CN118095105A - Airborne cable distributed capacitance obtaining method, device, medium and equipment - Google Patents
Airborne cable distributed capacitance obtaining method, device, medium and equipment Download PDFInfo
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Abstract
The embodiment of the application discloses a method, a device, a medium and equipment for acquiring a distributed capacitance of an airborne cable, which relate to the technical field of simulation of an airborne system.
Description
Technical Field
The application relates to the technical field of airborne system simulation, in particular to a method, a device, a medium and equipment for obtaining an airborne cable distributed capacitance.
Background
Typical cable distribution parameters comprise distribution capacitance, distribution inductance and the like, which are very important in the electromagnetic analysis simulation process of an airborne power supply system, a simulation circuit model is built according to the distribution parameters, electromagnetic transient analysis is further carried out on each system of the aircraft, and the accuracy of modeling and electromagnetic transient process analysis is directly affected by accurate calculation of the distribution parameters. When analysis simulation is performed, the distributed capacitance on the whole cable is required to be calculated, and as the cable cavity and the cable arrangement are continuously changed, and the cable wiring has great randomness, the distributed capacitance value of the same cable is also continuously changed, the calculation process is complex, the calculated amount is huge, the accuracy of obtaining the distributed capacitance is poor, the efficiency is low, and the acquisition level of the airborne cable distributed capacitance is low.
Disclosure of Invention
The application mainly aims to provide a method, a device, a medium and equipment for obtaining the distributed capacitance of an airborne cable, and aims to solve the problem that the level for obtaining the distributed capacitance of the airborne cable is low in the prior art.
In order to achieve the above object, the technical scheme adopted by the embodiment of the application is as follows:
in a first aspect, an embodiment of the present application provides a method for obtaining an airborne cable distributed capacitance, including the following steps:
Obtaining mapping parameters of a target cable; the mapping parameters comprise target transverse position coordinate data of a target cable and target cavity shape data of an airplane to which the target cable belongs;
Obtaining target distributed capacitance data of a target cable according to the mapping relation between the distributed capacitance and the mapping parameters; the mapping relation is established through a training neural network, and training data of the neural network comprise section cable distribution capacitance data of a plurality of different cables, transverse position coordinate data of the different cables and cavity shape data of an aircraft to which the different cables belong.
In a possible implementation manner of the first aspect, before obtaining the target distributed capacitance data of the target cable according to the mapping relation between the distributed capacitance and the mapping parameter, the method further includes:
And taking the distributed capacitance data of the cross section cables of different cables as an output layer of the neural network, and taking the transverse position coordinate data of the different cables and the cavity shape data of the aircraft to which the different cables belong as an input layer of the neural network, so as to train the neural network to establish a mapping relation.
In a possible implementation manner of the first aspect, before obtaining the target distributed capacitance data of the target cable according to the mapping relation between the distributed capacitance and the mapping parameter, the method further includes:
acquiring a plurality of different cables;
Segmenting different cables along the extending direction of the cables to obtain a plurality of cable sections;
and calculating the distributed capacitance of each cable section to obtain section cable distributed capacitance data of a plurality of different cables.
In one possible implementation manner of the first aspect, calculating the distributed capacitance of each cable section includes:
Calculating the distributed capacitance of a single cable on the section of each cable;
according to the potential distribution of the cable positions, the induced charges of the single cable at the positions of other single cables are obtained;
and calculating the distributed capacitance of each cable section according to the induction charges of the single cable at the positions of other single cables.
In one possible implementation manner of the first aspect, calculating a distributed capacitance of a single cable on each cable section includes:
Obtaining potential distribution equation and boundary condition in the cavity section of the aircraft to which each cable section belongs;
solving a potential distribution equation and boundary conditions to obtain potential values on square boundaries; the sides of the square boundary are centered on the axle center of the single cable and coincide with the divided grids;
Obtaining an electric field at the square boundary according to the potential value and the distance between the two square boundaries corresponding to the potential value;
Obtaining charge density according to the electric field at the square boundary, and superposing all the charge densities at the square boundary to obtain induction charges at the square boundary;
and calculating the distributed capacitance of the single cable according to the voltage and the induction charges at the square boundary.
