Ice melting control method and system based on flexible conductor connection technology

Abstract

The invention relates to the technical field of information, and discloses an ice melting control method and system based on a flexible conductor connection technology, and the method comprises the steps: obtaining real-time icing data and real-time wind power data of a wire in a target region, so as to determine the icing state change parameters and deformation characteristics of the wire, a wire state comprehensive model is constructed by combining the slippage degree of the wire between different material layers, and then a butt joint strategy of the ice melting mechanical arm is generated; generating a micro adjustment strategy of the ice melting mechanical arm based on feedback data after the docking strategy is executed so as to determine locking optimization parameters of the ice melting mechanical arm, and further controlling the preheating electrode to execute pre-ice melting operation; the method comprises the following steps: quantifying resistivity distribution data required by directional diversion based on icing state change in a pre-ice-melting process so as to execute directional diversion operation, adjusting locking optimization parameters by combining execution effect data, and generating an ice-melting control strategy based on the adjusted locking optimization parameters so as to execute the ice-melting control strategy; and the ice melting efficiency is improved by using a flexible conductor connection technology.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method and system for controlling ice melting based on flexible conductor connection technology. Background Technology

[0002] Flexible conductor connection technology is crucial in the field of power transmission, especially in extreme environments such as high-altitude mountainous areas. The stability and reliability of the connection between iced conductors and de-icing machinery directly affect the de-icing effect, power grid operation safety, and power supply continuity. Currently, most traditional flexible conductor connection technologies rely on single sensors or fixed connections. However, this approach struggles to adapt to the dynamic changes in conductor icing conditions, particularly when complex factors such as heterogeneous ice crystal structures, composite deformation, and interlayer slippage are combined, easily leading to connection failures and consequently, low de-icing efficiency.

[0003] Therefore, how to solve the problem of high failure rate and low de-icing efficiency of existing flexible conductor connection technology when the conductor is dynamically changed by icing has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] This invention provides a de-icing control method and system based on flexible conductor connection technology, which solves the problem that existing flexible conductor connection technologies have a high failure rate when the conductor's icing state changes dynamically, resulting in low de-icing efficiency.

[0005] To address the aforementioned technical problems, the first aspect of this invention provides a method for controlling ice melting based on flexible conductor connection technology, comprising: Real-time icing data and real-time wind data of the conductor within the target area are obtained to determine the distribution characteristics of the conductor under various heterogeneous ice crystal structures and the deformation data under wind action, thereby obtaining icing state change parameters and deformation characteristics. When the deformation characteristics exceed the preset deformation threshold, the temperature gradient data of the conductor is obtained to determine the degree of slippage of the conductor between different material layers, the slippage influence coefficient is obtained, and combined with the icing state change parameters and the deformation characteristics to construct a comprehensive conductor state model; Based on the comprehensive model of the conductor state, the docking strategy of the ice-melting robotic arm is determined, and the real-time feedback data of the dual-spectrum imaging system after the docking strategy is executed is obtained, so as to determine the micro-adjustment strategy for controlling the ice-melting robotic arm. The locking optimization parameters of the ice-melting robotic arm are determined according to the micro-motion adjustment strategy to control the preheating electrode to perform the pre-ice-melting operation and monitor the changes in the icing state during the pre-ice-melting operation to quantify the resistivity distribution data required for directional current conduction. The contact pressure of the preheating electrode is adjusted using the resistivity distribution data to perform a directional flow guidance operation. The locking optimization parameters are adjusted based on the execution effect data of the directional flow guidance operation, and an ice-melting control strategy is generated and executed based on the adjusted locking optimization parameters.

[0006] A second aspect of the present invention provides an ice-melting control system based on flexible conductor connection technology, comprising: The change characteristic determination module is used to acquire real-time icing data and real-time wind data of the conductor within the target area, so as to determine the distribution characteristics of the conductor under various heterogeneous ice crystal structures and the deformation data under wind action, and obtain icing state change parameters and deformation characteristics. The comprehensive model construction module is used to acquire the temperature gradient data of the conductor when the deformation characteristics exceed the preset deformation threshold to determine the degree of slippage of the conductor between different material layers, obtain the slippage influence coefficient, and combine it with the icing state change parameters and the deformation characteristics to construct a comprehensive conductor state model; The adjustment strategy generation module is used to determine the docking strategy of the ice-melting robotic arm based on the comprehensive model of the conductor state, and to obtain real-time feedback data of the dual-spectrum imaging system after the docking strategy is executed, so as to determine the micro-motion adjustment strategy for controlling the ice-melting robotic arm. The distributed data quantization module is used to determine the locking optimization parameters of the ice-melting robotic arm according to the micro-motion adjustment strategy, so as to control the preheating electrode to perform the pre-ice-melting operation and monitor the changes in the icing state during the pre-ice-melting operation, so as to quantify the resistivity distribution data required for directional current conduction. The control strategy execution module is used to adjust the contact pressure of the preheating electrode through the resistivity distribution data to perform a directional flow operation, adjust the locking optimization parameters according to the execution effect data of the directional flow operation, and generate an ice-melting control strategy based on the adjusted locking optimization parameters for execution.

[0007] Compared with the prior art, the beneficial effects of the embodiments of the present invention are as follows: By acquiring real-time icing data, wind data, and temperature gradient data of the conductor within the target area, a comprehensive and accurate understanding of the conductor's distribution characteristics, deformation, and slippage between different material layers under various heterogeneous ice crystal structures can be achieved, helping to improve the targeting and effectiveness of ice melting. A comprehensive conductor state model is constructed by considering the influence of multiple factors on the conductor's state, providing a scientific basis for determining the docking strategy, micro-motion adjustment strategy, and locking optimization parameters of the ice melting robot arm, improving the accuracy and rationality of decision-making. The micro-motion adjustment strategy and locking optimization parameters of the ice melting robot arm are continuously adjusted based on real-time feedback data, and the execution effect of the directional flow guidance operation is also considered. Further data optimization enables precise control of the de-icing process, improving de-icing efficiency and quality, reducing damage to conductors, and ensuring the safe and stable operation of the power system. By quantifying the resistivity distribution data required for directional current conduction, the de-icing operation becomes more scientific and precise, allowing adjustment of the contact pressure of the preheating electrodes based on the actual resistivity of the conductors to achieve directional current conduction. The de-icing strategy generated using flexible conductor connection technology can adapt to dynamic changes in icing conditions, while achieving precise de-icing of iced conductors, improving de-icing efficiency and uniformity, reducing residual deformation, effectively solving the problem of conductor icing under severe weather conditions, and ensuring the safe and stable operation of the power system. Attached Figure Description

[0008] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart of an ice-melting control method based on flexible conductor connection technology provided in a certain embodiment of the present invention; Figure 2 This is a structural diagram of an ice-melting control system based on flexible conductor connection technology provided in a certain embodiment of the present invention; Figure label: Among them, 10 is the change characteristic determination module; 20 is the comprehensive model construction module; 30 is the adjustment strategy generation module; 40 is the distributed data quantification module; and 50 is the control strategy execution module. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings and examples. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0011] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0012] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the system or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0013] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is merely for describing specific embodiments and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0014] Flexible conductor connection technology refers to the technology used to achieve electrical connections between flexible circuits, stretchable electronic devices, or flexible display elements. Its core objective is to maintain reliable mechanical and electrical performance under dynamic deformations such as bending, stretching, and torsion. Flexible conductor connection technology is of critical significance for power transmission in extreme environments such as high-altitude mountainous areas. Specifically, in high-altitude mountainous areas, icing of conductors forms various heterogeneous ice crystal structures. This structure not only causes a significant gradient change in the surface resistivity of the conductor but also alters the aerodynamic shape of the conductor and increases additional load. Furthermore, under wind force, iced conductors are prone to severe combined bending and torsional deformation. This complex external mechanical stress, combined with the deformation and the internal temperature gradient generated by environmental temperature fluctuations or current thermal effects (affected by the icing layer), acts on the layered structure of the aluminum-clad steel core conductor, leading to relative slippage between layers. This exacerbates the uneven distribution of internal stress and the complexity of condition assessment. These factors are coupled together, making the conductor's condition highly dynamic, which traditional static or semi-dynamic connection methods cannot accurately address. Based on this, the present invention constructs a comprehensive model of conductor state that considers conductor deformation, interlayer slippage, and changes in icing state. It also utilizes a dual-spectrum imaging system to adjust the six-axis motion, micro-motion positioning, and locking between the ice-melting robotic arm and the conductor in the ice-melting mechanical device in real time. Simultaneously, it optimizes the progressive contact pressure distribution between the preheating electrode and the conductor in the ice-melting mechanical device to adapt to the dynamic changes in conductor state under icing conditions, thereby improving ice-melting efficiency.

