Paying-off method for construction of large-fall power transmission line

Abstract

The invention provides a large-fall power transmission line construction paying-off method, and belongs to the technical field of line construction. According to the method, firstly, an unmanned aerial vehicle is used for collecting topographic data of a construction area, and preliminary path planning is completed by means of GIS; and then, through a reinforcement learning algorithm, in combination with terrain, path and geological conditions, carrying out intelligent optimization on the preliminarily planned path, and determining an optimal paying-off path. During pay-off, a random forest algorithm is adopted to predict tension change, and the tension of a pay-off device is automatically adjusted; meanwhile, the self-adaptive control algorithm flexibly adjusts the tension control strategy according to the terrain and tension data monitored in real time. In addition, the sensor continuously monitors the paying-off speed and the wire body position, manual intervention is immediately carried out once abnormity is found, and it is guaranteed that the paying-off process is stable and continuous. According to the method, the construction efficiency and safety of the large-fall power transmission line are remarkably improved, the risks of line body swinging and tension out-of-control are effectively reduced, and a powerful technical support is provided for electric power infrastructure construction in complex terrains.

Description

Technical Field

[0001] The present invention relates to the technical field of line construction, and particularly to a method for stringing a large-drop transmission line during construction. Background Art

[0002] When constructing a transmission line in areas with complex natural terrains such as mountains and canyons, the large-drop terrain poses a severe challenge to the stringing operation. Traditional stringing methods often struggle to adapt to terrain changes, leading to problems such as easy swinging of the wire body, uneven stringing speed, and difficulty in controlling the tension. This not only affects the construction efficiency but also may pose a threat to the line quality and operation safety. Therefore, it is particularly important to explore an improved stringing method without changing the hardware of the existing stringing device. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for stringing a large-drop transmission line during construction. Aiming at the deficiencies of the existing technology, the present invention focuses on the optimization of the stringing strategy, aiming to improve the efficiency, accuracy, and safety of the stringing operation of the transmission line in a large-drop environment by optimizing the construction strategy using the existing stringing device. This method significantly improves the efficiency and safety of the construction of the large-drop transmission line, effectively reduces the risk of wire body swinging and tension out of control, and provides strong technical support for the construction of power infrastructure in complex terrains.

[0004] To achieve the above purpose, the present invention provides a method for stringing a large-drop transmission line during construction, including the following steps:

[0005] S1. Use a drone to obtain the terrain data of the construction area and conduct a preliminary path planning in combination with the geographic information system;

[0006] S2. Introduce a reinforcement learning method, comprehensively consider the terrain data, path information, and geological conditions, intelligently optimize the preliminary planned path, and select the optimal stringing path;

[0007] S3. Prepare the corresponding stringing device, wire body material, and safety protection measures according to the optimal stringing path;

[0008] S4. During the stringing process, use the random forest algorithm to predict the change of the stringing tension and automatically adjust the tension control system of the stringing device according to the prediction result;

[0009] S5. Introduce an adaptive control algorithm and automatically adjust the tension control strategy according to the real-time monitored terrain changes and tension data;

[0010] S6. Use sensors to monitor the stringing speed and wire body position in real time, and immediately make manual adjustments when abnormalities are found to ensure the smooth and continuous stringing process.

[0011] Preferably, in step S1, a drone is used to obtain the terrain data of the construction area, and preliminary path planning is carried out in combination with the geographic information system. The specific operations are as follows:

[0012] S11. Use a drone to obtain the terrain data of the construction area;

[0013] S12. Input the terrain data into the GIS system and calculate the shortest path between two points;

[0014]

[0015] Among them, L represents the total path length; n represents the number of points on the path; (X i ,Y i ,Z i ) represents the coordinates of the i-th point on the path; (X i+1 ,Y i+1 ,Z i+1 ) represents the coordinates of the (i + 1)-th point on the path.

[0016] Preferably, in step S2, a reinforcement learning method is introduced. Considering the terrain data, path information, and geological conditions comprehensively, the preliminary planned path is intelligently optimized to select the optimal transmission line path. The specific operations are as follows:

[0017] S21. Calculate the comprehensive evaluation index of geological conditions according to the geological structure, rock stratum stability, construction difficulty, and quality of road-building materials;

[0018] G = ω 1 × geological structure + ω 2 × rock stratum stability + ω 3 × construction difficulty + ω 4 × quality of road-building materials;

[0019] Among them, G represents the comprehensive evaluation index of geological conditions; ω 1 , ω 2 , ω 3 , ω 4 represents the weight of the corresponding factor;

[0020] S22. Define the state space S:

[0021] S = {(X, Y, Z, P, G)|(X, Y, Z) ∈ terrain data, P ∈ path information, G ∈ geological conditions};

[0022] Among them, (X, Y, Z) represents the three-dimensional coordinates of each point on the path; P represents the path information;

[0023] Define the action space A:

[0024] A = {a 1 ,a2 ,…,a m} = {Steering angle, Forward distance};

[0025] Among them, a m represents the m-th possible action;

[0026] Define the reward function:

[0027] R(S,A) = -c(S,A) + r 目标 (S');

[0028] Among them, c(S,A) represents the cost of executing action A in state S; r 目标 (S') represents the reward for reaching the target position; S' represents the next state;

[0029] S23. Use the Q-learning algorithm to update the state-action value function Q(S,A);

[0030]

[0031] Among them, α represents the learning rate; γ represents the discount factor; r represents the immediate reward; A' represents the next action; Q(S,A)' represents the updated state-action value;

[0032] Select the action with the highest value as the optimal wire laying path.

[0033] Preferably, in step S4, during the wire laying process, use the random forest algorithm to predict the change of wire laying tension, and automatically adjust the tension control system of the wire laying device according to the prediction result. The specific operation is as follows:

[0034] S41. Collect historical wire laying operation data;

[0035] Preprocess the historical wire laying operation data, including data cleaning and data normalization;

[0036] Extract 70% of the samples from the preprocessed data as the training set and 30% of the samples as the test set;

[0037] Set the maximum depth of each tree to 10 and the minimum number of samples in the leaf node to 5;

[0038] Use the scikit-learn library in Python for model training to obtain the tension prediction model;

[0039] S42. Adjust the tension control system of the wire laying device according to the prediction result of the tension prediction model;

[0040] T control = T target + ΔT × sign(T predicted-T target );

[0041] Among them, T control represents a control instruction; T target represents the target tension value; ΔT represents the allowable tension fluctuation range; T predicted represents the predicted tension value; sign represents the sign function used to determine the tension adjustment direction.

[0042] Preferably, the historical wire pay-off operation includes the following characteristics: tension value, wire speed, coil diameter, ambient temperature, ambient humidity.

[0043] Preferably, in step S5, an adaptive control algorithm is introduced, and according to the terrain changes and tension data monitored in real time, the tension control strategy is automatically adjusted. The specific operations are as follows:

[0044] T control = f(T predicted , T actual , T target , TC);

[0045] Among them, f represents the adaptive control function; T actual represents the actually measured tension value; TC represents the terrain change parameter.

[0046] Preferably, in step S6, a sensor is used to monitor the wire pay-off speed and wire position in real time. When an abnormality is found, manual adjustment is immediately carried out to ensure the smooth and continuous wire pay-off process. The specific operations are as follows:

[0047] S61. Calculate the deviation between the actually measured tension value and the target tension value;

[0048] ΔO = T actual - T target ;

[0049] Among them, ΔO represents the deviation;

[0050] S62. When the actual tension exceeds the preset safety range, that is, |ΔT| > ΔT safe , where ΔT safe represents the safety tension deviation range, then trigger the emergency response mechanism, and then manual intervention is carried out.

[0051] Therefore, the present invention adopts the above-mentioned construction wire pay-off method for large-drop transmission lines, and the beneficial technical effects are as follows:

[0052] (1) Improvement of construction efficiency and accuracy:

[0053] Traditional construction methods for large-drop transmission lines often rely on manual path planning and tension control, which are not only inefficient but also difficult to ensure accuracy. At the same time, traditional methods are usually designed for specific types of stringing devices, lacking flexibility.

[0054] In the present invention, terrain data is collected by drones and combined with GIS for preliminary path planning, and then a reinforcement learning algorithm is introduced for intelligent optimization, which can quickly determine the optimal stringing path. This method not only improves the construction efficiency but also significantly enhances the accuracy of path planning. In addition, the present invention aims to utilize existing stringing devices (regardless of their specific models) and does not directly rely on specific stringing device models. Instead, this method improves the efficiency, accuracy, and safety of stringing operations for transmission lines in large-drop environments by optimizing construction strategies, such as using a random forest algorithm to predict tension changes and an adaptive control algorithm to adjust the tension control strategy.

[0055] (2) Enhancement of safety and reliability:

[0056] When traditional methods are used for construction in large-drop terrains, the wire body is prone to swinging, the stringing speed is uneven, and the tension is difficult to control, which not only affects the construction efficiency but also may pose a threat to the line quality and operation safety.

[0057] The method of the present invention effectively reduces the risk of wire body swinging and tension out-of-control by dynamically regulating the stringing process. At the same time, sensors are used to continuously monitor the stringing speed and the position of the wire body, and manual adjustment is immediately carried out once an abnormality is detected, ensuring the smooth and continuous stringing process. These measures significantly enhance the safety and reliability of construction.

[0058] (3) Improvement of flexibility and versatility:

[0059] Traditional methods are usually designed for specific types of stringing devices, lacking flexibility and being difficult to adapt to changes in different terrains and construction conditions.

[0060] The method of the present invention does not depend on specific stringing device models but utilizes existing stringing devices and improves efficiency by optimizing construction strategies. This makes the method have higher flexibility and versatility under different terrains and construction conditions and can be more widely applied to the construction of transmission lines in various complex environments. Description of the Drawings

[0061] Figure 1 is a flowchart of a method for stringing construction of a large-drop transmission line according to the present invention;

[0062] Figure 2 is a path diagram of intelligent optimization by reinforcement learning;

[0063] Figure 3 is a tension prediction diagram of the random forest algorithm. Detailed implementation manners

[0064] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0065] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs.

[0066] Embodiment 1

[0067] As Figure 1 shown, it is a flowchart of a method for laying wires during the construction of a large-drop transmission line according to the present invention, which specifically includes the following steps:

[0068] S1. Use a drone to obtain the topographic data of the construction area, and combine it with a geographic information system for preliminary path planning. The specific operations are as follows:

[0069] S11. Use a drone to obtain the topographic data of the construction area;

[0070] S12. Input the topographic data into the GIS system and calculate the shortest path between two points;

[0071]

[0072] Among them, L represents the total length of the path; n represents the number of points on the path; (X i , Y i , Z i ) represents the coordinates of the i-th point on the path; (X i+1 , Y i+1 , Z i+1 ) represents the coordinates of the (i + 1)-th point on the path.

[0073] S2. As Figure 2 shown, introduce a reinforcement learning method, comprehensively consider the topographic data, path information, and geological conditions, and perform intelligent optimization on the preliminary planned path to select the optimal wire-laying path. The specific operations are as follows:

[0074] S21. Calculate the comprehensive evaluation index of geological conditions according to the geological structure, rock layer stability, construction difficulty, and quality of road-building materials;

[0075] G = ω 1 × geological structure + ω 2 × rock layer stability + ω 3 × construction difficulty + ω 4 × quality of road-building materials;

[0076] Among them, G represents the comprehensive evaluation index of geological conditions; ω 1 , ω 2 , ω3 and ω 4 represent the weights of the corresponding factors;

[0077] Quantify the geological structure, rock stratum stability, construction difficulty, and quality of road construction materials;

[0078] Quantification of geological structure:

[0079]

[0080] Among them, I 构造 represents the geological structure quantification index; W i represents the weight of the i-th geological structure feature; C i represents the corresponding quantification value; n represents the total number of geological structure features;

[0081] Quantification of rock stratum stability:

[0082] I 岩石 = RQD × f(structural plane);

[0083] Among them, I 岩石 represents the rock stability quantification index; RQD represents the rock quality index; f(structural plane) represents the quantification function of the development degree of the structural plane;

[0084] Quantification index of construction difficulty:

[0085] I 施工难度 = k 1 × terrain difficulty + k 2 × geological difficulty + k 3 × climate difficulty + k 4 × technical difficulty;

[0086] Among them, I 施工难度 represents the quantification index of construction difficulty; k 1 , k 2 , k 3 , k 4 represent the weights of the corresponding factors;

[0087] Quantification of the quality of road construction materials:

[0088] I 材料 = a 1 × mechanical properties + a 2 × durability + a 3 × particle size distribution;

[0089] Among them, I 材料 represents the quantification index of the quality of road construction materials; a 1 , a 2 , a 3 represent the weights of the corresponding indexes;

[0090] In summary, the comprehensive evaluation index of geological conditions is as follows:

[0091] G = ω 1 × I 构造 + ω 2 × I 岩石 + ω 3 × I 施工难度 + ω 4 × I 材料 ;

[0092] S22. Define the state space S:

[0093] S = {(X, Y, Z, P, G)|(X, Y, Z) ∈ topographic data, P ∈ path information, G ∈ geological conditions};

[0094] Among them, (X, Y, Z) represents the three-dimensional coordinates of each point on the path; P represents path information;

[0095] Define the action space A:

[0096] A = {a 1 , a 2 , …, a m} = {steering angle, forward distance};

[0097] Among them, a m represents the m-th possible action;

[0098] Define the reward function:

[0099] R(S, A) = -c(S, A) + r 目标 (S');

[0100] Among them, c(S, A) represents the cost of executing action A in state S; r 目标 (S') represents the reward for reaching the target position; S' represents the next state;

[0101] S23. Use the Q-learning algorithm to update the state-action value function Q(S, A);

[0102]

[0103] Among them, α represents the learning rate; γ represents the discount factor; r represents the immediate reward; A' represents the next action; Q(S, A)' represents the updated state-action value;

[0104] Select the action with the highest value as the optimal wire-laying path.

[0105] S3. According to the optimal wire-laying path, prepare the corresponding wire-laying devices, wire materials and safety protection measures.

[0106] 1) Path confirmation:

[0107] Confirm the optimal wire laying path obtained from the intelligent path planning in step S2.

[0108] Convert the path data into construction drawings and construction manuals to ensure that the construction team can clearly understand the path direction and relevant parameters.

[0109] 2) Preparation of wire laying device:

[0110] Select a suitable wire laying device, such as a tension wire laying machine, according to the length of the optimal wire laying path, terrain undulation and geological conditions.

[0111] Conduct necessary inspections and maintenance on the wire laying device to ensure its reliability and safety during construction.

[0112] 3) Preparation of wire body materials:

[0113] Determine the specifications and quantities of the required wire body materials according to the length of the path and the technical requirements of the transmission line.

[0114] Prepare wire body materials, including conductors, insulators, fittings, etc., and conduct necessary quality inspections.

[0115] 4) Preparation of safety protection measures:

[0116] Formulate detailed safety operation procedures, including the use of personal protective equipment, emergency measures in case of emergencies, etc.

[0117] Prepare safety protection equipment, such as safety belts, safety helmets, insulating gloves, etc.

[0118] Set up emergency braking devices and wire body recovery mechanisms to deal with possible emergencies during construction.

[0119] 5) Training of construction personnel:

[0120] Conduct safety training and skill training for construction personnel to ensure that they are familiar with construction drawings, construction manuals and the operation methods of wire laying devices.

[0121] The training content includes but is not limited to wire laying techniques, safety regulations, emergency response measures, etc.

[0122] 6) Transportation of construction equipment and materials:

[0123] Plan the transportation routes of construction equipment and materials to ensure that they can reach the construction site safely and in a timely manner.

[0124] Prepare corresponding transportation tools and ensure safety during transportation.

[0125] 7) Preparation of the construction site:

[0126] Conduct a survey of the construction site to ensure that the construction site meets the construction conditions, such as ground bearing capacity, construction space, etc.

[0127] Clean up the construction site and remove obstacles that may affect the construction.

[0128] 8) Construction plan formulation:

[0129] According to the construction drawings and construction manuals, formulate a detailed construction plan, including construction progress, personnel division of labor, equipment usage plan, etc.

[0130] Determine the key nodes and milestones to monitor the construction progress and quality.

[0131] 9) Environment and weather assessment:

[0132] Evaluate the environmental and weather conditions during construction, especially factors such as wind, rain, snow, etc. that may affect the wire laying operation.

[0133] Formulate a plan to deal with bad weather to ensure construction safety.

[0134] 10) Establishment of communication and coordination mechanism:

[0135] Establish a communication mechanism between the construction site and the command center to ensure the timely transmission of information.

[0136] Coordinate the work between different construction teams to ensure the coordinated progress of construction operations.

[0137] The above 10 steps are routine operations during the construction process and will not be elaborated in detail here.

[0138] S4. As Figure 3 shown, during the wire laying process, use the random forest algorithm to predict the change of wire laying tension, and automatically adjust the tension control system of the wire laying device according to the prediction results. The specific operations are as follows:

[0139] S41. Collect historical wire laying operation data;

[0140] Historical wire laying operations include the following characteristics: tension value, wire speed, coil diameter, ambient temperature, ambient humidity.

[0141] Collect the wire laying operation data for the past 6 months and perform preprocessing (data cleaning and data normalization) to obtain a data set.

[0142] Bootstrap sampling: Extract 70% of the samples from the preprocessed data as the training set and 30% of the samples as the test set;

[0143] Construct decision trees: Set the maximum depth of each tree to 10 and the minimum number of samples in the leaf nodes to 5;

[0144] Training model: Use the scikit-learn library in Python to train the model and obtain a tension prediction model;

[0145] Integrated prediction: The predicted value of each tree is the average of the target values on the leaf nodes, and the final predicted value is the average of all trees;

[0146] Evaluate the model performance through the root mean square error;

[0147] During the actual wire laying process, input the current wire speed, coil diameter, temperature, and humidity in real time, and use the trained model to predict the tension value.

[0148] S42. Adjust the tension control system of the wire laying device according to the prediction result of the tension prediction model;

[0149] T control = T target + ΔT × sign(T predicted - T target );

[0150] Among them, T control represents the control instruction; T target represents the target tension value; ΔT represents the allowable tension fluctuation range; T predicted represents the predicted tension value; sign represents the sign function, which is used to determine the tension adjustment direction.

[0151] S5. Introduce an adaptive control algorithm, and automatically adjust the tension control strategy according to the real-time monitored terrain changes and tension data. The specific operations are as follows:

[0152] T control = f(T predicted , T actual , T target , TC);

[0153] Among them, f represents the adaptive control function; T actual represents the actually measured tension value; TC represents the terrain change parameter.

[0154] Specifically,

[0155] T control = K p × (T target - T actual ) + K pred × (T predicted - T actual ) + K C × T C ;

[0156] Among them, Kp is the proportional gain based on the difference between the actual tension and the target tension; K pred represents the proportional gain based on the difference between the predicted tension and the actual tension; K C represents the proportional gain based on the terrain change.

[0157] S6. Use sensors to monitor the wire laying speed and the position of the wire body in real time. When abnormalities are found, manual adjustment is immediately carried out to ensure the smooth and continuous wire laying process. The specific operations are as follows:

[0158] S61. Calculate the deviation between the actually measured tension value and the target tension value;

[0159] ΔO = T actual - T target ;

[0160] where, ΔO represents the deviation;

[0161] S62. When the actual tension exceeds the preset safety range at that time, that is, |ΔT| > ΔT safe , where ΔT safe represents the safety tension deviation range, then trigger the emergency response mechanism, and then manual intervention is carried out.

[0162] It should be noted that the content not elaborated in detail in the present invention is all prior art and is well known to those skilled in the art.

[0163] Therefore, by adopting the above-mentioned method for laying wires in the construction of large-drop transmission lines, the present invention significantly improves the efficiency and safety of the construction of large-drop transmission lines, effectively reduces the risks of wire body swing and tension out of control, and provides strong technical support for the construction of power infrastructure in complex terrains.

[0164] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for laying out a large drop transmission line, characterized in that: The following steps are involved: S1. Use drones to obtain terrain data of the construction area and combine it with the geographic information system to carry out preliminary path planning; S2, introduce reinforcement learning method, comprehensively consider terrain data, path information and geological conditions, intelligently optimize the preliminary planning path, and select the optimal laying-out path; S3. Prepare corresponding wire laying devices, wire materials and safety protection measures according to the optimal wire laying path; S4. During the wire-paying process, the random forest algorithm is used to predict the change in wire-paying tension, and the tension control system of the wire-paying device is automatically adjusted according to the prediction results; S5, introduce adaptive control algorithm to automatically adjust tension control strategy according to real-time monitored terrain changes and tension data; S6. Use sensors to monitor the line-paying speed and line position in real time. When abnormalities are found, manual adjustments are made immediately to ensure a smooth and continuous line-paying process.

