CN109963287B - Antenna direction angle optimization method, device, equipment and medium - Google Patents
Antenna direction angle optimization method, device, equipment and medium Download PDFInfo
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Abstract
The invention discloses an antenna direction angle optimization method, device, equipment and medium. The method comprises the following steps: acquiring user MR data, and performing rasterization positioning on the user MR data so as to position the user MR data to an optimal spatial grid; acquiring the average signal intensity and the weak coverage signal sampling point of the optimal space grid, and screening out a weak coverage cell according to the occupation ratio of the average signal intensity and the weak coverage signal sampling point; and calculating the optimal direction angle capable of improving the weak coverage cell problem based on the antenna horizontal azimuth diagram, and automatically optimizing the antenna direction angle according to the optimal direction angle. The method and the device guide the parameter adjustment of the antenna based on the actual coverage condition, do not need to invest a large amount of manpower and material resources to carry out field test or analysis, and effectively improve the efficiency of acquiring actual coverage data and screening weak coverage cells and the efficiency of optimizing the direction angle.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, and a medium for optimizing an antenna direction angle.
Background
With the advance of the LTE network construction, the problem of urban area deep coverage is increasingly highlighted, how to optimize antenna feeder parameters to achieve the maximum coverage effect becomes an important subject, the current antenna direction angle optimization adjustment has great blindness, and the direction angle setting and optimization cannot be performed specifically according to the actual coverage condition, the adjustment of the base station antenna feeder direction angle in daily optimization is adjusted only by the experience of optimization personnel and road test results, and the solution guidance for the LTE deep coverage problem is poor.
The current method for adjusting the cell antenna direction angle parameters has the following problems:
1) the cell coverage effect is obtained through field test, the workload is large, the test area is incomplete, and the optimal parameter adjustment scheme cannot be obtained;
2) evaluating the coverage effect based on MR data statistics instead of positioning users, resulting in unavailable cell parameter adjustment schemes;
3) the existing cell parameter adjustment is based on the work parameter information, and if the work parameter information is wrong, the pattern output is greatly influenced.
Therefore, a solution is needed that can perform targeted direction angle setting and optimization according to the actual coverage situation.
Disclosure of Invention
The embodiment of the invention provides an antenna direction angle optimization method, an antenna direction angle optimization device, a storage medium and equipment, which are used for guiding the parameter adjustment of an antenna based on the actual coverage condition without investing a large amount of manpower and material resources for field test or analysis, so that the efficiency of acquiring actual coverage data and screening weak coverage cells is improved, and meanwhile, an optimal direction angle adjustment suggestion is calculated and output by utilizing an advanced genetic algorithm, so that the direction angle optimization efficiency is improved.
In a first aspect, an embodiment of the present invention provides an antenna direction angle optimization method, where the method includes:
acquiring user MR data, and performing rasterization positioning on the user MR data so as to position the user MR data to an optimal spatial grid;
acquiring the average signal intensity and the weak coverage signal sampling point of the optimal space grid, and screening out a weak coverage cell according to the occupation ratio of the average signal intensity and the weak coverage signal sampling point;
and calculating the optimal direction angle capable of improving the weak coverage cell problem based on the antenna horizontal azimuth diagram, and automatically optimizing the antenna direction angle according to the optimal direction angle.
In a second aspect, an embodiment of the present invention provides an antenna direction angle optimizing apparatus, where the apparatus includes:
the data acquisition and positioning module is used for acquiring user MR data and performing rasterization positioning on the user MR data so as to position the user MR data to an optimal space grid;
the weak coverage cell screening module is used for acquiring the average signal intensity and the weak coverage signal sampling points of the optimal space grid and screening the weak coverage cells according to the average signal intensity and the occupation ratio of the weak coverage signal sampling points;
and the antenna direction angle optimization module is used for calculating the optimal direction angle capable of improving the weak coverage cell problem based on the antenna horizontal direction diagram, and automatically optimizing the antenna direction angle according to the optimal direction angle.
