CN110300413B - Communication station simulation planning method and system based on density algorithm - Google Patents
Communication station simulation planning method and system based on density algorithm Download PDFInfo
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- CN110300413B CN110300413B CN201910555535.8A CN201910555535A CN110300413B CN 110300413 B CN110300413 B CN 110300413B CN 201910555535 A CN201910555535 A CN 201910555535A CN 110300413 B CN110300413 B CN 110300413B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/155—Ground-based stations
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention discloses a communication site simulation planning method and a system based on a density algorithm, wherein the method comprises the following steps: step S1, importing all mobile communication station data of the planned area D; step S2, forming a plurality of site clusters for all mobile communication sites of the planning area D according to density connectivity among the sites based on a density algorithm to obtain a site cluster set; and step S3, calling a map software interface, importing the site cluster set obtained in the step S2 into map software, and displaying different site clusters in the map software in a distinguishing way.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a communication site simulation planning method and system based on a density algorithm.
Background
The existing mobile communication station planning is usually to arrange and arrange the layout to areas with large flow of people according to experience, stations are set up in specific positions non-actively, and a demand department puts forward demands to measure station sites and then construct the stations. At present, the planning and design of communication sites are mainly handled by communication design institutes, and engineers of the communication sites give network construction departments to operators for implementation after making site coverage area planning drawings.
In the prior art, due to the lack of a site-by-site coverage simulation tool in planning, a planning engineer needs to repeatedly and practically survey and operate, a large amount of time is spent, and the working efficiency is reduced. In an actual engineering workload example, in a network with a scale of 2000 base stations, if 100 sites are needed to be added for site selection to ensure reasonable distribution of the sites and continuous coverage of the network, site selection calculation and site selection coverage areas of geographic positions generally need 4 persons/day to complete, and in the case of emergency project requirements, great pressure is applied to engineers, and as a result, the work is done half.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a communication site simulation planning method and system based on a density algorithm, so that the updated area coverage connection condition of the communication site is simulated and planned in real time, and the working efficiency is improved.
In order to achieve the above object, the present invention provides a communication station simulation planning method based on density algorithm, which comprises the following steps:
step S1, importing all mobile communication station data of the planned area D;
step S2, forming a plurality of site clusters for all mobile communication sites of the planning area D according to density connectivity among the sites based on a density algorithm to obtain a site cluster set;
and step S3, calling a map software interface, importing the site cluster set obtained in the step S2 into map software, and performing differential presentation on different site clusters.
Preferably, the density linking means that if there is a site m in the sample, so that both the site q and the site p belong to the neighborhood of the site m and the number of sites included in the neighborhood is greater than or equal to the minimum number of sites threshold MinPts, then the site q, p is preferably density linked from the site m, and step S2 further includes:
s200, when unprocessed sites still exist in the planning area D, randomly selecting any site P from the planning area D, and marking the site P as a processed state;
step S201, calculating the number of sites within a preset neighborhood radius Eps of the site P, comparing the number of the sites with a preset minimum site number threshold MinPts within the neighborhood of the site, and determining the site P as a noise point according to a comparison result or entering step S202;
step S202, a site cluster Ci is newly built, a site P is added into the site cluster Ci, a neighborhood site cluster N is newly built, and sites which are connected with the site P in density are added into the neighborhood site cluster N;
step S203, if the neighborhood site cluster N has unprocessed sites, selecting a site Pa for processing;
step S204, if the site Pa is in an unprocessed state, marking the site Pa as a processed state, calculating the number of sites within a preset neighborhood radius Eps of the site Pa, comparing the number of the sites with a preset threshold MinPts of the minimum number of sites within the neighborhood of the site, and entering step S205 or directly entering step S206 according to the comparison result;
step S205, adding the site connected with the site Pa density into the neighborhood site cluster N;
step S206, judging whether any site cluster Ci member of the site Pa, if not, adding the site cluster Ci member to the site cluster Pa, returning to the step S203 until all sites in the neighborhood site cluster N are processed, and if so, directly returning to the step S203 until all sites in the neighborhood site cluster N are processed;
and step S207, returning to step S200 until all stations in the planning area D are processed.
