CN117544970A - Method, device, equipment and storage medium for updating interference feature library - Google Patents
Method, device, equipment and storage medium for updating interference feature library Download PDFInfo
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Abstract
The application provides an updating method, device, equipment and storage medium of an interference feature library, which are used for collecting network management data of interference cells in a target area in a preset duration period to generate frequency domain features and time domain features corresponding to the cells; comparing the frequency domain features and the time domain features of each cell with feature samples in a preset interference feature library respectively to obtain comparison results; clustering the frequency domain features and the time domain features of the unknown interference based on the K-means algorithm, updating the unknown frequency domain features and the unknown time domain features into an interference feature library, carrying out interference positioning on cells with the same unknown frequency domain features and the same unknown time domain features, confirming positioning of an interference source, acquiring an interference name and an investigation suggestion of the interference source according to positioning of the interference source, and filling the interference name and the investigation suggestion, so that the effects of positioning the unknown interference source and rapidly expanding the interference feature library are achieved.
Description
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, and a storage medium for updating an interference feature library.
Background
In a wireless communication system, a cell (cell) refers to an area covered by one base station or a part of a base station (sector antenna) in a wireless communication system, in which a mobile station can reliably communicate with the base station through a wireless channel. Interference is a key factor affecting network quality, and has significant impact on call quality, dropped calls, handover, congestion, network coverage, capacity, etc. At present, a wireless communication system mainly in the 900MHz frequency band has a large number of illegal repeater stations which are installed by users, so that the problem of stronger uplink interference is caused.
The traditional main interference checking mode is to adopt a manual mode to identify and position interference when the cell noise is high, for example, interference data is acquired on site through a frequency spectrograph, time-frequency waveforms of different interference types are identified, or physical layer data is acquired through a special test terminal and demodulated, the Identification (ID) of an interference cell is identified, and then an industrial parameter table is checked for checking. Therefore, there is a need for an automated and intelligent way to identify and locate interference, wherein establishing an interference feature library is the basis for intelligent processing, and interference can be identified and located according to the type of interference collected in the interference feature library. However, the interference types not recorded in the feature library cannot be processed in time in this way, so that the interference feature library needs to be updated regularly.
The current updating mode of the interference feature library is basically an off-line mode, and when a new interference type appears in the investigation, technicians responsible for feature library maintenance manually collect new interference sources, summarize the features of the new interference sources and update the features to the existing interference feature library. This approach is highly hysteretic, sporadic and inefficient in updating.
Disclosure of Invention
The application provides an interference feature library updating method, device, equipment and storage medium, which are used for solving the problems of high hysteresis and scattered and low efficiency of updating in the prior art.
In a first aspect, the present application provides a method for updating an interference feature library, including:
collecting network management data of interference cells in a target area in a preset duration period, and generating frequency domain features and time domain features corresponding to each cell;
comparing the frequency domain features and the time domain features of each cell with feature samples in a preset interference feature library respectively to obtain comparison results, wherein the comparison results comprise an interference type and unknown interference, the feature samples comprise frequency domain feature samples and time domain feature samples, and the interference feature library comprises mapping relations between each frequency domain feature sample and the interference type and between each time domain feature sample and the interference type;
Clustering the frequency domain features and the time domain features of which the comparison results are unknown interference based on a K-means algorithm, taking the obtained frequency domain feature clustering center as the unknown frequency domain features, taking the obtained time domain feature clustering center as the unknown time domain features, and respectively updating the unknown frequency domain features and the unknown time domain features into an interference feature library;
selecting cells with the same unknown frequency domain characteristic and the same unknown time domain characteristic as target cells, carrying out interference positioning on the target cells, confirming positioning of an interference source, acquiring an interference name and an investigation suggestion of the interference source according to the positioning of the interference source, and filling the interference name and the investigation suggestion into the unknown frequency domain characteristic and the unknown time domain characteristic corresponding to the target cells.
Optionally, as described above, the method includes the step of collecting network management data of an interfering cell in the target area, where the network management data includes a first base noise average value of a physical resource block in each period and a second base noise average value of a physical resource block in each frequency band, and generating a frequency domain feature and a time domain feature corresponding to each cell, and the method includes:
generating a frequency domain array according to the frequency band sequence from the second base noise average value of the cell, and taking the frequency domain array as the frequency domain characteristic of the cell;
Generating a time domain array according to a time period sequence according to a first background noise average value of a cell in each time period in a preset time period, and taking the time domain array as the time domain characteristic of the cell.
Optionally, in the method as described above, the comparing the frequency domain feature and the time domain feature of each cell with feature samples in a preset interference feature library respectively to obtain a comparison result includes:
calculating the similarity between the frequency domain features and the frequency domain feature samples, taking the frequency domain feature sample with the highest similarity with the frequency domain features as a first feature sample, taking the highest similarity as a first similarity, and judging whether the first similarity is larger than a first similarity threshold value or not;
if yes, judging that the comparison result of the frequency domain features is the interference type corresponding to the first feature sample;
if not, judging the comparison result of the frequency domain characteristics as unknown interference;
calculating the similarity between the time domain feature and the time domain feature sample, taking the time domain feature sample with the highest similarity with the time domain feature as a second feature sample, taking the highest similarity as a second similarity, and judging whether the second similarity is larger than a second similarity threshold;
If yes, judging that the comparison result of the time domain features is the interference type corresponding to the second feature sample;
if not, judging the comparison result of the time domain features as unknown interference.
