Icing thickness calculation method fusing big Beidou reasoning model and wire mechanical analysis

Abstract

The embodiment of the invention relates to the technical field of power systems, in particular to an icing thickness calculation method and device fusing a big Beidou reasoning model and wire mechanical analysis and electronic equipment. The method comprises the following steps: collecting Beidou positioning original data and wire inherent parameters of a target power transmission line; inputting the Beidou positioning original data into a preset Beidou reasoning large model, and calling the Beidou reasoning large model to calculate an ionosphere delay error of a region corresponding to the target power transmission line; based on the ionosphere delay error, correcting coordinate information in the Beidou positioning original data; calculating the initial sag of the conductor according to the corrected coordinates; and inputting the initial sag and the inherent parameters of the wire into the icing thickness inversion model, carrying out inversion calculation on the equivalent icing thickness of the target power transmission line through the icing thickness inversion model, and outputting the icing thickness obtained through inversion calculation. The method improves the robustness and precision of icing monitoring in a complex environment, and can guarantee the safe and stable operation of a power grid.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, device and electronic equipment for calculating icing thickness by integrating a BeiDou inference model and conductor mechanics analysis. Background Technology

[0002] Conductor icing poses a significant threat to the safe operation of high-voltage transmission lines. Excessive icing thickness can easily lead to accidents such as ice flashover, line breaks, and tower collapses. Timely and accurate monitoring of icing thickness is a core component of power grid disaster prevention and early warning. Developing a high-precision, interference-resistant method for calculating icing thickness is of great practical significance for ensuring the safety of 500kV and above high-voltage transmission lines and meeting the needs of intelligent disaster prevention in the power grid.

[0003] Currently, there are two main technological approaches to power grid conductor icing monitoring: one relies on static meteorological station data, which indirectly infers icing conditions through environmental data such as temperature, humidity, and wind speed collected by the stations, or uses direct measurement methods such as mechanical measurement and image processing. Traditional monitoring methods are weak against environmental interference, easily affected by factors such as rain, snow, dense fog, and sunlight, resulting in insufficient monitoring accuracy. They are ill-suited to complex outdoor power grid scenarios and cannot meet the demands for intelligent and high-precision power grid monitoring.

[0004] Therefore, how to enhance the anti-interference capability of icing monitoring in complex outdoor environments and achieve high-precision, real-time calculation of icing thickness on high-voltage transmission lines is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide at least one method, device, and electronic equipment for calculating icing thickness that integrates the BeiDou inference model with conductor mechanics analysis, which can improve the robustness and accuracy of icing monitoring in complex environments and ensure the safe and stable operation of the power grid.

[0006] To address the aforementioned technical problems, at least one embodiment of this application provides a method for calculating icing thickness that integrates a large-scale BeiDou inference model with conductor mechanics analysis, comprising: Collect raw BeiDou positioning data and conductor parameters of the target transmission line; The raw BeiDou positioning data is input into a preset BeiDou inference model, and the BeiDou inference model is called to calculate the ionospheric delay error of the area corresponding to the target transmission line. Based on the ionospheric delay error, the coordinate information in the original BeiDou positioning data is corrected to obtain the corrected coordinates. The initial sag of the conductor is calculated based on the corrected coordinates; The initial sag and the inherent parameters of the conductor are input into the ice thickness inversion model. The equivalent ice thickness of the target transmission line is calculated by inverting the ice thickness through the ice thickness inversion model, and the calculated ice thickness is output. The ice thickness inversion model is generated based on the parabolic model and the conductor state equation.

[0007] In one embodiment, the method for calculating icing thickness by integrating the BeiDou inference model with conductor mechanics analysis further includes: Obtain ionospheric correlation parameters; the ionospheric correlation parameters include: ionospheric total electron content matrix, ionospheric disturbance factor, and geomagnetic activity index; The BeiDou positioning raw data is then input into a preset BeiDou inference model, and the BeiDou inference model is called to calculate the ionospheric delay error of the area corresponding to the target transmission line. Specifically, the BeiDou positioning raw data and the ionospheric correlation parameters are input into a preset BeiDou inference model, and the BeiDou inference model is called to calculate the ionospheric delay error of the area corresponding to the target transmission line.

[0008] In one embodiment, before inputting the raw BeiDou positioning data and the ionospheric correlation parameters into a preset BeiDou inference model, the method further includes: Signal data of base stations surrounding the target transmission line are obtained through a nationwide ground-based augmentation system. The total electron content matrix of the ionosphere is supplemented and calibrated using signal data from the surrounding base stations.

[0009] In one embodiment, the method for calculating icing thickness by integrating the BeiDou inference model with conductor mechanics analysis further includes: Acquire conductor surface temperature data and conductor temperature change parameters; the conductor temperature change parameters include: conductor thermal expansion coefficient and conductor sag reference value at a preset standard temperature; Based on the conductor temperature change parameters, calculate the influence of the conductor sag on the conductor surface temperature data. The initial sag is superimposed with the influence quantity to obtain the maximum sag of the conductor; The process of inputting the initial sag and the conductor's inherent parameters into the icing thickness inversion model, calculating the equivalent icing thickness of the target transmission line through the icing thickness inversion model, and outputting the calculated icing thickness specifically involves: inputting the maximum sag of the conductor and the conductor's inherent parameters into the icing thickness inversion model, calculating the equivalent icing thickness of the target transmission line through the icing thickness inversion model, and outputting the calculated icing thickness.

[0010] In one embodiment, the ice thickness inversion model is a coupled equation based on the parabolic model and the real-time conductor state equation; The core coefficients of the coupling equation include: first model coefficients, second model coefficients, third model coefficients, and fourth model coefficients; The first model coefficients are calculated by relating the elastic modulus of the conductor to the corrected horizontal distance between the conductor suspension points; The second model coefficients are determined by the relationship between the conductor cross-sectional area and the ice weight ratio. The third model coefficient is calculated by combining the conductor thermal expansion coefficient, the difference between the preset standard temperature and the conductor surface temperature data; The fourth model coefficient is determined by comprehensively considering the conductor tension, conductor elastic modulus, and conductor sag reference value at the preset standard temperature.

[0011] In one embodiment, the method for calculating icing thickness by integrating the BeiDou inference model with conductor mechanics analysis further includes: Collect wind load data and conductor tension data for the target transmission line; Adjust the coefficients of the second model based on the wind load data; The coefficients of the first model are adjusted based on the conductor tension data.

[0012] In one embodiment, when the target transmission line is a non-uniformly iced line, calculating the initial sag of the conductor based on the corrected coordinates includes: The corrected coordinates are divided into several segments; Fit a segment parabola based on the coordinates within each segment, and calculate the initial sag of the corresponding segment based on the segment parabola; The process involves inputting the initial sag and the conductor's inherent parameters into the icing thickness inversion model, calculating the equivalent icing thickness of the target transmission line using the icing thickness inversion model, and outputting the calculated icing thickness. Specifically: The initial sag and the inherent parameters of the conductor in each section are input into the ice thickness inversion model. The equivalent ice thickness in each section is calculated by inverting the ice thickness inversion model to obtain the ice thickness of each section. The icing thickness of each section is weighted according to its length to obtain the overall equivalent icing thickness of the target transmission line.

[0013] In one embodiment, when the elevation difference of the target transmission line belongs to the standard of a large elevation difference line, before calculating the initial sag of the corresponding section based on the parabola of the section, the method further includes: The fitting parameters of the parabola in the section are adjusted according to the elevation difference correction factor.