In one possible implementation manner of the first aspect, solving the potential distribution equation and the boundary condition to obtain the potential value on the square boundary includes:
And solving a potential distribution equation and boundary conditions by adopting a finite difference method to obtain potential values on square boundaries.
In a possible implementation manner of the first aspect, the neural network further includes a hidden layer, and before training the neural network to establish the mapping relationship, the method further includes:
establishing input data according to the transverse position coordinate data of different cables and cavity shape data of an aircraft to which the cables belong;
And establishing the transfer function from the input layer to the hidden layer according to the center vector of the transfer function, the variance of the transfer function and the input data.
In a second aspect, an embodiment of the present application provides an airborne cable distributed capacitance obtaining apparatus, including:
The acquisition module is used for acquiring the mapping parameters of the target cable; the mapping parameters comprise target transverse position coordinate data of a target cable and target cavity shape data of an airplane to which the target cable belongs;
The mapping module is used for obtaining target distributed capacitance data of the target cable according to the mapping relation between the distributed capacitance and the mapping parameters; the mapping relation is established through a training neural network, and training data of the neural network comprise section cable distribution capacitance data of a plurality of different cables, transverse position coordinate data of the different cables and cavity shape data of an aircraft to which the different cables belong.
In a third aspect, an embodiment of the present application provides a computer readable storage medium storing a computer program, where the computer program when loaded and executed by a processor implements the method for obtaining an on-board cable distributed capacitance provided in any one of the first aspects.
In a fourth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, where,
The memory is used for storing a computer program;
the processor is configured to load and execute a computer program to cause the electronic device to perform the on-board cable distribution capacitance obtaining method as provided in any one of the first aspects above.
Compared with the prior art, the application has the beneficial effects that:
The embodiment of the application provides a method, a device, a medium and equipment for obtaining the distributed capacitance of an airborne cable, wherein the method comprises the following steps: obtaining mapping parameters of a target cable; the mapping parameters comprise target transverse position coordinate data of a target cable and target cavity shape data of an airplane to which the target cable belongs; obtaining target distributed capacitance data of a target cable according to the mapping relation between the distributed capacitance and the mapping parameters; the mapping relation is established through a training neural network, and training data of the neural network comprise section cable distribution capacitance data of a plurality of different cables, transverse position coordinate data of the different cables and cavity shape data of an aircraft to which the different cables belong. According to the application, the nonlinear corresponding relation between the distributed capacitance and the transverse position coordinate and the shape of the aircraft cavity is solved by a neural network learning mode, a large amount of sample calculation is not needed, the calculated amount can be greatly reduced, the acquisition of the distributed capacitance is not hindered by the cavity shape and the random cable distribution by the learning and prediction capabilities of the neural network, the corresponding and more accurate distributed capacitance can be obtained for any mapping parameter, and the data which are input in the mapping relation are the transverse position coordinate and the aircraft cavity shape are data which are easy to be directly measured in practical application, so that the calculation difficulty can be reduced, the acquisition efficiency is improved, and the acquisition level of the airborne cable distributed capacitance is further improved.
Drawings
FIG. 1 is a schematic diagram of an electronic device in a hardware operating environment according to an embodiment of the present application;
Fig. 2 is a flow chart of a method for obtaining an on-board cable distributed capacitance according to an embodiment of the present application;
Fig. 3 is an application schematic diagram of the method for obtaining the distributed capacitance of the airborne cable provided by the embodiment of the application on a cable;
FIG. 4 is a cross-sectional view of FIG. 3 taken along the direction of extension of the cable;
fig. 5 is a schematic diagram of grid division by a finite difference method in the method for obtaining the distributed capacitance of the on-board cable according to the embodiment of the present application;
FIG. 6 is an enlarged view of a portion of FIG. 5 at the dashed circle;
FIG. 7 is a schematic diagram showing the calculation of the distributed capacitance c ij between the two lines;
Fig. 8 is a schematic diagram of a neural network in the method for obtaining the distributed capacitance of the airborne cable according to the embodiment of the present application;
fig. 9 is a schematic block diagram of an on-board cable distributed capacitance obtaining device according to an embodiment of the present application;
the marks in the figure: 101-processor, 102-communication bus, 103-network interface, 104-user interface, 105-memory.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The main solutions of the embodiments of the present application are: obtaining mapping parameters of a target cable; the mapping parameters comprise target transverse position coordinate data of a target cable and target cavity shape data of an airplane to which the target cable belongs; obtaining target distributed capacitance data of a target cable according to the mapping relation between the distributed capacitance and the mapping parameters; the mapping relation is established through a training neural network, and training data of the neural network comprise section cable distribution capacitance data of a plurality of different cables, transverse position coordinate data of the different cables and cavity shape data of an aircraft to which the different cables belong.