Optionally, in the method as described above, the clustering the frequency domain feature and the time domain feature of the unknown interference based on the K-means algorithm, using the obtained frequency domain feature cluster center as the unknown frequency domain feature, and using the obtained frequency domain feature cluster center as the unknown time domain feature includes:
taking the frequency domain characteristic with the comparison result of unknown interference as a first data set, and selecting a first preparation clustering center from the first data set by combining the frequency domain characteristic sample based on a preset first K value;
the first preparation clustering center is adopted, K-means algorithm operation is carried out by combining the frequency domain features with the comparison results of unknown interference, an updated first clustering center is obtained, and the first clustering center is used as the unknown frequency domain feature;
taking the time domain characteristics with the comparison result of unknown interference as a second data set, and selecting a second preparation clustering center from the second data set by combining the time domain characteristic samples based on a preset second K value;
and carrying out K-means algorithm operation by adopting the second preparation clustering center and combining time domain features with all comparison results as unknown interference to obtain an updated second clustering center, and taking the second clustering center as the unknown time domain feature.
Optionally, the method as described above, wherein the selecting a first preliminary clustering center from the first data set in combination with the frequency domain feature samples includes:
acquiring a third class center set in the frequency domain feature sample, wherein the third class center set comprises a plurality of third class centers, calculating a first distance between the frequency domain feature of the unknown interference and each third class center as a comparison result, and taking the maximum value of the first distance as a first target distance;
and traversing the frequency domain features of which the comparison results are unknown interference, and taking the first K-value frequency domain features with the largest first target distance as a first preliminary clustering center.
Optionally, the method as described above, the selecting a second preliminary clustering center from the second data set in combination with the time domain feature samples includes:
a third class center set in the time domain feature sample is obtained, the third class center set comprises a plurality of third class centers, a second distance between the time domain feature of the unknown interference and each third class center is calculated according to the comparison result, and the maximum value of the second distance is used as a second target distance;
and traversing the time domain features of which the comparison results are unknown interference, and taking the second K-value time domain features with the largest second target distance as a second preliminary clustering center.
Optionally, the method for performing interference positioning on the target cell and confirming positioning of an interference source includes:
selecting a cell with a distance smaller than a preset value from a target cell as a cooperative cell in the target area;
and confirming the positioning of the interference source by adopting a triangular positioning method according to the positioning of the target cell and the cooperative cell.
In a second aspect, the present application provides an apparatus for updating an interference feature library, including:
the acquisition module is used for collecting network management data of the interference cells in the target area in a preset duration period and generating frequency domain features and time domain features corresponding to the cells;
the comparison module is used for respectively comparing the frequency domain characteristics and the time domain characteristics of each cell with characteristic samples in a preset interference characteristic library to obtain a comparison result, wherein the comparison result comprises an interference type and unknown interference, the characteristic samples comprise frequency domain characteristic samples and time domain characteristic samples, and the interference characteristic library comprises mapping relations between each frequency domain characteristic sample and the interference type and between each time domain characteristic sample and the interference type;
the updating module is used for respectively clustering the frequency domain features and the time domain features of which the comparison results are unknown interference based on a K-means algorithm, taking the obtained frequency domain feature clustering center as an unknown frequency domain feature, taking the obtained time domain feature clustering center as an unknown time domain feature, and respectively updating the unknown frequency domain features and the unknown time domain features into an interference feature library;
The filling module is used for selecting cells with the same unknown frequency domain characteristics and the same unknown time domain characteristics as target cells, carrying out interference positioning on the target cells, confirming positioning of an interference source, acquiring an interference name and an investigation suggestion of the interference source according to the positioning of the interference source, and filling the interference name and the investigation suggestion into the unknown frequency domain characteristics and the unknown time domain characteristics corresponding to the target cells.
In a third aspect, the present application provides an electronic device, including a memory, a processor, and computer-executable instructions stored in the memory and executable on the processor, the processor implementing the interference feature library updating method of any one of the first aspects when executing the computer-executable instructions.
In a fourth aspect, the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the interference feature library updating method of any one of the first aspects above.
According to the method, the device, the equipment and the storage medium for updating the interference feature library, network management data of interference cells in a target area are collected in a preset duration period, and frequency domain features and time domain features corresponding to the cells are generated; comparing the frequency domain features and the time domain features of each cell with feature samples in a preset interference feature library respectively to obtain comparison results, wherein the comparison results comprise an interference type and unknown interference, the feature samples comprise frequency domain feature samples and time domain feature samples, and the interference feature library comprises mapping relations between each frequency domain feature sample and the interference type and between each time domain feature sample and the interference type; clustering the frequency domain features and the time domain features of which the comparison results are unknown interference based on a K-means algorithm, taking the obtained frequency domain feature clustering center as an unknown frequency domain feature, taking the obtained frequency domain feature clustering center as an unknown time domain feature, respectively updating the unknown frequency domain feature and the unknown time domain feature into an interference feature library, and selecting cells with the same unknown frequency domain feature and the same unknown time domain feature as target cells; and carrying out interference positioning on the target cell, confirming the positioning of an interference source, acquiring an interference name and an investigation suggestion of the interference source according to the positioning of the interference source, filling the interference name and the investigation suggestion into an unknown frequency domain feature and an unknown time domain feature corresponding to the target cell, and realizing the means of positioning the unknown interference source by utilizing the co-positioning of cells clustered into the same frequency domain class and the same time domain class in a certain area, thereby realizing the effects of positioning the unknown interference source and rapidly expanding an interference feature library.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is an application scenario schematic diagram of an interference feature library updating method provided in an embodiment of the present application.
