the device's memory; for example, it can be set. =0.4、 =0.3、 =0.3, to reflect the main impact of conductor vibration on line stability in certain scenarios. The processing unit then substitutes these indices and weights into the formula. The calculation yields the mechanical dynamic threat coefficient $$K_{m}$$. This coefficient will serve as a key input for subsequent image acquisition frame rate adjustment, ensuring that the monitoring frame rate can be increased promptly and effectively to capture critical events when the mechanical condition of the line is abnormal.

[0040] Through the above technical solution, this application provides a specific and accurate method for calculating the mechanical dynamic threat coefficient, solving the problem of inaccurate coefficients caused by the lack of specific calculation methods in traditional methods. By comprehensively considering three key indicators—the dominant frequency of conductor vibration, the impact acceleration of tower materials / fittings, and the conductor's tilt angle offset to the ground—and performing dimensionless processing, amplitude limiting, and weighted fusion, the mechanical dynamic stability of transmission lines can be comprehensively and accurately quantified. This precise quantification enables the system to more sensitively perceive potential risks to the line. For example, when the line experiences minor but continuous vibration, the mechanical dynamic threat coefficient will increase accordingly, prompting the monitoring system to adjust the image acquisition frame rate in a timely manner, avoiding missing critical events due to excessively large sampling intervals. Simultaneously, by introducing weight parameters, this method allows for flexible adjustment of the importance of various mechanical threat factors based on different line types, environmental conditions, or operation and maintenance strategies, further improving the adaptability and accuracy of risk assessment. Ultimately, this precise and adjustable mechanical dynamic threat coefficient provides a reliable decision-making basis for intelligently adjusting the image acquisition frame rate in the aforementioned visualization monitoring method. It significantly improves the monitoring system's early warning capability and response efficiency for line structure stability, effectively avoids the loss of key images in high-risk scenarios, and reduces resource waste during stable operation.

[0041] This application further proposes a method for calculating the external threat coefficient as follows:

[0042] The radar module acquires the target distance and its rate of change, as well as the rate of change of the spatial power frequency electric field intensity. This can be achieved in various ways. For example, it can use vehicle-mounted or fixed radar sensors to detect the target distance and its rate of change in real time, while simultaneously using electric field sensors to monitor the rate of change of the spatial power frequency electric field intensity around power transmission lines. Alternatively, the radar module can periodically transmit and receive electromagnetic waves, calculating the target distance and velocity using time-of-flight or the Doppler effect; the electric field sensor then outputs a corresponding electrical signal by sensing changes in the electric field.

[0043] Based on the radar target distance, a radar target distance index characterizing the distance to the target is obtained; the current radar target distance change rate is extracted to obtain a target approach rate index characterizing the urgency of the target approach; based on the fluctuation amplitude of the current spatial power frequency electric field intensity change rate, a spatial power frequency electric field intensity change rate index characterizing the degree of electric field anomaly is obtained; the processing methods for the radar target distance and distance change rate, as well as the spatial power frequency electric field intensity change rate, are as follows:

[0044] The radar target distance is calculated by comparing the current radar target distance with the radar protection zone radius threshold, resulting in a radar target distance index. The radar protection zone radius threshold is a preset distance value used to define the area around the power transmission line that requires special attention. When a target detected by the radar enters this radius, it is considered a potential threat. This threshold can be set based on the voltage level of the power transmission line, the surrounding environment (such as proximity to residential areas or major transportation routes), and the detection capabilities of the inspection equipment. For example, for high-voltage power transmission lines, the threshold can be set to 50 meters; for ultra-high-voltage lines, it can be set to 100 meters. This step aims to transform the raw radar target distance data into a dimensionless, standardized index to reflect the relative relationship between the target and the protection zone boundary. By comparing the distance data with different dimensions, a comparable scale can be achieved, facilitating subsequent calculations that integrate with other threat factors. For example, a simple linear ratio (radar target distance / radar protection zone radius threshold) can be used, or a nonlinear function (such as a logarithmic function) can be used for mapping to more precisely reflect the impact of distance on the threat level.

[0045] The target approach rate exponent is obtained by taking the inverse of the current radar target range change rate, truncating the non-negative portion, and then comparing it with the upper limit of the radar target approach rate saturation. The specific formula is as follows:

[0046]

[0047] in, To approximate the rate exponent, The radar target range change rate, This is the upper limit for radar target approach rate saturation. The upper limit is a preset speed value representing the maximum urgency of a target approaching a power line. When the target's approach speed reaches or exceeds this limit, its threat level is considered the highest. Its purpose is to standardize the target approach speed, preventing excessively high instantaneous speed values ​​from overly influencing the threat index calculation, while ensuring that the threat index accurately reflects the urgency at extreme approach speeds. This upper limit can be empirically set based on the power line's protection level and target type (such as drones, birds, etc.). For example, it can be set to 20 m / s, or based on historical data analysis, 99% of abnormal approach speeds can be used as the saturation upper limit. This step quantifies the urgency of a target approaching a power line. First, the inverse of the distance change rate is used to define "approaching" as a positive value and "moving away" as a negative value, thus unifying the threat direction. Next, the non-negative portion is truncated to ensure that only the approaching situation is considered, ignoring the non-threat state when the target is moving away. Finally, a ratio is made with the saturation upper limit to standardize the approach speed to an exponent between 0 and 1, allowing targets of different speed ranges to be effectively assessed. For example, in addition to direct ratio processing, piecewise functions can be used to generate exponents by employing different weights or mapping relationships in different speed ranges, in order to more flexibly reflect the threat level of the approximation rate.

[0048] The absolute value of the current rate of change of the spatial power frequency electric field intensity is compared with the abrupt change threshold of the power frequency electric field rate of change. A min function is then used to truncate the ratio to 1, yielding the spatial power frequency electric field intensity rate of change exponent. The abrupt change threshold of the power frequency electric field rate of change is a preset value used to determine whether abnormal disturbances have occurred in the power frequency electric field around the transmission line. When the absolute value of the spatial power frequency electric field intensity rate of change exceeds this threshold, an electric field anomaly is considered to exist, potentially indicating external object intrusion or line fault. Its function is to identify abnormal fluctuations in the electric field environment, serving as an important basis for external threat assessment. This threshold can be determined based on the operating voltage of the transmission line, ambient electromagnetic background noise, and historical electric field data analysis. For example, it can be set to 100V / (m·s), or significant changes outside the normal fluctuation range can be used as the abrupt change threshold through long-term monitoring. This step aims to assess the severity of spatial power frequency electric field disturbances. By taking the absolute value, the direction of electric field enhancement or weakening can be disregarded, focusing only on the magnitude of the change, as drastic changes, whether enhancement or weakening, may indicate anomalies. The electric field variation amplitude is standardized by comparing the ratio to a mutation threshold. The upper limit of the ratio is truncated to 1 using a min function to prevent the exponent from becoming excessively large under extreme electric field changes, which could unreasonably dominate the calculation of the overall external threat coefficient, ensuring the exponent remains within a reasonable range. For example, besides a simple linear ratio with truncation, nonlinear mapping methods such as the Sigmoid or Tanh functions can be used to map the electric field change rate to the interval between 0 and 1, thus more smoothly reflecting the degree of electric field disturbance.