In a third aspect, an embodiment of the present invention provides a computer device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method of the first aspect of the embodiments described above.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which computer program instructions are stored, which, when executed by a processor, implement the method of the first aspect in the foregoing embodiments.
The antenna direction angle optimization method, device, equipment and medium provided by the embodiments of the invention can obtain at least one of the following beneficial effects:
1) and based on MR rasterization positioning users, evaluating the cell coverage condition of the actual positioning user geographical position, and positioning weak coverage cells more accurately.
2) And adjusting cell parameters to solve the problem of a weak coverage area through the joint analysis of the area cells, reasonably evaluating the cost values of other cells and providing an optimal adjustment scheme of the area cells.
3) The method has the advantages of optimizing coverage of field test, and can also utilize the platform tool to carry out automatic output of an optimization scheme, thereby greatly reducing manpower and material resources and improving economic benefits.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention 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 shows a schematic flow chart of an antenna direction angle optimization method provided by the present invention.
Fig. 2 shows a schematic flow chart of the rasterized localization of the user MR data.
Fig. 3 shows a schematic diagram of a connection of a base station to a building within its coverage area.
Fig. 4 shows a schematic diagram of the relative position relationship and direction angle between the base station and the buildings in the coverage area.
Fig. 5 shows a flow chart of calculating the optimal direction angles of a plurality of weak coverage cells based on the K-means algorithm pair.
Fig. 6 shows a flow chart of clustering a plurality of weak coverage cells by a K-means algorithm.
Fig. 7 shows a schematic diagram of clustering a plurality of weak coverage cells by the K-means algorithm.
Fig. 8 is a schematic structural diagram of an antenna direction angle optimizing apparatus provided by the present invention.
Fig. 9 shows a schematic structural diagram of a computer device capable of implementing the antenna direction angle optimization method provided by the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention 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 invention by illustrating examples of the present invention.
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.
Example one
Fig. 1 shows a schematic flowchart of an antenna direction angle optimization method according to an embodiment of the present invention. As shown in fig. 1, as a preferred embodiment, the method includes:
s100, acquiring user MR data, and performing rasterization positioning on the user MR data to position the user MR data to an optimal space grid;
preferably, as shown in fig. 2, the rasterizing positioning the user MR data to position the user MR data to an optimal spatial grid specifically includes:
step S110, establishing a spatial grid characteristic fingerprint database based on the MR data of the user;
step S120, correcting the fingerprint database by adopting a longitude and latitude calibration algorithm;
step S130, matching the user MR data with the characteristic vectors in the fingerprint database, and distributing the user MR data to an optimal space grid capable of being matched.
Further preferably, step S110 specifically includes:
acquiring base station work parameter data, simulating the area to be planned into a three-dimensional space through simulation software based on the base station work parameter data of the area to be planned and user MR data, and dividing the three-dimensional space into a plurality of space grids, such as 5m multiplied by 5m grids; each space grid comprises grid information, and the grid information comprises base station parameter data, user MR data and other related information;
and calculating the signal strength (RSRP and received level strength) of the base station corresponding to each space grid by adopting a 3D ray propagation model, digitizing the grid information including the signal strength to form a characteristic vector, and obtaining a sample data set consisting of the characteristic vectors, namely a space grid characteristic fingerprint database.
Further preferably, in step S120, the longitude and latitude calibration algorithm is used to determine the accuracy of the sample data in the fingerprint database, and the sample data with low accuracy is removed to implement the correction of the fingerprint database.
The accuracy of the sample data depends on the density of base stations participating in the calculation and the distance between the base stations and the primary serving cell. The distance between the base station and the primary serving cell is specifically represented by a longitude and latitude distance between the base station and a User Equipment (UE) in the primary serving cell.
Due to the influence of the geographical environment on the propagation path, the signal transmission between the UE and the base station is usually not straight-line propagation, but curved-line propagation, especially in dense urban areas. The distance between the base station and the UE can be represented by a curve propagation distance of signals between the base station and the UE, and a time difference of arrival (TADV) of signals transmitted to the base station and the UE can represent a time difference generated by the signals propagating through an actual propagation path (i.e. curve propagation); therefore, the accuracy of the sample data can also be judged by TADV.
