A method and product for distributed layout joint optimization of de-icing ferromagnetic material sleeves

Abstract

The application discloses a deicing ferromagnetic material sleeve distributed layout combined optimization method and product, belongs to the field of power system anti-icing / melting equipment and intelligent operation and maintenance technology, the method scatters the candidate position set, combines the line geometry, the fitting forbidden area, the meteorological statistics and the icing risk, establishes the unit power and the deicing energy efficiency core; with the binary site selection variable and the segmented linear power variable as the decision, constructs the MILP model containing the coverage / time, the power upper limit, the minimum spacing, the forbidden area, the magnetic density and the linearization upper limit of the temperature rise, solves the model to obtain the optimal number, position and power gear, and converts the solving result into the construction level layout scheme and parameter list for output, which can directly guide the line reconstruction and new supporting in the high-cold, wet-cold and sea wind salt fog areas, and takes into account the energy consumption, device scale and whole life reliability, so as to provide a standardized and quantifiable optimization tool and realization path for power grid icing control.

Description

Technical Field

[0001] This application belongs to the field of power system anti-icing / de-icing equipment and intelligent operation and maintenance technology, specifically involving a method and product for the joint optimization of distributed layout of de-icing ferromagnetic material sleeves. Background Technology

[0002] Transmission lines are prone to icing and frosting in complex environments such as high altitude, cold and humid conditions, and sea breezes and salt spray. This leads to a significant increase in conductor weight and wind load, worsening of sag and phase-to-phase clearance, and increased corona losses, potentially causing tripping, galloping, or even line breakage. Existing engineering solutions mainly include current-driven de-icing, external DC / AC heaters, mechanical de-icing, and surface hydrophobic / anti-icing coatings. Current-driven de-icing relies on system operation mode and backup capacity, resulting in high energy consumption and limited availability. External heaters require additional power supplies and weather-resistant encapsulation, leading to high maintenance costs. Mechanical de-icing efficiency is greatly affected by terrain and weather, and poses a potential risk of damage to conductors and fittings. Coating solutions have limited durability and environmental adaptability, making it difficult to maintain performance under strong winds, salt spray, and UV-coupled aging conditions. Therefore, "on-site thermal de-icing" equipment for in-service lines, which requires no changes to operating methods and can be implemented without power outages, has become a key research and development focus in recent years.

[0003] Ferromagnetic sleeves utilize the alternating magnetic field generated by the power frequency current of the conductor, relying on soft magnetic materials such as iron-nickel alloys with high initial permeability and low coercivity, to couple magnetic flux and convert it into heat through hysteresis / eddy current effects, achieving localized, fixed-point de-icing. To adapt to live-line installation and maintenance, engineering applications often employ a split structure with two axially open half-rings and interlayer insulation, coupled with guiding and self-locking mechanisms to achieve rapid loop closure. These devices offer advantages such as not altering wiring connections, small size, and local heat release, making them suitable for deployment in "ice-prone weak points" such as tension / suspension clamps, equalizing rings, and spacers. However, publicly reported methods and current engineering practices generally have three shortcomings: First, the number and spatial location of sleeves are mostly based on experience or equidistant arrangement, without comprehensively considering span geometry, hardware no-placement zones, minimum / maximum spacing, current upper limit, and differences in meteorological-icing statistics, resulting in uneven energy distribution, local overheating, or redundant configuration; Second, there is a lack of a unified framework that explicitly constrains the material magnetic saturation boundary and surface temperature rise safety threshold into the site selection-capacity determination decision, which easily leads to problems such as early saturation, hot spots, and shortened lifespan; Third, a few methods attempt to coordinate the trade-off between "coverage range-energy consumption-quantity" using heuristic rules, but it is difficult to provide calculable, reproducible, and directly constructible optimization results, and the combination explosion becomes more prominent when the span scale increases.

[0004] Furthermore, the thermal effects of ferromagnetic sleeves exhibit significant spatial coupling and environmental sensitivity: the "de-icing contribution" of unit power at different locations to different risk zones is non-uniformly distributed due to the influence of conductor diameter, wind speed, convective heat transfer, and thermal diffusion; the relationship between power at different levels and material magnetic flux density and surface temperature is non-linear and affected by current fluctuations and wind-induced heat transfer changes. If an energy efficiency core of "unit power - unit time - de-icing energy" is not established during the layout phase and modeled uniformly with the engineering boundary, even if a more complex control strategy is adopted during operation, it will be difficult to fundamentally compensate for the unreasonable initial spatial configuration. Summary of the Invention

[0005] To address the problems existing in the prior art, this application proposes a joint optimization method and product for the distributed layout of de-icing ferromagnetic material sleeves. This joint optimization method is designed for the span scenarios of AC transmission lines. It focuses on the collaborative optimization of key parameters such as the number of sleeves, spatial location (non-equidistant array), and rated power (slot). It integrates multi-source data such as line geometry, hardware no-placement zones, minimum / maximum spacing, electrical safety boundaries (magnetic flux density upper limit, surface temperature rise upper limit), operating current constraints, meteorological statistics, and icing history to establish an energy efficiency core of "unit power - de-icing energy" and embed it into a mixed integer linear programming (MILP) model in a piecewise linear form. This forms a computable and reproducible site selection-capacity determination decision framework, which can output construction-level layout schemes and parameter lists, and is adapted to the engineering process of uninterrupted power supply assembly.