[0014] At least one embodiment of this application also provides an ice thickness calculation device that integrates a BeiDou inference model with conductor mechanics analysis, comprising: The data acquisition unit is used to collect the raw BeiDou positioning data of the target transmission line and the inherent parameters of the conductor; The ionospheric delay error calculation unit is used to input the BeiDou positioning raw data into a preset BeiDou inference model and call the BeiDou inference model to calculate the ionospheric delay error of the area corresponding to the target transmission line. The BeiDou positioning correction unit is used to correct the coordinate information in the original BeiDou positioning data based on the ionospheric delay error, so as to obtain the corrected coordinates. A conductor sag calculation unit is used to calculate the initial sag of the conductor based on the corrected coordinates. The icing thickness inversion unit is used to input the initial sag and the inherent parameters of the conductor into the icing thickness inversion model, calculate the equivalent icing thickness of the target transmission line through the icing thickness inversion model, and output the calculated icing thickness; the icing thickness inversion model is generated based on the parabolic model and the conductor state equation.

[0015] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the above-described method for calculating icing thickness by integrating the BeiDou inference big model and conductor mechanics analysis.

[0016] The ice thickness calculation method provided in this application, which integrates the BeiDou inference model and conductor mechanics analysis, first acquires the original BeiDou positioning data and the inherent parameters of the conductor, breaking through the limitations of traditional methods that rely on single data acquisition. Then, it calls the BeiDou inference model to calculate the ionospheric delay error of the corresponding area of ​​the target transmission line based on the acquired original BeiDou positioning data. Relying on the pre-trained BeiDou inference model, it captures the dynamic change law of the ionosphere and identifies real-time interference characteristics, solving the problem that traditional static formulas cannot adapt to the spatiotemporal fluctuations of the ionosphere, and achieving accurate quantification of the ionospheric delay error specific to the target line area. Based on the accurately quantified ionospheric delay error, the original BeiDou positioning data is directionally adjusted, which can improve the accuracy of the original coordinates to the centimeter level. Based on the centimeter-level corrected coordinates, the initial sag and the inherent parameters of the conductor are calculated and input into the ice thickness inversion model for inversion calculation. The ice thickness inversion model uses the coupling logic of the parabolic model and the conductor state equation to relate the geometric relationship between the ice weight and the sag, and also supplements the influence of the conductor's elastic deformation. The accuracy of the output equivalent ice thickness can directly match the power grid operation and maintenance standards.

[0017] This method breaks through the technical barrier of the disconnect between positioning data and mechanical calculation in traditional monitoring by integrating Beidou AI error correction and conductor mechanics analysis across fields. It constructs a closed-loop logic from data acquisition to result output, and the accuracy of ice thickness inversion is significantly better than that of traditional methods. It solves the core pain points of traditional methods, such as insufficient accuracy, poor scene adaptability, and difficulty in engineering implementation. Attached Figure Description

[0018] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0019] Figure 1 This is a flowchart of an embodiment of the present application providing a method for calculating icing thickness that integrates a large BeiDou inference model with conductor mechanics analysis; Figure 2 This is a schematic diagram illustrating the intraday variation of the ionosphere, provided in one embodiment of this application. Figure 3 This is a schematic diagram of the geometric relationship between the sag of a conductor and the coordinates of the suspension point based on the parabolic assumption, provided in one embodiment of this application. Figure 4 This is a schematic diagram of a non-uniform icing segment calculation provided in one embodiment of this application; Figure 5 This is a schematic diagram of an ice thickness calculation device that integrates BeiDou inference large model and conductor mechanical analysis, provided in one embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0021] This invention proposes a method for calculating icing thickness that integrates the BeiDou inference model and conductor mechanics analysis. The implementation details of this method are described below. The following content is only for the convenience of understanding and is not necessary for implementing this solution.

[0022] Example 1:

[0023] The specific process of the icing thickness calculation method integrating the BeiDou inference model and conductor mechanics analysis in this embodiment can be described as follows: Figure 1 As shown, it includes: Step 101: Collect the original BeiDou positioning data and conductor parameters of the target transmission line.

[0024] Through a pre-set data acquisition mechanism, two types of core basic data are obtained: First, for target transmission lines with a clearly defined monitoring range, i.e., specific high-voltage transmission line sections where icing thickness calculations are required, raw observation data of the conductor's spatial position is collected, including but not limited to the raw coordinate information corresponding to the BeiDou satellite signal, signal propagation time difference, and other positioning data that have not undergone error correction. The acquisition method is not limited in this embodiment. For example, data can be collected by BeiDou positioning modules deployed at the conductor suspension point (the connection point between the tower and the conductor) and at preset key points along the span. Second, the inherent parameters of the conductors used in the target transmission line are obtained. Inherent parameters refer to the core physical and mechanical properties that are determined at the time of manufacture or line design and do not change with the environment. In this embodiment, the inherent parameters of the conductors can be configured as one or more of the following: conductor diameter, conductor cross-sectional area, conductor elastic modulus, and conductor thermal expansion coefficient. The parameter type of inherent parameters is not limited in this embodiment. Taking the above data types as examples, the corresponding parameters can be configured according to the needs of subsequent data processing.

[0025] Step 102: Input the raw BeiDou positioning data into the preset BeiDou inference model, and call the BeiDou inference model to calculate the ionospheric delay error of the area corresponding to the target transmission line.

[0026] The ionosphere, as a dynamic region of the Earth's atmosphere affected by solar activity and geomagnetic variations, exhibits significant spatiotemporal fluctuations in its delay effect on BeiDou satellite signals, such as... Figure 2 The diagram shows the intraday variation of the ionosphere. It can be seen that during the active period (11:37:32–15:07:32), the ionospheric delay error fluctuates drastically, with a peak value close to 0.4 meters and a trough value as low as -0.3 meters. Even during the stable period (after 16:07:32), the ionospheric delay error is in the range of -0.1 to 0.1 meters.

[0027] The original BeiDou positioning data is affected by ionospheric delay, resulting in coordinate information deviations. To eliminate ionospheric errors, the original coordinates are corrected to a range that supports high-precision calculations (e.g., centimeter-level) to meet the needs of high-precision icing thickness analysis. In this step, the collected original BeiDou positioning data of the target transmission line is input into a pre-trained BeiDou inference model adapted to the power grid scenario. By calling the model's inference calculation function, the model, based on the ionospheric interference characteristics (such as abnormal signal propagation time and coordinate fluctuation patterns) contained in the input original data and combined with its learned ionospheric change patterns, captures the real-time state of the ionosphere and outputs the ionospheric delay error value directly corresponding to the geographical area where the target transmission line is located. This achieves dynamic quantification of delay errors and avoids the limitations of static correction.

[0028] The BeiDou inference model is an intelligent analysis model optimized for the dynamic disturbance characteristics of the ionosphere. This embodiment does not limit the specific model type; a typical example is the dual-layer long short-term memory-attention mechanism fusion network model. The long short-term memory (LSTM) structure can capture the time-series characteristics of ionospheric delay (such as hourly or minute-level changes), while the attention mechanism can focus on key parameters that significantly affect the error (such as the propagation path of a specific satellite signal and the ionospheric disturbance factor of the target area). Of course, other model types can also be used, which will not be elaborated upon here.