The TADV is adopted to judge the precision of the sample data, and the specific method comprises the following steps:
setting the actual propagation distance as S _ TADV, namely the distance reflected by TADV, and calculating the linear distance between the same UE and the same base station as S _ a according to sample data in the fingerprint database;
comparing the linear distance S _ a with the actual propagation distance S _ tadv, and if the relationship between the two satisfies: s _ a < ═ S _ tadv, namely the straight line distance is not greater than the actual propagation distance, the precision of the sample data is judged to be high; and if the relation between the two does not meet the condition, judging that the precision of the sample data is low, and rejecting the sample data.
Further preferably, step S130 specifically includes:
for the MR data of the user to be positioned, a reporting cell and a grid set to which the reporting cell belongs are determined, the MR data of the user to be positioned is compared with a plurality of characteristic vectors in the grid set according to a method of minimum Euclidean distance, a space grid to which the characteristic vector with the highest similarity with the MR data of the user to be positioned belongs is selected, and the position of the space grid is the position of the MR data of the user to be positioned, so that the positioning of the MR data of the user is realized.
Wherein the calculation formula for locating the user MR data to the optimal spatial grid is:
wherein, RSRPaiFPThe first signal strength of the ith serving cell is represented, that is, the actual signal strength contained in the MR data actually reported by the user represents the signal with the highest strength among a plurality of signals reported by the user; the RSRPai represents a second signal strength of the ith serving cell, namely, an emulated signal strength contained in the fingerprint library, and represents a signal with the highest signal strength in a plurality of emulated signals in the fingerprint library; k represents the number of serving cells to which the second signal strength relates, the serving cells including the primary serving cell and the neighbor serving cells; d is RSRPaiFPEuclidean distance values to RSRPai.
The smaller the value of d, the RSRPaiFPThe closer the Euclidean distance to the RSRPai is, the higher the similarity between the RSRPai and the RSRPai is, and when the value of d is smaller than a preset threshold value, the grid corresponding to the RSRPai is the optimal space grid.
The base station parameter data and the user MR data can be respectively obtained by the imported base station information table and the MR data file.
Step S2, obtaining the average signal intensity and the weak coverage signal sampling points of the optimal space grid, and screening out weak coverage cells according to the occupation ratio of the average signal intensity and the weak coverage signal sampling points;
specifically, the average signal strength and the signal sampling points in the user MR data positioned in the optimal space grid are obtained, and if the value of the average signal strength is smaller than the set weak coverage threshold value and the ratio of the weak coverage signal sampling points is larger than the set ratio, the cell corresponding to the optimal space grid is the weak coverage cell.
Wherein, step S2 specifically includes:
firstly, clustering users covered by a base station and buildings to which the users belong, specifically, based on the rasterization positioning of user MR data of building boundaries of a three-dimensional electronic map and signaling soft mining weak coverage cells, clustering the user MR data to the corresponding buildings, wherein the optimal space grid for positioning the user MR data is also the optimal space grid corresponding to the buildings;
and acquiring coverage data such as total sampling points, weak coverage sampling points, average signal intensity and the like of the building, storing the coverage data into a building level database, combining the electronic map with the positioning result of the MR data of the user to obtain a building level Thiessen diagram, and realizing the geographical presentation of the clustering result.
Secondly, each building is mapped with the base station to which the building belongs, so that the mapping relationship between the users in the building and the base station to which the building belongs is established, specifically, the central point of each building in the coverage area of the base station is connected with the base station to form a ray, as shown in fig. 3, the dotted line in fig. 3 represents the ray, the connection points of a plurality of dotted lines represent the base station, and the rectangular pattern represents the building in the coverage area of the base station.
The direction angle of the center point of each building relative to the base station to which the building belongs is calculated according to the GIS algorithm, the relative positions of the center point of the building and the base station to which the building belongs and the direction angle of the center point of the building relative to the base station to which the building belongs are shown in fig. 4, point a is the base station position point, and point B is the building center point.
