CN111901740B

CN111901740B - Data processing method, device and equipment

Info

Publication number: CN111901740B
Application number: CN201910367410.2A
Authority: CN
Inventors: 吴亚楠; 万菁晶; 张坚; 钟建; 魏宗静; 刁枫; 黄崴; 尉燕; 肖尧; 胡薇; 邬方习
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Group Sichuan Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Group Sichuan Co Ltd
Priority date: 2019-05-05
Filing date: 2019-05-05
Publication date: 2023-04-28
Anticipated expiration: 2039-05-05
Also published as: CN111901740A

Abstract

The embodiment of the invention discloses a data processing method, a device and equipment, wherein the method comprises the following steps: acquiring position information of a target poverty-relieving area; determining a distance between the target lean region and the base station based on position information of a predetermined base station and position information of the target lean region; determining the type of the lean region to which the target lean region belongs based on the distance; and determining a preset lean strategy corresponding to the lean region type to which the target lean region belongs. According to the method, the type of the lean region to which the target lean region belongs can be determined according to the distance between the target lean region and the base station, the data utilization rate can be improved, and meanwhile, the corresponding preset lean strategy can be determined according to the type of the lean region to which the target lean region belongs, so that the accuracy of accurate lean can be improved.

Description

Data processing method, device and equipment

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, and a device for processing data.

Background

Along with the continuous development of economic construction in China, accurate poverty relief and accurate poverty relief become important works to be solved, and in the process of the accurate poverty relief work, the accurate poverty relief informatization and platformization are widely applied.

The currently used accurate poverty-supporting system based on the mobile communication network can realize data acquisition, analysis and recording of poverty-supporting people through a big data system, then realize aggregation of poverty-supporting resources and processes on a platform, and connect the poverty-supporting people, government, society and staff through a mobile terminal to provide a unified information entrance for the government and the staff, so that accurate poverty supporting is realized.

However, when using a precision lean system for lean operation, there are the following problems: firstly, when the poor crowd is identified, the poor crowd is mainly dependent on external data sources, such as poor population lists provided by government and social enterprises and institutions, and the like, and the external data sources possibly contain more data, wherein the external data sources possibly contain more useless data, so that the data utilization rate is poor; secondly, for the poverty population in poverty area, because informatization construction is behind, the poverty information can not be timely obtained through the accurate poverty supporting system, and the accurate poverty supporting effect is poor.

Disclosure of Invention

The embodiment of the invention aims to provide a data processing method, device and equipment, which are used for solving the problems of low data utilization rate and poor accurate lean effect when lean operation is carried out through accurate lean in the prior art.

In order to solve the technical problems, the embodiment of the invention is realized as follows:

in a first aspect, a method for processing data provided by an embodiment of the present invention includes:

acquiring position information of a target poverty-relieving area;

determining a distance between the target lean region and the base station based on position information of a predetermined base station and position information of the target lean region;

determining the type of the lean region to which the target lean region belongs based on the distance;

and determining a preset lean strategy corresponding to the lean region type to which the target lean region belongs.

In a second aspect, an embodiment of the present invention provides a data processing apparatus, including:

the information acquisition module is used for acquiring the position information of the target poverty-relieving area;

a distance determining module for determining a distance between the target lean region and the base station based on position information of a predetermined base station and position information of the target lean region;

the type determining module is used for determining the type of the lean region to which the target lean region belongs based on the distance;

the strategy determining module is used for determining a preset lean strategy corresponding to the lean region type to which the target lean region belongs.

In a third aspect, an embodiment of the present invention provides an apparatus, including a processor, a memory, and a computer program stored on the memory and executable on the processor, where the computer program when executed by the processor implements the steps of the data processing method provided in the foregoing embodiment.

In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, where the computer program when executed by a processor implements the steps of the data processing method provided in the foregoing embodiment.

As can be seen from the technical solutions provided in the embodiments of the present invention, the location information of the target lean region is obtained, then the distance between the target lean region and the base station is determined based on the location information of the predetermined base station and the location information of the target lean region, the lean region type to which the target lean region belongs is determined based on the distance, and finally the preset lean strategy corresponding to the lean region type to which the target lean region belongs is determined. Therefore, only the position information of the target lean region and the position information of the preset base station are required to be acquired, the lean region type of the target lean region can be determined through the distance between the target lean region and the preset base station, more information of the target lean region is not required to be acquired, the use efficiency of data is improved, meanwhile, the corresponding preset lean strategy is determined according to the lean region type of the target lean region, lean work can be carried out on the target lean region more accurately, and the accurate lean efficiency is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a data processing method according to the present invention;

FIG. 2 is a flow chart of another method for processing data according to the present invention;

FIG. 3 is a schematic diagram of a target lean region determination method based on cluster optimization according to the present invention;

FIG. 4 is a schematic diagram of a data processing apparatus according to the present invention;

fig. 5 is a schematic structural diagram of a data processing apparatus according to the present invention.

Detailed Description

The embodiment of the invention provides a data processing method, device and equipment.

In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, shall fall within the scope of the invention.

Example 1

As shown in fig. 1, an embodiment of the present invention provides a data processing method, where an execution body of the method may be a server, and the server may be an independent server or a server cluster formed by a plurality of servers. The method specifically comprises the following steps:

in step S102, positional information of the target lean region is acquired.

The target barren region may be a poor crowd settlement region determined according to a predetermined assessment rule, and the target barren region may be obtained from a related document or information such as a government report, for example, the target barren region may be a specific village determined in the government report. The location information may be latitude and longitude information of the target poverty-relief area.

