CN111278013A - Automatic pseudo base station identification method and device - Google Patents

Automatic pseudo base station identification method and device Download PDF

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CN111278013A
CN111278013A CN201811474578.5A CN201811474578A CN111278013A CN 111278013 A CN111278013 A CN 111278013A CN 201811474578 A CN201811474578 A CN 201811474578A CN 111278013 A CN111278013 A CN 111278013A
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information
lte cell
data prediction
lte
gsm
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CN111278013B (en
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邵锐
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China Mobile Communications Group Co Ltd
China Mobile Group Shandong Co Ltd
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China Mobile Communications Group Co Ltd
China Mobile Group Shandong Co Ltd
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Abstract

The embodiment of the invention provides a method and a device for automatically identifying a pseudo base station, wherein the method comprises the following steps: acquiring actual detection information of an LTE cell; acquiring first LTE cell data prediction information according to a first LTE data prediction model, and acquiring first mean value information and first standard deviation information through a first storage model; obtaining LTE cell relative error information according to the LTE cell actual detection information and the first LTE cell data prediction information; and combining the first mean value information, the first standard deviation information and the LTE cell relative error information according to a first judgment rule to obtain first identification result information. By using the cell unit flow RRC connection information, the problem that changes caused by natural traffic increase cannot be eliminated only by judging the RRC connection times of the LTE cell is solved, historical information is considered, influences caused by time periods such as natural fluctuation and seasons are overcome, and the identification result is more accurate.

Description

Automatic pseudo base station identification method and device
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a method and a device for automatically identifying a pseudo base station.
Background
The pseudo base station can search mobile phone information within a certain radius range by taking the pseudo base station as a center, and can forcibly send fraud, promotion and other spam short messages to the mobile phone of a user by arbitrarily pretending the mobile phone number of other people, and the pseudo base station is usually arranged in an automobile or a relatively hidden place to send the spam short messages. When the pseudo base station operates, the user mobile phone signal is forcibly connected to the device and cannot be connected to the public telecommunication network, so that the normal use of the mobile phone user is influenced, and meanwhile, if the pseudo base station and the operator network are set to be the same frequency, serious interference is brought to the operator network.
In the prior art, pseudo base station identification is mainly performed subjectively or based on judgment of an absolute threshold of a change amplitude by abnormal increase of Radio Resource Control (RRC) connection times of an operator LTE base station, but only by using the increase of the RRC connection times without considering normal RRC connection times caused by service increase due to abrupt change of the RRC connection times, erroneous judgment is easily caused, and each base station has respective RRC fluctuation characteristics including time periods such as natural fluctuation and seasons, and the accuracy of judging the pseudo base station needs to be improved.
Therefore, how to more accurately find the pseudo base station has become a problem to be solved in the industry.
Disclosure of Invention
Embodiments of the present invention provide an automatic pseudo base station identification method and apparatus, so as to solve the technical problems mentioned in the foregoing background art, or at least partially solve the technical problems mentioned in the foregoing background art.
In a first aspect, an embodiment of the present invention provides an automatic pseudo base station identification method, including:
acquiring actual detection information of an LTE cell;
acquiring first LTE cell data prediction information according to a first LTE data prediction model, and acquiring first mean value information and first standard deviation information through a first storage model;
obtaining LTE cell relative error information according to the LTE cell actual detection information and the first LTE cell data prediction information;
and combining the first mean value information, the first standard deviation information and the LTE cell relative error information according to a first judgment rule to obtain first identification result information.
In a second aspect, an embodiment of the present invention provides an apparatus for automatically identifying a pseudo base station, including:
the acquisition module is used for acquiring the actual detection information of the LTE cell;
the prediction module is used for acquiring first LTE cell data prediction information according to the first LTE data prediction model and acquiring first mean value information and first standard deviation information through the first storage model;
the calculation module is used for obtaining LTE cell relative error information according to the LTE cell actual detection information and the first LTE cell data prediction information;
and the identification module is used for combining the first mean value information, the first standard deviation information and the LTE cell relative error information according to a first judgment rule to obtain first identification result information.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the pseudo base station automatic identification method according to the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the pseudo base station automatic identification method according to the first aspect.
According to the method and the device for automatically identifying the pseudo base station, provided by the embodiment of the invention, the LTE cell flow connection historical information is calculated through the LTE cell radio resource control connection historical information, the cell flow RRC connection information is used, the problem that the change caused by the increase of natural traffic cannot be eliminated only through the judgment of the LTE cell RRC connection times is solved, a first LTE data prediction model capable of realizing the prediction function is established through the LTE cell flow connection historical information, and meanwhile, the historical information is considered through the identification result information finally obtained by obtaining the storage model of the mean value information and the standard deviation information, the influences caused by time periods such as natural fluctuation and seasons are overcome, and the identification result is more accurate.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a flowchart of an automatic pseudo base station identification method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an automatic pseudo base station identification apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 invention.
