BIM technology-based multi-complex-subject ultrahigh-voltage line parameter calculation method and system

Abstract

The invention discloses a BIM technology-based multi-complex-subject ultrahigh-voltage line parameter calculation method and system, and belongs to the field of power system engineering, and the transmission method comprises the steps: collecting ultrahigh-voltage line design parameters and power transmission line commissioning measured data in a region, building a parameter influence factor table, and constructing a parameter-influence factor incidence matrix; importing data, calling a BIM modeling tool to construct a terrain, a tower footing and a lead trend, fusing geological parameters into a three-dimensional model, and generating a data model for intelligent parameter calculation; in combination with three-dimensional geometry, geological physics and mutual inductance influence, calculating parameters by adopting a classification intelligent algorithm; aHP weight analysis and a BP neural network model are combined, and model errors are automatically corrected. According to the method, the three-dimensional visual line model is constructed by using the BIM technology, key parameters such as the effective length of the wire and the distance between the wires can be accurately measured by combining information such as the wire laying height, topographic relief and tower footing difference, and the calculation precision of resistance, inductance and capacitance is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system engineering, and more specifically, particularly relates to a calculation method and system for ultra-high voltage line parameters of multiple complex subjects based on BIM technology. Background Art

[0002] With the continuous advancement of China's power grid construction, the scale and complexity of ultra-high voltage (330 kV and above) transmission lines have been increasing. In traditional line parameter design and calculation, it mainly relies on two-dimensional drawings and static empirical formulas, which are difficult to fully reflect the diversity and complexity of the line environment. There are significant deficiencies especially in the following aspects: The influence of complex terrain on line geometric parameters has not been fully considered. Currently, most line electrical parameters (such as resistance, inductance, capacitance, etc.) rely on ideal straight lines or simplified models, ignoring the geometric shape changes caused by terrain undulations, tower base height differences, conductor sag, etc. during the actual line erection process, resulting in calculation errors.

[0003] Traditional methods have not introduced environmental factors such as soil dielectric constant, conductivity, humidity, and geological distribution into parameter calculation, resulting in large deviations in parameters such as capacitance and inductance in different regions. In scenarios where multiple circuits share a corridor, double-circuit on the same tower, or adjacent operation, the electromagnetic coupling (mutual inductance) between lines significantly affects the overall parameter calculation, but traditional manual or two-dimensional methods are difficult to dynamically and accurately model the spatial relative position relationship. Currently, parameter calculation mostly uses manual input of design values, cannot be automatically linked with the BIM design model, lacks an intelligent correction and real-time feedback mechanism, and is difficult to meet the requirements of large-scale engineering design and multi-source data fusion. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above or existing problems of the calculation method and system for ultra-high voltage line parameters of multiple complex subjects based on BIM technology, the present invention is proposed.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] The embodiment of the present invention provides a method for calculating parameters of ultra-high voltage lines with multiple complex subjects based on BIM technology, including: collecting the design parameters of ultra-high voltage lines and the actual measured data of the operating transmission lines in the region, establishing a parameter influence factor table, and constructing a parameter-influence factor correlation matrix; importing data, calling a BIM modeling tool to construct the terrain, tower bases, and conductor routes, integrating geological parameters into the three-dimensional model, and generating a data model for intelligent parameter calculation; combining three-dimensional geometry, geological physics, and mutual inductance influence, and using a classified intelligent algorithm to calculate parameters; combining AHP weight analysis and BP neural network model to automatically correct model errors.

[0008] As a preferred solution of the method for calculating parameters of ultra-high voltage lines with multiple complex subjects based on BIM technology according to the present invention, it includes:

[0009] Obtain geological and climate information related to the line operating environment, and use testing equipment to obtain the actual measured data of the resistance value, inductance value, capacitance value, and mutual inductance value of the line; classify all the line parameters to be calculated according to electrical characteristics, analyze the influence factors of each type of parameter in electromagnetic theory, list the influence factors corresponding to each parameter, and attach a description of the function; define all the line parameters to be analyzed as a set, and define all the identified influence factors as another set, construct the corresponding relationship, and introduce the analytic hierarchy process to score the influence intensity between each influence factor and its corresponding parameter.