In the implementation, along with the continuous development of the economic construction of China, the accurate support and the accurate deception become key works which are urgently needed to be solved, in the process of accurate poverty relieving work, accurate poverty relieving informatization and platformization are widely applied. The currently used accurate poverty-supporting system based on the mobile communication network can realize data acquisition, analysis and recording of poverty-supporting people through a big data system, then realize aggregation of poverty-supporting resources and processes on a platform, and connect the poverty-supporting people, government, society and staff through a mobile terminal to provide a unified information entrance for the government and the staff, so that accurate poverty supporting is realized.

In addition, in the process of informatization of the accurate lean work, another processing mode is provided, namely, the accurate lean system based on the mobile client and the accurate lean system based on the big data analysis management platform, wherein the lean part can perform the lean work in the accurate lean system based on the mobile client, the lean crowd can acquire lean information (such as a Huimin policy and the like) through the accurate lean system based on the mobile client, and the accurate lean system based on the big data analysis management platform can provide the function of managing the authority, the working trace and other data of the lean part.

However, similar to the accurate lean system based on the mobile communication network, the accurate lean system based on the mobile client and the accurate lean system based on the big data analysis management platform also have the problems of lacking the capability of actively selecting data and poor data utilization rate when the accurate lean work is carried out. And the main target users of the accurate poverty-relieving system based on the mobile client and the accurate poverty-relieving system based on the big data analysis management platform are government poverty-relieving workers, and due to the fact that informatization construction is behind, poverty-relieving people cannot timely acquire poverty-relieving information from the two accurate poverty-relieving systems, and the accurate poverty-relieving effect is poor.

For this purpose, another implementation scheme is provided in the embodiment of the present invention, which specifically may include the following:

the positional information of the target barred region of each administrative region may be acquired from a government report disclosed on the internet, and for example, the barred region within each administrative region may be determined as the target barred region from the government report, and the positional information of the target barred region may be acquired. If there are barren village 1 and barren village 2 in the annual government report of administrative district a, barren village 1 and barren village 2 can be determined as target barren regions, and meanwhile, the position information of barren village 1 and barren village 2 (such as longitude and latitude information of barren village 1 and barren village 2 and the like) can be acquired. Alternatively, the lower economic development level area in each administrative area can be obtained as the target poverty-relieving area according to government economic reports.

In addition, call information of the mobile communication user can be acquired through the base station, then call information of the nearby mobile communication user acquired by taking the base station as a center is analyzed, and if one or more indexes in the call information of the mobile communication user meet a preset threshold value of the target lean region, the region where the nearby mobile communication user taking the base station as the center is located can be determined as the target lean region. For example, with the base station a as the center, call information of all mobile communication users in an area with a radius of 1 km may be acquired, and then the acquired call information of the mobile communication users may be analyzed to determine whether the call information of the mobile communication users in the area stores an index exceeding a call threshold of the target lean area. For example, the average monthly consumption of the mobile communication user in the area may be acquired, if the average monthly consumption of the mobile communication user in the area is less than the preset consumption threshold (i.e., the preset threshold) of the preset target lean area, the area with the radius of 1 km centered on the base station a may be determined as the target lean area, and the location information of the base station a may be determined as the location information of the target lean area.

Or, the call information of the mobile communication user in the user residence area can be obtained by taking the user residence area such as villages or residential communities as a unit, then whether the user residence area meets the preset condition of the target poverty-supporting area or not is judged based on the call information of the mobile communication user, the user residence area meeting the preset condition can be determined as the target poverty-supporting area, and the position information of the user residence area at the moment is the position information of the target poverty-supporting area.

In step S104, the distance between the target poverty-maintaining area and the base station is determined based on the position information of the predetermined base station and the position information of the target poverty-maintaining area.

The location information of the predetermined base station may be location information of a base station to be constructed included in the pre-stored base station construction information.

In practice, after the target poverty-supporting area is determined, a base station matching the position information of the target poverty-supporting area may be determined from the pre-stored base station construction information according to the position information of the target poverty-supporting area. For example, if the target poverty-maintaining area is the residential quarter 1, the base station information matching the position information of the residential quarter 1 (i.e., the target poverty-maintaining area) may be searched from the pre-stored base station construction information, if there are a plurality of base stations matching in the residential quarter, any one of the base stations may be determined as the base station matching the target poverty-maintaining area, and if the corresponding base station is not matched in the residential quarter, the base station closest to the residential quarter in straight line distance may be acquired from the pre-stored base station construction information as the base station matching the target poverty-maintaining area.

After determining the base station that matches the target barred zone, the distance between the target barred zone and the base station may be determined based on the location information of the predetermined base station and the location information of the target barred zone.

In step S106, the type of the supported lean region to which the target supported lean region belongs is determined based on the distance.

The types of the poverty-relieving areas can be base station types and non-base station types.

In practice, the type of the zones of the target zone of the poverty relief may be determined based on the distance between the zone of the target zone of the relief and the base station. For example, if one or more base stations are built in the target euthanasia region, the euthanasia region type to which the target euthanasia region belongs may be of the base station type, or if the distance between the target euthanasia region and the base station is not greater than a preset distance threshold, the euthanasia region type to which the target euthanasia region belongs may also be of the base station type. For example, the distance between the target lean region and the base station is 800 meters, the preset distance threshold is 1 km, and if the distance between the target lean region and the base station is smaller than the preset distance threshold, the lean region type to which the target lean region belongs may be a base station type. Conversely, if the distance between the target poverty-maintaining area and the base station is greater than the preset distance threshold, the poverty-maintaining area type to which the target poverty-maintaining area belongs may be a no base station type.

