CN114828026A - Base station planning method, device, equipment, storage medium and program product - Google Patents
Base station planning method, device, equipment, storage medium and program product Download PDFInfo
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Abstract
The application discloses a base station planning method, a device, equipment, a storage medium and a program product, wherein the space of a target building is divided into a plurality of MR space grids according to the contour information and MR data of the target building to obtain the grid data of the MR space grids; predicting the required flow corresponding to each MR space grid according to the grid data to obtain a first data set of a plurality of space grids; obtaining N base station planning schemes according to the building information of the target building; determining the setting data of each base station device in each base station planning scheme according to the base station planning schemes to obtain N second data sets corresponding to the N base station planning schemes; and the first data set is subjected to fuzzy measure matching with the N second data sets respectively, and the base station planning scheme corresponding to the second data set with the matching degree reaching the preset threshold is determined as the target base station planning scheme, so that the base station planning scheme is accurately configured, the cost is saved, and the base station planning efficiency is improved.
Description
Technical Field
The present application relates to the field of wireless channel technologies, and in particular, to a method, an apparatus, a device, a storage medium, and a program product for planning a base station.
Background
In the field of planning and optimization of communication networks, weak coverage means that the signal strength of an area covered by a communication network cannot meet the normal communication requirement of a user, and because the weak coverage directly affects the call quality of the user, it is very important to plan a base station for the weak coverage area.
At present, base station planning related to a weak coverage area mainly selects corresponding point locations and base station equipment according to field test results and through observation of field environments and expert experience. However, the planning of the base station equipment by different experts depends on personal experience and knowledge level, and therefore, the obtained base station planning scheme is prone to fail to achieve the expected effect, and resources are wasted.
Disclosure of Invention
The embodiment of the application provides a base station planning method, a base station planning device, base station planning equipment and a storage medium, which can obtain a more reasonable planning scheme according to different scenes and different buildings and improve the base station planning efficiency.
In a first aspect, an embodiment of the present application provides a base station planning method, including:
dividing the space of a target building into a plurality of MR space grids according to the contour information of the target building and the MR data to obtain grid data of the MR space grids;
predicting the required flow corresponding to each MR space grid according to the grid data to obtain a first data set of the plurality of space grids;
obtaining N base station planning schemes according to the building information of the target building, wherein N is a positive integer;
determining setting data of each base station device in each base station planning scheme according to the N base station planning schemes to obtain N second data sets corresponding to the N base station planning schemes;
and respectively carrying out fuzzy measure matching on the first data set and the N second data sets, and determining a base station planning scheme corresponding to the second data set with the matching degree reaching a preset threshold value as a target base station planning scheme.
In a second aspect, an embodiment of the present application provides a base station planning apparatus, including:
the acquisition module is used for dividing the space of a target building into a plurality of MR space grids according to the contour information of the target building and the MR data to obtain grid data of the MR space grids;
the prediction module is used for predicting the demand flow corresponding to each space grid according to the grid data to obtain a first data set of the plurality of space grids;
the planning module is used for obtaining N base station planning schemes according to the building information of the target building, wherein N is a positive integer;
a determining module, configured to determine, according to the N base station planning schemes, setting data of each base station device in each base station planning scheme to obtain N second data sets corresponding to the N base station planning schemes;
and the matching module is used for performing fuzzy measure matching on the first data set and the second data set corresponding to each base station planning scheme respectively, and determining the base station planning scheme corresponding to the second data set with the highest matching degree as a target base station planning scheme.
In a third aspect, an embodiment of the present application provides a base station planning apparatus, where the apparatus includes: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method of base station planning as shown in the first aspect.
In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instructions are stored, and when the program or instructions are executed by a processor, the base station planning method according to the first aspect is implemented.
In a fifth aspect, the present application provides a computer program product, and when executed by a processor of an electronic device, the instructions of the computer program product cause the electronic device to perform the base station planning method according to the first aspect.
