CN115314909A - Big data-based residential community mobile network base station planning method and system - Google Patents
Big data-based residential community mobile network base station planning method and system Download PDFInfo
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Abstract
The application discloses a method and a system for planning a residential community mobile network base station based on big data, which belong to the technical field of data processing, and the method comprises the following steps: collecting basic information of a target coverage community; acquiring data according to the boundary track coordinates based on a time acquisition period to obtain a service data set, and constructing a first dynamic label according to a user division result; evaluating the traffic data of the user and constructing a second dynamic label; generating a static label according to the basic information of the user to obtain a value degree distribution result of the user; obtaining a building information acquisition result; and planning the mobile network base station of the target coverage community according to the building information acquisition result and the user value degree distribution result. The method and the system solve the technical problems that the planning of the residential community mobile network base station is inaccurate and the communication requirements of users cannot be met in the prior art, achieve scientific and reasonable network base station planning construction, and improve the technical effects of planning accuracy and comprehensiveness.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a residential community mobile network base station planning method and system based on big data.
Background
With the rapid development of mobile internet services, the ability to enjoy good mobile networks anytime and anywhere has become a main appeal of a large number of users, and perfect network coverage has become a new demand of communities, so that the study on how to better plan the base station of the mobile network of the residential community has very important significance for improving the experience of the user network.
At present, communication operators rely on huge user quantity and abundant data resources to establish user figures, user demands are obtained through the user figures, and then mobile base station planning is carried out according to the user demands. Meanwhile, due to the blocking of intensive building buildings in the community, the coverage degree of signals cannot be subjected to standardized design, so that the difficulty of planning a base station is increased, and the network service condition of users in the community cannot be fully met. The technical problems that the planning of a residential community mobile network base station is inaccurate and the communication requirements of users cannot be met exist in the prior art.
Disclosure of Invention
The application aims to provide a method and a system for planning a residential community mobile network base station based on big data, which are used for solving the technical problems that the planning of the residential community mobile network base station is inaccurate and the communication requirements of users cannot be met in the prior art.
In view of the above problems, the present application provides a method and a system for planning a residential community mobile network base station based on big data.
In a first aspect, the present application provides a method for planning a residential community mobile network base station based on big data, where the method includes: acquiring basic information of a target coverage community, carrying out community boundary acquisition according to the basic information, and determining a boundary track coordinate based on a community boundary acquisition result; setting a time acquisition period, carrying out data acquisition on base station interaction data in the boundary track coordinate to obtain a service data set, carrying out user division based on the service data set and the time acquisition period to obtain a user division result, and constructing a first dynamic label of a user according to the user division result; performing service volume data evaluation of the user according to the service data set, and constructing a second dynamic label of the user based on a service volume data evaluation result; acquiring basic information of a user, generating a static label according to the basic information, and distributing user value degrees in a community according to the static label, the first dynamic label and the second dynamic label to obtain a user value degree distribution result; carrying out community building information acquisition on the target coverage community to obtain a building information acquisition result; and planning the mobile network base station of the target coverage community according to the building information acquisition result and the user value degree distribution result.
On the other hand, the application also provides a system for planning the base station of the residential community mobile network based on big data, wherein the system comprises: the track coordinate determination module is used for acquiring basic information of a target coverage community, carrying out community boundary acquisition according to the basic information and determining boundary track coordinates based on community boundary acquisition results; the first dynamic label obtaining module is used for setting a time collecting period, carrying out data collection on base station interaction data in the boundary track coordinate to obtain a service data set, carrying out user division based on the service data set and the time collecting period to obtain a user division result, and constructing a first dynamic label of a user according to the user division result; the second dynamic label building module is used for evaluating the service data of the user according to the service data set and building a second dynamic label of the user based on the service data evaluation result; the distribution result obtaining module is used for obtaining basic information of a user, generating a static label according to the basic information, and distributing user value degrees in a community according to the static label, the first dynamic label and the second dynamic label to obtain a user value degree distribution result; the information acquisition result acquisition module is used for carrying out community building information acquisition on the target coverage community to obtain a building information acquisition result; and the base station planning module is used for planning the mobile network base station of the target coverage community according to the building information acquisition result and the user value degree distribution result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps of acquiring basic information of a target coverage community, determining a community boundary, obtaining a boundary track coordinate when a base station is planned, setting a time acquisition period, carrying out data acquisition on base station interaction data in the boundary track coordinate according to the time acquisition period, obtaining a service data set in the community, further dividing users according to the service data set and the time acquisition period, obtaining user division results, constructing a first dynamic label of the users according to the user division results, evaluating service volume data of the users according to the service data set, constructing a second dynamic label of the users according to the obtained service volume data evaluation results, further obtaining a static label according to the basic information of the users, further carrying out user value distribution in the community according to the static label, the first dynamic label and the second dynamic label, obtaining a user value distribution result, then carrying out building information acquisition on the target coverage community, obtaining a building information acquisition result, and finally combining the building information acquisition result and the user value distribution result to plan a mobile network base station of the target coverage community. The technical effects of improving the reliability of the mobile network base station planning and improving the planning accuracy are achieved.