In addition, the types of the barren regions may also include a plurality of types, and the embodiment of the invention provides an optional and achievable barren region type, and the specific barren region type includes a region type that may be different according to different practical application scenarios, which is not limited in this embodiment of the invention.

In step S108, a preset lean strategy corresponding to the lean zone type to which the target lean zone belongs is determined.

In practice, different preset poverty relief strategies may be determined depending on the type of poverty relief zone to which the target poverty relief zone belongs. For example, if the type of the poverty-supporting region to which the target poverty-supporting region belongs is a base station type, the corresponding preset poverty-supporting strategy may be to pay continuous attention to the base station of the target poverty-supporting region; if the type of the poverty-supporting area to which the target poverty-supporting area belongs is a base station-free type, the corresponding preset poverty-supporting strategy can be to strengthen the base station construction of the target poverty-supporting area.

In addition, if the type of the support zone to which the plurality of target support zones belong is a base station-free type, when implementing the corresponding preset support strategy (i.e. enhancing the base station construction of the target support zone), the plurality of target support zones may be prioritized, and the preset support strategy may be implemented according to the priority of the target support zone. For example, the base station construction of the target lean region with higher priority (i.e., higher lean degree) may be ranked first according to the lean degree of the target lean region, and the base station construction of the target lean region may be strengthened first. Alternatively, the priority ranking may be performed according to the ratio of the lean population or the number of lean populations in the target lean region, which is not limited by the embodiment of the present invention.

The embodiment of the invention provides a data processing method, which comprises the steps of obtaining position information of a target lean region, determining the distance between the target lean region and a base station based on the position information of a preset base station and the position information of the target lean region, determining the lean region type of the target lean region based on the distance, and finally determining a preset lean strategy corresponding to the lean region type of the target lean region. Therefore, only the position information of the target lean region and the position information of the preset base station are required to be acquired, the lean region type of the target lean region can be determined through the distance between the target lean region and the preset base station, more information of the target lean region is not required to be acquired, the use efficiency of data is improved, meanwhile, the corresponding preset lean strategy is determined according to the lean region type of the target lean region, lean work can be carried out on the target lean region more accurately, and the accurate lean efficiency is improved.

Example two

As shown in fig. 2, an embodiment of the present invention provides a data processing method, where an execution body of the method may be a server, and the server may be an independent server or a server cluster formed by a plurality of servers. The method specifically comprises the following steps:

In step S202, area information to which the target user belongs is acquired.

The target user may be a poor population user acquired according to government reports, or may be a user determined according to registration information of mobile communication, or the target user may be a user in a poor area, and the area information may include longitude and latitude information.

In practice, if the target user is a poor population user acquired according to government reports, the target user's resident area may be acquired as the target user's area information from data sources available according to government reports or the like. Alternatively, the target user may lean the user in the area, and the lean area may be used as the area information to which the target user belongs. Or, the target user may be determined according to call information of the user in mobile communication, for example, a user whose average consumption is lower than a predetermined call threshold may be determined as the target user, and the resident area of the target user is determined according to registration information of the target user when the target user is registered as the mobile communication user, that is, the area information to which the target user belongs.

The method for determining the target user and the method for acquiring the regional information to which the target user belongs in the embodiment of the invention are not particularly limited.

In step S204, clustering is performed on the target user based on the region information to which the target user belongs, so as to obtain a clustering result.

In implementation, based on the region information of the target user, a plurality of different clustering algorithms can be used for clustering the target user, for example, a K-means clustering algorithm, a Clara algorithm and the like can be used for clustering the target user, so as to obtain a clustering result.

In practical applications, the processing manners of the step S204 may be various, and the following provides an alternative implementation manner, which can be specifically referred to the following steps one to four:

step one, determining a data point corresponding to longitude and latitude information of a target user, and determining a target neighborhood corresponding to the data point based on a preset radius.

In implementation, the target user may be converted into a plurality of data points according to the location information (i.e. latitude and longitude information) of the area to which the target user belongs, for example, the latitude and longitude information of the area to which the target user 1 belongs is: lat1 (longitude information) and lng1 (latitude information), the information of the data point 1 corresponding to the target user 1 is (lat 1, lng 1), and the like, so that the data points corresponding to all the target users can be obtained.

Based on the predetermined radius, the target neighborhood corresponding to the data point and the data points contained in the target neighborhood may be determined, for example, the predetermined radius may be 100 meters, taking the data point 1 as an example, the distance between the data point 1 and all the remaining data points may be acquired, then the data points with the distance smaller than or equal to 100 meters between the data point 1 may be classified into the target neighborhood corresponding to the data point 1, and assuming that, in addition to the data point 1, there are the data point 2 corresponding to the target user 2, the data point 3 corresponding to the target user 3, and the data point 4 corresponding to the target user 4, wherein the distance between the data point 2 and the data point 1 is 101 meters, the distance between the data point 3 and the data point 1 is 20 meters, and the distance between the data point 4 and the data point 1 is 150 meters, then the data point contained in the target neighborhood 1 corresponding to the data point 1 may be determined as the data point 3 according to the predetermined radius. The distances between all other data points and the data point 2 are acquired by taking the data point 2 as a center, then the target neighborhood 2 corresponding to the data point 2 and the data points included in the target neighborhood 2 are determined based on a preset radius (100 meters), and the target neighborhood 3 corresponding to the data point 3 and the data points included in the target neighborhood 3, and the target neighborhood 4 corresponding to the data point 4 and the data points included in the target neighborhood 3 can be respectively determined.