[0010] As a preferred solution of the method for calculating parameters of ultra-high voltage lines with multiple complex subjects based on BIM technology according to the present invention, it includes:

[0011] Use parametric modeling to draw the tower base entity and associate it with the ground model, construct a complete three-dimensional line channel structure, set data annotation nodes at the coordinate points where the tower bases are located or along the line path of the line, and bind the soil resistivity, moisture content, surface temperature, and geological layer thickness to each node, and embed the data into the BIM model object in the form of attribute fields;

[0012] Export all the line, tower base, conductor structures and their integrated parameters from the BIM model, structure the parameters required for the calculation formula uniformly, and convert each section of the line into a data unit.

[0013] As a preferred solution of the method for calculating parameters of ultra-high voltage lines with multiple complex subjects based on BIM technology according to the present invention, it includes:

[0014] The calculation parameters include line resistance, line inductance, line mutual inductance, line capacitance, the distance between the conductor and the ground, and the load and stability of the tower foundation structure. The calculation method of the line resistance is as follows:

[0015] ,

[0016] where ρ is the resistivity of the conductor material, L is the effective length, and A is the cross-sectional area;

[0017] The calculation method of the single-circuit line inductance is as follows:

[0018] ,

[0019] where μ0 is the magnetic permeability of vacuum, D is the conductor spacing, and r is the conductor radius;

[0020] The calculation method of the multi-circuit line mutual inductance is as follows:

[0021] ,

[0022] where D 12 is the minimum spacing between the two lines, and r1, r2 are the conductor radii of the two lines.

[0023] As a preferred solution of the method for calculating the parameters of the multi-complex-subject extra-high voltage line based on the BIM technology described in the present invention, wherein: combining three-dimensional geometry, geophysics, and mutual inductance influence, and using a classified intelligent algorithm to calculate the parameters, further including:

[0024] The calculation method of the line capacitance is as follows: ,

[0025] where is the vacuum permittivity, is the relative permittivity, D is the conductor spacing, r is the conductor radius,

[0026] The calculation method of the distance between the conductor and the ground is as follows: ,

[0027] where L line is the actual suspension length of the conductor, θ is the suspension angle of the conductor, and h is the ground height or the tower foundation height;

[0028] The calculation method of the load and stability of the tower foundation structure is as follows:

[0029] ,

[0030] where ρ air is the air density, A wind is the wind load area, and v is the wind speed;

[0031] Regression analysis or neural network is used to model each influencing factor to generate a high-precision calculation model; the input variables are extracted according to the BIM model and input into the intelligent algorithm for adaptive optimization.

[0032] As a preferred solution of the multi-complex-subject ultra-high voltage line parameter calculation method based on BIM technology according to the present invention, it includes:

[0033] Construct a judgment matrix to evaluate the contribution of each influencing factor to the parameter error, and output the weight coefficient of each factor as the input of the neural network;

[0034] The learning and training steps of the BP neural network include designing parameters of the input layer, combining geological parameters with environmental parameters, using the measured electrical parameters of the output layer as the target value, and backpropagating the error to automatically correct the weights of the hidden layer.

[0035] As a preferred solution of the multi-complex-subject ultra-high voltage line parameter calculation method based on BIM technology according to the present invention, it includes: constructing a judgment matrix to evaluate the contribution of each influencing factor to the parameter error, and outputting the weight coefficient of each factor as the input of the neural network, including:

[0036] By comparing each influencing factor pairwise, using the scale 1-9 to represent their relative importance, and making pairwise comparisons through experts or data analysis; normalizing the judgment matrix, for the normalized matrix, calculating the average value of each row, verifying the consistency of the judgment matrix by calculating the maximum eigenvalue and consistency index of the judgment matrix, and obtaining the weight coefficients of each influencing factor.