[0006] This application is achieved through the following technical solution:

[0007] A method for joint optimization of distributed layout of de-icing ferromagnetic material sleeves includes:

[0008] Collect and process geometric data and basic operation and maintenance data of the target span line to determine the engineering physical boundary and safety threshold, and form a computable data model and parameter library; the collected data includes span length, conductor type and diameter, hardware coordinates and no-fire zone boundary, conductor operating current range, historical meteorological statistics and historical icing records.

[0009] Based on topography, hardware proximity, characteristics of high-incidence icing areas, and meteorological statistics, the entire line is divided into several risk sections along the axial direction, and the equivalent icing energy required for each risk section is estimated based on the target icing time.

[0010] The target span is discretized into a set of candidate positions by step size. The de-icing contribution of unit power at a candidate position to a certain risk section is obtained. The nonlinear relationship between power and magnetic flux density, and between power and surface temperature rise is processed into coefficients that can be directly entered into linear programming by table lookup and piecewise linearization.

[0011] The acquired data is input into a pre-built MILP model to solve for the optimal number of sleeves, optimal spatial location, and corresponding power level. The MILP model uses binary location variables and piecewise linear power variables as decision-making parameters, and uses coverage / time, total power limit, minimum installation spacing, no-release zone, upper limit of magnetic flux density, and upper limit of surface temperature rise as constraints. The optimization objective is to minimize the weighted average of energy consumption and device quantity. The output is the optimal number of sleeves, optimal spatial location, and corresponding power level.

[0012] The solution results are converted into construction-level files and digital unit outputs.

[0013] In some implementations, the constructed MILP model consists of an input layer, a variable layer, a constraint layer, a target layer, and an output layer;

[0014] The input layer gathers three types of information: candidate positions and corresponding construction boundaries obtained along the span at fixed step lengths; risk segment division along the span and calculation of the equivalent melting energy required for each risk segment within the target melting time; and the obtained unit power contribution to the de-icing of a certain risk segment at a certain candidate position, as well as the linearized upper bound of magnetic flux density and surface temperature rise.

[0015] The variable layer includes two types of decisions: the location variable of whether to place the sleeve at a certain candidate position, and the power level variable of which preset heating level each sleeve adopts;

[0016] The constraint layer is used to visualize the engineering physical boundaries: coverage / time constraints require all risk sections to receive sufficient de-icing energy within the target de-icing time; the total power upper limit restricts the total heat generation allowed within the span; the coupling between site selection and power ensures that power can only be allocated to selected locations and does not exceed their gear limit; the no-release zone constraint ensures that sleeves are not placed near restricted areas; the minimum installation spacing constraint limits the density of adjacent sleeves through a sliding window; the upper bound of magnetic flux density and the upper bound of surface temperature rise ensure that early magnetic saturation and hot spots are avoided during the solution process;

[0017] The target layer uses a weighted sum of energy consumption and device quantity as the optimization criterion.

[0018] The output layer transforms the solution results into engineering-usable deliverables, including the optimal number of sleeves, the optimal spatial position, and the corresponding power level.

[0019] In some implementations, the output layer can also cluster spatially adjacent site selection points to form a partitioning scheme based on spatial proximity clustering.

[0020] In some implementations, the collection and processing of geometric data and basic operation and maintenance data of the target span line includes:

[0021] The original data undergoes consistency verification and outlier removal. A unified coordinate system and time reference are established, and engineering physical boundaries and safety thresholds are determined, including the upper limit of total power, minimum installation spacing, upper limit of magnetic flux density, and upper limit of surface temperature rise. Finally, a calculable data model and parameter library are formed.

[0022] In some implementations, estimating the equivalent melting energy required for each risk segment based on the target melting time includes:

[0023] Combining the target melting time with the initial ice thickness estimate, the equivalent melting energy required within the target melting time is calculated according to the energy conservation relationship between the latent heat term and the sensible heat term, and segment-level weights are given.

[0024] In some implementations, the step-size discretization of the target span into a set of candidate locations to obtain the de-icing contribution of unit power at a candidate location to a certain risk segment includes:

[0025] By combining line geometry data and fitting coordinates, a no-discharge geometry and an installable window are generated. A lookup table relationship between power and surface heat flux density is established through bench testing or linear calibration. The local thermal coupling efficiency is estimated by combining thermally conductive insulating lining, contact state and queue heat transfer coefficient. Based on this, the unit power de-icing contribution of candidate locations to risk sections is abstracted into an energy efficiency core.