[0029] To adapt to power grid scenarios, the BeiDou inference model needs to incorporate a large amount of ionospheric data from the areas where power grid transmission lines are located (such as ionospheric characteristics of lines at different voltage levels and in different geographical environments (mountains / plains / high latitudes)) to accurately match the actual monitoring scenarios of power grid lines. The specific model training process is not limited in this embodiment and can be referenced from relevant technologies.

[0030] A large-scale BeiDou inference model based on the total electron content (TEC) and the ROTI (Redirected Electron Ingress) of the ionosphere, constructed using a Gaussian model and machine learning algorithm, is shown below: [Text {corrected coordinates} = text {original coordinates}\times\left(1-\frac{\Delta\text{TEC}\cdot\lambda^2}{40.3\cdotf^2}\right)] Where Delta\text{TEC} is the ionospheric residual, lambda is the signal wavelength, and f is the satellite frequency.

[0031] This embodiment uses the above formula as an example only. The model algorithm with other parameter types or coefficients can refer to the explanation in this embodiment, and will not be repeated here.

[0032] Step 103: Based on the ionospheric delay error, the coordinate information in the original BeiDou positioning data is corrected to obtain the corrected coordinates.

[0033] Using the ionospheric delay error of the target transmission line area calculated through the BeiDou inference model as the core correction basis, the system accurately adapts to the ionospheric environment of the line area. Adjustments are made to the original BeiDou positioning data according to a preset quantization correction logic to eliminate the influence of ionospheric disturbances on the positioning signal. The final output is a corrected 3D coordinate system that has undergone error compensation and closely matches the actual spatial position of the conductor. Compared to the uncorrected original BeiDou coordinates, the corrected 3D coordinates use the ionospheric delay error specific to the line area as the correction benchmark. A quantization correction logic is established by combining parameters such as signal wavelength and satellite frequency, ultimately improving the coordinate accuracy to the centimeter level of ±1~3cm. This accuracy ensures that the error in subsequent conductor sag calculations (which require capturing millimeter-level vertical offsets) is controlled within the engineering allowable range, laying a core positional benchmark for icing thickness inversion errors.

[0034] The coordinate correction process can combine the ionospheric delay error of the area corresponding to the target transmission line and the inherent characteristic parameters of the BeiDou positioning signal (such as signal wavelength and satellite frequency) to further convert the ionospheric delay error into a deviation that can be directly applied to the coordinates. The three-dimensional coordinate information in the original BeiDou positioning data is processed according to a preset quantization correction logic (such as adjusting the values ​​of each dimension of the original coordinates in a directional manner through the correlation between signal propagation delay and coordinate deviation). The specific correction processing logic and the algorithm for calculating the deviation are not limited in this embodiment, and the corresponding algorithm configuration can be made according to the actual application scenario.

[0035] Step 104: Calculate the initial sag of the conductor based on the corrected coordinates.

[0036] Ice accumulation on conductors increases their weight, leading to a significant increase in sag. There is a clear mechanical mapping relationship between the two: the greater the ice thickness, the more significant the increase in sag. The initial sag is the basis for quantifying this mapping relationship. In this step, based on the three-dimensional coordinates of the conductor after eliminating ionospheric interference in the early stage, the coordinate data of key points (such as suspension points and midpoints at preset intervals) are extracted through coordinate analysis. Using geometric analysis or curve fitting, the maximum vertical distance of the conductor from the suspension points at both ends to the lowest point within the span is calculated. This distance is the initial sag of the conductor.

[0037] The specific sag calculation algorithm is not limited in this embodiment. Since the static configuration of the conductor under its own gravity is approximately parabolic, a parabolic fitting model can be selected, such as... Figure 3The diagram illustrates the geometric relationship between the sag of a conductor and the coordinates of the suspension point, based on the parabolic assumption. The conductor is suspended parabolically between points A and B, with a horizontal span of l and an initial sag of f at the midpoint C. M The initial tension at the suspension point is σ0.

[0038] Of course, other model algorithms can also be used, and all can be described in the description of this embodiment, which will not be repeated here.

[0039] It should be noted that the initial sag in this step is the basic sag value without taking into account dynamic environmental factors such as temperature and wind load. It mainly reflects the spatial sag characteristics of the conductor under its own weight and initial tension.

[0040] Step 105: Input the initial sag and conductor inherent parameters into the icing thickness inversion model, calculate the equivalent icing thickness of the target transmission line through the icing thickness inversion model, and output the icing thickness obtained from the inversion calculation.

[0041] The calculated initial conductor sag and collected conductor inherent parameters are simultaneously input into a pre-constructed icing thickness inversion model. The icing thickness inversion model, through its built-in algorithm, uses the input initial sag (baseline value) and inherent parameters (basic physical quantities) to inversely deduce the icing weight causing the conductor sag change, and then converts it into an equivalent icing thickness easily applicable in engineering. This means that any non-uniform icing that may actually exist on the conductor is equivalent to a uniformly covered thickness value, and finally outputs the specific value of this equivalent icing thickness as the core data for power grid icing monitoring and early warning.

[0042] The ice thickness inversion model is constructed by coupling a parabolic model with the conductor's equation of state. Traditional indirect ice thickness calculations based on temperature, humidity, and wind speed often have an error exceeding 20%, while purely empirical formulas ignore the differences in actual operating conditions. This step couples the parabolic model with the conductor's equation of state. The parabolic model is used to describe the geometric relationship between the change in conductor weight and the increase in sag after icing, while the conductor's equation of state is used to supplement the influence of conductor elastic deformation on the sag-ice relationship. The two work together to form a complete mechanical inversion logic, which considers both the geometric relationship between ice weight and sag and the influence of conductor elastic deformation. The initial sag input is a measured reference value corrected by Beidou positioning (not the design theoretical value), and the conductor's inherent parameters are the actual values ​​matched with the actual conductor. Finally, the ice thickness inversion error is much lower than that of traditional methods, which can meet the high-precision requirements of 500kV and above high-voltage lines for ice monitoring.

[0043] Based on the above introduction, the ice thickness calculation method integrating the BeiDou inference model and conductor mechanics analysis provided in this embodiment first acquires the original BeiDou positioning data and the inherent parameters of the conductor, breaking through the limitations of traditional methods that rely on single data acquisition. Then, it calls the BeiDou inference model to calculate the ionospheric delay error of the corresponding area of ​​the target transmission line based on the acquired original BeiDou positioning data. Relying on the pre-trained BeiDou inference model, it captures the dynamic change law of the ionosphere and identifies real-time interference characteristics, solving the problem that traditional static formulas cannot adapt to the spatiotemporal fluctuations of the ionosphere, and achieving accurate quantification of the ionospheric delay error specific to the target line area. Based on the accurately quantified ionospheric delay error, the original BeiDou positioning data is directionally adjusted, which can improve the accuracy of the original coordinates to the centimeter level. Based on the centimeter-level corrected coordinates, the initial sag and inherent parameters of the conductor are calculated and input into the ice thickness inversion model for inversion calculation. The ice thickness inversion model uses the coupling logic of the parabolic model and the conductor state equation to relate the geometric relationship between the ice weight and the sag, and also supplements the influence of the conductor's elastic deformation. The accuracy of the output equivalent ice thickness can directly match the power grid operation and maintenance standards.

[0044] This method breaks through the technical barrier of the disconnect between positioning data and mechanical calculation in traditional monitoring by integrating Beidou AI error correction and conductor mechanics analysis across fields. It constructs a closed-loop logic from data acquisition to result output, and the accuracy of ice thickness inversion is significantly better than that of traditional methods. It solves the core pain points of traditional methods, such as insufficient accuracy, poor scene adaptability, and difficulty in engineering implementation.