[0037] A multi-complex-subject ultra-high voltage line parameter calculation system based on BIM technology includes: a parameter collection module for collecting ultra-high voltage line design parameters and the measured data of the in-service transmission lines in the region, establishing a parameter influence factor table, and constructing a parameter-influence factor correlation matrix; a three-dimensional channel construction module for importing data, calling the BIM modeling tool to construct the terrain, tower bases and conductor routes, integrating the geological parameters into the three-dimensional model, and generating a data model for intelligent parameter calculation; a parameter calculation module for calculating parameters using a classification intelligent algorithm by combining three-dimensional geometry, geological physics and mutual inductance influence; a model optimization module for automatically correcting the model error by combining AHP weight analysis and BP neural network model.

[0038] The beneficial effects of the present invention are as follows: By using BIM technology to construct a three-dimensional visualization model of the line, and combining information such as the height of the conductor layout, terrain undulation, and differences in tower bases, key parameters such as the effective length of the conductor and the conductor spacing can be accurately measured, significantly improving the calculation accuracy of resistance, inductance, capacitance, etc. It is especially suitable for complex scenarios such as mountainous areas and hilly areas. By introducing geophysical parameters such as soil type, conductivity, dielectric constant, and humidity into the three-dimensional model, the electrical characteristics closely related to the environment such as capacitance and inductance are comprehensively corrected, which is applicable to the dynamic calculation of line parameters in different regions and different seasons. Based on the three-dimensional model, the spatial distance and relative layout relationship between multiple circuits are obtained in real time, and the coupling effect of the circuits is dynamically calculated in combination with the mutual inductance formula, which can accurately evaluate the electromagnetic interference and induced voltage between the circuits and enhance the reliability of system simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0040] Figure 1 It is a flowchart of a method for calculating parameters of a multi-complex-subject ultra-high voltage line based on BIM technology provided by an embodiment of the present invention.

[0041] Figure 2 It is a schematic structural diagram of a system for calculating parameters of a multi-complex-subject ultra-high voltage line based on BIM technology provided by an embodiment of the present invention.

[0042] Figure 3 It schematically shows a structural diagram of a medium according to an embodiment of the present invention.

[0043] Figure 4 It schematically shows a structural diagram of a computing device according to an embodiment of the present invention.

[0044] In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In order to make the above-mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention with reference to the drawings of the specification.

[0046] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0047] Secondly, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure or characteristic that may be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0048] Embodiment

[0049] The following refers to Figure 1 , Figure 1 FIG. is a flowchart of a method for calculating parameters of a multi-complex-subject extra-high voltage line based on BIM technology provided for an embodiment of the present invention. It should be noted that the implementation manner of the present invention can be applied to any applicable scenario.

[0050] Figure 1 The process of the method for calculating parameters of a multi-complex-subject extra-high voltage line based on BIM technology provided for an embodiment of the present invention shown in FIG. includes:

[0051] S1: Collect the design parameters of the extra-high voltage line and the actual measured data of the transmission line in operation in the region (the actual measured data in operation includes the actual measured data before operation and / or in operation), establish a parameter influence factor table, and construct a parameter-influence factor correlation matrix.

[0052] Preferably, obtain the geological and climate information related to the line operation environment, and use the test equipment to obtain the actual measured data of the resistance value, inductance value, capacitance value and mutual inductance value of the line; classify all the line parameters to be calculated according to their electrical characteristics, analyze the influence factors of each type of parameter in electromagnetic theory, list the influence factors corresponding to each parameter, and attach a description of the function; define all the line parameters to be analyzed as a set, and define all the identified influence factors as another set, construct the corresponding relationship, and introduce the analytic hierarchy process to score the influence intensity between each influence factor and its corresponding parameter.

