CN111080473B - Area identification method, apparatus, computer device and readable storage medium - Google Patents
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Abstract
The application relates to a method, a device, computer equipment and a readable storage medium for identifying a region. The station area identification method comprises the following steps: acquiring an identification signal sent by a platform region host; determining channel parameters according to the identification signals, wherein the channel parameters refer to parameters of a communication channel between the station area host and the user; and inputting the channel parameters into a target classifier obtained by training in advance, and determining whether the current user belongs to the platform region corresponding to the platform region host. The method for identifying the station area can accurately identify the station area to which the user belongs.
Description
Technical Field
The present disclosure relates to the field of power systems, and in particular, to a method and apparatus for identifying a region, a computer device, and a readable storage medium.
Background
With the continuous development of power grid construction, lean management of a transformer area has been called a trend. The identification of the users in the transformer area is the basis for the electric power company to realize the lean management of the transformer area and reduce the loss. However, the identification of the station area is difficult to implement in the online field, and the main reasons are as follows: part of old cells and the lines along the street gate are complex; the user data of the platform area is inaccurate and even lost due to imperfect information of the platform area, untimely information updating and the like; as the work such as the transformation of the cable is performed, the intersecting lines of adjacent stations become complicated and difficult to identify.
As one-user one-meter systems have become popular, some area identification technologies have emerged on this basis. These zone identification techniques are largely divided into two categories, carrier-based communication techniques and pulse-current-based techniques. Both of these techniques transmit an identification signal between the distribution transformer and the subscriber to identify the zone to which the subscriber belongs.
However, in the conventional area identification technology, crosstalk information transmitted by other distribution transformers exists in identification signals transmitted between the distribution transformer and the user, so that the identification of the identified area is inaccurate.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, and a readable storage medium for identifying a region.
In order to achieve the above object, in one aspect, an embodiment of the present application provides a method for identifying a region, where the method includes:
acquiring an identification signal sent by a platform region host;
determining channel parameters according to the identification signals, wherein the channel parameters refer to parameters of a communication channel between the station area host and the user terminal;
and inputting the channel parameters into a target classifier obtained by training in advance, and determining whether the current user belongs to the station area corresponding to the station area host.
In one embodiment, the determining the channel parameter according to the identification signal includes:
detecting an identification sequence in the identification signal according to a matched filtering algorithm;
obtaining a standard identification sequence, wherein the standard identification sequence refers to a sequence of signals sent by a station area host of a station area to which the user terminal belongs;
and determining the channel parameters according to the identification sequence and the standard identification sequence.
In one embodiment, the determining the channel parameter according to the identification sequence and the standard identification sequence includes:
based on a Fourier transform algorithm, determining the frequency spectrums of the identification sequence and the standard identification sequence respectively to obtain an identification frequency spectrum and a standard frequency spectrum;
determining a channel frequency response according to the ratio of the identification spectrum to the standard spectrum;
and performing dimension reduction processing on the channel frequency response based on a principal component analysis algorithm to obtain the channel parameters.
In one embodiment, the performing the dimension reduction processing on the channel frequency response based on the principal component analysis algorithm to obtain the channel parameter includes:
performing a de-averaging process on the channel frequency response to obtain a de-averaging matrix;
calculating a covariance matrix of the de-averaging matrix;
calculating the eigenvalue and eigenvector of each element in the covariance matrix based on an eigenvalue decomposition algorithm to obtain an eigenvalue matrix and an eigenvector corresponding to each element in the eigenvalue matrix;
selecting elements in the eigenvalue matrix according to a preset rule to obtain a target eigenvalue matrix, and obtaining eigenvectors corresponding to all elements in the target eigenvalue matrix to obtain a vector matrix;
and multiplying the vector matrix by the channel frequency response to obtain the channel parameter.
In one embodiment, the method further comprises:
obtaining a standard channel parameter sample and a crosstalk channel parameter sample;
and respectively inputting the standard channel parameter sample and the crosstalk channel parameter sample into an initial classifier, and training the initial classifier based on an error back propagation algorithm to obtain the target classifier.
