CN116916363A

CN116916363A - Method, device, equipment, medium and product for predicting base station out-of-service influence

Info

Publication number: CN116916363A
Application number: CN202211373805.1A
Authority: CN
Inventors: 吴超
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Group Hubei Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Group Hubei Co Ltd
Priority date: 2022-11-04
Filing date: 2022-11-04
Publication date: 2023-10-20

Abstract

The application discloses a method, a device, equipment, a medium and a product for predicting the out-of-service influence of a base station. The method comprises the steps of obtaining a same-frequency measurement report and a different-frequency measurement report; preprocessing the same-frequency measurement report and the different-frequency measurement report to obtain a plurality of target measurement reports; obtaining a target measurement report corresponding to each time slicing of each calling number according to the reporting time and the calling number of each target measurement report; obtaining a corresponding service cell and an optimal neighbor cell of the service cell according to the target measurement report; obtaining the number of times of occurrence of the optimal neighbor cell in the target measurement report corresponding to each time slicing and the number of times of occurrence of the neighbor cell of the service cell, and obtaining the migration proportion of the service cell to each neighbor cell; and determining a predicted value of the out-of-service influence of the base station according to the migration proportion. According to the embodiment of the application, when the influence of the base station out-of-service on the user perception is predicted, the prediction accuracy can be improved.

Description

Method, device, equipment, medium and product for predicting base station out-of-service influence

Technical Field

The application belongs to the technical fields of wireless communication, knowledge graph and natural language processing, and particularly relates to a method, a device, equipment, a medium and a product for predicting the base station out-of-service influence.

Background

With the increase of the number of terminals, the service types and the complexity of the network structure, the operator network is increasingly full of data of different types and different sources, the number of communication base stations is huge, and the influence of the base station out-of-service of different scenes on the perception of users is different.

In the prior art, a large amount of information fed back by the data is reserved for analyzing the network through simple data collection and storage, analysis and prediction technology and labor investment.

However, in the prior art, when predicting the influence of the base station out-of-service on the user perception, the prediction accuracy is low.

Disclosure of Invention

The embodiment of the application provides a method, a device, equipment, a medium and a product for predicting the influence of base station out-of-service, which can improve the prediction accuracy when predicting the influence of base station out-of-service on user perception.

In a first aspect, an embodiment of the present application provides a method for predicting a base station out-of-service impact, where the method includes:

acquiring a common-frequency measurement report and a different-frequency measurement report, wherein the common-frequency measurement report comprises a measurement report of a first neighboring cell under the same frequency band, the different-frequency measurement report comprises a measurement report of the first neighboring cell under different frequency bands, and the first neighboring cell is a neighboring cell of a serving cell of a base station;

Preprocessing the same-frequency measurement report and the different-frequency measurement report to obtain a plurality of target measurement reports, wherein the calling number and the service cell identifier of each target measurement report are different;

obtaining a target measurement report corresponding to each time slicing of each calling number according to the reporting time and the calling number of each target measurement report;

obtaining a corresponding service cell and an optimal neighbor cell of the service cell according to a target measurement report corresponding to each calling number in each time slicing;

obtaining the number of times of occurrence of the optimal neighbor cell in the target measurement report corresponding to each time slicing and the number of times of occurrence of the neighbor cell of the service cell, and obtaining the migration proportion of the service cell to each neighbor cell;

and determining a predicted value of the out-of-service influence of the base station according to the migration proportion.