According to the base station planning method provided by the embodiment of the application, the space of a target building is divided into a plurality of MR space grids according to the contour information and the MR data of the target building, and grid data of the MR space grids are obtained; predicting the demand flow corresponding to each space grid according to the grid data to obtain a first data set of the plurality of space grids; obtaining N base station planning schemes according to the building information of the target building; determining setting data of each base station device in each base station planning scheme according to the N base station planning schemes to obtain N second data sets corresponding to the N base station planning schemes; the first data set is matched with the N second data sets in fuzzy measure respectively, the base station planning scheme corresponding to the second data set with the highest matching degree is determined as the target base station planning scheme, therefore, the base station planning scheme can be accurately configured according to the data information of a target building, the base station planning scheme highly matched with the target building is obtained, meanwhile, errors caused by subjectivity of the manual base station planning scheme can be avoided, the planning standardization of the base station is improved, the cost is saved, and the planning efficiency of the base station is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a base station planning method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a base station planning apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative only and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
At present, operators solve the coverage problem of different scenes through different types of diversified base stations, as shown in the following figure, wherein a macro station mainly solves the continuous coverage of a basic coverage layer, an indoor distribution system solves the indoor coverage, and various micro base stations solve the blind area or heat supplement of the coverage edge or blind area of the macro station. Aiming at the planning of the weak coverage building, corresponding point positions and base station equipment are selected mainly according to field test results and through observation of field environment and by utilizing self network planning experience.
The existing equipment type selection and planning scheme for building planning is evaluated by experts according to experience sites, and different experts have different tendencies on understanding of equipment capacity and application of equipment in different scenes, so the result difference is often caused by individual cognitive difference in the equipment type selection and planning scheme. Put into the network, individual differences evolve into network differences, and as the network scale grows, the differences will also gradually grow. Whether the plans with large differences have influence on the future network or not, whether the generated influence causes that the expected effect cannot be achieved after the network is built and operated or not, the result cannot be estimated, the process cannot be controlled, and the scheme can be different from person to person and is different. Therefore, providing a uniform link budget-based intelligent planning method according to the coverage capability of the base station by intelligently identifying the weak coverage area of the building has become a research focus of wireless network planning work.
Therefore, in order to improve the base station planning effect, the method provides a uniform link budget-based intelligent planning method according to the base station coverage capacity by intelligently identifying the weak coverage area of the building, and is convenient for the determination of base station planning schemes under different scenes, so that the purposes of higher efficiency, simpler and more convenient method, more reasonable obtained base station planning schemes and base station planning under various scenes are achieved.
In order to solve the problems in the prior art, embodiments of the present application provide a method, an apparatus, a device, and a storage medium for planning a base station. First, a base station planning method provided in the embodiment of the present application is described below.
Fig. 1 shows a flowchart of a base station planning method according to an embodiment of the present application. As shown in fig. 1, the method includes the following steps 101 to 105.
The target building may be any building in a mobile communication network cell in which the base station device needs to be installed, for example: buildings in areas with poor coverage problems. The target building can be a single building or all buildings in a preset area, and can be set according to actual requirements, and the method is not limited in the application. The MR data may be measurement information reported to the wireless network by the user during the service execution process, and can accurately reflect the coverage condition of the wireless network signal.
Specifically, the space in which the target building is located may be divided into a plurality of MR space grids according to the contour information of the target building and the MR data, and grid data (i.e., grid data including the MR data and the three-dimensional spatial position data) of each MR space grid may be obtained.
In some embodiments, step 101 may comprise:
vectorizing a target building according to a three-dimensional map of the target building to obtain three-dimensional vector data of the target building;
and dividing the target building into a plurality of MR space grids according to the three-dimensional vector data and the MR data of the target building to obtain grid data corresponding to the MR space grids.
The contour information of the target building may be obtained by an aerial remote sensing image and an aerial laser radar technology, for example: the ground LiDAR can be used for obtaining ground feature information at a head-up or head-up angle in a short distance, and building contour information can also be extracted from ground LiDAR data.