And secondly, detecting the data points one by one, if the number of the data points contained in the target adjacent area is not smaller than a preset point threshold value, establishing a cluster based on the data points, and adding the data points contained in the target adjacent area into the candidate set.

In implementation, after determining the target neighborhood corresponding to the data point, the number of data points included in the target neighborhood may be detected, if the number of data points included in the target neighborhood is smaller than a predetermined point threshold, the data point corresponding to the target neighborhood may be determined to be noise, for example, taking the above data point 1 as an example, assuming that the predetermined point threshold is 1, and only the data point 4 is included in the target neighborhood 1 corresponding to the data point 1, the data point 1 may be determined to be noise.

If the number of data points contained in the target neighborhood of the data point is not smaller than the preset point threshold, a cluster is built based on the data points, and the data points contained in the target neighborhood are added into the candidate set, and if the number of data points contained in the target neighborhood 2 corresponding to the data point 2 is 3 and is larger than the preset point threshold (for example, the preset point threshold is 2), a cluster 1 can be built based on the data point 2, and then the data points (for example, the data point 3 and the data point 4) contained in the target neighborhood 2 corresponding to the data point 2 can be added into the candidate set 1 corresponding to the cluster 1.

And thirdly, detecting the data points contained in the candidate set, and adding the data points into the cluster if the number of the data points contained in the target neighborhood is smaller than a preset point threshold value.

In implementation, the data points included in the candidate set may be detected, and still based on the examples in the first and second steps, the candidate set corresponding to the data point 2 includes the data point 3 and the data point 4, the number of data points included in the target neighborhood of the two data points may be detected, and assuming that the preset point threshold is 2, the number of data points included in the target neighborhood 3 corresponding to the data point 3 is 1, and the number of data points included in the target neighborhood 4 corresponding to the data point 4 is 3, the data point 3 may be added to the cluster 1 corresponding to the data point 2, and then the data point 4 may be deleted from the candidate set 1 of the data point 2.

And step four, determining a clustering result based on the clustering clusters.

In practice, still based on the example in step three above, the final cluster may have cluster 1 based on data point 2 and cluster 2 based on data point 4, where data point 2 and data point 3 are included in cluster 1 corresponding to data point 2, and data 1 and data point 4 may be included in cluster 2 corresponding to data point 4. The corresponding clustering results may be cluster 1 and cluster 2, where cluster 1 includes target user 1 corresponding to data point 2 and target user 3 corresponding to data point 3, and cluster 2 includes target user 2 corresponding to data point 1 and target user 4 corresponding to data point 4.

In step S206, a cluster area in which the number of target users included in the cluster result is greater than a predetermined number threshold is determined as a target euthanasia area.

In implementation, after the target users are clustered, the number of target users included in the clustered result may be detected, and if the number of target users included in the clustered result is greater than a predetermined number threshold, the clustered region corresponding to the clustered result may be determined as the target lean region.

In addition, the processing manner of the step S206 may be varied, and the following alternative implementation manner is provided, which may be specifically referred to the following steps one to three:

step one, acquiring the information of the poverty-rest priority of the target user.

In implementation, information such as the lean degree of the target user can be obtained as lean priority information according to public information such as government reports, or lean priority information of the target user can be determined according to call information of the target user.

And step two, determining the rest priority information of the clustering areas corresponding to different categories in the clustering result according to the rest priority information of the target user.

In an implementation, after acquiring the information of the barren priority of the target user, the information of the barren priority of the clustering areas corresponding to different categories may be determined based on the information of the barren priority of the target user in the clustering result. Based on the example in step S204, after the clustering processing is performed on the target user 1, the target user 2, the target user 3 and the target user 4, the obtained clustering results are the cluster 1 and the cluster 2, wherein the cluster 1 includes the target user 1 and the target user 3, the cluster 2 includes the target user 1 and the target user 4, if the lean priority of the target user 1 is 1, the lean priority of the target user 2 is 5, the lean priority of the target user 3 is 5, and the lean priority of the target user 4 is 1, then it may be determined that the lean priority of the cluster 1 is lower than the lean priority of the cluster 2 according to the priority of the target user.

In addition, the method for determining the information of the lean priority of the clustering areas corresponding to different types in the clustering result can be various, for example, the information of the lean priority of the clustering areas corresponding to different types can be determined according to the duty ratio of the target users with higher lean priority among the target users contained in different types in the clustering result, and the specific determining method can be different according to different practical application scenes.

And thirdly, determining the clustering area corresponding to the category greater than the preset excellent lean priority threshold value as a target lean area based on the lean priority information of the clustering areas corresponding to different categories.

In step S208, the positional information of the target lean region is determined.

In implementation, the location information of the target lean area may be determined according to the clustering result, for example, in step S204, when the target user is clustered, the corresponding target neighborhood may be determined according to the data point corresponding to the target user, so as to establish a cluster, and then in this clustering process, the data point corresponding to the cluster may be used as the core object of the cluster, that is, the location information of the target lean area corresponding to the cluster may be the location information of the core object of the cluster.

For example, based on the example in step S204, cluster 1 contains data point 2 and data point 3, where cluster 1 is a cluster created by data point 2, then data point 2 may be the core object of cluster 1, and if the location information of data point 2 is (lat 2, lng 2), then the location information of the target poverty-maintaining area corresponding to this cluster 1 may be (lat 2, lng 2).

In addition, the position information of the center point of the target lean region may be used as the position information of the target lean region, and the method for determining the position information of the target lean region according to the embodiment of the present invention is not particularly limited.