Fig. 1 is a flowchart of an automatic pseudo base station identification method according to an embodiment of the present invention, as shown in fig. 1, including:
step 110, acquiring actual detection information of the LTE cell;
step 120, acquiring first LTE cell data prediction information according to the first LTE data prediction model, and acquiring first mean value information and first standard deviation information through the first storage model;
step 130, obtaining relative error information of the LTE cell according to the actual detection information of the LTE cell and the data prediction information of the first LTE cell;
step 140, combining the first mean value information, the first standard deviation information and the LTE cell relative error information according to a first determination rule to obtain first identification result information.
Specifically, in step 110, the actual detection information of the LTE cell described herein refers to the RRC connection times information of the cell covered by the LTE base station, which is actually detected by the LTE base station.
Step 120 specifically includes acquiring first LTE cell data prediction information, specifically, determining LTE cell unit traffic connection information of a first LTE data prediction model at a certain time; the first mean information and the first standard deviation information described herein are derived from information in the LTE relative error information base of the first stored model.
The steps of establishing the first LTE data prediction model and establishing the first storage model described in the embodiments of the present invention are both performed before the step of obtaining the first LTE cell data prediction information.
Step 130 is specifically obtaining the relative error information RE of the LTE cell as described hereintThe specific steps of (a) may be:
Figure BDA0001891874700000041
wherein XtPredicting information for first LTE cell data, where utAnd actually detecting information for the LTE cell.
Step 140 specifically shows that the first determination rule described in the embodiment of the present invention specifically means if RE is presenttMean +2 × std; judging that abnormal fluctuation exists, wherein the first identification result information is that the LTE station is a pseudo base station; if REt<mean +2 × std; judging that abnormal fluctuation does not exist, wherein the first identification result information is that the LTE station is a normal base station; wherein mean refers to first mean information, std refers to first standard deviation information.
The first decision rule described here is not identified as uniform LTE cell relative error information REtThe threshold judgment standard is that the number of unit flow RRC connections and the coverage area of each LTE base station have a specific fluctuation trend, so the embodiment of the invention provides the relative error information RE for each LTE celltMaking statistics and calculating REtMean ofStandard deviation std, according to the statistical property of normal distribution, when RE istWhen the occurrence is in the region outside 2 times the standard deviation std on both sides of the mean, the probability is 4.55%, which is considered to be a small probability event, so we finally determine the first judgment rule.
According to the embodiment of the invention, the LTE cell flow connection historical information is calculated through the LTE cell radio resource control connection historical information, the cell flow RRC connection information is used, the problem that the change caused by the increase of natural traffic cannot be eliminated only through the judgment of the LTE cell RRC connection times is solved, a first LTE data prediction model capable of realizing the prediction function is established through the LTE cell flow connection historical information, and meanwhile, the identification result information finally obtained through the storage model of the average value information and the standard deviation information is obtained, the historical information is considered, the influence caused by time periods such as natural fluctuation and seasons is overcome, and the identification result of a pseudo base station is more accurate.
On the basis of the foregoing embodiment, before the step of obtaining the first LTE cell data prediction information according to the first LTE data prediction model, the method further includes:
acquiring LTE cell wireless resource control connection historical information in a first preset time period, and acquiring LTE cell unit flow connection historical information in the first preset time period according to the LTE cell wireless resource control connection historical information;
performing time-series conversion on the LTE cell unit flow connection historical information to obtain LTE cell unit flow database information;
and establishing a first LTE data prediction model according to the LTE cell unit flow database information.
Specifically, the first preset time period described in the embodiment of the present invention may refer to 4 weeks, that is, 4 × 7 × 24 hours, where the LTE cell radio resource control connection history information described herein refers to a set of LTE cell radio resource control connection information within 4 × 7 × 24 hours, and the cell data herein is iterated in a first-in first-out manner; the LTE cell traffic connection history information described in the embodiment of the present invention is a set of LTE cell radio resource control connection information/(LTE uplink data traffic + LTE downlink data traffic), and because only LTE cell radio resource control connection information cannot exclude changes due to natural traffic growth, the LTE cell traffic connection history information is specifically used in the embodiment of the present invention.
And performing batch machine learning after the LTE cell unit flow database information is obtained, and finally obtaining a first LTE data prediction model.