Drawings
In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained according to the provided drawings without inventive effort.
Fig. 1 is a schematic flow chart of a method for planning a residential community mobile network base station based on big data according to an embodiment of the present application;
fig. 2 is a schematic flow chart illustrating a process of constructing a first dynamic tag of a user according to a user partition result in a method for planning a base station of a residential community mobile network based on big data according to an embodiment of the present application;
fig. 3 is a schematic flow chart illustrating planning of a mobile network base station of a target coverage community in a big data based residential community mobile network base station planning method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a residential community mobile network base station planning system based on big data according to the present application.
Description of reference numerals: the system comprises a track coordinate determination module 11, a first dynamic label obtaining module 12, a second dynamic label constructing module 13, a distribution result obtaining module 14, an information acquisition result obtaining module 15 and a base station planning module 16.
Detailed Description
The method and the system for planning the residential community mobile network base station based on the big data solve the technical problems that the residential community mobile network base station is inaccurate in planning and cannot meet the communication requirements of users in the prior art. The technical effects of scientific and reasonable network base station planning construction and improvement of planning accuracy and comprehensiveness are achieved.
According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws and regulations.
In the following, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments of the present application, and it is to be understood that the present application is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. It should be further noted that, for the convenience of description, only some but not all of the relevant portions of the present application are shown in the drawings.
Example one
As shown in fig. 1, the present application provides a big data based residential community mobile network base station planning method, wherein the method includes:
step S100: acquiring basic information of a target coverage community, carrying out community boundary acquisition according to the basic information, and determining a boundary track coordinate based on a community boundary acquisition result;
specifically, the target coverage community refers to any community that needs to be planned by a mobile network base station. The basic information is related information capable of reflecting basic conditions of the target coverage community, and comprises the following steps: geographic location, community area planning, community direction angle, community boundary information, etc. The community boundary acquisition result is obtained by acquiring the area range contained in the community and determining the boundary condition. The boundary track coordinates refer to geographical position coordinates of boundaries of the community, optionally include position coordinates of connection points of the boundaries of the community, and are collected in the clockwise direction. The technical effects of collecting basic information of a planned target coverage community and providing basic data for subsequent planning are achieved.
Step S200: setting a time acquisition period, carrying out data acquisition on base station interaction data in the boundary track coordinate to obtain a service data set, carrying out user division based on the service data set and the time acquisition period to obtain a user division result, and constructing a first dynamic label of a user according to the user division result;
further, as shown in fig. 2, the user partition is performed based on the service data set and the time acquisition period to obtain a user partition result, and a first dynamic label of the user is constructed according to the user partition result, in step S200 of the embodiment of the present application, the method further includes:
step S210: constructing an initial dividing label weight value;
step S220: constructing a user set according to the service data set;
step S230: screening communication users in the service data set through the time acquisition period, and dividing the users of the user set according to screening results to obtain user division results, wherein the user division results comprise resident division results and mobile user division results;
step S240: and marking the value weight of the user classification result according to the initial classification label weight value, and constructing a first dynamic label of the user according to the marking result and the user classification result.