[0026] In some implementations, the nonlinear relationships between power and magnetic flux density, and between power and surface temperature rise, are processed using table lookup and piecewise linearization to convert them into coefficients that can be directly incorporated into linear programming, including:

[0027] For each power level, obtain the curves showing the relationship between power and magnetic flux density, and between power and surface temperature rise, and create tables for different weather conditions and typical contact conditions.

[0028] Each curve is approximated by several straight line segments, forming coefficients that can be directly called by the solver.

[0029] In some embodiments, the sleeve comprises two semi-annular cylindrical sections with axial openings. The two semi-annular cylindrical sections are joined together at the axial openings to form a closed-loop magnetic circuit. The outer surface is cylindrical, and the inner hole matches the outer diameter of the transmission line conductor.

[0030] Secondly, this application proposes an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the embodiments of the above-described joint optimization method.

[0031] Thirdly, this application proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the embodiments of the above-described joint optimization method.

[0032] This application proposes a joint optimization method for the distributed layout of de-icing ferromagnetic material sleeves. The method disperses the distance across candidate locations into a set, and combines line geometry, hardware no-displacement zones, meteorological statistics, and icing risk to establish a unit power and de-icing energy efficiency core. Using binary location variables and piecewise linear power variables as decision-making parameters, a MILP model is constructed, including coverage / time, power upper limit, minimum spacing, no-displacement zones, magnetic flux density, and upper bound of temperature rise linearization. Solving the model yields the optimal quantity, location, and power level. The solution results are then converted into a construction-level layout scheme and parameter list for output. This method can directly guide the modification and construction of new supporting facilities in high-altitude, cold, humid, and sea-breathing salt-fog regions, taking into account energy consumption, equipment scale, and full-life-cycle reliability. It provides a standardized and quantifiable optimization tool and implementation path for power grid icing control.

[0033] This method, when used with a ferromagnetic sleeve, enables non-equidistant array arrangement under power-off conditions. Compared with equidistant or empirical arrangements, this method can significantly reduce energy consumption per unit length and maximum surface temperature within a given ice-melting time, reduce the number of devices, and improve overall life-cycle reliability.

[0034] Accordingly, the electronic device and computer-readable storage medium proposed in this application also possess the same technical effects as described above. Attached Figure Description

[0035] The accompanying drawings, which are included to provide a further understanding of the embodiments of this application and form part of this application, do not constitute a limitation on the embodiments of this application. In the drawings:

[0036] Figure 1 This is a flowchart of the joint optimization method proposed in the embodiments of this application;

[0037] Figure 2 A schematic diagram of the MILP model architecture constructed for the embodiments of this application;

[0038] Figure 3 This is a schematic diagram of the sleeve structure used in the embodiments of this application;

[0039] Figure 4 This is a schematic diagram of the sleeve arrangement optimized using the joint optimization method proposed in the embodiments of this application;

[0040] Figure 5 This is a schematic diagram of the joint optimization device proposed in the embodiments of this application;

[0041] Figure 6 This is a schematic diagram of the joint optimization system architecture proposed in the embodiments of this application;

[0042] Figure 7 This is a schematic diagram of the electronic device proposed in the embodiments of this application;

[0043] Figure 8 This is a schematic diagram of a computer-readable storage medium proposed in an embodiment of this application;

[0044] Figure reference numerals and corresponding component names:

[0045] 1-Wedge-shaped limiting / self-locking groove, 2-Thermoconductive and electrically insulating liner, 3-Wire, 4-Ferromagnetic material sleeve, 200-Joint optimization device, 201-Acquisition unit, 202-Demand analysis unit, 203-Linearization processing unit, 204-Model solving unit, 205-Output unit, 300-Joint optimization system, 301-Input device, 302-Output device, 303-Processor A, 304-Memory A, 400-Electronic device, 410-Memory B, 420-Processor B, 411-Computer program A, 500-Computer-readable storage medium, 511-Computer program B. Detailed Implementation

[0046] In the following, the terms “comprising” or “may include” as used in the various embodiments of this application indicate the presence of a function, operation, or element of the invention and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in the various embodiments of this application, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, element, component, or combination of the foregoing and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or adding one or more combinations of the foregoing.

[0047] In various embodiments of this application, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0048] The terms used in the various embodiments of this application (such as "first," "second," etc.) may modify various constituent elements in the various embodiments, but do not limit the corresponding constituent elements. For example, the above terms do not limit the order and / or importance of the elements. The above terms are only used for the purpose of distinguishing one element from other elements. For example, a first user device and a second user device refer to different user devices, although both are user devices. For example, without departing from the scope of the various embodiments of this application, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element.

[0049] It should be noted that if a description is made of "connecting" one component to another, then the first component can be directly connected to the second component, and a third component can be "connected" between the first and second components. Conversely, when a component is "directly connected" to another component, it can be understood that there is no third component between the first and second components.

[0050] The terminology used in the various embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the various embodiments of this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. The terms (such as those defined in a generally used dictionary) are to be interpreted as having the same meaning as in the context of the relevant technical field and are not to be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this application are only for explaining this application and are not intended to limit this application.