Preferably, the step S1 further includes:
step S201a, calculating the distance between the station P and other stations in the planning area D;
step S201b, comparing the distances between other sites and the site P with a preset neighborhood distance radius parameter Eps one by one to obtain all other sites with the distances smaller than the neighborhood distance radius parameter Eps;
step S201c, counting all the stations obtained in step S201b, comparing the counted result with a preset minimum station number threshold value MinPts, if the counted result is smaller than the minimum station number threshold value MinPts, marking the station as a noise point, adding the noise point to the noise cluster, and returning to step S200, otherwise, directly entering step S202.
Preferably, in step S201a, the distance between two stations is calculated by using a distance calculation formula between two points on the earth.
Preferably, in step S204, if the number of stations is greater than or equal to the preset minimum threshold MinPts of the number of stations in the neighborhood of the station, step S205 is performed, otherwise, step S206 is performed.
Preferably, if the station Pa is in the processed state, the process directly proceeds to step S206.
Preferably, in step S3, different clusters of sites are marked with different colors.
Preferably, the method further comprises:
and step S4, adding a plurality of communication stations into the planning area D, returning to step S1, and re-executing the steps S1 to S3 to obtain the optimal planning result.
In order to achieve the above object, the present invention further provides a communication site simulation planning system based on a density algorithm, including:
a site data importing unit, configured to import all mobile communication site data of the planning area D;
and the site cluster generating unit is used for forming a plurality of site clusters from all the mobile communication sites of the planning area D according to the density connectivity among the sites based on a density algorithm to obtain a site cluster set Cn.
And the output presentation unit is used for calling a map software interface, importing the site cluster set obtained by the site cluster set generation unit into the map software, and performing differential presentation on different site clusters in the map software.
Compared with the prior art, the communication station simulation planning method and the communication station simulation planning system based on the density algorithm can be used for simulating the communication station according to the actual requirements at any time, the situation of dense and sparse clustering distribution of sites meeting the conditions of site number threshold MinPts and neighborhood distance radius parameter Eps is presented on the electronic map, planners rely on the electronic map, according to the clustering distribution condition of the stations, the relay stations can be arranged in the sparse areas of the corresponding stations to ensure the continuity of the coverage of the mobile communication stations, the method can lead in or update the site data for a plurality of times, freely input a plurality of site number threshold values MinPts and values of the neighborhood distance radius parameter Eps, the method simulates the coverage condition of a simulation site in a visual form by the electronic map, solves the problems that in the current coverage planning process of the mobile communication site, the manual site planning efficiency is low, the batch drawing operation cannot be carried out, and the newly added sites are randomly distributed.
Drawings
FIG. 1 is a flow chart illustrating the steps of a communication site simulation planning method based on density algorithm according to the present invention;
FIG. 2 is a schematic diagram of the concept of density connectivity in the present invention;
FIG. 3 is a detailed flowchart of step S2 according to an embodiment of the present invention;
fig. 4 is a flowchart of obtaining a site cluster set Cn of a planning region D based on a density algorithm in an embodiment of the present invention;
FIG. 5 is a diagram illustrating the results of a site cluster set Cn according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a site coverage situation after 5 communication sites are added in the embodiment of the present invention;
FIG. 7 is a diagram illustrating site cluster merging after neighborhood related parameters have been changed in an exemplary embodiment of the invention;
FIG. 8 is a system architecture diagram of a communication site simulation planning system based on density algorithm according to the present invention;
fig. 9 is a detailed structure diagram of the site cluster set generating unit 802 in the embodiment of the present invention.
Detailed Description
Other advantages and capabilities of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification by describing embodiments of the present invention with specific embodiments and by referring to the attached drawings. The invention is capable of other and different embodiments and its several details are capable of modification in various other respects, all without departing from the spirit and scope of the present invention.
Fig. 1 is a flowchart illustrating steps of a communication site simulation planning method based on a density algorithm according to the present invention. As shown in fig. 1, the communication site simulation planning method based on the density algorithm of the present invention includes the following steps:
step S1, importing all mobile communication station data of the planning area D, where the station data refers to longitude and latitude data of each mobile communication station and is represented in the form of floating point numbers, and in the specific embodiment of the present invention, the station data structure is shown in table 1 below.