[0049] Substituting the radar target range index, target approach rate index, and spatial power frequency electric field intensity change rate index into the formula Obtain the external threat coefficient , A value approaching 0 indicates no external object intrusion or a stable electric field, signifying the safety of the monitoring channel. A value approaching 1 indicates that an object is approaching the conductor at high speed at close range, or that the field strength is experiencing a severe disturbance, requiring immediate full-frame recording. This is the radar target range index. To approximate the rate exponent, This refers to the rate of change exponent of the power frequency electric field intensity in space. This step aims to synthesize the three exponents mentioned above and calculate the final external threat coefficient using a mathematical model. This coefficient comprehensively and quantitatively reflects the combined threat level of external intrusion and electric field disturbance, guiding subsequent frame rate adjustment. The formula uses an exponential function to nonlinearly combine the distance exponent and the approach rate exponent, ensuring that the closer the distance and the faster the approach speed, the larger the product term, reflecting the urgency of the threat. Simultaneously, a max function is used to ensure that the rate of change exponent of electric field intensity independently and directly affects the final external threat coefficient; even without external object intrusion, severe electric field disturbances can be identified as a high threat. The processor receives the three exponents as input, performs floating-point operations according to the formula, and calculates... The calculation results are directly used to determine the danger level of the current external environment and serve as an important basis for adjusting the image acquisition frame rate.

[0050] This application's solution provides multi-dimensional, real-time raw data for external threat assessment by acquiring radar target range and its rate of change, as well as the rate of change of the space power frequency electric field intensity. Subsequently, this raw data is transformed into standardized radar target range indices, target approach rate indices, and space power frequency electric field intensity change rate indices, quantifying target proximity, urgency of approach, and degree of electric field anomaly, respectively. These indices are then cleverly integrated into a formula. The exponential function combination term in this formula This function can non-linearly reflect the complex threat of external object intrusion; that is, the closer the object and the faster its approach speed, the larger the value of this term, indicating a more urgent threat. Simultaneously, by using a max function to compare this complex threat with the rate of change exponent of the spatial power frequency electric field intensity, it ensures that whether it is physical intrusion (such as drones or birds) or electrical disturbance (such as partial discharge or flashover), as long as either threat reaches a high level, the external threat coefficient can be independently adjusted. Pushing for higher values. This design enables the system to respond comprehensively and sensitively to different types of external threats, avoiding false alarms or missed detections that might result from a single sensor or simple threshold judgment. Ultimately, this precisely calculated external threat coefficient... This will serve as a key input for generating the demand-operating condition coordination coefficient, thereby affecting the adjustment of the target image acquisition frame rate. This enables the visualization monitoring method for power transmission line inspection to intelligently adjust its working mode according to the actual threat level of the external environment, achieving on-demand monitoring.

[0051] The following is a concrete example. Assume a power transmission line monitoring device is equipped with a millimeter-wave radar module and an electric field sensor. When the radar module detects a drone approaching the power line at a speed of 10 meters per second from a distance of 50 meters, and simultaneously the electric field sensor detects an abnormal fluctuation in the rate of change of the spatial power frequency electric field intensity, with the fluctuation amplitude reaching 80% of a preset abrupt change threshold, the system first converts the radar target distance of 50 meters into a radar target distance index. For example, if the radar protection zone radius threshold is 100 meters, then It might be calculated as 0.5. Next, the target approach rate of 10 m / s is converted into a target approach rate exponent. For example, if the upper limit of radar target approach rate saturation is 20 meters per second, then It may be calculated as 0.5. Simultaneously, the fluctuation amplitude of the electric field intensity change rate is converted into the spatial power frequency electric field intensity change rate exponent. For example, if the fluctuation amplitude reaches 80% of the sudden change threshold, then It might be calculated as 0.8. These exponents are then substituted into the formula. Perform the calculations. Specifically, The calculated value is 0.8. This is a relatively high external threat factor. (0.8) indicates that there is a significant external threat, whether it is an approaching drone or an abnormal electric field, requiring the system to take immediate action.

[0052] Through the above technical solution, this application can comprehensively perceive the external environment, avoiding the limitations of a single sensor data source and effectively solving the problem of false triggering or ignoring key threats caused by single sensor data or simple threshold judgment in traditional methods. By converting radar target distance, distance change rate, and spatial power frequency electric field intensity change rate into standardized exponents and substituting them into a specific fusion formula, a refined and quantitative assessment of external intrusion and electric field disturbance threats is achieved. The ingenuity of this formula lies in that it not only comprehensively considers the combined effects of target distance and approach speed, but also ensures through the max function that electric field anomalies can independently and directly trigger high-threat responses, thereby significantly reducing the false triggering rate and missed detection rate. This comprehensive assessment mechanism enables the monitoring device to more accurately identify real external threats, increasing the image acquisition frame rate only when necessary, avoiding unnecessary frequent startups and high power consumption operation, effectively saving energy, and significantly improving the reliability and effectiveness of power transmission line inspection and visualization monitoring.

[0053] This application further proposes a method for calculating the demand-operating condition coordination coefficient as follows:

[0054] The monitoring system acquires ambient light intensity and signal strength (RSSI). Ambient light intensity refers to the light intensity of the environment in which the monitoring device is located, directly affecting the quality and clarity of image acquisition. Ambient light intensity can be acquired in various ways, such as real-time measurement using the built-in illumination detection function of a photoresistor, photodiode, or CMOS / CCD image sensor integrated into the monitoring device. Another method is to acquire illumination data of the area through an external environmental sensor network and transmit it to the monitoring device. Signal strength (RSSI) refers to the wireless signal received between the monitoring device and a communication network (such as a 4G / 5G network). Its function is to assess the reliability and bandwidth of data transmission, thus affecting the real-time transmission capability of image data. RSSI is usually automatically measured and provided by the built-in wireless communication module of the monitoring device, for example, by querying the communication module's registers or API interface. Alternatively, it can be measured using an external wireless signal strength meter, and the data can be input into the monitoring device.