The time-series conversion described in the embodiment of the invention refers to converting the LTE cell unit flow connection historical information in the original data format into LTE cell unit flow database information in the time-series data format; the LTE cell unit traffic connection history information in the original data format may be:
period 1
Figure BDA0001891874700000051
Period 2
Figure BDA0001891874700000061
Period 3
Figure BDA0001891874700000062
The LTE cell unit traffic database information in the time-series data format may be:
cell sequence 1
Figure BDA0001891874700000063
Cell sequence 2
Figure BDA0001891874700000064
Cell sequence 3
Figure BDA0001891874700000071
According to the embodiment of the invention, each LTE base station collects the LTE cell unit flow connection historical information, the data influence caused by the change caused by the increase of natural traffic is eliminated, and the established first LTE data prediction model can be used for obtaining the data prediction information of the first LTE cell, so that the subsequent steps can be favorably carried out.
On the basis of the foregoing embodiment, before the step of obtaining the first mean information and the first standard deviation information through the first storage model, the method further includes:
acquiring second LTE cell data prediction information in a first preset time period according to the first LTE data prediction model;
and generating an LTE relative error information base according to the second LTE cell data prediction information and the LTE cell radio resource control connection historical information, so as to generate a first storage model according to the LTE relative error information base.
Specifically, the second LTE cell data prediction information described in the embodiment of the present invention refers to a set of LTE cell data prediction information in a first preset time period, where the first preset time period described herein may refer to 4 weeks, that is, 4 × 7 × 24 hours.
Generating a LTE cell relative error information collection RE by using the second LTE cell data prediction information and the LTE cell radio resource control connection history informationt1The method specifically comprises the following steps:
Figure BDA0001891874700000072
wherein Xt1Predicting information for second LTE cell data, where ut1And controlling connection history information for LTE cell wireless resources.
Obtaining a set RE of relative error information of an LTE cellt1Then, we can get the first storage model by the machine learning technique of the collection; here, we can obtain the relative error information set RE based on the LTE cell through the first storage modelt1And obtaining first mean information and first standard deviation information.
The embodiment of the invention acquires the relative error information collection RE of the LTE cellt1The obtained first storage model can be based on the LTE cell relative error information collection REt1And acquiring more reliable first mean value information and first standard deviation information so as to facilitate the subsequent steps.
On the basis of the foregoing embodiment, before the step of obtaining the first LTE data prediction information through the first LTE data prediction model, the method further includes:
acquiring the update history information of the GSM position of a preset area in a first preset time period;
performing time-series conversion on the historical information of the GSM position update of the preset area to obtain a GSM position update database of the preset area;
and establishing a second GSM data prediction model according to the GSM position updating database of the preset area.
Specifically, the preset area described in the embodiment of the present invention refers to a GSM base station cell in an area covered by an LTE base station cell in the above embodiment, and the GSM location update history information described in the embodiment of the present invention refers to a collection of GSM cell location update detection information in a first preset time period; the GSM position updating historical information is iterated in a first-in first-out mode; and updating the database according to the GSM position of the preset area to perform mechanical learning, and finally obtaining a second GSM data prediction model.
The time-series conversion described in the embodiments of the present invention is consistent with the time-series conversion steps described in the embodiments, and specific steps may refer to the above-mentioned parts, which are not described herein again.
The method and the device for predicting the GSM data can be used for predicting the GSM data prediction information and are beneficial to the implementation of subsequent steps.
On the basis of the foregoing embodiment, after the step of obtaining the first recognition result information, the method further includes:
acquiring GSM position updating detection information of a preset area;
acquiring GSM data prediction information through a second GSM data prediction model, and acquiring second mean value information and second standard deviation information through a second storage model;
updating detection information and the GSM data prediction information according to the GSM position of the preset area to obtain GSM relative error information;
according to a second judgment rule, combining the GSM relative error information, the second mean value information and the second standard deviation information to obtain first auxiliary identification information;
and obtaining second identification result information according to the first identification result and the first auxiliary identification information.
Specifically, in the embodiment of the present invention, the acquisition of the GSM location update detection information in the preset area may be realized according to the GSM base stations in the preset area, where the GSM location update detection information described herein refers to location update quantity information of the GSM base stations in the preset area.
Obtaining GSM relative error information RE described in the embodiments of the present inventiont2The method specifically comprises the following steps:
Figure BDA0001891874700000091
wherein, Xt2Predicting information for GSM data, ut2And updating the detection information for the GSM position of the preset area.
The second determination rule described in the embodiment of the present invention may be if REt2≥mean1+2×std1(ii) a Judging that abnormal fluctuation exists, wherein the first identification result information is that the LTE station is a pseudo base station; if REt2<mean1+2×std1(ii) a Judging that abnormal fluctuation does not exist, wherein the first identification result information is that the LTE station is a normal base station; wherein mean is1Is the first mean value information, std1Refers to the first standard deviation information.