Fig. 2 is a flowchart of an interference feature library updating method provided in an embodiment of the present application.
Fig. 3 is a schematic diagram of an interference feature library updating device according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an electronic device of an interference feature library updating apparatus according to an embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
In the related art, the current updating mode of the interference feature library is basically an off-line mode, and when a new interference type appears in the investigation, technicians responsible for feature library maintenance manually collect new interference sources, summarize the features of the new interference sources, update the features of the new interference sources to the existing interference feature library, and the feature library updating of the method is scattered and low-efficiency. In addition, the online feature library update is explored, the type recognition is carried out based on the existing feature library, unrecognized samples are sampled, and the samples are sent to a feature library analysis platform; the feature library analysis platform counts the statistical features and behavior pattern features of the sample, matches the statistical features and behavior pattern features with unrecorded types, determines corresponding statistical features and behavior pattern features, and forms a feature library file or a feature entry; and then updated into the protocol feature library. In the method, in actual interference management, unidentified samples are matched with the types which are not stored and recorded at present, and if the matching is successful, the types of the interference which are not recorded cannot be processed, so that the batch updating of the feature library is not facilitated.
Aiming at the technical problems, the embodiment of the application aims to provide an interference feature library updating method, device, equipment and storage medium, wherein the core concept of the method is as follows: based on the comparison of the frequency domain features and the time domain features of each cell with the feature samples in the preset interference feature library, the comparison result is obtained, the frequency domain features and the time domain features of the unknown interference are further clustered through the K-means algorithm, and the co-location of the cells clustered into the same frequency domain class and the same time domain class in a certain area is utilized, so that the location of the unknown interference source is realized, the application range of the platform is greatly expanded, and the detection and the elimination of the high-efficiency power-assisted interference are further realized.
In order to better understand the scheme of the embodiment of the present application, an application scenario related to the embodiment of the present application is first described below.
Referring to fig. 1, fig. 1 is a schematic application scenario diagram of an interference feature library updating method provided in an embodiment of the present application, and as shown in fig. 1, the method includes a network device 100 and a server 200. The network device 100 may be a device for communicating with a terminal device in a target area, the network device 100 may be a base station (base transceiver station, BTS) in a global system for mobile communications (global system of mobile communication, GSM) system or code division multiple access (code division multiple access, CDMA), a base station (nodeB, NB) in a wideband code division multiple access (wide band code division multiple access, WCDMA) system, an evolved base station (evolutional nodeB, eNB or eNodeB) in an LTE system, a wireless controller in a cloud wireless access network (cloud radio access network, CRAN) scenario, or the network device may be a relay station, an access point, a vehicle-mounted device, a wearable device, a network device in a G network, or a network device in an evolved PLMN network, etc., which is not limited in this application. The wireless communication system constructed by the network device 100 in the target area may be interfered due to an illegal repeater installed by the user, installation of a gate of a parking lot, and the like.
In this embodiment, the server 200 may generate frequency domain features and time domain features corresponding to each cell by collecting network management data of interference cells in the target area, and compare the frequency domain features and the time domain features based on a preset interference feature library, and if the comparison result is not a known interference type, divide the comparison result into unknown interference; further, clustering the frequency domain features and the time domain features of which the comparison results are unknown interference based on a K-means algorithm to obtain the unknown frequency domain features and the unknown time domain features, and updating the unknown frequency domain features and the unknown time domain features to an interference feature library; selecting cells with the same unknown frequency domain characteristics and the same unknown time domain characteristics as target cells, positioning the target cells to acquire interference names and investigation suggestions of interference sources, filling the interference names and investigation suggestions into an interference characteristic library, and finishing updating of the interference characteristic library.
The following describes the technical solution of the present application and how the technical solution of the present application solves the above 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.
Fig. 2 is a flowchart of an interference feature library updating method provided in an embodiment of the present application. As shown in fig. 2, the method of the present embodiment includes:
s201: and collecting network management data of the interference cells in the target area in a preset duration period, and generating frequency domain features and time domain features corresponding to the cells.
The execution subject of the embodiment of the application may be a server, or may be an interference feature library updating system in the server, where the interference feature library updating system may be implemented by software.
It can be understood that the frequency domain feature in this embodiment is a feature related to a frequency band in the network management data, and specifically may refer to a background noise average value of each frequency band physical resource module (Physical Resource Block, PRB) of the cell; in this embodiment, the time domain feature is a feature related to a time period in the network management data, and specifically may refer to a background noise average value of PRBs of each time period (for example, 1 hour as time granularity) of a cell.
S202: comparing the frequency domain features and the time domain features of each cell with feature samples in a preset interference feature library respectively to obtain comparison results, wherein the comparison results comprise an interference type and unknown interference, the feature samples comprise frequency domain feature samples and time domain feature samples, and the interference feature library comprises mapping relations between each frequency domain feature sample and the interference type and between each time domain feature sample and the interference type.