[0053] Furthermore, through the integration of the GIS and BIM systems, obtain the following data:

[0054]

[0055] Use a high-precision electrical parameter tester to perform multiple measurements in spring, summer and winter:

[0056]

[0057] Construct a parameter and influence factor set and a mapping relationship: Define a parameter set: P = {R, L, M, C}, influence factor set:

[0058] F = {f1 = ρ, f2 = L, f3 = A, f4 = T, f5 = D, f6 = r, f7 = μ, f8 = εr , f9 = D 12}; Taking the influence factor of R as an example, construct a 4×4 judgment matrix: ,

[0059] Calculate the maximum eigenvalue λ of the result max ≈4.13, CI = 0.043, RI = 0.90, CR≈0.048 < 0.1, the judgment matrix passes the consistency test, and the normalized weight vector w is obtained R ={0.42, 0.28, 0.13, 0.17},

[0060] For each group of parameters p i The influence factor weight w i constitutes the input vector, and the output is the prediction error Δp of this parameter i = p meas - p calc , and the trainable neural network structure is like: 4-16-8-1 (input 4 factors, output 1 error).

[0061] S2: Import data, call the BIM modeling tool to construct the terrain, tower foundation and conductor alignment, and integrate the geological parameters into the 3D model to generate a data model for intelligent parameter calculation.

[0062] Preferably, use the parametric modeling method to draw the tower foundation entity and associate it with the ground model, construct a complete 3D line channel structure, set data annotation nodes at the coordinate points where the tower foundation is located or along the line path of the line, and bind the soil resistivity, moisture content, surface temperature and geological layer thickness to each node, and embed the data into the BIM model object in the form of attribute fields;

[0063] Export all line, tower foundation, conductor structures and their integrated parameters from the BIM model, structure the parameters required for the calculation formula uniformly, and convert each section of the line into a data unit.

[0064] Furthermore, import the measurement coordinate points, line path files, design drawings, etc. into the modeling platform;

[0065] The ground model is constructed using the elevation DEM model + measured geological profile; use the parametric modeling tool to construct the 3D entity structure of the tower foundation; each tower foundation is anchored to the digital ground model terrain through its spatial coordinates P(x, y, z); establish terrain constraint parameters, and align the bottom surface of the tower foundation with the surface elevation; if the surface slope > 15°, automatically adjust the shape of the tower foundation or set the slope retaining wall parameters; the tower foundation number such as T-001 is bound to the path line segment.

[0066] Furthermore, use the electrical BIM family to generate a spatial curve by using path line segments + parameter control; automatically generate a clearance warning when the wire crosses objects such as houses and water bodies, and set nodes: the position of each tower base; set "marking nodes" every 100 meters along the line or where the geology changes significantly; each node has a unique number, such as N-001, N-002…, and is associated with three-dimensional coordinates. In the BIM platform, each node is an "intelligent data object". Export the structure and parameters, and unify them into a calculation unit. Use the Revit API or IFC export tool to extract the line model structure; each section of the line is defined as a "line data unit"; each unit contains the following structured information:

[0067] {"segment_id": "S-001",

[0068] "start_tower": "T-001",

[0069] "end_tower": "T-002",

[0070] "length": 158.6,

[0071] "avg_height": 24.7,

[0072] "wire_type": "LGJ-400",

[0073] "soil_resistivity": 39.5,

[0074] "water_content": 25.3,

[0075] "surface_temp": 19.1,

[0076] "geo_layer_thickness": 2.8}

[0077] Bind calculation parameters such as resistance, inductance, and capacitance to this unit; unify all fields into the database for the neural network or simulation platform to read; the advantage of each section of the line as a data unit supports segment-based simulation calculations; supports horizontal comparative analysis; facilitates intelligent diagnosis and historical data backtracking.

[0078] S3: Combine three-dimensional geometry, geophysics, and mutual inductance effects, and use a classification-based intelligent algorithm to calculate parameters.

[0079] Preferably, the calculation parameters include line resistance, line inductance, line mutual inductance, line capacitance, the distance between the wire and the ground, and the load and stability of the tower base structure. The calculation method of the line resistance is:

[0080] ,

[0081] where ρ is the resistivity of the wire material, L is the effective length, and A is the cross-sectional area;

[0082] The calculation method for the inductance of a single-circuit line is:

[0083] ,

[0084] where μ0 is the permeability of free space, D is the distance between wires, and r is the radius of the wire;

[0085] The calculation method for the mutual inductance of multi-circuit lines is:

[0086] ,

[0087] where D 12 is the minimum distance between two lines, and r1, r2 are the radii of the wires of the two lines.