2. A method for laying out a large-drop transmission line according to claim 1, characterized in that: In step S1, the terrain data of the construction area is obtained by using a drone, and preliminary path planning is performed in combination with a geographic information system. The specific operations are as follows: S11. Use drones to obtain topographic data of the construction area; S12, input the terrain data into the GIS system and calculate the shortest path between two points; Where L represents the total length of the path; n represents the number of points on the path; (X i ,Y i ,Z i ) represents the coordinates of the i-th point on the path; (X i+1 ,Y i+1 ,Z i+1 ) represents the coordinates of the i+1th point on the path.

3. A method for laying out a large drop transmission line according to claim 2, characterized in that: In step S2, the reinforcement learning method is introduced to comprehensively consider the terrain data, path information and geological conditions, and the preliminary planned path is intelligently optimized to select the optimal laying-out path. The specific operations are as follows: S21. Calculate the comprehensive evaluation index of geological conditions based on geological structure, rock stability, construction difficulty and quality of road construction materials; G = ω1×geological structure+ω2×rock stability+ω3×construction difficulty+ω4×quality of road construction materials; Among them, G represents the comprehensive evaluation index of geological conditions; ω1, ω2, ω3, and ω4 represent the weights of the corresponding factors; S22. Define the state space S: S = {(X, Y, Z, P, G)|(X, Y, Z)∈ terrain data, P∈ path information, G∈ geological conditions}; Among them, (X, Y, Z) represents the three-dimensional coordinates of each point on the path; P represents the path information; Define the action space A: A={a1,a2,…,a m } = {steering angle, forward distance}; Among them, a m represents the mth possible action; Define the reward function: R(S,A)=-c(S,A)+r 目标 (S'); Where c(S,A) represents the cost of executing action A in state S; r 目标 (S') represents the reward for reaching the target position; S' represents the next state; S23, use Q-learning algorithm to update the state-action value function Q(S,A); Among them, α represents the learning rate; γ represents the discount factor; r represents the immediate reward; A' represents the next action; Q(S,A)' represents the updated state-action value; The action with the highest value is selected as the optimal release path.

4. A method for laying out a large-drop transmission line according to claim 3, characterized in that: In step S4, during the wire-paying process, the random forest algorithm is used to predict the change in wire-paying tension, and the tension control system of the wire-paying device is automatically adjusted according to the prediction result. The specific operation is as follows: S41, collecting historical wire laying operation data; Preprocess the historical wireline operation data, including data cleaning and data normalization; Extract 70% of the samples from the preprocessed data as the training set and 30% of the samples as the test set; Set the maximum depth of each tree to 10 and the minimum number of sample leaf nodes to 5; Use Python's scikit-learn library to train the model and obtain the tension prediction model; S42, adjusting the tension control system of the pay-off device according to the prediction result of the tension prediction model; T control =T target +ΔT×sign(T predicted -T target ); Among them, T control Indicates control instructions; T target Indicates the target tension value; ΔT indicates the allowable tension fluctuation range; T predicted Represents the predicted tension value; sign represents the sign function, which is used to determine the tension adjustment direction.

5. A method for laying out a large-drop transmission line according to claim 4, characterized in that: The historical wire laying operation includes the following characteristics: tension value, wire speed, coil diameter, ambient temperature, and ambient humidity.

6. A method for laying out a large drop transmission line according to claim 5, characterized in that: In step S5, an adaptive control algorithm is introduced to automatically adjust the tension control strategy according to the real-time monitored terrain changes and tension data. The specific operations are as follows: T control =f(T predicted ,T actual ,T target ,TC); Where f represents the adaptive control function; T actual It represents the actual measured tension value; TC represents the terrain change parameter.

7. A method for laying out a large drop transmission line according to claim 6, characterized in that: In step S6, sensors are used to monitor the wire-laying speed and wire position in real time. When an abnormality is found, manual adjustments are immediately made to ensure a smooth and continuous wire-laying process. The specific operations are as follows: S61, calculating the deviation between the actually measured tension value and the target tension value; ΔO=T actual -T target ; Among them, ΔO represents the deviation; S62, the actual tension at that time exceeded the preset safety range, that is, |ΔT|>ΔT safe , where ΔT safe If the tension exceeds the safe tension deviation range, the emergency response mechanism is triggered and manual intervention is performed.

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