Let m denote a building, NmWhen the number of users in the building m is represented, the included angle between the building m and the base station (the direction angle of the point B relative to the point a) is:
the average signal strength of building m, i.e. its optimal spatial grid, is:
the proportion of the sampling points of the weak coverage signal is as follows:
wherein rsrp (i) represents the received signal strength of the ith user in building m, NRSRP (i) < weak coverage thresholdIndicating a judgment result for judging whether the strength of the signal received by the ith user is less than the weak coverage threshold value, if so, NRSRP (i) < weak coverage thresholdIs 1, if not NRSRP (i) < weak coverage thresholdIs 0.
If the average signal strength RSRP _ avgmLess than the weak coverage threshold value and the occupation Rate of the sampling point of the weak coverage signalmIf the ratio is larger than a certain set ratio, the cell to which the building m belongs is a weak coverage cell.
The weak coverage threshold value and the set proportion can be set and adjusted according to actual conditions.
Step S3, based on the antenna horizontal azimuth map, calculating the optimal direction angle capable of improving the weak coverage cell problem, and automatically optimizing the antenna direction angle according to the optimal direction angle;
for the best direction angle involving multiple weakly covered cells, it can be calculated as follows:
firstly, setting an adjustment step length of an antenna direction angle, and adjusting the antenna direction angle of each weak coverage cell according to the set adjustment step length; the adjustment step length can be set according to an industrial standard or an actual situation;
secondly, estimating the signal strength received by the adjusted MR data sampling point based on the antenna horizontal azimuth diagram; preferably, the signal strength received by the adjusted MR data sampling point is estimated according to the gain difference at different angles in the antenna horizontal azimuth diagram.
In particular for sample point localization from MR dataSetting the antenna direction angle theta 1 before adjustment and the antenna direction angle after adjustment as theta 2 for the ith grid, and when the direction angles are theta 1 and theta 2 based on the horizontal azimuth diagram of the antenna, the horizontal lobe gains of the connection line between the position of the ith grid and the antenna are respectively Gainθ1(i) And Gainθ2(i) And acquiring the received signal strength RSRP of the MR data sampling point when the direction angle is theta 1θ1(i) And estimating the strength of the signal received by the MR sampling point after the antenna direction angle is adjusted as follows:
RSRPθ2(i)＝RSRPθ1(i)+Gainθ2(i)-Gainθ1(i)。
thirdly, based on the relation between the RSRP before and after the antenna direction angle adjustment and the user number of the weak coverage cell, the average level of the weak coverage cell is iteratively calculated, the user number of the weak coverage cell can be reflected by the ratio of sampling points of the weak coverage signal, and the iterative calculation formula is as follows:
as can be seen from the above calculation formula, the antenna direction angle θ is the optimal solution of the direction angle when the Rate _ weak coverage (θ) reaches the maximum value, i.e., the direction angle with the best coverage.
The iteration converges when the Rate _ weak coverage (θ) reaches a maximum or the iteration has reached a maximum number of iterations.
The optimal direction angles of a plurality of weak coverage cells are calculated by adopting the mode, and the calculation amount is too large. For example, if the adjustment range of the direction angle is-10 to 10 degrees and the adjustment step is 5 degrees for any one of the weak coverage cells, the best coverage direction angle can be obtained through (10+10)/5+1 to 5 iterations, and assuming that 100 weak coverage cells are found in one local network, N to 5 should be theoretically performed100≈7.8×1069The direction angle with the best coverage can be obtained by the iteration operation.