[0088] Preferably, the calculation method for the line capacitance is: ,

[0089] where is the permittivity of free space, is the relative permittivity, D is the distance between wires, r is the radius of the wire,

[0090] The calculation method for the distance between the wire and the ground is: ,

[0091] where L line is the actual suspension length of the wire, θ is the suspension angle of the wire, and h is the ground height or the tower base height;

[0092] The calculation method for the load and stability of the tower base structure is:

[0093] ,

[0094] where ρ air is the air density, A wind is the wind load area, and v is the wind speed;

[0095] Regression analysis or neural networks are used to model each influencing factor to generate a high-precision calculation model; the input variables are extracted from the BIM model and input into the intelligent algorithm for adaptive optimization.

[0096] Furthermore, a judgment matrix A of the relative contribution degree of the influencing factors is constructed, and the relative importance is represented by the 1-9 scale method: ,

[0097] The corresponding factors are: ρ, L, A, nj in sequence;

[0098] Through normalization and consistency test, the normalized principal eigenvector is obtained as the weight coefficient vector W of each influencing factor: W R =[0.52, 0.26, 0.14, 0.08], that is, the resistivity of the material has the greatest influence on the resistance (52%), and the number of joints has the least influence (8%).

[0099] Use a multi-layer perceptron (MLP) neural network to model the parameter error. The model structure is as follows:

[0100] Input layer: The collected influencing factors, such as ρ, L, A, nj, Output layer: The target parameter value (such as resistance R). Taking the line segment D1 as an example:

[0101]

[0102] It is transformed into the input vector X = [0.0283, 1200, 400, 2];

[0103] There is an error between the model prediction value and the measured value, and the weights of the input factors need to be adjusted backward; Residual analysis: ,

[0104] If ε exceeds the threshold, the inverse inference module is activated;

[0105] Using the idea of backpropagation, the sensitivity of the feedback error to each factor is used to update the contribution weights of the influencing factors: ,

[0106] where η is the learning rate, is the i-th influencing factor. After each iteration converges, the new weights are used for the next input feature adjustment.

[0107] S4: Combine AHP weight analysis and BP neural network model to automatically correct the model error.

[0108] Preferably, a judgment matrix is constructed to evaluate the contribution of each influencing factor to the parameter error, and the weight coefficients of each factor are output as the input of the neural network;

[0109] The learning and training steps of the BP neural network include designing parameters for the input layer, combining geological parameters with environmental parameters, using the measured electrical parameters of the output layer as the target value, and backpropagating the error to automatically correct the weights of the hidden layer.

[0110] Preferably, by comparing each influencing factor pairwise, using the scale 1-9 to represent their relative importance, and comparing them pairwise through experts or data analysis; normalize the judgment matrix. For the normalized matrix, calculate the average value of each row, verify the consistency of the judgment matrix by calculating the maximum eigenvalue and consistency index of the judgment matrix, and obtain the weight coefficients of each influencing factor.

[0111] Further, based on expert evaluation or statistical methods, a relative importance matrix of impact factors is formed:

[0112] ,

[0113] The eigenvector (weighted average) is obtained by normalizing each column to get the weights of each factor: W R = [0.39, 0.23, 0.12, 0.10, 0.08, 0.08]

[0114] That is, ρ contributes the most to the resistance, accounting for 39%; L is the second, accounting for 23%; the number of joints and soil moisture content are the smallest, each accounting for 8%;

[0115] Input and output vectors:

[0116]

[0117] Among them, all inputs are first normalized or standardized; the output value is the electrical parameter measured by the line test equipment; the input data of the neural network is bound to the data of the tower base, conductor, and coordinate points in the BIM model; each line segment object corresponds to a data unit; the weight vector is embedded in the model attribute field as the input data for electrical simulation and safety assessment.