[0052] This application proposes a joint optimization method for the distributed layout of de-icing ferromagnetic material sleeves. This method uses mixed integer linear programming (MILP) as its core, and performs unified modeling and simultaneous solution for three types of decisions: sleeve quantity, spatial location, and rated power. It takes the target de-icing time as a hard constraint, minimizes energy consumption and the number of devices, and explicitly incorporates engineering safety boundaries such as material magnetic saturation and surface temperature rise. This results in a non-equidistant array scheme that can be directly used for on-site construction. Unlike traditional equidistant or empirical layouts, this method quantifies the influencing factors such as thermal, magnetic, and aerodynamic factors into optimizable parameters and constraints during the planning stage, ensuring the computability and reproducibility of the scheme.

[0053] Specifically, such as Figure 1 As shown, the joint optimization method proposed in this application includes the following steps:

[0054] Step 110, Data Acquisition and Modeling: This involves collecting and processing basic geometric and operational data of the target span line to determine engineering physical boundaries and safety thresholds, ultimately forming a computable data model and parameter library. Specifically, this includes collecting data on span length, conductor type and diameter, hardware coordinates and no-parking zone boundaries, and the rated and commonly used operating current range of the conductors. Simultaneously, it extracts multi-year meteorological statistics (temperature, wind speed, relative humidity, precipitation / icing days, etc.) and historical icing records. Then, it performs consistency checks and outlier removal on the raw data, unifies the coordinate system and time reference, and determines engineering physical boundaries and safety thresholds (total power limit, minimum / maximum installation spacing, magnetic flux density limit, surface temperature rise limit), ultimately forming a computable data model and parameter library.

[0055] Step 120, Risk Segment Division and Demand Estimation: Based on terrain and hardware proximity, characteristics of high-incidence icing areas, and meteorological statistics, the entire span of the line is divided along the axial direction into several physically meaningful risk segment sets (indicating where the entire span needs to be guaranteed for de-icing: clustered into segments of unequal length based on terrain, hardware proximity, and icing statistics, with attributes such as initial icing, target time, and segment-level weight; segments may contain no-firework zones), and the equivalent de-icing energy required for each risk segment is estimated based on the target de-icing time; specifically, based on terrain and hardware proximity, characteristics of high-incidence icing areas, and meteorological statistics... Statistically, the entire line span is divided into several physically significant risk sections along the axial direction. For each risk section, the equivalent melting energy required within the target melting time is calculated based on the design target melting time and the initial ice thickness (or ice mass), according to the energy conservation relationship of latent heat and sensible heat. Segment-level weights are also assigned to prioritize critical components. Specifically, the volume and mass of the ice accretion can be obtained based on the conductor diameter, length, and initial ice thickness of the risk section. The process involves heating the ice from its current temperature to 0°C (sensible heat) and converting the 0°C ice into water (latent heat). This primarily involves convective heat dissipation (stronger with higher wind speeds) and radiative heat dissipation, with consideration given to heat conduction from components such as fittings when necessary. The total heat required to melt the ice body is added to the total heat loss throughout the process to obtain the total heat that must be delivered to the ice-covered surface. Combined with the comprehensive thermal efficiency (effective transfer ratio from device to ice accretion) obtained from bench / field calibration, the required surface heat is used to calculate the input energy that the device needs to provide. Calculate and summarize the equivalent melting energy target for each risk segment; recalculate quickly when temperature, wind speed, or target melting time changes. Weight settings: Obtain the following scoring factors (example weights): Fitting proximity / critical component (0.35), icing statistical intensity (0.30), crossing / importance (0.20), historical defects / maintenance accessibility (0.15). Score each scoring factor ([0,100]), and sum them by weight to obtain the target value. Normalization It can also be graded into three levels: A=1.0, B=0.7, C=0.4.