In a second aspect, an embodiment of the present application provides a device for predicting a base station out-of-service impact, where the device includes:

the first acquisition module is used for acquiring a same-frequency measurement report and a different-frequency measurement report, wherein the same-frequency measurement report comprises a measurement report of a first neighbor cell under the same frequency band, the different-frequency measurement report comprises a measurement report of the first neighbor cell under different frequency bands, and the first neighbor cell is a neighbor cell of a serving cell of a base station;

The preprocessing module is used for preprocessing the same-frequency measurement report and the different-frequency measurement report to obtain a plurality of target measurement reports, and the calling number and the service cell identifier of each target measurement report are different;

the first obtaining module is used for obtaining a target measurement report corresponding to each time slicing of each calling number according to the reporting time and the calling number of each target measurement report;

the second obtaining module is used for obtaining a corresponding service cell and an optimal neighbor cell of the service cell according to the target measurement report corresponding to each time slicing of each calling number;

the second acquisition module is used for acquiring the number of times of occurrence of the optimal neighbor cell in the target measurement report corresponding to each time slicing of each calling number and the number of times of occurrence of the neighbor cell of the service cell, and obtaining the migration proportion of the service cell to each neighbor cell;

and the determining module is used for determining a predicted value of the base station out-of-service influence according to the migration proportion.

In a third aspect, an embodiment of the present application provides an electronic device, including:

a processor and a memory storing computer program instructions;

the processor when executing the computer program instructions carries out the steps of a method of predicting the base station out-of-service impact as in any one of the embodiments of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of a method for predicting a base station out-of-service impact as in any of the embodiments of the first aspect.

In a fifth aspect, embodiments of the present application provide a computer program product, instructions in which, when executed by a processor of an electronic device, cause the electronic device to perform the steps of a method of predicting a base station out-of-service impact as in any of the embodiments of the first aspect.

According to the method, the device, the equipment, the medium and the product for predicting the base station out-of-service influence, which are provided by the embodiment of the application, the same-frequency measurement report and the different-frequency measurement report are obtained, the same-frequency measurement report and the different-frequency measurement report are preprocessed to obtain the multi-item mark measurement report, the calling number and the service cell identifier of each item mark measurement report are different, the target measurement report corresponding to each calling number in each time slice is obtained according to the reporting time and the calling number of each item mark measurement report, then the corresponding service cell and the optimal neighbor cell of the service cell are obtained, and then the number of times of occurrence of the optimal neighbor cell and the number of times of occurrence of the neighbor cell of the service cell in the target measurement report are obtained, so that the migration proportion of the service cell to each neighbor cell is obtained. Therefore, the predicted value of the base station out-of-service influence is determined according to the migration proportion, and the quantity of the base station out-of-service influence on the user perception is quantized when the influence of the base station out-of-service on the user perception is collected and predicted, so that the prediction accuracy is improved.

Drawings

In order to more clearly illustrate the technical solution of the embodiments of the present application, the drawings that are needed to be used in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.

Fig. 1 is a flow chart of a method for predicting a base station out-of-service effect according to an embodiment of the present application;

FIG. 2 is a flow chart of a specific implementation of step S120;

FIG. 3 is a flow chart of a specific implementation of step S130;

fig. 4 is a flow chart of another method for predicting a base station out-of-service impact according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a prediction apparatus for a base station out-of-service influence according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings and the detailed embodiments. It should be understood that the particular embodiments described herein are meant to be illustrative of the application only and not limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the application by showing examples of the application.

It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

As described in the background art, the inventor finds that, in the existing prediction method of the base station service withdrawal influence, with the increase of the number of terminals, the service type and the complexity of the network structure, great challenges are brought to data exploration, analysis, understanding and presentation, structured, semi-structured and unstructured data of various sources need to be collected and sorted manually, different means are used for cleaning and noise reduction, and unified standardization is performed, then analysis processing can be performed, and the influence of base station service withdrawal of different scenes on user perception is different, and simple data induction and human input often cannot accurately predict the influence of base station service withdrawal on user perception.

