CN116087659A - Data processing method, device, electronic equipment and storage medium - Google Patents
Data processing method, device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a data processing method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring at least one set of cable geometric parameters corresponding to at least two cables; the cable geometric parameters comprise cable spacing, distance between each cable and a reference conductor and cable radius; inputting at least one group of cable geometric parameters into a crosstalk parameter prediction model which is trained in advance, and obtaining a crosstalk parameter prediction result corresponding to each cable; a probability distribution curve corresponding to each crosstalk parameter is determined to determine a target cable spacing of at least two cables at respective cable radii and a target distance from a reference conductor based on the distribution curve. The technical scheme of the embodiment of the invention realizes the effect of effectively improving the calculation efficiency while ensuring the calculation precision.
Description
Technical Field
The present invention relates to the field of electrical systems, and in particular, to a data processing method, apparatus, electronic device, and storage medium.
Background
Crosstalk is electromagnetic interference generated by interaction of electromagnetic fields among cables in various electrical systems, and can reduce the safety of the system when severe, so that the system cannot work normally. Due to the production and manufacturing process of the cables or displacement of the electrical system in which the cables are located, the radius of the cables, the height of the cables to the ground and the relative distance between the cables are changed, and crosstalk is further changed. As an important prediction target of electromagnetic compatibility design of an electrical system, in order to ensure good electromagnetic compatibility performance of the system, cable crosstalk simulation test has become one of the most important problems in the field of electromagnetic compatibility.
Currently, the quantization analysis of the uncertainty of the cable crosstalk is generally to model the cable crosstalk through a transmission line theory, and predict the probability distribution of the induced voltage or the induced current at the near end or the far end of the cable crosstalk by adopting a Monte Carlo Method (MC).
However, since the MC method is a result calculated based on a large amount of random sample data, it has a relatively high calculation cost, resulting in low calculation efficiency for quantifying the cable crosstalk uncertainty.
Disclosure of Invention
The invention provides a data processing method, a data processing device, electronic equipment and a storage medium, so as to achieve the effect of effectively improving the calculation efficiency while ensuring the calculation precision.
According to an aspect of the present invention, there is provided a data processing method comprising:
acquiring at least one set of cable geometric parameters corresponding to at least two cables; the cable geometric parameters comprise cable spacing, distance between each cable and a reference conductor and cable radius;
inputting the at least one group of cable geometric parameters into a crosstalk parameter prediction model which is trained in advance, and obtaining a crosstalk parameter prediction result corresponding to each cable; the crosstalk parameter prediction model is obtained by training based on the geometric parameters of the sample cable and corresponding crosstalk parameter labeling information; the crosstalk parameter prediction result comprises crosstalk parameters corresponding to each group of cable geometric parameters; the crosstalk parameter is at least one of a cable near-end induced current, a cable near-end induced voltage, a cable far-end induced current and a cable far-end induced voltage;
a probability distribution curve corresponding to the crosstalk parameter prediction result is determined to determine a target cable spacing of the at least two cables at respective cable radii and a target distance from a reference conductor based on the probability distribution curve.
According to another aspect of the present invention, there is provided a data processing apparatus comprising:
the cable geometric parameter acquisition module is used for acquiring at least one group of cable geometric parameters corresponding to at least two cables; the cable geometric parameters comprise cable spacing, distance between each cable and a reference conductor and cable radius;
the crosstalk parameter prediction result determining module is used for inputting the at least one group of cable geometric parameters into a crosstalk parameter prediction model which is trained in advance to obtain a crosstalk parameter prediction result corresponding to each cable; the crosstalk parameter prediction model is obtained by training based on the geometric parameters of the sample cable and corresponding crosstalk parameter labeling information; the crosstalk parameter prediction result comprises crosstalk parameters corresponding to each group of cable geometric parameters; the crosstalk parameter is at least one of a cable near-end induced current, a cable near-end induced voltage, a cable far-end induced current and a cable far-end induced voltage;
and the probability distribution curve determining module is used for determining a probability distribution curve corresponding to the crosstalk parameter prediction result so as to determine a target cable distance between the at least two cables and a reference conductor under the corresponding cable radius based on the probability distribution curve.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data processing method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a data processing method according to any one of the embodiments of the present invention.
