CN107205245B

CN107205245B - Hot spot area automatic identification method and device

Info

Publication number: CN107205245B
Application number: CN201710452773.7A
Authority: CN
Inventors: 鲁向东
Original assignee: Individual
Current assignee: Individual
Priority date: 2016-08-31
Filing date: 2016-08-31
Publication date: 2020-12-08
Anticipated expiration: 2036-08-31
Also published as: CN106376032B; CN107205245A; CN106376032A

Abstract

The invention relates to a method and a device for automatically identifying a hot spot area. The method comprises the following steps: triggering a hot spot area identification step aiming at a cell to be identified according to a preset cell service quality index degradation threshold; the hot spot region identification step includes: searching each homogeneous degraded cell according to the condition that the same service quality index of the co-sited adjacent cell of the cell to be identified and each adjacent-sited adjacent cell around the co-sited adjacent cell are degraded; and triggering a hot spot area determining process according to the ratio of the number of the searched homogeneous deteriorated cells to the number of the associated adjacent cells analyzed in the hot spot identification process, wherein the hot spot area determining process comprises the step of carrying out closed connection on the associated adjacent cells analyzed in the hot spot identification process so as to form a hot spot area. The invention can automatically identify the hot spot area, and has high accuracy and high efficiency.

Description

Hot spot area automatic identification method and device

The application is a divisional application of Chinese patent application with the application number of 201610799023.2, the application date of 2016, 8, month and 31, and the invention and creation name of 'hot spot area automatic identification method and device'.

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for automatically identifying a hot spot area.

Background

At present, a mobile wireless network is in a rapid development stage, network load begins to face a large-traffic test, and characteristics of high user density, high concurrency and large traffic are presented in a local hot spot area. The key point of network operation work is to grasp and ensure the service quality and customer perception of the hot spot area in time.

The existing hot spot area monitoring capability mainly manually combs out network elements needing important attention by previously planning the range in which service hot spots are likely to occur, and continuously pays attention during the guarantee period. The method can accurately monitor the network monitoring range to the hot spot area, and solves the monitoring energy efficiency of daily foreseeable hot spot area service load and network quality.

However, the hot spot area is set through manual intervention, the labor cost consumption of manual carding and manual configuration is high, and the automatic capturing capability of the hot spot area is more prominent and efficient through large signaling data analysis based on the scheme.

According to the current signaling data acquisition capacity, the problems and the defects of realizing the automatic capturing of the hot spot area and the monitoring of the service quality exist, and the method comprises the following points:

1. the real-time acquisition capacity of large signaling data needs to be further improved at present, and particularly, a gap exists in the delay control capacity of index data, and the gap has the performance of increasing and deteriorating along with the expansion of an acquisition range. The data delay will directly affect the ability to capture sudden concurrency problems in hot spot areas.

2. The diversity of bottom data sources relates to signaling, network management, dial testing and drive testing, the difficulty of convergence of upper data is improved, convergence composite operation is carried out on different data sources, different time dimensions and different time delays, and the high requirement is provided for the logical operation capability of a data processing layer.

3. The hot spot identification adopts a dynamic baseline and a transverse comparison algorithm, historical data for generating the dynamic baseline are all based on 5-minute acquisition and the granularity of a whole network cell, and meanwhile, due to the burstiness of a hot spot area, a data range needing transverse comparison cannot be agreed in advance, so that all dynamic baselines are triggered and calculated in real time, and the efficiency of the calculation method is low.

Disclosure of Invention

Technical problem

In view of this, the technical problem to be solved by the present invention is how to automatically identify hot spot areas.

Solution scheme

The invention provides an automatic identification method of a hot spot area, which comprises the following steps:

under the condition that a cell to be identified with the number of users larger than a first threshold and/or the traffic larger than a second threshold appears, triggering a hot spot area identification step aiming at the cell to be identified;

the hot spot region identification step includes:

acquiring related service data of the co-sited adjacent cell and each adjacent station adjacent cell of the cell to be identified at the same time;

connecting to each adjacent station adjacent cell around the common station adjacent cell by taking the common station adjacent cell as a center original point to determine a coverage area;

calculating service density data corresponding to the coverage area according to the service data of the co-station adjacent cell, the service data of each adjacent station adjacent cell and the coverage area;

extracting effective data from the service density data of all the co-sited adjacent cells and the historical period of each adjacent station adjacent cell in the coverage area, and calculating corresponding dynamic baselines;

determining whether the coverage area belongs to a hotspot original area or not according to the dynamic baseline and preset tolerance;

and forming a hot spot area based on the hot spot original area.