In this step, the preset interference feature library is previously constructed by the frequency domain feature sample and the time domain feature sample, so that the frequency domain feature and the time domain feature generated in the previous step can be respectively compared with the feature samples in the interference feature library. Specifically, whether the interference feature library has the target feature sample matched with the frequency domain feature or the time domain feature can be judged by calculating the similarity, if so, the interference type corresponding to the target feature sample is used as the interference type of the frequency domain feature or the time domain feature, and if not, the comparison result of the frequency domain feature or the time domain feature is confirmed to be unknown interference.
S203: clustering the frequency domain features and the time domain features of which the comparison results are unknown interference based on a K-means algorithm, taking the obtained frequency domain feature clustering center as the unknown frequency domain features, taking the obtained time domain feature clustering center as the unknown time domain features, and respectively updating the unknown frequency domain features and the unknown time domain features into an interference feature library.
In this step, for the frequency domain features and the time domain features, the comparison result is unknown interference, that is, the cell interference is not identified, an unsupervised K-means algorithm may be used to cluster the frequency domain array and the time domain array, so that M types of frequency domain feature cluster centers and N types of time domain feature cluster centers are obtained by calculation according to preset different K values (for example, M sets are set for the frequency domain array) and the cluster centers are updated into the feature library.
S204: selecting cells with the same unknown frequency domain characteristic and the same unknown time domain characteristic as target cells to perform interference positioning on the target cells, confirming positioning of an interference source, acquiring an interference name and an investigation suggestion of the interference source according to the positioning of the interference source, and filling the interference name and the investigation suggestion into the unknown frequency domain characteristic and the unknown time domain characteristic corresponding to the target cells.
It can be understood that, due to the propagation characteristics of electromagnetic waves, the external interference presents regional characteristics, that is, cells in a region may present similar background noise frequency domain characteristics and time domain characteristics, so that a target cell is determined based on the same unknown frequency domain characteristics and the cells with the same unknown time domain characteristics, and positioning of an interference source is performed according to the target cell, so as to obtain an interference name and an investigation suggestion, so as to fill in information of the unknown frequency domain characteristics and the unknown time domain characteristics in an interference characteristic library.
According to the interference feature library updating method provided by the embodiment, network management data of interference cells in a target area are collected in a preset duration period, and frequency domain features and time domain features corresponding to the cells are generated; comparing the frequency domain features and the time domain features of each cell with feature samples in a preset interference feature library respectively to obtain comparison results, wherein the comparison results comprise an interference type and unknown interference, the feature samples comprise frequency domain feature samples and time domain feature samples, and the interference feature library comprises mapping relations between each frequency domain feature sample and the interference type and between each time domain feature sample and the interference type; clustering the frequency domain features and the time domain features of which the comparison results are unknown interference based on a K-means algorithm, taking the obtained frequency domain feature clustering center as an unknown frequency domain feature, taking the obtained frequency domain feature clustering center as an unknown time domain feature, respectively updating the unknown frequency domain feature and the unknown time domain feature into an interference feature library, and selecting cells with the same unknown frequency domain feature and the same unknown time domain feature as target cells; and carrying out interference positioning on the target cell, confirming the positioning of an interference source, acquiring an interference name and an investigation suggestion of the interference source according to the positioning of the interference source, filling the interference name and the investigation suggestion into an unknown frequency domain feature and an unknown time domain feature corresponding to the target cell, and realizing the means of positioning the unknown interference source by utilizing the co-positioning of cells clustered into the same frequency domain class and the same time domain class in a certain area, thereby realizing the effects of positioning the unknown interference source and rapidly expanding an interference feature library.
The technical scheme of the above-mentioned interference feature library updating method is described in detail below.
In a possible implementation manner, the network management data includes a first base noise average value of a physical resource block in each period and a second base noise average value of a physical resource block in each frequency band, and the method for updating an interference feature library provided in this embodiment determines a time domain feature according to the first base noise average value of each cell in the network management data, and determines a frequency domain feature according to the second base noise average value of each cell.
Specifically, collecting network management data of an interference cell in a target area, generating a frequency domain feature and a time domain feature corresponding to each cell, including: generating a frequency domain array according to the frequency band sequence from the second base noise average value of the cell, and taking the frequency domain array as the frequency domain characteristic of the cell; generating a time domain array according to a time period sequence according to a first background noise average value of a cell in each time period in a preset time period, and taking the time domain array as the time domain characteristic of the cell.
It can be understood that, the network management data of the interfering cell may further include a base station name, a cell name, a date, a time period, and the like, when the acquisition duration is 24 hours and 1 hour is used as the duration granularity to calculate, then the time domain array t= [ the first background noise average value at 0 and the first background noise average value at 1..the first background noise average value at 23 ], the frequency domain array f= [ the second background noise average value of the PRB 0 uplink, the second background noise average value of the PRB 1 uplink, ], the PRB N uplink second background noise average value ], where N is the maximum PRB number-1.
In this embodiment, the time domain feature is determined according to the first bottom noise average value of each cell in the network management data, the frequency domain feature is determined according to the second bottom noise average value of each cell, and the data of the interference cell is analyzed by combining the frequency domain feature and the time domain feature, so that more accurate and more reasonable determination of the interference type and the interference source is facilitated.
