CN113591541A - Processing method and device for debugging data of duplexer - Google Patents
Processing method and device for debugging data of duplexer Download PDFInfo
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Abstract
The application provides a method and a device for processing debugging data of a duplexer, which are used for providing accurate data support through data processing when the signal waveform of the duplexer is adjusted. The method comprises the following steps: acquiring task data of a debugging task of the duplexer, wherein the debugging task is used for adjusting the waveform of a filtering signal of the duplexer to a target waveform, and the filtering signal is specifically a signal subjected to filtering processing by a filter included in the duplexer; inputting the task data into a screw data prediction model, so that the screw data prediction model predicts target screw data matched with a target waveform according to the task data, wherein the screw data prediction model is obtained by training an initial model through task sample data marked with corresponding screw sample data, the target screw data is state data of a screw when the waveform of a filtering signal of a target duplexer is adjusted to the target waveform, and the screw is used for adjusting the waveform of the filtering signal of the duplexer; and extracting and outputting target screw data output by the screw data prediction model.
Description
Technical Field
The present application relates to the field of communications, and in particular, to a method and an apparatus for processing debug data of a duplexer.
Background
In a communication system, a duplexer is used as a pilot frequency duplex radio station, is a main accessory of a relay station, and is used for isolating a transmitting signal from a receiving signal and ensuring that the transmitting and receiving of the signals can work normally at the same time.
In the context of the increasing proximity of 5G commercialization, the concerned 5G base station is required to have a transmission-stable, high-quality wireless communication capability, whereas in the 5G base station, a duplexer is an inevitable device through which signals in a communication link are selected and controlled according to frequency, a specific frequency signal is selected to pass while an unnecessary frequency signal is suppressed.
In the existing research process of the related art, the inventor finds that during the production or deployment process of the duplexer, a worker needs to adjust a screw on the duplexer according to an operation experience to adjust a signal waveform to a production-specified waveform, and the adjustment process obviously wastes time and labor and is low in efficiency.
Disclosure of Invention
The application provides a method and a device for processing debugging data of a duplexer, which are used for providing accurate data support through data processing when the signal waveform of the duplexer is adjusted.
In a first aspect, the present application provides a method for processing debug data of a duplexer, where the method includes:
acquiring task data of a debugging task of the duplexer, wherein the debugging task is used for adjusting the waveform of a filtering signal of the duplexer to a target waveform, and the filtering signal is specifically a signal subjected to filtering processing by a filter included in the duplexer;
inputting the task data into a screw data prediction model, so that the screw data prediction model predicts target screw data matched with a target waveform according to the task data, wherein the screw data prediction model is obtained by training an initial model through task sample data marked with corresponding screw sample data, the target screw data is state data of a screw when the waveform of a filtering signal of a target duplexer is adjusted to the target waveform, and the screw is used for adjusting the waveform of the filtering signal of the duplexer;
and extracting and outputting target screw data output by the screw data prediction model.
With reference to the first aspect of the present application, in a first possible implementation manner of the first aspect of the present application, before acquiring task data of a debugging task of a duplexer, the method further includes:
acquiring task sample data marked with corresponding screw sample data;
and sequentially inputting the sample data of each task into an initial model, carrying out forward propagation, calculating a loss function according to the screw data output by the model, carrying out backward propagation according to the calculation result of the loss function, optimizing the parameters of the model, and taking the model after model training as a screw data prediction model when preset model training conditions are completed.
In combination with the first possible implementation manner of the first aspect of the present application, in the second possible implementation manner of the first aspect of the present application, a random forest model is specifically adopted for an initial model, in a model training process, a main loop process is called, a training set including a plurality of task sample data is randomly sampled for the t-th time, m times are collected in total, a sampling set Dt including m samples is obtained, the sampling set Dt is used for training a t-th decision tree model Gt, when a model node is trained, partial sample features in all sample features on the node are randomly selected, and an optimal feature is selected from the partial sample features for left and right sub-tree division of a decision tree.
With reference to the first possible implementation manner of the first aspect of the present application, in a third possible implementation manner of the first aspect of the present application, before the model training, the method further includes:
and under a min-max standardization mode, carrying out normalization processing on a plurality of task sample data, so that values in the data are mapped into a range interval from 0 to 1.