[0055] Based on ambient illuminance, an ambient illuminance index is generated, whose value decreases as illuminance decreases and has a non-zero lower limit; based on signal strength RSSI, a signal strength RSSI index is generated, whose value decreases as signal strength weakens and is constrained by amplitude limiting; the mechanical dynamic threat coefficient and the external threat coefficient are weighted and combined to obtain the threat fusion quantity; the specific processing methods for ambient illuminance and signal strength RSSI are as follows:

[0056] Based on the comparison between the current ambient illuminance and the upper limit of ambient illuminance saturation, an ambient illuminance index is generated to indicate the suitability of ambient lighting. When the ambient illuminance is below the upper limit of ambient illuminance saturation, the ambient illuminance index decreases as the ambient illuminance decreases, but does not fall below the lower limit threshold for maintaining the operating condition. The specific calculation formula is as follows:

[0057]

[0058] in, The ambient light intensity index. For ambient light intensity, This represents the upper limit of ambient light saturation. The operating condition maintenance lower limit threshold is used to prevent the frame rate from dropping to zero and causing the device to completely shut down in complete darkness. The ambient light saturation upper limit defines the threshold at which ambient light reaches an optimal or sufficient level. When the ambient light exceeds this value, the lighting conditions are considered sufficiently good, and the ambient light index can reach its maximum value of 1. Its function is to provide an upper limit reference point for the calculation of the ambient light index, preventing the index from continuing to grow indefinitely under excessively strong light. For example, it can be set to 5000 lux, indicating that the image acquisition conditions are very ideal when the illuminance reaches this value on a sunny day; or it can be set to 10000 lux to accommodate the need for higher lighting conditions. The operating condition maintenance lower limit threshold defines the lowest non-zero value that the ambient light index can reach under extremely unfavorable lighting conditions. Its function is to ensure that even in a completely dark environment, the ambient light index will not drop to zero, thereby preventing the demand-operating condition coordination coefficient from completely dropping to zero, avoiding complete device shutdown or cessation of image acquisition, and maintaining basic monitoring capabilities. For example, it can be set to 0.1, indicating that the ambient illuminance index is at least 0.1 under the worst lighting conditions; or it can be set to 0.05 to allow for a lower minimum operating capability. The weak signal lower limit defines the starting point at which the RSSI (Resonance Signal Strength Index) is considered a "weak signal." When the signal strength is below this value, the system begins to consider communication conditions poor and adjusts the RSSI index accordingly. Its function is to provide a low-signal reference point for calculating the RSSI index, distinguishing between normal communication and weak signal communication. For example, it can be set to -90dBm, indicating that a value below this is considered a weak signal; or it can be set to -85dBm to identify weak signal conditions earlier.

[0059] The upper limit of ambient light saturation can be obtained through calibration experiments on the image quality of the image sensor mounted on the device. The minimum illuminance at which the image signal-to-noise ratio (SNR) and sharpness evaluation value reach a stable plateau (i.e., further increasing the illuminance no longer significantly improves the image quality) is gradually increased in a dark room. For example, the calibration result is 5000 lux.

[0060] The RSSI index is obtained by comparing the difference between the current signal strength RSSI and the weak signal lower limit with the difference between the upper limit of communication signal strength saturation and the weak signal lower limit. The ratio is then truncated using max and min functions to set the upper and lower limits to 1 and the lower signal maintenance threshold, respectively. The lower signal maintenance threshold ensures that the RSSI index does not fall below this value when the signal strength approaches its weakest point, preventing the device from completely stopping image acquisition due to poor communication quality. This step dynamically assesses data transmission conditions based on the actual communication signal strength and quantifies it into an index. By introducing the lower signal maintenance threshold, the index is ensured to remain non-zero even when the signal is extremely weak, thus preventing the system from completely stopping image acquisition and data transmission due to poor communication quality. This can be achieved by obtaining the RSSI value through a wireless communication module (such as a 4G / 5G module) and then having a microcontroller or dedicated processing chip perform the above ratio and truncation processing. The upper limit of communication signal strength saturation defines the threshold at which the RSSI is considered a "strong signal." When the signal strength reaches or exceeds this value, the communication conditions are considered sufficiently good, and the RSSI index can reach its maximum value of 1. Its function is to provide an upper limit reference point for the calculation of the RSSI index, preventing the index from continuing to grow indefinitely when the signal is too strong. For example, it can be set to -60dBm, indicating that the communication conditions are very ideal when the signal strength reaches this value; or it can be set to -55dBm to accommodate the need for higher signal quality. The lower limit threshold for signal maintenance defines the lowest non-zero value that the RSSI index can reach under extremely unfavorable signal conditions. Its function is to ensure that even under extremely weak signal conditions, the RSSI index will not drop to zero, thereby preventing the demand-condition coordination factor from completely reaching zero and preventing the equipment from completely stopping image acquisition due to poor communication quality, thus guaranteeing a minimum data transmission capacity. For example, it can be set to 0.08, indicating that under the worst signal conditions, the RSSI index is at least 0.08; or it can be set to 0.03 to allow for a lower minimum transmission capacity. Based on the comparison between the current ambient illuminance and the upper limit of ambient illuminance saturation, an ambient illuminance index is generated to determine the suitability of the ambient light. This step dynamically assesses the image acquisition conditions based on the actual ambient illuminance and quantifies it into an index. By introducing a lower limit threshold to maintain the operating conditions, the index is ensured to remain non-zero even under extremely low light conditions, thus preventing the system from completely shutting down due to insufficient light. This can be achieved by collecting ambient illuminance data using light sensors such as photoresistors and photodiodes, and then having a microcontroller or dedicated processing chip execute the above formula for calculation.

[0061] The lower limit for weak communication signal strength can be found in the technical specifications of the 4G / 5G communication module used in the device. Locate the minimum received signal strength threshold required to guarantee a minimum uplink rate (e.g., not less than 1Mbps, sufficient for transmitting compressed images). For example, if a module specification states that a basic connection can be maintained at -105dBm, but a rate of 1Mbps requires a minimum of -95dBm, then the lower limit for weak communication signal strength can be set to -95dBm.

[0062] The threat fusion quantity is multiplicatively coupled with the ambient illuminance index and the RSSI signal strength index to obtain the demand-operating condition synergy coefficient. This coefficient is positively correlated with the threat fusion quantity, ambient illuminance index, and RSSI signal strength index. The specific calculation formula is as follows:

[0063]

[0064] in, This is the demand-operating condition coordination coefficient. A value close to 0 indicates a low threat level and harsh operating conditions, suggesting the use of the lowest possible standby frame rate. A value close to 1 indicates an imminent threat and excellent lighting and signal conditions, supporting the highest video frame rate. As a threat to fusion, These are operating condition constraints. For mechanical dynamic threat coefficient, External threat coefficient, The ambient light intensity index. The RSSI index represents the signal strength. and All are threat fusion weights, and Threat fusion weight and It is used to balance the dynamic threat coefficient of machinery. and external threat coefficient The parameters representing the relative importance of demand-condition synergy in the calculation of demand-condition synergy factors. These weights are typically pre-defined, for example, based on historical data analysis or expert experience. and A value of 0.5 indicates that both threats are equally important. In certain application scenarios, the value can be dynamically adjusted via remote configuration or adaptive algorithms based on specific needs or monitoring priorities, assigning a higher priority to a particular type of threat.