In this embodiment, high-definition three-dimensional map-based building contour vectorization may be adopted to extract contour information of a target building.
Specifically, vectorizing a target building through a high-definition three-dimensional map of the target building, and extracting contour points (interest points) of the target building, for example: the method comprises the steps of obtaining the longitude and latitude of the center, the height and the geographic contour of a target building and the longitude and latitude of interest point data through a high-definition three-dimensional map, processing the obtained contour point data to obtain a point array of the contour of the target building, carrying out primary classification on the obtained point array, segmenting and linearly fitting straight lines corresponding to the edge of the contour, and finally resolving the intersection point of the straight lines to obtain the corner point position of the target building. And then acquiring MR data of the region where the target building is located, and determining the MR three-dimensional space grid to which the target building belongs according to the MR data. And fitting the MR three-dimensional space grid of the target building and the contour information (including corner position information) to obtain MR space grid data of the target building.
In this embodiment, the MR three-dimensional space grid of the target building and the contour information (including corner position information) are fitted, a unique position vector may be generated for each MR space grid through longitude and latitude coordinates of the corner position, and MR space grid data of the target building is generated according to the MR data and the position vector corresponding to each MR three-dimensional space grid (that is, the MR space grid data includes a three-dimensional space grid position vector and MR data). The three-dimensional space grid may be set according to actual requirements, for example 5m x 5 m.
In some embodiments, a fingerprint library location algorithm may be utilized to determine the three-dimensional spatial grid to which the target building belongs based on the measurement reports of the cell to which the target building belongs. Specifically, for any three-dimensional space grid, a plurality of grid sampling points can be set in the three-dimensional space grid; the signal intensity of each base station, of which the current service range can reach one or more grid sampling points in the three-dimensional space grid, at a corresponding grid reference point in the three-dimensional space grid can be calculated by using a 3D propagation model, a field intensity feature vector corresponding to a cell to which a target building belongs can be formed on the basis of geographical position information and signal intensity information contained in a measurement report of the cell to which the target building belongs, and the three-dimensional space grid to which the cell to which the target building belongs can be judged on the basis of the Euclidean distance similarity between the field intensity feature vector corresponding to the cell to which the target building belongs and the field intensity feature vector in a fingerprint library.
In some embodiments, the latitude and longitude of the MR data may be determined by using the level information of the main serving cell and the neighboring cell in the MR data, and combining the latitude and longitude of the main serving cell and the neighboring cell and the transmission power, and then the field strength information is rasterized, that is, the wireless network is divided into a plurality of grids according to a certain scale.
And step 102, predicting the required flow corresponding to each MR space grid according to the grid data to obtain a first data set of a plurality of space grids.
Specifically, for any three-dimensional space grid to which the target building belongs, the currently required service flow of each MR space grid, that is, the flow coverage amount that each MR space grid needs to be increased, is determined according to the MR data of the target building.
In some embodiments, for any one MR space grid of a target building, in the case where the target building is one primary cell belonging to the MR space grid, the current traffic of the MR space grid is determined based on the subject cell traffic of the target building and the cell traffic of the other respective primary cells belonging to the MR space grid. And then, calculating the predicted service flow by using a flow prediction model according to the longitude and latitude of the center of the MR space grid. The currently required service traffic of the MR space grid (the service traffic that needs to be satisfied yet, that is, the required traffic corresponding to the MR space grid) can be determined according to the difference between the expected service traffic and the current traffic of the MR space grid.
It should be noted that in step 102, there are various calculation methods for determining the required flow rate corresponding to the MR space grid in the prior art, which are not described in detail in this application.
In one example, the currently required traffic flow for each MR spatial grid may be denoted as Y ═ Y 1 ,y 2 ，…，y n In which y is 1 ,y 2 ，…，y n Refers to the currently required traffic flow for the respective MR space grid.