With reference to the first aspect of the present application, in a fourth possible implementation manner of the first aspect of the present application, the task data includes a waveform offset, a main waveform peak value, and an auxiliary waveform peak value of the target waveform, and the target screw data includes a screw model and a screw screwing amount.
In a second aspect, the present application provides an apparatus for processing debug data of a duplexer, the apparatus including:
the duplexer comprises an acquisition unit, a debugging unit and a filtering unit, wherein the acquisition unit is used for acquiring task data of a debugging task of the duplexer, the debugging task is used for adjusting the waveform of a filtering signal of the duplexer to a target waveform, and the filtering signal is specifically a signal subjected to filtering processing by a filter included in the duplexer;
the prediction unit is used for inputting the task data into a screw data prediction model so that the screw data prediction model predicts target screw data matched with a target waveform according to the task data, the screw data prediction model is obtained by training an initial model through task sample data marked with corresponding screw sample data, the target screw data is state data of a screw when the waveform of a filtering signal of a target duplexer is adjusted to the target waveform, and the screw is used for adjusting the waveform of the filtering signal of the duplexer;
and the output unit is used for extracting and outputting the target screw data output by the screw data prediction model.
With reference to the second aspect of the present application, in a first possible implementation manner of the second aspect of the present application, the apparatus further includes a training unit, configured to train the apparatus to perform training
Acquiring task sample data marked with corresponding screw sample data;
and sequentially inputting the sample data of each task into an initial model, carrying out forward propagation, calculating a loss function according to the screw data output by the model, carrying out backward propagation according to the calculation result of the loss function, optimizing the parameters of the model, and taking the model after model training as a screw data prediction model when preset model training conditions are completed.
In combination with the first possible implementation manner of the second aspect of the present application, in the second possible implementation manner of the second aspect of the present application, a random forest model specifically adopted by the initial model is invoked during a model training process to perform a t-th random sampling on a training set containing a plurality of task sample data, the t-th random sampling is performed m times in total to obtain a sampling set Dt containing m samples, the t-th decision tree model Gt is trained by using the sampling set Dt, when a model node is trained, partial sample features in all sample features on the node are randomly selected, and an optimal feature is selected from the partial sample features to perform left and right subtree division of the decision tree.
With reference to the first possible implementation manner of the second aspect of the present application, in a third possible implementation manner of the second aspect of the present application, the training unit is further configured to:
and under a min-max standardization mode, carrying out normalization processing on a plurality of task sample data, so that values in the data are mapped into a range interval from 0 to 1.
With reference to the second aspect of the present application, in a fourth possible implementation manner of the second aspect of the present application, the task data includes a waveform offset amount, a main waveform peak value, and an auxiliary waveform peak value of the target waveform, and the target screw data includes a screw model and a screw screwing amount.
In a third aspect, the present application provides a processing device for debugging data of a duplexer, including a processor and a memory, where the memory stores a computer program, and the processor executes the method provided in the first aspect of the present application or any possible implementation manner of the first aspect of the present application when calling the computer program in the memory.
In a fourth aspect, the present application provides a computer-readable storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the method provided in the first aspect of the present application or any one of the possible implementations of the first aspect of the present application.
From the above, the present application has the following advantageous effects:
aiming at the adjustment of the signal waveform of the duplexer, the application provides a data processing means, by configuring a screw data prediction model in advance, when determining an adjustment task of adjusting the waveform of the filtering signal of the duplexer to a target waveform, the task data of the task can be input into the screw data prediction model, so that the screw data prediction model predicts target screw data matched with the target waveform according to the task data, wherein the filtering signal is specifically a signal subjected to filtering processing by a filter included in the duplexer, the target screw data is state data of a screw describing the adjustment of the waveform of the filtering signal of the target duplexer to the target waveform, the screw is used for adjusting the waveform of the filtering signal of the duplexer, and under the condition, due to the introduced artificial intelligence means, the target screw data required by the adjustment task is analyzed more scientifically and accurately through a neural network model, therefore, accurate data support is provided through data processing, the signal waveform of the duplexer can be accurately adjusted according to the target screw data, time and labor are saved through the accurate data support provided through the data processing, and the adjusting efficiency can be obviously improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for processing debug data of a duplexer according to the present application;
fig. 2 is a schematic structural diagram of a processing apparatus for debugging data of a duplexer according to the present application;
fig. 3 is a schematic structural diagram of a processing device for debugging data of a duplexer according to the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the description and in the claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Moreover, the terms "comprises," "comprising," and any other variation 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 modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus. The naming or numbering of the steps appearing in the present application does not mean that the steps in the method flow have to be executed in the chronological/logical order indicated by the naming or numbering, and the named or numbered process steps may be executed in a modified order depending on the technical purpose to be achieved, as long as the same or similar technical effects are achieved.