Further, step S240 in the embodiment of the present application further includes:
step S241: counting the occurrence frequency of the resident in the time acquisition period in the resident division result to obtain frequency statistical data;
step S242: generating a frequency influence coefficient through the frequency statistical data, and adjusting the identification result based on the frequency influence coefficient to obtain an identification adjustment result;
step S243: and constructing a first dynamic label of the user according to the identification adjustment result and the user division result.
Specifically, the initial division label weight value is a label established according to different attributes of the community users, and the weight value occupied by different community users during community base station planning is determined according to the attributes. Alternatively, the user attributes may be residential users and floating users. The time acquisition cycle is a time period for acquiring data of the base station data in the boundary track coordinate, and is set by a worker, and is not limited herein. The base station interactive data refers to base station network data information and service flow data information in boundary track coordinates. And then, constructing a user set according to the base station interactive data, wherein the user set is a user using a corresponding service in the community. And dividing the users in the user set from two dimensions of the service type and the service time to obtain a user division result. Whether the user is a residential user or a floating user can be determined according to the purpose of using the mobile network and the time of using the mobile network. The user division result comprises a resident division result and a mobile user division result. And the resident dividing result is obtained by dividing the resident users in the community. The floating user division result is obtained by dividing users who do not live in the community but who appear in the community and use the mobile network. And performing value degree weight identification on the user division result according to the initial division label weight value, wherein the identification result reflects the contribution degree of user information to the construction of the community mobile network base station. And then, constructing a first dynamic label of the user according to the identification result and the user division result, and performing labeling construction on the user so as to lay a cushion for subsequent further analysis. The first dynamic label refers to the information of relative dynamic change of the user using the mobile network in the community, and comprises the time of using the network.
Illustratively, new labels can be further derived by summarizing user data of each network user, terminal and the like, and the new labels are used for reflecting the overall situation of community business information. When community users are gathered, the conditions of single-user multi-terminal, double-card mobile phones and the like need to be considered in the gathering process, and operations such as duplicate removal and correction are carried out on data. Therefore, the reliability of the data in the service data set is ensured, and the accuracy of planning and construction is improved.
Specifically, the frequency statistical data reflects the occurrence frequency of the resident in the time acquisition period, so that the frequency of the resident using the mobile network in the community can be obtained, and the corresponding traffic can be adjusted. And obtaining a corresponding frequency influence coefficient according to the frequency statistical data and the frequency value. And the frequency influence coefficient reflects the influence of the user network use condition corresponding to the frequency on the network base station planning. The larger the frequency influence coefficient is, the larger the influence of the corresponding frequency value on the planning and construction of the network base station is. And correspondingly adjusting the identification result according to the frequency influence coefficient to obtain the identification adjustment result. And the identification adjustment result is obtained after the value of the user is accurately corrected. The reliability and accuracy of the value corresponding to the identifier can be improved. Therefore, the technical effects of improving the data quality and providing reliable data for planning by finely preprocessing the data are achieved.
Further, step S240 in the embodiment of the present application further includes:
step S244: obtaining privileged user information in the user set;
step S245: generating a privilege association coefficient based on the privileged user information;
step S246: performing identification optimization of the identification result according to the privilege association coefficient to obtain an identification optimization result;
step S247: and constructing a first dynamic label of the user according to the identification optimization result and the user division result.
Specifically, the privileged user information is basic information of a privileged user using a mobile network, including the number of users and the user ratio. And then obtaining a corresponding privilege association coefficient according to the information of the privileged user, wherein the privilege association coefficient reflects the influence degree of the mobile network use condition of the privileged user on the mobile network construction. And then optimizing the identification result according to the privilege association coefficient, thereby obtaining the identification optimization result. The identification optimization result is obtained by considering the influence of the privileged user on the construction. And then, obtaining a first dynamic label of the user according to the identification optimization result and the user division result. The technical effects of deeply mining the user information and improving the utilization rate of the user information are achieved.
Step S300: performing service data evaluation on the user according to the service data set, and constructing a second dynamic label of the user based on a service data evaluation result;
specifically, after the service data set is obtained, the service volume of the mobile network of the user is obtained according to the service data information. Optionally, the service volume data includes service traffic, network time, service type, and the like. And evaluating the traffic data to obtain the service use condition of the user. The second dynamic label reflects the service use condition of the user. And acquiring user information corresponding to the label according to the second dynamic label.