Typical cable distribution parameters comprise distribution capacitance, distribution inductance and the like, the cable distribution parameters are very important in the electromagnetic analysis simulation process of an airborne power supply system, a simulation circuit model is built according to the distribution parameters, electromagnetic transient analysis is further carried out on each system of the aircraft, the accuracy of modeling and electromagnetic transient process analysis is directly affected by accurate calculation of the distribution parameters, and electromagnetic design and inspection of the aircraft are affected.
At present, some problems exist in random cable distributed capacitance calculation in any aircraft cavity, such as a multi-conductor distributed capacitance automatic calculation method based on a finite element method with the application number 201910729179.7, which is only limited to cable distribution with a single section, and the distributed capacitance change on the whole cable needs multiple times of calculation and is complicated in calculation. For another example, a method and a system for calculating the distributed capacitance inside a multi-core cable with application number 201910717967.4 need a capacitance meter to perform auxiliary measurement, and for an airborne cable, the test workload is huge.
The airborne cables are distributed in each cavity of the aircraft, one group of cables often comprises a plurality of wires, the cables are randomly distributed in the cavity of the aircraft, and the wires in the cables are randomly twisted, so that the distributed capacitance is not unique and has huge variation. Two factors that have a greater impact on the calculation of the distributed capacitance of the on-board cable are the shape of the cable cavity and the location of the individual wires in the cable. During installation, the same group of cables can pass through a plurality of cavities of the aircraft, and the shapes of all the cavities are irregular and different, so that the distributed capacitances of the same group of cables in different cavities are greatly different. Meanwhile, the cable wiring has great randomness, all wires of the cable in the same cavity are not fixed in arrangement, all wires in the cable are randomly twisted together, and therefore the distributed capacitance of the cable along the line is different in the same cavity. When analysis simulation is performed, the distributed capacitance on the whole cable is required to be calculated, and as the cable cavity and the cable arrangement are continuously changed, the distributed capacitance value of the same cable is also continuously changed, the calculation process is complex, and the calculation amount is huge.
In order to meet the calculation accuracy, the cables are required to be split into segments with extremely small lengths in the longitudinal direction in a segmentation processing mode, the distributed capacitance on each segment is approximately unique due to the extremely small lengths, and in order to achieve the effect, each group of cables is at least divided into dozens of hundred segments, so that the calculation amount is huge. Because the length is very small, each line segment corresponds to a unique cavity shape, a cable position and a distributed capacitance value, a unique nonlinear corresponding relation exists among the cavity shape, the cable position and the distributed capacitance value, after the corresponding relation is determined, the distributed capacitance can be directly calculated through cavity shape information and cable position information, but obviously, the workload is huge, and high-efficiency acquisition is difficult to realize.