[0015] In one embodiment, such as Figure 1 As shown, the first aspect of the present invention provides a method for controlling ice melting based on flexible conductor connection technology, comprising: S1. Obtain real-time icing data and real-time wind data of the conductor within the target area to determine the distribution characteristics of the conductor under various heterogeneous ice crystal structures and the deformation data under wind action, and obtain icing state change parameters and deformation characteristics. In one embodiment, step S1 includes: Real-time icing data of the conductor within the target area is acquired, and ice crystal lattice parameters of various icing depths are obtained by X-ray diffraction technology to divide the ice layers. The density gradient method is used to determine the density of each ice layer in order to construct a correspondence table between icing depth and ice layer density. Based on the correspondence table, the resistance value of each ice layer is determined by the four-probe method, and combined with the thickness of each ice layer, the resistivity of each ice layer is quantified. Based on the resistivity described, a distribution function of resistivity as a function of ice depth is established using linear interpolation, and the resistivity gradient value is obtained by quantification through the distribution function. The ice thickness data of the conductor at multiple times is obtained by using the time series method to quantify the ice thickness growth rate of the conductor, and combined with the resistivity gradient value to quantify the resistivity change rate of the conductor. The crystal structure ratio data of each ice layer at each time point are obtained, and the ice crystal structure evolution rate is determined by the time derivative of the hexagonal ice volume fraction. Combined with the ice thickness growth rate and the resistivity change rate, ice state change parameters are constructed to determine the ice development stage.

[0016] Specifically, this invention acquires real-time surface icing sample data of conductors within a target area as real-time icing data. The target area is a region prone to conductor icing, such as high-altitude mountainous areas or other extreme environments. An X-ray diffractometer is used to scan the icing cross-section of the sample data, allowing X-ray beams to penetrate the icing layer. The ice crystal lattice parameters are identified based on the differences in diffraction angles of different ice crystal structures, and the proportions of hexagonal and cubic ice in each layer are recorded. Hexagonal ice exhibits a hexagonal symmetrical lattice structure, with diffraction peaks located at specific angles, while cubic ice exhibits different diffraction patterns. Since abrupt changes in lattice parameters indicate alterations in ice layer properties, different ice layer boundaries are delineated accordingly. The thickness of each ice layer is measured, and the actual density of each ice layer is determined using the density gradient method commonly used in existing technologies to establish a correspondence between icing depth and ice layer density. Ice density typically decreases from the inner to the outer layer. The inner layer, due to greater pressure, has a more compact ice crystal arrangement, reaching a density of up to 0.92 g / cm³, while the newly formed outer layer has a density of only about 0.85 g / cm³. The ice structure can be detected using infrared spectroscopy or lidar technology, combined with machine learning algorithms to analyze surface characteristics (such as roughness and reflectivity) to determine the ice crystal distribution.

[0017] Based on the correspondence table, the resistance of each ice layer is measured using the four-probe method. In this method, four probes are in contact with the ice layer; two probes carry current, while the other two measure voltage. The resistance of each ice layer is calculated using Ohm's law, and combined with the thickness information, the resistivity of each layer is quantified using the resistivity formula (resistivity = resistance × cross-sectional area / length, where cross-sectional area is the product of thickness and width). Alternatively, the resistivity of a layer can be calculated based on the probe spacing, current value, and measured voltage, combined with the ice layer thickness at that location. Furthermore, the resistivity of each ice layer can also be obtained non-contactly by introducing a resistive-capacitive sensor or a microwave sensor, utilizing the difference in dielectric constant between ice and water.

[0018] Since ice at different depths has different crystal ratios and densities, its resistivity varies. This invention obtains discrete resistivity data points by measuring at multiple depth locations, and constructs a continuous resistivity-depth distribution function that varies with ice depth by linearly connecting adjacent measurement points using linear interpolation. The derivative of this function is then used to obtain the resistivity gradient. The gradient value reflects the rate of change of resistivity with depth. A larger gradient value indicates drastic changes in the internal structure of the ice, which usually occurs at the boundary between different ice layers.

[0019] The time series method is used to acquire ice thickness data of the conductor at multiple time points. Data can be obtained by periodically measuring ice thickness with an ice thickness sensor or by manual observation and recording. Specifically, by measuring the ice thickness every 30 minutes and monitoring continuously for 24 hours, the complete process data of ice growth can be obtained. Then, based on this data, the thickness difference between adjacent time points is divided by the time interval to quantify the ice thickness growth rate of the conductor. Based on the correlation between the resistivity gradient value and the ice thickness growth rate and the resistivity change rate, a mathematical relationship model is constructed, and then the resistivity change rate of the conductor is quantified using this mathematical relationship model.

[0020] By comparing the crystal structure ratios of different ice layers at different times and calculating the time derivative of the volume fraction of hexagonal ice, the ice crystal structure evolution rate is determined. Since hexagonal ice is a common ice crystal structure, changes in its volume fraction reflect the evolution of the ice crystal structure. Combining the ice thickness growth rate, resistivity change rate, and ice crystal structure evolution rate, ice accretion state change parameters are obtained. Based on the magnitude of these parameters, the ice development stage can be determined, such as the initial stage, growth stage, and stable stage. Specifically, if the ice thickness growth rate exceeds 0.5 mm / hour and the resistivity change rate is positive, the ice is considered to be in a rapid growth stage.

[0021] This invention obtains detailed information on conductor icing in the target area, enabling a comprehensive and accurate understanding of the physical characteristics of icing and providing a solid foundation for subsequent analysis of icing state changes. By quantifying icing change indicators, it reflects the development dynamics of icing more scientifically and accurately. By constructing icing state change parameters to determine the icing development stage, it can clearly understand the state of icing, thereby taking more targeted countermeasures, improving de-icing efficiency, and ensuring the safe operation of the power system.

[0022] In one embodiment, step S1 further includes: Based on the icing state change parameters and the real-time wind data, the deflection distribution of the conductor under lateral load is quantified by the catenary equation to determine the bending deformation data of the conductor. Based on the bending deformation data and the real-time wind data, the torsional moment generated by the wind is determined, and combined with the torsional stiffness of the conductor, the torsion angle of the conductor is determined, and combined with the bending deformation data, the composite deformation of the conductor at each position is determined. Based on the composite deformation, the deformation gradient between adjacent positions in the conductor is calculated using the finite difference method, and the strain value at each position in the conductor is determined according to the deformation gradient to obtain the strain distribution data of the conductor. The bending deformation data, the torsion angle, and the strain distribution data are combined to generate the deformation characteristics of the conductor.

[0023] Specifically, this invention uses the icing thickness growth rate and resistivity change rate from the icing state change parameters, as well as real-time wind data, including wind speed and direction, to determine the additional mass per unit length of the conductor based on the icing thickness. It then calculates the lateral load exerted by the wind on the iced conductor. Since icing thickness directly affects the windward area (approximately 20% increase for every 10 mm increase in thickness), this invention discretizes the conductor into multiple micro-elements and integrates the wind force applied to each micro-element to obtain the total lateral load distribution. Next, based on the calculated lateral load distribution, iterative or direct methods are used to solve the catenary equation, quantifying the conductor's deflection distribution under lateral load. Based on the deflection distribution, the degree of conductor bending is further analyzed (through derivative calculations, geometric derivations, etc.), determining the conductor's bending deformation data, such as curvature, bending angle, and maximum bending amplitude. Alternatively, a three-dimensional dynamic model of the conductor can be established by combining the finite element method and real-time wind data, using wind data and icing state change parameters as input, and outputting the conductor's deflection, torsional moment, and strain distribution. Furthermore, the stress on the conductor under lateral loads (mainly generated by wind and the gravitational component of icing) can be considered. A mechanical model can be constructed using the catenary equation, and the icing state variation parameters and real-time wind data can be substituted into the mechanical model to output the conductor deflection distribution data. When the wind speed reaches 15 m / s, the lateral displacement at the midpoint of the conductor can reach 5% of the span, and the maximum bending amplitude usually occurs near the center of the span, where the stress concentration is highest.

[0024] The torsional moment is calculated using fluid dynamics principles and moment balance equations based on the calculated bending deformation data and real-time wind data. The product of the conductor's polar moment of inertia and shear modulus is used as its torsional stiffness, and the torsion angle is determined using the torsion angle formula (torsion angle = torsional moment / torsional stiffness). The composite deformation can be decomposed into components in three orthogonal directions: longitudinal displacement, lateral displacement, and torsion angle. Based on the bending deformation data and torsion angle, a geometric synthesis method is used to determine the composite deformation at each location of the conductor. Alternatively, energy synthesis and finite element method synthesis can also be used to calculate the composite deformation of the conductor.