In order to solve the problems in the prior art, the embodiment of the application provides a prediction method for the out-of-service influence of a base station, which is characterized in that a common-frequency measurement report and a different-frequency measurement report are obtained, the common-frequency measurement report and the different-frequency measurement report are preprocessed to obtain a multi-item standard measurement report, the calling number and the service cell identifier of each standard measurement report are different, the target measurement report corresponding to each calling number in each time slicing is obtained according to the reporting time and the calling number of each standard measurement report, then the corresponding service cell and the optimal neighbor cell of the service cell are obtained, and then the frequency of occurrence of the optimal neighbor cell and the frequency of occurrence of the neighbor cell of the service cell in the target measurement report are obtained, so that the migration proportion of the service cell to each neighbor cell is obtained. Therefore, the predicted value of the base station out-of-service influence is determined according to the migration proportion, and the quantity of the base station out-of-service influence on the user perception is quantized when the influence of the base station out-of-service on the user perception is collected and predicted, so that the prediction accuracy is improved.

The method for predicting the base station out-of-service influence provided by the embodiment of the application is described in detail through specific embodiments and application scenes thereof with reference to the accompanying drawings.

Fig. 1 is a flow chart of a method for predicting a base station out-of-service effect according to an embodiment of the present application. As shown in fig. 1, the method for predicting the base station out-of-service influence provided by the embodiment of the application may include the following steps:

s110, acquiring a same-frequency measurement report and a different-frequency measurement report;

s120, preprocessing the same-frequency measurement report and the different-frequency measurement report to obtain a plurality of target measurement reports, wherein the calling number and the service cell identifier of each target measurement report are different;

s130, obtaining a target measurement report corresponding to each time slicing of each calling number according to the reporting time and the calling number of each target measurement report;

s140, obtaining a corresponding service cell and an optimal neighbor cell of the service cell according to a target measurement report corresponding to each time slicing of each calling number;

s150, obtaining the number of times of occurrence of the optimal neighbor cell in the target measurement report corresponding to each time slicing of each calling number and the number of times of occurrence of the neighbor cell of the serving cell, and obtaining the migration proportion of the serving cell to each neighbor cell;

s160, determining a predicted value of the base station out-of-service influence according to the migration proportion.

Therefore, the same-frequency measurement report and the different-frequency measurement report are obtained, the same-frequency measurement report and the different-frequency measurement report are preprocessed to obtain a multi-item standard measurement report, the calling number and the service cell identifier of each item standard measurement report are different, the target measurement report corresponding to each time slicing of each calling number is obtained according to the reporting time and the calling number of each item standard measurement report, then the corresponding service cell and the optimal neighbor cell of the service cell are obtained, and then the number of times of occurrence of the optimal neighbor cell and the number of times of occurrence of the neighbor cell of the service cell in the target measurement report are obtained, so that the migration proportion of the service cell to each neighbor cell is obtained. Therefore, the predicted value of the base station out-of-service influence is determined according to the migration proportion, and the quantity of the base station out-of-service influence on the user perception is quantized when the influence of the base station out-of-service on the user perception is collected and predicted, so that the prediction accuracy is improved.

A specific implementation of each of the above steps is described below.

In some embodiments, in S110, there are many types of entries in a measurement report (Measurement Report, MR) log, and in this embodiment of the present application, the on-channel measurement report and the off-channel measurement report are mainly obtained, where the on-channel measurement report includes a measurement report of a first neighbor cell in the same frequency band, the off-channel measurement report includes a measurement report of the first neighbor cell in a different frequency band, and the first neighbor cell is a neighbor cell corresponding to a serving cell of a base station.

It should be noted that only one neighbor cell frequency point measurement result is included in each co-frequency measurement report and inter-frequency measurement report, and the following eight information data needs to be acquired: call number of measurement report, upload time of measurement report (accurate to millisecond/ms), service cell identification, frequency point of service cell, physical cell identification of neighbor cell, frequency point of neighbor cell, and reference signal received power (Reference Signal Receiving Power, RSRP) of neighbor cell.

In some embodiments, in S120, the measurement reports are summarized according to the acquired common-frequency measurement report and the inter-frequency measurement report, where each measurement report belongs to a serving cell identifier and a call number, and thus, each acquired measurement report is preprocessed to obtain a multi-label measurement report.