TABLE 1
| Site ID | Longitude (G) | Latitude |
| 0 | 113.3279294 | 23.14740292 |
| 1 | 113.3689938 | 23.14053526 |
| 2 | 113.2627869 | 23.14833291 |
| 3 | 113.3689952 | 23.12512955 |
| 4 | 113.3669967 | 23.13939939 |
| 5 | 113.3279263 | 23.14150005 |
| 6 | 113.3429884 | 23.14588769 |
| 7 | 113.3839615 | 23.13621485 |
| … | … | … |
Step S2, forming a plurality of site clusters for all mobile communication sites of the planning area D according to density connectivity among the sites based on a density algorithm, and obtaining a site cluster set Cn.
The density-connected means that if there is a site m in the sample such that both the site q and the site p belong to the neighborhood of the site m (i.e., the radius length Eps centered on the site m) and the number of sites included in the neighborhood is greater than or equal to the minimum site number threshold value MinPts (5 shown in fig. 2), the site q and the site p are density-connected from the site m.
Specifically, as shown in fig. 3, step S2 further includes:
step S200, when unprocessed sites still exist in the planning area D, randomly selecting any site P from the planning area D, and marking the site P as a processed state. Specifically, all sites in the planned area D are initialized to an unprocessed state, all the sites are processed one by one, and when there are unprocessed sites in the planned area D, any one of the sites P is randomly selected.
Step S201, calculating the number of sites within a preset neighborhood radius Eps of the site P, comparing the number of sites with a preset minimum site number threshold MinPts within the site neighborhood, and determining whether the site P is a noise point or entering step S202 according to the comparison result.
In an embodiment of the present invention, specifically, step S201 further includes:
in step S201a, the distance between the station P and other stations in the planned area D is calculated. In the embodiment of the present invention, all the sites in the planning region D are unprocessed sites, and all the processed sites are added to the corresponding site cluster.
In an embodiment of the present invention, the calculation of the distance between two stations can be obtained by using a calculation formula of the distance between two points on the earth.
Specifically, the Distance between two points on the earth is calculated as follows:
taking the PYTHON calculation method as an example:
two communication stations are provided, and the distance (unit is meter) between two communication stations, namely a communication station 1 (long 0, lat0) and a communication station 2 (long 1, lat1) is calculated by the following method:
lat0＝radians(lat0)
lat1＝radians(lat1)
lng0＝radians(lng0)
lng1＝radians(lng1)
dlng＝fabs(lng0-lng1)
dlat＝fabs(lat0-lat1)
h＝hav(dlat)+cos(lat0)*cos(lat1)*hav(dlng)
distance＝2*EARTH_RADIUS*asin(sqrt(h))*1000
it should be noted that there are many formulas for calculating the distance between two points on the earth, which is only one of the ways, and the present invention is not limited thereto. Here, the filling values of the communication station 1(lng0, lat0) and the communication station 2(lng1, lat1) are (base station longitude value, base station latitude value), and assume the following forms: the distance between the two points of the communication station 1(114.325721, 22.706023) and the communication station 2(113.969720, 22.597897) is 38459.52213130572 (meters).
Step S201b, comparing the distances between each other site and the site P with the preset neighborhood distance radius parameter Eps, to obtain all other sites whose distances are smaller than the neighborhood radius Eps, that is, the other sites whose distances are smaller than the neighborhood radius Eps are sites within the preset neighborhood distance radius parameter Eps of the site P.
That is, the neighborhood distance radius parameter Eps is preset, and in the embodiment of the present invention, the neighborhood distance radius parameter Eps is preset to be 1000 meters, that is, the site having the distance from the site P smaller than the neighborhood distance radius parameter 1000 meters is obtained through step S201 b.
Step S201c, counting all the stations obtained in step S201b, comparing the counted result with a preset minimum station number threshold MinPts, if the counted result is smaller than the minimum station number threshold MinPts, indicating that the station P is a noise point, marking the station P as a noise point, adding the noise point to the noise cluster, returning to step S200, otherwise, directly entering step S202.
That is, the minimum site number threshold MinPts needs to be preset in the present invention, in the embodiment of the present invention, the minimum site number threshold MinPts in the site neighborhood is preset to 10 (one), that is, if the site number in the site P preset neighborhood distance radius parameter Eps is less than 10, P is marked as a noise point, and the step S200 is returned to, otherwise, the next step S202 is performed.