[0025] Based on the composite deformation obtained through quantization, the conductor is discretized into multiple small segments. The finite difference method is used to calculate the deformation gradient between adjacent positions in the conductor. The deformation gradient is obtained by calculating the ratio of the difference in deformation between adjacent positions to the distance between them. According to the mechanical properties of the material, there is a certain relationship between the deformation gradient and strain. For small deformation cases, the strain can be approximately equal to the deformation gradient. Therefore, the strain value at each position in the conductor is determined based on the calculated deformation gradient. The strain values ​​at each position of the conductor are organized and analyzed to establish a strain distribution function along the length of the conductor, obtaining the strain distribution data of the conductor. This data reflects the magnitude and distribution of strain at different positions of the conductor. The strain value can also be obtained by dividing the difference in displacement between adjacent points by the original length.

[0026] By combining the quantified bending deformation data, torsion angle, and strain distribution data, the deformation characteristics of the conductor can be obtained. These characteristics can comprehensively and accurately describe the deformation state of the conductor under icing and wind action, providing an important basis for subsequent analysis and decision-making.

[0027] This invention comprehensively considers icing state change parameters and real-time wind data, enabling a comprehensive and accurate assessment of conductor deformation in complex environments. By quantifying key indicators such as conductor deflection distribution, torsion angle, and strain distribution, it can promptly identify potential safety hazards such as excessive deformation and stress concentration, allowing for proactive preventative and remedial measures to avoid conductor breakage due to excessive deformation and ensure the stable operation of the power system. Accurate deformation characteristic data helps to gain a deeper understanding of the combined impact of icing and wind on conductors, providing a reference for developing more scientific and reasonable de-icing and wind protection strategies. The use of mathematical methods such as the finite difference method to calculate deformation gradients and strain values ​​improves the accuracy and reliability of data analysis, reduces the impact of human error and approximate calculations, and makes the assessment of conductor deformation more precise.

[0028] S2. When the deformation characteristics exceed the preset deformation threshold, the temperature gradient data of the conductor is obtained to determine the degree of slippage of the conductor between different material layers, the slippage influence coefficient is obtained, and combined with the icing state change parameters and the deformation characteristics to construct a comprehensive conductor state model. Specifically, the conductor deformation threshold is set based on the conductor's mechanical properties and safety margin requirements. When a conductor deforms under external force, its internal stress increases accordingly. The bending amplitude reflects the degree to which the conductor deviates from its original straight state, usually expressed as a percentage of the span. The torsion angle reflects the degree to which the conductor rotates around its own axis. When these deformation parameters exceed the allowable range, they can cause relative movement of the internal materials of the conductor, especially in multi-layered composite conductors. Therefore, if the bending amplitude or torsion angle in the conductor's deformation characteristics exceeds the preset deformation threshold, infrared thermal imager data of the conductor surface temperature distribution is acquired, thermocouples are used to measure the temperature values ​​at different depths inside the conductor, and the rate of temperature change with radial distance is calculated based on the correspondence between radial position and temperature to obtain the conductor's temperature gradient data.

[0029] In one embodiment, acquiring the temperature gradient data of the conductor to determine the degree of slippage of the conductor between different material layers and obtaining the slippage influence coefficient includes: The temperature gradient data, steel core thermal expansion coefficient, and aluminum strand thermal expansion coefficient of the conductor are obtained to quantify the difference in expansion of each material layer of the conductor under the action of the temperature gradient. Based on the difference in expansion and the interlayer contact length of the conductor, the relative displacement per unit length is determined, and the differential thermal effect value between the steel and aluminum layers is determined based on the relative displacement. Based on the differential thermal effect value and the friction coefficient of the interlayer contact surface of the conductor, the interlayer slip resistance is determined, and combined with the relative displacement, the degree of interlayer slip of the conductor is quantified. The slip influence coefficient is obtained by quantifying the degree of interlayer slip and the preset slip reference value.

[0030] Specifically, the calculation of the temperature gradient involves the rate of temperature change with spatial location. In the radial direction of the conductor, due to the difference in thermal conductivity between the steel core and the aluminum stranded wire, the temperature exhibits a non-linear distribution from the inside out. The difference in the coefficient of thermal expansion is the fundamental cause of interlayer slip; the coefficient of thermal expansion of steel is approximately 11 × 10⁻⁶. -6 / ℃, while the coefficient of thermal expansion of aluminum is approximately 23×10. -6 / ℃, the product of temperature gradient, corresponding conductor length and expansion coefficient is used as the formula to calculate the expansion amount. The expansion amount of steel core and aluminum stranded wire are calculated separately. The difference in expansion amount of conductor is obtained by subtracting the former from the latter. The relative displacement per unit length is obtained by dividing the difference in expansion amount by the interlayer contact length. This relative displacement is used as the value of differential thermal effect between steel and aluminum layers of conductor.

[0031] During the manufacturing process of the conductor, a certain preload exists between the steel core and the aluminum stranded wire. This preload generates positive pressure, which in turn generates friction. The coefficient of friction is typically between 0.3 and 0.5. The interlayer slip resistance is calculated by multiplying the differential thermal effect value by the friction coefficient of the interlayer contact surface. The ratio of the relative displacement per unit length to the interlayer slip resistance is used as the degree of interlayer slippage. Finally, the slippage influence coefficient is obtained based on the ratio of the degree of slippage to a preset slippage reference value. The preset slippage reference value is a safety threshold determined according to conductor design specifications and historical operating experience. When the ratio of the actual degree of slippage to the reference value approaches or exceeds 1, it indicates that the interlayer bonding state of the conductor has changed significantly, requiring corresponding maintenance measures.

[0032] This invention, by acquiring temperature gradient data and quantifying the degree of slippage between different material layers, can more accurately understand the actual working state of the conductor under temperature change environment, providing a key basis for the safe operation assessment of the conductor, avoiding conductor failures caused by interlayer slippage, and ensuring the stability of power transmission.

[0033] After normalizing the slip influence coefficient, icing state change parameters, and deformation characteristics, the values ​​are summed according to preset weighting coefficients to obtain the comprehensive conductor state value. Based on the correspondence between the comprehensive state value and the spatial coordinates of each point on the conductor, a comprehensive conductor state model containing conductor position and state information is constructed. The weight of the icing thickness growth rate is typically set to 0.4, as it directly affects the conductor's load condition; the deformation characteristic weight is 0.35, reflecting structural safety; and the slip influence coefficient weight is 0.25, reflecting the degree of internal damage. The comprehensive state value is obtained through weighted summation; a larger value indicates a more severe deviation from the normal conductor state. The comprehensive conductor state model constructed in this invention comprehensively considers multiple factors such as conductor position, heterogeneous ice crystal structure state changes, composite deformation, and interlayer slip, effectively coping with harsh environments, especially the complex state changes of conductors under icing conditions in high-altitude mountainous areas.

[0034] S3. Determine the docking strategy of the ice-melting robotic arm based on the comprehensive model of the conductor state, and obtain the real-time feedback data of the dual-spectrum imaging system after the docking strategy is executed, so as to determine the micro-adjustment strategy for controlling the ice-melting robotic arm. In one embodiment, determining the docking strategy of the ice-melting robotic arm based on the comprehensive model of the conductor state includes: Based on the comprehensive model of the conductor state, the spatial coordinate sequence of the conductor is determined. The Jacobian matrix method is used to solve the joint angles of the ice-melting robot arm. Combined with the safety distance corresponding to the radius of curvature of the conductor, several trajectory points are determined to obtain the six-axis motion trajectory of the ice-melting robot arm. Based on the six-axis motion trajectory, the position difference and attitude angle change between adjacent trajectory points are quantified to adjust the angle of each joint and obtain the docking angle adjustment amount. The average motion speed is determined by the six-axis motion trajectory and the preset task cycle, and the maximum contact force of the conductor is determined based on the strain distribution data. Based on the docking angle adjustment, the average movement speed, and the maximum contact force, a docking strategy for the ice-melting robotic arm is constructed.