Therefore, the application provides a solution, the nonlinear corresponding relation between the distributed capacitance and the transverse position coordinate and the shape of the aircraft cavity is solved by a neural network learning mode, a large amount of sample calculation is not needed, the calculated amount can be greatly reduced, the acquisition of the distributed capacitance is not hindered by the distribution of the cavity shape and the random cable by the learning and prediction capability of the neural network, the corresponding and more accurate distributed capacitance can be obtained for any mapping parameter, and the input data in the mapping relation is the data which is easily and directly measured in practical application, so that the efficiency of acquisition can be reduced, and the acquisition level of the airborne cable distributed capacitance can be further improved.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an electronic device of a hardware running environment according to an embodiment of the present application, where the electronic device may include: a processor 101, such as a central processing unit (Central Processing Unit, CPU), a communication bus 102, a user interface 104, a network interface 103, a memory 105. Wherein the communication bus 102 is used to enable connected communication between these components. The user interface 104 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 104 may also include standard wired, wireless interfaces. The network interface 103 may alternatively comprise a standard wired interface, a wireless interface, such as a wireless FIdelity (WI-FI) interface. The Memory 105 may alternatively be a storage device independent of the foregoing processor 101, where the Memory 105 may be a high-speed random access Memory (Random Access Memory, RAM) Memory, or may be a stable Non-Volatile Memory (NVM), such as at least one disk Memory; the processor 101 may be a general purpose processor including a central processing unit, a network processor, etc., as well as a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
As shown in fig. 1, the memory 105, which is a storage medium, may include an operating system, a network communication module, a user interface module, and an on-board cable distributed capacitance obtaining device.
In the electronic device shown in fig. 1, the network interface 103 is mainly used for data communication with a network server; the user interface 104 is mainly used for data interaction with a user; the processor 101 and the memory 105 in the present application may be provided in an electronic device, where the electronic device invokes the on-board cable distributed capacitance obtaining device stored in the memory 105 through the processor 101, and executes the on-board cable distributed capacitance obtaining method provided in the embodiment of the present application.
Referring to fig. 2, based on the hardware device of the foregoing embodiment, an embodiment of the present application provides a method for obtaining an on-board cable distributed capacitance, including the following steps:
s00: and taking the distributed capacitance data of the cross section cables of different cables as an output layer of the neural network, and taking the transverse position coordinate data of the different cables and the cavity shape data of the aircraft to which the different cables belong as an input layer of the neural network, so as to train the neural network to establish a mapping relation.
In the implementation process, the nonlinear relation between the cavity shape and the cable position and the distributed capacitance, namely the mapping relation, is solved by utilizing a neural network, and after the corresponding relation exists, the distributed capacitance data can be obtained rapidly and accurately by inputting the cavity shape and the cable position which are easy to measure and obtain. The key point in establishing the mapping relation is that a plurality of accurate distributed capacitances are obtained through calculation to serve as sample data of the training neural network, specifically, before the target distributed capacitance data of the target cable are obtained according to the mapping relation between the distributed capacitances and the mapping parameters, the method further comprises:
acquiring a plurality of different cables;
Segmenting different cables along the extending direction of the cables to obtain a plurality of cable sections;
and calculating the distributed capacitance of each cable section to obtain section cable distributed capacitance data of a plurality of different cables.
In the specific implementation process, the airborne cable shown in fig. 3 is used for illustration, other different cables all adopt the same calculation principle, the cables are firstly segmented along the extending direction of the cables to obtain a plurality of cable sections, the cables in an aircraft cavity are shown in fig. 3, four cable sections are obtained through segmentation, the cables in the example rotate in a clockwise direction for embodying the randomness of cable installation, the rotation angle is random, meanwhile, due to the gravity factor, a certain sag exists in the middle of the cables, the extending direction is the Y-axis direction shown by a coordinate axis, the cross section of the extending direction is shown in fig. 4, seven single cables are contained in one cable, and are marked as l 1、l2…l7, and the distributed capacitance data of the cable sections of different cables are obtained by calculating the distributed capacitance of each cable section.
Specifically, calculating the distributed capacitance for each cable section includes:
Calculating the distributed capacitance of a single cable on the section of each cable;
according to the potential distribution of the cable positions, the induced charges of the single cable at the positions of other single cables are obtained;
and calculating the distributed capacitance of each cable section according to the induction charges of the single cable at the positions of other single cables.
In the implementation process, since a section contains multiple cables, the distributed capacitance of a single cable can be calculated first, taking l 1 as an example, except that voltages corresponding to l 1 are all set to 0, and then the capacitance is calculated through the relationship between the voltages U 1 on the capacitance c and l 1 and the charge Q' on the capacitance c and l 1, namely:
Meanwhile, the potential distribution of other single cable positions at the moment is extracted, the induced charges of l 1 at the other cable positions are calculated, and similarly, the distributed capacitance c ij of each cable section can be obtained according to the following formula:
Further, calculating the distributed capacitance of the single cable on each cable section includes:
Obtaining potential distribution equation and boundary condition in the cavity section of the aircraft to which each cable section belongs;
solving a potential distribution equation and boundary conditions, planning two square boundaries which surround the cable and have different side lengths near the single cable, wherein the sides of the two square boundaries are centered on the axis of the single cable, and the boundaries are coincident with the divided grids to obtain potential values on the two square boundaries.
Obtaining an electric field at a square boundary according to the potential value and the distance between two square boundaries corresponding to the potential value;
Obtaining charge density according to the electric field at the square boundary, and superposing all the charge densities at the square boundary to obtain induction charges at the square boundary;
and calculating the distributed capacitance of the single cable according to the voltage and the induction charges at the square boundary.
In the specific implementation process, the distributed capacitance of a single cable l 1 is calculated, the potential distribution in the section of the aircraft cavity is solved according to a field distribution equation and boundary conditions, the boundary conditions required by calculation are set on the surface of the cavity and the surface of the cable, the potential in the cavity meets the Laplacian equation, the aircraft cavity is grounded, the potential on the boundary of the cavity is 0, the potential exists on the boundary of the surface of the cable, and the value is consistent with the voltage of the cable. The potential distribution equation and boundary conditions in the cavity section are determined by the method:
Wherein, For the potential distribution of the cavity section,/>For potential distribution on the boundary of cavity section,/>For the potential distribution at the boundary of the cable l 1, S v and S 1 are the surface of the cavity of the aircraft and the surface of the cable, respectively, as shown in fig. 3, x and y represent the x coordinate position and the y coordinate position, respectively, and ∂ is a partial differential symbol.
Because the cross section of the cavity is irregular, the solution of the potential distribution equation and the boundary condition can be performed by adopting a finite difference method, as shown in fig. 5, which is a grid division performed by the finite difference method, and fig. 6 is a partial enlarged view of a dotted line circle in fig. 5, wherein the finite difference method is a method for solving a numerical solution of partial differential (or ordinary differential) equation and equation set solution problems, namely solving the differential equation under certain solution conditions, and the solution conditions to be met on the boundary of a space region are called boundary value conditions. And solving the potential distribution equation and the boundary condition by adopting a finite difference method to obtain:
Where k is the number of iterations, i is the number of grid rows, j is the number of grid columns, To accelerate the convergence factor. The divided grid length is/>. Two square boundaries R 1 and R 2 with different side lengths surrounding the cable are planned nearby the cable, the two squares are centered on the axis of the cable, the boundaries are overlapped with the divided grids, and the side lengths are different by 2 times/>. Solving the potential value/>, on the boundary of two squares by the methodAnd/>As shown in fig. 6, the square boundary electric field E is calculated as:
Wherein, The distance between the potentials at the boundaries of the two squares is shown in figure 6. Calculating boundary charge density/> from boundary electric field EAll charge densities on the cavity cross section boundary are superimposed, and then the induction charge Q on the cavity cross section boundary is obtained:
In the method, in the process of the invention, For the dielectric constant of air in the cavity, R is a square boundary, and the charge Q is an induced current generated by the charge Q' on the surface of the cable at the boundary of the cross section of the cavity, and the values of the charge Q and the induced current are consistent and opposite in polarity, namely:
Considering the case of a plurality of individual cables in a group of cables, denoted as l 1、l2…ln, the matrix form of the equation for calculating capacitance described above is expanded to obtain:
In the calculation, the actual voltage value on each single cable l 1、l2…li…ln is not required to be obtained through actual measurement, and only the voltage U 1、U2…Ui…Un on each cable is required to be set to be different values in the calculation. Taking l 1 as an example, all voltages except U 1 are set to 0, by The distributed capacitance of a single cable can be calculated, namely the distributed capacitance c i on the diagonal line in the capacitance matrix in the above type; the potential distribution of other single cable positions at this time is extracted by the double square method, and l 2 is taken as an example, as shown in fig. 7, the distribution capacitance between two wires is calculated, the double squares are R 1 'and R 2', the induced charge Q″ of l 1 at other cable positions is calculated, and the distribution capacitance between two wires can be obtained according to the following formula, namely, all the distribution capacitances c 12、c21…cij…cin、cni except for the diagonal in the capacitance matrix:
after the distributed capacitance of some sample data is accurately calculated, the constructed neural network is divided into an input layer and an output layer as shown in fig. 8, wherein the input layer of the neural network is a cavity shape and a cable transverse position coordinate during training, and the output layer is the distributed capacitance of the cable and can be expressed in a matrix form. The neural network further comprises a hidden layer, and before training the neural network to establish the mapping relationship, the method further comprises:
establishing input data according to the transverse position coordinate data of different cables and cavity shape data of an aircraft to which the cables belong;
And establishing the transfer function from the input layer to the hidden layer according to the center vector of the transfer function, the variance of the transfer function and the input data.
The transfer function f (a j) from the input layer to the hidden layer is:
Where e is the base of the natural logarithmic function, m j is the center vector of the transfer function, For the variance of the transfer function, T is the transposed symbol, a j is the j-th input of the input layer, converted from p, which is the combination of the cavity shape information and the cable lateral position coordinate information, the i-th combination p i can be written as:
Where s i is the i-th cavity shape information and (x i,zi) is the transverse position coordinates of each individual cable in the cable. The output of this network is the distributed capacitance c j:
where h is the number of training samples, β is a weighting coefficient, and can be written as:
where m max is the maximum distance between all center vectors. And converting the output of the output layer into a matrix mode, thus obtaining the distributed capacitance matrix.
After training is completed, other cavity shape information and cable position information are input into the input layer, a specified distributed capacitance can be obtained at the output layer, and the matrix expression of the calculated distributed capacitance C is as follows by taking the cable shown in fig. 4 as an example:
s10: obtaining mapping parameters of a target cable; the mapping parameters comprise target transverse position coordinate data of a target cable and target cavity shape data of an airplane to which the target cable belongs.
S20: obtaining target distributed capacitance data of a target cable according to the mapping relation between the distributed capacitance and the mapping parameters; the mapping relation is established through a training neural network, and training data of the neural network comprise section cable distribution capacitance data of a plurality of different cables, transverse position coordinate data of the different cables and cavity shape data of an aircraft to which the different cables belong.
In the specific implementation process, after the mapping relation between the transverse position coordinates and the plane cavity shape and the distributed capacitance is established through the neural network training in the mode, in the application stage, the cable needing to obtain the distributed capacitance, namely the mapping parameter of the target cable, is obtained through direct measurement, and then the parameter is input into the neural network, so that the target distributed capacitance data can be obtained quickly and accurately according to the mapping relation.
In this embodiment, the nonlinear correspondence between the distributed capacitance and the transverse position coordinate and the shape of the aircraft cavity is solved by the neural network learning mode, so that a large amount of sample calculation is not required, the calculation amount can be greatly reduced, the acquisition of the distributed capacitance is not hindered by the distribution of the cavity shape and the random cable by the learning and prediction capabilities of the neural network, the corresponding and more accurate distributed capacitance can be obtained for any mapping parameter, and the data which are input in the mapping relation are the transverse position coordinate and the shape of the aircraft cavity and are obtained by direct measurement in practical application, so that the acquisition efficiency can be reduced, and the acquisition level of the airborne cable distributed capacitance is further improved.
Referring to fig. 9, based on the same inventive concept as in the previous embodiment, an embodiment of the present application further provides an airborne cable distributed capacitance obtaining apparatus, including:
The acquisition module is used for acquiring the mapping parameters of the target cable; the mapping parameters comprise target transverse position coordinate data of a target cable and target cavity shape data of an airplane to which the target cable belongs;
The mapping module is used for obtaining target distributed capacitance data of the target cable according to the mapping relation between the distributed capacitance and the mapping parameters; the mapping relation is established through a training neural network, and training data of the neural network comprise section cable distribution capacitance data of a plurality of different cables, transverse position coordinate data of the different cables and cavity shape data of an aircraft to which the different cables belong.
It should be understood by those skilled in the art that the division of each module in the embodiment is merely a division of a logic function, and may be fully or partially integrated onto one or more actual carriers in practical application, and the modules may be fully implemented in a form of software called by a processing unit, or may be fully implemented in a form of hardware, or implemented in a form of a combination of software and hardware, and it should be noted that each module in the on-board cable distributed capacitance obtaining apparatus in this embodiment is in one-to-one correspondence with each step in the on-board cable distributed capacitance obtaining method in the foregoing embodiment, so that a specific implementation of this embodiment may refer to an implementation manner of the on-board cable distributed capacitance obtaining method described herein and will not be repeated herein.
Based on the same inventive concept as in the foregoing embodiments, an embodiment of the present application further provides a computer readable storage medium storing a computer program, where the computer program, when loaded and executed by a processor, implements the method for obtaining the distributed capacitance of the on-board cable according to the embodiment of the present application.
Based on the same inventive concept as in the previous embodiments, an embodiment of the present application further provides an electronic device, including a processor and a memory, wherein,
The memory is used for storing a computer program;
the processor is used for loading and executing the computer program so as to enable the electronic equipment to execute the method for obtaining the distributed capacitance of the airborne cable.
In some embodiments, the computer readable storage medium may be FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; but may be a variety of devices including one or any combination of the above memories. The computer may be a variety of computing devices including smart terminals and servers.
In some embodiments, the executable instructions may be in the form of programs, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and they may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.
As an example, executable instructions may, but need not, correspond to files in a file system, may be stored as part of a file that holds other programs or data, such as in one or more scripts in a hypertext markup language (HTML, hyper Text Markup Language) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).
As an example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices located at one site or distributed across multiple sites and interconnected by a communication network.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of embodiments, it will be clear to a person skilled in the art that the above embodiment method may be implemented by means of software plus a necessary general hardware platform, but may of course also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk) comprising instructions for causing a multimedia terminal device (which may be a mobile phone, a computer, a television receiver, or a network device, etc.) to perform the method according to the embodiments of the present application.
In summary, the application provides a method, a device, a medium and equipment for obtaining distributed capacitance of an onboard cable, wherein the method comprises the following steps: obtaining mapping parameters of a target cable; the mapping parameters comprise target transverse position coordinate data of a target cable and target cavity shape data of an airplane to which the target cable belongs; obtaining target distributed capacitance data of a target cable according to the mapping relation between the distributed capacitance and the mapping parameters; the mapping relation is established through a training neural network, and training data of the neural network comprise section cable distribution capacitance data of a plurality of different cables, transverse position coordinate data of the different cables and cavity shape data of an aircraft to which the different cables belong. According to the application, the nonlinear corresponding relation between the distributed capacitance and the transverse position coordinate and the shape of the aircraft cavity is solved by a neural network learning mode, a large amount of sample calculation is not needed, the calculated amount can be greatly reduced, the acquisition of the distributed capacitance is not hindered by the cavity shape and the random cable distribution by the learning and prediction capabilities of the neural network, the corresponding and more accurate distributed capacitance can be obtained for any mapping parameter, and the data which are input in the mapping relation are the transverse position coordinate and the aircraft cavity shape are data which are easy to be directly measured in practical application, so that the calculation difficulty can be reduced, the acquisition efficiency is improved, and the acquisition level of the airborne cable distributed capacitance is further improved.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
Claims (10)
1. The method for obtaining the distributed capacitance of the airborne cable is characterized by comprising the following steps of:
Obtaining mapping parameters of a target cable; the mapping parameters comprise target transverse position coordinate data of the target cable and target cavity shape data of an airplane to which the target cable belongs;
Obtaining target distributed capacitance data of the target cable according to the mapping relation between the distributed capacitance and the mapping parameters; the mapping relation is established through a training neural network, and training data of the neural network comprise section cable distribution capacitance data of a plurality of different cables, transverse position coordinate data of the different cables and cavity shape data of an airplane to which the different cables belong.