And 103, obtaining N base station planning schemes according to the building information of the target building.
Where N is a positive integer, the building information of the target building may include: the longitude and latitude of the target building, the floor height, the floor number, the number of elevators, the structure type and the like.
Specifically, according to the building information of the target building, the setting point position and the equipment type of the base station equipment in the area range where the target building is located are judged, and due to the diversity of the base station equipment type and the engineering parameters, various different planning schemes can be obtained.
In some embodiments, multiple base station planning scenarios may be derived through expert evaluation.
In some embodiments, obtaining N base station planning schemes according to the building information of the target building may include:
acquiring building information of a target building according to a three-dimensional map of the target building;
determining the type of base station equipment corresponding to the building information according to a preset rule;
and obtaining N base station planning schemes according to the type of the base station equipment, the base station setting point position and the engineering parameters of the base station equipment.
Specifically, scene information of an area where the longitude and the latitude of the center of the target building are located can be obtained through a high-definition map building contour technology and a crawler technology, and the type of the base station equipment to be used is preliminarily selected according to the principle that the scene corresponds to the type of the base station equipment.
In some embodiments, a scenario corresponding to the target building may be determined according to the building information of the target building, and then a base station planning scheme corresponding to the scenario may be obtained from the planning experience database.
In one example, multiple base station planning plans for the target building may be initially obtained from historical base station equipment planning plans for other areas in the database. For example, a base station planning model is constructed through historical data (including base station planning schemes and corresponding building information of the schemes in real time) and expert experience, and a plurality of base station planning schemes which can be suitable for a target building are obtained through inputting building information of the target building.
And step 104, determining the setting data of each base station device in each base station planning scheme according to the N base station planning schemes to obtain N second data sets corresponding to the N base station planning schemes.
Wherein the setting data of the base station device may include: mounting point location, downtilt angle (mechanical and electronic downtilt), azimuth, and may also include other fixed engineering parameters.
In some embodiments, the setting data of the base station apparatus includes: the base station type, the number of base station equipment corresponding to the base station type and the set point position of each base station equipment.
In one example, the second data set of the base station planning scheme may be represented as X ═ { X ═ X 1 ，x 2 ，…，x m In which x 1 ，x 2 ，…，x m Each parameter referring to the setting data of the base station apparatus may include: the installation position, the downtilt angle (mechanical downtilt and electronic downtilt), the azimuth angle, and other fixed engineering parameters such as maximum 27.2W of the transmission power of RRU5158-fa, 8T8R of the transmission channel, 1885-1915MHz of the transmission frequency range, 2010-2025MHz of the reception frequency range; the front-to-back ratio of the 8-channel FAD conventional antenna is larger than or equal to 23, the horizontal lobe width is 65 degrees, the vertical lobe width is 5 degrees, the gain is 15.5dBi, and the like.
And 105, performing fuzzy measure matching on the first data set and the N second data sets respectively, and determining a base station planning scheme corresponding to the second data set with the matching degree reaching a preset threshold value as a target base station planning scheme.
The target planning scheme is a planning scheme which can meet the requirements of a target building, has the lowest implementation cost and does not cause resource waste.
Specifically, the first data corresponding to the target building is matched one by one with the second data set corresponding to the base station planning scheme, and the base station planning scheme matched with the target building (i.e. the planning scheme with the matching degree reaching a preset threshold) is determined by a fuzzy matching method.
In some embodiments, fuzzy measure matching is performed on the first data set and the N second data sets, and determining a base station planning scheme corresponding to the second data set whose matching degree reaches a preset threshold as the target base station planning scheme may include:
calculating a first ambiguity metric value of the first data set and N second ambiguity metric values corresponding to the N second data sets according to the ambiguity integral;
matching the first fuzzy measurement value with N second fuzzy measurement values respectively to obtain N matching degree values of the first fuzzy measurement value and each second fuzzy measurement value;
and determining a target base station planning scheme according to the corresponding second data set with the matching degree value reaching the preset threshold value.