For the above method, in a possible implementation manner, constructing a hotspot region based on the hotspot original region includes:

and combining the hot spot original areas with adjacent boundaries to form the hot spot area under the condition that a plurality of hot spot original areas exist.

For the above method, in a possible implementation manner, extracting valid data from the traffic density data of the history period of all the co-sited neighboring cells and each neighboring-sited neighboring cell in the coverage area, and calculating a corresponding dynamic baseline includes:

extracting the service density data of the historical period of the number of users and/or the traffic in d days from the co-sited adjacent cell and each adjacent station adjacent cell in the coverage area;

according to the percentage a% of preset effective values, d x a% effective data are selected from the extracted service density data of the historical period, wherein the effective data are d x a% data with the minimum variance in the extracted service density data of the historical period;

and calculating the mean value E and the variance sigma of the selected d × a% effective data, and calculating the dynamic base line M by using the formula M ═ E + sigma.

For the above method, in a possible implementation manner, determining whether the coverage area belongs to a hotspot original area according to the dynamic baseline and a preset tolerance includes:

by the formula M (1+ r)_n) To calculate the n-th level of traffic fluctuation threshold, where r_nThe tolerance with the service fluctuation level of n levels is adopted, and n is a positive integer;

and comparing the fluctuation condition of the service density data of the historical period of the coverage area with a service fluctuation threshold, and determining whether the coverage area belongs to a hotspot original area.

For the above method, in a possible implementation manner, the method further includes:

and rendering by adopting corresponding colors on the GIS map according to the service fluctuation level of the coverage area.

The invention also provides an automatic identification method of the hotspot area, which comprises the following steps:

triggering a hot spot area identification step aiming at a cell to be identified according to a preset cell service quality index degradation threshold;

the hot spot region identification step includes:

searching each homogeneous degraded cell according to the condition that the same service quality index of the co-sited adjacent cell of the cell to be identified and each adjacent-sited adjacent cell around the co-sited adjacent cell are degraded;

and triggering a hot spot area determining process according to the ratio of the number of the searched homogeneous deteriorated cells to the number of the associated adjacent cells analyzed in the hot spot identification process, wherein the hot spot area determining process comprises the step of carrying out closed connection on the associated adjacent cells analyzed in the hot spot identification process so as to form a hot spot area.

For the above method, in a possible implementation manner, searching for each homogeneous degraded cell according to a condition that a same service quality indicator of a co-sited neighboring cell of the cell to be identified and each neighboring-sited neighboring cell around the co-sited neighboring cell are degraded includes:

under the condition that the co-sited adjacent cell of the cell to be identified and each adjacent station adjacent cell have the same service quality index degradation, searching whether the homogeneous degraded cell of the cell to be identified exists or not;

if the cells exist, executing a searching step according to the currently searched homogeneous degraded cell, wherein the searching step comprises the following steps: searching whether the co-sited adjacent cell and the adjacent station adjacent cell of the currently searched homogeneous degradation cell have the homogeneous degradation cell of the currently searched homogeneous degradation cell;

and if so, continuing to execute the searching step according to the currently searched homogeneous deteriorated cell until the homogeneous deteriorated cell of the currently searched homogeneous deteriorated cell cannot be searched.

and rendering by adopting corresponding colors on the GIS map according to the service quality degradation level of each cell in the hot spot area.

The invention also provides a device for automatically identifying the hot spot area, which comprises:

the device comprises a triggering module, a hot spot area identification module and a hot spot area identification module, wherein the triggering module is used for triggering the hot spot area identification module to identify a hot spot area of a cell to be identified under the condition that the number of users is greater than a first threshold and/or the traffic is greater than a second threshold;

the hot spot region identification module includes:

a service data acquiring unit, configured to acquire service data of the co-sited neighboring cell of the cell to be identified and service data of each neighboring-sited neighboring cell at the same time;

a coverage area determining unit, configured to use the co-sited neighboring cell as a center origin, and connect to each neighboring-site neighboring cell around the co-sited neighboring cell to determine a coverage area;

a service density calculation unit, configured to calculate service density data corresponding to the coverage area according to the service data of the co-sited neighboring cell, the service data of each neighboring-sited neighboring cell, and the coverage area;

the dynamic baseline calculation unit is used for extracting effective data from the service density data of all the co-sited adjacent cells and the historical periods of all the adjacent-sited adjacent cells in the coverage area and calculating corresponding dynamic baselines;

the hot spot area determining unit is used for determining whether the coverage area belongs to a hot spot original area according to the dynamic baseline and a preset tolerance; and forming a hot spot area based on the hot spot original area.