In a possible implementation manner, the method for updating the interference feature library provided in this embodiment determines whether the frequency domain feature or the time domain feature can match the feature sample by calculating the similarity between the feature sample and the frequency domain feature/time domain feature, so as to determine the comparison result.
Specifically, for the frequency domain feature, the similarity between the frequency domain feature and the frequency domain feature sample can be calculated, the frequency domain feature sample with the highest similarity with the frequency domain feature is taken as a first feature sample, the highest similarity is taken as a first similarity, and whether the first similarity is larger than a first similarity threshold value is judged; if yes, judging that the comparison result of the frequency domain features is the interference type corresponding to the first feature sample; if not, judging the comparison result of the frequency domain characteristics as unknown interference.
Similarly, for the time domain feature, the similarity between the time domain feature and the time domain feature sample can be calculated, the time domain feature sample with the highest similarity with the time domain feature is taken as a second feature sample, the highest similarity is taken as a second similarity, and whether the second similarity is larger than a second similarity threshold value is judged; if yes, judging that the comparison result of the time domain features is the interference type corresponding to the second feature sample; if not, judging the comparison result of the time domain features as unknown interference.
It will be appreciated that the first similarity threshold and the second similarity threshold may be the same value or different values based on different requirements; the first similarity threshold/the second similarity threshold may also be the same or different for different types of interference.
In this embodiment, by calculating the similarity between the feature sample and the frequency domain feature/time domain feature, it is determined whether the frequency domain feature or the time domain feature can be matched with the feature sample, so as to determine the comparison result, and it is able to quickly determine whether the frequency domain feature/time domain feature meets the known interference type, so that it is convenient to locate the interference and update the interference feature library subsequently.
In a possible implementation manner, the method for updating the interference feature library provided in this embodiment combines feature samples to select a first preliminary clustering center/a second preliminary clustering center from frequency domain features/time domain features with comparison results of unknown interference, and then combines the first preliminary clustering center/the second preliminary clustering center with each frequency domain feature with comparison results of unknown interference to perform K-means algorithm operation to obtain final unknown frequency domain features/unknown time domain features.
Specifically, for the frequency domain features, taking the frequency domain features with the comparison result of unknown interference as a first data set, and selecting a first preparation clustering center from the first data set by combining the frequency domain feature samples based on a preset first K value; and carrying out K-means algorithm operation by adopting the first preparation clustering center and combining the frequency domain features with the comparison results of unknown interference to obtain an updated first clustering center, and taking the first clustering center as the unknown frequency domain feature.
It will be appreciated that the step of selecting a first preliminary clustering center from the first data set in combination with the frequency domain feature samples may comprise: acquiring a third class center set in the frequency domain feature sample, wherein the third class center set comprises a plurality of third class centers, calculating a first distance between the frequency domain feature of the unknown interference and each third class center as a comparison result, and taking the maximum value of the first distance as a first target distance; and traversing the frequency domain features of which the comparison results are unknown interference, and taking the first K-value frequency domain features with the largest first target distance as a first preliminary clustering center.
Specifically, a point is randomly selected from the first data set, a first distance between the point and the third class center is calculated, the maximum value of the first distance is used as a first target distance, then the first data set is traversed until a first K-value point (frequency domain feature) is selected, and a first K-value frequency domain feature with the maximum first target distance is used as a first preliminary clustering center. And then using the first K value first preparation clustering centers to run a standard K-means algorithm, distributing the frequency domain feature with each comparison result being unknown interference to the cluster with the nearest center point, and updating the center point position (changing the category relation between the sample and the cluster) for each cluster with the iteration completion until the termination condition is reached, wherein the updated first clustering centers are the unknown frequency domain features.
Likewise, regarding the time domain characteristics, taking the time domain characteristics with the comparison result of unknown interference as a second data set, and selecting a second preparation clustering center from the second data set by combining the time domain characteristics based on a preset second K value; and carrying out K-means algorithm operation by adopting the second preparation clustering center and combining time domain features with all comparison results as unknown interference to obtain an updated second clustering center, and taking the second clustering center as the unknown time domain feature.
It will be appreciated that the step of selecting a second preliminary clustering center from the second data set in combination with the time domain feature samples may comprise: a third class center set in the time domain feature sample is obtained, the third class center set comprises a plurality of third class centers, a second distance between the time domain feature of the unknown interference and each third class center is calculated according to the comparison result, and the maximum value of the second distance is used as a second target distance; and traversing the time domain features of which the comparison results are unknown interference, and taking the second K-value time domain features with the largest second target distance as a second preliminary clustering center.
In this embodiment, the unrecognized cell frequency domain arrays may be clustered into a first K value class, the unrecognized time domain arrays may be clustered into a second K value class, and the clustering center may be updated into the feature library, and meanwhile, the interference type code, the feature description, the frequency band and the bandwidth information may be filled.