In order to better divide the same-frequency measurement report and different-frequency measurement report, a multi-label measurement report is obtained, as shown in fig. 2, S120 may specifically include:

s121, combining the first measurement report and the second measurement report to obtain a target measurement report under the condition that the first measurement report and the second measurement report comprise the same calling number and service cell identification;

s122, in the case that at least one of the calling number and the service cell identifier included in the first measurement report and the second measurement report is different, the first measurement report and the second measurement report are respectively determined to be different target measurement reports.

As an example, each measurement report is to be obtained by associating with a cell identifier and a call number, for example, in the case that the first measurement report and the second measurement report include the same call number and cell identifier, the measurement report is to be calculated as a measurement report under one call number, and then the first measurement report and the second measurement report are combined to obtain one target measurement report; in the case that at least one of the call number and the cell identity included in the first measurement report and the second measurement report is different, that is, the same call number may be used in two different cells, two different target measurement reports are calculated, and the two different target measurement reports cannot be combined into one target measurement report, that is, the pre-processing measurement report is performed simultaneously based on the call number and the cell, where the first measurement report and the second measurement report may be the same frequency measurement report or the measurement report in the different frequency measurement report.

It should be noted that the measurement report in which "the frequency point of the serving cell", "the RSRP value of the neighboring cell", and "the frequency point of the neighboring cell" are arbitrarily invalid values is filtered out.

In order to further divide the first measurement report and the second measurement report, also after S122, the steps of:

deleting the second target measurement report when the time difference between the uploading time of the second target measurement report and the uploading time of the first target measurement report is smaller than the first preset time;

and when the time difference between the uploading time of the second target measurement report and the uploading time of the first target measurement report is greater than or equal to a first preset time, reserving the first target measurement report and the second target measurement report, wherein the first target measurement report and the second target measurement report are any one of the multi-item target measurement report.

As an example, the same calling number may be recycled in the same serving cell, in order to avoid repeated calculation, a time threshold, that is, a first preset time, for example, the first preset time is 3s, the target measurement reports are ordered according to the uploading time, starting with the uploading time point of the first target measurement report, and if the time difference between the uploading time point of the second target measurement report and the uploading time point of the first target measurement report is less than 3s, deleting the second target measurement report; if the time difference between the uploading time point of the second target measurement report and the uploading time point of the first target measurement report is greater than or equal to 3s, the first target measurement report and the second target measurement report are reserved and are sequentially carried out.

In some embodiments, in S130, the target measurement report has a relevant reporting period, and each reporting period reports multiple measurement results, and each analysis is performed only for all target measurement reports on the same time point on one call of a cell, so that time slicing is performed on all target measurement reports under each call number, and the target measurement report corresponding to each time slicing of each call number is obtained mainly according to the reporting time and the call number of each target measurement report.

As an example, in order to ensure that, at each analysis, only all target measurement reports at the same time point on one call of one cell are performed, as shown in fig. 3, S130 may specifically include:

s131, determining the reporting time of a target measurement report reported by the first one of the target measurement reports as a reference time;

s132, determining the second target measurement report as a measurement report corresponding to the first time slicing under the condition that the time difference between the reporting time of the second target measurement report and the reference time is smaller than the first preset time;

s133, determining the second target measurement report as a measurement report corresponding to a second time slice when the time difference between the reporting time of the second target measurement report and the reference time is greater than or equal to the first preset time and less than the second preset time, wherein the first time slice is adjacent to the second time slice.

For example, all the target measurement reports under the same call number are ordered according to the uploading time, the uploading time of the first target measurement report is determined as the reference time from the first target measurement report, and if the time difference between the uploading time of the second target measurement report and the reference time is smaller than the first preset time, the second target measurement report is determined as the measurement report corresponding to the first time slicing; if the time difference between the uploading time of the second target measurement report and the reference time is greater than or equal to the first preset time and less than the second preset time, determining the second target measurement report as a measurement report corresponding to the second time slicing, and determining the uploading time of the second target measurement report as a new reference time, wherein the preset time can be set according to the reporting period of the target measurement report.