In one embodiment, the acquiring the standard channel parameter samples and the crosstalk channel parameter samples includes:
acquiring a plurality of training signals sent by a host computer of a target platform area;
determining a plurality of standard channel parameters according to the plurality of training signals to obtain a standard channel parameter sample;
acquiring a plurality of crosstalk signals sent by a host in a non-target area;
and determining a plurality of crosstalk channel parameters according to the plurality of crosstalk signals to obtain the crosstalk channel parameter samples.
In one embodiment, the method further comprises:
if the current user belongs to the platform region corresponding to the platform region host, acquiring user terminal information corresponding to the current user, demodulating the identification signal, and acquiring platform region identification information and phase sequence information in the identification signal, wherein the phase sequence information is used for representing the phase sequence to which the identification signal belongs;
and sending the user terminal information, the platform area identification information and the phase sequence information to a management system.
On the other hand, the embodiment of the application also provides a device for identifying the area, which comprises:
the identification signal acquisition module is used for acquiring an identification signal sent by the host computer of the platform area;
a channel parameter determining module, configured to determine a channel parameter according to the identification signal, where the channel parameter is a parameter of a communication channel between the station host and the user terminal;
and the platform region determining module is used for inputting the channel parameters into a target classifier obtained by training in advance and determining whether the current user belongs to the platform region corresponding to the platform region host.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method as described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor realizes the steps of the method as described above.
According to the method, the device, the computer equipment and the readable storage medium for identifying the area, the identification signal sent by the area terminal is obtained, the channel parameters are estimated according to the identification sequence, the channel parameters are input into the target classifier, and the area to which the user belongs can be judged according to the output result of the target classifier. Because the channel parameters of the signals transmitted by the host in the platform area and the crosstalk signals transmitted by other host in the platform area are obviously distinguished, whether the signals received by the current user terminal are the signals transmitted by the host in the platform area or not can be judged according to the channel parameters, so that whether the current user belongs to the platform area where the host in the platform area is located or not can be judged. The method provided by the embodiment can accurately identify the area to which the user belongs, has higher identification working efficiency, and can avoid wasting manpower and material resources.
Drawings
Fig. 1 is a schematic application environment diagram of a method for identifying a region according to an embodiment of the present application;
fig. 2 is a flowchart illustrating steps of a method for identifying a cell according to an embodiment of the present application;
fig. 3 is a flowchart illustrating steps of a method for identifying a cell according to an embodiment of the present application;
fig. 4 is a flowchart illustrating steps of a method for identifying a cell according to an embodiment of the present application;
fig. 5 is a flowchart illustrating steps of a method for identifying a cell according to an embodiment of the present application;
fig. 6 is a flowchart illustrating steps of a method for identifying a cell according to an embodiment of the present application;
fig. 7 is a flowchart illustrating steps of a method for identifying a region according to an embodiment of the present application;
fig. 8 is a schematic diagram of a station area identifying apparatus according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Referring to fig. 1, the method for identifying a domain may be used to identify a distribution transformer domain to which a user belongs. The method is particularly applied to the distribution transformer area identification system as shown in fig. 1. The distribution transformer area identification system comprises a plurality of area hosts 10, a plurality of user terminals 20 and the like, wherein each user terminal is provided with a user terminal, and each user terminal is in communication connection with the area host of the distribution transformer area to which the user terminal belongs. The host of the platform region sends signals to the corresponding user terminals through channels, the corresponding user terminals can receive the signals sent by the host of the platform region, and when signal interference occurs, other user terminals can also receive the signals sent by the host of the platform region. The host may be, but not limited to, a computer host, an internet host, a mini computer host, etc., and one host may configure a plurality of terminals. The user terminals may be, but are not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. Both the host and the user terminal comprise a memory capable of storing data and a computer program, and a processor capable of processing the computer program.
The following describes the technical solution of the present application and how the technical solution of the present application solves the technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Referring to fig. 2, an embodiment of the present application provides a method for identifying a cell, and the embodiment of the present application is described by taking application of the method to any ue as an example. The method comprises the following steps:
s100, acquiring an identification signal sent by a platform region host.
The identification signal may include an identification sequence, a zone identifier, and a phase sequence number, where the zone identifier refers to information about a zone where the zone host is located, for example: the location of the area, the number of the area itself, etc. The host in the area can send signals to the users on the phases A, B and C, if the host in the area sends signals to the users on the phase A, the phase sequence number is A; and if the station area host sends a signal to a user on the phase B, the phase sequence number is B. The user terminal of the user corresponding to the platform region where the platform region host is located can acquire the identification signal sent by the platform region host.
S200, determining channel parameters according to the identification signals, wherein the channel parameters refer to parameters of a communication channel between the station area host and the user terminal.
The channel refers to a path of channel transmission between the cell host and the user terminal. The channel parameters may include bandwidth, signal-to-noise ratio, and transmission rate. The channel parameters will be different if the channels for signal transmission between the cell host and the user terminal are different. And if the channel of the identification signal transmitted between the station area host and the corresponding user terminal is different from the channel of the crosstalk signal transmitted by other station area hosts to the user terminal, the channel parameters of the identification signal and the crosstalk signal are different. There are various methods for determining channel parameters from the identification signal and the crosstalk signal, for example: the present embodiment does not impose any limitation on the beamforming algorithm, the spectrum estimation algorithm, the parameter subspace estimation algorithm, and the like, as long as the functions thereof can be realized.
S300, inputting the channel parameters into a target classifier which is obtained through training in advance, and determining whether the current user belongs to the platform region corresponding to the platform region host.
The target classifier is obtained by training according to a plurality of channel parameters and a preset initial classifier. The target classifier obtained through training can accurately identify the type of the channel parameter. The channel parameters are input into the target classifier, which can output corresponding results. The output results may include: the current user belongs to the area corresponding to the area host, and the current user does not belong to the area corresponding to the area host. For example: the station area host A sends an identification signal to the user terminal A, the user terminal A receives the identification signal, calculates channel parameters according to the identification signal, and inputs the channel parameters into the target classifier. If the output of the target classifier is 1, the user where the user terminal A is located is indicated to belong to the platform where the platform host A is located; if the output of the target classifier is 0, it indicates that the user where the user terminal a is located does not belong to the zone where the zone host a is located. Of course, other results may be output according to the difference of the target classifier.
In this embodiment, the channel parameter is determined according to the identification signal sent by the host station of the station area by acquiring the identification signal. And inputting the channel parameters into a target classifier obtained by training in advance, and determining whether the current user belongs to the station area corresponding to the station area host. Because the signals sent by the district host and the crosstalk signals sent by other district hosts are obviously different from each other through the channel parameters transmitted by the channels, whether the current user terminal receives the signals sent by the district host or not can be judged according to the channel parameters, so that whether the current user belongs to the district where the district host is located or not can be judged. Therefore, the identification of the area to which the user belongs can be accurately realized, the identification work efficiency is high, and the waste of manpower and material resources can be avoided.
Referring to fig. 3, this embodiment relates to a possible implementation manner of determining a channel parameter according to the identification signal, and S200 includes:
s210, detecting the identification sequence of the identification signal according to a matched filtering algorithm.
The matched filtering algorithm means that after filtering, the ratio of the instantaneous power of the signal at the output end of the filter to the average power of the noise (namely, the signal to noise ratio) is maximized, when the useful signal and the noise enter the filter at the same time, the useful signal has a peak value at a certain moment, and the noise signal is restrained. The identification signal may include a sequence sent by the host of the station area to which the current user belongs, a sequence of crosstalk signals sent by other host of the station area, and other noise sequences. Since the identification sequence is a known sequence, the sequence of the identification sequence contained in the received identification signal can be detected by matched filtering.
S220, a standard identification sequence is obtained, wherein the standard identification sequence refers to a sequence of signals sent by a station area host of a station area to which the user terminal belongs.
The user terminal stores the identification sequence in the identification signal sent by the platform host, and the identification sequence is called a standard identification sequence. The standard identification sequence adopts an M sequence, wherein the M sequence is the most basic PN sequence adopted in a code division multiple access (CodeDivisionMultipleAccess, CDMA) system, and is the short name of the longest linear feedback shift register sequence.
And S230, determining the channel parameters according to the identification sequence and the standard identification sequence.
And calculating the channel parameters through a correlation algorithm according to the identification sequence received by the user terminal and the known standard identification sequence. The present embodiment does not impose any limitation on the method of calculating the channel parameters as long as the functions thereof can be realized.
Referring to fig. 4, in one possible implementation manner of determining the channel parameter according to the identification sequence and the standard identification sequence, S230 includes:
s231, respectively determining the frequency spectrums of the identification sequence and the standard identification sequence based on a Fourier transform algorithm to obtain an identification frequency spectrum and a standard frequency spectrum.
The fourier transform algorithm is a transform algorithm for transforming a signal from a time domain to a frequency domain, and in the field of signal processing, an input signal can be represented in the frequency domain according to the size of the frequency by using the fourier transform algorithm, so that the frequency spectrum of the input signal can be obtained. The identification sequence and the standard identification sequence are used as input signals, and the identification frequency spectrum and the standard frequency spectrum can be obtained through a Fourier transform algorithm.
S232, determining the channel frequency response according to the ratio of the identification frequency spectrum to the standard frequency spectrum.
It is assumed that the identification spectrum is denoted by Y (k), the standard spectrum is denoted by X (k), and the channel frequency response is denoted by H (k). The channel frequency responseWhere k is the sequence number of the discrete frequency sampling point.
S233, performing dimension reduction processing on the channel frequency response based on a principal component analysis algorithm to obtain the channel parameters.
The principal component analysis algorithm mainly projects high-dimensional data into a lower-dimensional space. For the channel frequency response H (k), a principal component analysis algorithm may be used to perform a dimension reduction process, i.e., the channel frequency response in k dimensions is reduced to n dimensions, and the channel frequency response in n dimensions is denoted as the channel parameter. Referring to fig. 5, a specific dimension reduction process is as follows:
and S2331, performing a de-averaging process on the channel frequency response to obtain a de-averaging matrix.
Firstly, the channel frequency response of each dimension is averaged, and the corresponding average value is subtracted from the value in the channel frequency of each dimension to obtain a de-averaging matrix.
And S2332, calculating a covariance matrix of the de-averaging matrix.
And S2333, calculating the characteristic value and the characteristic vector of each element in the covariance matrix based on a characteristic decomposition method to obtain a characteristic value matrix and the characteristic vector corresponding to each element in the characteristic value matrix.
S2334, selecting elements in the eigenvalue matrix according to a preset rule to obtain a target eigenvalue matrix, and obtaining eigenvectors corresponding to all elements in the target eigenvalue matrix to obtain a vector matrix.
The preset rule is that the eigenvalues are firstly ordered from big to small, and the largest n eigenvalues in the eigenvalue matrix are selected to obtain the target eigenvalue matrix due to the requirement of n-dimensional channel frequency response. And respectively taking eigenvectors corresponding to n eigenvalues in the target eigenvalue matrix as row vectors to form a vector matrix, wherein the obtained vector matrix is also n-dimensional.
And S2335, multiplying the vector matrix by the channel frequency response to obtain the channel parameters.
By using the vector matrix of n dimensions as the channel frequency response H (k), the channel frequency response of k dimensions can be reduced to n dimensions, and the channel parameters of n dimensions can be obtained.
Referring to fig. 6 and 7, the method for identifying a region further includes:
s400, obtaining standard channel parameter samples and crosstalk channel parameter samples.
When training the initial classifier, a large amount of data is required, and samples of standard channel parameters and samples of crosstalk channel parameters are required to be acquired. The specific process of obtaining the standard channel parameter sample and the crosstalk channel parameter sample is as follows:
s410, a plurality of training signals sent by a host computer of a target station area are obtained.
S420, determining a plurality of standard channel parameters according to the training signals to obtain the standard channel parameter samples.
S430, obtaining a plurality of crosstalk signals sent by the non-target area host.
S440, determining a plurality of crosstalk channel parameters according to the plurality of crosstalk signals, and obtaining the crosstalk channel parameter samples.
In this embodiment, the method for determining the standard channel parameter samples and the crosstalk channel parameter samples by using the acquired plurality of training signals and the plurality of crosstalk signals is similar to the method in the above embodiment, and will not be described herein again.
S500, respectively inputting the standard signal parameter sample and the crosstalk signal parameter sample into an initial classifier, and training the initial classifier based on an error back propagation algorithm to obtain the target classifier.
The error back propagation based algorithm includes an input layer, an intermediate layer, and an output layer. The input layer includes a plurality of neurons for receiving the channel parameters, the number of neurons corresponding to a dimension of the channel parameters. The intermediate layer may comprise a hidden layer, the number of neurons in the hidden layer being determined after a number of training, the intermediate layer being arranged to transform the channel parameters. The output layer may include two neurons, when the channel parameter of the host of the station area to which the channel parameter dimension of the input layer belongs is input, the target output of the output neuron 1 is 1, and the target output of the output neuron 2 is 0; when the channel parameter input to the input layer is a crosstalk channel parameter, the target output of the output neuron 1 is 0, and the target output of the output neuron 2 is 1. Neuron weights for the input layer and the output layer may be estimated by the error back propagation algorithm.
With continued reference to fig. 6, the method for identifying a region further includes:
s600, performing S600; and if the current user belongs to the platform area corresponding to the platform area host, acquiring user terminal information corresponding to the current user.
The user terminal information may include an identification of the user terminal itself, location information of the user terminal, and the like. If the current user is judged to belong to the area where the area host computer is located, recording the user terminal information of the current user.
S700, demodulating the identification signal to obtain the platform area identification information and the phase sequence information in the identification signal, wherein the phase sequence information is used for representing the phase sequence to which the identification signal belongs.
Because the identification signal sent by the station area host is modulated, the user terminal needs to demodulate the received identification signal, so that station area identification information and phase sequence information in the identification signal can be obtained.
S800, the user terminal information, the platform area identification information and the phase sequence information are sent to a management system.
After the identification of the areas is completed, the user terminal information, the area identification information and the phase sequence information are sent to a management system, so that the management system can know whether the user corresponding to each area changes or not, and correspondingly adjust the management of power supply by the change, thereby better managing and serving the users of all areas.
It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described above may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, and the order of execution of the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with at least a part of the sub-steps or stages of other steps or other steps.
Referring to fig. 8, an embodiment of the present application provides a station area identifying apparatus 30, which includes: the identification signal acquisition module 100, the channel parameter determination module 200, and the station area determination module 300. Wherein,
the identification signal acquisition module 100 is configured to acquire an identification signal sent by a host in a station area;
the channel parameter determining module 200 is configured to determine a channel parameter according to the identification signal, where the channel parameter is a parameter of a communication channel between the base station and the user terminal;
the platform region determining module 300 is configured to input the channel parameter into a target classifier obtained by training in advance, and determine whether the current user belongs to a platform region corresponding to the platform region host.
In one embodiment, the channel parameter determining module 200 is further configured to detect an identification sequence in the identification signal according to a matched filtering algorithm; obtaining a standard identification sequence, wherein the standard identification sequence refers to a sequence of signals sent by a station area host of a station area to which a user terminal belongs; and determining the channel parameters according to the identification sequence and the standard identification sequence.
In one embodiment, the channel parameter determining module 200 is further configured to determine the frequency spectrums of the identification sequence and the standard identification sequence based on a fourier transform algorithm, so as to obtain an identification frequency spectrum and a standard frequency spectrum; determining a channel frequency response according to the ratio of the identification spectrum to the standard spectrum; and performing dimension reduction processing on the channel frequency response based on a principal component analysis algorithm to obtain the channel parameters.
In one embodiment, the channel parameter determining module 200 is further configured to perform a process of de-averaging the channel frequency response to obtain an averaging matrix; calculating a covariance matrix of the de-averaging matrix; calculating the eigenvalue and eigenvector of each element in the covariance matrix based on an eigenvalue decomposition algorithm to obtain an eigenvalue matrix and an eigenvector corresponding to each element in the eigenvalue matrix; selecting elements in the eigenvalue matrix according to a preset rule to obtain a target eigenvalue matrix, and obtaining eigenvectors corresponding to all elements in the target eigenvalue matrix to obtain a vector matrix; and multiplying the vector matrix by the channel frequency response to obtain the channel parameter.
In one embodiment, the station area identifying apparatus 30 further includes: a sample acquisition module 400 and a target classifier determination module 500. Wherein,
the sample acquiring module 400 is configured to acquire a standard channel parameter sample and a crosstalk channel parameter sample;
the target classifier determining module 500 is configured to input the standard channel parameter sample and the crosstalk channel parameter sample into an initial classifier respectively, and train the initial classifier based on an error back propagation algorithm to obtain the target classifier.
In one embodiment, the sample acquiring module 400 is further configured to acquire a plurality of training signals sent by the target site host; determining a plurality of standard channel parameters according to the plurality of training signals to obtain a standard channel parameter sample; acquiring a plurality of crosstalk signals sent by a host in a non-target area; and determining a plurality of crosstalk channel parameters according to the plurality of crosstalk signals to obtain the crosstalk channel parameter samples.
In one embodiment, the station area identifying apparatus 30 further includes: an information acquisition module 600 and an information transmission module 700. Wherein,
the information obtaining module 600 is configured to obtain, if a current user belongs to a zone corresponding to the zone host, user terminal information corresponding to the current user, demodulate the identification signal, and obtain zone identification information and phase sequence information in the identification signal, where the phase sequence information is used to characterize a phase sequence to which the identification signal belongs.
The information sending module 700 is configured to send the user terminal information, the platform area identification information, and the phase sequence information to a management system.
For specific limitation of the area identifying device 30, reference may be made to the limitation of the area identifying method hereinabove, and the description thereof will not be repeated here. The respective modules in the above-described zone identification device 30 may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Referring to fig. 9, in one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing source data, report data and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a report generation method.
It will be appreciated by those skilled in the art that the structure shown in fig. 9 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor, when executing the computer program, performing the steps of:
acquiring an identification signal sent by a platform region host;
determining channel parameters according to the identification signals, wherein the channel parameters refer to parameters of a communication channel between the station area host and the user terminal;
and inputting the channel parameters into a target classifier obtained by training in advance, and determining whether the current user belongs to the station area corresponding to the station area host.
The specific processes and beneficial effects of executing the computer program by the computer device processor provided in the above embodiments to implement the above method steps are similar to those of the corresponding method embodiments, and are not described herein again.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring an identification signal sent by a platform region host;
determining channel parameters according to the identification signals, wherein the channel parameters refer to parameters of a communication channel between the station area host and the user terminal;
and inputting the channel parameters into a target classifier obtained by training in advance, and determining whether the current user belongs to the station area corresponding to the station area host.
The specific processes and beneficial effects of implementing the steps of the method according to the above embodiment are similar to those of the corresponding method embodiment, and are not described herein.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method for identifying a region, the method comprising:
acquiring an identification signal sent by a platform region host;
determining channel parameters according to the identification signals, wherein the channel parameters refer to parameters of a communication channel between the station area host and the user terminal;
inputting the channel parameters into a target classifier obtained by training in advance, and determining whether the current user belongs to a station area corresponding to the station area host;
wherein said determining a channel parameter from said identification signal comprises: detecting an identification sequence in the identification signal according to a matched filtering algorithm; obtaining a standard identification sequence, wherein the standard identification sequence refers to a sequence of signals sent by a station area host of a station area to which the user terminal belongs; based on a Fourier transform algorithm, determining the frequency spectrums of the identification sequence and the standard identification sequence respectively to obtain an identification frequency spectrum and a standard frequency spectrum; determining a channel frequency response according to the ratio of the identification spectrum to the standard spectrum; performing a de-averaging process on the channel frequency response to obtain a de-averaging matrix; calculating a covariance matrix of the de-averaging matrix; calculating the eigenvalue and eigenvector of each element in the covariance matrix based on an eigenvalue decomposition algorithm to obtain an eigenvalue matrix and an eigenvector corresponding to each element in the eigenvalue matrix; selecting elements in the eigenvalue matrix according to a preset rule to obtain a target eigenvalue matrix, and obtaining eigenvectors corresponding to all elements in the target eigenvalue matrix to obtain a vector matrix; and multiplying the vector matrix by the channel frequency response to obtain the channel parameter.
2. The method as recited in claim 1, further comprising:
obtaining a standard channel parameter sample and a crosstalk channel parameter sample;
and respectively inputting the standard channel parameter sample and the crosstalk channel parameter sample into an initial classifier, and training the initial classifier based on an error back propagation algorithm to obtain the target classifier.
3. The method of claim 2, wherein the obtaining standard channel parameter samples and crosstalk channel parameter samples comprises:
acquiring a plurality of training signals sent by a host computer of a target platform area;
determining a plurality of standard channel parameters according to the plurality of training signals to obtain a standard channel parameter sample;
acquiring a plurality of crosstalk signals sent by a host in a non-target area;
and determining a plurality of crosstalk channel parameters according to the plurality of crosstalk signals to obtain the crosstalk channel parameter samples.
4. The method according to claim 1, wherein the method further comprises:
if the current user belongs to the platform region corresponding to the platform region host, acquiring user terminal information corresponding to the current user, demodulating the identification signal, and acquiring platform region identification information and phase sequence information in the identification signal, wherein the phase sequence information is used for representing the phase sequence to which the identification signal belongs;
and sending the user terminal information, the platform area identification information and the phase sequence information to a management system.
5. A station area identification device, characterized in that the device comprises:
the identification signal acquisition module is used for acquiring an identification signal sent by the host computer of the platform area;
a channel parameter determining module, configured to determine a channel parameter according to the identification signal, where the channel parameter is a parameter of a communication channel between the station host and the user terminal;
the platform region determining module is used for inputting the channel parameters into a target classifier obtained by training in advance and determining whether the current user belongs to a platform region corresponding to the platform region host;
wherein said determining a channel parameter from said identification signal comprises: detecting an identification sequence in the identification signal according to a matched filtering algorithm; obtaining a standard identification sequence, wherein the standard identification sequence refers to a sequence of signals sent by a station area host of a station area to which the user terminal belongs; based on a Fourier transform algorithm, determining the frequency spectrums of the identification sequence and the standard identification sequence respectively to obtain an identification frequency spectrum and a standard frequency spectrum; determining a channel frequency response according to the ratio of the identification spectrum to the standard spectrum; performing a de-averaging process on the channel frequency response to obtain a de-averaging matrix; calculating a covariance matrix of the de-averaging matrix; calculating the eigenvalue and eigenvector of each element in the covariance matrix based on an eigenvalue decomposition algorithm to obtain an eigenvalue matrix and an eigenvector corresponding to each element in the eigenvalue matrix; selecting elements in the eigenvalue matrix according to a preset rule to obtain a target eigenvalue matrix, and obtaining eigenvectors corresponding to all elements in the target eigenvalue matrix to obtain a vector matrix; and multiplying the vector matrix by the channel frequency response to obtain the channel parameter.
6. The apparatus of claim 5, further comprising a sample acquisition module and a target classifier determination module, wherein the sample acquisition module is configured to acquire standard channel parameter samples and crosstalk channel parameter samples; the target classifier determining module is used for respectively inputting the standard channel parameter sample and the crosstalk channel parameter sample into an initial classifier, and training the initial classifier based on an error back propagation algorithm to obtain the target classifier.
7. The apparatus of claim 6, wherein the sample acquisition module is further configured to acquire a plurality of training signals sent by a target site host; determining a plurality of standard channel parameters according to the plurality of training signals to obtain a standard channel parameter sample; acquiring a plurality of crosstalk signals sent by a host in a non-target area; and determining a plurality of crosstalk channel parameters according to the plurality of crosstalk signals to obtain the crosstalk channel parameter samples.
8. The apparatus of claim 5, further comprising an information acquisition module and an information transmission module; the information acquisition module is used for acquiring user terminal information corresponding to the current user if the current user belongs to a platform region corresponding to the platform region host, demodulating the identification signal and acquiring platform region identification information and phase sequence information in the identification signal, wherein the phase sequence information is used for representing a phase sequence to which the identification signal belongs; and the information sending module is used for sending the user terminal information, the platform area identification information and the phase sequence information to a management system.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 4 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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