[0065] The proposed solution achieves intelligent dynamic adjustment of the image acquisition frame rate of the transmission line inspection visualization monitoring device by finely calculating the demand-operating condition coordination coefficient. First, the system acquires the current ambient light intensity, signal strength RSSI, and preset threat fusion weights. and These parameters form the basis for assessing the feasibility of image acquisition and the urgency of monitoring. Next, based on the real-time acquired ambient light intensity, the system generates an ambient light intensity index. This index not only reflects the direct impact of lighting conditions on image quality, but also, through its non-zero lower limit design, ensures that the device can maintain a minimum image acquisition capability even in extremely poor lighting conditions, preventing complete shutdown due to complete darkness. Simultaneously, based on the real-time acquired signal strength RSSI, the system generates the signal strength RSSI index. This index quantifies the impact of communication quality on data transmission and, through amplitude limiting constraints, ensures that the device can still maintain basic communication and data return capabilities even when the signal is extremely weak, preventing monitoring failure due to communication interruption. Subsequently, the system processes these operating condition indices (ambient light intensity index)... and RSSI index (and the previously calculated mechanical dynamic threat coefficient) and external threat coefficient To integrate. Specifically, the mechanical dynamic threat coefficient. and external threat coefficient First, through threat fusion weighting and A weighted summation is performed to form a comprehensive threat fusion item. This item comprehensively reflects both the structural stability risks of the line itself and the risks of external intrusion. Then, this threat fusion item is combined with the operating condition constraints. Multiply by each product to obtain the final demand-operating condition coordination coefficient. This multiplication operation is the core of this solution, cleverly coupling monitoring needs (threat level) with execution feasibility (operating conditions). The demand-operating condition synergy coefficient only increases when the threat level is high and operating conditions (light and signal) are good. Only when the threat level is low, or operating conditions are poor (insufficient lighting or weak signal), will the coefficient approach 1, indicating a need for high frame rate acquisition. Conversely, if the threat level is low, or operating conditions are poor (insufficient lighting or weak signal), even if the threat is high, the coefficient will decrease accordingly. This avoids ineffective high frame rate acquisition when effective acquisition or transmission conditions are not available, effectively saving energy and optimizing data transmission. Ultimately, this demand-operating-condition coordination coefficient... This will serve as a key input for adjusting the target image acquisition frame rate, along with the energy survival constraint coefficient. Together, they determine the final frame rate, thereby achieving intelligent, efficient, and adaptive management of the image acquisition frame rate of the power transmission line inspection visualization monitoring device.

[0066] As a specific implementation, the visual monitoring device for power transmission line inspection can be equipped with an ambient light sensor, such as a photodiode or an integrated ambient light sensor chip, to measure ambient light intensity in real time. Simultaneously, the device's built-in wireless communication module (such as a cellular module supporting 4G / 5G communication) can periodically report its Received Signal Strength Indication (RSSI) value. Threat fusion weights can be preset in the processing unit. It is 0.6. A value of 0.4 indicates that, in the current application scenario, dynamic mechanical threats are more important than external threats. When the current ambient illuminance is 5000 lux, the processing unit can calculate the ambient illuminance index based on a preset mapping relationship (e.g., when the illuminance is below 10000 lux, the index decreases linearly proportionally, but not below 0.1). For example, if the upper limit of saturation is 10,000 lux, the lower limit threshold for maintaining the operating condition is... If it is 0.1, then Meanwhile, when the acquired RSSI signal strength is -85dBm, the processing unit can calculate the RSSI exponent based on a preset signal strength-exponential mapping relationship (for example, a linear mapping when RSSI is between -90dBm and -60dBm, with an exponent of 0.1 below -90dBm and an exponent of 1 above -60dBm). For example, if the weak signal lower limit is -90dBm, the saturation upper limit is -60dBm, and the signal maintenance lower limit threshold is 0.1, then... =0.167. Assuming the currently calculated mechanical dynamic threat coefficient... The external threat coefficient is 0.8 (indicating a high mechanical risk). A value of 0.3 indicates a low risk of external intrusion. At this point, the processing unit substitutes these exponents and coefficients into the formula. Perform the calculation. That is, The final demand-operating condition coordination coefficient is obtained. The value is approximately 0.055. This low coefficient indicates that despite the high mechanical threat, the device will tend to use a lower image acquisition frame rate due to the generally poor lighting and signal conditions, in order to balance monitoring needs with the feasibility of actual operating conditions.

[0067] Through the above technical solution, this application provides an accurate and adaptive method for calculating the demand-condition coordination coefficient, effectively solving the problem of inaccurate calculations and improper frame rate adjustment caused by traditional methods when fusing threat factors and operating conditions. This solution comprehensively considers ambient light intensity, signal strength RSSI, mechanical dynamic threat coefficient, and external threat coefficient, and employs a multiplicative fusion mechanism to ensure that the image acquisition frame rate is only increased when monitoring needs are urgent and operating conditions are favorable. This avoids ineffective high-frame-rate acquisition due to misjudgment in high-threat but harsh operating conditions, and also avoids wasting resources on unnecessary frequent acquisitions in low-threat but favorable operating conditions. Thus, intelligent dynamic adjustment of the image acquisition frame rate is achieved, significantly improving the resource utilization efficiency and monitoring reliability of the monitoring device in complex field environments, ensuring the effective capture of critical events, and maximizing the device's endurance.

[0068] This application further proposes a method for calculating the energy survival constraint coefficient as follows:

[0069] The system obtains the remaining battery capacity and ambient temperature. Remaining battery capacity (SOC) refers to the current usable charge of the battery, usually expressed as a percentage. It can be monitored and estimated in real time by the device's internal battery management system (BMS), for example, using coulomb counting or open-circuit voltage methods. Ambient temperature refers to the real-time temperature of the external environment in which the monitoring device is located. It can be measured using internal or external integrated temperature sensors, such as thermistors or integrated temperature sensors.

[0070] Based on the comparison between the remaining battery capacity and the critical threshold of remaining battery charge, an energy adequacy index is generated to characterize the sufficiency of energy supply. When the remaining battery capacity is lower than the critical threshold of remaining battery charge, the energy adequacy index decreases as the remaining battery capacity decreases, and the lowest value after the decrease is limited by the minimum operating maintenance threshold. The specific calculation formula is as follows:

[0071]

[0072] in, For the energy abundance index, This refers to the remaining battery capacity. This represents the critical threshold for remaining battery power. To maintain the minimum operating threshold, The limit constraint value approaching 0 ensures heartbeat monitoring; the remaining battery power threshold is a preset percentage of remaining battery capacity used to determine whether the battery has entered a low-power state, for example, it can be set to 20% or 30%. The minimum operating maintenance threshold is an index value between 0 and 1, representing the minimum allowable value of the energy adequacy index when the battery power is extremely low, for example, it can be set to 0.05 or 0.1. Energy Adequacy Index It is a dimensionless index used to quantify the sufficiency of battery power supply, reflecting the device's operational potential in terms of power.

[0073] Based on the degree of deviation of the ambient temperature from the optimal operating reference temperature, a temperature suitability index is generated to characterize the suitability of the temperature. Specifically, when the deviation is greater than or equal to half the allowable temperature difference width, the temperature suitability index is set as the minimum temperature protection threshold. When the deviation is less than half the allowable temperature difference width, the temperature suitability index increases as the deviation decreases, until it reaches a maximum value of 1. The specific calculation formula is as follows:

[0074]

[0075] in, The temperature suitability index, For ambient temperature, For optimal operating reference temperature, To allow for a half-width temperature difference, The minimum temperature protection threshold is the lowest operating capability coefficient retained under extreme temperatures. The optimal operating reference temperature refers to the ideal ambient temperature at which the monitoring device performs best, consumes the least power, or has the longest battery life during design and operation; for example, it can be set to 22°C. The permissible temperature difference half-width is half a temperature range, representing the allowable range of ambient temperature deviation from the optimal operating reference temperature. The permissible temperature difference half-width can be determined based on the industrial-grade or wide-temperature-range operating temperature range of the main chips in the device (such as processors, communication modules, and batteries). If the guaranteed operating temperature range of all core chips is -20°C to 60°C, and the optimal operating temperature is 25°C, then the permissible temperature difference half-width can be min(|-20-25|,|60-25|) = 35°C. For performance protection, a more conservative value can be used, for example, the permissible temperature difference half-width can be 15°C. The minimum temperature protection threshold is an index value between 0 and 1, representing the lowest permissible value of the temperature suitability index under extreme ambient temperatures; for example, it can be set to 0.1 or 0.2. Temperature Suitability Index It is a dimensionless index used to quantify the suitability of ambient temperature for the operation of a device, reflecting the device's operating potential in terms of temperature.

[0076] Multiply the energy abundance index by the temperature suitability index to obtain the energy survival constraint coefficient. , A value close to 1 indicates sufficient energy and suitable temperature, allowing the equipment to operate at full capacity. A value close to 0 indicates depleted battery or extreme temperature, forcing the device into the lowest power consumption frame rate mode. This coefficient comprehensively characterizes the device's ability to continue operating under current energy and ambient temperature conditions.

[0077] This scheme introduces a critical threshold for remaining battery charge, a minimum operational sustainment threshold, an optimal operating reference temperature, a permissible temperature difference half-width, and a minimum temperature protection threshold, combined with corresponding calculation formulas, to achieve a refined assessment and constraint of the device's continuous operating capability. This mechanism effectively prevents the device from completely shutting down when the battery is depleted or under extreme temperatures, ensuring the continuity and reliability of monitoring. This energy survival constraint coefficient... Together with the demand-condition coordination coefficient, it is used to obtain the target image acquisition frame rate and adjust the current value. This ensures that the image acquisition strategy not only considers the monitoring requirements and execution feasibility, but also fully considers the device's own energy and environmental tolerance. Thus, while ensuring the monitoring effect, it maximizes the extension of the device's field operation time and avoids monitoring interruption due to energy depletion or harsh environment.

[0078] For example, a visual monitoring device for power transmission line inspection can integrate a battery management unit and an environmental sensing unit. The battery management unit is responsible for real-time monitoring of the remaining battery capacity, for example, by integrating the battery charging and discharging current and calibrating it in conjunction with the battery voltage. The environmental sensing unit includes a high-precision temperature sensor to collect the ambient temperature in real time. When calculating the energy survival constraint coefficient, the processor first obtains the current remaining battery capacity from the battery management unit and the current ambient temperature from the environmental sensing unit. Preset parameters can be stored in the device's non-volatile memory; for example, the critical threshold for remaining battery capacity can be set to 25%, and the minimum operating maintenance threshold can be set to 0.08. The optimal operating reference temperature can be set to 20°C, the allowable temperature difference half-width can be set to 15°C, and the minimum temperature protection threshold can be set to 0.15. When the remaining battery capacity is 15% (below the 25% critical threshold), the processor calculates the energy adequacy index according to a formula, for example... Meanwhile, if the ambient temperature is -10℃, and the optimal operating reference temperature is 20℃, with a permissible temperature difference half-width of 15℃, then the deviation is |-10-20|=30℃. Since 30℃ is greater than the permissible temperature difference half-width of 15℃, the processor will directly set the temperature suitability index to the minimum temperature protection threshold, i.e., 0.15. Finally, multiplying the calculated energy sufficiency index of 0.632 by the temperature suitability index of 0.15 yields the energy survival constraint coefficient. The value is 0.095. This low energy survival constraint factor will instruct the device to enter a lower image acquisition frame rate mode to maximize power savings and protect the device from operation at extreme low temperatures.

[0079] Through the above technical solution, this application effectively solves the problem that the monitoring device may be unable to maintain a minimum operating state when the battery capacity is nearly depleted or the ambient temperature is extreme, leading to monitoring interruption. Specifically, by introducing a critical threshold for remaining battery power and a minimum operating maintenance threshold, it ensures that the energy adequacy index does not completely drop to zero when the power is extremely low, thereby ensuring that the device can continuously perform basic functions such as "heartbeat" monitoring and avoiding complete dormancy due to power depletion. At the same time, by setting an optimal operating reference temperature, a permissible temperature difference half-width, and a minimum temperature protection threshold, the temperature suitability index can still maintain a non-zero value under extreme temperature conditions, thereby ensuring the device's minimum operating capability in harsh temperature environments. The energy survival constraint coefficient obtained by multiplying these two indices comprehensively reflects the device's power supply status and environmental tolerance, providing a comprehensive and robust basis for subsequent target image acquisition frame rate adjustment. This mechanism enables the device to intelligently adjust its operating mode according to its own energy and environmental conditions, maximizing the field operation time while ensuring monitoring continuity, and significantly improving the survivability and reliability of the power transmission line visualization monitoring device in complex environments.

