A method and system for intelligent identification and assessment of external damage risks in power transmission channels

Abstract

The present invention belongs to the technical field of power transmission channel monitoring and relates to a method and system for intelligently identifying and assessing the risk of power transmission channel failure. The method comprises the following steps: adaptively deploying low-cost, low-power monitoring terminals; constructing a diagnostic model for calculating the power transmission channel anomaly index; collecting current, voltage, and vibration data from the power transmission channel and inputting them into the diagnostic model to obtain the anomaly index of the power transmission channel; when the anomaly index of the power transmission channel is greater than an early warning threshold, flying a drone to the power transmission channel's failure risk area for rapid positioning; calculating the distance between a person or animal and the failure risk area to obtain a distance value; and selecting different levels of positioning and early warning modes based on the judgment results of the distance value and first and second thresholds. The present invention can solve the problem of difficulty in quickly responding to the risk of power transmission channel failure on rugged mountain roads or complex terrain.

Description

Technical Field

[0001] The present invention belongs to the technical field of power transmission channel monitoring and relates to a method and system for intelligently identifying and evaluating the risk of external damage in power transmission channels. Background Art

[0002] With the rapid development of power systems and the ever-expanding scale of power grids, the safe and stable operation of transmission channels has become increasingly important. However, the external environment facing transmission channels is becoming increasingly complex, and external risks (such as natural disasters, sabotage, and construction disruptions) are a frequent occurrence, posing a serious challenge to the safe operation of power grids. Traditional transmission channel monitoring methods rely primarily on manual inspections, which suffer from long inspection cycles, high costs, and low efficiency, making it difficult to promptly detect and address potential risks in transmission channels.

[0003] While some automated monitoring technologies are currently being applied to transmission channel monitoring, these technologies still have some shortcomings. For example, some monitoring systems can only monitor specific types of risks and lack comprehensive risk identification capabilities. Other systems, while able to identify multiple risks, need to improve their accuracy and real-time performance. Traditional monitoring methods are difficult to implement in areas with difficult directions, such as rugged mountain roads or complex terrain, resulting in these areas becoming monitoring blind spots, increasing the risk of power grid operation.

[0004] Based on the above problems, when facing areas with rugged mountain roads or complex terrain, it is easy to have weak navigation signals and get lost, making it difficult to quickly respond to the risk of external damage to power transmission channels on rugged mountain roads or complex terrain. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a method and system for intelligently identifying and evaluating the risk of external damage to a power transmission channel.

[0006] In a first aspect, the present invention provides a method for intelligently identifying and assessing the risk of external damage to a power transmission channel, which employs the following technical solutions:

[0007] A method for intelligently identifying and assessing the risk of external damage to a power transmission channel comprises the following steps:

[0008] S1. Build a model to dynamically divide monitoring section lengths based on total line length, historical data, and topography, and adaptively deploy low-cost, low-power monitoring terminals.

[0009] S2. Construct a diagnostic model for calculating the abnormality index of the transmission channel;

[0010] S3, collecting current data, voltage data, and vibration data of the power transmission channel, and inputting them into the diagnostic model to obtain an abnormality index of the power transmission channel;

[0011] S4. When the abnormal index of the transmission channel is greater than the warning threshold, the drone flies to the external damage risk area of ​​the transmission channel for rapid positioning;

[0012] S5. Calculate the distance between the person or animal and the external injury risk area to obtain a distance value;

[0013] S6. Determine the difference between the distance value and a preset first threshold and a preset second threshold;

[0014] S7. Select different levels of positioning and warning modes according to the judgment results of the distance value and the first threshold and the second threshold.

[0015] In a further embodiment of the present invention, the monitoring terminal includes: a current sensor, a voltage sensor, and a vibration sensor;

[0016] The current sensor is used to monitor the current changes of the line in the power transmission channel and promptly detect the abnormal current of the line;

[0017] The voltage sensor is used to monitor the voltage changes of the lines in the power transmission channel and promptly detect voltage anomalies of the lines;

[0018] The vibration sensor is used to monitor the vibration changes of the line in the power transmission channel and promptly detect the vibration abnormality of the line.