It should be noted that, when analyzing each target measurement report, the relevant information of the neighbor cell corresponding to the serving cell is recorded, such as the physical cell identifier of the neighbor cell, the frequency point of the neighbor cell, and the RSRP value.

In some embodiments, in S140, each acquired call number has a plurality of measurement results of neighboring cells in a target measurement report corresponding to each time slice, so that an optimal neighboring cell corresponding to a serving cell under each time slice needs to be obtained, so as to know to which neighboring cell the call number may be migrated after the serving cell is taken out of service.

In order to obtain the best neighbor of each calling number under each time slicing, the step S140 may specifically include the following steps:

obtaining the frequency band of the corresponding service cell, the frequency band of the adjacent cell and the RSRP value under each time slicing according to the obtained service cell corresponding to each time slicing;

according to the frequency bands of the service cell, the frequency bands of the adjacent cells are arranged step by step from high to low;

and under the condition that the acquired current RSRP value is larger than a preset RSRP threshold value, obtaining the optimal neighbor cell of the serving cell under the current time slicing.

As an example, for each time slice of each call number, the frequency point of the corresponding serving cell, the frequency points of all neighbor cells, and the corresponding RSRP value are obtained. Converting the frequency points into frequency bands, classifying according to different frequency bands, arranging the frequency bands of adjacent cells step by step from high to low according to the frequency bands of a service cell, obtaining a maximum RSRP value measured in each frequency band of the adjacent cells under the time slice and a corresponding adjacent cell identifier, if the maximum RSRP value is greater than a threshold value, for example, the threshold value can be 35, and if the maximum RSRP value is greater than 35, setting the adjacent cell as the optimal adjacent cell of the time slice of the calling number.

If no optimal neighbor is found, one of the neighbors with the largest RSRP value measured is found as the optimal neighbor.

It should be noted that "frequency point of neighbor cell+physical cell identifier of neighbor cell" of neighbor cell in the target measurement report is to be filtered out as the measurement report of the site where the serving cell is located, and the target measurement report in which the neighbor cell identifier is an invalid value in the target measurement.

In some embodiments, in S150, according to the obtained best neighbor cell of each call number in each time slicing, the number of occurrences of the best neighbor cell of each serving cell is counted, and for each counted best neighbor cell, 1 is added to the corresponding neighbor cell. Taking the number of times of occurrence of the best neighbor cell of each service cell as a numerator, taking the number of times of occurrence of all neighbor cells of the service cell as a denominator, and dividing by 100% to obtain the migration proportion of each neighbor cell.

In some embodiments, in S160, the influence of base station overstock on user perception is predicted according to the migration proportion, but it is noted that call loss is not deducted when calculating the migration proportion.

In order to better describe the whole scheme, the application also provides another implementation mode of the prediction method of the base station out-of-service influence, and the embodiment is specifically referred to as the following.

Referring to fig. 4, another flow chart of a method for predicting a base station out-of-service effect according to an embodiment of the present application includes the following steps: s410 to S480, which will be explained in detail below.

S460, obtaining the number of calls with zero number of neighbor cells called by the calling number in the multi-label measurement report and all the number of calls called by the calling number, and obtaining the loss proportion of the serving cell;

s470, determining the number of losses of the call arrival in the migration proportion according to the loss proportion.

As an example, some users or services may be out of service due to the serving cell, and the surrounding neighbor cells are not covered, so that the user or service loss is caused, the number of all calls of the calling number calls under one serving cell is counted, the number of calls of the calling number calls to the neighbor cells is zero, the number of the calls of the calling number calls to the neighbor cells is zero as a numerator, the number of all calls of the calling number calls is a denominator, the loss proportion of the serving cell is obtained by dividing by 100%, and the influence of the out-of-service of the base station on the user perception can be more accurately predicted by subtracting the loss proportion from the migration proportion.

S410 to S450 and S480 are the same as S110 to S160 in the above embodiments, and will not be described in detail here for brevity.