Step S202, a new site cluster Ci is newly built, a site P is added into the site cluster Ci, a neighborhood site cluster N is newly built, and sites which are connected with the site P in density are added into N. Specifically, the density concatenation means that if there is a site m in the sample such that both site q and site p belong within the neighborhood of site m (i.e., the radius length Eps centered on site m) and the number of sites contained within the neighborhood is greater than or equal to the minimum number of sites threshold MinPts, then site q, p is density-concatenated from site m.
Step S203, if the neighborhood site cluster N has unprocessed sites, selecting a site Pa for processing;
step S204, if the site Pa is in an unprocessed state, marking the site Pa as a processed state, calculating the number of sites in a preset neighborhood distance radius parameter Eps of the site Pa, comparing the number of the sites with a preset threshold value MinPts of the minimum number of sites in the neighborhood of the site, and entering step S205 or directly entering step S206 according to the comparison result, specifically, if the number of the sites is greater than or equal to the preset threshold value MinPts of the minimum number of sites in the neighborhood of the site, entering step S205, otherwise, entering step S206, and if the site Pa is in a processed state, directly entering step S206;
step S205, adding the sites connected with the site Pa density into a neighborhood site cluster N;
step S206, judging whether any site cluster Ci member of the site Pa, if not, adding the site cluster Ci member to the site cluster Ci, returning to the step S203 until all sites in the neighborhood site cluster N are processed, and if so, directly returning to the step S203 until all sites in the neighborhood site cluster N are processed.
And step S207, returning to step S200 until all stations in the planning area D are processed.
Fig. 4 is a flowchart of obtaining a site cluster set Cn of the planning region D based on a density algorithm in an embodiment of the present invention. In the specific embodiment of the invention, the specific process is as follows:
step 1, initializing all sites of a planning area D to be in an unprocessed state;
step 2, whether unprocessed sites still exist in the planning area D or not is judged, if yes, the step 3 is carried out, and if not, the step 14 is directly carried out;
step 3, randomly selecting any station P from the planning area D and marking the station P as a processed state;
step 4, calculating the number of sites within the site P neighborhood radius Eps, judging whether the number of sites within the site P neighborhood radius Eps is smaller than a minimum site number threshold value MinPts, if so, marking the site P as a noise point, adding the noise point to a noise cluster, and returning to the step 2; otherwise, entering step 5;
step 5, a new site cluster Ci is newly built, and a site P is added into the site cluster Ci;
step 6, a neighborhood site cluster N is newly established, and sites which are connected with the site P in density are added into the N;
step 7, processing the sites in the neighborhood site cluster N, judging whether unprocessed sites still exist in the neighborhood site cluster N, if so, entering step 8, otherwise, returning to step 2;
step 8, selecting a site Pa from the neighborhood site cluster N, judging whether the site Pa is in an unprocessed state, if so, entering step 9, and if not, directly entering step 12;
step 9, marking Pa as a processed state;
step 10, calculating the number of sites within the radius Eps of the Pa field of the site, and judging whether the number of the sites within the radius Eps of the Pa field of the site is greater than or equal to a minimum site number threshold value MinPts, if so, entering step 11, otherwise, entering step 12;
step 11, adding points connected with the Pa density into a neighborhood site cluster N, and entering step 12;
step 12, judging whether the station Pa is a member of any station cluster Ci; if yes, go to step 13, otherwise go to step 14 directly;
step 13, adding the site Pa to Ci;
and step 14, outputting all the site clusters Ci (i ═ 1, 2, 3, …, n).
After the calculation, the following station clusters are obtained, and the results are shown in table 2 below, in which different station clusters in the station cluster set are marked with classification cluster marks, where the classification cluster marks 0, 1, -1 represent 3 station clusters, where-1 is a noise cluster (a station that does not meet the requirement of step S201).
TABLE 2
| Classification cluster tagging | Site ID | Longitude (G) | Latitude |
| 0 | 0 | 113.3279294 | 23.14740292 |
| -1 | 1 | 113.3689938 | 23.14053526 |
| -1 | 2 | 113.2627869 | 23.14833291 |
| -1 | 3 | 113.3689952 | 23.12512955 |
| -1 | 4 | 113.3669967 | 23.13939939 |
| 0 | 5 | 113.3279263 | 23.14150005 |
| 0 | 6 | 113.3429884 | 23.14588769 |
| 1 | 7 | 113.3839615 | 23.13621485 |
| … | … | … | … |
And step S3, calling a map software interface, importing the site cluster set Cn obtained in the step S2 into map software, and performing differential presentation on different site clusters. In the specific embodiment of the invention, interfaces of map software such as Baidu maps, GOOGLE maps and the like are called, the results are imported into the map software for presentation, and different sites are clustered and marked with different colors and can be customized by a user.
For example, in the coordinate system of FIG. 5, the X-axis value is the longitude value and the Y-axis value is the latitude value. Each point in the graph is a communication site corresponding to longitude and latitude. Artificially defining the color of the classification cluster mark value, for example, the color of the communication station with 0 value is blue; the communication station with the value of 1 is yellow in color; the communication station color of the-1 value is a noisy communication station, and the result is presented as shown in fig. 5 (color difference not shown).
Preferably, after step S3, the method further includes the following steps:
and step S4, adding a plurality of communication stations into the planning area D, returning to step S1, and re-executing the steps S1 to S3 to obtain the optimal planning result.
In the embodiment of the present invention, in order to ensure that the yellow area station (the classification cluster is marked as 1) is continuously covered, 5 communication stations are added, and step S1 to step S3 are executed again. The added geographic coordinate data of the communication sites are shown in the following table 3:
TABLE 3
In the station cluster collection result of the planned area D obtained by the density algorithm through steps S1 to S3, the variation part is shown in table 4 below, where the 5 station classification cluster labels have values of 0, and due to the addition, the adjacent noise communication stations are continuously overlaid with the original yellow area in pieces, which can be seen in real time. In the actual site building operation, site building can be recommended to take points nearby.
TABLE 4
| Classification cluster tagging | Site ID | Longitude (longitude) | Latitude |
| 1 | 104 | 113.3712353 | 23.1333256 |
| 1 | 105 | 113.3725353 | 23.1333611 |
| 1 | 106 | 113.3734854 | 23.1326951 |
| 1 | 107 | 113.3745353 | 23.1354214 |
| 1 | 108 | 113.3754854 | 23.1362845 |
Fig. 6 is a schematic diagram of a site coverage situation after 5 communication sites are added.
If the correlation determination condition of the neighborhood is relaxed, for example, the minimum station count threshold value MinPts is 10 (m), and the neighborhood distance radius parameter Eps is 2000 (m), after steps S1 to S3 are re-executed, the result is that two station clusters are merged, as shown in fig. 7. The setting of the value of Eps is established only when the average coverage distance of the mobile communication station signal is required to be met, and the default coverage distance in the urban area of the 4G communication station is 1000 meters.
Fig. 8 is a system architecture diagram of a communication site simulation planning system based on a density algorithm according to the present invention, and as shown in fig. 8, the communication site simulation planning system based on a density algorithm according to the present invention includes:
a site data importing unit 801, configured to import all mobile communication site data of the planning area D, where the site data refers to longitude and latitude data of each mobile communication site, and is represented in a floating point format
A site cluster set generating unit 802, configured to form a plurality of site clusters for all mobile communication sites in the planning region D according to density connectivity among the sites based on a density algorithm, so as to obtain a site cluster set Cn.
The density concatenation means that if there is a site m in the sample such that both site q and site p belong within the neighborhood of site m (i.e., the radius length Eps centered on site m) and the number of sites contained within the neighborhood is greater than or equal to the minimum site number threshold value MinPts, then site q, p is density-concatenated from site m.
Specifically, as shown in fig. 9, the site cluster generating unit 802 further includes: the site selecting unit 8021 is configured to randomly select any one of the sites P from the planning area D when unprocessed sites still remain in the planning area D, and mark the selected site P as a processed state.
And the neighborhood site number calculation and comparison unit 8022 is used for calculating the site number within the preset neighborhood radius Eps of the site P, comparing the site number with a preset minimum site number threshold value MinPts in the site neighborhood, and determining whether the site P is a noise point or enters the site cluster establishing unit 8023 according to the comparison result.
In an embodiment of the present invention, the neighborhood site count calculation and comparison unit 8022 is specifically configured to:
and calculating the distance between the station P and other stations in the planning area D.
In an embodiment of the present invention, the calculation of the distance between two stations can be obtained by using a calculation formula of the distance between two points on the earth.
Specifically, the Distance between two points on the earth is calculated as follows:
taking the PYTHON calculation method as an example:
two communication stations are provided, and the distance (unit is meter) between two communication stations, namely a communication station 1 (long 0, lat0) and a communication station 2 (long 1, lat1) is calculated by the following method:
lat0＝radians(lat0)
lat1＝radians(lat1)
lng0＝radians(lng0)
lng1＝radians(lng1)
dlng＝fabs(lng0-lng1)
dlat＝fabs(lat0-lat1)
h＝hav(dlat)+cos(lat0)*cos(lat1)*hav(dlng)
distance＝2*EARTH_RADIUS*asin(sqrt(h))*1000
it should be noted that there are many formulas for calculating the distance between two points on the earth, which is only one of the ways, and the present invention is not limited thereto. Here, the filling values of the communication station 1(lng0, lat0) and the communication station 2(lng1, lat1) are (base station longitude value, base station latitude value), and assume the following forms: the distance between the two points of the communication station 1(114.325721, 22.706023) and the communication station 2(113.969720, 22.597897) is 38459.52213130572 (meters).
And comparing the distances between other sites and the site P with a preset neighborhood distance radius parameter Eps one by one to obtain all other sites with the distances smaller than the neighborhood radius Eps, namely, the other sites with the distances smaller than the neighborhood radius Eps are sites within the preset neighborhood distance radius parameter Eps of the site P.
That is, the neighborhood radius Eps is preset in the present invention, and in the embodiment of the present invention, the neighborhood distance radius parameter Eps is preset to 1000 meters, that is, other sites having a distance from the site P smaller than the neighborhood distance radius parameter 1000 meters are obtained.
Counting all the obtained sites, comparing the counting result with a preset minimum site number threshold value MinPts, if the counting result is smaller than the minimum site number threshold value MinPts, indicating that the site P is a noise point, marking the noise point as a noise point, adding the noise point to a site selection unit, and returning to the site selection unit, otherwise, directly entering the site cluster establishing unit 8023.
That is to say, the present invention also needs to preset a minimum site number threshold value MinPts, in the specific embodiment of the present invention, the minimum site number threshold value MinPts in the site neighborhood is 10 (one), that is, if the site number in the site P preset neighborhood distance radius parameter Eps is less than 10, the mark P is a noise point, otherwise, the site P enters the site cluster establishing unit 8023.
And the site cluster establishing unit 8023 is configured to newly establish a new site cluster Ci, add a site P to the site cluster Ci, newly establish a neighborhood site cluster N, and add sites connected with the site P in density to the neighborhood site cluster N. The density concatenation means that if a site m exists in the sample, so that both site q and site p belong to the neighborhood of site m (i.e., the radius length Eps with site m as the centroid) and the number of sites contained in the neighborhood is greater than or equal to the minimum site number threshold value MinPts, then site q, p is density-concatenated from site m;
the neighborhood site cluster traversal processing unit 8024 is configured to process the sites Pa in the neighborhood site cluster N one by one, mark the sites Pa in a processed state if the sites Pa are in an unprocessed state, calculate the number of sites within a preset neighborhood radius Eps of the sites Pa, compare the number of the sites with a preset minimum site number threshold MinPts in a neighborhood of the sites, and enter the neighborhood site cluster updating unit 8025 or the site cluster updating unit 8026 according to a comparison result, specifically, if the number of the sites is greater than or equal to the preset minimum site number threshold MinPts in the neighborhood of the sites, enter a new unit 8025 such as a neighborhood site cluster, or otherwise enter the site cluster updating unit 8026, and if the sites Pa is in a processed state, directly enter the site cluster updating unit 8026;
a neighborhood site cluster updating unit 8025, configured to add a site connected to the site Pa density to the neighborhood site cluster N;
and the site cluster updating unit 8026 is configured to determine whether any site cluster Ci member of the site Pa, add the site cluster Ci to the site cluster Ci if the site cluster Ci member is not a member of any site cluster Ci member, return to the neighborhood site cluster traversal processing unit 8024 until all sites in the neighborhood site cluster N are completely processed, and directly return to the neighborhood site cluster traversal processing unit 8024 until all sites in the neighborhood site cluster N are completely processed if the site cluster updating unit 8026 is a member of any site cluster Ci member.
And obtaining a site cluster set after calculation, and marking different site clusters in the site cluster set by using classification cluster marks, wherein the classification cluster marks 0, 1 and-1 represent 3 site clusters, and-1 is a noise cluster (sites which do not meet the requirements of a neighborhood site number calculation and comparison unit).
And the output presenting unit 803 is configured to invoke a map software interface, import the site cluster set Cn obtained by the site cluster set generating unit 802 into the map software, and perform differential presentation on different site clusters. In the specific embodiment of the invention, interfaces of map software such as Baidu maps, GOOGLE maps and the like are called, the results are imported into the map software for presentation, and different sites are clustered and marked with different colors and can be customized by a user.
Preferably, the communication site simulation planning system based on the density algorithm of the present invention further includes:
and the station adding and updating unit is used for adding a plurality of communication stations, adding the communication stations into the planning area D, returning to the station data importing unit 801, and re-executing the station data importing unit 801 to the output presenting unit 803 to obtain an optimal planning result.
In summary, the invention provides a communication site simulation planning method and system based on density algorithm, which can present the site dense and sparse cluster distribution condition meeting the conditions of site number threshold MinPts and neighborhood distance radius parameter Eps on an electronic map at any time according to the actual requirements, and a planner can set a relay site in the corresponding site sparse area according to the site cluster distribution condition by relying on the electronic map, thereby ensuring the continuity of the coverage of the mobile communication site And (5G) planning and simulating design of a communication station of a mobile communication network system.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Modifications and variations can be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the present invention. Therefore, the scope of the invention should be determined from the following claims.
Claims (7)
1. A communication station simulation planning method based on a density algorithm comprises the following steps:
step S1, importing all mobile communication station data of the planned area D;
step S2, forming a plurality of site clusters for all mobile communication sites of the planning area D according to density connectivity among the sites based on a density algorithm to obtain a site cluster set;
step S3, calling a map software interface, importing the site cluster set obtained in the step S2 into map software, and performing differential presentation on different site clusters;
the density connection means that if a site m exists in a sample, so that both the site q and the site p belong to the neighborhood of the site m, and the number of sites contained in the neighborhood is greater than or equal to a minimum site number threshold value MinPts, the site q and the site p are connected from the site m in density;
step S2 further includes:
s200, when unprocessed sites still exist in the planning area D, randomly selecting any site P from the planning area D, and marking the site P as a processed state;
step S201, calculating the number of sites within a preset neighborhood radius Eps of the site P, comparing the number of the sites with a preset minimum site number threshold MinPts within the neighborhood of the site, and determining the site P as a noise point according to a comparison result or entering step S202;
step S202, a site cluster Ci is newly built, a site P is added into the site cluster Ci, a neighborhood site cluster N is newly built, and sites which are connected with the site P in density are added into the neighborhood site cluster N;
step S203, if the neighborhood site cluster N has unprocessed sites, selecting a site Pa for processing;
step S204, if the site Pa is in an unprocessed state, marking the site Pa as a processed state, calculating the number of sites within a preset neighborhood radius Eps of the site Pa, comparing the number of the sites with a preset threshold MinPts of the minimum number of sites within the neighborhood of the site, and entering step S205 or directly entering step S206 according to the comparison result;
step S205, adding the site connected with the site Pa density into the neighborhood site cluster N;
step S206, judging whether any site cluster Ci member of the site Pa, if not, adding the site cluster Ci member to the site cluster Pa, returning to the step S203 until all sites in the neighborhood site cluster N are processed, and if so, directly returning to the step S203 until all sites in the neighborhood site cluster N are processed;
step S207, returning to step S200 until all stations in the planning area D are processed;
step S201 further includes:
step S201a, calculating the distance between the station P and other stations in the planning area D;
step S201b, comparing the distances between other sites and the site P with a preset neighborhood distance radius parameter Eps one by one to obtain all other sites with the distances smaller than the neighborhood distance radius parameter Eps;
step S201c, counting all the stations obtained in step S201b, comparing the counted result with a preset minimum station number threshold value MinPts, if the counted result is less than the minimum station number threshold value MinPts, marking the station as a noise point, adding the noise point to the noise cluster, and returning to step S200, otherwise, directly entering step S202.