2. The method for obtaining the distributed capacitance of the on-board cable according to claim 1, wherein before obtaining the target distributed capacitance data of the target cable according to the mapping relationship between the distributed capacitance and the mapping parameter, the method further comprises:
and taking the section cable distribution capacitance data of different cables as an output layer of the neural network, and taking the transverse position coordinate data of the different cables and the cavity shape data of an airplane to which the different cables belong as an input layer of the neural network, and training the neural network to establish the mapping relation.
3. The method for obtaining the distributed capacitance of the on-board cable according to claim 1, wherein before obtaining the target distributed capacitance data of the target cable according to the mapping relationship between the distributed capacitance and the mapping parameter, the method further comprises:
Acquiring a plurality of different cables;
Segmenting the different cables along the extending direction of the cables to obtain a plurality of cable sections;
and calculating the distributed capacitance of each cable section to obtain section cable distributed capacitance data of a plurality of different cables.
4. The method of claim 3, wherein said calculating the distributed capacitance of each of said cable sections comprises:
calculating the distributed capacitance of a single cable on each cable section;
according to the potential distribution of the cable positions, obtaining the induced charges of the single cable at the positions of other single cables;
and calculating the distributed capacitance of each cable section according to the induction charges of the single cable at the positions of other single cables.
5. The method of claim 4, wherein calculating the distributed capacitance of a single cable on each cable section comprises:
obtaining potential distribution equations and boundary conditions in a cavity section of an aircraft to which each cable section belongs;
Solving the potential distribution equation and the boundary condition to obtain a potential value on a square boundary; the edges of the square boundaries are centered on the axle center of the single cable and coincide with the divided grids;
Obtaining an electric field at the square boundary according to the potential value and the distance between the two square boundaries corresponding to the potential value;
obtaining charge density according to the electric field at the square boundary, and superposing all the charge densities at the square boundary to obtain induced charges at the square boundary;
and calculating the distributed capacitance of the single cable according to the voltage and the induction charges at the square boundaries.
6. The method of claim 5, wherein solving the potential distribution equation and the boundary condition to obtain the potential value on the square boundary comprises:
and solving the potential distribution equation and the boundary condition by adopting a finite difference method to obtain a potential value on a square boundary.
7. The on-board cable distributed capacitance obtaining method according to claim 2, wherein the neural network further comprises a hidden layer, and before the training of the neural network to establish the mapping relationship, the method further comprises:
Establishing input data according to the transverse position coordinate data of the different cables and cavity shape data of an aircraft to which the cables belong;
And establishing the transfer function from the input layer to the hidden layer according to a center vector of the transfer function, variance of the transfer function and the input data.
8. An airborne cable distributed capacitance obtaining device, comprising:
The acquisition module is used for acquiring mapping parameters of the target cable; the mapping parameters comprise target transverse position coordinate data of the target cable and target cavity shape data of an airplane to which the target cable belongs;
The mapping module is used for obtaining target distributed capacitance data of the target cable according to the mapping relation between the distributed capacitance and the mapping parameters; the mapping relation is established through a training neural network, and training data of the neural network comprise section cable distribution capacitance data of a plurality of different cables, transverse position coordinate data of the different cables and cavity shape data of an airplane to which the different cables belong.
9. A computer readable storage medium storing a computer program, wherein the computer program, when loaded and executed by a processor, implements the on-board cable distributed capacitance obtaining method according to any one of claims 1-7.
10. An electronic device comprising a processor and a memory, wherein,
The memory is used for storing a computer program;
the processor is configured to load and execute the computer program to cause the electronic device to perform the on-board cable distribution capacitance obtaining method according to any one of claims 1-7.
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