The division of the modules presented in this application is a logical division, and in practical applications, there may be another division, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed, and in addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some interfaces, and the indirect coupling or communication connection between the modules may be in an electrical or other similar form, which is not limited in this application. The modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in a plurality of circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present disclosure.
Before describing the method for processing debug data of a duplexer provided in the present application, the background related to the present application will be described first.
The method and the device for processing the debugging data of the duplexer and the computer readable storage medium can be applied to a processing device of the debugging data of the duplexer, and are used for providing accurate data support through data processing when the signal waveform of the duplexer is adjusted.
In the processing method for the debugging data of the duplexer, an execution main body of the processing method may be a processing device for the debugging data of the duplexer, or a processing device such as a server, a physical host, or a User Equipment (UE) that integrates the processing device for the debugging data of the duplexer. The device may be implemented in a hardware or software manner, the UE may specifically be a terminal device such as a smart phone, a tablet computer, a notebook computer, a desktop computer, or a Personal Digital Assistant (PDA), and the processing device of the debugging data of the duplexer may be set in a device cluster manner.
Further, in practical applications, the device form of the processing device for debugging data of the duplexer may be specifically adjusted according to a production environment or a deployment environment of the duplexer, for example, the processing device may be a control device on a production line, or may be a notebook computer at the hand of a worker, and may be specifically determined according to actual needs.
Next, a method for processing debug data of a duplexer provided by the present application will be described.
First, referring to fig. 1, fig. 1 shows a schematic flow chart of a processing method of debugging data of a duplexer in the present application, and the processing method of debugging data of a duplexer in the present application may specifically include the following steps:
step S101, acquiring task data of a debugging task of the duplexer, wherein the debugging task is used for adjusting the waveform of a filtering signal of the duplexer to a target waveform, and the filtering signal is specifically a signal which is subjected to filtering processing by a filter included in the duplexer;
step S102, inputting task data into a screw data prediction model, so that the screw data prediction model predicts target screw data matched with a target waveform according to the task data, wherein the screw data prediction model is obtained by training an initial model through task sample data marked with corresponding screw sample data, the target screw data is state data of a screw when the waveform of a filtering signal of a target duplexer is adjusted to the target waveform, and the screw is used for adjusting the waveform of the filtering signal of the duplexer;
and step S103, extracting and outputting the target screw data output by the screw data prediction model.
As can be seen from the embodiment shown in fig. 1, for the adjustment of the signal waveform of the duplexer, the present application proposes a data processing means, by configuring a screw data prediction model in advance, when determining an adjustment task of adjusting the waveform of the filtered signal of the duplexer to a target waveform, the task data of the task may be input into the screw data prediction model, so that the screw data prediction model predicts target screw data matched with the target waveform according to the task data, wherein the filtered signal is specifically a signal subjected to filtering processing by a filter included in the duplexer, the target screw data is state data of a screw describing the adjustment of the waveform of the filtered signal of the target duplexer to the target waveform, and the screw is used to adjust the waveform of the filtered signal of the duplexer, in this case, due to an introduced artificial intelligence means, target screw data required for the adjustment task is more scientifically and accurately analyzed by a neural network model, therefore, accurate data support is provided through data processing, the signal waveform of the duplexer can be accurately adjusted according to the target screw data, time and labor are saved through the accurate data support provided through the data processing, and the adjusting efficiency can be obviously improved.
The steps of the embodiment shown in fig. 1 and the possible implementation manner thereof in practical application will be described in detail.