2. The communication site simulation planning method based on the density algorithm as claimed in claim 1, wherein: in step S201a, the distance between two stations is calculated by using the distance calculation formula between two points on the earth.
3. The communication site simulation planning method based on the density algorithm as claimed in claim 1, wherein: in step S204, if the number of stations is greater than or equal to the preset minimum threshold MinPts of the number of stations in the neighborhood of the station, the process proceeds to step S205, otherwise, the process proceeds to step S206.
4. The communication site simulation planning method based on the density algorithm as claimed in claim 1, wherein: if the station Pa is in the processed state, the process proceeds to step S206.
5. The communication site simulation planning method based on the density algorithm as claimed in claim 1, wherein: in step S3, different clusters are marked with different colors.
6. The method for communication site simulation planning based on density algorithm as claimed in claim 1, wherein the method further comprises:
and step S4, adding a plurality of communication stations into the planning area D, returning to step S1, and re-executing the steps S1 to S3 to obtain the optimal planning result.
7. A communication site simulation planning system based on a density algorithm comprises:
a site data importing unit, configured to import all mobile communication site data of the planning area D;
a site cluster generating unit, configured to form a plurality of site clusters for all mobile communication sites in the planning area D according to density connectivity between the sites based on a density algorithm, so as to obtain a site cluster set;
the output presentation unit is used for calling a map software interface, importing the site cluster set obtained by the site cluster set generation unit into map software, and performing differential presentation on different site clusters in the map software;
in the site cluster aggregation generating unit, the density connection means that if a site m exists in the sample, so that both the site q and the site p belong to the neighborhood of the site m, and the number of sites included in the neighborhood is greater than or equal to the minimum site number threshold MinPts, the site q and the site p are density-connected from the site m;
in a site cluster set generating unit, forming a plurality of site clusters from all mobile communication sites in a planning area D according to density connectivity among the sites based on a density algorithm, wherein the method for obtaining the site cluster set comprises the following steps:
step S200, when unprocessed sites still exist in the planning area D, randomly selecting any site P from the planning area D, and marking the site P as a processed state;
step S201, calculating the number of sites within a preset neighborhood radius Eps of the site P, comparing the number of the sites with a preset minimum site number threshold MinPts within the neighborhood of the site, and determining the site P as a noise point according to a comparison result or entering step S202;
step S202, a site cluster Ci is newly built, a site P is added into the site cluster Ci, a neighborhood site cluster N is newly built, and sites which are connected with the site P in density are added into the neighborhood site cluster N;
step S203, if the neighborhood site cluster N has unprocessed sites, selecting a site Pa for processing;
step S204, if the site Pa is in an unprocessed state, marking the site Pa as a processed state, calculating the number of sites within a preset neighborhood radius Eps of the site Pa, comparing the number of the sites with a preset threshold MinPts of the minimum number of sites within the neighborhood of the site, and entering step S205 or directly entering step S206 according to the comparison result;
step S205, adding the site connected with the site Pa density into the neighborhood site cluster N;
step S206, judging whether any site cluster Ci member of the site Pa, if not, adding the site cluster Ci member to the site cluster Pa, returning to the step S203 until all sites in the neighborhood site cluster N are processed, and if so, directly returning to the step S203 until all sites in the neighborhood site cluster N are processed;
step S207, returning to step S200 until all stations in the planning area D are processed;
step S201 further includes:
step S201a, calculating the distance between the station P and other stations in the planning area D;
step S201b, comparing the distances between other sites and the site P with a preset neighborhood distance radius parameter Eps one by one to obtain all other sites with the distances smaller than the neighborhood distance radius parameter Eps;
step S201c, counting all the stations obtained in step S201b, comparing the counted result with a preset minimum station number threshold value MinPts, if the counted result is less than the minimum station number threshold value MinPts, marking the station as a noise point, adding the noise point to the noise cluster, and returning to step S200, otherwise, directly entering step S202.
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| CN115250476B (en) * | 2021-04-26 | 2023-12-15 | 中国移动通信集团设计院有限公司 | Wireless network planning simulation method, device, computing equipment and storage medium |
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