[0045] Example 2:

[0046] The generation of ionospheric delay error is directly related to electron concentration distribution, disturbance intensity, and geomagnetic environment. Relying solely on the original positioning data is insufficient to fully capture the coupling effect of these influencing factors. To compensate for the limitations of the original BeiDou positioning data in characterizing the ionospheric state, in the above-mentioned method for calculating icing thickness by integrating the BeiDou inference model and conductor mechanics analysis, a step of acquiring and fusing ionospheric correlation parameters can be added in the pre-processing step of calculating ionospheric delay error by calling the BeiDou inference model.

[0047] Specifically, ionospheric correlation parameters are obtained that precisely match the geographical region corresponding to the target transmission line. Ionospheric correlation parameters refer to a set of physical quantities that directly or indirectly reflect the physical state of the ionosphere and are closely related to the propagation errors of satellite positioning signals such as BeiDou. Ionospheric correlation parameters include, but are not limited to: the total electron content matrix of the ionosphere, the ionospheric disturbance factor, and the geomagnetic activity index.

[0048] Among them, the total ionospheric electron content matrix presents the spatial distribution characteristics of electron concentration in the target area of ​​the ionosphere in a rasterized form, which can intuitively reflect the gradient changes in ionospheric electron content within the region; the ionospheric disturbance factor is a parameter characterizing the intensity of short-term fluctuations in ionospheric electron content (such as the ionospheric rate of change index), which can be used to capture the characteristics of sudden ionospheric disturbances caused by solar activity, geomagnetic storms, etc.; the geomagnetic activity index (such as the geomagnetic Kp index) quantitatively reflects the activity level of the geomagnetic field. Since geomagnetic changes directly affect the electron trajectory of the ionosphere, it is a key external correlation parameter for analyzing ionospheric stability. The three types of parameters are standardized in format (unified timestamp and spatial coordinate system) to ensure that they are completely matched with the spatiotemporal dimensions of the original BeiDou positioning data, forming a complete set of ionospheric state data.

[0049] Then step 102 inputs the original BeiDou positioning data into the preset BeiDou inference model and calls the BeiDou inference model to calculate the ionospheric delay error of the area corresponding to the target transmission line. The corresponding adjustment is: input the original BeiDou positioning data and ionospheric correlation parameters into the preset BeiDou inference model and call the BeiDou inference model to calculate the ionospheric delay error of the area corresponding to the target transmission line.

[0050] In this embodiment, ionospheric correlation parameters such as the total electron content matrix, ionospheric disturbance factor, and geomagnetic activity index are obtained and input together with the original BeiDou positioning data into the BeiDou inference model. The integration of multi-dimensional ionospheric correlation parameters enables the model to distinguish error characteristics under different ionospheric environments (such as calm and disturbed periods, high electron concentration areas and low electron concentration areas), avoiding misjudgments due to the lack of key influencing factors. At the same time, the spatial distribution characteristics of the total electron content matrix and the temporal variation law of the geomagnetic activity index can help the model accurately locate local ionospheric anomalies in the target line area, making the output delay error more consistent with the actual ionospheric environment of the line, providing a higher quality error benchmark for subsequent coordinate correction, and further strengthening the accuracy foundation of the entire icing thickness calculation method from the data input level.

[0051] It should be noted that the total electron content matrix obtained is mostly generated based on large-scale spatial observations. Although it can reflect macroscopic distribution characteristics, it is prone to data deviation in local areas where the target transmission line is located (such as mountainous areas, high-altitude areas, and other complex terrain areas). In order to further improve the local authenticity of the ionospheric total electron content matrix data and ensure that the data is highly consistent with the actual ionospheric state of the target line, before inputting the original BeiDou positioning data and ionospheric correlation parameters into the preset BeiDou inference model, the signal data of the base stations around the target transmission line can be obtained through the national ground-based augmentation system. Then, the signal data of the surrounding base stations can be used to supplement and calibrate the ionospheric total electron content matrix.

[0052] By accessing the data service interface of the national ground-based augmentation system, real-time signal data from dedicated reference stations around the target transmission line is obtained. Here, the surrounding base stations specifically refer to the frame reference stations or regional stations deployed within the national ground-based augmentation system. The core signal data includes pseudorange, carrier phase, signal propagation time difference, and precise coordinate information of the base station itself, which are received by the BeiDou satellite. Using these base station signal data as a reference, and combining reference factors such as the geometric relationship between the precise coordinates of the base station and the satellite signal propagation path, the actual ionospheric electron content in the local area around the line is inferred. This is then compared with the corresponding grid values ​​in the previously obtained wide-area ionospheric total electron content matrix to correct local deviations caused by wide-area interpolation in the matrix. Finally, a calibrated ionospheric total electron content matrix that fits the actual environment of the line is generated.

[0053] The reference stations of the national ground-based augmentation system possess professional-grade observation accuracy. Their signal data directly reflects the actual ionospheric characteristics around the target line, effectively correcting systematic errors in the wide-area matrix in local regions. This upgrades the total electron content matrix of the ionosphere from macroscopic adaptation to local precision, avoiding model misjudgments caused by data deviations. Furthermore, the calibrated matrix has stronger spatiotemporal correlation with the original BeiDou positioning data. When both are input into the model, they help establish a more accurate mapping relationship between ionospheric state and positioning error, making the output ionospheric delay error more closely match the actual situation of the line. This provides a more reliable error benchmark for subsequent coordinate correction, further compressing the error space of the entire icing thickness calculation process from the input data source, and enhancing the applicability of the method in complex geographical scenarios. Of course, this step can be omitted; this embodiment does not impose any limitations on it.

[0054] Example 3:

[0055] In addition to the combined effects of its own weight and the weight of ice accumulation, the conductor sag is also affected by temperature changes. Increased temperature causes the conductor to expand and elongate, increasing the sag, while decreased temperature causes the conductor to contract and decrease the sag. To avoid interference from ambient temperature on the sag and resulting in deviations in the ice thickness calculation, the following steps can be further performed based on the above embodiments: Step 106: Obtain the surface temperature data of the conductor and the temperature change parameters of the conductor.

[0056] The change in conductor sag is essentially the result of the combined effects of increased sag due to icing weight and expansion / contraction caused by temperature changes. If the initial sag without temperature correction is directly used for inversion, the increased sag caused by thermal expansion at high temperatures may be misinterpreted as increased icing, or the decreased sag caused by conductor contraction at low temperatures may be misinterpreted as icing melting. This misinterpretation can lead to increased errors in icing thickness calculations, especially in mountainous areas with diurnal temperature variations exceeding 15°C or during seasonal transitions. To avoid this, this step involves collecting the conductor's body temperature and corresponding temperature change data.

[0057] Among them, real-time acquisition of the conductor body temperature can control the interference error of temperature on sag to the millimeter level. Specifically, data can be acquired by a high-precision temperature sensor attached to the surface of the conductor. In this embodiment, the acquisition method is not limited.

[0058] Obtain the temperature change data corresponding to the conductor, including but not limited to: the conductor's coefficient of thermal expansion and the conductor's sag reference value at a preset standard temperature. The conductor's coefficient of thermal expansion reflects the quantitative relationship between temperature change and conductor length expansion / contraction. The preset standard temperature is the reference temperature at which the conductor's mechanical properties are stable, typically taken as 20℃. The sag reference value at the preset standard temperature serves as a reference value free from environmental interference.