In the present application, the duplexer may specifically be a device on any communication equipment, for example, a duplexer that needs to be configured on a 5G base station.
In the duplexer, the functions are mainly realized by two different filters, and a filter is respectively arranged on a signal receiving link and a signal sending link to isolate a transmitting signal from a receiving signal, so that the normal operation of signal transmitting work and signal receiving work is ensured.
A plurality of screws are generally reserved in the duplexer, the screws are related to the resonant cavity pitch of the filter, the screws are adjusted, the resonant cavity pitch can be changed, the filtering signal of the filter is further changed, and the effect of adjusting the filtering signal of the duplexer is achieved.
Because the influence factor that involves in the adjustment process is comparatively complicated, and the screw is general more in quantity, consequently if go on by the staff through operation experience in current regulation scheme, have the problem that takes a lot of trouble and laboursome, lead to inefficiency.
The Artificial Intelligence (AI) is introduced, and the adjustment guidance of the screw is realized through a neural network model.
The method comprises the steps of training a model in advance, obtaining task sample data marked with corresponding screw sample data, sequentially inputting each task sample data into an initial model, conducting forward propagation, calculating a loss function according to the screw data output by the model, conducting backward propagation according to a loss function calculation result, optimizing model parameters, and taking the model after model training as a screw data prediction model when preset model training conditions are completed.
The involved neural network model can be different types of models such as a YOLOv3 model, a ResNet model, an R-CNN model, a Fast R-CNN model, a Mask R-CNN model, an SSD model and the like.
As the task sample data of the training set, the content thereof includes the corresponding desired adjustment, also called the target waveform reached after the adjustment, and at the same time, the screw sample data is also labeled.
The task sample data can be configured by staff, or can be recorded in the actual debugging process, and the task data is extracted to be used as the task sample data, and even the task sample data can be obtained by the equipment through a large amount of measurement and collection.
The screw data prediction model is generally understood to predict a screw-in amount corresponding to a current target signal waveform, that is, target screw data, based on a relationship between a screw-in amount and an output waveform of a duplexer configured in the model.
In addition, it should be understood that, in the practical application process, if the same duplexer is targeted, it is obvious that the relationship between the screw-in amount and the output waveform is generally fixed and unchanged, so that the target waveform (task data) can be directly input into the model, that is, the corresponding screw-in amount can be accurately obtained, and data guidance is provided for the adjustment of the filtering signal.
If the adjustment effect of the filtering signal may be different in consideration of the duplexers of different models or the duplexers of the same model having different filters, the model of the duplexer or the filter model may also be considered in the above model training and model application processes, so as to achieve a more accurate prediction effect.
For example, not only a specific target signal waveform, but also a specific duplexer model, a filter model, and even other specific influencing elements that may be involved may be included in the task data, so that the screw data prediction model may more specifically and accurately predict the target screw data required to reach the target signal waveform.
The target waveform indicated in the task data may indicate waveform characteristics of the target waveform, such as a waveform offset, a main waveform peak, and an auxiliary waveform peak, for example, in practical applications, two auxiliary waveform peaks, i.e., an auxiliary waveform peak 1 and an auxiliary waveform peak 2, may be configured.
On the other hand, the screw data related to the model may include the screw model and the screw-in amount.
It is understood that the task data and the screw data may be directly expressed in text, or may be presented in combination with data forms such as images and tables.
For example, if the target screw data output by the screw data prediction model is output to the operator, the target screw data may be directly in an image form, so that the equipment can be directly loaded and displayed.
Of course, the output of the target screw data may be specifically adjusted according to actual needs, for example, the output mode may be adjusted according to different output objects, or a unified data format may also be adopted, and the output objects perform conversion of the data format.
For example, if a display device is configured or the target screw data is displayed through the display device, it is obvious that the display device may perform data conversion on the target screw data according to a display policy configured by the display device itself after obtaining the target screw data transmitted by the processing device of the debugging data of the duplexer, and display the target screw data in an adaptive display manner.
Of course, the display device, even the display screen, may belong to the processing device itself of the commissioning data of the diplexer.
In addition, the application aims at the training of the model and also provides a training mechanism so as to further improve the training effect and further guarantee the high prediction precision of the model.
The method comprises the steps that a random forest model specifically adopted by an initial model calls a main cycle process in a model training process, the training set containing a plurality of task sample data is subjected to the t-th random sampling, m times of sampling are acquired in total, the sampling set Dt containing m samples is obtained, the t-th decision tree model Gt is trained by the sampling set Dt, when model nodes are trained, partial sample features in all sample features on the nodes are randomly selected, and an optimal feature is selected from the partial sample features to divide left and right subtrees of a decision tree.
Specifically, the random forest model is composed of a decision tree, voting selection is performed on classification results of a plurality of weak classifiers, so that a strong classifier is formed, a plurality of sample data are extracted, each sample contains a plurality of characteristics, N training samples are extracted from a training set randomly and in a replacement mode (namely a Bootstrap sampling method), and corresponding screw regulation guide parameters are obtained through model learning.
The random forest model principle is that the weak classifier iteration times T are performed by taking an input as a sample set D { (x1, y1), (x2, y2),. }, (xm, ym) }, (x is waveform feature data, y is nut data), and the output is a final strong classifier h (x):
and (3) carrying out a main loop process (T is 1, 2, … and T) of random forest model calling, carrying out the T-th random sampling on a training set containing a plurality of task sample data, collecting m times in total to obtain a sampling set Dt containing m samples, training a T-th decision tree model Gt by using the sampling set Dt, randomly selecting partial sample characteristics from all sample characteristics on nodes when training model nodes, and selecting an optimal characteristic from the partial sample characteristics to divide left and right subtrees of the decision tree.
In the scheme, the random forest model is mainly used for predicting the model of the sample screw and the screwing amount of the sample screw.
In addition, considering that the dimensions of different attribute characteristics in the task sample data may have inconsistency and the difference between the data may be large, in order to avoid the influence of the situation on the training effect and the training precision of the model, preprocessing can be performed, and the difference between the data is eliminated through the preprocessing of the data.
For example, prior to model training, the method may further comprise:
and under a min-max standardization mode, carrying out normalization processing on a plurality of task sample data, so that values in the data are mapped into a range interval from 0 to 1.
The min-max normalization method is to transform the sequences x1, x 2.
Then the new sequence y1, y 2.
The above is the introduction of the processing method of the debugging data of the duplexer provided by the present application, and in order to better implement the processing method of the debugging data of the duplexer provided by the present application, the present application also provides a processing device of the debugging data of the duplexer.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a processing apparatus for debugging data of a duplexer in the present application, in which the processing apparatus 200 for debugging data of a duplexer specifically includes the following structure:
an obtaining unit 201, configured to obtain task data of a debugging task of a duplexer, where the debugging task is used to adjust a waveform of a filtering signal of the duplexer to a target waveform, and the filtering signal is specifically a signal subjected to filtering processing by a filter included in the duplexer;
the prediction unit 202 is configured to input the task data into a screw data prediction model, so that the screw data prediction model predicts target screw data matched with a target waveform according to the task data, where the screw data prediction model is obtained by training an initial model through task sample data labeled with corresponding screw sample data, the target screw data is state data of a screw when a waveform of a filter signal of a target duplexer is adjusted to a target waveform, and the screw is used for adjusting a waveform of the filter signal of the duplexer;
and an output unit 203 for extracting and outputting the target screw data output by the screw data prediction model.
In an exemplary implementation, the apparatus further comprises a training unit 204 for
Acquiring task sample data marked with corresponding screw sample data;
and sequentially inputting the sample data of each task into an initial model, carrying out forward propagation, calculating a loss function according to the screw data output by the model, carrying out backward propagation according to the calculation result of the loss function, optimizing the parameters of the model, and taking the model after model training as a screw data prediction model when preset model training conditions are completed.
In another exemplary implementation manner, a random forest model is specifically adopted for the initial model, in the model training process, a main loop process is called, a training set containing a plurality of task sample data is randomly sampled for the t-th time, m times are collected in total to obtain a sampling set Dt containing m samples, the sampling set Dt is used for training the t-th decision tree model Gt, when model nodes are trained, partial sample features in all sample features on the nodes are randomly selected, and an optimal feature is selected from the partial sample features to divide left and right subtrees of the decision tree.
In another exemplary implementation, the training unit 204 is further configured to:
and under a min-max standardization mode, carrying out normalization processing on a plurality of task sample data, so that values in the data are mapped into a range interval from 0 to 1.
In yet another exemplary implementation, the task data includes a waveform offset amount, a main waveform peak value, and an auxiliary waveform peak value of the target waveform, and the target screw data includes a screw model and a screw screwing amount.
Referring to fig. 3, fig. 3 shows a schematic structural diagram of a processing device for debugging data of a duplexer in the present application, specifically, the processing device for debugging data of a duplexer in the present application may include a processor 301, a memory 302, and an input/output device 303, where the processor 301 is configured to implement steps of a processing method for debugging data of a duplexer in the corresponding embodiment of fig. 1 when executing a computer program stored in the memory 302; alternatively, the processor 301 is configured to implement the functions of the units in the corresponding embodiment shown in fig. 2 when executing the computer program stored in the memory 302, and the memory 302 is configured to store the computer program required by the processor 301 to execute the processing method of the debug data of the duplexer in the corresponding embodiment shown in fig. 1.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 302 and executed by the processor 301 to accomplish the present application. One or more modules/units may be a series of computer program instruction segments capable of performing certain functions, the instruction segments being used to describe the execution of a computer program in a computer device.
The processing device of the debugging data of the duplexer may include, but is not limited to, the processor 301, the memory 302, and the input-output device 303. It will be understood by those skilled in the art that the illustration is merely an example of the processing device of the debugging data of the duplexer, and does not constitute a limitation of the processing device of the debugging data of the duplexer, and may include more or less components than those illustrated, or combine some components, or different components, for example, the processing device of the debugging data of the duplexer may further include a network access device, a bus, etc., and the processor 301, the memory 302, the input-output device 303, etc. are connected by the bus.
The Processor 301 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center of the processing device for debugging data of the duplexer, various interfaces and lines connecting the various parts of the whole device.
The memory 302 may be used to store computer programs and/or modules, and the processor 301 implements various functions of the computer device by running or executing the computer programs and/or modules stored in the memory 302 and invoking data stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from use of the processing device of the debugging data of the duplexer, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The processor 301, when executing the computer program stored in the memory 302, may specifically implement the following functions:
acquiring task data of a debugging task of the duplexer, wherein the debugging task is used for adjusting the waveform of a filtering signal of the duplexer to a target waveform, and the filtering signal is specifically a signal subjected to filtering processing by a filter included in the duplexer;
inputting the task data into a screw data prediction model, so that the screw data prediction model predicts target screw data matched with a target waveform according to the task data, wherein the screw data prediction model is obtained by training an initial model through task sample data marked with corresponding screw sample data, the target screw data is state data of a screw when the waveform of a filtering signal of a target duplexer is adjusted to the target waveform, and the screw is used for adjusting the waveform of the filtering signal of the duplexer;
and extracting and outputting target screw data output by the screw data prediction model.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the processing apparatus and the device for debugging data of a duplexer and the corresponding units thereof described above may refer to the description of the method in the embodiment corresponding to fig. 1, and are not described herein again in detail.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
For this reason, the present application provides a computer-readable storage medium, where multiple instructions are stored, where the instructions can be loaded by a processor to execute steps in the processing method of the debug data of the duplexer in the embodiment corresponding to fig. 1 in the present application, and specific operations may refer to descriptions of the processing method of the debug data of the duplexer in the embodiment corresponding to fig. 1, which are not repeated herein.
Wherein the computer-readable storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the instructions stored in the computer-readable storage medium can execute the steps of the processing method for the debug data of the duplexer in the embodiment corresponding to fig. 1 in the present application, the beneficial effects that can be achieved by the processing method for the debug data of the duplexer in the embodiment corresponding to fig. 1 in the present application can be achieved, for details, see the foregoing description, and are not repeated herein.
The method, the apparatus, the device and the computer-readable storage medium for processing the debugging data of the duplexer provided by the present application are described in detail above, and a specific example is applied in the present application to explain the principles and embodiments of the present application, and the description of the above embodiment is only used to help understanding the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method for processing debugging data of a duplexer, the method comprising:
acquiring task data of a debugging task of a duplexer, wherein the debugging task is used for adjusting the waveform of a filtering signal of the duplexer to a target waveform, and the filtering signal is specifically a signal subjected to filtering processing by a filter included in the duplexer;
inputting the task data into a screw data prediction model, so that the screw data prediction model predicts target screw data matched with the target waveform according to the task data, wherein the screw data prediction model is obtained by training an initial model through task sample data marked with corresponding screw sample data, the target screw data is state data of a screw when the waveform of a filtering signal of the target duplexer is adjusted to a target waveform, and the screw is used for adjusting the waveform of the filtering signal of the duplexer;
and extracting and outputting the target screw data output by the screw data prediction model.
2. The method of claim 1, wherein before the obtaining task data for the debugging task of the duplexer, the method further comprises:
acquiring the task sample data marked with the corresponding screw sample data;
and sequentially inputting each task sample data into the initial model, performing forward propagation, calculating a loss function according to the screw data output by the model, performing backward propagation according to a calculation result of the loss function, optimizing model parameters, and taking the model after model training as the screw data prediction model when preset model training conditions are completed.
3. The method according to claim 2, wherein a random forest model specifically adopted by the initial model calls a main loop process in a model training process to randomly sample a training set containing a plurality of task sample data for a t-th time, and the random forest model is collected for m times in total to obtain a sampling set Dt containing m samples, the sampling set Dt is used to train a t-th decision tree model Gt, when a model node is trained, partial sample features in all sample features on the node are randomly selected, and an optimal feature in the partial sample features is selected to divide left and right subtrees of the decision tree.
4. The method of claim 2, wherein prior to model training, the method further comprises:
and under a min-max standardization mode, carrying out normalization processing on a plurality of task sample data, so that values in the data are mapped into a range interval from 0 to 1.
5. The method of claim 1, wherein the task data includes a waveform offset, a main waveform peak, and an auxiliary waveform peak of the target waveform, and the target screw data includes a screw model and a screw amount of screw screwing.
6. An apparatus for processing debug data of a duplexer, the apparatus comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring task data of a debugging task of a duplexer, the debugging task is used for adjusting the waveform of a filtering signal of the duplexer to a target waveform, and the filtering signal is specifically a signal which is subjected to filtering processing by a filter included in the duplexer;
the prediction unit is used for inputting the task data into a screw data prediction model so that the screw data prediction model predicts target screw data matched with the target waveform according to the task data, the screw data prediction model is obtained by training an initial model through task sample data marked with corresponding screw sample data, the target screw data is state data of a screw when the waveform of the filtering signal of the target duplexer is adjusted to the target waveform, and the screw is used for adjusting the waveform of the filtering signal of the duplexer;
and the output unit is used for extracting and outputting the target screw data output by the screw data prediction model.
7. The apparatus according to claim 6, further comprising a training unit for training
Acquiring the task sample data marked with the corresponding screw sample data;
and sequentially inputting each task sample data into the initial model, performing forward propagation, calculating a loss function according to the screw data output by the model, performing backward propagation according to a calculation result of the loss function, optimizing model parameters, and taking the model after model training as the screw data prediction model when preset model training conditions are completed.
8. The apparatus according to claim 7, wherein in the model training process, a main loop process is invoked to perform a t-th random sampling on a training set containing a plurality of task sample data, m times are collected in total to obtain a sampling set Dt containing m samples, the sampling set Dt is used to train a t-th decision tree model Gt, when a model node is trained, a partial sample feature is randomly selected from all sample features on the node, and an optimal feature is selected from the partial sample feature to perform left and right sub-tree division of the decision tree.
9. The apparatus of claim 7, wherein the training unit is further configured to:
and under a min-max standardization mode, carrying out normalization processing on a plurality of task sample data, so that values in the data are mapped into a range interval from 0 to 1.
10. The apparatus of claim 6, wherein the task data comprises a waveform offset, a main waveform peak, and an auxiliary waveform peak of the target waveform, and the target screw data comprises a screw model and a screw amount of screwing.
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