[0035] Specifically, the spatial coordinates of the guide wire are obtained through laser ranging or image recognition, and each coordinate point corresponds to a comprehensive state value output by a comprehensive guide wire state model. The Jacobian matrix describes the linear transformation relationship between the velocity of the end effector of the robotic arm and the velocities of each joint. First, a kinematic model of the ice-melting robotic arm is established to determine the functional relationship between the position and orientation of the end effector and the angles of each joint; then, the velocity relationship is obtained by differentiating with respect to time. Given the target position and orientation of the end effector (i.e., the spatial coordinates of the guide wire), the angles of each joint are obtained by solving inverse kinematics. The nonlinear equations can be iteratively solved using numerical methods (such as the Newton-Raphson method) to obtain the joint angle sequence that satisfies the position and orientation requirements of the end effector. Based on the radius of curvature of the guide wire, the safe distance between the robotic arm and the guide wire is determined by consulting relevant safety standards or through experiments. Based on the determined spatial coordinate sequence of the guide wire, a sphere (in three-dimensional space) or a circle (in two-dimensional projection plane) is drawn with each coordinate point as the center and the safe distance as the radius. Points on the movement path of the robotic arm that do not intersect with these spheres or circles are selected as trajectory points. Path planning algorithms (such as A* algorithm, RRT algorithm, etc.) can be used to search for the optimal sequence of trajectory points from the starting point to the target point, considering safety distance constraints. These determined trajectory points are then connected in a specific order, and combined with the solved joint angles, the six-axis motion trajectory of the ice-melting robotic arm from the starting position to the target position is obtained. Spline interpolation can be used for trajectory planning to ensure smooth and continuous robotic arm movement, avoiding abrupt acceleration and deceleration.

[0036] For adjacent trajectory points on a six-axis motion path, calculate the position difference between them. For attitude angle changes, if Euler angles are used to represent the attitude, calculate the rotation angle changes around the x, y, and z axes respectively; if a rotation matrix is ​​used to represent the attitude, the attitude change can be calculated through matrix operations. Based on the position difference between adjacent trajectory points and the attitude angle changes, the adjustment amount of the joint angle is calculated using the inverse (or pseudo-inverse) of the Jacobian matrix of the robotic arm: because the Jacobian matrix describes the relationship between the end effector speed and the joint speed, the speed change of the end effector is obtained by differentiating the position difference and attitude angle changes, and then the joint speed change is solved using the inverse relationship of the Jacobian matrix, and then integrated to obtain the joint angle adjustment amount. Alternatively, an error feedback-based control method, such as proportional-integral-derivative (PID) control, can be used, where the position difference and attitude angle changes are used as error signals, and the PID controller calculates the joint angle adjustment amount to obtain the docking angle adjustment amount between the ice-melting robotic arm and the guide wire.

[0037] The average motion speed is obtained by dividing the total length of the six-axis motion trajectory by the preset task cycle. The task cycle is preset according to the operation requirements, with a typical value of 30-60 seconds to complete one docking operation. The calculation of the average motion speed takes into account the acceleration and deceleration process, and the actual peak speed will be about 20% higher than the average. The stress at each point of the conductor is calculated by strain distribution data, and the maximum contact force at each position of the conductor is determined according to the relationship between stress and contact force (considering factors such as the force-bearing area of ​​the conductor). Alternatively, the stress-strain analysis of the conductor can be performed using the finite element method to obtain a more accurate strain distribution and maximum contact force.

[0038] The docking control command is generated based on the docking angle adjustment, the speed control command is generated based on the average motion speed, and the force control command is generated based on the maximum permissible contact force. These commands constitute the docking strategy between the ice-melting robotic arm and the guide wire, and the docking attitude, motion speed, and contact force are adjusted through the command sequence. Alternatively, the docking angle adjustment, average motion speed, and maximum contact force can be used as input parameters to construct a comprehensive docking strategy. Based on these parameters, motion control rules for the robotic arm can be formulated. For example, when approaching the guide wire, the joint angle is adjusted according to the docking angle adjustment to allow the robotic arm to approach the guide wire with a suitable attitude; the motion time of the robotic arm is controlled according to the average motion speed to ensure docking is completed within the task cycle; and the force when the robotic arm contacts the guide wire is controlled according to the maximum contact force to avoid excessive contact force damaging the guide wire. Furthermore, intelligent control methods such as fuzzy control and expert systems can be used to dynamically adjust the robotic arm's docking strategy according to different working conditions and parameter combinations.

[0039] This invention, by solving the angles of each joint of the ice-melting robotic arm, enables the robotic arm to reach the target position more accurately, achieving high-precision docking with the conductor, reducing docking errors, and improving the reliability of ice-melting operations. By determining the trajectory point based on the safety distance corresponding to the conductor's radius of curvature, collisions between the robotic arm and the conductor or other obstacles during movement can be avoided, ensuring the safety of both the robotic arm and the conductor and reducing operational risks. Adjusting the docking angle makes the robotic arm's movement smoother, reducing impact and vibration during movement, and improving the robotic arm's stability and service life. Determining the maximum contact force of the conductor based on strain distribution data and incorporating it into the docking strategy allows the robotic arm to apply appropriate force when contacting the conductor, avoiding damage due to excessive contact force or weak docking due to insufficient contact force, adapting to the ice-melting needs of conductors under different conditions. By determining the average movement speed through the six-axis motion trajectory and preset task cycle, the movement time of the robotic arm is rationally planned, improving the efficiency of ice-melting operations and shortening operation time while ensuring docking quality and safety.

[0040] In one embodiment, acquiring real-time feedback data from the dual-spectral imaging system after executing the docking strategy to determine the micro-adjustment strategy for controlling the ice-melting robotic arm includes: The visible light image data and infrared thermal image data of the dual-spectrum imaging system after executing the docking strategy are acquired as real-time feedback data, and the difference between the actual contact area and the target contact area is quantified based on the real-time feedback data to obtain the pose deviation parameter. The translational fine-tuning amount of the ice-melting robotic arm is determined based on the pose deviation parameters, and the three-axis rotational fine-tuning amount is determined based on the angular deviation between the contact area contour and the ideal contour. The contact speed is determined based on the posture deviation parameters and the preset adjustment cycle, and combined with the translational fine adjustment amount and the three-axis rotational fine adjustment amount to obtain the posture parameters of the micro-motion stage. The contact area of ​​the preheating electrode is obtained, and the pressure value is determined according to the distance from each contact point in the contact area to the center of the preheating electrode, so as to generate a progressive contact pressure distribution using a time-incrementing function; Based on the micro-motion stage pose parameters and the progressive contact pressure distribution, a micro-motion adjustment strategy for controlling the ice-melting robotic arm is determined.

[0041] Specifically, this invention acquires visible light image data and infrared thermal image data from a dual-spectrum imaging system after executing the docking strategy as real-time feedback data. It aligns the coordinate systems of the two spectral images using a feature point matching method to extract the temperature distribution and contour boundaries of the contact area between the preheating electrode and the wire in the ice-melting mechanical device. This allows for the calculation of the difference between the actual contact area and the target contact area, and the determination of pose deviation parameters based on the area difference and temperature distribution non-uniformity. This invention identifies areas in the infrared image with temperatures higher than the ambient temperature as effective contact areas by setting a temperature threshold. The target contact area is pre-determined based on the electrode specifications and wire diameter, typically 70%-80% of the electrode electrode area. Temperature distribution non-uniformity is calculated by determining the standard deviation of the temperature within the contact area; a larger standard deviation indicates poorer contact quality. When the actual contact area is less than the target value by 50 square millimeters or the temperature standard deviation exceeds 5 degrees Celsius, pose adjustment is required. The positional deviation parameters are determined based on geometric analysis and thermodynamic principles. The positional deviation components include three dimensions: lateral offset, longitudinal offset, and height deviation. The lateral offset is calculated by comparing the distance between the electrode center and the conductor axis. The longitudinal offset is determined by the distance between the electrode end face and the preset contact point. The height deviation reflects whether the electrode indentation depth is insufficient or excessive. The angle deviation is obtained by the angle between the major axis of the fitted contact profile ellipse and the ideal direction.

[0042] The Euclidean distance between the lateral and longitudinal offsets in the pose deviation parameters is used as the translational fine-tuning amount for the robotic arm. Then, by analyzing the angular deviation between the contact area contour and the ideal contour, and combining this with the kinematic model of the robotic arm, the rotational fine-tuning amounts around the x, y, and z axes are determined. The ratio of the absolute value of the pose deviation parameter to the preset adjustment cycle is used as the contact speed. The typical value of the preset adjustment cycle is 2-5 seconds, within which one fine-tuning cycle is completed. The contact speed is determined following the principle of "slow contact, fast adjustment," with the initial contact speed controlled below 5 mm / s, and the maximum adjustment speed increased to 20 mm / s. Even when exceeding this value, the speed remains the same. Finally, the contact speed is combined with the translational fine-tuning amount and the three-axis rotational fine-tuning amount to obtain the pose parameters for the micro-motion stage.