According to the embodiment of the invention, the first auxiliary identification information is finally obtained through considering the GSM position updating detection information and through the second GSM data prediction model and the second storage model, and the judgment of the pseudo base station can be assisted through the first auxiliary identification information. The embodiment of the invention effectively considers the problem that the LTE terminal can not accurately position the pseudo base station only by depending on the statistical indexes of the LTE side because part of the pseudo base stations cause the LTE terminal to reside in the GSM network for a long time, and improves the accuracy of pseudo base station identification.
On the basis of the foregoing embodiment, before the step of obtaining the second mean information and the second standard deviation information through the second storage model, the method further includes:
acquiring second GSM data prediction information in a first preset time period according to a second GSM data prediction model;
and generating a GSM relative error information base according to the second GSM data prediction information and the preset regional GSM position updating historical information, so as to generate a second storage model according to the GSM relative error information base.
Specifically, the second GSM data prediction information described in the embodiment of the present invention refers to a collection of GSM location update detection information in a preset area within a first preset time period.
The GSM relative error information base described in the embodiment of the invention is a collection of GSM relative error information generated according to second GSM data prediction information and preset regional GSM position update historical information, so as to obtain the GSM relative error information base; and performing machine learning after obtaining the GSM relative error information base to obtain a second storage model, wherein the second storage model can output second mean value information and second standard deviation information according to the GSM relative error information base.
According to the embodiment of the invention, the GSM relative error information base is generated, so that the second storage model is established according to the GSM relative error information base to obtain the second mean value information and the second standard deviation information, and the subsequent steps are convenient to carry out.
On the basis of the above embodiment, the method further includes:
acquiring real-time interfered resource block information;
generating second auxiliary identification information according to the real-time interfered resource block information and a third judgment rule;
and generating third identification result information according to the first identification result information and the second auxiliary identification information.
The obtaining of the real-time interfered resource block information described in the embodiment of the present invention may be obtained according to a base station, where the third determination rule in the embodiment of the present invention specifically indicates that the number of interfered resource blocks is equal to 25 or 50, and the number of interfered resource blocks corresponds to 5MHZ and 10MZ bandwidths of a pseudo base station, respectively, and at this time, the obtained second auxiliary identification information is the pseudo base station; here, the second auxiliary identification information may help to identify the pseudo base station.
The embodiment of the invention assists the identification of the pseudo base station by acquiring the real-time interfered resource block information, so that the identification of the pseudo base station is more accurate.
Fig. 2 is a schematic structural diagram of an automatic pseudo base station identification device according to an embodiment of the present invention, as shown in fig. 2, including: an acquisition module 210, a prediction module 220, a calculation module 230, and a recognition module 240; the obtaining module 210 is configured to obtain actual detection information of an LTE cell; the prediction module 220 is configured to obtain first LTE cell data prediction information according to a first LTE data prediction model, and obtain first mean information and first standard deviation information through a first storage model; the calculating module 230 is configured to obtain LTE cell relative error information according to the LTE cell actual detection information and the first LTE cell data prediction information; the identification module 240 is configured to obtain first identification result information according to a first determination rule by combining the first mean information, the first standard deviation information, and the LTE cell relative error information.
The apparatus provided in the embodiment of the present invention is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.
According to the embodiment of the invention, the LTE cell flow connection historical information is calculated through the LTE cell radio resource control connection historical information, the cell flow RRC connection information is used, the problem that the change caused by the increase of natural traffic cannot be eliminated only through the judgment of the LTE cell RRC connection times is solved, a first LTE data prediction model capable of realizing the prediction function is established through the LTE cell flow connection historical information, and meanwhile, the identification result information finally obtained through the storage model of the average value information and the standard deviation information is obtained, the historical information is considered, the influence caused by time periods such as natural fluctuation and seasons is overcome, and the identification result of a pseudo base station is more accurate.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device may include: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a communication bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the communication bus 304. Processor 301 may call logic instructions in memory 303 to perform the following method: acquiring actual detection information of an LTE cell; acquiring first LTE cell data prediction information according to a first LTE data prediction model, and acquiring first mean value information and first standard deviation information through a first storage model; obtaining LTE cell relative error information according to the LTE cell actual detection information and the first LTE cell data prediction information; and combining the first mean value information, the first standard deviation information and the LTE cell relative error information according to a first judgment rule to obtain first identification result information.