[0056] Step 130: Energy efficiency core calibration and piecewise linearization to generate candidate points: Discretize the target span into a set of candidate locations (representing where sleeve installation is permitted: candidate locations discretized along the axial direction with small step sizes under constraints such as geometry, safety clearance, restricted areas, and minimum / maximum spacing, with attributes such as power rating and installation cost). Combine this with line geometry, hardware restricted areas, meteorological statistics, and icing risk to establish an energy efficiency core. The nonlinear relationships between power and magnetic flux density, and between power and surface temperature rise are processed using table lookup and piecewise linearization to become directly applicable to linear programming. Coefficients; specifically, the target span is discretized along the axial direction into a set of candidate positions according to the step size, and a no-release set and an installable window (minimum / maximum spacing rule) are generated by combining the line geometry and hardware coordinates; a "power-surface heat flux density" lookup table relationship is established through bench tests or field calibration, and the local thermal coupling efficiency is estimated by combining factors such as thermally conductive insulating lining, contact state and queue heat transfer coefficient. Based on this, the unit power de-icing contribution of candidate points to the risk section is abstracted into an energy efficiency kernel. If necessary, an influence domain kernel is introduced to characterize the spatial attenuation of heat diffusion. Specifically, in a controllable environment (temperature Under adjustable conditions (temperature, wind speed, contact pressure / fit), the sleeve is given a step power output, and the surface temperature rise and equivalent heat flux are recorded. Multiplicative correction coefficients (obtained from bench tests or field sampling) are given based on factors such as thermally conductive insulating lining, contact state, and queue arrangement. Based on the thermal diffusion and convection carry-away effect along the conductor axis, an influence domain is established where "the contribution decreases with distance." The effective de-icing contribution per unit power of candidate points in each risk segment is allocated, and the resulting allocation matrix (candidate point × risk segment) is the energy efficiency core. In subsequent optimization processes, the decision-making power of candidate points will be... The power is multiplied by the energy efficiency kernel to obtain the ice-melting energy obtained in each risk segment within the target time. This energy is then compared and constrained with the energy requirement (i.e., the equivalent ice-melting energy target) for each risk segment. Simultaneously, the power-magnetic flux density and power-surface temperature rise relationships are piecewise linearized using a lookup table, resulting in matrices and upper bound functions that can be directly embedded into linear programming. Specifically, for each power level, three corresponding relationships are recorded: power and surface heat flux density, power and surface temperature rise, and power and magnetic flux density. Tables are created for several meteorological conditions (temperature / wind speed) and typical contact states. Each curve is approximated by several "breakpoint-straight line segments," forming coefficients that can be directly used by the solver. During optimization, a standard method of "level selection + convex combination" is used to achieve fast interpolation. The piecewise straight lines of "power → surface temperature rise / magnetic flux density" provide upper bound constraints (not exceeding the material temperature rise and magnetic saturation thresholds), which are directly added to the model as linear inequalities, automatically eliminating unsafe levels. In practical applications, input the current meteorological and geometric data → select / correct the lookup table → generate the "energy efficiency kernel matrix + safety upper bound" → solve the MILP to obtain the "installation location and power level".

[0057] Step 140: Input the data obtained in the above steps into the pre-constructed MILP model to solve for the optimal number of sleeves, optimal location, and power level. Specifically, the constructed MILP model uses binary location variables and piecewise linear power variables as decision-making parameters. It constructs constraints including coverage / time, total power limit, minimum installation spacing, no-release zone, upper limit of magnetic flux density, and upper limit of surface temperature rise. The MILP model with the weighted minimum of energy consumption and device quantity as the optimization objective outputs the optimal number, optimal location, and power level. Here, the binary location variable represents whether to place sleeves at candidate points, and the discrete power level variable represents the rated power level selected at that location. Magnetic flux density and surface temperature rise are embedded using a lookup table or piecewise linearization method. The objective function is the weighted minimization of energy consumption and device quantity terms.

[0058] The MILP solver is invoked to solve the MILP model. Reasonable relative / absolute intervals and time limits are set to obtain the optimal or near-optimal solution that satisfies all constraints. The minimum number of sleeves, the optimal spatial location, and the corresponding power level are output. Spatially adjacent site selection points are clustered to form distinctions, and the constructability of the solution is checked a second time.

[0059] Step 150: Generate and output the construction layout drawing and parameter list based on the solution results: Convert the solution results into construction-level documents and digital lists, including location coordinates, numbers, power levels and zone identifiers, installation window and no-release boundary descriptions, and quality control indicators (concentricity, fit, minimum electrical clearance, locking torque, etc.). On-site, under power-off conditions, implement the two-half ring alignment and stepped guide self-centering according to the drawing, and lock the ring with the wedge mechanism to complete the closed-loop magnetic circuit assembly. Verify the baseline file through infrared and electrical parameter checks, and finally archive and accept the data to provide standardized input and traceability basis for subsequent operation, maintenance, and optional online optimization.

[0060] Furthermore, the relationship between the MILP model structure constructed in this application embodiment and the main constraints is as follows: Figure 2As shown, a mixed-integer linear programming layout model is given from five levels: input, variable, constraint, objective, and output. The input layer gathers three types of core information: (1) spatial candidates, namely, candidate positions obtained along the span at fixed step lengths and corresponding no-fire zones, minimum installation spacing, and other construction boundaries; (2) risk requirements, namely, the span is segmented according to meteorological statistics and icing history, and the equivalent melting energy required for each segment within the target melting time is calculated; (3) energy efficiency core, namely, the de-icing contribution of unit power at a certain candidate position to a certain risk segment obtained through bench tests or on-site calibration and gray box thermal model, and the nonlinear relationship between power and magnetic flux density, and power and surface temperature rise is pre-processed by table lookup and piecewise linearization to form a coefficient library that can be directly used for linear programming. The variable layer contains three types of decisions: the location variable of whether to place the sleeve at a certain candidate position, the power level variable of which preset heating level is used for each sleeve, and the coverage slack amount used to ensure feasibility under extreme working conditions. The constraint layer visualizes the physical boundaries of the project: coverage / time constraints require all risk segments to receive sufficient de-icing energy within the target de-icing time; the total power limit restricts the total heat generation allowed within the span; the coupling between site selection and power ensures that power can only be allocated to selected locations and does not exceed their power level limit; no-load zone constraints ensure that equipment is not placed near restricted areas such as fittings; the minimum installation spacing limits the density of adjacent devices through a sliding window; the upper bound of magnetic flux density and the upper bound of surface temperature rise are given by piecewise linearized lookup table functions, thus avoiding early magnetic saturation and hot spots during the solution process. The target layer uses a weighted sum of energy consumption and device quantity as the optimization criterion and imposes penalties on coverage slack variables, pursuing optimal energy efficiency while suppressing over-configuration. The output layer transforms the solution results into engineering-usable deliverables, including the minimum number of devices (optimal number of devices), optimal installation locations, corresponding power level configurations, and zoning schemes formed by spatial proximity clustering. Finally, it generates layout diagrams and parameter lists that can be directly used for uninterrupted power supply construction, realizing a closed-loop process from data modeling to on-site implementation.