For the above apparatus, in a possible implementation manner, the hot spot region determining unit is further configured to, in the case that there are multiple hot spot original regions, merge hot spot original regions whose boundaries are adjacent to each other to form a hot spot region.

For the above apparatus, in a possible implementation manner, the dynamic baseline calculation unit is further configured to:

For the apparatus, in a possible implementation manner, the hot spot region determining unit is further configured to:

calculating the nth-level service fluctuation threshold by adopting a formula M (1+ rn), wherein rn is the tolerance of the service fluctuation level at n levels, and n is a positive integer;

For the above apparatus, in a possible implementation manner, the method further includes:

and the rendering module is used for rendering the GIS map by adopting corresponding colors according to the service fluctuation level of the coverage area.

the triggering module is used for triggering the hot spot area identification module to identify the hot spot area of the cell to be identified according to the preset cell service quality index degradation threshold;

the hot spot region identification module includes:

the searching unit is used for searching each homogeneous degraded cell according to the condition that the same service quality index of the co-sited adjacent cell of the cell to be identified and each adjacent-sited adjacent cell around the co-sited adjacent cell are degraded;

and the hot spot area determining unit is used for triggering a hot spot area determining process according to the ratio of the number of the searched homogeneous deteriorated cells to the number of the associated adjacent cells analyzed in the hot spot identification process, wherein the hot spot area determining triggering process comprises the step of carrying out closed connection on the associated adjacent cells analyzed in the hot spot identification process so as to form a hot spot area.

For the apparatus, in a possible implementation manner, the search unit is further configured to:

and the rendering module is used for rendering by adopting corresponding colors on the GIS map according to the service quality degradation level of each cell in the hot spot area.

Advantageous effects

Compared with the existing manual judgment method, the method can automatically identify the hot spot region, complete the automatic capture of the hot spot region by setting the hot spot automatic capture threshold, save the labor cost and have the advantages of high accuracy and high efficiency.

Furthermore, the invention can refer to the co-station adjacent cell of the cell to be identified and the historical data related to each adjacent station adjacent cell for collaborative analysis, can effectively analyze and evaluate the burst problem of the hot spot region through the periodic data, and avoids the time delay of real-time data acquisition.

Furthermore, the invention collects the bottom data step by step to form the associated analysis data of different levels, thereby reducing the computing power and the requirement for outputting the final result, and providing the contrast condition according to different data levels.

Furthermore, the invention extracts effective data by analyzing the history data related to the co-sited adjacent cell and each adjacent station adjacent cell of the cell to be identified, and calculates the corresponding dynamic baseline without real-time acquisition and real-time starting calculation, thereby improving the identification efficiency and the calculation requirement.

Other features and aspects of the present invention will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the invention and, together with the description, serve to explain the principles of the invention.

Fig. 1 illustrates a flowchart of a method for automatically identifying a hot spot region according to an embodiment of the present invention;

FIG. 2 illustrates a flow chart of a method for automatically identifying hot spot areas according to another embodiment of the present invention;

FIG. 3 illustrates a flow chart of a method for automatic identification of hot spot areas according to another embodiment of the invention;

FIG. 4 illustrates a flow chart of a method for automatic identification of hot spot areas according to another embodiment of the invention;

fig. 5 is a schematic diagram illustrating a coverage area calculation manner in the hot spot region automatic identification method according to another embodiment of the present invention;

fig. 6 is a schematic diagram illustrating a rendering effect in a hot spot region automatic identification method according to another embodiment of the present invention;

fig. 7 is a schematic structural diagram illustrating an automatic hot spot area recognition apparatus according to an embodiment of the present invention;

fig. 8 is a schematic structural diagram illustrating an automatic hot spot area recognition apparatus according to another embodiment of the present invention.

Detailed Description

Various exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In some instances, methods, procedures, components, and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present invention.