Step S400: acquiring basic information of a user, generating a static label according to the basic information, and distributing user value degrees in a community according to the static label, the first dynamic label and the second dynamic label to obtain a user value degree distribution result;
specifically, the basic information is the use condition of the user, and includes a user address, a user living area and the like. The static label is used for describing information that the user is relatively stable, and optionally, the static label comprises a detailed address, a belonging street, the number of community users, community population and the like. Evaluating the value degree of the users in the community according to the static label, the first dynamic label and the second dynamic label, and then determining the value degree distribution according to the positions of the users to obtain the value degree distribution result of the users. The user value degree distribution result refers to a result of objectively presenting mobile network requirements in a community. Therefore, the technical effect of improving the planning accuracy of the mobile network base station is achieved.
Step S500: carrying out community building information acquisition on the target coverage community to obtain a building information acquisition result;
specifically, community building information of the target coverage community is collected, and the distribution condition of buildings in the target community and building foundation information are obtained, so that a building information collection result is obtained. The building information acquisition result comprises information which can reflect the distribution condition of buildings in the community, such as the distribution position of the buildings, the height of the buildings and the like. Therefore, basic information is provided for the subsequent position construction of the base station.
Step S600: and planning the mobile network base station of the target coverage community according to the building information acquisition result and the user value degree distribution result.
Further, as shown in fig. 3, step S600 in the embodiment of the present application further includes:
step S610: obtaining building height information and building position coordinate information according to the building information acquisition result;
step S620: obtaining preset base station height information, and performing multi-angle signal connection line fitting according to the preset base station height information, the building height information and the building position coordinate information to obtain a multi-angle connection line fitting result, wherein the multi-angle connection line fitting result comprises signal influence value data at each angle;
step S630: and planning the mobile network base station of the target coverage community according to the multi-angle connecting line fitting result and the user value degree distribution result.
Specifically, the building height information reflects the height of the building in the community, and provides a basis for the interference situation of the building on the base station signal. The building position coordinate information is information reflecting the position situation of the building in the community. The preset base station height information is the preset height condition of the accurately constructed mobile network base station. The multi-angle signal connection fitting is to measure the signal fitting condition when the base station is located at different positions and forms different angles with the building by taking the building position coordinate information as a reference and combining preset base station height information and building height information and moving the base station position. And obtaining the signal conditions of the base station at different angles according to the multi-angle line fitting result. The signal impact value data refers to a value of the base station signal that is affected by the building. And after the multi-angle connecting line fitting result is obtained, determining the distribution quantity and the distribution position of the mobile network base station by combining the user value degree distribution result. Optionally, in an area with a high user value, the coverage degree of the mobile network base station is better, the conditions that the base station is affected at different angles can be obtained through the multi-angle connection line fitting result, and an angle with a small influence is obtained from the multi-angle connection line fitting result, so that the mobile network base station is set. Therefore, the goal of planning the construction of the mobile network base station of the community is achieved, and the technical effect of improving the accuracy of planning is achieved.
Further, step S620 in this embodiment of the present application further includes:
step S621: judging whether the number of the preset base stations exceeds a preset threshold value or not;
step S623: when the number of the preset base stations exceeds a preset threshold value, comparing and screening signal influence value data of each position in the target coverage community to obtain a comparison and screening result;
step S624: and obtaining the multi-angle connecting line fitting result according to the comparison screening result.
Specifically, the influence of signals of other base stations does not need to be considered when a single base station is established, and when the number of the preset base stations exceeds a preset threshold value, the influence of signals of each position of the community after each base station is established needs to be comprehensively considered. The predetermined number of base stations is the minimum number of base stations which are preset among community base stations and do not need to consider mutual influence. And when the number of the preset base stations exceeds a preset threshold value, acquiring the signal influence value condition of each position of the target coverage community, and comparing and screening the obtained data. The comparison screening result is obtained by comparing the numerical values of the signal influence value data with each other. And according to the comparison screening result, comprehensively selecting a result with the minimum integral signal influence value from the comparison screening result to perform multi-angle line connection fitting, so as to obtain a multi-angle line connection fitting result. Therefore, the technical effect of improving the integrity of the planning is achieved.
Further, step S600 in the embodiment of the present application further includes:
step S640: obtaining a mobile network base station planning scheme of the target coverage community;
step S650: fitting through the mobile network base station planning scheme and the building information acquisition result, and generating a plurality of signal monitoring points through the user value degree distribution result;
step S660: performing signal detection of a fitting result on the plurality of signal monitoring points to generate detection verification data;
step S670: generating a feedback parameter according to the matching result of the detection verification data and the user value degree distribution result;
step S680: and optimizing the planning scheme of the mobile network base station according to the feedback parameters.
Specifically, the mobile network base station planning scheme is determined according to the basic situation of the target coverage community, the mobile network base station planning scheme is fused with the building information acquisition result, the base station position and the building position are fitted, and then the signal monitoring points are set according to the user value degree distribution result and the position with higher user value degree distribution. And respectively carrying out signal detection on the fitting results of the plurality of signal monitoring points, and carrying out detection verification on the transmission quality of the signals so as to obtain the detection verification data. The detection verification data is data reflecting signal quality conditions, and comprises signal coverage, signal quality, connection power and the like. And determining whether the signals of all the distribution areas meet the requirement of corresponding value degrees according to the detection verification data and the result user value degree distribution result, if not, obtaining feedback parameters for adjusting the signals of the non-satisfied areas, and optimizing the mobile network base station planning scheme according to the feedback parameters. Therefore, the technical effects of carrying out feedback control on the planning scheme and improving the planning quality are achieved.
In summary, the method for planning the residential community mobile network base station based on big data provided by the present application has the following technical effects:
the method comprises the steps of collecting the basic condition of a target coverage community, mainly collecting the boundary condition of the community, determining the coordinate of a boundary track, providing reliable data for determining the range for planning a base station, collecting data of base station interaction data in the boundary track coordinate in a time collection period to obtain a business data set, dividing users according to the business data set and the time collection period, dividing the users into residential users and mobile users, distinguishing the contribution degree of user information to the base station, constructing a first dynamic label of the users according to the user division result, evaluating the business volume data of the users according to the business data set, constructing a second dynamic label of the users according to the business volume data evaluation result, providing accurate data for planning by obtaining relatively fixed information of the users, obtaining a static label according to the basic information, evaluating the value degree of the users in the community according to the static label, the first dynamic label and the second dynamic label, obtaining a user value degree distribution result, determining the value degree distribution condition, improving the coverage target of the base station, further effectively planning the building value of the target coverage community, and combining the building value distribution condition of the target coverage community with the mobile network planning of the users. The technical effects of carrying out optimal mobile network base station planning and improving planning quality and planning efficiency are achieved.
Example two
Based on the same inventive concept as the method for planning the residential community mobile network base station based on the big data in the foregoing embodiment, as shown in fig. 4, the present application further provides a system for planning the residential community mobile network base station based on the big data, wherein the system includes:
the track coordinate determination module 11 is used for acquiring basic information of a target coverage community, performing community boundary acquisition according to the basic information, and determining boundary track coordinates based on a community boundary acquisition result;
a first dynamic tag obtaining module 12, where the first dynamic tag obtaining module 12 is configured to set a time collection period, perform data collection on base station interaction data in the boundary trajectory coordinate, obtain a service data set, perform user partitioning based on the service data set and the time collection period, obtain a user partitioning result, and construct a first dynamic tag of a user according to the user partitioning result;
the second dynamic tag building module 13 is configured to perform service data evaluation on the user according to the service data set, and build a second dynamic tag of the user based on a service data evaluation result;
the distribution result obtaining module 14 is configured to obtain basic information of a user, generate a static tag according to the basic information, and perform user value distribution in a community according to the static tag, the first dynamic tag, and the second dynamic tag to obtain a user value distribution result;
the information acquisition result obtaining module 15, where the information acquisition result obtaining module 15 is configured to perform community building information acquisition on the target coverage community to obtain a building information acquisition result;
and the base station planning module 16 is configured to plan a mobile network base station of the target coverage community according to the building information acquisition result and the user value degree distribution result.
Further, the system further comprises:
a weight value construction unit for constructing an initial dividing label weight value;
the user set constructing unit is used for constructing a user set according to the service data set;
a user division result obtaining unit, configured to perform communication user screening in the service data set through the time acquisition period, perform user division of the user set according to a screening result, and obtain a user division result, where the user division result includes a resident division result and a mobile user division result;
and the weight identification unit is used for carrying out value weight identification on the user classification result according to the initial classification label weight value and constructing a first dynamic label of the user according to the identification result and the user classification result.
Further, the system further comprises:
the frequency statistical data obtaining unit is used for counting the occurrence frequency of the residents in the time acquisition period in the resident dividing result to obtain frequency statistical data;
an identification adjustment result obtaining unit, configured to generate a frequency influence coefficient through the frequency statistical data, and perform identification result adjustment based on the frequency influence coefficient to obtain an identification adjustment result;
and the first dynamic label obtaining unit is used for constructing a first dynamic label of the user according to the identification adjusting result and the user dividing result.
Further, the system further comprises:
the coordinate information acquisition unit is used for acquiring building height information and building position coordinate information according to the building information acquisition result;
a connection line fitting result obtaining unit, configured to obtain height information of a predetermined base station, and perform multi-angle signal connection line fitting according to the height information of the predetermined base station, the building height information, and the building position coordinate information to obtain a multi-angle connection line fitting result, where the multi-angle connection line fitting result includes signal influence value data at each angle;
and the network base station planning unit is used for planning the mobile network base station of the target coverage community according to the multi-angle connecting line fitting result and the user value degree distribution result.
Further, the system further comprises:
a predetermined threshold judgment unit for judging whether the number of predetermined base stations exceeds a predetermined threshold;
the comparison screening unit is used for comparing and screening signal influence value data of each position in the target coverage community when the number of the preset base stations exceeds a preset threshold value to obtain a comparison screening result;
and the multi-angle fitting result obtaining unit is used for obtaining the multi-angle connecting line fitting result according to the comparison screening result.
Further, the system further comprises:
a privileged user information obtaining unit configured to obtain privileged user information in the user set;
a privilege association coefficient generation unit for generating a privilege association coefficient based on the privileged user information;
the identification optimization unit is used for carrying out identification optimization on the identification result according to the privilege association coefficient to obtain an identification optimization result;
and the optimization construction unit is used for constructing a first dynamic label of the user according to the identification optimization result and the user division result.
Further, the system further comprises:
a mobile planning scheme obtaining unit, configured to obtain a mobile network base station planning scheme of the target coverage community;
the system comprises a signal monitoring point obtaining unit, a building information acquisition unit and a monitoring point management unit, wherein the signal monitoring point obtaining unit is used for fitting through the mobile network base station planning scheme and the building information acquisition result and generating a plurality of signal monitoring points through the user value degree distribution result;
the detection verification data generation unit is used for carrying out signal detection on the fitting results of the plurality of signal monitoring points to generate detection verification data;
a feedback parameter generating unit, configured to generate a feedback parameter according to a matching result of the detection verification data and the user value degree distribution result;
and the planning scheme optimization unit is used for optimizing the planning scheme of the mobile network base station through the feedback parameters.
In the present description, each embodiment is described in a progressive manner, and the main point of the description of each embodiment is that the embodiment is different from other embodiments, and the aforementioned method for planning a base station of a residential community mobile network based on big data in the first embodiment of fig. 1 and the specific example are also applicable to the system for planning a base station of a residential community mobile network based on big data in the present embodiment. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method for planning a residential community mobile network base station based on big data is characterized by comprising the following steps:
acquiring basic information of a target coverage community, carrying out community boundary acquisition according to the basic information, and determining a boundary track coordinate based on a community boundary acquisition result;
setting a time acquisition period, carrying out data acquisition on base station interaction data in the boundary track coordinate to obtain a service data set, carrying out user division based on the service data set and the time acquisition period to obtain a user division result, and constructing a first dynamic label of a user according to the user division result;
performing service volume data evaluation of the user according to the service data set, and constructing a second dynamic label of the user based on a service volume data evaluation result;
acquiring basic information of a user, generating a static label according to the basic information, and distributing user value degrees in a community according to the static label, the first dynamic label and the second dynamic label to obtain a user value degree distribution result;
carrying out community building information acquisition on the target coverage community to obtain a building information acquisition result;
and planning the mobile network base station of the target coverage community according to the building information acquisition result and the user value degree distribution result.
2. The method of claim 1, wherein user partitioning is performed based on the service data set and the time acquisition period to obtain user partitioning results, and a first dynamic tag of a user is constructed according to the user partitioning results, further comprising:
constructing an initial dividing label weight value;
constructing a user set according to the service data set;
screening communication users in the service data set through the time acquisition period, and carrying out user division on the user set according to a screening result to obtain a user division result, wherein the user division result comprises a resident division result and a mobile user division result;
and performing value weight identification on the user division result according to the initial division label weight value, and constructing a first dynamic label of the user according to the identification result and the user division result.
3. The method of claim 2, wherein the method further comprises:
counting the occurrence frequency of the resident in the time acquisition period in the resident division result to obtain frequency statistical data;
generating a frequency influence coefficient through the frequency statistical data, and adjusting the identification result based on the frequency influence coefficient to obtain an identification adjustment result;
and constructing a first dynamic label of the user according to the identification adjustment result and the user division result.
4. The method of claim 1, wherein the method further comprises:
obtaining building height information and building position coordinate information according to the building information acquisition result;
obtaining preset base station height information, and performing multi-angle signal connection line fitting according to the preset base station height information, the building height information and the building position coordinate information to obtain a multi-angle connection line fitting result, wherein the multi-angle connection line fitting result comprises signal influence value data at each angle;
and planning the mobile network base station of the target coverage community according to the multi-angle connecting line fitting result and the user value degree distribution result.
5. The method of claim 4, wherein the method further comprises:
judging whether the number of the preset base stations exceeds a preset threshold value or not;
when the number of the preset base stations exceeds a preset threshold value, comparing and screening signal influence value data of each position in the target coverage community to obtain a comparison and screening result;
and obtaining the multi-angle connecting line fitting result according to the comparison screening result.
6. The method of claim 2, wherein the method further comprises:
obtaining privileged user information in the set of users;
generating a privilege association coefficient based on the privileged user information;
performing identification optimization of the identification result according to the privilege association coefficient to obtain an identification optimization result;
and constructing a first dynamic label of the user according to the identification optimization result and the user division result.
7. The method of claim 1, wherein the method further comprises:
obtaining a mobile network base station planning scheme of the target coverage community;
fitting through the mobile network base station planning scheme and the building information acquisition result, and generating a plurality of signal monitoring points through the user value degree distribution result;
performing signal detection of fitting results on the plurality of signal monitoring points to generate detection verification data;
generating a feedback parameter according to the matching result of the detection verification data and the user value degree distribution result;
and optimizing the planning scheme of the mobile network base station according to the feedback parameters.
8. A big data based residential community mobile network base station planning system, the system comprising:
the track coordinate determination module is used for acquiring basic information of a target coverage community, performing community boundary acquisition according to the basic information, and determining boundary track coordinates based on a community boundary acquisition result;
the first dynamic label obtaining module is used for setting a time collecting period, carrying out data collection on base station interactive data in the boundary track coordinate to obtain a service data set, carrying out user division based on the service data set and the time collecting period to obtain a user division result, and constructing a first dynamic label of a user according to the user division result;
the second dynamic label building module is used for evaluating the service data of the user according to the service data set and building a second dynamic label of the user based on the evaluation result of the service data;
the distribution result acquisition module is used for acquiring basic information of a user, generating a static label according to the basic information, and distributing user value degrees in a community according to the static label, the first dynamic label and the second dynamic label to acquire a user value degree distribution result;
the information acquisition result acquisition module is used for carrying out community building information acquisition on the target coverage community to obtain a building information acquisition result;
and the base station planning module is used for planning the mobile network base station of the target coverage community according to the building information acquisition result and the user value degree distribution result.
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