[0080] This application further proposes a method for calculating the target image acquisition frame rate as follows:

[0081] The system acquires the minimum allowed frame rate, maximum allowed frame rate, demand-condition coordination factor, and energy survival constraint factor. The minimum allowed frame rate refers to the lowest image acquisition frequency that the monitoring device must maintain under all circumstances to ensure basic heartbeat monitoring or status recording even under the most unfavorable conditions. This minimum frame rate is typically determined by the device's hardware capabilities, system design, and minimum monitoring requirements. It can be pre-set and stored in the device's configuration parameters; for example, it can be set to one frame per minute or one frame per hour to ensure the device can still provide basic data feedback in extremely low-power modes. The maximum allowed frame rate refers to the maximum image acquisition frequency that the monitoring device can achieve under ideal operating conditions and sufficient energy supply, representing the upper limit of the device's performance. This maximum frame rate is also determined by hardware specifications such as the device's image sensor, processor performance, storage bandwidth, and data transmission capabilities. It can be pre-set, for example, to 25 frames per second or 30 frames per second to capture rapidly changing event details.

[0082] Based on the product of the demand-operating condition coordination coefficient and the energy survival constraint coefficient, an interpolation mapping is performed between the device's minimum allowed frame rate and the device's maximum allowed frame rate to obtain the target image acquisition frame rate. Specifically, when the product approaches 0, the target image acquisition frame rate approaches the device's minimum allowed frame rate; when the product approaches 1, the target image acquisition frame rate approaches the device's maximum allowed frame rate. The specific interpolation mapping formula is as follows:

[0083]

[0084] in, The frame rate for acquiring the target image. This is the demand-operating condition coordination coefficient. Energy survival constraint coefficient, The minimum frame rate allowed by the device, The highest frame rate allowed by the device. Demand-condition coordination factor. This is an indicator that comprehensively assesses the urgency of current monitoring needs and the feasibility of image acquisition (i.e., operating conditions). The coefficient typically ranges from 0 to 1, where a higher value indicates an urgent monitoring need and suitable environmental conditions (such as lighting and signal strength) for high-quality image acquisition; a lower value indicates a less urgent monitoring need or harsh environmental conditions unsuitable for high-frame-rate acquisition. Energy Survival Constraint Coefficient This is an indicator that comprehensively evaluates the device's current power supply status and environmental tolerance. The value of this coefficient also ranges from 0 to 1, where a higher value indicates that the device has sufficient battery power and a suitable ambient temperature, supporting high-power operation; a lower value indicates insufficient battery power or extreme ambient temperature, requiring power consumption limitation to extend operating time. The formula uses the minimum frame rate as a benchmark and superimposes a demand-condition coordination coefficient based on the difference between the maximum and minimum frame rates. and energy survival constraint coefficient The increment determined by the product of these factors is used to calculate the target image acquisition frame rate. This calculation method cleverly couples monitoring needs, operating conditions, and energy constraints, ensuring that the dynamic adjustment of the frame rate can respond to changes in the external environment while also taking into account the device's own operating status.

[0085] The solution in this application obtains the minimum frame rate allowed by the acquisition device. The device allows a maximum frame rate This sets clear upper and lower limits for the dynamic adjustment of the image acquisition frame rate, ensuring that the frame rate remains within the acceptable performance range of the device. Based on this, a demand-condition coordination coefficient is introduced. This coefficient comprehensively reflects the line's mechanical dynamic threats, external intrusion risks, and current operating conditions such as ambient light and communication signal strength, enabling frame rate adjustment to intelligently respond to actual monitoring needs and changes in the external environment. Simultaneously, the energy survival constraint coefficient... The frame rate is constrained from the perspective of the device's own energy reserves and environmental temperature tolerance, avoiding power depletion due to over-collection or damage to the device in extreme environments. This is achieved by using these two key coefficients... and By incorporating a product into the frame rate calculation formula, this solution achieves deep coupling between monitoring needs, operating conditions, and energy constraints. This means that the frame rate can only reach a high level when monitoring needs are urgent, operating conditions are excellent, the device has sufficient energy, and the ambient temperature is suitable; conversely, any adverse factor will lead to a corresponding decrease in the frame rate. This mechanism enables the device to optimize power consumption and extend battery life to the maximum extent while ensuring that critical information is not lost, based on real-time conditions. This solves the problems of power redundancy, loss of critical images, or false triggering that exist in traditional fixed frame rate or single threshold triggering modes.

[0086] As a specific implementation method, the device can be set to allow a minimum frame rate. The device allows a maximum frame rate of 1 frame per minute. The frame rate is 25 frames per second. When the transmission line is in normal operation, with no significant mechanical vibration or external intrusion risk, and with sufficient ambient light and good communication signal, the demand-condition coordination coefficient is... It may be close to 1. Meanwhile, if the device's battery has sufficient charge and the ambient temperature is suitable, the energy survival constraint coefficient... It may also be close to 1. In this case, according to the formula... Target image acquisition frame rate The frame rate will approach the maximum, such as 25 frames per second, to capture detailed information about the line's operation. Conversely, when severe vibrations are detected in the conductor or a high-speed approach of an unidentified flying object, leading to a decrease in the demand-condition coordination coefficient... The energy survival constraint factor increases, but if the device's battery power is low or the ambient temperature is too high / too low, this will lead to an energy survival constraint factor. If the frame rate of the target image acquisition is reduced, then... A balance will be struck between high demand and low energy consumption. For example, adjusting to 5 frames per second will ensure continuous monitoring of abnormal events while effectively controlling power consumption. If the line is in a stable state for a long period of time and the energy supply is tight, both coefficients may be lower, and the target frame rate will approach the minimum frame rate, such as 1 frame per minute, to maximize the standby time of the device.

[0087] Through the above technical solution, this application can intelligently and dynamically adjust the image acquisition frame rate according to the actual operating status of the transmission line, external environmental conditions, and the energy status of the monitoring device itself. This effectively avoids the problem of losing key images in high-risk scenarios due to excessively large sampling intervals in the traditional fixed frame rate mode, and also avoids power consumption and data transmission redundancy caused by high-frequency acquisition during stable operation. At the same time, compared with the single threshold triggering mode, this solution significantly reduces the probability of false triggering by fusing the coordination coefficient and constraint coefficient generated from multi-source data, enabling the device to respond to monitoring needs more accurately, thereby greatly improving energy utilization efficiency and the continuous operation capability of the device while ensuring monitoring effectiveness.

[0088] In some of the solutions described above in this application, a visual monitoring method for power transmission line inspection is proposed to dynamically adjust the image acquisition frame rate through multi-source data fusion. However, in implementing these methods, there is a lack of a dedicated device to efficiently and reliably execute them, resulting in existing monitoring devices suffering from low sampling efficiency, high false triggering rate, and excessive energy consumption due to fixed frame rates or single threshold triggering modes.

[0089] In response, this application proposes a visual monitoring device for transmission line inspection, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned visual monitoring method for transmission line inspection.

[0090] The memory in this device is the medium used to store data and instructions. The concept encompasses various types of digital storage technologies. For example, non-volatile memory (such as flash memory and EEPROM) can be used to store computer programs and configuration parameters, while volatile memory (such as DDR SDRAM and SRAM) can be used to store runtime data, instantaneous data collected by sensors, and intermediate results generated during calculations. The memory provides data persistence and fast access capabilities for the entire monitoring system, and is fundamental to ensuring the stable operation of the monitoring method. The processor is the core computing unit of the device, responsible for executing the instructions in the computer program. The processor can be a microcontroller (MCU), such as a chip based on the ARM Cortex-M series core, which integrates a CPU, memory, and various peripheral interfaces; or it can be a microprocessor (MPU), such as a chip based on the ARM Cortex-A series core, which has stronger computing power and more complex operating system support. The choice of processor depends on the required computing performance, power budget, and system complexity. The computer program is a pre-written set of instructions that defines the specific logic and algorithms of the visualization monitoring method. This program can be embedded in memory as firmware or run as an application on an operating system. The steps involved in implementing a visual monitoring method for power transmission line inspection by executing a computer program mean that the processor reads and executes the computer program in memory to complete the entire process from data acquisition, data fusion, coefficient calculation to final frame rate adjustment. This includes controlling sensors to acquire data, performing complex mathematical operations to calculate mechanical dynamic threat coefficients, external threat coefficients, demand-condition coordination coefficients, and energy survival constraint coefficients, and dynamically adjusting the image acquisition frame rate based on these coefficients.

[0091] The solution in this application integrates the aforementioned visualization monitoring method for power transmission line inspection into a dedicated device, achieving a tight integration of method and hardware. Specifically, the memory provides stable storage space for the computer program and real-time monitoring data, ensuring the continuity and integrity of the data stream and avoiding frame rate adjustment failures due to data loss or interruption. The processor, as the computing core, can efficiently execute the complex algorithms coded in the computer program, fusing and processing multi-source data such as conductor vibration frequency, tower / fitting impact acceleration, conductor tilt angle offset, radar target distance and distance change rate, spatial power frequency electric field intensity change rate, ambient light intensity, RSSI signal strength, remaining battery capacity, and ambient temperature. Through these processes, the processor can accurately calculate the mechanical dynamic threat coefficient, external threat coefficient, demand-condition coordination coefficient, and energy survival constraint coefficient, and ultimately dynamically acquire and adjust the target image acquisition frame rate based on these coefficients. This integrated design enables the device to intelligently optimize image acquisition strategies based on the actual operating status of the transmission line, changes in the external environment, and its own energy status, thereby overcoming the limitations of traditional fixed frame rate or single threshold triggering modes. By executing this computer program, the device transforms abstract monitoring methods into practical, operable functions, ensuring that the entire monitoring system operates efficiently, accurately, and energy-savingly.

[0092] The following is a specific example. As a concrete implementation, this visualization monitoring device for power transmission line inspection can employ an embedded system architecture. The memory can consist of a 32MB NOR Flash chip (for storing firmware and configuration parameters) and a 64MB DDR SDRAM chip (for storing real-time sensor data, image cache, and runtime variables). The processor can be a low-power, high-performance embedded microprocessor, such as an ARM Cortex-A7-based processor, which integrates multiple ADCs, SPI, I2C, UART, and other communication interfaces, as well as an image processing unit. The computer program can be written in C and run on a lightweight real-time operating system (RTOS), such as FreeRTOS. Upon device startup, the processor first loads the computer program from the NOR Flash into the DDR SDRAM for execution. After program startup, the processor periodically collects analog data such as conductor vibration frequency, tower / fitting impact acceleration, conductor tilt angle offset to ground, ambient light intensity, remaining battery capacity, and ambient temperature through the ADC interface. Simultaneously, communication with the radar module and RSSI module is achieved via SPI or I2C interfaces to acquire digital data such as radar target distance and distance change rate, and signal strength RSSI. The change rate of the spatial power frequency electric field intensity can be collected by a dedicated electric field sensor and processed by the processor. Based on this real-time acquired data, the computer program calculates the mechanical dynamic threat coefficient, external threat coefficient, demand-condition coordination coefficient, and energy survival constraint coefficient sequentially according to preset algorithms and formulas (as described in claims 1 to 9). Finally, based on these coefficients, the program calculates the target image acquisition frame rate and adjusts the current image acquisition frame rate to the target value by controlling the registers of the image sensor module or sending control commands. For example, when severe vibration of the line is detected or an object approaches at high speed, the frame rate is increased to capture key details; while when the line is stable, lighting is sufficient, and energy is abundant, the frame rate is optimized according to the demand-condition coordination coefficient and energy survival constraint coefficient to balance monitoring effectiveness and energy consumption.

[0093] Through the above technical solution, the device of this application can efficiently and reliably execute the visualization monitoring method, thereby solving the problems of low sampling efficiency, high false trigger rate, and excessive energy consumption caused by the fixed frame rate or single threshold trigger mode of existing monitoring devices. This device, through the close integration of hardware and software, realizes intelligent fusion analysis and dynamic frame rate adjustment of multi-source monitoring data, ensuring that image acquisition is carried out with the optimal strategy under different operating conditions. It can effectively capture key events while significantly reducing redundant data and energy consumption, greatly improving the intelligence level and operational efficiency of transmission line inspection.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A visual monitoring method for power transmission line inspection, characterized in that, Includes the following steps: The system collects conductor vibration frequency, tower material / fitting impact acceleration, and conductor tilt angle offset to the ground, which reflect the conductor's operating status. After fusion processing, it generates a mechanical dynamic threat coefficient that characterizes the structural stability of the line itself. The radar target distance and distance change rate, as well as the change rate of the space power frequency electric field intensity, which reflect the environment of the monitoring channel, are collected and fused to generate an external threat coefficient that characterizes the risk of external intrusion and the degree of electric field disturbance. The ambient light intensity and signal strength RSSI, which reflect the image acquisition conditions, are collected and combined with the mechanical dynamic threat coefficient and the external threat coefficient to generate a demand-condition coordination coefficient that characterizes the monitoring needs and the feasibility of execution. The remaining battery capacity, which reflects the power supply status of the device, and the ambient temperature, which reflects the environmental tolerance, are collected and constrained to generate an energy survival constraint coefficient that characterizes the continuous operation capability. Based on the demand-condition coordination coefficient and energy survival constraint coefficient, the target image acquisition frame rate is obtained and the current value is adjusted.

2. The visual monitoring method for transmission line inspection according to claim 1, characterized in that, The mechanical dynamic threat coefficient is calculated as follows: Acquire the dominant frequency of conductor vibration, the impact acceleration of tower materials / fittings, and the conductor's tilt angle relative to the ground; After dimensionless and amplitude-limiting processing of the conductor vibration dominant frequency, tower material / fitting impact acceleration, and conductor tilt angle offset to the ground, the corresponding conductor vibration dominant frequency index, tower material / fitting impact acceleration index, and conductor tilt angle offset to the ground index are generated. The conductor vibration dominant frequency index, tower material / fitting impact acceleration index, and conductor ground tilt offset index are weighted and fused, and the fusion result is subjected to saturation characteristic nonlinear mapping to obtain the mechanical dynamic threat coefficient; wherein, the mechanical dynamic threat coefficient increases monotonically with the increase of the fusion result.

3. The visual monitoring method for transmission line inspection according to claim 2, characterized in that, The methods for handling the dominant frequency of conductor vibration, the impact acceleration of tower materials / fittings, and the conductor's tilt angle offset to the ground are as follows: The current conductor vibration dominant frequency and tower material / fitting impact acceleration are compared with the upper limit of conductor vibration frequency monitoring and the upper limit of impact acceleration threshold, respectively. After truncating the upper limit of the ratio to 1, the conductor vibration dominant frequency index and tower material / fitting impact acceleration index are obtained. The absolute value of the current conductor's tilt angle offset to the ground is compared with the maximum allowable tilt angle of the conductor to the ground, and the ratio is truncated to an upper limit of 1 to obtain the conductor's tilt angle offset index.

4. The visual monitoring method for transmission line inspection according to claim 1, characterized in that, The external threat coefficient is calculated as follows: Acquire radar target range and range change rate, as well as the rate of change of spatial power frequency electric field intensity; Based on the radar target distance, a radar target distance index is obtained that characterizes the distance to the target. Extract the current radar target range change rate to obtain the target approach rate index, which characterizes the urgency of the target approach. Based on the fluctuation amplitude of the current spatial power frequency electric field intensity change rate, the spatial power frequency electric field intensity change rate index, which characterizes the degree of electric field anomaly, is obtained; Substituting the radar target range index, target approach rate index, and spatial power frequency electric field intensity change rate index into the formula Obtain the external threat coefficient ,in, This is the radar target range index. To approximate the rate exponent, It is the exponent of the rate of change of the power frequency electric field intensity in space.

5. The visual monitoring method for transmission line inspection according to claim 4, characterized in that, The processing method for the radar target range and range change rate, as well as the spatial power frequency electric field intensity change rate, is as follows: The radar target distance index is obtained by comparing the current radar target distance with the radar protection zone radius threshold. After taking the inverse of the current radar target range change rate, the non-negative part is truncated and then compared with the upper limit of radar target approach rate saturation to obtain the target approach rate index. The ratio of the absolute value of the current rate of change of the spatial power frequency electric field intensity to the threshold of the sudden change of the power frequency electric field intensity is processed, and the upper limit of the ratio is truncated to 1 to obtain the index of the rate of change of the spatial power frequency electric field intensity.

6. The visual monitoring method for transmission line inspection according to claim 1, characterized in that, The demand-condition coordination coefficient is calculated as follows: Acquire ambient light intensity and signal strength RSSI; Based on ambient illuminance, an ambient illuminance index is generated, which decreases as illuminance decreases and has a non-zero lower limit. Based on the RSSI signal strength, a RSSI exponent is generated whose value decreases as the signal weakens and is constrained by amplitude limiting. The threat fusion quantity is obtained by weighting and combining the mechanical dynamic threat coefficient and the external threat coefficient. The demand-condition synergy coefficient is obtained by multiplicatively coupling the threat fusion quantity with the ambient light index and the RSSI signal strength index; among which, the demand-condition synergy coefficient is positively correlated with the threat fusion quantity, the ambient light index, and the RSSI signal strength index.

7. The visual monitoring method for transmission line inspection according to claim 6, characterized in that, The specific processing method for ambient light intensity and RSSI signal strength is as follows: Based on the comparison between the current ambient illuminance and the upper limit of ambient illuminance saturation, an ambient illuminance index is generated to determine the suitability of ambient light. When the ambient illuminance is lower than the upper limit of ambient illuminance saturation, the ambient illuminance index decreases as the ambient illuminance decreases, but does not fall below the lower limit threshold of the operating condition maintenance. The RSSI index is obtained by comparing the difference between the current signal strength RSSI and the lower limit of weak signal strength with the difference between the upper limit of saturation and the lower limit of weak signal strength. The ratio is then truncated to 1 and the lower limit of signal maintenance threshold, respectively.

8. The visual monitoring method for transmission line inspection according to claim 1, characterized in that, The energy survival constraint coefficient is calculated as follows: Obtain the remaining battery capacity and ambient temperature; Based on the comparison between the remaining battery capacity and the critical threshold of the remaining battery charge, an energy sufficiency index is generated to characterize the sufficiency of energy supply. When the remaining battery capacity is lower than the critical threshold of the remaining battery charge, the energy sufficiency index decreases as the remaining battery capacity decreases, and the lowest value after the decrease is limited by the minimum operating maintenance threshold. A temperature suitability index is generated based on the degree of deviation of the ambient temperature from the optimal working reference temperature, which characterizes the suitability of the temperature. Specifically, when the deviation is greater than or equal to half the allowable temperature difference, the temperature suitability index is set to the minimum temperature protection threshold; when the deviation is less than half the allowable temperature difference, the temperature suitability index increases as the deviation decreases, until it reaches a maximum value of 1. The energy sufficiency index is multiplied by the temperature suitability index to obtain the energy survival constraint coefficient.

9. The visual monitoring method for transmission line inspection according to claim 1, characterized in that, The target image acquisition frame rate is calculated as follows: Obtain the device's minimum allowed frame rate, the device's maximum allowed frame rate, the demand-condition coordination coefficient, and the energy survival constraint coefficient; Based on the product of the demand-operating condition coordination coefficient and the energy survival constraint coefficient, an interpolation mapping is performed between the minimum frame rate allowed by the device and the maximum frame rate allowed by the device to obtain the target image acquisition frame rate. Specifically, when the product approaches 0, the target image acquisition frame rate approaches the minimum frame rate allowed by the device; when the product approaches 1, the target image acquisition frame rate approaches the maximum frame rate allowed by the device.

10. A visual monitoring device for power transmission line inspection, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the visual monitoring method for power transmission line inspection as described in any one of claims 1 to 9.

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