As for the optimal direction angles related to a plurality of weak coverage cells, as shown in fig. 5, the first embodiment of the present invention performs calculation based on a K-means clustering algorithm, and the specific process is as follows:
step S310, dividing a plurality of weak coverage cells into a plurality of clusters (clusters) through a K-means clustering algorithm;
step S320, setting the adjustment step length of the antenna direction angle, and adjusting the antenna direction angle of each cluster according to the set adjustment step length;
step S330, estimating the received signal strength of the MR data sampling points of each cluster after the direction angle of the antenna is adjusted based on the antenna horizontal azimuth diagram;
step S340, performing iterative computation on the average levels of a plurality of clusters based on the relation between the RSRP received by the MR data sampling points in each cluster and the number of users in the cluster before and after the adjustment of the antenna direction angle; and when the calculation result reaches the maximum value, the corresponding antenna direction angle is the optimal solution of the direction angle, namely the direction angle with the best coverage or the optimal direction angle. When the calculation result reaches the maximum value or the iteration reaches the maximum iteration number, the iteration converges.
The specific calculation principle is similar to the iterative calculation formula, and the iteration of the weak coverage cells is changed into the iteration of a plurality of clusters;
the K-means algorithm is a typical distance-based clustering algorithm, and adopts distance as an evaluation index of similarity, i.e., the closer the distance between two objects is, the greater the similarity is. The algorithm considers clusters to be composed of closely spaced objects, and therefore targets the resulting compact and independent clusters as final targets.
In the algorithm, firstly, k objects are randomly selected as initial clustering centers, each initial clustering center represents a cluster, and the selection of k initial clustering center points has great influence on a clustering result; secondly, as for each object left in the data set, each object is reassigned to the nearest cluster according to the distance between each object and the center of each cluster in each iteration; after all data objects are examined, one iteration operation is completed, and a new clustering center is calculated. The criterion function used by the algorithm is shown as follows:
wherein k represents the number of initial clusters (or the number of initial clusters), j represents one of the base stations to be adjusted, and xjIndicates the position of the jth base station to be adjusted, SiThe range of the ith initial cluster is represented, mui represents the center of the ith initial cluster, and V represents the square sum of the difference value of the position of each base station to be adjusted and the position of the center of the initial cluster.
The objective of this function is to minimize the sum of squared errors in a cluster, i.e. the above-mentioned value of V, i.e. the distance of a data object in the same cluster to its center point should be as small as possible. If the V value is not changed before and after one iteration, the algorithm is converged.
Based on the above principle, in the first embodiment of the present invention, the optimal direction angles of the plurality of weak coverage cells are calculated in the K-means clustering algorithm-based manner, so that the calculation of the optimal direction angles is effectively simplified, and the calculation amount is greatly reduced.
Step S310 is shown in fig. 6, and the specific implementation process is as follows:
step S311, a selection step, in which a specified number of base stations are randomly selected from a plurality of base stations with weak coverage and direction angles needing to be adjusted, and the selected base stations serve as initial clustering centers;
step S312, a measuring step, namely measuring the similarity (distance) between each residual base station and each initial clustering center, and distributing each base station to the cluster to which the closest initial clustering center belongs according to the similarity to obtain a plurality of new clusters;
step S313, a calculating step, namely recalculating a new cluster center of each new cluster, specifically, calculating a mean value of similarity of each base station in each new cluster;
and step S314, iteration step, step S312 and step S313 are iterated until the new clustering center is equal to the initial clustering center or is smaller than a specified threshold value, and the algorithm is ended.
The implementation process is shown in fig. 7, where the black circular pattern in fig. 7 is located at a position representing an initial cluster center selected at random, and circles a to E represent the remaining base stations, and a new cluster center, that is, a new position where the black circular pattern in fig. 7 moves, may be calculated by measuring distances between the circles a to E and the two initial cluster centers.
The weak coverage cell can be divided into a plurality of irrelevant clusters through the algorithm, and A is arranged1,A2,…,AM×NEach weak coverage cell iterates for K times and is divided into N clusters, and each cluster has M cells, so that K needed to iterate originally can be calculated by the algorithmM×NSub-optimization to require only iteration KMAnd multiplied by N times, the calculation amount is greatly reduced.