The method comprises the steps of obtaining the same-frequency measurement report and the different-frequency measurement report, preprocessing the same-frequency measurement report and the different-frequency measurement report to obtain a plurality of standard measurement reports, obtaining a target measurement report corresponding to each time slicing of each call number according to the reporting time and the call number of each standard measurement report, obtaining a corresponding service cell and an optimal neighbor cell of the service cell, obtaining the occurrence times of the optimal neighbor cell and the occurrence times of the neighbor cell of the service cell in the target measurement report, obtaining the migration proportion of the service cell to each neighbor cell, and obtaining the loss proportion of the service cell according to the number of the neighbor cells. Therefore, the predicted value of the base station out-of-service influence is determined by efficiently integrating resources according to the migration proportion and the loss proportion, and the quantity of the base station out-of-service influence on the user perception is quantized when the influence of the base station out-of-service on the user perception is collected and predicted, so that the prediction accuracy is improved.

It should be noted that, the application scenario described in the foregoing embodiment of the present application is for more clearly describing the technical solution of the embodiment of the present application, and does not constitute a limitation on the technical solution provided by the embodiment of the present application, and as a person of ordinary skill in the art can know, with the appearance of a new application scenario, the technical solution provided by the embodiment of the present application is also applicable to similar technical problems.

Based on the same inventive concept, the application also provides a device for predicting the base station out-of-service influence, which is specifically described in detail with reference to fig. 5.

Fig. 5 is a schematic structural diagram of a prediction apparatus for a base station out-of-service influence according to an embodiment of the present application.

As shown in fig. 5, the apparatus 500 for predicting the base station out-of-service influence may include:

a first obtaining module 501, configured to obtain a common-frequency measurement report and a different-frequency measurement report, where the common-frequency measurement report includes a measurement report of a first neighboring cell in the same frequency band, and the different-frequency measurement report includes a measurement report of the first neighboring cell in a different frequency band, and the first neighboring cell is a neighboring cell of a serving cell of a base station;

the preprocessing module 502 is configured to preprocess the same-frequency measurement report and the different-frequency measurement report to obtain a plurality of target measurement reports, where the call number and the serving cell identifier of each target measurement report are different;

a first obtaining module 503, configured to obtain, according to the reporting time and the call number of each target measurement report, a target measurement report corresponding to each time slice by each call number;

a second obtaining module 504, configured to obtain, according to the target measurement report corresponding to each time slicing of each call number, a corresponding serving cell, and an optimal neighbor cell of the serving cell;

A second obtaining module 505, configured to obtain the number of occurrences of the best neighbor cell in the target measurement report corresponding to each time slicing, and the number of occurrences of the neighbor cell of the serving cell, so as to obtain a migration proportion of the serving cell to each neighbor cell;

and the determining module 506 is configured to determine a predicted value of the base station out-of-service impact according to the migration proportion.

The following describes the base station out-of-service influence prediction apparatus 500 in detail, and is specifically described as follows:

in some embodiments, in order to better divide the on-channel measurement report and the off-channel measurement report into multiple-label measurement reports, the preprocessing module 502 may specifically include:

an obtaining sub-module, configured to combine the first measurement report and the second measurement report to obtain a target measurement report when the first measurement report and the second measurement report include the same call number and serving cell identifier, where the first measurement report and the second measurement report are measurement reports in the same frequency measurement report and different frequency measurement report;

a determining sub-module, configured to determine the first measurement report and the second measurement report as different target measurement reports, respectively, when at least one of the call number and the serving cell identity included in the first measurement report and the second measurement report are different.

In some embodiments, to further divide the first measurement report and the second measurement report, the apparatus 500 for predicting the base station out-of-service impact may further include the following modules:

the deleting module is used for deleting the second target measurement report when the time difference between the uploading time of the second target measurement report and the uploading time of the first target measurement report is smaller than the first preset time after the first measurement report and the second measurement report are respectively determined to be different target measurement reports;

the reservation module is configured to reserve the first target measurement report and the second target measurement report when a time difference between an uploading time of the second target measurement report and an uploading time of the first target measurement report is greater than or equal to a first preset time, where the first target measurement report and the second target measurement report are any one of the multiple target measurement reports.