In step S210, the longitude information and latitude information of the target poverty-relieving area, the longitude information and latitude information of the base station are substituted into the following formula,

and determining the distance information of the target poverty-relieving area and the base station.

Wherein L is the distance between the target area and the base station, R is the earth radius, lng1 is the longitude information of the target area, lng2 is the longitude information of the base station, lat1 is the latitude information of the target area, and lat2 is the latitude information of the base station.

In step S212, base station signal coverage information is acquired.

In an implementation, the base station information coverage information may be acquired based on measurement reports (MR, measurement Report) of mobile communications, e.g., the base station signal coverage information may be acquired via whole network MR weak coverage library information, such as whether MR weak coverage information is present or not.

In step S214, signal coverage information in the target area is determined based on the base station signal coverage information and the position information of the target area.

In step S216, the type of the lean region to which the target lean region belongs is determined based on the distance and the signal coverage information.

The type of the poverty-relieving area can comprise a base station-free type, a base station-free signal coverage type, a base station-free and signal coverage weak type and the like.

In implementation, based on the distance between the base station and the target lean region, and the signal coverage information of the target lean region, the lean region type to which the target lean region belongs may be determined, for example, if the distance between the base station and the target lean region is greater than a predetermined distance threshold, the lean region type to which the target lean region belongs may be a base station-free type, such as the predetermined distance threshold may be 2000 meters, and if the distance between the target lean region and the base station is 2500 meters, the lean region type to which the target lean region belongs may be a base station-free type.

If the distance between the base station and the target lean region is not greater than the predetermined distance threshold and the signal coverage information is less than the first predetermined signal threshold, the lean region type to which the target lean region belongs is a base station-present or non-signal coverage type, for example, the distance between the target lean region and the base station is 1000 meters, less than the predetermined distance threshold (e.g., 2000 meters), and no signal coverage (i.e., the signal coverage information is less than the first predetermined signal threshold) in the target lean region, the lean region type to which the target lean region belongs is a base station-present or non-signal coverage type, or the distance between the target lean region and the base station is not greater than the predetermined distance threshold, but the target lean region is within the predetermined signal distance threshold (e.g., 200 meters), the lean region type to which the target lean region belongs may also be a base station-present or non-signal coverage type.

If the distance is not greater than the preset distance threshold value, the signal coverage information is not less than the first preset signal threshold value and is less than the second preset signal threshold value, the type of the lean region to which the target lean region belongs is a base station and the signal coverage is weak type, and the situation that connection signals are weak in the target lean region is easy to generate disconnection although connection is possible.

In step S218, a preset lean strategy corresponding to the lean zone type to which the target lean zone belongs is determined.

In an implementation, according to different types of the supported lean areas, corresponding preset supported lean strategies are determined, for example, if the supported lean area type to which the target supported lean area belongs is a base station with or without signal coverage type, the preset supported lean strategy may be enhanced coverage optimization, and if the supported lean area type to which the target supported lean area belongs is a base station with or without signal coverage weak type, the preset supported lean strategy may be a strategy that continuously focuses on the signal coverage situation.

The embodiment of the invention does not specifically limit the type of the lean region to which the target lean region belongs, does not specifically limit the preset lean strategy, and can be different according to the application scene of poems.

As shown in fig. 3, if the target users are not clustered according to the region information to which the target users belong, but the target barred regions are determined by administrative region division, both the region a and the region B can be used as the target barred regions, and accordingly, base stations (i.e., the base station a and the base station B) need to be respectively established in the two regions. Based on the scheme of the invention, the target users can be clustered to determine the target barren regions, if the barren region type of the target barren regions is a base station-free type, a corresponding preset barren strategy (such as base station construction) can be implemented, namely, only one base station is required to be constructed in the target barren regions, thus meeting the use needs of the target barren users, reducing the resource waste and improving the accurate barren efficiency.

According to the scheme provided by the invention, the evidence analysis is carried out, and the evidence analysis results show that 299 base stations are planned and built for 66 poor population gathering areas, and 351 signal coverage problem points are optimized. The MR coverage rate of the target lean area is improved from 76.2% to 95.8%, the month average flow rate of the target user is increased from 1.75GB to 3.37GB, the day average complaint amount of the target user is reduced from 62 to 14, and the accurate lean effect is obvious.

Example III

The above method for processing data provided by the embodiment of the present invention is based on the same concept, and the embodiment of the present invention further provides a device for processing data, as shown in fig. 4.

The data processing device comprises: an information acquisition module 401, a distance determination module 402, a type determination module 403, and a policy determination module 404, wherein:

an information acquisition module 401, configured to acquire position information of a target lean region;

a distance determining module 402, configured to determine a distance between the target lean region and the base station based on position information of a predetermined base station and position information of the target lean region;

a type determining module 403, configured to determine, based on the distance, a type of the lean region to which the target lean region belongs;

a policy determining module 404, configured to determine a preset lean policy corresponding to a lean region type to which the target lean region belongs.

In the embodiment of the present invention, the information obtaining module 401 includes:

the information acquisition unit is used for acquiring the regional information of the target user, wherein the regional information comprises longitude and latitude information;

the clustering unit is used for carrying out clustering processing on the target user based on the regional information of the target user to obtain a clustering result;

and the area determining unit is used for determining the clustering areas, which are included in the clustering result and have the number of the target users larger than a preset number threshold, as target poverty-supporting areas and determining the position information of the target poverty-supporting areas.