Based on the above, the first embodiment of the present invention has the following beneficial effects:
1) positioning user MR data based on an MR rasterization positioning method, determining the signal coverage condition of a cell to which the user MR data belongs according to actual positioning, and more accurately screening out weak coverage cells;
2) based on a horizontal antenna azimuth diagram and a K-means clustering algorithm, clustering analysis on the weak coverage cell can be realized, the optimal direction angle to be adjusted is determined more accurately, and an optimal adjustment scheme is output;
3) the embodiment has the advantages of optimizing the signal coverage condition based on field test, integrates the function of an automatic output optimization scheme, greatly reduces the consumption of manpower and material resources, and improves the economic benefit.
Example two
Correspondingly to the embodiment of the present invention, fig. 8 shows a schematic structural diagram of an antenna direction angle optimizing device provided in the second embodiment of the present invention, where the device includes: the system comprises a data acquisition and positioning module 201, a weak coverage cell screening module 202 and an antenna direction angle optimization module 204.
The data acquisition and positioning module 201 is configured to acquire user MR data, and perform rasterization positioning on the user MR data to position the user MR data to an optimal spatial grid.
And the weak coverage cell screening module 202 is configured to obtain the average signal strength of the optimal space grid and a weak coverage signal sampling point, and screen out a weak coverage cell according to the signal strength of the optimal space grid and the ratio of the weak coverage signal sampling points.
And an antenna direction angle optimization module 204, configured to calculate an optimal direction angle capable of improving the problem of the weak coverage cell based on the antenna horizontal direction diagram, and automatically optimize the antenna direction angle according to the optimal direction angle.
For the above specific limitations on the antenna direction angle optimizing apparatus, reference may be made to the first embodiment, and details are not described herein again.
EXAMPLE III
Correspondingly to the embodiment of the present invention, fig. 9 shows a schematic structural diagram of a computer device provided in the third embodiment of the present invention, and the device may include a processor 301 and a memory 302 storing computer program instructions.
In particular, the processor 301 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present invention.
The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement the antenna direction optimization method according to any one of the first embodiment.
In one example, the device may also include a communication interface 303 and a bus 310. As shown in fig. 9, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 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 invention.
For the above specific limitations on the computer device, reference may be made to embodiment one, and details are not described here.
Example four
Correspondingly to the fourth embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored; the computer program instructions, when executed by a processor, implement any of the above-described method for antenna direction angle optimization.
For the above specific limitations of the computer-readable storage medium, reference may be made to embodiment one, and details are not repeated here.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the 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 invention 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 invention.
The functional blocks shown in the above-described 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 invention 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 patent describe some methods or systems based on a series of steps or devices. However, the present invention 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.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention 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 invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A method for antenna directive angle optimization, the method comprising:
acquiring user MR data, and performing rasterization positioning on the user MR data so as to position the user MR data to an optimal spatial grid;
acquiring the average signal intensity and the weak coverage signal sampling point of the optimal space grid, and screening out a weak coverage cell according to the occupation ratio of the average signal intensity and the weak coverage signal sampling point;
calculating an optimal direction angle capable of improving the problem of the weak coverage cell based on an antenna horizontal azimuth map, and automatically optimizing the antenna direction angle according to the optimal direction angle;
dividing the weak coverage cells into a plurality of clusters through a K-means clustering algorithm;
setting an adjustment step length of the antenna direction angle, and adjusting the antenna direction angle of each cluster according to the set adjustment step length;
estimating the received signal strength of the MR data sampling points of each cluster after the direction angle of the antenna is adjusted based on the antenna horizontal azimuth diagram;
and performing iterative computation on the average levels of a plurality of clusters based on the relationship between the signal strength received by the MR data sampling points in each cluster and the number of users in the cluster before and after the adjustment of the antenna direction angle to obtain the optimal direction angle.
2. The method according to claim 1, wherein the rasterizing positioning the user MR data to position the user MR data to an optimal spatial grid, specifically comprises:
establishing a spatial grid feature fingerprint database based on the user MR data;
correcting the fingerprint database by adopting a longitude and latitude calibration algorithm;
and matching the user MR data with the characteristic vectors in the fingerprint database, and distributing the user MR data to a corresponding optimal space grid.