According to the technical scheme, at least one group of cable geometric parameters corresponding to at least two cables are obtained, then the at least one group of cable geometric parameters are input into a crosstalk parameter prediction model which is trained in advance, a crosstalk parameter prediction result corresponding to each cable is obtained, further, a probability distribution curve corresponding to the crosstalk parameter prediction result is determined, the target cable distance between at least two cables under the corresponding cable radius and the target distance between the at least two cables and a reference conductor are determined based on the probability distribution curve, the problems that in the prior art, calculation analysis is carried out based on a large amount of random sample data, the calculation cost is quite high, the calculation efficiency of cable crosstalk uncertainty quantification is low and the like are solved, and the effect that the calculation efficiency can be effectively improved while the calculation accuracy is ensured is achieved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a data processing method according to a first embodiment of the present invention;
FIG. 2 is a schematic illustration of a three-conductor model provided in accordance with a first embodiment of the present invention;
FIG. 3 is a graph showing probability distribution curve comparison analysis of a near-end induced current at 700 MHz according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a data processing apparatus according to a second embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing a data processing method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a data processing method according to a first embodiment of the present invention, where the method may be implemented by a data processing device, and the data processing device may be implemented in hardware and/or software, and the data processing device may be configured in a terminal and/or a server. As shown in fig. 1, the method includes:
s110, at least one group of cable geometric parameters corresponding to at least two cables are acquired.
It should be noted that, when the crosstalk problem is generated by the electromagnetic field interaction between cables in the electrical system, the technical solution provided in the present embodiment may be applied to a scenario in which the cable related geometric parameters that cause the crosstalk problem cannot be quantitatively analyzed, that is, the influence of the cable related geometric parameters on the cable crosstalk problem may be determined through numerical analysis based on the technical solution, so that the arrangement position of the cables may be adjusted to reduce electromagnetic interference between the cables.
In this embodiment, the cable is a generic term for optical cables, electric cables, and other objects, and may be used for controlling installation, connecting devices, transmitting power, and the like. The cable geometry parameters may be parameters characterizing the cable geometry. Optionally, the cable geometric parameters include cable spacing, a distance between each cable and the reference conductor, and cable radius. The cable distance may be a distance between any two cables. The reference conductor may be a conductor that only serves as a reference in the multi-conductor model. As shown in fig. 2, the three-conductor model is illustrated, wherein the three conductors are a cable 1, a cable 2 and a ground, the ground is a reference conductor, and L is the length of the cable and is 2m; r is the radius of the cable and obeys normal distribution with the average value of 0.0004m and the standard deviation of 0.0001 m; h1 and h2 are the heights of the cables to the ground and are respectively subjected to uniform distribution of [0.02m,0.025m ]; d is the lateral distance between the cables, i.e. the cable spacing, subject to a uniform distribution of 0.005m, 0.0072 m.
In the practical application process, all parameters included in the cable geometric parameters have corresponding value ranges, the value ranges can be predetermined according to practical situations, and when the cable geometric parameters corresponding to at least two cables are obtained, the value can be carried out according to the value ranges of all the parameters so as to obtain at least one group of cable geometric parameters meeting the requirements of users.
Optionally, acquiring at least one set of cable geometry parameters corresponding to at least two cables includes: a predetermined range of geometric parameters corresponding to the at least two cables is determined to determine at least one set of cable geometric parameters based on the predetermined range of geometric parameters.
In this embodiment, the preset geometric parameter range may be a range preset for limiting the value of the geometric parameter of the cable in the practical application process. Different preset geometrical parameter ranges may be corresponding to different cable geometrical parameters, and each preset geometrical parameter range may be determined based on a probability distribution function. For example, for a cable spacing, its corresponding preset geometrical parameter range may obey a uniform distribution of [0.005m, 0.0073 m ]; for the distance between the cable and the reference conductor, the corresponding preset geometric parameter range can obey the uniform distribution of [0.02m,0.025m ]; for the cable radius, the corresponding preset geometric parameter range can obey a normal distribution with the mean value of 0.0004m and the standard deviation of 0.0001 m.
In a specific implementation, when obtaining the cable geometry parameters corresponding to at least two cables, a preset geometry parameter range corresponding to the cable spacing of the cables, a preset geometry parameter range corresponding to the distance between the cable and the reference conductor, and a preset geometry parameter range corresponding to the cable radius may be first determined, and further, values may be arbitrarily taken in the corresponding preset geometry parameter ranges and randomly combined to obtain at least one set of cable geometry parameters.
S120, inputting at least one group of cable geometric parameters into a crosstalk parameter prediction model which is trained in advance, and obtaining a crosstalk parameter prediction result corresponding to each cable.
In this embodiment, after at least one set of cable geometric parameters is obtained, the at least one set of cable geometric parameters may be input into a pre-trained crosstalk parameter prediction model to process the at least one set of cable geometric parameters based on the model. The crosstalk parameter prediction model may be a deep neural network model that is trained in advance and used for predicting crosstalk parameters between cables. Alternatively, the crosstalk parameter prediction model may be a deep neural network model (Deep Neural Network, DNN) built up of a plurality of fully connected layers, at least one normalization layer, and at least one random inactivation layer. The random inactivation (dropout) layer is formed by traversing the nodes of each layer of the neural network, and then setting a keep_prob (node retention probability) for the neural network of the layer, that is, the probability that the nodes of the layer have the keep_prob is retained, and the value of the keep_prob can be between 0 and 1. By setting the retention probability of the nodes of the layer of the neural network, the neural network cannot deviate to a certain node, so that the weight of each node cannot be excessively large, and the overfitting of the neural network is reduced.
In this embodiment, the crosstalk parameter prediction result includes crosstalk parameters corresponding to each set of cable geometric parameters, that is, at least one set of cable geometric parameters is input into the crosstalk parameter prediction model, so as to obtain the crosstalk parameters corresponding to each set of cable geometric parameters. The crosstalk parameter may include at least one of a cable proximal induced current, a cable proximal induced voltage, a cable distal induced current, and a cable distal induced voltage.
It should be noted that, before the at least one cable geometric parameter is processed according to the crosstalk parameter prediction model, the model to be trained may be trained in advance, so as to determine the crosstalk parameter prediction result based on the trained crosstalk parameter prediction model.
Based on the above, the above technical means further includes: acquiring at least one training sample; inputting the training sample into a model to be trained to obtain an actual output result; based on the actual output result and crosstalk parameter labeling information, determining model loss, and performing model parameter adjustment on the model to be trained based on the model loss to obtain a crosstalk parameter prediction model.
The training samples comprise sample geometric parameters and corresponding crosstalk parameter labeling information.
It should be noted that, when the model to be trained is trained, the model to be trained may be trained based on different crosstalk parameter labeling information included in the training sample, so as to obtain a crosstalk parameter prediction model corresponding to the crosstalk parameter labeling information. Optionally, if the crosstalk parameter labeling information is cable near-end induced current labeling information, the crosstalk parameter labeling information corresponds to a model obtained by training, and after at least one group of cable geometric parameters are processed, cable near-end induced currents corresponding to each cable can be obtained; if the crosstalk parameter labeling information is cable near-end induced voltage labeling information, the crosstalk parameter labeling information corresponds to a model obtained through training, and cable near-end induced voltages corresponding to each cable can be obtained after at least one group of cable geometric parameters are processed; if the crosstalk parameter labeling information is cable far-end induced current labeling information, the crosstalk parameter labeling information corresponds to a model obtained by training, and cable far-end induced current corresponding to each cable can be obtained after at least one group of cable geometric parameters are processed; if the crosstalk parameter labeling information is cable far-end induced voltage labeling information, the crosstalk parameter labeling information corresponds to a model obtained through training, and cable far-end induced voltages corresponding to each cable can be obtained after at least one group of cable geometric parameters are processed.
It should be noted that, before training the model to be trained, a training sample needs to be acquired to train the model based on the training sample. In order to improve the accuracy of the model, training samples can be acquired as much and as much as possible.
In this embodiment, the sample cable geometric parameter may be determined by arbitrarily taking a value in a preset geometric parameter range corresponding to the cable spacing, arbitrarily taking a value in a preset geometric parameter range corresponding to the cable and the reference conductor, and arbitrarily taking a value in a preset geometric parameter range corresponding to the cable radius, and arbitrarily combining. The crosstalk parameter labeling information may correspond to each set of sample cable geometric parameters, i.e., each set of sample cable geometric parameters corresponds to one crosstalk parameter labeling information.
In a specific implementation, first, the geometric parameters of the sample cable and corresponding crosstalk parameter labeling information can be obtained, at least one training sample is constructed based on the information, after the at least one training sample is obtained, the at least one training sample can be input into a model to be trained to obtain an actual output result, further, after the actual output result is obtained, model loss of the model to be trained can be determined according to the actual output result and the crosstalk parameter labeling information included in the training sample, and further, model parameters are corrected based on the model loss. Specifically, when model parameters in the model to be trained are corrected by using model loss, the loss function may be converged as a training target, for example, whether the training error is smaller than a preset error, whether the error change tends to be stable, or whether the current iteration number is equal to the preset number. If the training error of the loss function is smaller than the preset error or the error variation trend tends to be stable, the training of the model to be trained can be indicated to be completed, and at the moment, the iterative training can be stopped. If the current condition is detected not to be met, other training samples can be further obtained to train the model to be trained until the training error of the loss function is within a preset range. When the training error of the loss function reaches convergence, the trained model to be trained can be used as a crosstalk parameter prediction model, namely, at least one group of cable geometric parameters are input into the credit evaluation model at the moment, and the crosstalk parameter prediction result corresponding to each cable can be accurately obtained.
Illustratively, when the model to be trained is a DNN model, the DNN itself is composed of more hidden layers, and if the input quantity of a single node on the hidden layer is x and the output quantity is y, the relationship between y and x can be expressed as:
y＝σ(ω·x+b)
where ω represents the linear mapping, b represents the bias term, σ (·) represents the nonlinear transfer function, x represents the input quantity per node, and y represents the output quantity per node.
In this embodiment, the ReLu function may be used as a σ (·) function of all nodes in the DNN, meanwhile, the values of ω and b are randomly allocated before model training, and in the network training process, the values of ω and b are continuously updated by a back propagation method until the error between the output result and the corresponding crosstalk parameter labeling information is approximately zero, where the specific evaluation index function is:
where N represents the number of training samples, y i Representing the actual output result, y * The crosstalk parameter labeling information is represented, ω represents a linear map, b represents a bias term, x represents an input amount of each node, and y represents an output amount of each node.
Further, after the trained crosstalk parameter prediction model is obtained, at least one group of cable geometric parameters can be input into the crosstalk parameter prediction model, so that a crosstalk parameter prediction result corresponding to each cable is obtained.
S130, determining a probability distribution curve corresponding to the crosstalk parameter prediction result, so as to determine a target cable distance between at least two cables and a reference conductor under corresponding cable radiuses based on the probability distribution curve.
In this embodiment, the probability distribution curve may be a curve for characterizing a distribution rule of all crosstalk parameters in the crosstalk parameter prediction result.
In the practical application process, after the crosstalk parameter prediction result is obtained, in order to determine a distribution rule corresponding to the crosstalk parameter prediction result, so as to determine the target cable distance and the target distance based on the distribution rule, statistical analysis can be performed on the crosstalk parameter prediction result to obtain a corresponding probability distribution curve.
Optionally, determining a probability distribution curve corresponding to the crosstalk parameter prediction result includes: and determining probability distribution curves of all crosstalk parameters under preset frequency in a crosstalk parameter prediction result based on a multi-conductor transmission line theory.
It should be understood by those skilled in the art that the multi-conductor transmission line (Multiconductor Transmission Line, MTL) theory is mainly a method for researching a transmission line model formed by three or more conductors, and describing distribution parameters in a matrix manner to extend the two-conductor transmission line theory. For the problem of the microstructure, the method can be realized by a field analysis method, and common field analysis methods include an electromagnetic field moment method, an electromagnetic field time domain finite difference method and an electromagnetic field finite element method. The field analysis method is too complex and has large calculation amount, and the multi-conductor transmission line theory is a method for deducing and solving a path through a field method. The preset frequency may be any frequency, and optionally, may be 700 mhz or 800 mhz.
In a specific implementation, after the crosstalk parameter prediction result is obtained, each crosstalk parameter included in the crosstalk parameter prediction result may be statistically analyzed according to the multi-conductor transmission line theory, so as to determine a probability distribution curve of each crosstalk parameter under a preset frequency. Exemplary, as shown in fig. 3, a graph is shown comparing probability distribution curves of the near-end induced current obtained based on the DNN model and the base Yu Mengte karlo Method (MC) at 700 mhz. As can be seen from fig. 3: the near-end induced current obtained based on the DNN model can effectively quantify the uncertainty of cable crosstalk, and the calculation time of DNN is 90 seconds, and the calculation time of MC method is 147 seconds, so that the effect of remarkably improving the calculation efficiency on the premise of ensuring the calculation accuracy is achieved.
Further, after the probability distribution curve is obtained, the probability distribution curve may be analyzed to determine a target cable spacing of at least two cables at respective cable radii and a target distance from the reference conductor. The target cable spacing may be a spacing corresponding to when at least two cables do not have a cable crosstalk phenomenon. Accordingly, the target distance may be a distance corresponding to when no cable crosstalk phenomenon occurs between at least two cables and the reference conductor.