In an embodiment of the present invention, the clustering unit is configured to:

determining a data point corresponding to longitude and latitude information of the target user, and determining a target neighborhood corresponding to the data point based on a preset radius;

detecting the data points one by one, if the number of the data points contained in the neighborhood of the data points is not smaller than a preset point threshold value, establishing a target cluster based on the data points, and adding the data points contained in the neighborhood of the data points into a candidate set;

detecting the data points contained in the candidate set, and adding the data points into the target cluster if the number of the data points contained in the neighborhood corresponding to the data points is smaller than a preset point threshold;

in an embodiment of the present invention, the area determining unit is configured to:

acquiring the information of the poverty-rest priority of the target user;

according to the information of the lean priority of the target user, determining the information of the lean priority of the clustering areas corresponding to different categories in the clustering result;

and determining the clustering area corresponding to the category greater than the preset excellent lean priority threshold as a target lean area based on the lean priority information of the clustering areas corresponding to the different categories.

In an embodiment of the present invention, the apparatus further includes:

the signal acquisition module is used for acquiring the signal coverage information of the base station;

an information determining module, configured to determine signal coverage information in the target area based on the base station signal coverage information and the position information of the target area;

the type determining module is used for:

and determining the type of the lean region to which the target lean region belongs based on the distance and the signal coverage information.

In the embodiment of the invention, the position information of the target poverty-relief area comprises longitude information and latitude information of the target poverty-relief area, and the position information of the base station comprises longitude information and latitude information of the base station;

the distance determining module is used for:

substituting the longitude information and latitude information of the target poverty-relieving area and the longitude information and latitude information of the base station into the following formula,

determining distance information between the target poverty-relieving area and the base station;

In the embodiment of the invention, the type of the poverty-relieving area comprises a base station-free type, a base station-with-signal-coverage weak type and a base station-with-signal-without-signal-coverage type;

the type determination module comprises:

a first type determining unit, configured to, if the distance is greater than a predetermined distance threshold, determine that a type of the lean region to which the target lean region belongs is a base station-free type;

a second type determining unit, configured to, if the distance is not greater than a predetermined distance threshold and the signal coverage information is smaller than a first predetermined signal threshold, determine that a type of a poverty-supporting area to which the target poverty-supporting area belongs is a base station-presence/non-signal coverage type;

and a third type determining unit, configured to, if the distance is not greater than a predetermined distance threshold, and the signal coverage information is not less than a first predetermined signal threshold and less than a second predetermined signal threshold, determine that the type of the lean region to which the target lean region belongs is a base station and signal coverage weak type.

The embodiment of the invention provides a data processing device, which is used for acquiring position information of a target lean region, determining the distance between the target lean region and a base station based on the position information of a preset base station and the position information of the target lean region, determining the lean region type of the target lean region based on the distance, and finally determining a preset lean strategy corresponding to the lean region type of the target lean region. Therefore, only the position information of the target lean region and the position information of the preset base station are required to be acquired, the lean region type of the target lean region can be determined through the distance between the target lean region and the preset base station, more information of the target lean region is not required to be acquired, the use efficiency of data is improved, meanwhile, the corresponding preset lean strategy is determined according to the lean region type of the target lean region, lean work can be carried out on the target lean region more accurately, and the accurate lean efficiency is improved.

Example IV

Figure 5 is a schematic diagram of the hardware architecture of an apparatus implementing various embodiments of the invention,

the apparatus 500 includes, but is not limited to: radio frequency unit 501, network module 502, audio output unit 503, input unit 504, sensor 505, display unit 506, user input unit 507, interface unit 508, memory 509, processor 510, and power source 511. It will be appreciated by those skilled in the art that the device structure shown in fig. 5 is not limiting of the device and that the device may include more or fewer components than shown, or certain components may be combined, or a different arrangement of components. In the embodiment of the invention, the equipment comprises, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted terminal, a wearable equipment, a pedometer and the like.

Wherein the processor 510 is configured to obtain location information of the target lean region;

a processor 510 further configured to determine a distance between the target barren region and the base station based on location information of a predetermined base station and location information of the target barren region;

processor 510 is further configured to determine, based on the distance, a type of the lean region to which the target lean region belongs;

Further, the processor 510 is further configured to determine a preset lean strategy corresponding to a lean zone type to which the target lean zone belongs.

In addition, the processor 510 is further configured to obtain area information that the target user belongs to, where the area information includes latitude and longitude information;

in addition, the processor 510 is further configured to perform clustering processing on the target user based on the region information to which the target user belongs, so as to obtain a clustering result;

in addition, the processor 510 is further configured to determine, as a target lean region, a cluster region in which the number of target users included in the cluster result is greater than a predetermined number threshold, and determine location information of the target lean region.

In addition, the processor 510 is further configured to determine a data point corresponding to latitude and longitude information to which the target user belongs, and determine a target neighborhood corresponding to the data point based on a predetermined radius;

in addition, the processor 510 is further configured to detect the data points one by one, and if the number of data points included in the target neighboring area is not less than a preset point threshold, establish a cluster based on the data points, and add the data points included in the target neighboring area to a candidate set;

In addition, the processor 510 is further configured to detect data points included in the candidate set, and if the number of data points included in the target neighborhood is less than a preset point threshold, add the data points to the cluster;

in addition, the processor 510 is further configured to determine the clustering result based on the clustering cluster.

In addition, the processor 510 is further configured to obtain the lean priority information of the target user;

in addition, the processor 510 is further configured to determine, according to the lean priority information of the target user, lean priority information of clustering areas corresponding to different categories in the clustering result;

in addition, the processor 510 is further configured to determine, based on the information of the lean priorities of the clustered regions corresponding to the different categories, a clustered region corresponding to a category greater than a preset threshold of excellent lean priority as the target lean region.