[0019] In a further embodiment of the present invention, the model for dynamically dividing the monitoring segment length satisfies the following formula:

[0020]

[0021] Where, ΔL i represents the length of the i-th monitoring section; L represents the total length of the line; A i It is a coefficient directly related to the fault location accuracy; B i is the coefficient of other factors that affect the length of the monitoring section; C i is the correction coefficient, which is used to fine-tune the model for dynamically dividing the monitoring segment length;

[0022] Z i It represents the weighted sum of the total length of the line, historical data, and topography, and satisfies the following formula:

[0023] Z i =α i ·λ1+β i ·λ2+γ i ·λ3

[0024] Z is the Z of all individuals j from 1 to N j The sum of satisfies the following formula:

[0025]

[0026] Where λ1 is the importance index α i The weight coefficient of λ2 is the historical data β i The weight coefficient, λ3 represents the topography coefficient γ i The weight coefficient of L represents the total length of the transmission channel.

[0027] In a further embodiment of the present invention, the diagnostic model for the power transmission channel abnormality index comprises the following steps:

[0028] Current abnormality index:

[0029]

[0030] Voltage anomaly index:

[0031]

[0032] Vibration abnormality index:

[0033]

[0034] The weighted sum of the abnormality index EDRI is calculated using the weighted average method, satisfying the following formula:

[0035] R=w1×CAI+w2×VAI+w3×AAI

[0036] Among them, w1 represents the weight coefficient of the current anomaly index, w2 represents the weight coefficient of the voltage anomaly index, and w3 represents the weight coefficient of the vibration anomaly index. w1, w2, and w3 are determined based on actual application scenarios and expert experience.

[0037] The current measured by the current sensor is I, and the normal current range is [I min , I max ]; the voltage measured by the voltage sensor is V, and the normal voltage range is [V min , V max ]; the vibration amplitude measured by the vibration sensor is A, and the normal vibration range is [A min , A max ].

[0038] A further embodiment of the present invention, step S4, comprises the following steps:

[0039] In the diagnostic model of the transmission channel anomaly index, the R value range is between 0 and 1. The larger the R value, the higher the risk of transmission channel failure. Based on historical data and operation and maintenance experience, a reasonable warning threshold R is set. thresh , used to determine whether there is a risk of external damage in the transmission channel;

[0040] If R≤R thresh , indicating that the weighted sum R of the abnormal index does not exceed the warning threshold of the transmission channel external damage risk, indicating that no external damage risk has been found in this area. The monitoring data of this area is recorded in the historical data to optimize the diagnosis model;

[0041] If R>R thresh , indicating that the weighted sum of the abnormal index exceeds the warning threshold of the transmission channel external failure risk, indicating that the external failure risk is found in the area, the system triggers the alarm mechanism and provides detailed information of the abnormal data.

[0042] A further solution of the present invention, step S5, comprises the following steps:

[0043] There are construction workers or idle personnel around the external damage risk area, or even animals or livestock around the external damage risk area of ​​the transmission channel. The drone is equipped with a ranging device to monitor the distance between people or animals and the external damage risk area in real time.

[0044] A further embodiment of the present invention, step S6, comprises the following steps:

[0045] With the risk area of ​​external injury as the center, a first threshold and a second threshold are pre-set according to the distance value; when the distance value is greater than the first threshold, it indicates that the person or animal is in a safe area; when the distance value is less than the first threshold but greater than the second threshold, it indicates that the person or animal is in a warning area; when the distance value is less than the second threshold, it indicates that the person or animal is in a dangerous area.

[0046] A further solution of the present invention, step S7, comprises the following steps:

[0047] When people or animals are in a safe area, the drone lights up with a green safety light to assist maintenance personnel in quickly locating the fault point.

[0048] When people or animals are in the warning area, the drone flashes a yellow warning light and emits a shrill alarm sound. The yellow warning light reminds people around the risk area to pay attention to safety, and also reminds operation and maintenance personnel of the presence of wild animals and livestock. The shrill alarm sound can scare away animals or livestock around the risk area.