In some embodiments, in order to ensure that each analysis is performed only for all the on-channel measurement reports and off-channel measurement reports at the same time point on one call of one cell, the first obtaining module 503 may specifically include:

a determining submodule, configured to determine a reporting time of a target measurement report reported by a first one of the plurality of target measurement reports as a reference time;

A determining submodule, configured to determine the second target measurement report as a measurement report corresponding to the first time slicing when a time difference between an upload time of the second target measurement report and the reference time is less than a first preset time;

and the determining submodule is used for determining the second target measurement report as the measurement report corresponding to the second time slicing when the time difference between the uploading time of the second target measurement report and the reference time is larger than or equal to the first preset time and smaller than the second preset time, and the first time slicing is adjacent to the second time slicing.

In some embodiments, in order to obtain the best neighbor of each calling number under each time slicing, the second obtaining module 504 may specifically include:

the acquisition sub-module is used for acquiring the frequency band of the corresponding service cell, the frequency band of the adjacent cell and the reference signal received power RSRP under each time slicing according to the acquired service cell corresponding to each time slicing;

an arrangement sub-module, configured to arrange the frequency bands of the neighboring cells step by step from high to low according to the frequency bands of the serving cell;

and the obtaining submodule is used for obtaining the optimal neighbor cell of the serving cell under the current time slicing under the condition that the obtained current RSRP value is larger than the preset RSRP threshold value.

In some embodiments, the apparatus 500 for predicting the base station out-of-service impact may further include the following modules:

a second obtaining module 504, configured to obtain the migration ratio of the serving cell to each neighbor cell, and obtain the number of calls with zero number of neighbor cells called by the calling number in the multi-label measurement report, and all the number of calls called by the calling number, so as to obtain the loss ratio of the serving cell;

a determining module 506, configured to determine the number of calls arriving in the migration proportion according to the loss proportion.

Fig. 6 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.

The device may comprise a processor 601 and a memory 602 storing computer program instructions in the electronic device 600.

In particular, the processor 601 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application.

Memory 602 may include mass storage for data or instructions. By way of example, and not limitation, memory 602 may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the above. The memory 602 may include removable or non-removable (or fixed) media, where appropriate. Memory 602 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 602 is a non-volatile solid state memory.

In particular embodiments, memory 602 includes Read Only Memory (ROM). The ROM may be mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these, where appropriate. The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to methods in accordance with aspects of the present disclosure.

The processor 601 implements any of the data processing methods of the above embodiments by reading and executing computer program instructions stored in the memory 602.

In one example, electronic device 600 may also include a communication interface 603 and a bus 610. As shown in fig. 6, the processor 601, the memory 602, and the communication interface 603 are connected to each other through a bus 610 and perform communication with each other.

The communication interface 603 is mainly used for implementing communication between each module, apparatus, unit and/or device in the embodiment of the present application.

Bus 610 includes hardware, software, or both that couple components of the predictive device of base station overstock effects to each other. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 610 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.

By way of example, the electronic device 600 may be a cell phone, tablet, notebook, palm, in-vehicle electronic device, ultra-mobile personal computer (UMPC), netbook, or Personal Digital Assistant (PDA), among others.

The electronic device 600 may execute the method for predicting the base station out-of-service influence in the embodiment of the present application, so as to implement the method and apparatus for predicting the base station out-of-service influence described in connection with fig. 1 and 5.

In addition, in combination with the method for predicting the base station out-of-service influence in the above embodiment, the embodiment of the application may be implemented by providing a computer readable storage medium. The computer readable storage medium has stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement a method for predicting the out-of-service impact of any of the base stations in the above embodiments. Examples of computer readable storage media include non-transitory computer readable storage media such as portable disks, hard disks, random Access Memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROM or flash memories), portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, and the like.

It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.

The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.

It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.

Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.