The fuzzy measure can solve the multi-attribute decision problem that the attributes have correlation and are not additivity. The method can represent the comprehensive importance degree of one or more attributes, and can accurately describe the mutual relation among the attributes. The ambiguity measure value over the measurable space (vector construction space) can be calculated by Choquet ambiguity integration. Choquet fuzzy integral sign: (c) integral whole number fd mu.
In one example, for any measurable space (vector construction space): (Y, P (Y)). Defining a fuzzy metric value of a measurable space as
Fuzzy measure value
The method is obtained by calculating the following formulas (1) to (5):
step 1: for fuzzy measure therein
There may be:
wherein Y is { Y ═ Y 1 ,y 2 ，…，y n Converting each variable in the building space into a spatial fuzzy measure,
the lower limit value is set as the value of,
is the upper limit value. Thus, a set is obtained: m ═ { u | u ═ u i ≤u≤u r }；
Step 2: if when it is used
For the ambiguity measure in the metric space (Y, P (Y)), then there is a metric function f: Y → (— ∞, + ∞). Then
Has the following integral:
y i(f) ＝inf{∫fdu|u∈M} (4)
y r(f) ＝sup{∫fdu|u∈M} (5)
where integral whole number fdu is the Choquet integral, which results in a measure of ambiguity as to the building space
In some embodiments, the first data set Y ═ Y, above 1 ,y 2 ，…，y n Obtaining a first ambiguity measure value for the first data set using equations (1) - (5) above, where y 1 ,y 2 ，…，y n Refers to the currently required traffic flow for each MR space grid;
setting the second data set X to { X ═ X 1 ，x 2 ，…，x m Obtaining a second ambiguity measure for the second data set using equations (1) - (5) above, wherein x 1 ，x 2 ，…，x m Refers to each parameter of the setting data of the base station apparatus. Similarly, a second ambiguity measure value for a plurality of second data sets may be obtained.
After the first ambiguity measure value of the first data set and the second ambiguity measure value of the second data set are obtained, the first ambiguity measure value of the first data set and the second ambiguity measure value of the second data set are matched one by one. If the first fuzzy measure value and the high matching item of the first fuzzy measure value exist (the matching degree is higher than a preset threshold), the base station planning scheme corresponding to the second fuzzy measure in the high matching result can be determined as the target base station planning scheme.
According to the base station planning method provided by the embodiment of the application, the space of a target building is divided into a plurality of MR space grids according to the contour information and the MR data of the target building, and grid data of the MR space grids are obtained; predicting the demand flow corresponding to each space grid according to the grid data to obtain a first data set of the plurality of space grids; obtaining N base station planning schemes according to the building information of the target building; determining setting data of each base station device in each base station planning scheme according to the N base station planning schemes to obtain N second data sets corresponding to the N base station planning schemes; the first data set is matched with the N second data sets in fuzzy measure respectively, the base station planning scheme corresponding to the second data set with the highest matching degree is determined as the target base station planning scheme, therefore, the base station planning scheme can be accurately configured according to the data information of a target building, the base station planning scheme highly matched with the target building is obtained, meanwhile, errors caused by subjectivity of the manual base station planning scheme can be avoided, the planning standardization of the base station is improved, the cost is saved, and the planning efficiency of the base station is improved.
To facilitate understanding of the base station planning method provided in this embodiment, a practical application of the base station planning method is provided herein for explanation, specifically referring to the following example:
analyzing problem building (target building) information through a high-definition three-dimensional map, matching a proper diversified base station by using an NDEE (network Diagnosis modelled on Experience evolution) matching algorithm, and outputting a reasonable intelligent planning scheme, wherein the method comprises the following specific steps:
1. the method comprises the steps of performing vectorization extraction on building outline based on a high-definition three-dimensional map, taking problem scene information and problem area building information as input, mainly comprising center longitude and latitude and building basic characteristics (floor height, floor number, elevator number, structure type and the like), and acquiring building information by adopting a building outline vectorization technology based on the high-definition three-dimensional map according to the center longitude and latitude.
2. Combining the map contour information of the building, selecting diversified base station models and covering capacity around the target building by using an NDEE (network Diagnosis model based on Experience evaluation) matching algorithm, performing ergodic point location evaluation in a problem area, performing point location evaluation by machine learning (combining the covering condition of the problem, complaint condition, potential market estimation value and base station covering capacity), selecting the point location with the best evaluation result and the most comprehensive point location, and outputting point location detailed information (longitude and latitude, direction angle, hanging height and the like) and equipment quantity by combining the map information according to the point location.
(1) And selecting a basic station type, namely acquiring scene information of an area where the longitude and the latitude of the center are located by utilizing the high-definition map building outline technology and the crawler technology, and primarily selecting the station type to be used according to the principle that the scene corresponds to the station type.
(2) The network equipment planning attribute parameters are respectively defined as a plurality of variables, namely, hanging height, mechanical downward inclination, electronic downward inclination, transmitting power, azimuth angle, antenna channel number, RRU channel number, antenna horizontal half-power angle, antenna vertical half-power angle, antenna support frequency band and other functional parameters, to form a variable set (namely, the second data set X ═ X 1 ，x 2 ，…，x m })。
Meanwhile, network problems of a target problem building or building are defined as a plurality of variables, namely 10 m-10 m three-dimensional space grids formed according to MR weak coverage grids, each grid can exist as one variable, predicted traffic is calculated by using a flow prediction model according to the longitude and latitude of the center of the grid, and the capacity requirement of each grid is used as one variable to form another variable set (namely, the first data set Y (Y-Y) is used as another variable set 1 ,y 2 ，…，y n })。
(3) And matching the first data set with the second data set by using an NDEE matching algorithm to obtain the best matching base station planning scheme.
S1: the pedometer is set to 0; to begin a preliminary matching of the diversified device capability parameters (second data set)/field data set (first data set), i.e. to start withCollecting the diversified device capability parameters and the field data, and setting the field data X as { X ═ X 1 ，x 2 ，…，x m } generation of one
Measure of blur at P (X).
S2: if the high matching item of the diversified device capability parameter/field data set exists, directly outputting a matching result (namely, directly performing awakening matching according to a second data set of the first data set to obtain a matching result, comparing the matching result with a preset threshold value to obtain a base station planning scheme corresponding to the second data set which is most matched), and skipping to the step S5, otherwise, performing the next step;
s3: directly judging the field data through a threshold value, namely, manually and directly judging the building space data if a proper planning scheme is not matched;
s4: performing fusion processing on the field data, namely performing parameter set correction and recalculation according to the field data or other newly added known parameters to generate a group of new fuzzy measures u (P (X ^) further on the set X 1 ，x 2 ，…，x m Sorting and deleting mismatching points, which can be generated by MR grids, field flow prediction, etc
Is provided with
And the following treatments were carried out:
further obtaining:
wherein the content of the first and second substances,
thus, sorting the data can result in a high match of the field data of the various capability parameters. If the item meets the requirement and the agreement of manual examination is obtained, the item can be fed back to the planning experience database through the evolutionary function.
S5: adding 1 to the pedometer; if the field data is detected to have a high matching item in threshold judgment, outputting the planning station type, the number and the optimal point position, updating the planning experience database, promoting the evolution of the planning experience database, and if none of the field data has a high matching item but the pedometer exceeds the threshold, quitting, and performing manual intervention to obtain a planning scheme.
3. And (4) combining the station type, the equipment quantity, the point location information (planning scheme) and the map modeling simulation, and outputting a simulation effect.
As shown in fig. 2, an apparatus 200 for planning a base station is further provided in the embodiment of the present application, and includes an obtaining module 201, a predicting module 202, a planning module 203, a determining module 204, and a matching module 205.
The acquiring module 201 is configured to divide the space of the target building into a plurality of MR space grids according to the contour information of the target building and the MR data, so as to obtain grid data of the plurality of MR space grids.
The prediction module 202 is configured to predict a demand flow corresponding to each spatial grid according to the grid data, so as to obtain a first data set of multiple spatial grids.
And the planning module 203 is configured to obtain N base station planning schemes according to the building information of the target building, where N is a positive integer.
The determining module 204 is configured to determine, according to the N base station planning schemes, setting data of each base station device in each base station planning scheme, to obtain N second data sets corresponding to the N base station planning schemes.
A matching module 205, configured to perform fuzzy measure matching on the first data set and the N second data sets, and determine a base station planning scheme corresponding to the second data set with the highest matching degree as a target base station planning scheme.
In some embodiments, the obtaining module includes:
the vectorization submodule is used for vectorizing the target building according to the three-dimensional map of the target building to obtain the contour information of the target building;
and the dividing submodule is used for dividing the target building into a plurality of MR space grids according to the three-dimensional vector data and the MR data of the target building to obtain grid data corresponding to the MR space grids.
In some embodiments, the planning module 203 is specifically configured to:
acquiring building information of a target building according to a three-dimensional map of the target building;
determining the type of base station equipment corresponding to the building information according to a preset rule;
and obtaining N base station planning schemes according to the type of the base station equipment, the base station setting point position and the engineering parameters of the base station equipment.
In some embodiments, the setting data of the base station apparatus includes: the base station type, the number of base station equipment corresponding to the base station type and the set point position of each base station equipment.
The execution module 202 is specifically configured to:
in some embodiments, the setting data of the base station device includes: the base station type, the number of base station equipment corresponding to the base station type and the set point position of each base station equipment.
In some embodiments, the matching module 205 is specifically configured to:
calculating a first ambiguity metric value of the first data set and N second ambiguity metric values corresponding to the N second data sets according to the ambiguity integral;
matching the first fuzzy measurement value with N second fuzzy measurement values respectively to obtain N matching degree values of the first fuzzy measurement value and each second fuzzy measurement value;
and determining the planning scheme of the target base station according to the second data set corresponding to the matching degree value reaching a preset threshold value. .
According to the base station planning device provided by the embodiment of the application, the space of a target building is divided into a plurality of MR space grids according to the contour information and the MR data of the target building, and grid data of the MR space grids are obtained; predicting the demand flow corresponding to each space grid according to the grid data to obtain a first data set of the plurality of space grids; obtaining N base station planning schemes according to the building information of the target building; determining setting data of each base station device in each base station planning scheme according to the N base station planning schemes to obtain N second data sets corresponding to the N base station planning schemes; the first data set is matched with the N second data sets in fuzzy measure respectively, the base station planning scheme corresponding to the second data set with the highest matching degree is determined as the target base station planning scheme, therefore, the base station planning scheme can be accurately configured according to the data information of a target building, the base station planning scheme highly matched with the target building is obtained, meanwhile, errors caused by subjectivity of the manual base station planning scheme can be avoided, the planning standardization of the base station is improved, the cost is saved, and the planning efficiency of the base station is improved.
Fig. 2 shows a hardware structure diagram of an electronic device provided in an embodiment of the present application.
The electronic device may comprise a processor 301 and a memory 302 in which computer program instructions are stored.
Specifically, the processor 301 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory may include Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to the methods according to an aspect of the present disclosure.
The processor 301 implements any one of the base station planning methods in the above embodiments by reading and executing computer program instructions stored in the memory 302.
In one example, the electronic device can also include a communication interface 303 and a bus 304. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 304 to complete communication therebetween.
The communication interface 303 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiment of the present application.
In addition, in combination with the base station planning method in the foregoing embodiment, the embodiment of the present application may provide a computer storage medium to implement. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement any of the base station planning methods in the above embodiments.