In addition, the logic instructions in the memory 303 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer can execute the methods provided by the above method embodiments, for example, the method includes: acquiring actual detection information of an LTE cell; acquiring first LTE cell data prediction information according to a first LTE data prediction model, and acquiring first mean value information and first standard deviation information through a first storage model; obtaining LTE cell relative error information according to the LTE cell actual detection information and the first LTE cell data prediction information; and combining the first mean value information, the first standard deviation information and the LTE cell relative error information according to a first judgment rule to obtain first identification result information.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores server instructions, and the computer instructions enable a computer to execute the method for automatically identifying a pseudo base station provided in the foregoing embodiment, for example, the method includes: acquiring actual detection information of an LTE cell; acquiring first LTE cell data prediction information according to a first LTE data prediction model, and acquiring first mean value information and first standard deviation information through a first storage model; obtaining LTE cell relative error information according to the LTE cell actual detection information and the first LTE cell data prediction information; and combining the first mean value information, the first standard deviation information and the LTE cell relative error information according to a first judgment rule to obtain first identification result information.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Claims (10)

1. A pseudo base station automatic identification method is characterized by comprising the following steps:
acquiring actual detection information of an LTE cell;
acquiring first LTE cell data prediction information according to a first LTE data prediction model, and acquiring first mean value information and first standard deviation information through a first storage model;
obtaining LTE cell relative error information according to the LTE cell actual detection information and the first LTE cell data prediction information;
and combining the first mean value information, the first standard deviation information and the LTE cell relative error information according to a first judgment rule to obtain first identification result information.
2. The method of claim 1, wherein the step of obtaining the first LTE cell data prediction information according to the first LTE data prediction model is preceded by the method further comprising:
acquiring LTE cell wireless resource control connection historical information in a first preset time period, and acquiring LTE cell unit flow connection historical information in the first preset time period according to the LTE cell wireless resource control connection historical information;
performing time-series conversion on the LTE cell unit flow connection historical information to obtain LTE cell unit flow database information;
and establishing a first LTE data prediction model according to the LTE cell unit flow database information.
3. The method of claim 2, wherein the step of obtaining the first mean information and the first standard deviation information through the first storage model is preceded by the method further comprising:
acquiring second LTE cell data prediction information in a first preset time period according to the first LTE data prediction model;
and generating an LTE relative error information base according to the second LTE cell data prediction information and the LTE cell radio resource control connection historical information, so as to generate a first storage model according to the LTE relative error information base.
4. The method of claim 1, wherein the step of obtaining the first LTE data prediction information through the first LTE data prediction model is preceded by the method further comprising:
acquiring the update history information of the GSM position of a preset area in a first preset time period;
performing time-series conversion on the historical information of the GSM position update of the preset area to obtain a GSM position update database of the preset area;
and establishing a second GSM data prediction model according to the GSM position updating database of the preset area.
5. The method of claim 4, wherein after the step of obtaining the first recognition result information, the method further comprises:
acquiring GSM position updating detection information of a preset area;
acquiring GSM data prediction information through a second GSM data prediction model, and acquiring second mean value information and second standard deviation information through a second storage model;
updating detection information and the GSM data prediction information according to the GSM position of the preset area to obtain GSM relative error information;
according to a second judgment rule, combining the GSM relative error information, the second mean value information and the second standard deviation information to obtain first auxiliary identification information;
and obtaining second identification result information according to the first identification result and the first auxiliary identification information.
6. The method of claim 5, wherein the step of obtaining the second mean information and the second standard deviation information through the second storage model is preceded by the method further comprising:
acquiring second GSM data prediction information in a first preset time period according to a second GSM data prediction model;
and generating a GSM relative error information base according to the second GSM data prediction information and the preset regional GSM position updating historical information, so as to generate a second storage model according to the GSM relative error information base.
7. The method of any of claims 1-3, further comprising:
acquiring real-time interfered resource block information;
generating second auxiliary identification information according to the real-time interfered resource block information and a third judgment rule;
and generating third identification result information according to the first identification result information and the second auxiliary identification information.
8. An automatic pseudo base station identification device, comprising:
the acquisition module is used for acquiring the actual detection information of the LTE cell;
the prediction module is used for acquiring first LTE cell data prediction information according to the first LTE data prediction model and acquiring first mean value information and first standard deviation information through the first storage model;
the calculation module is used for obtaining LTE cell relative error information according to the LTE cell actual detection information and the first LTE cell data prediction information;
and the identification module is used for combining the first mean value information, the first standard deviation information and the LTE cell relative error information according to a first judgment rule to obtain first identification result information.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the pseudo base station automatic identification method according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the pseudo base station automatic identification method according to any one of claims 1 to 7.
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