[0118] Further, the Saaty scale method (1 - 9) is used to compare the impact factors pairwise, and the scale explanations are as follows:

[0119]

[0120] Construct a judgment matrix A (6×6) after expert scoring:

[0121] ,

[0122] Normalize each column of the judgment matrix:

[0123] Column sum = [2.277, 5.383, 11.583, 9.167, 17.5, 22]

[0124] Divide each element in the matrix by the sum of its column to obtain the normalized matrix A norm : ,

[0125] Calculate the average value of each row of the normalized matrix to obtain the weights of each factor W = [w1, w2,..., w6]:

[0126] ,

[0127] The explanatory material resistivity has the highest weight, accounting for 42.09%; the soil moisture content is the lowest, accounting for 4.26%.

[0128] Multiply the judgment matrix A by the weight vector W to obtain a new vector, and then divide each element by the corresponding weight and take the average:

[0129] ,

[0130] The consistency ratio CR, when n = 6, RI≈1.24: CR = 0.00 < 0.10, and the consistency verification passes.

[0131] Input the finally obtained weight as the pre-weight of the influencing factor when each sample is input, or use it as the feature combination weight coding:

[0132]

[0133] After introducing the method of the exemplary embodiment of the present invention, next, refer to Figure 2 To describe a BIM technology-based multi-complex-subject ultra-high voltage line parameter calculation system according to an exemplary embodiment of the present invention, the system includes:

[0134] A parameter collection module, configured to collect ultra-high voltage line design parameters and pre-operation measured data, establish a parameter influencing factor table, and construct a parameter-influencing factor association matrix;

[0135] A three-dimensional channel construction module, configured to import data, call a BIM modeling tool to construct the terrain, tower bases, and conductor routes, and integrate geological parameters into the three-dimensional model to generate a data model for intelligent parameter calculation;

[0136] A parameter calculation module, configured to calculate parameters using a classified intelligent algorithm by combining three-dimensional geometry, geological physics, and mutual inductance effects;

[0137] A model optimization module, configured to automatically correct model errors by combining AHP weight analysis and a BP neural network model.

[0138] After introducing the method and device of the exemplary embodiment of the present invention, next, refer to Figure 3 To describe a computer-readable storage medium according to an exemplary embodiment of the present invention, please refer to Figure 3, which shows that the computer-readable storage medium is an optical disc 30, on which a computer program (i.e., program product) is stored. When the computer program is run by a processor, it will implement the steps described in the above method embodiments. For example, collect the design parameters of the extra-high voltage line and the measured data before commissioning, establish a parameter influence factor table, and construct a parameter-influence factor correlation matrix; import data, call the BIM modeling tool to construct the terrain, tower foundation, and wire alignment, fuse the geological parameters into the three-dimensional model, and generate a data model for intelligent parameter calculation; combine three-dimensional geometry, geological physics, and mutual inductance influence, and use a classified intelligent algorithm to calculate parameters; combine AHP weight analysis and BP neural network model to automatically correct model errors. The specific implementation methods of each step will not be repeated here.

[0139] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other optical and magnetic storage media, which will not be elaborated one by one here.

[0140] After introducing the methods, devices, and media of the exemplary embodiments of the present invention, next, refer to Figure 4 a computing device for calculating the parameters of an extra-high voltage line with multiple complex subjects based on BIM technology according to the exemplary embodiments of the present invention.

[0141] Figure 4 FIG. shows a block diagram of an exemplary computing device 40 suitable for implementing the embodiments of the present invention. The computing device 40 may be a computer system or a server. Figure 4 The shown computing device 40 is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0142] As Figure 4 shown, the components of the computing device 40 may include, but are not limited to: one or more processors or processing units 401, a system memory 402, and a bus 403 connecting different system components (including the system memory 402 and the processing unit 401).

[0143] The computing device 40 typically includes various computer system-readable media. These media can be any available media accessible by the computing device 40, including volatile and non-volatile media, removable and non-removable media.