[0043] The initial pressure value is allocated based on the contact area between the preheating electrode and the wire, and the distance from each contact point within this area to the center of the preheating electrode. The pressure change rate is calculated by dividing the pressure difference between adjacent points by the distance between the points, and a progressive contact pressure distribution is generated using a time-increasing function. The contact area is divided into several small grids, and contact points are set at the center or specific locations of each small grid. A pressure distribution model can be established based on the distance from each contact point within the contact area to the center of the preheating electrode. Generally, the pressure gradually decreases with distance from the center to achieve a uniform contact pressure distribution. A Gaussian distribution function can be used to describe the pressure distribution. To achieve progressive contact, a time-increasing function (such as a linear or exponential function) is used to modulate the pressure distribution, resulting in a progressive contact pressure distribution that changes over time.

[0044] Based on the progressive contact pressure distribution and the pose parameters of the micro-motion stage, control commands are generated in the order of position adjustment followed by pressure adjustment. Through coordinated motion of each axis, translational and rotational fine-tuning, as well as contact speed control, are achieved, thus determining a micro-motion adjustment scheme that includes the execution sequence and parameter configuration. The generation of control commands follows a priority principle: position adjustment takes precedence over angle adjustment, and coarse adjustment takes precedence over fine adjustment. Translational adjustment is performed first to roughly align the electrodes, followed by angle fine-tuning to optimize the contact posture, and finally, progressive pressure is applied to complete reliable docking. The execution sequence includes position feedback checkpoints; after each adjustment action is completed, the system re-acquires dual-spectral images to verify the adjustment effect.

[0045] This invention quantifies the difference between the actual contact area and the target contact area as a posture deviation parameter, enabling precise detection of positional and posture deviations in the ice-melting robotic arm during the docking process. This allows for targeted micro-adjustments, significantly improving docking accuracy, ensuring good contact between the preheating electrode and the conductor, and enhancing the ice-melting effect. Based on the posture deviation parameter, the invention determines translational and rotational fine-tuning amounts and generates a progressive contact pressure distribution according to the contact area conditions. This makes the contact process between the preheating electrode and the conductor more stable and uniform, avoiding problems such as poor contact or conductor damage caused by sudden excessive pressure or uneven contact, thus ensuring contact quality. Furthermore, this solution dynamically adjusts the posture and contact pressure of the ice-melting robotic arm based on real-time feedback data, allowing the system to adapt to changes in the conductor's state under different working conditions, improving the adaptability and stability of the entire ice-melting system.

[0046] S4. Determine the locking optimization parameters of the ice-melting robotic arm according to the micro-motion adjustment strategy, so as to control the preheating electrode to perform the pre-ice-melting operation and monitor the changes in the icing state during the pre-ice-melting operation, so as to quantify the resistivity distribution data required for directional current conduction. In one embodiment, determining the locking optimization parameters of the ice-melting robotic arm according to the micro-adjustment strategy to control the preheating electrode to perform the pre-ice-melting operation includes: Based on the micro-adjustment strategy, the pressure value and position coordinates of each contact point are determined to quantify the eccentricity between the point of application of the resultant pressure force and the center of the preheating electrode, and the initial locking torque is obtained to control the ice-melting robotic arm to perform the locking action. The locking deformation amplitude and locking strain distribution of the ice-melting robotic arm when performing the locking action are obtained to quantify the conductor deformation increment under the current locking torque. When the conductor deformation increment exceeds a preset increment threshold, the initial locking torque is updated to obtain the target torque value. The loading rate is determined based on the transition time from the initial locking torque to the target torque value, and the holding time is determined by the deformation time characteristic curve of the conductor under constant stress. Combined with the target torque value and the loading rate, the locking optimization parameters are obtained. The contact pressure between the preheating electrode and the wire, as well as the preheating duration of the preheating electrode, are determined by the locking optimization parameters. Based on the contact pressure and the preheating duration, the preheating electrode is controlled to perform a pre-de-icing operation.

[0047] Specifically, this invention extracts the pressure value and position coordinates of each contact point based on the progressive contact pressure distribution data in the micro-motion adjustment strategy. The pressure value of each contact point can also be collected in real time by a sensor array. Each sensor corresponds to a specific contact area, and the position coordinates are represented in a polar coordinate system relative to the center of the electrode. By integrating the pressure value of each contact point with its distance from the reference axis, the total torque is obtained. Dividing this torque by the resultant pressure force yields the eccentricity. The product of the eccentricity and the resultant pressure force is used as the initial locking torque. The robotic arm is then driven to perform the locking action according to the control command corresponding to the initial locking torque.

[0048] Real-time feedback is a crucial means of ensuring locking quality. Based on real-time feedback during the initial locking torque execution process, the deformation amplitude and strain distribution are read. Specifically, this invention pre-establishes a relationship model between conductor deformation and locking torque based on the conductor's material mechanical properties and geometric dimensions. The deformation increment of the conductor under different locking torques is pre-determined experimentally, and a functional relationship between the deformation increment and locking torque is fitted. The deformation amplitude and strain distribution are input into this function to obtain the conductor deformation increment under the current locking torque. When the calculated conductor deformation increment exceeds a preset increment threshold, the initial locking torque is corrected using a neural network algorithm or fuzzy control algorithm based on the current conductor state and deformation increment, yielding the target torque value. Alternatively, the torque increment step size can be reduced by gradually increasing the torque and monitoring the deformation response to obtain the target torque value. The adjustment of the torque increment step size employs an adaptive strategy: when the deformation response is linear, the step size can be larger; when approaching the limit, the step size automatically decreases to 10%-20% of the initial value.

[0049] The loading rate is obtained by dividing the difference between the target torque and the initial locking torque by the time interval between them. Based on the deformation-time characteristic curve of the conductor material under constant stress, the holding time required for deformation stabilization is determined. Combining the loading rate, holding time, and target torque value yields the optimized locking parameters. The conductor material undergoes creep under constant stress, meaning the deformation increases slowly over time. The deformation-time characteristic curve is obtained experimentally; a typical curve is logarithmic, showing rapid initial deformation growth followed by gradual stabilization. The holding time is determined based on the time required for the deformation to reach 95% of its stable value, typically 30-60 seconds.

[0050] The contact pressure between the preheating electrode and the wire is controlled by the target torque value in the locking optimization parameters, and the preheating duration is determined based on the holding time. Finally, a stepped power control is used to gradually increase the electrode temperature, performing a pre-de-icing operation. The contact pressure and contact thermal resistance are inversely proportional; for every 10% increase in pressure, the contact thermal resistance decreases by 15%-20%. The stepped power control uses a segmented increasing method: the initial stage sets the power to 30% of the rated power, lasting for 1 minute, allowing the electrode temperature to slowly rise to 50℃; the second stage increases to 60% of the rated power, raising the electrode temperature to 80℃; the final stage reaches the rated power, with the electrode temperature stabilizing at around 120℃. This gradual heating avoids thermal shock and prevents uneven melting caused by sudden cracking of the ice layer.

[0051] This invention obtains the initial locking torque by quantifying the eccentricity between the point of application of the resultant pressure force and the center of the preheating electrode. This enables more precise control of the locking action of the de-icing robotic arm, reducing poor contact caused by unstable locking and ensuring reliable contact between the preheating electrode and the conductor. It also quantifies the conductor deformation increment by acquiring the locking deformation amplitude and strain distribution, and updates the locking torque when it exceeds a preset threshold. This avoids damage to the conductor due to excessive locking force, ensuring the structural integrity and electrical performance of the conductor. Furthermore, it determines the loading rate based on the transition time from the initial locking torque to the target torque value, and determines the holding time by combining the conductor deformation time characteristic curve, obtaining optimized locking parameters. This allows for accurate control of the contact pressure and preheating duration of the preheating electrode, making the pre-de-icing operation more scientific and reasonable, and improving de-icing efficiency and quality.

[0052] In one embodiment, the monitoring of changes in the icing state during the pre-melting operation to quantify the resistivity distribution data required for directional current conduction includes: The temperature change data during the pre-melting operation is obtained to determine the melting boundary position, and the ice thickness change value and the water flow direction data generated by melting are quantified based on the melting boundary position. Multiple measurement points are set based on the melting boundary location and the water flow direction data, and the resistance value of each measurement point is determined by the four-electrode method. This resistance value is then combined with the ice thickness change value to obtain the resistivity of each measurement point. Based on the resistivity of each measurement point, a spatial distribution of resistivity along the water flow direction is established, and the resistivity distribution data required for the directional flow guidance is obtained.