In addition, the processor 510 is further configured to obtain base station signal coverage information;

in addition, the processor 510 is further configured to determine signal coverage information in the target area based on the base station signal coverage information and the location information of the target area;

in addition, the processor 510 is further configured to determine, based on the distance and the signal coverage information, a type of the lean region to which the target lean region belongs.

Further, the location information of the target poverty-rest area includes longitude information and latitude information of the target poverty-rest area, and the location information of the base station includes longitude information and latitude information of the base station; the processor 510 is further configured to substitute the longitude information and latitude information of the target balustrade region, the longitude information and latitude information of the base station into the following formula,

In addition, the type of the poverty-relieving area comprises a base station-free type, a base station-with-signal-coverage weak type and a base station-with-signal-without-signal-coverage type; the processor 510 is further configured to, if the distance is greater than a predetermined distance threshold, determine that the type of the lean region to which the target lean region belongs is a base station-free type;

in addition, the processor 510 is further configured to, if the distance is not greater than a predetermined distance threshold and the signal coverage information is less than a first predetermined signal threshold, determine that the type of the lean region to which the target lean region belongs is a base station-presence/non-signal coverage type;

In addition, the processor 510 is further configured to, if the distance is not greater than a predetermined distance threshold, the signal coverage information is not less than a first predetermined signal threshold and is less than a second predetermined signal threshold, and the type of the lean region to which the target lean region belongs is a base station and signal coverage weak type.

The embodiment of the invention provides equipment, which is used for determining the distance between a target poverty-relieving area and a base station by acquiring the position information of the target poverty-relieving area, then determining the poverty-relieving area type of the target poverty-relieving area based on the position information of a preset base station and the position information of the target poverty-relieving area, and finally determining a preset poverty-relieving strategy corresponding to the poverty-relieving area type of the target poverty-relieving area. Therefore, only the position information of the target lean region and the position information of the preset base station are required to be acquired, the lean region type of the target lean region can be determined through the distance between the target lean region and the preset base station, more information of the target lean region is not required to be acquired, the use efficiency of data is improved, meanwhile, the corresponding preset lean strategy is determined according to the lean region type of the target lean region, lean work can be carried out on the target lean region more accurately, and the accurate lean efficiency is improved.

It should be understood that, in the embodiment of the present invention, the radio frequency unit 501 may be used to receive and send information or signals during a call, specifically, receive downlink data from a base station, and then process the downlink data with the processor 510; and, the uplink data is transmitted to the base station. Typically, the radio frequency unit 501 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 501 may also communicate with networks and other devices through a wireless communication system.

The device provides wireless broadband internet access to the user via the network module 502, such as helping the user to send and receive e-mail, browse web pages, and access streaming media, etc.

The audio output unit 503 may convert audio data received by the radio frequency unit 501 or the network module 502 or stored in the memory 509 into an audio signal and output as sound. Also, the audio output unit 503 may also provide audio output (e.g., call signal reception sound, message reception sound, etc.) related to a specific function performed by the device 500. The audio output unit 503 includes a speaker, a buzzer, a receiver, and the like.

The input unit 504 is used for receiving an audio or video signal. The input unit 504 may include a graphics processor (Graphics Processing Unit, GPU) 5051 and a microphone 5042, the graphics processor 5051 processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 506. The image frames processed by the graphics processor 5051 may be stored in the memory 509 (or other storage medium) or transmitted via the radio frequency unit 501 or the network module 502. Microphone 5042 may receive sound and may be capable of processing such sound into audio data. The processed audio data may be converted into a format output that can be transmitted to the mobile communication base station via the radio frequency unit 501 in case of a phone call mode.

The device 500 further comprises at least one sensor 505, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor that can adjust the brightness of the display panel 5061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 5061 and/or backlight when the device 500 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when the accelerometer sensor is stationary, and can be used for recognizing the gesture of equipment (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking) and the like; the sensor 505 may further include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which are not described herein.

The display unit 506 is used to display information input by a user or information provided to the user. The display unit 506 may include a display panel 5061, and the display panel 5061 may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD), an Organic Light-Emitting Diode (OLED), or the like.

The user input unit 507 is operable to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the device. Specifically, the user input unit 507 includes a touch panel 5071 and other input devices 5072. Touch panel 5071, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on touch panel 5071 or thereabout using any suitable object or accessory such as a finger, stylus, etc.). Touch panel 5071 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 510, and receives and executes commands sent by the processor 510. In addition, the touch panel 5071 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 5071, the user input unit 507 may include other input devices 5072. In particular, other input devices 5072 may include, but are not limited to, physical keyboards, function keys (e.g., volume control keys, switch keys, etc.), trackballs, mice, joysticks, and so forth, which are not described in detail herein.

Further, the touch panel 5071 may be overlaid on the display panel 5061, and when the touch panel 5071 detects a touch operation thereon or thereabout, the touch operation is transmitted to the processor 510 to determine a type of touch event, and then the processor 510 provides a corresponding visual output on the display panel 5061 according to the type of touch event. Although in fig. 5, the touch panel 5071 and the display panel 5061 are provided as two separate components to implement the input and output functions of the device, in some embodiments, the touch panel 5071 may be integrated with the display panel 5061 to implement the input and output functions of the device, which is not limited herein.

The interface unit 508 is an interface to which an external device is connected to the apparatus 500. For example, the external devices may include a wired or wireless headset port, an external power (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 508 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the apparatus 500 or may be used to transmit data between the apparatus 500 and an external device.