[0061] Furthermore, the split-type ferromagnetic material sleeve structure used in the embodiments of this application is as follows: Figure 3As shown, the sleeve comprises two semi-annular cylindrical sections (made of iron-nickel alloy) with axial openings. The two semi-annular cylindrical sections meet at the axial openings to form a closed magnetic circuit. The outer surface is cylindrical, and the inner hole matches the outer diameter of the wire. Specifically, a wedge-shaped limiting / self-locking groove 1, axially extending along the opening end of the cylinder, is located at the joint. The cross-section is wedge-shaped or dovetail-shaped and is used to engage with a matching wedge or eccentric locking component to achieve self-centering, circumferential clamping, and axial limiting during assembly. It also provides stable preload and anti-loosening capability under wind vibration and thermal cycling conditions. This groove is usually arranged along the entire length, with a wedge angle of less than 8°. It can be combined with a backstop structure and a specified locking torque to ensure the fit and concentricity of the mating surfaces, thereby reducing the magnetic gap and improving the continuity of the magnetic circuit. The inner surface of the sleeve is covered with a thermally conductive and electrically insulating liner 2, forming a functional interface between the conductor and the magnet substrate. This maintains electrical isolation while minimizing thermal resistance, facilitating the efficient transfer of hysteresis / eddy current heat generated within the sleeve to the conductor and its icing layer. The liner is preferably made of a material system with high thermal conductivity and high electrical strength (such as alumina or aluminum nitride filled resin, polyimide composite layer, etc.), with a thickness typically in the tens to hundreds of micrometers range. Its volume resistivity and dielectric strength meet the requirements of the charged environment, and its surface density and rounded corners reduce partial discharge and wear. Overall, the wedge-shaped self-locking groove ensures rapid and reliable charged assembly and long-term mechanical stability, while the thermally conductive and electrically insulating liner guarantees dual thermoelectric functions and interface durability. This allows the sleeve to achieve strong coupling and low magnetic gap magnetothermal conversion under the influence of the conductor's power frequency magnetic field, enabling efficient de-icing and anti-re-icing. In practical applications, the two semi-circular cylinders are placed on both sides of the conductor without interrupting power. Only the guide surfaces slide axially and then lock in place within the self-locking groove, ensuring concentricity and fit after closing. This forms a closed magnetic circuit with low leakage flux, low loss, and adjustable parameters, which is used for on-site, controllable electromagnetic heating to melt ice on the line.

[0062] Furthermore, the non-equidistant sleeve arrangement optimized by the joint optimization method proposed in the embodiments of this application is as follows: Figure 4 As shown, ferromagnetic sleeves 4 are installed at non-equidistant intervals along the axial direction of the AC transmission line conductor 3 (such as steel-cored aluminum stranded wire or large-section aluminum stranded wire). The sleeves are two-section semi-annular cylindrical structures with iron-nickel alloy laminations, providing guiding alignment and wedge-shaped self-locking. After assembly, they are electrically isolated from the conductor and the magnetic circuit is closed. Figure 4As can be seen, the sleeves exhibit a locally denser and overall sparser distribution within the span. This non-equidistant placement is determined by the MILP model under constraints such as target de-icing time, magnetic saturation (i.e., upper limit of magnetic flux density), upper limit of surface temperature rise, minimum installation spacing, and no-displacement zones. Priority is given to covering sections with high icing risk or high thermal coupling efficiency, while controlling total power and the number of devices. This output corresponds to the installation coordinates and power level of each sleeve, and can be used to generate a zoning and construction layout list. This facilitates rapid assembly and quality verification under uninterrupted power conditions, thereby achieving the required de-icing capacity with fewer devices and lower energy consumption, and improving overall life-cycle reliability.

[0063] Based on the same technical concept described above, this application also proposes a distributed layout joint optimization device for de-icing ferromagnetic material sleeves, such as... Figure 5 As shown, the joint optimization device 200 includes:

[0064] Acquisition unit 201 is used for data acquisition and modeling. The specific data acquisition and modeling process is as described in the joint optimization method above, and will not be repeated here.