[0053] Specifically, this invention uses thermal imaging equipment to monitor the temperature distribution on the ice-covered surface of the conductor based on the temperature field generated during the pre-melting process, obtaining temperature change data. This data presents a concentric circle pattern centered on the contact point of the preheating electrode, with the temperature decreasing outwards from the center. When the temperature in a certain area exceeds 0°C, the ice in that area begins to melt. The melting boundary is determined using an isotherm tracking method; the 0°C isotherm is the solid-liquid phase transition interface. The calculation of the ice thickness change is based on the principle of thermal balance: the heat transferred per unit time equals the latent heat required to melt the ice plus the heat lost to the environment. By observing the melting boundary position at different times, the rate of thickness reduction, i.e., the ice thickness change, can be calculated. The water flow direction is influenced by gravity and surface tension, forming a specific flow path on the conductor surface. Meltwater on inclined conductors flows along the direction of maximum slope, while horizontal conductors form a circling flow. Therefore, the water flow direction can be inferred from the shape change of the melting boundary and the inclination of the ice surface. For example, if the melting boundary is inclined to one side and the ice surface has a certain slope, then the water flow direction is downwards along the slope and towards the melting boundary. The thickness of the ice layer and its changes can also be obtained using an ultrasonic thickness gauge.

[0054] Based on the location of the melting boundary and the direction of water flow, measurement points are rationally arranged in the icing area. The measurement points should cover the vicinity of the melting boundary and the area through which the water flows to ensure a comprehensive reflection of the impact of changes in icing state on resistivity. Measurement points can be set at regular intervals (e.g., 20 cm) along the conductor, with the density of measurement points appropriately increased on both sides of the melting boundary. The arrangement of measurement points follows the principle of equal spacing, with a typical spacing of 5-10 cm. The resistance value of each measurement point reflects the conductivity characteristics of the icing at that location. Then, the resistance value of each measurement point is determined using the four-electrode method and combined with the change in icing thickness to obtain the resistivity of each measurement point. The resistivity can be calculated according to the resistance law, and the cross-sectional area is related to the change in icing thickness and the diameter of the conductor.

[0055] The location information of each measurement point and its corresponding resistivity value are interpolated using interpolation algorithms (such as linear interpolation, spline interpolation, etc.) to obtain a continuous spatial distribution of resistivity. The interpolation algorithm can be optimized based on the water flow direction to make the resistivity distribution more reflective of the influence of water flow on resistivity. The established spatial distribution of resistivity is analyzed to extract key information for directional flow guidance. For example, the direction and magnitude of resistivity gradient changes can be determined. The resistivity gradient can indicate the direction of current or heat flow, providing a basis for directional flow guidance. The resistivity distribution data is output in the form of charts (such as resistivity change curves along the water flow direction, resistivity distribution cloud maps, etc.) or data files for subsequent directional flow guidance control strategies. The spatial distribution of resistivity exhibits a clear gradient characteristic: near the melting boundary, resistivity decreases significantly due to increased water content; the resistivity in the dry ice region far from the melting zone remains high. This gradient distribution provides a natural current path for subsequent directional flow guidance.

[0056] This invention accurately determines the melting boundary location by using temperature change data, thereby quantifying the changes in ice thickness and water flow direction data to comprehensively understand the dynamic changes in ice cover and provide accurate basic information for subsequent ice melting operations. By establishing the spatial distribution of resistivity along the water flow direction, the resistivity distribution data required for directional flow guidance is obtained, which helps to guide the distribution of current or heat generated during the ice melting process based on resistivity differences, achieving more precise and efficient directional flow guidance, and improving the ice melting effect and energy utilization rate.

[0057] S5. Adjust the contact pressure of the preheating electrode using the resistivity distribution data to perform a directional flow guidance operation. Adjust the locking optimization parameters based on the execution effect data of the directional flow guidance operation and generate an ice-melting control strategy based on the adjusted locking optimization parameters for execution. Specifically, this invention calculates the resistivity gradient value based on resistivity distribution data. The gradient value is obtained by dividing the difference in resistivity between adjacent measuring points by the distance. Then, a threshold segmentation method is used to classify areas with resistivity 20% higher than the average value as high-resistivity regions and areas with resistivity 20% lower than the average value as low-resistivity regions. This establishes a boundary between low-resistivity and high-resistivity regions, allowing adjustment of the preheating electrode contact pressure to prioritize current flow through the low-resistivity path, thereby performing directional current guidance. Displacement sensors record the increase in bending deformation and interlayer slip displacement of the conductor during the current guidance process as conductor deformation data. The adjustment of contact pressure directly affects the current distribution. When the electrode applies greater pressure to the low-resistivity region, the contact resistance further decreases, creating a current concentration effect. Displacement sensors are placed at key locations on the conductor, such as suspension points, midpoints of spans, and stress concentration areas. These sensors can capture minute displacement changes in the conductor in real time with an accuracy of 0.01 mm. The increase in bending deformation is calculated by comparing the deflection difference before and after current guidance, while interlayer slip is obtained by measuring the relative displacement of marked points.

[0058] In one embodiment, adjusting the locking optimization parameters based on the execution effect data of the directional flow guidance operation, and generating an ice-melting control strategy based on the adjusted locking optimization parameters for execution, includes: The temperature rise, conductor deformation data, and current data at various positions of the conductor are obtained during the directional flow guidance operation, and the ice melting efficiency is determined based on the temperature rise and the input electrical energy. The residual deformation of the conductor is determined based on the conductor deformation data, and the current density uniformity is determined based on the deviation between each current data and the average current. This is combined with the ice melting efficiency and the residual deformation to obtain the execution effect data. When the execution effect data does not reach the preset effect target, the locking optimization parameters are adjusted to obtain parameter improvement values ​​to determine the current intensity data of the conductor, and the execution time is determined according to the remaining icing thickness of the conductor; The control strategy is generated and executed using the current intensity data and the execution time.

[0059] Specifically, this invention determines the temperature rise by the temperature difference before and after the directional flow guidance operation, and obtains the conductor deformation data and current data at each position of the conductor during the operation. The input electrical energy is calculated by multiplying the voltage, current and time, and the ice melting efficiency is calculated by the ratio of the temperature rise to the input electrical energy.

[0060] After the current diversion is completed and the conductor has cooled to ambient temperature, the residual deformation is measured. The difference between the conductor deformation data and the initial deformation is used to obtain the current density uniformity. Next, the deviation between the current at each measuring point and the average current is calculated to determine the current density uniformity. The evaluation of current density uniformity uses statistical methods: 10-15 measuring points are selected on the conductor surface, and the current value at each point is measured using a clamp meter. First, the average current at all measuring points is calculated, and then the deviation of each measuring point from the average value is calculated. The smaller the deviation, the more uniform the current distribution. When the maximum deviation exceeds 30% of the average value, it indicates a significant current concentration phenomenon, requiring adjustment of the contact state between the preheating electrode and the conductor. Combining the current density uniformity, de-icing efficiency, and residual deformation yields the execution effect data.

[0061] If the current density uniformity is lower than the set value or the residual deformation exceeds the allowable value, it indicates that the current-guided ice-melting effect has not achieved the preset target. The locking optimization parameters can be adjusted based on the difference between the effect and the target to obtain the parameter improvement value. Specifically, if the residual deformation is too large, the locking force can be appropriately increased; if the current density uniformity is poor, the locking position can be adjusted to improve the wire connection. Experimental design methods can be used to determine the direction and magnitude of parameter adjustment through multiple experiments. The parameter improvement value is obtained by comparing the parameter changes before and after adjustment. For example, increasing the locking optimization parameter from 50N to 75N results in an improvement of 25%.

[0062] Based on the parameter improvement values, the current intensity is reduced in areas of high resistivity and increased in areas of low resistivity to obtain current intensity data. The remaining ice thickness measured by thermal imaging is divided by the current ice-melting efficiency to determine the required contact time. The remaining ice thickness is monitored in real time using thermal imaging technology, and the internal thickness is estimated based on the difference between the ice surface temperature and the ambient temperature. Then, a control strategy is constructed and executed based on the adjusted current intensity data and the control command corresponding to the contact time, and it is judged whether the effect of this directional ice-melting operation meets the standard. If it does not meet the standard, the parameter improvement value is recalculated and the directional flow guidance operation is performed again until the ice-melting effect meets the standard. In addition, the current intensity data can be based on a relationship model between current intensity, locking optimization parameters, and ice condition established by empirical formulas or experimental data. This model takes the parameter improvement value and its corresponding improved locking optimization parameters and the remaining ice thickness as inputs, and the current intensity data as outputs.