[0059] Step 107: Calculate the influence of conductor sag on conductor sag based on conductor temperature change parameters and conductor surface temperature data. Based on the physical principle of thermal expansion and contraction, and according to a pre-set quantization logic, the effect of real-time temperature changes relative to a reference temperature on the sag is calculated. When the temperature is higher than the reference temperature, the effect is positive, increasing the sag; when the temperature is lower than the reference temperature, the effect is negative, decreasing the sag. This embodiment does not limit the specific quantization logic. For better understanding, one method for calculating the effect is: the product of the conductor's thermal expansion coefficient, the reference sag value at the reference temperature, and (the conductor surface temperature data minus the preset reference temperature). Of course, other quantization logics can also be used, all of which are described in this embodiment and will not be elaborated further here.

[0060] Step 108: Superimpose the initial sag with the influence quantity to obtain the maximum sag of the conductor; The temperature influence quantity mentioned above is algebraically superimposed with the initial sag calculated based on the corrected coordinates in the early stage. Finally, the maximum sag of the conductor after removing temperature interference is obtained, which is only related to the conductor's own weight, ice weight and initial tension. This sag is the core input parameter for ice thickness inversion.

[0061] The initial sag and conductor inherent parameters are input into the icing thickness inversion model. The equivalent icing thickness of the target transmission line is calculated through the icing thickness inversion model, and the calculated icing thickness is output. The corresponding adjustment is as follows: the maximum sag and conductor inherent parameters are input into the icing thickness inversion model. The equivalent icing thickness of the target transmission line is calculated through the icing thickness inversion model, and the calculated icing thickness is output.

[0062] Based on the above introduction, this embodiment constructs a precise input system combining real-time conductor surface temperature, conductor-specific thermal expansion coefficient, and standard sag benchmark value. This system avoids adaptation deviations caused by traditional general parameters or ambient temperature. Relying on quantitative logic, it accurately calculates the impact of temperature on sag. The superimposed maximum conductor sag completely eliminates temperature expansion and contraction interference, ensuring that the sag data input to the model only carries mechanical information related to gravity, icing, and initial tension, significantly improving parameter purity. In model application, the maximum conductor sag replaces the initial sag in the inversion, focusing the coupling logic between the parabolic model and the conductor state equation on the core correlation between icing weight and sag increment, significantly reducing system errors caused by non-icing factors. Ultimately, the precise inversion results can be directly connected to engineering standards such as power grid icing early warning and de-icing activation, providing technically rigorous and practically guiding support for intelligent disaster prevention of high-voltage lines.

[0063] Example 4:

[0064] The icing thickness inversion model does not rely on a single model to describe the relationship between conductor icing and sag. Instead, it integrates the geometric characteristics of the parabolic model with the mechanical characteristics of the real-time conductor state equations to form a coupled equation that combines geometric configuration description with mechanical deformation laws. To further enhance the rigor of the model's mechanical logic and the accuracy of parameter adaptation, this embodiment refines the definition of the inversion model's construction system and core parameters. The inherent mechanical properties of the conductor, spatial geometric parameters, environmental dynamic factors, and standard condition parameters are transformed into mathematically quantifiable terms that can be directly invoked by the coupled equations, ensuring that the equations can accurately map the multiphysics mechanism behind the icing-sag relationship.

[0065] Specifically, the core coefficients of the coupled equations include: the first model coefficient, the second model coefficient, the third model coefficient, and the fourth model coefficient. These four core coefficients are the quantitative support carriers of the coupled equations, corresponding to the four key dimensions of conductor mechanical properties, icing load influence, temperature dynamic effects, and standard condition benchmark connection.

[0066] Among them, the first model coefficient focuses on the fundamental relationship between conductor mechanics and spatial geometry. It is calculated by linking the conductor's elastic modulus (reflecting the conductor's inherent ability to resist elastic deformation) with the corrected horizontal distance of the conductor's suspension point (obtained based on BeiDou positioning correction coordinates). This anchors the fundamental mapping relationship between the conductor's mechanical properties and spatial configuration, providing a bottom-level quantitative basis for the equation to describe the conductor's elastic deformation.

[0067] The second model coefficient focuses on the direct impact of icing load. It is determined by the relationship between conductor cross-sectional area (the basic physical quantity of conductor bearing icing load) and ice weight ratio (a quantitative index of the force exerted by the weight of icing on the conductor, which is directly related to the thickness of icing). It accurately quantifies the driving effect of the weight of icing on conductor sag and is the core mathematical expression of the icing factor in the equation.

[0068] The third model coefficient focuses on the regulating effect of temperature dynamics. It is calculated by combining the conductor thermal expansion coefficient (reflecting the inherent relationship between temperature change and conductor expansion and contraction), the preset standard temperature (the reference temperature for stable conductor mechanical properties), and the difference between conductor surface temperature data (a quantitative value of real-time ambient temperature fluctuations). It can dynamically capture the influence of conductor thermal expansion and contraction caused by temperature changes on sag, making up for the shortcomings of traditional models that ignore temperature interference.

[0069] The fourth model coefficient focuses on the mechanical connection between actual working conditions and standard conditions. It is determined by comprehensively considering the conductor tension (stable stress state of conductor under standard conditions), conductor elastic modulus, and conductor sag reference value (stable geometric state of conductor under standard conditions) at the preset standard temperature. This provides a stable mechanical and geometric reference for the equation, ensuring that the sag calculation under actual working conditions is always based on the conductor reference state, and avoiding inversion deviations caused by reference ambiguity.

[0070] The four core coefficients, through a clear physical parameter association logic, quantify the core influencing dimensions such as the inherent properties of the conductor, icing load, temperature effect, and standard condition benchmark, enabling the model to fully map the sag change mechanism under the coupling effect of multiple physical fields, and providing rigorous quantitative support for the accurate inversion of icing thickness.

[0071] Example 5:

[0072] After clarifying the quantitative basis of the core coefficients (first to fourth model coefficients) of the ice thickness inversion coupling equation, before substituting the coefficients into the coupling equation for ice thickness inversion, a multi-physics parameter acquisition and coefficient dynamic adjustment step can be added to further improve the model's adaptability to complex working conditions.

[0073] Furthermore, based on the above embodiment four, wind load data and conductor tension data of the target transmission line can be collected in real time. For example, wind load data including wind speed, wind direction and wind deflection angle calculated therefrom can be collected in real time by wind sensors deployed along the target transmission line, and real-time conductor tension data can be obtained by conductor tension sensors.

[0074] For the core coefficients of the above-mentioned coupling equation, dynamic optimization can be performed according to the preset mechanical correlation logic. Specifically, the second model coefficients can be adjusted according to the wind load data, and the first model coefficients can be adjusted according to the conductor tension data. The adjusted coefficients are then substituted into the coupling equation to carry out the ice thickness inversion.

[0075] Wind load alters the direction of force on the conductor through wind deflection angle, reducing the effective vertical icing load. The second model coefficient, originally determined by the relationship between conductor cross-sectional area and ice weight ratio, is adjusted to match the weakening effect on the effective vertical force on the conductor when wind load increases, considering the wind deflection angle. A larger wind deflection angle results in a lower equivalent proportion of the vertical icing load on the conductor, allowing for a downward adjustment of the second model coefficient. Simultaneously, conductor tension fluctuates in real-time with changes in ice weight and wind load, directly affecting the degree of elastic deformation. Correspondingly, the first model coefficient, originally calculated by the relationship between conductor elastic modulus and the corrected horizontal distance from the suspension point, can be adjusted to match the actual effect of conductor elastic deformation based on the difference between real-time conductor tension data and standard tension, and the change in conductor elastic elongation as tension increases.