The memory 509 may be used to store software programs as well as various data. The memory 509 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory 509 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

The processor 510 is a control center of the device, connecting the various parts of the overall device using various interfaces and lines, performing various functions of the device and processing the data by running or executing software programs and/or modules stored in the memory 509, and invoking data stored in the memory 509, thereby performing overall monitoring of the device. Processor 510 may include one or more processing units; preferably, the processor 510 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 510.

The device 500 may also include a power source 511 (e.g., a battery) for powering the various components, and preferably the power source 511 may be logically connected to the processor 510 via a power management system that performs functions such as managing charging, discharging, and power consumption.

Preferably, the embodiment of the present invention further provides an apparatus, which includes a processor 510, a memory 509, and a computer program stored in the memory 509 and capable of running on the processor 510, where the computer program when executed by the processor 510 implements each process of the foregoing data processing method embodiment, and the same technical effects can be achieved, and for avoiding repetition, a description is omitted herein.

Example five

The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the processes of the data processing method embodiment, and can achieve the same technical effects, so that repetition is avoided and no further description is given here. Wherein the computer readable storage medium is selected from Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.

The embodiment of the invention provides a computer readable storage medium, which is used for obtaining the position information of a target poverty-supporting area, then determining the distance between the target poverty-supporting area and a base station based on the position information of a preset base station and the position information of the target poverty-supporting area, determining the poverty-supporting area type of the target poverty-supporting area based on the distance, and finally determining a preset poverty-supporting strategy corresponding to the poverty-supporting area type of the target poverty-supporting area. Therefore, only the position information of the target lean region and the position information of the preset base station are required to be acquired, the lean region type of the target lean region can be determined through the distance between the target lean region and the preset base station, more information of the target lean region is not required to be acquired, the use efficiency of data is improved, meanwhile, the corresponding preset lean strategy is determined according to the lean region type of the target lean region, lean work can be carried out on the target lean region more accurately, and the accurate lean efficiency is improved.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or 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, embedded processor, 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that 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 one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.

Claims

1. A method of processing data, the method comprising:

acquiring position information of a target poverty-relieving area;

determining the type of the lean region to which the target lean region belongs based on the distance, wherein the type of the lean region comprises a base station type and a non-base station type;

determining a preset poverty-relieving strategy corresponding to the poverty-relieving area type to which the target poverty-relieving area belongs;

wherein, under the condition that the type of the lean region to which the target lean region belongs is a base station-free type, a preset lean strategy corresponding to the type of the lean region to which the target lean region belongs is to strengthen the base station construction of the target lean region;

the obtaining the position information of the target poverty-relieving area comprises the following steps:

obtaining regional information of a target user, wherein the regional information comprises longitude and latitude information;

clustering the target users based on the regional information of the target users to obtain clustering results;

determining a clustering area, which is included in the clustering result and has the number of the target users larger than a preset number threshold, as a target lean area, and determining position information of the target lean area;

The determining, as a target lean region, a cluster region in which the number of target users included in the cluster result is greater than a predetermined number threshold includes:

acquiring the information of the poverty-rest priority of the target user;

2. The method according to claim 1, wherein the clustering the target user based on the region information to which the target user belongs to obtain a clustering result includes:

detecting the data points one by one, if the number of the data points contained in the target adjacent area is not smaller than a preset point threshold value, establishing a cluster based on the data points, and adding the data points contained in the target adjacent area into a candidate set;

Detecting data points contained in the candidate set, and adding the data points into the cluster if the number of the data points contained in the target neighborhood is smaller than a preset point threshold;

and determining the clustering result based on the clustering cluster.

3. The method of claim 1, wherein prior to determining the type of the region of the target based on the distance, the method further comprises:

acquiring base station signal coverage information;

determining signal coverage information in the target area based on the base station signal coverage information and the position information of the target area;

the determining, based on the distance, a type of the lean region to which the target lean region belongs, including:

4. The method of claim 1, wherein the location information of the target balustrade region comprises longitude information and latitude information of the target balustrade region, and the location information of the base station comprises longitude information and latitude information of the base station;

the determining, based on the acquired location information of the base station, a distance between the target poverty-maintaining area and the base station information includes:

5. The method of claim 4, wherein the type of the region of poverty relief comprises a no base station type, a base station with weak signal coverage type, a base station with no signal coverage type;

the determining, based on the distance and the signal coverage information, a type of the lean region to which the target lean region belongs, includes:

if the distance is greater than a preset distance threshold value, the type of the lean region to which the target lean region belongs is a base station-free type;

if the distance is not greater than a preset distance threshold value and the signal coverage information is smaller than a first preset signal threshold value, the type of the lean region to which the target lean region belongs is a base station-provided signal coverage type or a base station-non-signal coverage type;

And if the distance is not greater than a preset distance threshold value, the signal coverage information is not less than a first preset signal threshold value and less than a second preset signal threshold value, and the type of the lean region to which the target lean region belongs is a base station and signal coverage is weak type.

6. A data processing apparatus, the apparatus comprising:

the type determining module is used for determining the type of the lean region to which the target lean region belongs based on the distance, wherein the type of the lean region comprises a base station type and a non-base station type;

the strategy determining module is used for determining a preset lean strategy corresponding to the lean region type to which the target lean region belongs;

The information acquisition module is specifically configured to:

acquiring the information of the poverty-rest priority of the target user;

7. An apparatus comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program implementing the steps of the method of processing data as claimed in any one of claims 1 to 5 when executed by the processor.

8. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the data processing method according to any one of claims 1 to 5.