In this embodiment, a first preliminary clustering center/second preliminary clustering center is selected from frequency domain features/time domain features with unknown interference by combining feature samples, then the first preliminary clustering center/second preliminary clustering center is combined with frequency domain features with unknown interference by each comparison result to perform K-means algorithm operation, so as to obtain final unknown frequency domain features/unknown time domain features, improvement is performed based on the K-means algorithm, selection of the preliminary clustering center is performed by combining feature samples, and iterative operation is performed by combining the frequency domain features with unknown interference by the comparison result, so that the obtained convergence result better meets the requirement of the application on the clustering center.
In a possible implementation manner, the method for updating the interference feature library provided in this embodiment realizes the positioning of the unknown interference source by using the co-positioning of cells clustered into the same frequency domain class and the same time domain class in a certain area.
Specifically, performing interference positioning on the target cell, and confirming positioning of an interference source, including: selecting a cell with a distance smaller than a preset value from a target cell as a cooperative cell in the target area; and confirming the positioning of the interference source by adopting a triangular positioning method according to the positioning of the target cell and the cooperative cell.
It can be understood that, because of the propagation characteristics of electromagnetic waves, external interference presents regional characteristics, that is, cells in a region may present similar background noise frequency domain characteristics and time domain characteristics, and for unidentified interference types, cells clustered into the same frequency domain class and the same time domain class are selected as a group to perform interference positioning. And selecting cells with the distance within a preset value from the cells clustered into a group to perform cooperative interference source positioning, such as a triangulation positioning method, so as to position the interference source. After the position of the interference source is positioned, further manual on-site confirmation is carried out, a specific interference name is determined, and an investigation suggestion is given. And further writing the interference name and the investigation suggestion into a frequency domain sample library and a time domain sample library of the interference feature library. Therefore, a new interference source type is self-learned in one-time network operation, the interference positioning of unknown interference names is completed, and meanwhile, an interference feature library is expanded.
In the embodiment, the unknown interference source positioning is realized by utilizing the co-positioning of the cells clustered into the same frequency domain class and the same time domain class in a certain area, so that the application range of the platform is greatly expanded, and the efficient power-assisted interference is more effectively detected and cleared.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required in the present application.
It should be further noted that, although the steps in the flowchart are sequentially shown as indicated by arrows, the steps are not necessarily sequentially performed in the order 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 a portion of the steps in the flowcharts 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, the order in which the sub-steps or stages are performed is not necessarily sequential, and may be performed in turn or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Fig. 3 is a schematic diagram of an interference feature library updating device according to an embodiment of the present application. As shown in fig. 3, the interference feature library updating apparatus includes:
the acquisition module 31 is configured to collect network management data of an interference cell in a target area in a preset duration period, and generate a frequency domain feature and a time domain feature corresponding to each cell;
the comparison module 32 is configured to compare the frequency domain features and the time domain features of each cell with feature samples in a preset interference feature library, to obtain a comparison result, where the comparison result includes an interference type and an unknown interference, the feature samples include a frequency domain feature sample and a time domain feature sample, and the interference feature library includes a mapping relation for storing each frequency domain feature sample and an interference type, and each time domain feature sample and an interference type;
an updating module 33, configured to cluster the frequency domain feature and the time domain feature of which the comparison result is unknown interference based on a K-means algorithm, take the obtained frequency domain feature cluster center as an unknown frequency domain feature, take the obtained time domain feature cluster center as an unknown time domain feature, and update the unknown frequency domain feature and the unknown time domain feature into an interference feature library respectively;
And the filling module 34 is used for selecting cells with the same unknown frequency domain characteristics and the same unknown time domain characteristics as target cells, carrying out interference positioning on the target cells, confirming the positioning of an interference source, acquiring the interference name and the investigation suggestion of the interference source according to the positioning of the interference source, and filling the interference name and the investigation suggestion into the unknown frequency domain characteristics and the unknown time domain characteristics corresponding to the target cells.
In one possible design, the network management data includes a first base noise average value of the physical resource blocks in each period and a second base noise average value of the physical resource blocks in each frequency band, and the acquisition module 31 is specifically configured to:
generating a frequency domain array according to the frequency band sequence from the second base noise average value of the cell, and taking the frequency domain array as the frequency domain characteristic of the cell;
generating a time domain array according to a time period sequence according to a first background noise average value of a cell in each time period in a preset time period, and taking the time domain array as the time domain characteristic of the cell.
In one possible design, it is used in particular:
calculating the similarity between the frequency domain features and the frequency domain feature samples, taking the frequency domain feature sample with the highest similarity with the frequency domain features as a first feature sample, taking the highest similarity as a first similarity, and judging whether the first similarity is larger than a first similarity threshold value or not;
If yes, judging that the comparison result of the frequency domain features is the interference type corresponding to the first feature sample;
if not, judging the comparison result of the frequency domain characteristics as unknown interference;
calculating the similarity between the time domain feature and the time domain feature sample, taking the time domain feature sample with the highest similarity with the time domain feature as a second feature sample, taking the highest similarity as a second similarity, and judging whether the second similarity is larger than a second similarity threshold;
if yes, judging that the comparison result of the time domain features is the interference type corresponding to the second feature sample;
if not, judging the comparison result of the time domain features as unknown interference.