Claims

1. The method for predicting the base station out-of-service influence is characterized by comprising the following steps:

acquiring a same-frequency measurement report and a different-frequency measurement report, wherein the same-frequency measurement report comprises a measurement report of a first neighboring cell under the same frequency band, the different-frequency measurement report comprises a measurement report of the first neighboring cell under different frequency bands, and the first neighboring cell is a neighboring cell of a serving cell of a base station;

preprocessing the same-frequency measurement report and the different-frequency measurement report to obtain a plurality of target measurement reports, wherein the call number and the service cell identifier of each target measurement report are different;

obtaining the number of times of occurrence of the optimal neighbor cell in a target measurement report corresponding to each time slicing and the number of times of occurrence of the neighbor cell of the service cell, and obtaining the migration proportion of the service cell to each neighbor cell;

and determining a predicted value of the base station out-of-service influence according to the migration proportion.

2. The method of claim 1, wherein the preprocessing the on-channel measurement report and the off-channel measurement report to obtain a plurality of target measurement reports comprises:

combining a first measurement report and a second measurement report to obtain a target measurement report under the condition that the first measurement report and the second measurement report comprise the same calling number and service cell identification, wherein the first measurement report and the second measurement report are measurement reports in the same frequency measurement report and different frequency measurement report;

In the case that at least one of the call number and the serving cell identity included in the first measurement report and the second measurement report is different, the first measurement report and the second measurement report are respectively determined as different target measurement reports.

3. The method of claim 2, wherein after the determining the first measurement report and the second measurement report as different target measurement reports, respectively, the method further comprises:

4. The method according to claim 1, wherein the obtaining the target measurement report corresponding to each time slice for each call number according to the uploading time and the call number of each target measurement report comprises:

Determining the reporting time of a target measurement report reported by a first one of the target measurement reports as a reference time;

determining the second target measurement report as a measurement report corresponding to the first time slicing under the condition that the time difference between the uploading time of the second target measurement report and the reference time is smaller than the first preset time;

and determining the second target measurement report as a measurement report corresponding to a second time slice when the time difference between the uploading time of the second target measurement report and the reference time is greater than or equal to a first preset time and less than a second preset time, wherein the first time slice is adjacent to the second time slice.

5. The method according to claim 1, wherein the obtaining the corresponding serving cell and the best neighbor cell of the serving cell according to the target measurement report corresponding to each call number at each time slice comprises:

obtaining the frequency band of the corresponding service cell, the frequency band of the adjacent cell and the Reference Signal Received Power (RSRP) under each time slicing according to the obtained service cell corresponding to each time slicing;

6. The method of claim 1, wherein after the obtaining the migration ratio of the serving cell to each neighbor cell, the method further comprises:

acquiring the number of calls with zero number of neighbor cells called by the calling number in the multi-item mark measurement report and all the number of calls called by the calling number, and obtaining the loss proportion of the service cell;

and determining the number of call losses in the migration proportion according to the loss proportion.

7. A base station out-of-service impact prediction apparatus, the apparatus comprising:

the first acquisition module is used for acquiring a same-frequency measurement report and a different-frequency measurement report, wherein the same-frequency measurement report comprises a measurement report of a first neighboring cell under the same frequency band, the different-frequency measurement report comprises a measurement report of the first neighboring cell under different frequency bands, and the first neighboring cell is a neighboring cell of a serving cell of a base station;

the second acquisition module is used for acquiring the number of times of occurrence of the optimal neighbor cell and the number of times of occurrence of the neighbor cell of the service cell in the target measurement report corresponding to each time slicing to obtain the migration proportion of the service cell to each neighbor cell;

8. An electronic device, the device comprising:

a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements the steps of a method for predicting a base station out-of-service impact as claimed in any one of claims 1-6.

9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the steps of a method of predicting a base station out-of-service impact according to any one of claims 1-6.

10. A computer program product, characterized in that instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the steps of the method of predicting a base station out-of-service impact according to any one of claims 1-6.