[0063] This invention, by quantifying de-icing efficiency, residual deformation, and current density uniformity, can comprehensively and accurately evaluate the execution effect of directional current conduction operations, providing a reliable basis for subsequent optimization. Adjusting the locking optimization parameters based on the execution effect data can effectively improve the state of the conductor connection, reduce connection resistance, and improve the stability and efficiency of current transmission, thereby enhancing the de-icing effect. Based on the parameter improvement values, current intensity data is determined, and the execution time is determined in conjunction with the remaining ice thickness of the conductor, generating and executing a de-icing control strategy. This allows for scientific and reasonable control of the de-icing process according to actual conditions, avoiding over-de-icing or under-de-icing, and improving energy utilization and de-icing safety. The entire scheme forms a closed-loop control system. By continuously monitoring the execution effect and adjusting parameters, it can adapt to different icing conditions and environmental changes, improving the reliability and stability of the de-icing system.

[0064] This application addresses the problem of high failure rates in existing flexible conductor connection technologies when the conductor icing state changes dynamically, leading to low de-icing efficiency. To address this, a de-icing control method based on flexible conductor connection technology is designed. This method acquires real-time icing data, wind data, and temperature gradient data of the conductor within the target area, enabling a comprehensive and accurate understanding of the conductor's distribution characteristics, deformation, and slippage between different material layers under various heterogeneous ice crystal structures. This improves the targeting and effectiveness of de-icing. A comprehensive conductor state model is constructed by considering the influence of multiple factors on the conductor state, providing a scientific basis for determining the docking strategy, micro-motion adjustment strategy, and locking optimization parameters of the de-icing robotic arm, thus improving the accuracy and rationality of decision-making. By continuously adjusting the micro-motion adjustment strategy and locking optimization parameters of the de-icing robotic arm through real-time feedback data, and further optimizing based on the execution effect data of the directional flow guidance operation, refined control of the de-icing process can be achieved, improving de-icing efficiency and quality, reducing damage to the conductor, and ensuring the safety and stability of the power system. The system operates by quantifying the resistivity distribution data required for directional flow guidance, making the de-icing operation more scientific and precise. It can adjust the contact pressure of the preheating electrode according to the actual resistivity of the conductor to achieve directional flow guidance. When dynamic changes in the state of the iced conductor are detected, the dual-spectrum imaging system collects real-time icing data of the heterogeneous ice crystal structure on the conductor surface, thereby quantifying the icing thickness distribution and resistivity gradient. Simultaneously, it assesses the conductor deformation parameters under wind action and adaptively adjusts the six-axis motion trajectory, micro-motion positioning accuracy, and locking torque of the de-icing robotic arm based on this data. It also optimizes the progressive contact pressure distribution of the preheating electrode to ensure quality stability during the three-stage connection process of pre-de-icing, directional flow guidance, and post-locking, while also improving de-icing efficiency. The de-icing strategy generated using flexible conductor connection technology can adapt to dynamic changes in the icing state, achieving precise de-icing of the iced conductor, improving de-icing efficiency and uniformity, reducing residual deformation, effectively solving the problem of conductor icing under severe weather conditions, and ensuring the safe and stable operation of the power system.

[0065] It should be noted that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders.

[0066] In another embodiment, such as Figure 2 As shown, a second aspect of the present invention provides an ice-melting control system based on flexible conductor connection technology, comprising: The change feature determination module 10 is used to acquire real-time icing data and real-time wind data of the conductor within the target area, so as to determine the distribution characteristics of the conductor under various heterogeneous ice crystal structures and the deformation data under wind action, and obtain icing state change parameters and deformation characteristics. The comprehensive model construction module 20 is used to acquire the temperature gradient data of the conductor when the deformation characteristics exceed the preset deformation threshold, determine the degree of slippage of the conductor between different material layers, obtain the slippage influence coefficient, and combine it with the icing state change parameters and the deformation characteristics to construct a comprehensive conductor state model. The adjustment strategy generation module 30 is used to determine the docking strategy of the ice-melting robotic arm based on the comprehensive model of the conductor state, and to obtain real-time feedback data of the dual-spectrum imaging system after the docking strategy is executed, so as to determine the micro-motion adjustment strategy for controlling the ice-melting robotic arm. The distributed data quantization module 40 is used to determine the locking optimization parameters of the ice-melting robotic arm according to the micro-motion adjustment strategy, so as to control the preheating electrode to perform the pre-ice-melting operation and monitor the changes in the icing state during the pre-ice-melting operation, so as to quantify the resistivity distribution data required for directional current conduction. The control strategy execution module 50 is used to adjust the contact pressure of the preheating electrode through the resistivity distribution data to perform a directional flow operation, adjust the locking optimization parameters according to the execution effect data of the directional flow operation, and generate an ice-melting control strategy based on the adjusted locking optimization parameters for execution.

[0067] It should be noted that the various modules in the aforementioned ice-melting control system based on flexible conductor connection technology can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module. For specific limitations regarding the ice-melting control system based on flexible conductor connection technology, please refer to the limitations of the ice-melting control method based on flexible conductor connection technology mentioned above; both have the same function and role, and will not be repeated here.

[0068] In summary, this invention relates to the field of information technology and discloses a de-icing control method and system based on flexible conductor connection technology. The method acquires real-time icing data and real-time wind data of conductors within a target area to determine the icing state change parameters and deformation characteristics of the conductors. A comprehensive conductor state model is constructed by combining the degree of slippage between different material layers, thereby generating a docking strategy for the de-icing robotic arm. Based on the feedback data after the docking strategy is executed, a micro-motion adjustment strategy for the de-icing robotic arm is generated to determine the locking optimization parameters, thereby controlling the preheating electrode to perform pre-de-icing operations. Based on the icing state changes during the pre-de-icing process, the resistivity distribution data required for directional flow is quantified to execute the directional flow operation. The locking optimization parameters are adjusted based on the execution effect data, and a de-icing control strategy is generated and executed based on the adjusted locking optimization parameters. Flexible conductor connection technology is used to improve de-icing efficiency.

[0069] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0070] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A method for controlling ice melting based on flexible conductor connection technology, characterized in that, include: Real-time icing data and real-time wind data of the conductor within the target area are obtained to determine the distribution characteristics of the conductor under various heterogeneous ice crystal structures and the deformation data under wind action, thereby obtaining icing state change parameters and deformation characteristics. When the deformation characteristics exceed the preset deformation threshold, the temperature gradient data of the conductor is obtained to determine the degree of slippage of the conductor between different material layers, the slippage influence coefficient is obtained, and combined with the icing state change parameters and the deformation characteristics to construct a comprehensive conductor state model; Based on the comprehensive model of the conductor state, the docking strategy of the ice-melting robotic arm is determined, and the real-time feedback data of the dual-spectrum imaging system after the docking strategy is executed is obtained, so as to determine the micro-adjustment strategy for controlling the ice-melting robotic arm. The locking optimization parameters of the ice-melting robotic arm are determined according to the micro-motion adjustment strategy to control the preheating electrode to perform the pre-ice-melting operation and monitor the changes in the icing state during the pre-ice-melting operation to quantify the resistivity distribution data required for directional current conduction. The contact pressure of the preheating electrode is adjusted using the resistivity distribution data to perform a directional flow guidance operation. The locking optimization parameters are adjusted based on the execution effect data of the directional flow guidance operation, and an ice-melting control strategy is generated and executed based on the adjusted locking optimization parameters.

2. The ice-melting control method based on flexible conductor connection technology according to claim 1, characterized in that, The process involves acquiring real-time icing data and real-time wind data of the conductor within the target area to determine the distribution characteristics of the conductor under various heterogeneous ice crystal structures and the deformation data under wind action, thereby obtaining icing state change parameters and deformation characteristics, including: Real-time icing data of the conductor within the target area is acquired, and ice crystal lattice parameters of various icing depths are obtained by X-ray diffraction technology to divide the ice layers. The density gradient method is used to determine the density of each ice layer in order to construct a correspondence table between icing depth and ice layer density. Based on the correspondence table, the resistance value of each ice layer is determined by the four-probe method, and combined with the thickness of each ice layer, the resistivity of each ice layer is quantified. Based on the resistivity described, a distribution function of resistivity as a function of ice depth is established using linear interpolation, and the resistivity gradient value is obtained by quantification through the distribution function. The ice thickness data of the conductor at multiple times is obtained by using the time series method to quantify the ice thickness growth rate of the conductor, and combined with the resistivity gradient value to quantify the resistivity change rate of the conductor. The crystal structure ratio data of each ice layer at each time point are obtained, and the ice crystal structure evolution rate is determined by the time derivative of the hexagonal ice volume fraction. Combined with the ice thickness growth rate and the resistivity change rate, ice state change parameters are constructed to determine the ice development stage.