In one possible design, the update module 33 is specifically configured to:
taking the frequency domain characteristic with the comparison result of unknown interference as a first data set, and selecting a first preparation clustering center from the first data set by combining the frequency domain characteristic sample based on a preset first K value;
the first preparation clustering center is adopted, K-means algorithm operation is carried out by combining the frequency domain features with the comparison results of unknown interference, an updated first clustering center is obtained, and the first clustering center is used as the unknown frequency domain feature;
Taking the time domain characteristics with the comparison result of unknown interference as a second data set, and selecting a second preparation clustering center from the second data set by combining the time domain characteristic samples based on a preset second K value;
and carrying out K-means algorithm operation by adopting the second preparation clustering center and combining time domain features with all comparison results as unknown interference to obtain an updated second clustering center, and taking the second clustering center as the unknown time domain feature.
In one possible design, the update module 33 is specifically configured to:
acquiring a third class center set in the frequency domain feature sample, wherein the third class center set comprises a plurality of third class centers, calculating a first distance between the frequency domain feature of the unknown interference and each third class center as a comparison result, and taking the maximum value of the first distance as a first target distance;
and traversing the frequency domain features of which the comparison results are unknown interference, and taking the first K-value frequency domain features with the largest first target distance as a first preliminary clustering center.
In one possible design, the update module 33 is specifically configured to:
a third class center set in the time domain feature sample is obtained, the third class center set comprises a plurality of third class centers, a second distance between the time domain feature of the unknown interference and each third class center is calculated according to the comparison result, and the maximum value of the second distance is used as a second target distance;
And traversing the time domain features of which the comparison results are unknown interference, and taking the second K-value time domain features with the largest second target distance as a second preliminary clustering center.
In one possible design, the fill module 34 is specifically configured to:
selecting a cell with a distance smaller than a preset value from a target cell as a cooperative cell in the target area;
and confirming the positioning of the interference source by adopting a triangular positioning method according to the positioning of the target cell and the cooperative cell.
It should be understood that the above-described device embodiments are merely illustrative, and that the device of the present application may be implemented in other ways. For example, the division of the units/modules in the above embodiments is merely a logic function division, and there may be another division manner in actual implementation. For example, multiple units, modules, or components may be combined, or may be integrated into another system, or some features may be omitted or not performed.
In addition, each functional unit/module in each embodiment of the present application may be integrated into one unit/module, or each unit/module may exist alone physically, or two or more units/modules may be integrated together, unless otherwise specified. The integrated units/modules described above may be implemented either in hardware or in software program modules.
Fig. 4 is a schematic structural diagram of an electronic device of an interference feature library updating apparatus according to an embodiment of the present application. As shown in fig. 4, the electronic device of this embodiment includes: at least one processor 40 (only one shown in fig. 4), a memory 41, and a computer program stored in the memory 41 and executable on the at least one processor 40, the processor 40 implementing the steps in any of the various method embodiments described above when executing the computer program.
The electronic device may include, but is not limited to, a processor 40, a memory 41. It will be appreciated by those skilled in the art that fig. 4 is merely an example of an electronic device and is not meant to be limiting, and may include more or fewer components than shown, or may combine certain components, or different components, such as may also include input-output devices, network access devices, etc.
The processor 40 may be a central processing unit (Central Processing Unit, CPU), the processor 40 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The specific implementation process of the processor 401 may refer to the above-mentioned method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
The memory 41 may in some embodiments be an internal storage unit of the electronic device, such as a memory of the electronic device. The memory 41 may in other embodiments also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 41 may also include both an internal storage unit and an external storage device of the electronic device. The memory 41 is used to store an operating system, application programs, boot loader (BootLoader), data, and other programs and the like, such as program codes of computer programs and the like. The memory 41 may also be used to temporarily store data that has been output or is to be output.
The embodiments of the present application also provide a computer readable storage medium storing a computer program, where the computer program when executed by a processor implements steps of the foregoing method embodiments.
The computer readable storage medium described above may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). The processor and the readable storage medium may reside as discrete components in the electronic device described above.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments. The technical features of the foregoing embodiments may be arbitrarily combined, and for brevity, all of the possible combinations of the technical features of the foregoing embodiments are not described, however, all of the combinations of the technical features should be considered as being within the scope of the disclosure.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. An interference feature library updating method, comprising:
collecting network management data of interference cells in a target area in a preset duration period, and generating frequency domain features and time domain features corresponding to each cell;
comparing the frequency domain features and the time domain features of each cell with feature samples in a preset interference feature library respectively to obtain comparison results, wherein the comparison results comprise an interference type and unknown interference, the feature samples comprise frequency domain feature samples and time domain feature samples, and the interference feature library comprises mapping relations between each frequency domain feature sample and the interference type and between each time domain feature sample and the interference type;
clustering the frequency domain features and the time domain features of which the comparison results are unknown interference based on a K-means algorithm, taking the obtained frequency domain feature clustering center as the unknown frequency domain features, taking the obtained time domain feature clustering center as the unknown time domain features, and respectively updating the unknown frequency domain features and the unknown time domain features into an interference feature library;
selecting cells with the same unknown frequency domain characteristic and the same unknown time domain characteristic as target cells, carrying out interference positioning on the target cells, confirming positioning of an interference source, acquiring an interference name and an investigation suggestion of the interference source according to the positioning of the interference source, and filling the interference name and the investigation suggestion into the unknown frequency domain characteristic and the unknown time domain characteristic corresponding to the target cells.
2. The method according to claim 1, wherein the network management data includes a first base noise average value of physical resource blocks in each period and a second base noise average value of physical resource blocks in each frequency band, and the collecting network management data of the interfering cells in the target area generates frequency domain features and time domain features corresponding to each cell, and includes:
generating a frequency domain array according to the frequency band sequence from the second base noise average value of the cell, and taking the frequency domain array as the frequency domain characteristic of the cell;
generating a time domain array according to a time period sequence according to a first background noise average value of a cell in each time period in a preset time period, and taking the time domain array as the time domain characteristic of the cell.
3. The method of claim 1, wherein the comparing the frequency domain feature and the time domain feature of each cell with feature samples in a preset interference feature library to obtain a comparison result includes:
calculating the similarity between the frequency domain features and the frequency domain feature samples, taking the frequency domain feature sample with the highest similarity with the frequency domain features as a first feature sample, taking the highest similarity as a first similarity, and judging whether the first similarity is larger than a first similarity threshold value or not;
If yes, judging that the comparison result of the frequency domain features is the interference type corresponding to the first feature sample;
if not, judging the comparison result of the frequency domain characteristics as unknown interference;
calculating the similarity between the time domain feature and the time domain feature sample, taking the time domain feature sample with the highest similarity with the time domain feature as a second feature sample, taking the highest similarity as a second similarity, and judging whether the second similarity is larger than a second similarity threshold;
if yes, judging that the comparison result of the time domain features is the interference type corresponding to the second feature sample;
if not, judging the comparison result of the time domain features as unknown interference.
4. The method according to claim 1, wherein the clustering the frequency domain features and the time domain features of the unknown interference based on the K-means algorithm, respectively, using the obtained frequency domain feature cluster center as the unknown frequency domain feature, and using the obtained frequency domain feature cluster center as the unknown time domain feature, includes:
taking the frequency domain characteristic with the comparison result of unknown interference as a first data set, and selecting a first preparation clustering center from the first data set by combining the frequency domain characteristic sample based on a preset first K value;
The first preparation clustering center is adopted, K-means algorithm operation is carried out by combining the frequency domain features with the comparison results of unknown interference, an updated first clustering center is obtained, and the first clustering center is used as the unknown frequency domain feature;
taking the time domain characteristics with the comparison result of unknown interference as a second data set, and selecting a second preparation clustering center from the second data set by combining the time domain characteristic samples based on a preset second K value;
and carrying out K-means algorithm operation by adopting the second preparation clustering center and combining time domain features with all comparison results as unknown interference to obtain an updated second clustering center, and taking the second clustering center as the unknown time domain feature.
5. The method of claim 1, wherein said selecting a first preliminary cluster center from said first data set in combination with said frequency domain feature samples comprises:
acquiring a third class center set in the frequency domain feature sample, wherein the third class center set comprises a plurality of third class centers, calculating a first distance between the frequency domain feature of the unknown interference and each third class center as a comparison result, and taking the maximum value of the first distance as a first target distance;
And traversing the frequency domain features of which the comparison results are unknown interference, and taking the first K-value frequency domain features with the largest first target distance as a first preliminary clustering center.
6. The method of claim 1, wherein said selecting a second preliminary cluster center from said second data set in combination with said time domain feature samples comprises:
a third class center set in the time domain feature sample is obtained, the third class center set comprises a plurality of third class centers, a second distance between the time domain feature of the unknown interference and each third class center is calculated according to the comparison result, and the maximum value of the second distance is used as a second target distance;
and traversing the time domain features of which the comparison results are unknown interference, and taking the second K-value time domain features with the largest second target distance as a second preliminary clustering center.
7. The method of claim 1, wherein said performing interference location on the target cell, confirming location of an interference source, comprises:
selecting a cell with a distance smaller than a preset value from a target cell as a cooperative cell in the target area;
and confirming the positioning of the interference source by adopting a triangular positioning method according to the positioning of the target cell and the cooperative cell.
8. An interference feature library updating apparatus, comprising:
the acquisition module is used for collecting network management data of the interference cells in the target area in a preset duration period and generating frequency domain features and time domain features corresponding to the cells;
the comparison module is used for respectively comparing the frequency domain characteristics and the time domain characteristics of each cell with characteristic samples in a preset interference characteristic library to obtain a comparison result, wherein the comparison result comprises an interference type and unknown interference, the characteristic samples comprise frequency domain characteristic samples and time domain characteristic samples, and the interference characteristic library comprises mapping relations between each frequency domain characteristic sample and the interference type and between each time domain characteristic sample and the interference type;
the updating module is used for respectively clustering the frequency domain features and the time domain features of which the comparison results are unknown interference based on a K-means algorithm, taking the obtained frequency domain feature clustering center as an unknown frequency domain feature, taking the obtained time domain feature clustering center as an unknown time domain feature, and respectively updating the unknown frequency domain features and the unknown time domain features into an interference feature library;
the filling module is used for selecting cells with the same unknown frequency domain characteristics and the same unknown time domain characteristics as target cells, carrying out interference positioning on the target cells, confirming positioning of an interference source, acquiring an interference name and an investigation suggestion of the interference source according to the positioning of the interference source, and filling the interference name and the investigation suggestion into the unknown frequency domain characteristics and the unknown time domain characteristics corresponding to the target cells.
9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 7.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 7.
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