[0049] When people or animals are in a dangerous area, the drone flashes a red emergency light and emits a dazzling light at the people or animals; the red emergency light reminds operation and maintenance personnel to avoid harming frightened wild animals and livestock, and the dazzling light can scare people or animals away from the risk area.

[0050] A further embodiment of the present invention, step S7, further comprises the following steps:

[0051] Through the voice broadcast device equipped on the drone, the surrounding construction workers are informed of the current situation, location, and safe distance of the external demolition area, reminding surrounding people to pay attention to safety.

[0052] In a second aspect, the present invention provides an intelligent identification and assessment system for the risk of external damage to power transmission channels, which adopts the following technical solutions:

[0053] An intelligent identification and assessment system for transmission channel external damage risk, the system includes a monitoring terminal layout module, a diagnosis model construction module, an abnormality index calculation module, an abnormality index diagnosis module, a distance value calculation module, a distance value diagnosis module, and an early warning mode selection module;

[0054] The monitoring terminal layout module is used to build a model for dynamically dividing the monitoring section length based on the total line length, historical data, and topography, and to adaptively layout low-cost, low-power monitoring terminals;

[0055] A diagnostic model building module is used to build a diagnostic model for calculating the abnormality index of the transmission channel;

[0056] an abnormality index calculation module, used to collect current data, voltage data, and vibration data of the power transmission channel, and input the data into the diagnostic model to obtain an abnormality index of the power transmission channel;

[0057] The abnormality index diagnosis module is used to compare the abnormality index of the transmission channel with the warning threshold. The drone flies to the external damage risk area of ​​the transmission channel for rapid positioning.

[0058] A distance value calculation module calculates the distance between a person or animal and the external damage risk area to obtain a distance value;

[0059] A distance value diagnosis module is used to determine the difference between the distance value and a preset first threshold value and a preset second threshold value;

[0060] The warning mode selection module is used to select different levels of positioning and warning modes according to the judgment results of the distance value and the first threshold and the second threshold.

[0061] In summary, the present invention has the following beneficial technical effects:

[0062] 1. This invention uses a phased monitoring strategy, including comprehensive monitoring, risk location, and precise monitoring, to rapidly locate and accurately assess the risk of external failures in power transmission channels. This is particularly true in rugged mountainous areas or complex terrain. The use of intelligent tools such as drones significantly improves identification accuracy and efficiency, thereby enhancing the safety and reliability of power grid operations. The use of low-cost, low-power distributed fault location monitoring terminals for preliminary identification effectively reduces unnecessary resource investment.

[0063] 2. This invention utilizes the drone's eye-catching lights and piercing alarms to enable maintenance personnel to quickly locate fault areas. Using images captured by the drone's high-definition camera and infrared thermal imager, it analyzes potential hazards or abnormal phenomena (such as tree growth, construction activities, and animal activity) around the transmission corridor. This alerts construction personnel and other personnel near areas at risk of transmission corridor damage to pay attention to safety, and can also scare off animals or livestock in these areas.

[0064] 3. The present invention is designed for rugged mountain roads or complex terrains, where maintenance personnel are prone to weak navigation signals and may get lost. By combining images taken by drones and data from on-site monitoring, maintenance personnel can more accurately locate the fault point. The maintenance personnel can use the voice broadcast device equipped on the drone to inform surrounding construction personnel of the current situation, location, and safe distance of the external damage area, reminding surrounding personnel to pay attention to safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of the intelligent identification and assessment method of external failure risks in transmission channels is disclosed.

[0066] Figure 2 The hardware block diagram of a distributed fault location monitoring terminal is disclosed.

[0067] Figure 3 The structural schematic diagram of the intelligent identification and assessment system for external damage risks of transmission channels is disclosed. DETAILED DESCRIPTION

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0069] The following is combined with Figure 1-3 The preferred embodiments of the present invention are described in detail.

[0070] See attached Figure 1 This paper proposes an intelligent identification and assessment method for transmission channel failure risks. This method uses transmission channels on rugged mountain roads or complex terrain as research objects. By using intelligent means such as drones, this method can quickly locate and accurately assess transmission channel failure risks, thereby improving the safety and reliability of power grid operations. The method includes the following steps:

[0071] S1. Build a model to dynamically divide monitoring section lengths based on total line length, historical data, and topography, and adaptively deploy low-cost, low-power monitoring terminals.

[0072] S2. Construct a diagnostic model for calculating the abnormality index of the transmission channel;

[0073] S3, collecting current data, voltage data, and vibration data of the power transmission channel, and inputting them into the diagnostic model to obtain an abnormality index of the power transmission channel;

[0074] S4. When the abnormal index of the transmission channel is greater than the warning threshold, the drone flies to the external damage risk area of ​​the transmission channel for rapid positioning;

[0075] S5. Calculate the distance between the person or animal and the external injury risk area to obtain a distance value;

[0076] S6. Determine the difference between the distance value and a preset first threshold and a preset second threshold;

[0077] S7. Select different levels of positioning and warning modes according to the judgment results of the distance value and the first threshold and the second threshold.

[0078] In one embodiment of the present invention, see the attached Figure 2 , the monitoring terminal includes: a current sensor, a voltage sensor, a vibration sensor, and a communication unit;

[0079] Current sensors are used to monitor current changes in the power transmission lines and promptly detect abnormal currents in the lines.

[0080] Voltage sensors are used to monitor voltage changes in power transmission lines and detect voltage anomalies in a timely manner.

[0081] Vibration sensors are used to monitor the vibration changes of the lines in the transmission channel and detect abnormal vibration of the lines in a timely manner;

[0082] The communication unit is used to transmit the data collected by the current sensor, voltage sensor, and vibration sensor to the central processing module. The communication unit ensures real-time data transmission and remote communication through a highly reliable communication protocol.

[0083] In one embodiment of the present invention, step S1 includes the following steps:

[0084] According to the specific conditions of the line and operation and maintenance experience, different weights are assigned to the total length of the line, historical data, and topography. A model for dynamically dividing the length of the monitoring section is constructed, and the key locations of the monitoring terminals are dynamically divided and comprehensively analyzed.

[0085] In one embodiment of the present invention, the model for dynamically dividing the monitoring segment length satisfies the following formula:

[0086]

[0087] Where, ΔL i represents the length of the i-th monitoring section; L represents the total length of the line; A i It is a coefficient directly related to the fault location accuracy; B i is the coefficient of other factors that affect the length of the monitoring section; C i is the correction coefficient, which is used to fine-tune the model for dynamically dividing the monitoring segment length;

[0088] Z i It represents the weighted sum of the total length of the line, historical data, and topography, and satisfies the following formula:

[0089] Z i =α i ·λ1+β i ·λ2+γ i ·λ3

[0090] Where λ1 is the importance index α i The weight coefficient of λ2 is the historical data β i The weight coefficient, λ3 represents the topography coefficient γ i The weight coefficient needs to be set according to the actual situation; L represents the total length of the transmission channel.

[0091] Z is the Z of all individuals j from 1 to N j The sum of satisfies the following formula:

[0092]

[0093] For example, the total length of the transmission channel is set to L=100 km; the weight coefficient of the importance index λ1=0.5, the weight coefficient of the historical data λ1=0.3, and the weight coefficient of the topography coefficient λ3=0.2.

[0094] The coefficients associated with each monitoring segment are set according to actual conditions, including:

[0095] The relevant coefficients of monitoring segment 1 are set as follows: α1 = 0.6, β1 = 0.2, γ1 = 0.2;

[0096] The relevant coefficients of monitoring section 2 are set as follows: α1 = 0.5, β1 = 0.3, γ1 = 0.2.

[0097] The fault location accuracy coefficient is set based on the performance of the monitoring equipment and the signal transmission quality, including:

[0098] Monitoring section 1: A1 = 1.2 (indicates that the fault location accuracy of monitoring section 1 is higher);

[0099] Monitoring section 2: A2 = 1.0 (indicating that the fault location accuracy of monitoring section 2 is moderate).

[0100] Other factors that affect the monitoring segment length are set based on monitoring costs and data transmission delays, including:

[0101] Monitoring segment 1: B1 = 1.0 (indicating moderate cost and delay for monitoring segment 1);

[0102] Monitoring segment 2: B2 = 0.9 (indicating that monitoring segment 2 needs to be optimized to reduce cost and delay).

[0103] The correction coefficient is fine-tuned according to the actual situation, including:

[0104] Monitoring segment 1: C1 = 1.0 (indicates that monitoring segment 1 is moderate);

[0105] Monitoring section 2: C2 = 1.05 (indicating that monitoring section 2 requires fine-tuning to improve economic efficiency).

[0106] Substitute the above parameters into the formula to calculate the length ΔL of the i-th monitoring segment i The length of is obtained by the following formula:

[0107] Weighted sum of monitoring segment 1: Z1 = 0.6 × 0.5 + 0.2 × 0.3 + 0.2 × 0.2 = 0.37;

[0108] The weighted sum of monitoring segment 2: Z2 = 0.5 × 0.5 + 0.3 × 0.3 + 0.2 × 0.2 = 0.365.

[0109] Calculate the total sum Z′ based on the weighted sum of all monitoring segments;

[0110] Calculate the length of each monitoring section ΔL using the formula i , satisfying the following formula:

[0111] Length of monitoring segment 1:

[0112] Length of monitoring section 2:

[0113] And so on, calculate the length of all monitoring segments.

[0114] In one embodiment of the present invention, the diagnostic model for the power transmission channel abnormality index includes the following steps:

[0115] Abnormal current indicates problems such as short circuit, overload or equipment failure; voltage fluctuations may cause equipment damage or unstable operation; excessive vibration may be caused by wind deviation, dancing, etc., and long-term vibration may cause line fatigue and fracture.

[0116] The current sensor measures the current I, and the normal current range is [I min , I max ];

[0117] The voltage measured by the voltage sensor is V, and the normal voltage range is [V min , V max ];

[0118] The vibration amplitude measured by the vibration sensor is A, and the normal vibration range is [A min , A max ].

[0119] Define the abnormal index of current, voltage, and vibration to meet the following formula:

[0120] Current Anomaly Index (CAI):

[0121]

[0122] Voltage Anomaly Index (VAI):

[0123]

[0124] Vibration Anomaly Index (AAI):

[0125]

[0126] The weighted sum of the abnormality index EDRI is calculated using the weighted average method, satisfying the following formula:

[0127] R=w1×CAI+w2×VAI+w3×AAI

[0128] Among them, w1 represents the weight coefficient of the current anomaly index, w2 represents the weight coefficient of the voltage anomaly index, and w3 represents the weight coefficient of the vibration anomaly index; w1, w2, and w3 are determined based on actual application scenarios and expert experience.

[0129] The current sensor, voltage sensor and vibration sensor in the monitoring terminal are used to collect the current data, voltage data and vibration data of the line in the transmission channel to provide basic data for subsequent fault location and analysis; the current data, voltage data and vibration data of the line are cleaned, converted and integrated to obtain pre-processed data; the pre-processed data is transmitted to the central processing module through the communication unit.

[0130] In one embodiment of the present invention, step S4 includes the following steps:

[0131] In the diagnostic model of the transmission channel anomaly index, the R value range is between 0 and 1. The larger the R value, the higher the risk of transmission channel failure. Based on historical data and operation and maintenance experience, a reasonable warning threshold R is set. thresh , used to determine whether there is a risk of external damage in the transmission channel.

[0132] If R≤R thresh , indicating that the weighted sum R of the abnormal index does not exceed the warning threshold of the transmission channel external damage risk, indicating that no external damage risk has been found in this area. The monitoring data of this area is recorded in the historical data to optimize the diagnosis model;

[0133] If R>R thresh , indicating that the weighted sum of the abnormal index exceeds the warning threshold of the transmission channel external failure risk, indicating that the external failure risk is found in the area, the system triggers the alarm mechanism and provides detailed information of the abnormal data.

[0134] In one embodiment of the present invention, the system triggers an alarm mechanism and provides detailed information about abnormal data, including the following steps:

[0135] If the monitoring terminal identifies the risk of external damage to the transmission channel, it will initially locate the risk area of ​​the transmission channel, and the drone will quickly respond and head to the risk area. The drone is pre-equipped with a high-definition camera, infrared thermal imager, rangefinder, sound warning device, voice broadcast device, strong light equipment and warning lights;

[0136] As the drone follows a pre-set route to the risk zone, its high-definition camera and infrared thermal imager simultaneously capture and analyze the transmission lines and their surroundings, identifying potential hazards around the lines, including tree growth, construction activities, and animal activity.

[0137] In one embodiment of the present invention, step S5 includes the following steps:

[0138] There are construction workers or idle personnel around the external damage risk area, or even animals or livestock around the external damage risk area of ​​the transmission channel. The drone is equipped with a ranging device to monitor the distance between people or animals and the external damage risk area in real time.

[0139] In one embodiment of the present invention, step S6 includes the following steps:

[0140] With the risk of injury area as the center, a first threshold and a second threshold are pre-set based on the distance value. When the distance value is greater than the first threshold, it indicates that the person or animal is in a safe area; when the distance value is less than the first threshold but greater than the second threshold, it indicates that the person or animal is in a warning area; when the distance value is less than the second threshold, it indicates that the person or animal is in a dangerous area.

[0141] In one embodiment of the present invention, step S7 includes the following steps:

[0142] When people or animals are in a safe area, the drone lights up a green safety light to assist operation and maintenance personnel in quickly locating the fault point.

[0143] When people or animals are in the warning area, the drone flashes yellow warning lights and emits a shrill alarm sound; the yellow warning light reminds people around the external damage risk area to pay attention to safety, and also reminds operation and maintenance personnel that there are wild animals and livestock; the shrill alarm sound can scare away animals or livestock around the external damage risk area.

[0144] When people or animals are in a dangerous area, the drone flashes a red emergency light and emits a dazzling bright light towards them. The red emergency light reminds operators to avoid harming frightened wild animals and livestock, and the dazzling bright light can scare people or animals away from the risk area.

[0145] Operation and maintenance personnel can use the voice broadcast device equipped on the drone to inform surrounding construction personnel of the current situation, location, and safe distance of the external demolition area, reminding surrounding personnel to pay attention to safety.

[0146] See attached Figure 3 The present invention also proposes an intelligent identification and assessment system for the risk of external damage to a transmission channel, which includes a monitoring terminal layout module, a diagnosis model construction module, an abnormality index calculation module, an abnormality index diagnosis module, a distance value calculation module, a distance value diagnosis module, and an early warning mode selection module.

[0147] The monitoring terminal layout module is used to build a model for dynamically dividing the monitoring section length based on the total line length, historical data, and topography, and to adaptively layout low-cost, low-power monitoring terminals;

[0148] A diagnostic model building module is used to build a diagnostic model for calculating the abnormality index of the transmission channel;

[0149] an abnormality index calculation module, used to collect current data, voltage data, and vibration data of the power transmission channel, and input the data into the diagnostic model to obtain an abnormality index of the power transmission channel;

[0150] The abnormality index diagnosis module is used to compare the abnormality index of the transmission channel with the warning threshold. The drone flies to the external damage risk area of ​​the transmission channel for rapid positioning.

[0151] A distance value calculation module calculates the distance between a person or animal and the external damage risk area to obtain a distance value;

[0152] A distance value diagnosis module is used to determine the difference between the distance value and a preset first threshold value and a preset second threshold value;

[0153] The warning mode selection module is used to select different levels of positioning and warning modes according to the judgment results of the distance value and the first threshold and the second threshold.