Besides determining the target cable spacing and the target distance based on the probability distribution curve, the target cable spacing and the target distance can be adjusted based on other statistical characteristic parameters, so that the finally obtained target cable spacing and the target distance can be more accurate.
On the basis of the above embodiments, the method further comprises: and respectively determining the mean and/or variance corresponding to each crosstalk parameter in the crosstalk parameter prediction result so as to update the target cable spacing and the target distance based on the mean and/or variance.
In a specific implementation, after the crosstalk parameter prediction result is obtained, the mean value and/or the variance corresponding to each crosstalk parameter included in the prediction result may be determined, and then, the target cable distance and the target distance may be updated based on the rule represented by the mean value and/or the variance.
According to the technical scheme, at least one group of cable geometric parameters corresponding to at least two cables are obtained, then the at least one group of cable geometric parameters are input into a crosstalk parameter prediction model which is trained in advance, a crosstalk parameter prediction result corresponding to each cable is obtained, further, a probability distribution curve corresponding to the crosstalk parameter prediction result is determined, the target cable distance between at least two cables under the corresponding cable radius and the target distance between the reference conductor are determined based on the probability distribution curve, the problems that in the prior art, calculation analysis is carried out based on a large amount of random sample data, the calculation cost is quite high, the calculation efficiency of cable crosstalk uncertainty quantification is low and the like are solved, and the effect that calculation accuracy is guaranteed and meanwhile calculation efficiency can be effectively improved is achieved.
Example two
Fig. 3 is a schematic structural diagram of a data processing apparatus according to a second embodiment of the present invention. As shown in fig. 3, the apparatus includes: the crosstalk parameter prediction result determining module 220 and the probability distribution curve determining module 230 are connected to the cable geometric parameter obtaining module 210.
The cable geometric parameter obtaining module 210 is configured to obtain at least one set of cable geometric parameters corresponding to at least two cables; the cable geometric parameters comprise cable spacing, distance between each cable and a reference conductor and cable radius;
the crosstalk parameter prediction result determining module 220 is configured to input the at least one set of cable geometric parameters into a crosstalk parameter prediction model that is trained in advance, so as to obtain a crosstalk parameter prediction result corresponding to each cable; the crosstalk parameter prediction model is obtained by training based on the geometric parameters of the sample cable and corresponding crosstalk parameter labeling information; the crosstalk parameter prediction result comprises crosstalk parameters corresponding to each group of cable geometric parameters; the crosstalk parameter is at least one of a cable near-end induced current, a cable near-end induced voltage, a cable far-end induced current and a cable far-end induced voltage;
a probability distribution curve determining module 230 is configured to determine a probability distribution curve corresponding to the crosstalk parameter prediction result, so as to determine a target cable spacing and a target distance between the at least two cables and a reference conductor under the corresponding cable radius based on the probability distribution curve.
According to the technical scheme, at least one group of cable geometric parameters corresponding to at least two cables are obtained, then the at least one group of cable geometric parameters are input into a crosstalk parameter prediction model which is trained in advance, a crosstalk parameter prediction result corresponding to each cable is obtained, further, a probability distribution curve corresponding to the crosstalk parameter prediction result is determined, the target cable distance between at least two cables under the corresponding cable radius and the target distance between the reference conductor are determined based on the probability distribution curve, the problems that in the prior art, calculation analysis is carried out based on a large amount of random sample data, the calculation cost is quite high, the calculation efficiency of cable crosstalk uncertainty quantification is low and the like are solved, and the effect that calculation accuracy is guaranteed and meanwhile calculation efficiency can be effectively improved is achieved.
Optionally, the cable geometric parameter obtaining module 210 is specifically configured to determine a preset geometric parameter range corresponding to the at least two cables, so as to determine the at least one set of cable geometric parameters based on the preset geometric parameter range.
Optionally, the probability distribution curve determining module 230 is specifically configured to determine a probability distribution curve of each crosstalk parameter in the crosstalk parameter prediction result under a preset frequency based on a multi-conductor transmission line theory.
Optionally, the apparatus further includes: and the average value determining module.
And the average value determining module is used for respectively determining the average value and/or the variance corresponding to each crosstalk parameter in the crosstalk parameter prediction result so as to update the target cable distance and the target distance based on the average value and/or the variance.
Optionally, the apparatus further includes: the device comprises a training sample determining module, an actual output result determining module and a model parameter adjusting module.