3. The ice-melting control method based on flexible conductor connection technology according to claim 2, characterized in that, The process of acquiring real-time icing data and real-time wind data of the conductor within the target area to determine the distribution characteristics of the conductor under various heterogeneous ice crystal structures and the deformation data under wind action, thereby obtaining icing state change parameters and deformation characteristics, further includes: Based on the icing state change parameters and the real-time wind data, the deflection distribution of the conductor under lateral load is quantified by the catenary equation to determine the bending deformation data of the conductor. Based on the bending deformation data and the real-time wind data, the torsional moment generated by the wind is determined, and combined with the torsional stiffness of the conductor, the torsion angle of the conductor is determined, and combined with the bending deformation data, the composite deformation of the conductor at each position is determined. Based on the composite deformation, the deformation gradient between adjacent positions in the conductor is calculated using the finite difference method, and the strain value at each position in the conductor is determined according to the deformation gradient to obtain the strain distribution data of the conductor. The bending deformation data, the torsion angle, and the strain distribution data are combined to generate the deformation characteristics of the conductor.

4. The ice-melting control method based on flexible conductor connection technology according to claim 1, characterized in that, The process of acquiring temperature gradient data of the conductor to determine the degree of slippage of the conductor between different material layers and obtaining the slippage influence coefficient includes: The temperature gradient data, steel core thermal expansion coefficient, and aluminum strand thermal expansion coefficient of the conductor are obtained to quantify the difference in expansion of each material layer of the conductor under the action of the temperature gradient. Based on the difference in expansion and the interlayer contact length of the conductor, the relative displacement per unit length is determined, and the differential thermal effect value between the steel and aluminum layers is determined based on the relative displacement. Based on the differential thermal effect value and the friction coefficient of the interlayer contact surface of the conductor, the interlayer slip resistance is determined, and combined with the relative displacement, the degree of interlayer slip of the conductor is quantified. The slip influence coefficient is obtained by quantifying the degree of interlayer slip and the preset slip reference value.

5. The ice-melting control method based on flexible conductor connection technology according to claim 3, characterized in that, The method for determining the docking strategy of the ice-melting robotic arm based on the comprehensive model of the conductor state includes: Based on the comprehensive model of the conductor state, the spatial coordinate sequence of the conductor is determined. The Jacobian matrix method is used to solve the joint angles of the ice-melting robot arm. Combined with the safety distance corresponding to the radius of curvature of the conductor, several trajectory points are determined to obtain the six-axis motion trajectory of the ice-melting robot arm. Based on the six-axis motion trajectory, the position difference and attitude angle change between adjacent trajectory points are quantified to adjust the angle of each joint and obtain the docking angle adjustment amount. The average motion speed is determined by the six-axis motion trajectory and the preset task cycle, and the maximum contact force of the conductor is determined based on the strain distribution data. Based on the docking angle adjustment, the average movement speed, and the maximum contact force, a docking strategy for the ice-melting robotic arm is constructed.

6. The ice-melting control method based on flexible conductor connection technology according to claim 1, characterized in that, The step of acquiring real-time feedback data from the dual-spectral imaging system after executing the docking strategy to determine the micro-adjustment strategy for controlling the ice-melting robotic arm includes: The visible light image data and infrared thermal image data of the dual-spectrum imaging system after executing the docking strategy are acquired as real-time feedback data, and the difference between the actual contact area and the target contact area is quantified based on the real-time feedback data to obtain the pose deviation parameter. The translational fine-tuning amount of the ice-melting robotic arm is determined based on the pose deviation parameters, and the three-axis rotational fine-tuning amount is determined based on the angular deviation between the contact area contour and the ideal contour. The contact speed is determined based on the posture deviation parameters and the preset adjustment cycle, and combined with the translational fine adjustment amount and the three-axis rotational fine adjustment amount to obtain the posture parameters of the micro-motion stage. The contact area of ​​the preheating electrode is obtained, and the pressure value is determined according to the distance from each contact point in the contact area to the center of the preheating electrode, so as to generate a progressive contact pressure distribution using a time-incrementing function; Based on the micro-motion stage pose parameters and the progressive contact pressure distribution, a micro-motion adjustment strategy for controlling the ice-melting robotic arm is determined.

7. The ice-melting control method based on flexible conductor connection technology according to claim 6, characterized in that, The step of determining the locking optimization parameters of the ice-melting robotic arm according to the micro-adjustment strategy to control the preheating electrode to perform the pre-ice-melting operation includes: Based on the micro-adjustment strategy, the pressure value and position coordinates of each contact point are determined to quantify the eccentricity between the point of application of the resultant pressure force and the center of the preheating electrode, and the initial locking torque is obtained to control the ice-melting robotic arm to perform the locking action. The locking deformation amplitude and locking strain distribution of the ice-melting robotic arm when performing the locking action are obtained to quantify the conductor deformation increment under the current locking torque. When the conductor deformation increment exceeds a preset increment threshold, the initial locking torque is updated to obtain the target torque value. The loading rate is determined based on the transition time from the initial locking torque to the target torque value, and the holding time is determined by the deformation time characteristic curve of the conductor under constant stress. Combined with the target torque value and the loading rate, the locking optimization parameters are obtained. The contact pressure between the preheating electrode and the wire, as well as the preheating duration of the preheating electrode, are determined by the locking optimization parameters. Based on the contact pressure and the preheating duration, the preheating electrode is controlled to perform a pre-de-icing operation.

8. The ice-melting control method based on flexible conductor connection technology according to claim 7, characterized in that, The monitoring of changes in the icing state during the pre-melting operation to quantify the resistivity distribution data required for directional current conduction includes: The temperature change data during the pre-melting operation is obtained to determine the melting boundary position, and the ice thickness change value and the water flow direction data generated by melting are quantified based on the melting boundary position. Multiple measurement points are set based on the melting boundary location and the water flow direction data, and the resistance value of each measurement point is determined by the four-electrode method. This resistance value is then combined with the ice thickness change value to obtain the resistivity of each measurement point. Based on the resistivity of each measurement point, a spatial distribution of resistivity along the water flow direction is established, and the resistivity distribution data required for the directional flow guidance is obtained.

9. The ice-melting control method based on flexible conductor connection technology according to claim 1, characterized in that, The step of adjusting the locking optimization parameters based on the execution effect data of the directional flow operation, and generating an ice-melting control strategy based on the adjusted locking optimization parameters for execution, includes: The temperature rise, conductor deformation data, and current data at various positions of the conductor are obtained during the directional flow guidance operation, and the ice melting efficiency is determined based on the temperature rise and the input electrical energy. The residual deformation of the conductor is determined based on the conductor deformation data, and the current density uniformity is determined based on the deviation between each current data and the average current. This is combined with the ice melting efficiency and the residual deformation to obtain the execution effect data. When the execution effect data does not reach the preset effect target, the locking optimization parameters are adjusted to obtain parameter improvement values ​​to determine the current intensity data of the conductor, and the execution time is determined according to the remaining icing thickness of the conductor; The control strategy is generated and executed using the current intensity data and the execution time.

10. An ice-melting control system based on flexible conductor connection technology, characterized in that, include: The change characteristic determination module is used to acquire real-time icing data and real-time wind data of the conductor within the target area, so as to determine the distribution characteristics of the conductor under various heterogeneous ice crystal structures and the deformation data under wind action, and obtain icing state change parameters and deformation characteristics. The comprehensive model construction module is used to acquire the temperature gradient data of the conductor when the deformation characteristics exceed the preset deformation threshold to determine the degree of slippage of the conductor between different material layers, obtain the slippage influence coefficient, and combine it with the icing state change parameters and the deformation characteristics to construct a comprehensive conductor state model; The adjustment strategy generation module is used to determine the docking strategy of the ice-melting robotic arm based on the comprehensive model of the conductor state, and to obtain real-time feedback data of the dual-spectrum imaging system after the docking strategy is executed, so as to determine the micro-motion adjustment strategy for controlling the ice-melting robotic arm. The distributed data quantization module is used to determine the locking optimization parameters of the ice-melting robotic arm according to the micro-motion adjustment strategy, so as to control the preheating electrode to perform the pre-ice-melting operation and monitor the changes in the icing state during the pre-ice-melting operation, so as to quantify the resistivity distribution data required for directional current conduction. The control strategy execution module is used to adjust the contact pressure of the preheating electrode through the resistivity distribution data to perform a directional flow operation, adjust the locking optimization parameters according to the execution effect data of the directional flow operation, and generate an ice-melting control strategy based on the adjusted locking optimization parameters for execution.

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