3. The method according to claim 2, wherein the creating a spatial grid feature fingerprint library based on the user MR data specifically comprises:
simulating a region to be planned into a three-dimensional space, and dividing the three-dimensional space into a plurality of space grids; each spatial grid comprises grid information, wherein the grid information comprises base station parameter data and user MR data;
and calculating the signal intensity of each base station corresponding to the space grid, digitizing the grid information including the signal intensity to form a characteristic vector, and obtaining a sample data set consisting of the characteristic vectors, namely a space grid characteristic fingerprint database.
4. The method according to claim 2, wherein the correcting the fingerprint database by using the latitude and longitude calibration algorithm specifically comprises: and judging the precision of the sample data in the fingerprint database by adopting a longitude and latitude calibration algorithm, and rejecting the sample data with low precision.
5. The method according to claim 2, wherein matching the user MR data with the feature vectors in the fingerprint library assigns the user MR data to a corresponding optimal spatial grid, specifically comprising:
determining a reporting cell of the user MR data and a grid set to which the reporting cell belongs;
comparing the user MR data with the characteristic vectors in the space grid characteristic fingerprint database according to a minimum Euclidean distance method;
and selecting the space grid to which the feature vector with the highest similarity with the MR data of the user belongs as the optimal space grid.
6. The method according to claim 1, wherein screening out the weak coverage cells according to the average signal strength and the ratio of the weak coverage signal sampling points comprises:
judging whether the average signal strength is smaller than a set weak coverage threshold value or not and whether the ratio of sampling points of weak coverage signals is larger than a set ratio or not;
and if the value of the average signal strength is smaller than a set weak coverage threshold value and the ratio of the sampling points of the weak coverage signals is larger than a set proportion, the cell corresponding to the optimal space grid is a weak coverage cell.
7. The method according to claim 1, wherein the dividing the plurality of weak coverage cells into a plurality of clusters by a K-means clustering algorithm specifically comprises:
randomly selecting a specified number of base stations from a plurality of base stations needing to adjust the direction angle as an initial clustering center;
measuring the similarity between each residual base station and the initial clustering center, and distributing each base station to the cluster to which the nearest initial clustering center belongs according to the similarity to obtain a plurality of new clusters;
calculating the mean value of the similarity of each base station in each new cluster to obtain a new cluster center of each new cluster;
and iterating the measuring step and the calculating step until the new clustering center is equal to the initial clustering center or less than a specified threshold value.
8. An antenna directive angle optimizing apparatus, characterized in that the apparatus comprises:
the data acquisition and positioning module is used for acquiring user MR data and performing rasterization positioning on the user MR data so as to position the user MR data to an optimal space grid;
the weak coverage cell screening module is used for acquiring the average signal intensity and the weak coverage signal sampling points of the optimal space grid and screening the weak coverage cells according to the average signal intensity and the occupation ratio of the weak coverage signal sampling points;
the antenna direction angle optimization module is used for calculating an optimal direction angle capable of improving the problem of the weak coverage cell based on an antenna horizontal direction diagram and automatically optimizing the antenna direction angle according to the optimal direction angle;
the antenna direction angle optimization module is specifically used for dividing the weak coverage cells into a plurality of clusters through a K-means clustering algorithm;
the antenna direction angle optimization module is further specifically used for setting an adjustment step length of the antenna direction angle, and adjusting the antenna direction angle of each cluster according to the set adjustment step length;
the antenna direction angle optimization module is further specifically used for predicting the received signal strength of the MR data sampling points of each cluster after the antenna direction angle is adjusted based on the antenna horizontal direction diagram;
the antenna direction angle optimization module is further specifically configured to perform iterative computation on average levels of the multiple clusters based on a relationship between signal strength received by the MR data sampling points in each cluster and the number of users in the cluster before and after the adjustment of the antenna direction angle, so as to obtain an optimal direction angle.
9. A computer device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory that, when executed by the processor, implement the method of any of claims 1-7.
10. A computer-readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1-7.
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