The training sample determining module is used for acquiring at least one training sample; the training samples comprise sample geometric parameters and corresponding crosstalk parameter labeling information;
the actual output result determining module is used for inputting the training sample into the model to be trained to obtain an actual output result;
and the model parameter adjustment module is used for determining model loss based on the actual output result and the crosstalk parameter labeling information so as to adjust the model parameters of the model to be trained based on the model loss and obtain a crosstalk parameter prediction model.
Optionally, the crosstalk parameter prediction model includes a plurality of fully connected layers, at least one normalization layer, and at least one random deactivation layer.
Optionally, the positional relationship between the at least two cables is parallel.
The data processing device provided by the embodiment of the invention can execute the data processing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example III
Fig. 5 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as data processing methods.
In some embodiments, the data processing method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. One or more of the steps of the data processing method described above may be performed when the computer program is loaded into RAM 13 and executed by processor 11. Alternatively, in other embodiments, the processor 11 may be configured to perform the data processing method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of data processing, comprising:
acquiring at least one set of cable geometric parameters corresponding to at least two cables; the cable geometric parameters comprise cable spacing, distance between each cable and a reference conductor and cable radius;
inputting the at least one group of cable geometric parameters into a crosstalk parameter prediction model which is trained in advance, and obtaining a crosstalk parameter prediction result corresponding to each cable; the crosstalk parameter prediction model is obtained by training based on the geometric parameters of the sample cable and corresponding crosstalk parameter labeling information; the crosstalk parameter prediction result comprises crosstalk parameters corresponding to each group of cable geometric parameters; the crosstalk parameter is at least one of a cable near-end induced current, a cable near-end induced voltage, a cable far-end induced current and a cable far-end induced voltage;
a probability distribution curve corresponding to the crosstalk parameter prediction result is determined to determine a target cable spacing of the at least two cables at respective cable radii and a target distance from a reference conductor based on the probability distribution curve.
2. The method of claim 1, wherein the obtaining at least one set of cable geometry parameters corresponding to at least two cables comprises:
a predetermined range of geometric parameters corresponding to the at least two cables is determined to determine the at least one set of cable geometric parameters based on the predetermined range of geometric parameters.
3. The method of claim 1, wherein the determining a probability distribution curve corresponding to the crosstalk parameter predictor comprises:
and determining a probability distribution curve of each crosstalk parameter under a preset frequency in the crosstalk parameter prediction result based on a multi-conductor transmission line theory.
4. The method as recited in claim 1, further comprising:
and respectively determining a mean value and/or a variance corresponding to each crosstalk parameter in the crosstalk parameter prediction result, so as to update the target cable spacing and the target distance based on the mean value and/or the variance.
5. The method as recited in claim 1, further comprising:
acquiring at least one training sample; the training samples comprise sample geometric parameters and corresponding crosstalk parameter labeling information;
inputting the training sample into a model to be trained to obtain an actual output result;
and determining model loss based on the actual output result and the crosstalk parameter labeling information, so as to adjust the model parameters of the model to be trained based on the model loss, and obtain a crosstalk parameter prediction model.
6. The method of claim 1, wherein the crosstalk parameter prediction model comprises a plurality of fully connected layers, at least one normalization layer, and at least one random deactivation layer.
7. The method of claim 1, wherein the positional relationship between the at least two cables is parallel.
8. A data processing apparatus, comprising:
the cable geometric parameter acquisition module is used for acquiring at least one group of cable geometric parameters corresponding to at least two cables; the cable geometric parameters comprise cable spacing, distance between each cable and a reference conductor and cable radius;
the crosstalk parameter prediction result determining module is used for inputting the at least one group of cable geometric parameters into a crosstalk parameter prediction model which is trained in advance to obtain a crosstalk parameter prediction result corresponding to each cable; the crosstalk parameter prediction model is obtained by training based on the geometric parameters of the sample cable and corresponding crosstalk parameter labeling information; the crosstalk parameter prediction result comprises crosstalk parameters corresponding to each group of cable geometric parameters; the crosstalk parameter is at least one of a cable near-end induced current, a cable near-end induced voltage, a cable far-end induced current and a cable far-end induced voltage;
and the probability distribution curve determining module is used for determining a probability distribution curve corresponding to the crosstalk parameter prediction result so as to determine a target cable distance between the at least two cables and a reference conductor under the corresponding cable radius based on the probability distribution curve.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data processing method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions for causing a processor to implement the data processing method of any one of claims 1-7 when executed.
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