The embodiment of the invention respectively determines the measurement standards from two aspects of important regional quality assurance effect and network performance management process, monitors the fluctuation condition of the cell service plane indexes (such as flow, user number and the like), combines the index values (such as quality deterioration) of the signaling control plane, reasonably sets province, city, district and county and important assurance hotspot triggering rules by using big data analysis, realizes important regional quality monitoring analysis, and timely grasps and ensures the service quality and customer perception of important regions.

First, 3 fixed scenes such as province, city, and prefecture, and all the cells belonging to the important security scene area, for example, can be regarded as important areas in a region.

Then, hotspot identification is performed based on the basic data. By way of example, the set of signaling-based metrics may include: user number, traffic volume, control plane index, signaling plane index, etc. 4 types. The method relates to the links of web browsing, video service, instant messaging, application downloading, network attachment, bearer establishment, TAU updating, domain name inquiry, connection establishment and the like. The set of metrics may all take the spatial dimension of the radio cell with 5 minutes as the granularity of the time statistic. For example, the types and corresponding meanings of the indexes can be referred to in table 1, but table 1 is only an example, and some or all indexes in table 1 may be used as basic data for hot spot identification, or other indexes similar to table 1 may be used as basic data for hot spot identification.

TABLE 1

The hot spot identification (or hot spot service identification) is to find out the service with the highest usage amount and the highest increment amount in the area or with obvious change of a service model according to a certain calculation rule by mainly adopting a threshold value, a fluctuation threshold and a transverse comparison method, and to grasp the condition that a user in the area accesses the service in time. The process of automatic identification of hot spots is described below in several embodiments.

Example 1

Fig. 1 is a flowchart illustrating an automatic hot spot area identification method according to an embodiment of the present invention. As shown in fig. 1, the method for automatically identifying a hot spot region may mainly include:

step 101, under the condition that a cell to be identified with the number of users larger than a first threshold and/or the traffic larger than a second threshold appears, step 102 is triggered. For example, each cell of the important area may be monitored, and when a user density and/or a traffic density of a certain cell of the important area is found to be suddenly changed, the certain cell may be regarded as a cell to be identified.

Step 102, performing hotspot area identification on the cell to be identified, which may specifically include:

step 1021, acquiring relevant service data of the co-sited adjacent cell of the cell to be identified and each adjacent station adjacent cell at the same time; wherein, the co-station adjacent cell of a cell is other cells in the coverage of the base station to which the cell belongs; the neighbor cells of one cell are all cells within the coverage of the base station directly adjacent to the home base station of the cell.

Step 1022, connecting to each neighboring station neighboring cell around the co-sited neighboring cell with the co-sited neighboring cell as a center origin to determine a coverage area;

step 1023, according to the service data of the co-station adjacent cell, the service data of each adjacent station adjacent cell and the coverage area, calculating service density data corresponding to the coverage area;

step 1024, extracting effective data from the service density data of all the co-sited neighboring cells and the history period of each neighboring-sited neighboring cell in the coverage area, and calculating corresponding dynamic baselines;

and 1025, determining whether the coverage area belongs to a hot spot original area or not according to the dynamic baseline and a preset tolerance.

In addition, in step 1026, a hotspot region is formed based on the hotspot original region. Specifically, if there are multiple hot spot original areas, the hot spot original areas adjacent to the boundary may be merged to form a hot spot area, so as to form a complete hot spot area identification result.

For example, step 1024 may specifically include:

extracting service density data of a historical period of the number of users and/or the service volume within d days from the co-station adjacent region and each adjacent station adjacent region; the number of days d can be adjusted to 7, 30 and the like according to the week and month cycle requirements, the maximum number can be set to 365, the default number can be selected to be the optimal value of 30, the number can be flexibly set according to specific application requirements, and the specific numerical value of d is not limited in the invention.

Then, in step 1025, the formula M × (1+ r) may be employed_n) To calculate the n-th level of traffic fluctuation threshold, where r_nThe tolerance with the service fluctuation level of n levels is shown, and n is a positive integer. And then comparing the fluctuation condition of the service density data of the historical period of the coverage area with a service fluctuation threshold to determine whether the coverage area belongs to the hotspot original area.

Finally, in step 103, a corresponding color may be used to render on a GIS (Geographic Information System) map according to the service fluctuation level of the coverage area. See fig. 6.