[0154] The modules can be implemented in whole or in part through software, hardware, or a combination thereof, supporting hardware embedded in or independent of a processor in a computer device, and also supporting software stored in a memory in a computer device so that the processor can call and execute operations corresponding to the modules.

[0155] It should be noted that the user information (including but not limited to user device information and personal information, etc.) and data (including but not limited to data used for analysis, stored data and displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0156] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for intelligent identification and assessment of transmission channel external damage risk, characterized in that: The following steps are involved: S1. Based on the total line length, historical data, and topography, a model for dynamically dividing the monitoring segment length is constructed to adaptively deploy low-cost, low-power monitoring terminals. The model for dynamically dividing the monitoring segment length satisfies the following formula: ； in, Indicates the The length of each monitoring segment; Indicates the total length of the line; It is a coefficient directly related to the fault location accuracy; is the coefficient of other factors that affect the length of the monitoring section; is the correction coefficient, which is used to fine-tune the model for dynamically dividing the monitoring segment length; Represents the weighted sum of the total length of the line, historical data, and topography. Yes all sum; S2. Construct a diagnostic model for calculating the abnormality index of the transmission channel; S3, collecting current data, voltage data, and vibration data of the power transmission channel, and inputting them into the diagnostic model to obtain an abnormality index of the power transmission channel; S4. When the abnormal index of the transmission channel is greater than the warning threshold, the drone flies to the external damage risk area of ​​the transmission channel for rapid positioning; S5. Calculate the distance between the person or animal and the external injury risk area to obtain a distance value; S6. Determine the difference between the distance value and a preset first threshold and a preset second threshold; S7. Select different levels of positioning and warning modes based on the distance value and the first and second threshold values; With the risk area of ​​external injury as the center, a first threshold and a second threshold are pre-set according to the distance value; when the distance value is greater than the first threshold, it indicates that the person or animal is in a safe area; when the distance value is less than the first threshold but greater than the second threshold, it indicates that the person or animal is in a warning area; when the distance value is less than the second threshold, it indicates that the person or animal is in a dangerous area.

2. The method for intelligent identification and assessment of transmission channel external damage risk according to claim 1 is characterized in that: The monitoring terminal includes: a current sensor, a voltage sensor, and a vibration sensor; The current sensor is used to monitor the current changes of the line in the power transmission channel and promptly detect the abnormal current of the line; The voltage sensor is used to monitor the voltage changes of the lines in the power transmission channel and promptly detect voltage anomalies of the lines; The vibration sensor is used to monitor the vibration changes of the line in the power transmission channel and promptly detect the vibration abnormality of the line.

3. The method for intelligent identification and assessment of transmission channel external damage risk according to claim 1 is characterized in that: Satisfies the following formula: ； All individuals From 1 to N The sum of satisfies the following formula: ； in, Is the total length of the line The weight coefficient of It is historical data The weight coefficient of Representing topography The weight coefficient of The total length of the line representing the transmission channel.

4. The method for intelligent identification and assessment of transmission channel external damage risk according to claim 3 is characterized in that: The diagnostic model of the transmission channel abnormality index includes the following steps: current abnormality index: ； Voltage anomaly index: ； Vibration abnormality index: ； The weighted sum of the abnormality index EDRI is calculated using the weighted average method, satisfying the following formula: R=w1×CAI+w2×VAI+w3×AAI; Among them, w1 represents the weight coefficient of the current anomaly index, w2 represents the weight coefficient of the voltage anomaly index, and w3 represents the weight coefficient of the vibration anomaly index. w1, w2, and w3 are determined based on actual application scenarios and expert experience. The current measured by the current sensor is , the normal current range is [ , ]; the voltage measured by the voltage sensor is , the normal voltage range is [ , ]; the vibration amplitude measured by the vibration sensor is , the normal range of vibration is [ , ].