[0144] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 4021 and / or cache memory 4022. Computing device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 4023 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown in the figure, commonly referred to as a "hard disk drive"). Although not shown in Figure 4 the figure, a disk drive for reading and writing removable non-volatile disks (such as "floppy disks") and an optical disk drive for reading and writing removable non-volatile optical disks (such as CD-ROM, DVD-ROM or other optical media) may be provided. In these cases, each drive may be connected to bus 403 through one or more data media interfaces. System memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0145] A program / utility 4025 having a set (at least one) of program modules 4024 may be stored, for example, in system memory 402, and such program modules 4024 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, and an implementation of a network environment may be included in each or some combination of these examples. Program modules 4024 generally execute the functions and / or methods in the embodiments described in the present invention.

[0146] Computing device 40 may also communicate with one or more external devices 404 (such as a keyboard, pointing device, display, etc.). Such communication may be through an input / output (I / O) interface 405. Also, computing device 40 may further communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 406. As Figure 4 shown, network adapter 406 communicates with other modules (such as processing unit 401, etc.) of computing device 40 through bus 403. It should be understood that although not shown in Figure 4 the figure, other hardware and / or software modules may be used in conjunction with computing device 40.

[0147] The processing unit 401 executes various functional applications and data processing by running the programs stored in the system memory 402. For example, it collects the design parameters of extra-high voltage lines and the measured data before commissioning, establishes a parameter influence factor table, and constructs a parameter-influence factor correlation matrix; imports data, calls the BIM modeling tool to construct the terrain, tower bases, and conductor routes, integrates the geological parameters into the 3D model, and generates a data model for intelligent parameter calculation; combines 3D geometry, geophysics, and mutual inductance effects, and uses classified intelligent algorithms to calculate parameters; combines AHP weight analysis and BP neural network model to automatically correct model errors. The specific implementation methods of each step will not be repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the synchronous escape wiring device based on multi-commodity flow are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present invention, the features and functions of the two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0148] In the description of the present invention, it should be noted that the terms "first", "second", and "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0149] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0150] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. Also, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0151] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] In addition, in each embodiment of the present invention, each functional unit may be integrated into a processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit.

[0153] If the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0154] Finally, it should be noted that the above-described embodiments are only specific implementation manners of the present invention, used to illustrate the technical solutions of the present invention, and are not intended to limit it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the technical field can still modify the technical solutions described in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements for some of the technical features; and these modifications, changes, or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0155] In addition, although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution.

Claims

1. A calculation method for ultra-high voltage line parameters of multiple complex subjects based on BIM technology, characterized in that, Including: Collect the design parameters of ultra - high - voltage lines and the actual measured data of the in - service transmission lines in the region, establish a parameter influence factor table, and construct a parameter - influence factor correlation matrix; Import the data, call the BIM modeling tool to construct the terrain, tower bases, and conductor routes, integrate the geological parameters into the 3D model, and generate a data model for intelligent parameter calculation; Combine three - dimensional geometry, geophysics, and mutual inductance effects, and use classification - type intelligent algorithms to calculate parameters; Combine AHP weight analysis and BP neural network model to automatically correct model errors.

2. The method for calculating parameters of a multi-complex-subject ultra-high voltage line based on BIM technology according to claim 1, wherein The collection of the design parameters of ultra - high - voltage lines and the actual measured data of the in - service transmission lines in the region, establishing a parameter influence factor table, and constructing a parameter - influence factor correlation matrix includes: Obtain the geological and climate information related to the line operation environment, and use test equipment to obtain the actual measured data of the resistance value, inductance value, capacitance value, and mutual inductance value of the line; classify all the line parameters to be calculated according to electrical characteristics. For each type of parameter, analyze its influence factors in electromagnetic theory, list the influence factors corresponding to each parameter, and attach a description of the function; define all the line parameters to be analyzed as a set, and define all the identified influence factors as another set, construct the corresponding relationship, and introduce the analytic hierarchy process to score the influence intensity between each influence factor and its corresponding parameter.

3. The calculation method of ultra-high voltage line parameters for multiple complex entities based on BIM technology according to claim 1, characterized in that, The calling of the BIM modeling tool to construct the terrain, tower bases, and conductor routes, integrating the geological parameters into the 3D model, and generating a data model for intelligent parameter calculation includes: Use parametric modeling to draw the tower - base entities and associate them with the ground model, construct a complete 3D line channel structure, set data - annotation nodes at the coordinate points where the tower bases are located or along the line path of the line, and bind the soil resistivity, moisture content, surface temperature, and geological layer thickness to each node, and embed the data into the BIM model object in the form of attribute fields; Export all the line, tower - base, conductor structures and their fusion parameters from the BIM model, structure the parameters required by the calculation formula uniformly, and convert each section of the line into a data unit.