[0076] Among them, the ice-weight ratio refers to the core physical quantity that characterizes the weight of ice accretion per unit length and unit cross-sectional area of ​​the conductor. Its essence is to quantify the intensity of the influence of ice load on the conductor's stress.

[0077] In this embodiment, by collecting wind load and tension data and dynamically adjusting the corresponding coefficients, the quantization logic of the coupled equations can be highly matched with the real-time stress state of the conductor. This ensures that the coefficients are always consistent with the actual stress and load state of the conductor, further compressing the inversion error at the parameter level. At the same time, it enables the model to respond in real time to changes in conductor stress under extreme weather conditions (such as strong winds accompanied by icing), avoiding the failure of fixed coefficients under complex working conditions and significantly enhancing the model's adaptability to harsh environments. Moreover, the entire adjustment process requires no manual intervention, resulting in low maintenance costs.

[0078] Example 6:

[0079] During the icing process of transmission lines, non-uniform icing is a common phenomenon in mountainous areas, canyons, and complex meteorological conditions. Its formation is often affected by topographic gradients, local meteorological field differences, and conductor dynamics. For example, in canyon terrain, local airflow vortices can lead to uneven water vapor condensation rates at different locations within the span, resulting in icing distributions that are thick in the middle and thin at both ends, or segmented with alternating thick and thin layers.

[0080] The icing distribution varies significantly within spans of non-uniformly iced transmission lines. Traditional solutions do not address this issue. However, using an overall parabolic fit to the initial sag may mask local icing characteristics. For instance, the sag increment in thick ice sections may be offset by thin ice sections, leading to a large discrepancy between the overall inverted icing thickness and the actual local icing state. This could even prevent the identification of potential thick ice hazard that could cause line breakage. To accurately capture the icing differences in each section and ensure that the inversion results reflect the true local icing state, this embodiment proposes that when the target transmission line is non-uniformly iced, step 104, calculating the initial sag of the conductor based on the corrected coordinates, can be performed as follows: Step 41: Divide the corrected coordinates into several segments.

[0081] Based on the characteristic differences of non-uniform icing, the overall coordinates of the conductor are divided into several continuous sub-regions with relatively uniform internal icing distribution, thus avoiding interference from cross-segment icing differences on sag calculation.

[0082] In this embodiment, no restrictions are placed on the zoning rules. The rules should be as close as possible to the actual icing pattern. The standard can be the vertical offset fluctuation threshold of the corrected coordinates or the preset span segment length. For example, if the vertical coordinate difference between adjacent key points exceeds 5mm, a new segment is defined. Another example is that a segment is defined every 50m along the line span. In this embodiment, only the above segmentation method is used as an example. Other methods can refer to the description in this embodiment.

[0083] Step 42: Fit a segment parabola based on the coordinates within each segment, and calculate the initial sag of the corresponding segment based on the segment parabola.

[0084] First, a specific parabola is fitted for the discrete coordinate points within each section. Even if the icing is not uniform, the static configuration of the conductor under its own gravity is still approximately parabolic. Then, combined with geometric parameters such as the horizontal distance between the conductor suspension points in the section, the parabola equation is substituted into it. For example, the initial sag of the section is calculated as (equivalent load of icing in the section × horizontal distance of the section²) / (8 × initial tension of the section × cos angle of elevation difference of the section). The initial sag of the section is obtained only, reflecting the weight and initial tension of the section itself and not affected by the difference in icing across sections, rather than the average sag of the entire line.

[0085] like Figure 4 The diagram shows a segmented calculation method for non-uniform icing. The conductor extends from suspension point A to B and is divided into several continuous segments (such as C). i-1 To C i C i To C i+1(etc.), the measurement and calculation of each segment follows the principle of local geometric feature extraction: the vertical offset fluctuation of the conductor geometry or the preset length threshold is used as the standard (as shown by adjacent node C in the figure). i The vertical variation between sections can reflect differences in icing distribution. The entire line is divided into several sub-regions to ensure relatively uniform icing distribution within each section. For each section, data is collected at horizontal distances of l. i (Horizontal projection length of the conductor within the section) and vertical elevation difference h i (The elevation difference between the two nodes of the section), combined with the corrected three-dimensional coordinates, fits a section-specific parabola to calculate the initial sag of the section, providing a local geometric benchmark for subsequent icing thickness inversion. During the segmented measurement and calculation process, abnormal coordinate points deviating from the overall trend of the section can be further identified and eliminated to ensure the accuracy of the segmented model. For example, if a node (such as C...)... i If the coordinate data of a node deviates significantly from the horizontal-vertical correlation pattern of other nodes in the same segment, it is identified as an outlier. After removing outliers, the segment parabola is refitted based on the remaining nodes to ensure that the segment geometric model reflects only the morphological changes caused by actual icing, rather than abnormal fluctuations caused by measurement errors or interference factors, thereby ultimately improving the accuracy of segmented icing thickness inversion.

[0086] Step 105 inputs the initial sag and conductor inherent parameters into the icing thickness inversion model, calculates the equivalent icing thickness of the target transmission line using the icing thickness inversion model, and outputs the calculated icing thickness, which is then adjusted accordingly: Step 51: Input the initial sag and conductor inherent parameters of each section into the icing thickness inversion model, and calculate the equivalent icing thickness in each section through the icing thickness inversion model to obtain the icing thickness of each section.

[0087] The initial sag and conductor inherent parameters of each segment are input into the icing thickness inversion model. The equivalent icing thickness of each segment is calculated separately by the model. Since the icing distribution within the segment is relatively uniform when the segment is divided, this one-segment-one-inversion mode can avoid interference from the icing difference across segments on the local calculation and accurately obtain the true icing level of each segment.

[0088] Step 52: Calculate the icing thickness of each section by weighting according to the length of each section to obtain the overall equivalent icing thickness of the target transmission line.

[0089] Using the actual length of each section as the weight, the icing thickness of each section obtained in step 51 is weighted and integrated. One weighted calculation method is: the sum of the products of the icing thickness of each section and the corresponding section length, divided by the sum of the lengths of each section: (icing thickness of section 1 × length of section 1 + icing thickness of section 2 × length of section 2 + ... + icing thickness of section n × length of section n) ÷ total line length. Finally, the overall equivalent icing thickness that can reflect the overall icing state of the line is obtained, which not only preserves the authenticity of local data, but also meets the engineering requirements of overall line stress analysis and disaster prevention early warning.

[0090] This embodiment focuses on overcoming the core pain point that local features of non-uniformly iced lines are masked by overall calculation. It divides the corrected coordinates into several continuous sub-regions, and can transform unevenly iced areas within a span into relatively uniformly iced sections based on differences in icing distribution. This avoids the problem of cross-segment icing differences canceling each other out in traditional overall calculations, and constructs the smallest accurate unit for subsequent local accurate calculations. This ensures that the icing features of each section can be captured independently, especially in key sections such as the windward slope and the middle of the span where thick ice is prone to form, thus preventing the omission of local risk points. At the piecewise fitting level, a dedicated parabola is fitted for each segment and the initial sag is calculated. Compared with fitting a single parabola for the whole, this method can accurately adapt to the real geometric configuration of each segment. The piecewise fitting fits the real distribution of discrete coordinate points in the segment, improving the accuracy of the calculated initial sag to the millimeter level. This completely avoids the local data distortion caused by averaging the sag in the overall fitting, providing a geometric benchmark that is highly matched with the actual local working conditions for the subsequent segment icing thickness inversion. Ultimately, this achieves the dual effect of accurate segment-level icing inversion and accurate local risk location, significantly enhancing the method's adaptability to complex and non-uniform icing scenarios.

[0091] To enhance understanding, this embodiment further introduces a specific calculation algorithm. The implementation methods of other algorithms can be referred to the description in this embodiment, and will not be repeated here.

[0092] In the core calculation step of ice thickness inversion, the relationship between the maximum sag of the conductor and the equivalent ice thickness is first established based on the parabolic model: the quantitative relationship between the maximum sag of the conductor f_M (the maximum sag of the conductor under ice load) and the equivalent ice thickness b is expressed by the formula f_M=frac{(\gamma_g+\gamma_i)l^2}{8\sigma_0\cos\beta}.

[0093] Where gamma_i=frac{0.9\pig}{4S}[(D+2b)^2-D^2]\times10^{-3} is the ice weight ratio, D is the conductor diameter, and S is the cross-sectional area.

[0094] To infer the equivalent icing thickness b from the measurable maximum sag f_M, it is necessary to simultaneously solve the conductor state equations (correcting for the influence of conductor elastic deformation on sag), ultimately obtaining the inverse formula: b=sqrt{C_1f_M+C_2f_M^2+C_3f_M(t-t_{sd})+C_4-g(f_M)}-\frac{D}{2} The coefficients C_1 to C_4 are determined by the conductor's elastic modulus, standard parameters, and geometric parameters corrected by BeiDou positioning. During the calculation process, the ionospheric delay error ΔI needs to be predicted by the BeiDou inference model to ensure that the geometric parameters used for coefficient calculation have centimeter-level accuracy.

[0095] For lines with non-uniform icing (significant differences in icing distribution within a span), a segmented calculation logic is required to obtain the overall equivalent icing thickness: First, the line is divided into n segments according to the icing distribution characteristics, and the icing thickness b_i of each segment is calculated (by inversely deducing from the aforementioned sag-icing thickness correlation model). Then, using the length l_i of each segment as the weight, a weighted average formula is applied: b=frac{\sum_{i=1}^{n}b_il_i}{\sum_{i=1}^{n}l_i} The overall equivalent icing thickness of the target transmission line is calculated to ensure that the results reflect both the actual local icing condition and meet the engineering requirements for the overall stress analysis of the line.

[0096] Example 7:

[0097] Meanwhile, in practical applications of transmission lines, lines with large elevation differences (more than 10% of the span length) where the ratio of the elevation difference of the conductor suspension point to the span length exceeds 10% are widely distributed in complex terrain areas such as mountains and hills. Due to the significant differences in the height of the suspension points, the stress state and geometric configuration of the conductors in these lines will undergo special changes with the elevation difference. Traditional segmented parabolic fitting for non-uniformly iced lines only determines the fitting parameters based on the basic correlation between horizontal and vertical coordinates, without considering the influence of elevation difference on conductor tension distribution and parabolic curvature, and without proposing adaptive solutions for such situations. Directly using traditional methods can easily lead to deviations between the fitted segmented parabolic curve and the actual suspension shape of the conductor. For example, the actual curvature of the conductor between the high and low suspension ends may be greater than the horizontal fitting result, which in turn reduces the accuracy of the initial sag in subsequent calculations and cannot meet the high-precision requirements for sag data in icing thickness inversion.

[0098] To address this, this embodiment further proposes a method for adapting and correcting large elevation differences. Specifically, after dividing the non-uniformly iced line into sections and before fitting the section parabola based on the coordinates within each section, it is determined whether the elevation difference of the target transmission line falls under the standard for large elevation difference lines. If so, the fitting parameters of the section parabola, such as the parabola curvature coefficient and vertex offset, can be adjusted according to a preset elevation difference correction factor (e.g., the elevation difference between the suspension points at both ends of the corresponding section). This ensures that the adjusted fitting parameters can adapt to the actual stress and geometric characteristics of the conductor under large elevation differences. One standard for large elevation difference lines is, for example, a ratio of the elevation difference between the suspension points of a section to the span length of the section > 10%. Of course, other standards can be configured according to the actual application scenario; this embodiment does not limit this.

[0099] By dynamically adjusting parameters through the elevation difference correction factor, the parabola of the section can accurately reflect the suspension configuration of the conductor's non-horizontal projection under large elevation differences, effectively eliminating fitting deviations caused by elevation differences, improving fitting accuracy, and controlling the subsequent initial sag calculation error within millimeters. At the same time, it can expand the scene adaptability, breaking through the limitation of traditional segmented fitting only being applicable to lines with gentle terrain. It can stably adapt to lines with large elevation differences and non-uniform icing in complex terrains such as mountains and hills, covering more actual power grid application scenarios.

[0100] Example 8:

[0101] This embodiment relates to an ice thickness calculation device that integrates a BeiDou inference model with conductor mechanics analysis. A schematic diagram of the device provided in this embodiment is shown below. Figure 5 As shown, it includes: a data acquisition unit 201, an ionospheric delay error calculation unit 202, a BeiDou positioning correction unit 203, a conductor sag calculation unit 204, and an ice thickness inversion unit 205.

[0102] Among them, the data acquisition unit 201 is used to collect the original Beidou positioning data of the target transmission line and the inherent parameters of the conductor; The ionospheric delay error calculation unit 202 is used to input the original BeiDou positioning data into the preset BeiDou inference model and call the BeiDou inference model to calculate the ionospheric delay error of the area corresponding to the target transmission line. The BeiDou positioning correction unit 203 is used to correct the coordinate information in the original BeiDou positioning data based on the ionospheric delay error, so as to obtain the corrected coordinates. The conductor sag calculation unit 204 is used to calculate the initial sag of the conductor based on the corrected coordinates; The icing thickness inversion unit 205 is used to input the initial sag and conductor inherent parameters into the icing thickness inversion model, calculate the equivalent icing thickness of the target transmission line through the icing thickness inversion model, and output the icing thickness calculated by the inversion. The icing thickness inversion model is generated based on the parabolic model and the conductor state equation.

[0103] It should be noted that the contents of the icing thickness calculation device integrating the BeiDou inference big model and the conductor mechanics analysis provided in this embodiment can be referred to in conjunction with the icing thickness calculation method integrating the BeiDou inference big model and the conductor mechanics analysis provided in the above embodiments. The repeated parts will not be repeated in this embodiment.

[0104] The icing thickness calculation device provided in this embodiment, which integrates the BeiDou inference model and conductor mechanics analysis, utilizes the ionospheric delay error calculation unit to call upon the BeiDou inference model. This dynamically quantifies the ionospheric delay error in the target area, and then uses the BeiDou positioning correction unit to achieve high-precision correction of the coordinate information. This provides a centimeter-level spatial reference for subsequent sag calculation, completely avoiding the icing inversion deviation caused by insufficient positioning accuracy in traditional methods. The conductor sag calculation unit uses the corrected coordinates as input to ensure that the initial sag calculation closely matches the actual geometric configuration of the conductor. The icing thickness inversion unit is based on an inversion model constructed from a parabolic model and the conductor's state equation. This model integrates the conductor's geometric shape and mechanical deformation laws, avoiding the shortcomings of a single model that ignores elastic deformation and load correlation, and significantly improving the accuracy of equivalent icing thickness inversion. This device can complete automated high-precision monitoring without manual intervention, meeting the real-time and accuracy requirements of intelligent disaster prevention monitoring in the power grid. It is also adaptable to icing monitoring scenarios for transmission lines of different specifications, providing reliable data support for power grid ice flashover accident early warning and anti-icing operation and maintenance.

[0105] Furthermore, it should be noted that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units do not exist in this embodiment.

[0106] Example 9:

[0107] Another embodiment of this application relates to an electronic device, such as... Figure 6As shown, it includes: at least one processor 301; and a memory 302 communicatively connected to at least one processor 301; wherein the memory 302 stores instructions that can be executed by at least one processor 301, and the instructions are executed by at least one processor 301 to enable at least one processor 301 to perform the steps of the ice thickness calculation method integrating the BeiDou inference big model and the conductor mechanics analysis in the above embodiments.

[0108] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0109] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0110] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for calculating icing thickness that integrates a large-scale BeiDou inference model with conductor mechanics analysis, characterized in that, include: Collect raw BeiDou positioning data and conductor parameters of the target transmission line; The raw BeiDou positioning data is input into a preset BeiDou inference model, and the BeiDou inference model is called to calculate the ionospheric delay error of the area corresponding to the target transmission line. Based on the ionospheric delay error, the coordinate information in the original BeiDou positioning data is corrected to obtain the corrected coordinates. The initial sag of the conductor is calculated based on the corrected coordinates; The initial sag and the inherent parameters of the conductor are input into the ice thickness inversion model. The equivalent ice thickness of the target transmission line is calculated by inverting the ice thickness through the ice thickness inversion model, and the calculated ice thickness is output. The ice thickness inversion model is generated based on the parabolic model and the conductor state equation.

2. The method for calculating icing thickness by integrating the BeiDou inference model with conductor mechanics analysis as described in claim 1, characterized in that, Also includes: Obtain ionospheric correlation parameters; The ionospheric correlation parameters include: the total electron content matrix of the ionosphere, the ionospheric disturbance factor, and the geomagnetic activity index; The BeiDou positioning raw data is then input into a preset BeiDou inference model, and the BeiDou inference model is called to calculate the ionospheric delay error of the area corresponding to the target transmission line. Specifically, the BeiDou positioning raw data and the ionospheric correlation parameters are input into a preset BeiDou inference model, and the BeiDou inference model is called to calculate the ionospheric delay error of the area corresponding to the target transmission line.

3. The method for calculating icing thickness by integrating the BeiDou inference model with conductor mechanics analysis as described in claim 2, is characterized in that... Before inputting the raw BeiDou positioning data and the ionospheric correlation parameters into the preset BeiDou inference model, the following steps are also included: Signal data of base stations surrounding the target transmission line are obtained through a nationwide ground-based augmentation system. The total electron content matrix of the ionosphere is supplemented and calibrated using signal data from the surrounding base stations.

4. The method for calculating icing thickness by integrating the BeiDou inference model with conductor mechanics analysis as described in claim 1, characterized in that, Also includes: Acquire conductor surface temperature data and conductor temperature change parameters; The conductor temperature change parameters include: the conductor thermal expansion coefficient and the conductor sag reference value at a preset standard temperature; Based on the conductor temperature change parameters, calculate the influence of the conductor sag on the conductor surface temperature data. The initial sag is superimposed with the influence quantity to obtain the maximum sag of the conductor; The process of inputting the initial sag and the conductor's inherent parameters into the icing thickness inversion model, calculating the equivalent icing thickness of the target transmission line through the icing thickness inversion model, and outputting the calculated icing thickness specifically involves: inputting the maximum sag of the conductor and the conductor's inherent parameters into the icing thickness inversion model, calculating the equivalent icing thickness of the target transmission line through the icing thickness inversion model, and outputting the calculated icing thickness.

5. The method for calculating icing thickness by integrating the BeiDou inference model with conductor mechanics analysis according to claim 4, characterized in that, The ice thickness inversion model is a coupled equation based on the parabolic model and the real-time conductor state equation; The core coefficients of the coupling equation include: first model coefficients, second model coefficients, third model coefficients, and fourth model coefficients; The first model coefficients are calculated by relating the elastic modulus of the conductor to the corrected horizontal distance between the conductor suspension points; The second model coefficients are determined by the relationship between the conductor cross-sectional area and the ice weight ratio. The third model coefficient is calculated by combining the conductor thermal expansion coefficient, the difference between the preset standard temperature and the conductor surface temperature data; The fourth model coefficient is determined by comprehensively considering the conductor tension, conductor elastic modulus, and conductor sag reference value at the preset standard temperature.

6. The method for calculating icing thickness by integrating the BeiDou inference model with conductor mechanics analysis as described in claim 5, is characterized in that... Also includes: Collect wind load data and conductor tension data for the target transmission line; Adjust the coefficients of the second model based on the wind load data; The coefficients of the first model are adjusted based on the conductor tension data.

7. The method for calculating icing thickness by integrating the BeiDou inference model with conductor mechanics analysis according to claim 1, characterized in that, When the target transmission line is a non-uniformly iced line, the initial sag of the conductor is calculated based on the corrected coordinates, including: The corrected coordinates are divided into several segments; Fit a segment parabola based on the coordinates within each segment, and calculate the initial sag of the corresponding segment based on the segment parabola; The process involves inputting the initial sag and the conductor's inherent parameters into the icing thickness inversion model, calculating the equivalent icing thickness of the target transmission line using the icing thickness inversion model, and outputting the calculated icing thickness. Specifically: The initial sag and the inherent parameters of the conductor in each section are input into the ice thickness inversion model. The equivalent ice thickness in each section is calculated by inverting the ice thickness inversion model to obtain the ice thickness of each section. The icing thickness of each section is weighted according to its length to obtain the overall equivalent icing thickness of the target transmission line.

8. The method for calculating icing thickness by integrating the BeiDou inference model with conductor mechanics analysis according to claim 7, characterized in that, When the elevation difference of the target transmission line belongs to the standard of a large elevation difference line, before calculating the initial sag of the corresponding section based on the parabola of the section, the method further includes: The fitting parameters of the parabola in the section are adjusted according to the elevation difference correction factor.

9. A device for calculating icing thickness that integrates a large-scale BeiDou inference model with conductor mechanics analysis, characterized in that, include: The data acquisition unit is used to collect the raw BeiDou positioning data of the target transmission line and the inherent parameters of the conductor; The ionospheric delay error calculation unit is used to input the BeiDou positioning raw data into a preset BeiDou inference model and call the BeiDou inference model to calculate the ionospheric delay error of the area corresponding to the target transmission line. The BeiDou positioning correction unit is used to correct the coordinate information in the original BeiDou positioning data based on the ionospheric delay error, so as to obtain the corrected coordinates. A conductor sag calculation unit is used to calculate the initial sag of the conductor based on the corrected coordinates. The icing thickness inversion unit is used to input the initial sag and the inherent parameters of the conductor into the icing thickness inversion model, calculate the equivalent icing thickness of the target transmission line through the icing thickness inversion model, and output the calculated icing thickness; the icing thickness inversion model is generated based on the parabolic model and the conductor state equation.

10. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the ice thickness calculation method that integrates the BeiDou inference big model and conductor mechanical analysis as described in any one of claims 1 to 8.

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