It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.
The functional blocks shown in the above structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable base station planning apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable base station planning apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As will be apparent to those skilled in the art, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.
Claims (10)
1. A method for base station planning, comprising:
dividing the space of a target building into a plurality of MR space grids according to the contour information of the target building and the MR data to obtain grid data of the MR space grids;
predicting the required flow corresponding to each MR space grid according to the grid data to obtain a first data set of the plurality of space grids;
obtaining N base station planning schemes according to the building information of the target building, wherein N is a positive integer;
determining setting data of each base station device in each base station planning scheme according to the N base station planning schemes to obtain N second data sets corresponding to the N base station planning schemes;
and respectively carrying out fuzzy measure matching on the first data set and the N second data sets, and determining a base station planning scheme corresponding to the second data set with the matching degree reaching a preset threshold value as a target base station planning scheme.
2. The method of claim 1, wherein the dividing the space of the target building into a plurality of MR space grids according to the contour information of the target building and the MR data, and obtaining grid data of the plurality of MR space grids comprises:
vectorizing the target building according to the three-dimensional map of the target building to obtain the contour information of the target building;
and dividing the target building into a plurality of MR space grids according to the contour information of the target building and the MR data to obtain grid data corresponding to the MR space grids.
3. The method of claim 1, wherein obtaining N base station planning plans according to the building information of the target building comprises:
acquiring building information of the target building according to the three-dimensional map of the target building;
determining the type of the base station equipment corresponding to the building information according to a preset rule;
and obtaining N base station planning schemes according to the type of the base station equipment, the base station setting point and the engineering parameters of the base station equipment.
4. The method of claim 1, wherein the setting data of the base station device comprises: the base station type, the number of base station equipment corresponding to the base station type and the set point position of each base station equipment.
5. The method according to claim 1, wherein the fuzzy measure matching is performed on the first data set and the N second data sets, and the base station planning scheme corresponding to the second data set with the matching degree reaching the preset threshold is determined as a target base station planning scheme, including:
calculating a first ambiguity metric value of the first data set and N second ambiguity metric values corresponding to the N second data sets according to ambiguity integration;
matching the first fuzzy measurement value with the N second fuzzy measurement values respectively to obtain N matching degree numerical values of the first fuzzy measurement value and each second fuzzy measurement value;
and determining the planning scheme of the target base station according to the second data set corresponding to the matching degree value reaching a preset threshold value.
6. A base station planning apparatus, comprising:
the acquisition module is used for dividing the space of a target building into a plurality of MR space grids according to the contour information of the target building and the MR data to obtain grid data of the MR space grids;
the prediction module is used for predicting the required flow corresponding to each MR space grid according to the grid data to obtain a first data set of the multiple space grids;
the planning module is used for obtaining N base station planning schemes according to the building information of the target building, wherein N is a positive integer;
a determining module, configured to determine, according to the N base station planning schemes, setting data of each base station device in each base station planning scheme to obtain N second data sets corresponding to the N base station planning schemes;
and the matching module is used for performing fuzzy measure matching on the first data set and the N second data sets respectively and determining a base station planning scheme corresponding to the second data set with the highest matching degree as a target base station planning scheme.
7. The apparatus of claim 6, wherein the obtaining module comprises:
the vectorization sub-module is used for vectorizing the target building according to the three-dimensional map of the target building to obtain the contour information of the target building;
and the dividing submodule is used for dividing the target building into a plurality of MR space grids according to the three-dimensional vector data of the target building and the MR data to obtain grid data corresponding to the MR space grids.
8. A base station planning apparatus, the apparatus comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method of base station planning as claimed in any of claims 1-5.
9. A readable storage medium, on which a program or instructions are stored, which program or instructions, when executed by a processor, carry out the steps of the base station planning method according to any one of claims 1-5.
10. A computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the base station planning method according to any of claims 1-5.
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