Example 2

Fig. 2 is a flowchart illustrating a method for automatically identifying a hot spot region according to another embodiment of the present invention. As shown in fig. 2, the method for automatically identifying a hot spot region mainly performs automatic identification of the hot spot region based on abrupt changes in user density and service density, and specifically may include:

step 201, when the number of users and/or the real-time data index of the service volume in a certain cell is larger than a certain preset static threshold, triggering hotspot area analysis. For example, each cell of the important area is monitored, and the user density and/or the traffic density of a certain cell of the important area are found to be suddenly changed.

Step 202, extracting the service data when the co-sited neighboring cell and the neighboring cell of the cell are the same.

And 203, connecting adjacent station neighboring cells around the common-station neighboring cell by taking the common-station neighboring cell as a center, and then sequentially connecting the adjacent station neighboring cells to form a coverage area with a closed space dimension.

Step 204, traffic density is the total amount of traffic/coverage area. For example, as shown in fig. 5, the coverage area is divided into a plurality of triangles, and the triangles are respectively calculated and combined.

Step 205, extracting the number of users in the cell or the data of the history period of 30 days of each service volume. The 30 days is only an example, and the selection can be flexibly performed according to the actual application scenario.

And step 206, selecting 30 a% of the data as effective data to calculate a dynamic baseline (in order to eliminate abnormal data therein) according to the percentage a% of the preset effective values.

Specifically, the effective data is the number of 30 × a% in which the variance is the smallest. The mean E and variance sigma of the selected 30 a% of the available data can be calculated, and the dynamic baseline M ═ E + sigma.

Step 207, after the dynamic baseline is calculated, the tolerances r1, r2, r3 and r4 need to be set, and finally a four-level fluctuation threshold is formed to distinguish the fluctuation levels. The 4 tolerances in table 2 are only an example, and can be flexibly set according to the actual application scenario.

TABLE 2

Direction of index fluctuation	First stage	Second stage	Three-stage	Four stages
					Upward fluctuation index	M*(1+r1)	M*(1+r2)	M*(1+r3)	M*(1+r4)

And step 208, if the service fluctuation level of the service density data of the history period of the coverage area is within the fluctuation threshold range, determining that the coverage area is the hot spot original area.

And 209, combining the hot spot original areas adjacent to the boundary to form complete area identification.

Example 3

Fig. 3 is a flowchart illustrating a method for automatically identifying a hot spot region according to another embodiment of the present invention. As shown in fig. 3, the method for automatically identifying a hot spot region may mainly include:

step 301, triggering step 302 according to a preset cell service quality index degradation threshold. Specifically, a service quality index degradation threshold of a cell may be preset, and as can be seen from table 1, there are various service quality indexes, including various KQIs, and also including indexes such as connection establishment, domain name query, network attachment, bearer establishment, and TAU, and each service quality index may set a corresponding degradation threshold. For example, when a certain service quality indicator of a certain cell in an important area is suddenly degraded, and the degraded service quality indicator is smaller than a degradation threshold, the step of identifying the hot spot area may be triggered.

Step 302, performing hotspot area identification on the cell to be identified, which may specifically include:

step 3021, searching for each homogeneous degraded cell according to the condition that the same service quality index of the co-sited neighboring cell of the cell to be identified and each neighboring-sited neighboring cell around the co-sited neighboring cell are degraded; the homogeneous degraded cell refers to a cell in which the degradation occurs with one or more service quality indicators of the cell to be identified. For example, referring to table 1, assuming that the HTTP single response success rates of both cell a and cell B are reduced by 10% or both exceed the service quality indicator degradation threshold, it may be considered that cell a is a homogeneous degraded cell of cell B, or cell B is a homogeneous degraded cell of cell a.

Step 3022, triggering a hot spot area determination process according to the ratio of the number of the searched homogeneous deteriorated cells to the number of the associated neighboring cells analyzed in the hot spot identification process, where the hot spot area determination triggering process includes performing closed connection on the associated neighboring cells analyzed in the hot spot identification process to form a hot spot area.

Wherein, step 3021 may include:

step 3021a, searching whether a homogeneous degraded cell of the cell to be identified exists or not under the condition that the co-sited neighboring cell of the cell to be identified and each neighboring-sited neighboring cell have the same service quality index degradation; if so, step 3021b is performed.

Step 3021b, performing a searching step 3021c according to the currently searched homogeneous deteriorated cell. In the searching step 3021c, it is searched whether the co-sited neighboring cell and the neighboring-sited neighboring cell of the currently searched homogeneous deteriorated cell have the homogeneous deteriorated cell of the currently searched homogeneous deteriorated cell.