4. The method for calculating ultra-high voltage line parameters of multiple complex subjects based on BIM technology according to claim 1, wherein, The combination of three - dimensional geometry, geophysics, and mutual inductance effects, and using classification - type intelligent algorithms to calculate parameters includes: The calculated parameters include line resistance, line inductance, line mutual inductance, line capacitance, the distance between the conductor and the ground, and the load and stability of the tower - base structure. The calculation method of the line resistance is: ， Where ρ is the resistivity of the conductor material, L is the effective length, and A is the cross - sectional area; The calculation method of the single - circuit line inductance is: ， Where μ0 is the permeability of free space, D is the conductor spacing, and r is the conductor radius; The calculation method of the multi - circuit line mutual inductance is: ， Among them, D 12 is the minimum distance between two lines, and r1, r2 are the conductor radii of the two lines.

5. The method for calculating the parameters of a multi-complex main body ultra-high voltage line based on the BIM technology according to claim 1 or 4, characterized in that The combination of three - dimensional geometry, geophysics, and mutual inductance effects, and using classification - type intelligent algorithms to calculate parameters also includes: The method for calculating the line capacitance is as follows: , Among them, is the vacuum permittivity, is the relative permittivity, D is the conductor spacing, and r is the conductor radius. The calculation method for the distance between the wire and the ground is as follows: , where L line is the actual suspension length of the wire, θ is the suspension angle of the wire, and h is the ground height or the tower base height; The calculation method of the load and stability of the tower - base structure is: ， Among them, ρ air is the air density, A wind is the wind load area, and v is the wind speed; Use regression analysis or neural network to model each influencing factor to generate a high - precision calculation model; the input variables are extracted from the BIM model and input into the intelligent algorithm for adaptive optimization.

6. The calculation method for ultra-high voltage line parameters of multiple complex subjects based on BIM technology according to claim 1, wherein The combination of AHP weight analysis and BP neural network model to automatically correct model errors includes: Construct a judgment matrix to evaluate the contribution of each influencing factor to the parameter error, and output the weight coefficients of each factor as the input of the neural network; The learning and training steps of the BP neural network include designing parameters for the input layer, combining geological parameters with environmental parameters, using the measured electrical parameters of the output layer as the target value, and performing error backpropagation to automatically correct the weights of the hidden layer.

7. The method for calculating the parameters of a multi-complex-subject extra-high voltage line based on the BIM technology according to claim 6, wherein The construction of the judgment matrix to evaluate the contribution of each influencing factor to the parameter error and output the weight coefficients of each factor as the input of the neural network includes: By comparing each influencing factor pairwise, using the scale 1-9 to represent their relative importance, and making pairwise comparisons through experts or data analysis; normalizing the judgment matrix, for the normalized matrix, calculating the average value of each row, verifying the consistency of the judgment matrix by calculating the maximum eigenvalue and the consistency index of the judgment matrix, and obtaining the weight coefficients of each influencing factor.

8. A multi-complex-subject ultra-high voltage line parameter calculation system based on BIM technology, characterized in that, It includes: A parameter collection module for collecting the design parameters of ultra-high voltage lines and the measured data of the operation of transmission lines in the region, establishing a parameter influence factor table, and constructing a parameter-influence factor correlation matrix; A three-dimensional channel construction module for importing data, calling a BIM modeling tool to construct the terrain, tower bases, and conductor orientations, integrating geological parameters into the three-dimensional model, and generating a data model for intelligent parameter calculation; A parameter calculation module for calculating parameters using a classification intelligent algorithm by combining three-dimensional geometry, geological physics, and mutual inductance effects; A model optimization module for automatically correcting the model error by combining AHP weight analysis and a BP neural network model.

Images