[0065] Demand analysis unit 202 is used for risk segmentation and demand estimation. The specific process of risk segmentation and demand estimation is as described in the joint optimization method above, and will not be repeated here.

[0066] The linearization processing unit 203 is used for energy efficiency kernel calibration and piecewise linearization to generate candidate points. The specific process of energy efficiency kernel calibration and piecewise linearization to generate candidate points is as described in the joint optimization method above, and will not be repeated here.

[0067] The model solving unit 204 is used to input the data acquired by the acquisition unit 201, the demand analysis unit 202, and the linearization processing unit 203 into the pre-built MILP model to solve for the optimal number of sleeves, the optimal position, and the power level. The specific model structure, construction process, and solution process are as described in the joint optimization method above, and will not be repeated here.

[0068] Additionally, output unit 205 is used to generate and output construction layout drawings and parameter lists based on the solution results. The specific process for generating construction layout drawings and parameter lists is as described in the joint optimization method above, and will not be repeated here.

[0069] Based on the same technical concept described above, this application also proposes a distributed layout joint optimization system for de-icing ferromagnetic material sleeves, such as... Figure 6 As shown, the joint optimization system 300 proposed in this application embodiment includes:

[0070] The system comprises an input device 301, an output device 302, a processor A303, and a memory A304; wherein the number of processors A303 and memory A304 can be one or more. Figure 6 The following description uses a processor A303 and a memory A304 as an example. The input device 301, output device 302, processor A303, and memory A304 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0071] Specifically, by calling the operation instructions stored in memory A304, processor A303 executes the following steps:

[0072] Data acquisition and modeling;

[0073] Risk segmentation and demand estimation;

[0074] Energy efficiency kernel calibration and piecewise linearization to generate candidate points;

[0075] The data obtained in the above steps are input into the pre-built MILP model to solve for the optimal number of sleeves, optimal position and power level;

[0076] The construction layout diagram and parameter list are generated and output based on the solution results.

[0077] Optionally, by calling the operation instructions stored in memory A304, processor A303 is also used to execute any of the embodiments in the corresponding examples of the above-described joint optimization method.

[0078] Based on the same technical concept described above, this application also proposes an electronic device, such as... Figure 7 As shown, the electronic device 400 includes: a memory B410, a processor B420, and a computer program A411 stored in the memory B410 and executable on the processor B420. When the processor B420 executes the computer program A411, it performs the following steps:

[0079] Data acquisition and modeling;

[0080] Risk segmentation and demand estimation;

[0081] Energy efficiency kernel calibration and piecewise linearization to generate candidate points;

[0082] The data obtained in the above steps are input into the pre-built MILP model to solve for the optimal number of sleeves, optimal position and power level;

[0083] The construction layout diagram and parameter list are generated and output based on the solution results.

[0084] Optionally, when processor B420 executes computer program A411, it can implement any of the embodiments in the corresponding examples of the above-described joint optimization method.

[0085] It should be noted that the electronic device proposed in this application embodiment is a device used to implement the above-mentioned joint optimization method. Therefore, based on the above-mentioned joint optimization method proposed in this application embodiment, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this application embodiment. Therefore, how the electronic device specifically implements the above-mentioned joint optimization method will not be described in detail here. Any electronic device used by those skilled in the art to implement the above-mentioned joint optimization method is within the scope of protection of this application.

[0086] Based on the same technical concept described above, embodiments of this application also propose a computer-readable storage medium, such as... Figure 8 As shown, the computer-readable storage medium 500 stores a computer program B511, which, when executed by a processor, performs the following steps:

[0087] Data acquisition and modeling;

[0088] Risk segmentation and demand estimation;

[0089] Energy efficiency kernel calibration and piecewise linearization to generate candidate points;

[0090] The data obtained in the above steps are input into the pre-built MILP model to solve for the optimal number of sleeves, optimal position and power level;

[0091] The construction layout diagram and parameter list are generated and output based on the solution results.

[0092] Optionally, when the computer program B511 is executed by the processor, it can implement any of the embodiments corresponding to the above-described joint optimization method.

[0093] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0094] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above description is only a specific embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for joint optimization of distributed layout of de-icing ferromagnetic material sleeves, characterized in that, include: Collect and process geometric data and basic operation and maintenance data of the target span line to determine the engineering physical boundary and safety threshold, and form a computable data model and parameter library; the collected data includes span length, conductor type and diameter, hardware coordinates and no-fire zone boundary, conductor operating current range, historical meteorological statistics and historical icing records. Based on topography, hardware proximity, characteristics of high-incidence icing areas, and meteorological statistics, the entire line is divided into several risk sections along the axial direction, and the equivalent icing energy required for each risk section is estimated based on the target icing time. The target span is discretized into a set of candidate positions by step size. The de-icing contribution of unit power at a candidate position to a certain risk section is obtained. The nonlinear relationship between power and magnetic flux density, and between power and surface temperature rise is processed into coefficients that can be directly entered into linear programming by table lookup and piecewise linearization. The acquired data is input into a pre-built MILP model to solve for the optimal number of sleeves, optimal spatial location, and corresponding power level. The MILP model uses binary location variables and piecewise linear power variables as decision-making parameters, and uses coverage / time, total power limit, minimum installation spacing, no-release zone, upper limit of magnetic flux density, and upper limit of surface temperature rise as constraints. The optimization objective is to minimize the weighted average of energy consumption and device quantity. The output is the optimal number of sleeves, optimal spatial location, and corresponding power level. The solution results are converted into construction-level files and digital unit outputs; the constructed MILP model consists of an input layer, a variable layer, a constraint layer, an objective layer, and an output layer. The input layer gathers three types of information: candidate positions and corresponding construction boundaries obtained along the span at fixed step lengths; risk segment division along the span and calculation of the equivalent melting energy required for each risk segment within the target melting time; and the obtained unit power contribution to the de-icing of a certain risk segment at a certain candidate position, as well as the linearized upper bound of magnetic flux density and surface temperature rise. The variable layer includes two types of decisions: the location variable of whether to place the sleeve at a certain candidate position, and the power level variable of which preset heating level each sleeve adopts; The constraint layer is used to visualize the engineering physical boundaries: coverage / time constraints require all risk sections to receive sufficient de-icing energy within the target de-icing time; the total power upper limit restricts the total heat generation allowed within the span; the coupling between site selection and power ensures that power can only be allocated to selected locations and does not exceed their gear limit; the no-release zone constraint ensures that sleeves are not placed near restricted areas; the minimum installation spacing constraint limits the density of adjacent sleeves through a sliding window; the upper bound of magnetic flux density and the upper bound of surface temperature rise ensure that early magnetic saturation and hot spots are avoided during the solution process; The target layer uses a weighted sum of energy consumption and device quantity as the optimization criterion. The output layer transforms the solution results into engineering-usable deliverables, including the optimal number of sleeves, the optimal spatial position, and the corresponding power level.

2. The method for joint optimization of distributed layout of de-icing ferromagnetic material sleeves according to claim 1, characterized in that, The output layer can also cluster spatially adjacent site selection points to form a partitioning scheme based on spatial proximity clustering.

3. The method for joint optimization of distributed layout of de-icing ferromagnetic material sleeves according to any one of claims 1-2, characterized in that, The aforementioned collection and processing of geometric data and basic operation and maintenance data of the target span line includes: The original data undergoes consistency verification and outlier removal. A unified coordinate system and time reference are established, and engineering physical boundaries and safety thresholds are determined, including the upper limit of total power, minimum installation spacing, upper limit of magnetic flux density, and upper limit of surface temperature rise. Finally, a calculable data model and parameter library are formed.

4. A method for joint optimization of distributed layout of de-icing ferromagnetic material sleeves according to any one of claims 1-2, characterized in that, The estimation of the equivalent melting energy required for each risk segment based on the target melting time includes: Combining the target melting time with the initial ice thickness estimate, the equivalent melting energy required within the target melting time is calculated according to the energy conservation relationship between the latent heat term and the sensible heat term, and segment-level weights are given.

5. A method for joint optimization of distributed layout of de-icing ferromagnetic material sleeves according to any one of claims 1-2, characterized in that, The method of discretizing the target span into a set of candidate locations by step size, and obtaining the de-icing contribution of unit power at a candidate location to a certain risk segment, includes: By combining line geometry data and fitting coordinates, a no-discharge geometry and an installable window are generated. A lookup table relationship between power and surface heat flux density is established through bench testing or linear calibration. The local thermal coupling efficiency is estimated by combining thermally conductive insulating lining, contact state and queue heat transfer coefficient. Based on this, the unit power de-icing contribution of candidate locations to risk sections is abstracted into an energy efficiency core.

6. A method for joint optimization of distributed layout of de-icing ferromagnetic material sleeves according to any one of claims 1-2, characterized in that, The aforementioned process of converting the nonlinear relationships between power and magnetic flux density, and between power and surface temperature rise, into coefficients that can be directly incorporated into linear programming using table lookup and piecewise linearization includes: For each power level, obtain the curves showing the relationship between power and magnetic flux density, and between power and surface temperature rise, and create tables for different weather conditions and typical contact conditions. Each curve is approximated by several straight line segments, forming coefficients that can be directly called by the solver.

7. A method for joint optimization of distributed layout of de-icing ferromagnetic material sleeves according to any one of claims 1-2, characterized in that, The sleeve includes two semi-annular cylindrical sections with axial openings. The two semi-annular cylindrical sections are joined together at the axial openings to form a closed-loop magnetic circuit. The outer surface is cylindrical, and the inner hole matches the outer diameter of the transmission line conductor.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the joint optimization method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the joint optimization method according to any one of claims 1-7.

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