If so, the searching step 3021c is continuously performed according to the currently searched homogeneous deteriorated cell until the homogeneous deteriorated cell of the currently searched homogeneous deteriorated cell is not searched, and the number of the searched homogeneous deteriorated cells may be calculated, and then step 3022 is performed.

Finally, in step 303, rendering may be performed on the GIS map by using corresponding colors according to the service quality degradation level of each cell in the hot spot region, see fig. 6.

Example 4

Fig. 4 is a flowchart illustrating a method for automatically identifying a hot spot region according to another embodiment of the present invention. As shown in fig. 4, the method for automatically identifying a hotspot region mainly performs automatic identification of a hotspot region based on network degradation, and specifically may include:

step 401, presetting a service quality index degradation threshold of a certain cell.

Step 402, triggering hot spot area analysis based on the service index degradation of the cell.

Step 403, analyzing whether the co-sited neighboring cell and the neighboring cell of the cell have the same service quality index degradation condition, if so, executing step 404.

And step 404, analyzing whether the co-sited adjacent cell and the adjacent sited adjacent cell of the homogeneous degraded cell have the homogeneous degraded cell according to the searched homogeneous degraded cell. If so, continue to step 404 to find a homogeneous degraded cell, otherwise stop the analysis. In the process of analyzing and searching for homogeneous degraded cells, the number of searched homogeneous degraded cells may be counted.

Step 405, triggering hot spot area identification according to the proportion of the number of homogeneous degraded cells relative to the number of associated adjacent cells in a one-time complete hot spot area analysis process, and performing closed connection on all analyzed cells (namely the associated adjacent cells) in the analysis process to automatically form a hot spot area.

As shown in fig. 6, hot spot area ranges are carried on the basis of a GIS map, and each hot spot area range can perform color rendering early warning according to the service fluctuation level or the service quality degradation level in the above embodiment. In the rendering process, an interpolation analysis algorithm can be adopted to form a gradual diffusion effect that the rendering color is gradually changed from dark to light by the service fluctuation source cell or the service degradation source cell.

The embodiment of the invention can automatically identify the hot spot area, and has the advantages of high accuracy and high efficiency compared with the existing manual judgment method. By setting the automatic hot spot capturing threshold and enabling the threshold preset value to be effective for a long time, the automatic hot spot region capturing is completed, and the labor cost is greatly saved.

Compared with the original acquisition capacity based on the network management side, the acquisition capacity based on the signaling data is greatly improved in both index real-time performance and service subdivision angle. By means of the hot spot automatic identification algorithm, monitoring of hot spot areas is free of dead corners, and the monitoring effectiveness of the hot spot areas can reach 100% at most.

The hot spot area automatic identification method provided by the embodiment of the invention re-interprets the definition of the hot spot area. The monitoring range is not repeatedly collected through manual collection, historical data analysis is performed to frame the monitoring range in advance, the hot spot area in the scheme is completely and dynamically generated, the hot spot coverage area is synchronously updated according to real-time index change, and the purpose of monitoring objects is achieved.

Example 5

Fig. 7 is a schematic structural diagram illustrating an automatic hot spot area recognition apparatus according to an embodiment of the present invention. As shown in fig. 7, the automatic identification apparatus for hot spot areas may specifically include:

the triggering module 51 is configured to trigger the hot spot area identification module to perform hot spot area identification on the cell to be identified when the cell to be identified in which the number of users is greater than a first threshold and/or the traffic is greater than a second threshold appears;

the hot spot area identifying module 53 includes:

a service data obtaining unit 531, configured to obtain service data of the co-sited neighboring cell and each neighboring-sited neighboring cell of the cell to be identified at the same time;

a coverage area determining unit 532, configured to use the co-sited neighboring cell as a center origin, and connect to each neighboring-site neighboring cell around the co-sited neighboring cell to determine a coverage area;

a service density calculating unit 533, connected to the service data obtaining unit 531 and the coverage area determining unit 532, respectively, for calculating service density data corresponding to the coverage area according to the service data of the co-sited neighboring cell, the service data of each neighboring-sited neighboring cell, and the coverage area;

the dynamic baseline calculation unit 534 is connected with the traffic density calculation unit and is used for extracting effective data from the traffic density data of the history periods of all the co-sited neighboring cells and all the neighboring-sited neighboring cells in the coverage area and calculating corresponding dynamic baselines;

a hot spot region determining unit 535, connected to the dynamic baseline calculating unit, for determining whether the coverage area belongs to a hot spot original region according to the dynamic baseline and a preset tolerance; and forming a hot spot area based on the hot spot original area.