5. The method for intelligent identification and assessment of transmission channel external damage risk according to claim 3 is characterized in that: Step S4 includes the following steps: In the diagnostic model of the transmission channel anomaly index, the R value range is between 0 and 1. The larger the R value, the higher the risk of transmission channel failure. Based on historical data and operation and maintenance experience, a reasonable warning threshold R is set. thresh , used to determine whether there is a risk of external damage in the transmission channel; If R≤R thresh , indicating that the weighted sum R of the abnormal index does not exceed the warning threshold of the transmission channel external damage risk, indicating that no external damage risk has been found in this area. The monitoring data of this area is recorded in the historical data to optimize the diagnosis model; If R>R thresh , indicating that the weighted sum of the abnormal index exceeds the warning threshold of the transmission channel external failure risk, indicating that the external failure risk is found in the area, the system triggers the alarm mechanism and provides detailed information of the abnormal data.

6. The method for intelligent identification and assessment of transmission channel external damage risk according to claim 5, characterized in that: Step S5 includes the following steps: There are construction workers or idle personnel around the external damage risk area, or even animals or livestock around the external damage risk area of ​​the transmission channel. The drone is equipped with a ranging device to monitor the distance between people or animals and the external damage risk area in real time.

7. The method for intelligent identification and assessment of transmission channel external damage risk according to claim 1 is characterized in that: Step S7 includes the following steps: When people or animals are in a safe area, the drone lights up with a green safety light to assist maintenance personnel in quickly locating the fault point. When people or animals are in the warning area, the drone flashes a yellow warning light and emits a shrill alarm sound. The yellow warning light reminds people around the risk area to pay attention to safety, and also reminds operation and maintenance personnel of the presence of wild animals and livestock. The shrill alarm sound can scare away animals or livestock around the risk area. When people or animals are in a dangerous area, the drone flashes a red emergency light and emits a dazzling light at the people or animals; the red emergency light reminds operation and maintenance personnel to avoid harming frightened wild animals and livestock, and the dazzling light can scare people or animals away from the risk area.

8. The method for intelligent identification and assessment of transmission channel external damage risk according to claim 7, characterized in that: Step S7 further includes the following steps: Through the voice broadcast device equipped on the drone, the surrounding construction workers are informed of the current situation, location, and safe distance of the external demolition area, reminding surrounding people to pay attention to safety.

9. An intelligent identification and assessment system for external damage risk of power transmission channels, characterized by: It includes monitoring terminal layout module, diagnosis model construction module, abnormal index calculation module, abnormal index diagnosis module, distance value calculation module, distance value diagnosis module, and early warning mode selection module; The monitoring terminal layout module is used to construct a model for dynamically dividing the monitoring segment length based on the total line length, historical data, and topography, and to adaptively layout low-cost, low-power monitoring terminals. The model for dynamically dividing the monitoring segment length satisfies the following formula: ； in, Indicates the The length of each monitoring segment; Indicates the total length of the line; It is a coefficient directly related to the fault location accuracy; is the coefficient of other factors that affect the length of the monitoring section; is the correction coefficient, which is used to fine-tune the model for dynamically dividing the monitoring segment length; Represents the weighted sum of the total length of the line, historical data, and topography. Yes all sum; A diagnostic model building module is used to build a diagnostic model for calculating the abnormality index of the transmission channel; an abnormality index calculation module, used to collect current data, voltage data, and vibration data of the power transmission channel, and input the data into the diagnostic model to obtain an abnormality index of the power transmission channel; The abnormality index diagnosis module is used to compare the abnormality index of the transmission channel with the warning threshold. The drone flies to the external damage risk area of ​​the transmission channel for rapid positioning. A distance value calculation module calculates the distance between a person or animal and the external injury risk area to obtain a distance value; a distance value diagnosis module is used to determine the size of the distance value and a preset first threshold value and a preset second threshold value; An early warning mode selection module is used to select different levels of positioning and early warning modes according to the judgment results of the distance value and the first threshold and the second threshold; With the risk area of ​​external injury as the center, a first threshold and a second threshold are pre-set according to the distance value; when the distance value is greater than the first threshold, it indicates that the person or animal is in a safe area; when the distance value is less than the first threshold but greater than the second threshold, it indicates that the person or animal is in a warning area; when the distance value is less than the second threshold, it indicates that the person or animal is in a dangerous area.

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