In a possible implementation manner, the hot spot region determining unit 535 is further configured to, in a case that there are multiple hot spot original regions, merge the hot spot original regions whose boundaries are adjacent to each other to form a hot spot region.

In one possible implementation, the dynamic baseline calculation unit 534 is further configured to:

In a possible implementation manner, the hot spot region determining unit 535 is further configured to:

In a possible implementation manner, the device for automatically identifying a hotspot region further includes:

and the rendering module 55 is connected with the hot spot region identification module 53, and is configured to render the GIS map in a corresponding color according to the service fluctuation level to which the coverage area belongs.

The hot spot area automatic identification device of the present embodiment can execute the hot spot area automatic identification method of embodiments 1 and 2. The same contents in this embodiment as those in the above embodiments have the same meanings, and are not described again.

Example 6

Fig. 8 is a schematic structural diagram illustrating an automatic hot spot area recognition apparatus according to another embodiment of the present invention. As shown in fig. 8, the automatic hot spot area identification device mainly performs automatic identification of a hot spot area based on network degradation, and the automatic hot spot area identification device may specifically include:

the triggering module 61 is configured to trigger the hot spot area identification module to identify a hot spot area of the cell to be identified according to a preset cell service quality index degradation threshold;

the hot spot area identifying module 63 includes:

a searching unit 631, configured to search each homogeneous degraded cell according to a condition that the same service quality indicator of the co-sited neighboring cell of the cell to be identified and each neighboring-sited neighboring cell around the co-sited neighboring cell are degraded;

a hot spot area determining unit 633, configured to trigger a hot spot area determining process according to a ratio of the number of the searched homogeneous deteriorated cells to the associated neighboring cells analyzed in the hot spot identification process, where the hot spot area determining process includes performing closed connection on the associated neighboring cells analyzed in the hot spot identification process to form a hot spot area.

In one possible implementation, the lookup unit 631 is further configured to:

In a possible implementation manner, the device for automatically identifying a hotspot region further includes: and the rendering module 65 is configured to render the GIS map in corresponding colors according to the service quality degradation level of each cell in the hot spot region.

The hot spot area automatic identification device of the present embodiment can execute the hot spot area automatic identification method of embodiments 3 and 4. The same contents in this embodiment as those in the above embodiments have the same meanings, and are not described again.

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims

1. A hot spot area automatic identification method is characterized by comprising the following steps:

the hot spot region identification step includes:

searching each homogeneous degradation cell according to the degradation condition of the same service quality index of the co-sited adjacent cell of the cell to be identified and each adjacent-sited adjacent cell around the co-sited adjacent cell, wherein the homogeneous degradation cell comprises at least one cell with the service quality index of the cell to be identified having the degradation condition at the same time;

triggering a hot spot area determining process according to the ratio of the number of the searched homogeneous deteriorated cells to the number of the analyzed associated adjacent cells in the hot spot identifying process, wherein the hot spot area determining triggering process comprises the steps of carrying out closed connection on the analyzed associated adjacent cells in the hot spot identifying process to form a hot spot area,

searching each homogeneous degradation cell according to the condition that the same service quality index of the co-sited neighbor cell of the cell to be identified and each neighbor cell of the neighboring cell of the co-sited neighbor cell is degraded, wherein the searching comprises the following steps:

2. The method of claim 1, further comprising:

3. An automatic identification device for hot spot areas, comprising:

the hot spot region identification module includes:

a searching unit, configured to search each homogeneous degraded cell according to a condition that a same service quality indicator of a co-sited neighboring cell of the cell to be identified and each neighboring-sited neighboring cell around the co-sited neighboring cell are degraded, where the homogeneous degraded cell includes at least one cell in which the service quality indicator of the cell to be identified and the service quality indicator of the cell to be identified are degraded at the same time;

a hot spot area determining unit, configured to trigger a hot spot area determining process according to a ratio of the number of the searched homogeneous deteriorated cells to the number of the associated neighboring cells analyzed in the hot spot identification process, where the hot spot area determining process includes performing closed connection on the associated neighboring cells analyzed in the hot spot identification process to form a hot spot area,

wherein the lookup unit is further configured to:

4. The apparatus of claim 3, further comprising: