CN113938954B - Load balancing optimization method, device and storage medium - Google Patents

Abstract

The present disclosure provides a load balancing optimization method, device, and storage medium. The method includes: obtaining network performance data and user perception data of an indoor distribution system, and then determining the load balancing optimization condition according to the network performance data and user perception data. The first target indoor sub-district; then use the indoor antenna coverage area corresponding to the first target indoor sub-district as a unit, quantify the first target indoor sub-district to obtain the target fragmentation; finally perform load balancing optimization for the target fragmentation, In order to achieve the load balance of the indoor distribution system. The present disclosure diversifies the conditions for load balancing determination. In addition, by dividing the antenna into small slices, each slice is optimized for load balancing. Compared with balancing parameter optimization and adding new equipment, it can solve the problem of limited optimization of equalization parameters and limited equipment deployment space. Avoid wasting human and/or material resources.

Description

Load balance optimization method, device and storage medium

technical field

The present disclosure relates to the technical field of communications, and in particular, to a load balancing optimization method, device and storage medium.

Background technique

In recent years, with the rapid development of wireless communication technology, the number of 4G and 5G users is also increasing rapidly. The complexity of network structure and diversification of services make the improvement of user perception more and more complicated. Among them, there are more and more complex indoor scenes such as high-rise buildings, subway lines, large venues, and airport high-speed rail stations. It is very difficult to balance the judgment and optimization, but there is also a big difference in the load among the various cells in these indoor scenes, which brings both opportunities and challenges.

At present, the traditional load balancing judgment and optimization method is to judge the load balancing only through the network performance data analysis of the network management platform, and then carry out the load balancing optimization by manually adding new equipment on site or adjusting the balancing parameters. In this solution, the conditions for judging load balancing are single; in addition, for indoor scenarios, the deployment space of new equipment is very restrictive, and the optimization of balancing parameters has high limitations. These optimization methods consume a lot of manpower and material resources.

Contents of the invention

In order to solve the above problems, the present disclosure provides a load balancing optimization method. It not only saves manpower and material resources, but also enables more accurate load balancing optimization.

In a first aspect, the present disclosure provides a load balancing optimization method, including:

Obtain network performance data and user perception data of the indoor distribution system, where the indoor distribution system includes at least two indoor sub-districts;

According to the network performance data and user perception data, determine the first target indoor sub-district with load balancing optimization conditions, where the load balancing optimization condition is an indoor sub-district with high load characteristics and poor user perception characteristics among at least two indoor sub-districts;

Taking the indoor antenna coverage area corresponding to the first target indoor sub-cell as a unit, quantifying the first target indoor sub-cell to obtain the target slice;

Load balancing optimization is carried out for target shards to achieve load balancing of indoor distribution systems.

In a possible implementation manner, performing load balancing optimization for the target slice to achieve load balancing of the indoor distribution system includes: determining the user terminal in the target slice as a sample point set; selecting a preset from the sample point set Threshold target sample points, redirect the target sample points to the second target indoor sub-district, the second target indoor sub-district is an indoor sub-district that does not have high load characteristics among at least two indoor sub-districts; after optimizing the allocation, re- Obtain the network performance data of the indoor distribution system, and determine whether the load balance of the indoor distribution system has been achieved according to the reacquired network performance data; if the load balance of the indoor distribution system has not been reached, reselect the preset threshold target from the sample point set The sample point is a step of redirecting the target sample point to the second target indoor sub-district until the load balance of the indoor distribution system is achieved.

In a possible implementation manner, determining whether to achieve load balancing of the indoor distribution system according to the reacquired network performance data includes at least one of the following:

If according to the re-acquired network performance data, it is determined that the first target indoor sub-district has high-load characteristics, and the second target indoor sub-district does not have high-load characteristics, then it is determined that the load balance of the indoor distribution system has not been reached; if according to the re-acquired network Performance data, determine that the first target indoor sub-district does not have high load characteristics, and the second target indoor sub-district has high load characteristics, then move the target sample point back to the first target indoor sub-district, and determine that the load of the indoor distribution system has not been reached Balanced; if it is determined that the first target indoor sub-district does not have high-load characteristics and the second target indoor sub-district does not have high-load characteristics according to the re-acquired network performance data, it is determined that the load balance of the indoor distribution system is reached; If it is determined that the first target indoor sub-district has high load characteristics and the second target indoor sub-district has high load characteristics, it is determined that the load balance of the indoor distribution system has been achieved.

In a possible implementation manner, selecting a preset threshold target sample points from the sample point set, and redirecting the target sample points to the second target room sub-district includes: after each preset time window, from the sample point A preset threshold target sample points are selected from the set, and the first target sample point is redirected to the second target room sub-district.

In a possible implementation manner, according to network performance data and user perception data, determining a first target indoor sub-district with load balancing optimization conditions includes: determining a first indoor sub-district with high load characteristics according to network performance data; According to the user perception data, determine the second indoor sub-district with poor user perception characteristics; according to the first indoor sub-district and the second indoor sub-district, determine the first target indoor sub-district with load balancing optimization conditions.

In a possible implementation manner, the network performance data includes cell downlink traffic and cell downlink PRB resource utilization, and according to the network performance data, determining the first indoor sub-cell with high load characteristics includes: determining that the cell downlink traffic is greater than or equal to the cell The downlink traffic threshold, and the indoor sub-cell whose downlink PRB resource utilization rate of the cell is greater than or equal to the cell downlink PRB resource utilization rate threshold is the first indoor sub-cell with high load characteristics.

In a possible implementation, the network performance data includes the downlink traffic of the cell and the number of RRC connected users in the cell, and according to the network performance data, determining the first indoor sub-district with high load characteristics includes: determining that the downlink traffic of the cell is greater than or equal to the downlink traffic of the cell The traffic threshold, and the indoor sub-cell with the number of RRC connected users in the cell greater than or equal to the threshold of the number of RRC connected users in the cell is the first indoor sub-cell with high load characteristics.

In a possible implementation, the user perception data includes cell channel quality, cell freezing times, cell delay and cell average rate, and according to the user perception data, determining the second indoor sub-cell with poor user perception characteristics includes: determining The cell channel quality is less than or equal to the cell channel quality threshold, the number of cell freeze times is greater than or equal to the cell freeze frequency threshold, the cell delay is greater than or equal to the cell delay threshold, and the cell average rate is less than or equal to the cell average rate threshold. A sub-district of the second room with poor user perception.

In a possible implementation manner, the load balancing optimization condition further includes: there is a second target indoor sub-district in the neighborhood of the first target indoor sub-district, and the second target indoor sub-district does not have high Indoor subdivision of load characteristics.

In a possible implementation manner, the method further includes: if there is no second target room subdivision, ending the procedure.

In a second aspect, the present disclosure provides a load balancing optimization device, including:

An acquisition module, configured to acquire network performance data and user perception data of an indoor distribution system, where the indoor distribution system includes at least two indoor sub-districts;

A determining module, configured to determine a first target indoor sub-district with load balancing optimization conditions based on network performance data and user perception data, where the load balancing optimization condition is at least two indoor sub-districts with high load characteristics and poor user perception characteristics Indoor district;

A quantization module, configured to quantify the first target indoor sub-cell by taking the indoor antenna coverage area corresponding to the first target indoor sub-cell as a unit, to obtain the target slice;

The optimization module is used to perform load balancing optimization on the target slice, so as to achieve the load balancing of the indoor distribution system.

In a possible implementation, the optimization module is specifically configured to: determine the user terminal in the target slice as a sample point set; select a preset threshold target sample point from the sample point set, and redirect the target sample point to the first Two target indoor sub-districts, the second target indoor sub-district is an indoor sub-district that does not have high load characteristics among at least two indoor sub-districts; after optimizing the allocation, reacquire the network performance data of the indoor distribution system, and based The network performance data determines whether the load balance of the indoor distribution system is reached; if the load balance of the indoor distribution system is not reached, the preset threshold target sample points are reselected from the sample point set, and the target sample points are redirected to the second target room The step of subdividing the cell until the load balance of the indoor distribution system is achieved.

In a possible implementation manner, the determination module is specifically used for at least one of the following:

If according to the re-acquired network performance data, it is determined that the first target indoor sub-district has high-load characteristics, and the second target indoor sub-district does not have high-load characteristics, then it is determined that the load balance of the indoor distribution system has not been reached; if according to the re-acquired network Performance data, determine that the first target indoor sub-district does not have high load characteristics, and the second target indoor sub-district has high load characteristics, then move the target sample point back to the first target indoor sub-district, and determine that the load of the indoor distribution system has not been reached Balanced; if it is determined that the first target indoor sub-district does not have high-load characteristics and the second target indoor sub-district does not have high-load characteristics according to the re-acquired network performance data, it is determined that the load balance of the indoor distribution system is reached; If it is determined that the first target indoor sub-district has high load characteristics and the second target indoor sub-district has high load characteristics, it is determined that the load balance of the indoor distribution system has been achieved.

In a possible implementation, the optimization module is specifically configured to: select a preset threshold target sample points from the sample point set after each preset time window, and redirect the target sample points to the second target room sub-district .

In a possible implementation manner, the determining module is specifically configured to: determine the first indoor sub-district with high load characteristics according to the network performance data; determine the second indoor sub-district with poor user perception characteristics according to the user perception data; The first room sub-district and the second room sub-district determine the first target room sub-district with load balancing optimization conditions.

In a possible implementation manner, the determining module is specifically configured to: determine that the cell downlink traffic is greater than or equal to the cell downlink traffic threshold, and the cell downlink PRB resource utilization rate is greater than or equal to the cell downlink PRB resource utilization threshold. The first compartment of the load characteristic is subdivided into plots.

In a possible implementation, the determination module is specifically used to: determine that the downlink flow of the cell is greater than or equal to the threshold of the downlink flow of the cell, and the number of RRC connected users of the cell is greater than or equal to the threshold of the number of RRC connected users of the cell as an indoor cell with high load characteristics The first room is divided into districts.

In a possible implementation manner, the determining module is specifically used to: determine that the cell channel quality is less than or equal to the cell channel quality threshold, the number of cell freeze times is greater than or equal to the cell freeze frequency threshold, and the cell delay is greater than or equal to the cell delay threshold, The indoor cell whose average cell rate is less than or equal to the cell average rate threshold is the second indoor cell with poor user perception.

In a possible implementation manner, the load balancing optimization condition further includes: there is a second target indoor sub-district in the neighborhood of the first target indoor sub-district, and the second target indoor sub-district does not have high Indoor subdivision of load characteristics.

In a possible implementation manner, the determining module is further configured to: if there is no second target indoor sub-district, end the procedure.

In a third aspect, the present disclosure provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

the memory stores computer-executable instructions;

The processor executes the computer-executed instructions stored in the memory, so as to implement the load balancing optimization method of the first aspect.

In a fourth aspect, the present disclosure discloses a computer-readable storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are used to implement the load balancing optimization method in the first aspect when executed by a processor.

In a fifth aspect, the present disclosure provides a computer program product, including a computer program. When the computer program is executed by a processor, the load balancing optimization method in the first aspect is implemented.

The disclosure provides a load balancing optimization method, device, and storage medium. By acquiring network performance data and user perception data of the indoor distribution system, the indoor distribution system includes at least two indoor sub-districts; and then according to the network performance data and user perception data, Determine the first target indoor sub-district with load balancing optimization conditions, wherein the load balancing optimization condition is an indoor sub-district with high load characteristics and poor user perception characteristics among at least two indoor sub-districts; then use the first target indoor sub-district The corresponding indoor antenna coverage area is taken as the unit, and the first target indoor sub-district is quantified to obtain the target fragmentation; finally, load balancing optimization is performed on the target fragmentation to achieve load balance of the indoor distribution system. The disclosure judges whether the indoor sub-district satisfies the load balancing condition according to the network performance data and the user perception data, so that the conditions for load balancing judgment are diversified. In addition, by dividing the indoor antenna into small slices, each slice area is optimized for load balance. Compared with balance parameter optimization and new equipment, it can solve the problem of limited optimization balance parameters and limited equipment deployment space. problems and avoid waste of human and/or material resources.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description These are some embodiments of the present disclosure. Those skilled in the art can also obtain other drawings based on these drawings without any creative effort.

FIG. 1 is a schematic diagram of an indoor distribution system provided by an embodiment of the present disclosure;

FIG. 2 is a flowchart of a load balancing optimization method provided by an embodiment of the present disclosure;

FIG. 3 is a flow chart of load balancing optimization for target fragments provided by another embodiment of the present disclosure;

FIG. 4 is a flow chart of load balancing optimization for target fragmentation provided by another embodiment of the present disclosure;

FIG. 5 is a schematic structural diagram of a load balancing optimization device provided by an embodiment of the present disclosure;

Fig. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments It is a part of the embodiments of the present disclosure, but not all of them. Based on the embodiments in the present disclosure, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present disclosure.

First, some technical terms involved in this disclosure are explained:

Base Station: that is, a public mobile communication base station is a form of a radio station, which refers to a radio transceiver station that transmits information between a mobile communication switching center and a mobile phone terminal in a certain radio coverage area. .

Cell ID: the identification code of the base station cell to which the serving cell belongs.

Longitude: The geographic longitude of the community.

Latitude: The geographic latitude of the community.

Delay: refers to the time required for a packet or packet to be transmitted from one end of a network to the other, and is a performance indicator for network operators to characterize the delay level.

Speed: It is the speed of network transmission, that is, the speed is the size of the speed or equivalent to the rate of change of file transmission, and it is a performance indicator for network operators to characterize the speed of transmission.

Channel Quality Indicator (Chartered Quality Institute, CQI for short): It is a measurement standard for communication quality of a wireless channel, and is a performance index for network operators to characterize channel quality.

Indoor distribution system: use the indoor antenna distribution system to evenly distribute the signal of the base station in every corner of the room, so as to ensure ideal signal coverage in the indoor area.

This disclosure provides a load balancing optimization method, device, and storage medium in order to solve the problems in the prior art that the conditions for load balancing judgment are single, and load balancing optimization takes a long time, high labor costs, low efficiency, and poor feasibility. Based on the two dimensions of performance data and user perception data, the judgment of load balancing optimization is made to diversify the conditions of load balancing judgment; and the load balancing optimization is carried out through the redistribution of coverage areas, which improves the efficiency of load balancing optimization, improves resource utilization, and reduces The cost and the feasibility of improving load balancing optimization can also improve user perception.

Fig. 1 is a schematic diagram of an indoor distribution system provided by an embodiment of the present disclosure. As shown in FIG. 1 , the indoor distribution system includes a base station 100 , wherein the indoor cells covered by the base station 100 include an indoor cell 101 , an indoor cell 102 and an indoor cell 103 . Wherein, the indoor cell 101 includes a terminal device 4 , and the indoor cell 102 includes a terminal device 1 , a terminal device 2 and a terminal device 3 .

In FIG. 1 , the indoor cell 102 has three terminal devices, and the network performance data and user perception data of the indoor cell 102 can be obtained from the base station 100 . According to the network performance data and user perception data of the indoor sub-cell 102, it can be judged whether the indoor sub-cell 102 is in a state of high load and poor perception performance. If the indoor sub-cell 102 is in a state of high load and poor perception performance, that is, has high load characteristics and poor user perception characteristics, then the indoor sub-cell 102 has load balancing optimization conditions.

Further, since the indoor sub-district 101 contains one terminal device, the load is less, and the probability of being in a low-load state is relatively high, therefore, the terminal equipment in the indoor sub-district 102 can be redirected to the indoor sub-district 101, For example, the terminal device 1 is redirected to the indoor cell 101 .

It should be clarified that whether the indoor sub-district has load balancing optimization conditions does not simply depend on the number of terminal devices contained in it, but is also related to the services performed by the terminal devices. Here is an example for the convenience of understanding, but does not constitute a Limitations on this Disclosure.

Since the present disclosure is aimed at optimizing the load balancing of indoor scenes, FIG. 1 uses indoor sub-districts as an example for illustration.

After the terminal equipment in the indoor cell 102 is redirected to the indoor cell 101, the load on the indoor cell 102 is reduced. In this case, if the indoor sub-cell 102 is in a low-load state and the indoor sub-cell 101 is still in a low-load state, it can be considered that the load balancing optimization is completed.

It should be noted that Fig. 1 is only a schematic diagram of an application scenario provided by the embodiment of the present disclosure, and the embodiment of the present disclosure does not limit the devices included in Fig. 1, nor does it limit the number of devices and the positions between devices in Fig. 1 The relationship is limited. In addition, the terminal device may be a mobile phone, or a computer or other communication device, which is not limited in the present disclosure.

Next, the load balancing optimization method is introduced through specific embodiments.

FIG. 2 is a flowchart of a load balancing optimization method provided by an embodiment of the present disclosure. As shown in Figure 2, the load balancing optimization method includes:

S201. Acquire network performance data and user perception data of an indoor distribution system, where the indoor distribution system includes at least two indoor sub-districts.

Specifically, the indoor distribution system may include a base station, as shown in FIG. 1 . Further, the base station may be an indoor base station, which is not limited in the present disclosure.

For example, the network performance data and user perception data of the indoor distribution system can be obtained from the base station through the network management platform. The network data can represent the load of each indoor cell, for example, information such as cell downlink physical resource block (Physical Resource Block, PRB for short) resource utilization rate, cell downlink traffic, and the average number of users in the cell. User perception data can represent the service usage of users in each indoor cell, such as cell freeze times, cell freeze rate, cell delay, cell channel quality, and cell average rate.

In addition, engineering physical parameters of the indoor distribution system can also be obtained, and the engineering physical parameters can include the longitude, latitude, cell number, cell name, and coverage area of the indoor cell.

In some embodiments, a database may be established according to the above-mentioned network performance data, user perception data, and engineering physical parameters. This database may include, but is not limited to, the following data:

Among them, the database is represented by Q _x , {C _i } represents the set of all indoor cells contained in the network performance database; {PRB _i } represents the set of downlink PRB resource utilization of the cell; {User _i } represents the number of RRC connected users in the cell set; {Flow _i } is characterized as a cell downlink flow set; {CQI _i } is represented as a cell channel quality set; {Caton _i } is represented as a set of cell stall times; {Delay _i } is represented as a cell delay set; _i } is characterized as a set of cell average rates.

Among them, the key fields involved in the network performance data in the database are shown in Table 1:

Table 1

The key fields involved in the user perception data in the database are shown in Table 2:

Table 2

The key fields involved in the engineering physical parameters in the database are shown in Table 3:

table 3

S202. According to the network performance data and user perception data, determine the first target indoor sub-district with load balancing optimization conditions, where the load balancing optimization condition is an indoor sub-district with high load characteristics and poor user perception characteristics among at least two indoor sub-districts .

Through the analysis of network performance data and user perception data, indoor sub-districts with high load characteristics and poor user perception characteristics can be selected from multiple indoor sub-districts. It can be understood that when the network performance data and/or user perception data are different, specific implementations corresponding to determining the first target indoor sub-cell with load balancing optimization conditions may also be different. An example is given below:

As an example, in a specific implementation, determining the first target indoor sub-district with load balancing optimization conditions according to network performance data and user perception data may include: determining the first indoor sub-district with high load characteristics according to network performance data Cells: According to user perception data, determine the second indoor sub-district with poor user perception characteristics; according to the first indoor sub-district and the second indoor sub-district, determine the first target indoor sub-district with load balancing optimization conditions.

Optionally, for the above obtained first indoor sub-district and second indoor sub-district, do an "and" algorithm matching operation to screen and clean the indoor sub-districts that satisfy both the high load feature and the poor user perception feature. The method of screening and cleaning out the first target indoor sub-district with load balancing optimization conditions is as follows:

Among them, Cell _j represents the first target indoor cell with load balancing optimization conditions, Cell _x represents the indoor cell with high load characteristics, and Cell _y represents the indoor cell with poor user perception characteristics.

As an example, the first target indoor sub-district with load balancing optimization conditions can also be represented by Table 4:

Table 4

In another specific implementation, according to the network performance data and user perception data, determining the first target indoor sub-district with load balancing optimization conditions may include: according to the network performance data, determining the first indoor sub-district with high load characteristics; According to the user perception data, the indoor sub-cells with poor user perception characteristics are determined from the first indoor sub-cells as the first target indoor sub-cells.

In another specific implementation, according to the network performance data and user perception data, determining the first target indoor sub-district with load balancing optimization conditions may include: according to the user perception data, determining the second indoor sub-district with poor user perception characteristics ; According to the network performance data, from the second indoor sub-districts, determine the indoor sub-districts with high load characteristics as the first target indoor sub-districts.

S203. Taking the indoor antenna coverage area corresponding to the first target indoor sub-cell as a unit, quantify the first target indoor sub-cell to obtain target slices.

Each indoor cell includes at least one indoor antenna, and each indoor antenna is responsible for covering an area of the indoor cell. By partitioning the previously determined first target indoor cell according to the coverage area of the indoor antenna:

Taking the area covered by each indoor antenna as a basic unit, finally, the first target indoor sub-cell is divided into multiple basic units, realizing the operation of target segmentation. Therefore, it is convenient to finely manage user terminal devices under multiple basic units.

S204. Perform load balancing optimization for the target slice, so as to achieve load balancing of the indoor distribution system.

After refining the indoor sub-cell, the problem of load balancing optimization is transformed from the indoor sub-cell to the coverage area of one of the indoor antennas in the indoor sub-cell (target fragmentation). By performing load balancing optimization on multiple target slices, the load balancing optimization of the indoor distribution system is realized.

In some embodiments, the load balance optimization condition may also include: there is a second target indoor sub-cell in the neighborhood of the first target indoor sub-cell, and at least two of the second target indoor sub-cells do not have high load characteristics room division area.

Optionally, combine the engineering physical parameters with the list of cells connected to the base station to carry out algorithm matching. After algorithm matching, filter and clean out the load balancing optimization conditions (there are indoor sub-units with high load characteristics and poor user perception characteristics under the same indoor base station) There are other indoor sub-districts that do not have high-load characteristics) as the first target indoor sub-district. The method of screening and cleaning out the first target indoor sub-district Cell _m with load balancing optimization conditions is as follows:

Among them, Cell _a represents the indoor sub-district with high load characteristics and poor user perception characteristics under the same indoor base station; Cell _a+1 represents the indoor sub-district without high load characteristics under the same indoor base station; NodeB _a represents the indoor base station; a represents The number of indoor cells under the same indoor base station.

In the above formula, NodeB _a not only includes the indoor cell Cell _a with high load characteristics and poor user perception, but also has the indoor cell Cell _a+1 without high load characteristics.

The first target room sub-district can be represented by Table 5:

table 5

Optionally, the load balancing optimization method of the present disclosure may further include: if there is no second target indoor sub-district, ending the procedure.

In the embodiment of the present disclosure, by obtaining the network performance data and user perception data of the indoor distribution system, wherein the indoor distribution system includes at least two indoor sub-districts; and then according to the network performance data and user perception data, determine the optimal conditions for load balancing The first target indoor sub-district, wherein the load balance optimization condition is an indoor sub-district with high load characteristics and poor user perception characteristics in at least two indoor sub-districts; then use the indoor sub-district antenna coverage area corresponding to the first target indoor sub-district As a unit, quantify the first target room into sub-districts to obtain the target fragmentation; finally, optimize the load balancing for the target fragmentation to achieve the load balance of the indoor distribution system. The disclosure judges whether the indoor sub-district satisfies the load balancing condition according to the network performance data and the user perception data, so that the conditions for load balancing judgment are diversified. In addition, by dividing the indoor antenna into small slices, each slice area is optimized for load balance. Compared with balance parameter optimization and new equipment, it can solve the problem of limited optimization balance parameters and limited equipment deployment space. problems and avoid waste of human and/or material resources.

Based on the above-mentioned embodiments, the specific implementation of load balancing optimization for the target slice to achieve load balancing of the indoor distribution system can be represented by the process shown in FIG. 3 . As shown in Figure 3, S204 may further include:

S301. Determine user terminals in the target slice as a set of sample points.

In order to perform load balancing optimization operations, it is first necessary to quantify the number of user terminals in the target shard, and form the above user terminals into a set of sample points. Each sample point in the set corresponds to a user terminal.

S302. Select preset threshold target sample points from the sample point set, and redirect the target sample points to the second target indoor sub-district, where the second target indoor sub-district is at least two indoor sub-districts that do not have high load characteristics Indoor district.

In order to perform load balancing optimization, at least one target sample point in the sample point set needs to be redirected to other indoor sub-districts.

Exemplarily, the preset threshold is an integer greater than or equal to 1, and the preset threshold can be changed. The second target room sub-cell may be adjacent to the first target room sub-cell.

The so-called redirection refers to directing the user terminal originally belonging to a certain target slice in the first target indoor sub-cell to within the coverage of a certain indoor antenna in the second target indoor sub-cell.

Further, in some embodiments, selecting a preset threshold target sample points from the sample point set, and redirecting the target sample points to the second target room sub-district may include: after each preset time window, from the sample Select preset threshold target sample points from the point set, and redirect the target sample points to the second target room sub-district.

Setting the time window can also be understood as setting a time period T, that is, redirecting at least one target sample point to the second target indoor sub-cell after each time period T elapses. The reason for setting the time period T is that the frequency of redirection can be flexibly adjusted according to actual conditions. For example, for multiple indoor cells, if each indoor cell cannot accommodate more user terminals, it cannot perform multiple redirection operations. In this case, the value of the time period T needs to be set larger. Exemplarily, it may be 10 seconds. If there is a first target indoor sub-district and multiple second target indoor sub-districts, the time period T may be set to a smaller value at this time. Specifically, it may be 5 seconds. In addition, the loss of the redirection device also needs to be considered. If the setting time period T is too small, the loss of the redirection device will be increased, thereby reducing the service life.

As an example, the above steps are as follows:

The indoor antenna coverage area after quantization and subdivision is determined as a sample point set {Antenna _p }; one or more target sample points are selected from the sample point set {Antenna _p }, and the preset time period for determining the user adjustment strategy is T, after each time period T, all target sample points are redirected to the indoor sub-district Cell _a+1 that does not have high load characteristics under the same indoor base station in the neighborhood. (The time period T can be set to 10, 20 or 60 seconds, etc. according to site needs).

In the embodiments of the present disclosure, by introducing a time window, the frequency of redirection can be flexibly set, thereby satisfying more application scenarios. In addition, compared with not setting a time window, the service life of the redirection device is improved.

S303. After the allocation is optimized, reacquire the network performance data of the indoor distribution system, and determine whether the load balance of the indoor distribution system is achieved according to the reacquired network performance data.

After each load balancing optimization, it is necessary to re-judge the load status of the entire indoor distribution system, and then judge whether further load balancing optimization can be performed.

For example, the load status of each indoor cell at this time may be judged by acquiring network performance data.

In some implementations, performing load balancing optimization on target shards to achieve load balancing of the indoor distribution system may include at least one of the following:

If according to the re-acquired network performance data, it is determined that the first target indoor sub-district has high-load characteristics, and the second target indoor sub-district does not have high-load characteristics, then it is determined that the load balance of the indoor distribution system has not been reached; if according to the re-acquired network Performance data, determine that the first target indoor sub-district does not have high load characteristics, and the second target indoor sub-district has high load characteristics, then move the target sample point back to the first target indoor sub-district, and determine that the load of the indoor distribution system has not been reached Balanced; if it is determined that the first target indoor sub-district does not have high-load characteristics and the second target indoor sub-district does not have high-load characteristics according to the re-acquired network performance data, it is determined that the load balance of the indoor distribution system is reached; According to the network performance data, it is determined that the first target indoor sub-district has high load characteristics, and the second target indoor sub-district has high load characteristics, and then it is determined that the load balance of the indoor distribution system has been achieved.

Exemplarily, the above steps can be written as:

If satisfied:

且/>

and/>

In the formula, the indoor cells connected to the base station NodeB _a include Cell _m , Cell _a+1 , ..., including at least two indoor cells. Among them, Cell _m is an indoor community with high load characteristics, and Cell _a+1 is an indoor community without high load characteristics.

If satisfied:

且/>

and/>

Then return to the previous step to re-optimize the allocation operation of the coverage area, and replace and select a new set of target sample points from the sample point set, and re-execute the above-mentioned redirection optimization allocation operation.

In the formula, the indoor cells attached to the base station NodeB _a include Cell _m , Cell _a+1 , ..., including at least two indoor cells, where Cell _m is an indoor cell that does not have high load characteristics, and Cell _{a +1} for an indoor community with high load characteristics.

like:

且/>

Case 1:

and/>

且/>

Case 2:

and/>

If one of the above conditions is satisfied, the above-mentioned redirection optimization allocation operation is stopped. Both of the above two conditions represent that each indoor cell (and each target segment inside the indoor cell) under the base station has reached a load balance state.

In the formula, the indoor cells under the base station NodeB _a include Cell _m , Cell _a+1 , ..., including at least 2 indoor cells, where, in case 1, Cell _m belongs to the indoor cells that do not have the characteristics of high load And Cell _a+1 also belongs to the indoor sub-district without high load characteristics; in case 2, Cell _m belongs to the indoor sub-district with high load characteristics, and Cell _a+1 also belongs to the indoor sub-district with high load characteristics.

S304. If the load balance of the indoor distribution system is not reached, reselect a preset threshold target sample points from the sample point set, and redirect the target sample points to the second target indoor sub-district until the indoor distribution system is reached. load balancing.

In the embodiment of the present disclosure, load balancing optimization is performed based on a time window for the first target indoor sub-district with load balancing optimization conditions. Specifically, load balancing optimization is performed on user terminals under the target slice. In the optimization process, when all the plots are indoor sub-districts with high load characteristics; or when all the sub-districts are indoor sub-districts without high load characteristics. At this point, the load balancing optimization ends, and the indoor sub-district under the base station realizes load balancing. In addition, for other situations, load balancing optimization will be further carried out. This method improves the comprehensiveness of the optimization, the optimization speed is fast, and the precision is strong.

Optionally, the network performance data may include cell downlink traffic and cell downlink PRB resource utilization. According to the network performance data, determining the first indoor sub-cell with high load characteristics may include:

It is determined that the downlink traffic of the cell is greater than or equal to the threshold of downlink traffic of the cell, and the indoor sub-cell whose downlink PRB resource utilization rate of the cell is greater than or equal to the threshold of the downlink PRB resource utilization of the cell is the first indoor sub-cell with high load characteristics.

In other embodiments, the network performance data may also include the downlink traffic of the cell and the number of RRC connected users of the cell. According to the network performance data, determining the first indoor sub-district with high load characteristics may include: determining that the downlink traffic of the cell is greater than or equal to that of the cell The downlink traffic threshold, and the indoor sub-cell with the number of RRC connected users in the cell greater than or equal to the threshold of the number of RRC connected users in the cell is the first indoor sub-cell with high load characteristics.

Exemplarily, the indoor sub-district Cell _x with high load characteristics needs to meet the following conditions:

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In the formula, among them, Flow _i represents the cell downlink traffic; Flow _th represents the cell flow threshold; PRB _i represents the cell downlink PRB resource utilization; PRB _th represents the cell downlink PRB resource utilization threshold; User _i represents the cell RRC The number of connected users; User _th is characterized as the threshold of the number of RRC connected users in the cell.

Specifically, the cell traffic threshold may be set to 8.4 GB, the cell downlink PRB resource utilization threshold may be set to 80%, and the cell RRC connection user number threshold may be set to 160. The first room subdivision with high load characteristics can be shown in Table 6:

Table 6

Optionally, in the above embodiment, the user perception data may include cell channel quality, cell freezing times, cell delay and cell average rate, and according to the user perception data, the second indoor sub-cell with poor user perception characteristics may be determined. include:

It is determined that the cell channel quality is less than or equal to the cell channel quality threshold, the number of cell freeze times is greater than or equal to the cell freeze frequency threshold, the cell delay is greater than or equal to the cell delay threshold, and the cell average rate is less than or equal to the cell average rate threshold. It is a sub-district for the second room with poor user perception characteristics.

For example, the indoor division cell Cell _y with poor user perception needs to meet the following conditions:

Among them, CQI _i represents the cell channel quality; CQI _th represents the cell channel quality threshold; Caton _i represents the cell stalling; Caton _th represents the cell stalling threshold; Delay _i represents the cell delay; Delay _th represents the cell time delay threshold; Speed _i is characterized as the average rate of the cell; Speed _th is represented as the average rate threshold of the cell.

In addition, it should be noted that, in some embodiments, the second indoor sub-cell with poor user perception may be one or more of the above conditions. The embodiment of the present disclosure does not limit it. Specifically, the cell channel quality threshold can be set to 10, the cell freeze threshold can be set to 70 times, the cell delay threshold can be set to 80 MS, and the cell average rate threshold can be set to 10 Mbps.

The second indoor sub-district with poor user perception characteristics can be represented by Table 7:

Table 7

Next, a method for performing load balancing optimization on target fragments provided by an embodiment is introduced through FIG. 4 . As shown in FIG. 4 , the method includes:

S401. Determine a first target indoor sub-district that meets load balancing optimization conditions.

This step has been described above and will not be repeated here.

S402. Taking the indoor antenna coverage area corresponding to the first target indoor cell as a unit, quantify the first target indoor cell to obtain a target segment, and determine user terminals in the target segment as a set of sample points.

S403. Select preset threshold target sample points from the sample point set, and redirect the target sample points to the second target indoor sub-district, where the second target indoor sub-district is at least two indoor sub-districts that do not have high load characteristics Indoor district.

S404. According to the network performance data of the indoor distribution system, determine whether there is an indoor sub-district with a high load characteristic in the indoor distribution system.

If it exists, execute S406; if not, execute S405.

S405. Stop the load balancing optimization operation.

S406. Determine whether the second target indoor sub-district is an indoor sub-district with high load characteristics.

If yes, execute S407, if not, return to step S403.

S407. Determine whether the first target indoor sub-district is an indoor sub-district with high load characteristics.

If yes, execute step S405; otherwise, execute step S408, and return to step S403.

It can be seen from the above that only when all the indoor sub-districts under the one-room sub-system have high load characteristics or all indoor sub-districts do not have high load characteristics, the load balance optimization is realized, and then the load balance optimization is stopped. For other cases, S403 needs to be further executed.

S408. Mark the current target sample point and put it back into the original sample point set.

The purpose of this disclosure is to build a load balance judgment model and a load balance optimization model, first output a list of indoor communities with load balance optimization conditions, and then perform load balance optimization adjustments to achieve the purpose of load balance and user perception improvement. Judging indoor sub-districts with load balancing optimization conditions includes: first step, judging indoor sub-districts with high load characteristics according to network performance data; second step, judging indoor sub-districts with poor user perception characteristics according to user perception data sub-district; the third step, for the indoor sub-district with the above-mentioned high load characteristics and poor user perception characteristics, combined with the engineering physical parameter base station data, it is judged by algorithm calculation that it has the load balancing optimization conditions (the same as the existing indoor sub-district under the base station) Indoor subdistricts with high load characteristics and poor user perception characteristics, and indoor subdivision scenarios of other indoor subdistricts with non-high loads) indoor subdistricts. Through the above algorithm screening, the indoor sub-districts with load balancing optimization conditions are obtained. Then, based on the coverage area of indoor sub-district antennas attached to indoor sub-districts, it is quantified and subdivided, and the subdivided coverage area is optimized based on the load balancing strategy based on the time window, and finally achieves the purpose of load balancing and user perception improvement optimization. For example, indoor scenes such as high-rise indoor buildings, underground railways, large indoor venues, and airport high-speed rail stations. All of the above are difficult points in property management, wiring, construction, and indoor scenes with high space constraints in the venue. This type of indoor scene has the characteristics of many user groups, concentrated users in a certain area, complex wireless environment, difficulty in site expansion and optimization, and concentrated business volume.

After introducing the load balance optimization method of the present disclosure, next, the load balance optimization device of the present disclosure is introduced through FIG. 5 . As shown in FIG. 5, the load balance optimization device 500 of the embodiment of the present disclosure includes:

An acquisition module 501, configured to acquire network performance data and user perception data of an indoor distribution system, where the indoor distribution system includes at least two indoor sub-districts;

Determining module 502, configured to determine a first target indoor sub-district with load balancing optimization conditions according to network performance data and user perception data, where the load balancing optimization condition is that at least two indoor sub-districts have high load characteristics and poor user perception characteristics The room division area;

The quantization module 503 is used to quantify the first target indoor sub-cell by taking the indoor antenna coverage area corresponding to the first target indoor sub-cell as a unit, to obtain the target slice;

The optimization module 504 is configured to perform load balancing optimization on target slices, so as to achieve load balancing of the indoor distribution system.

In a possible implementation, the optimization module 504 is specifically configured to: determine the user terminal in the target slice as a set of sample points; select a preset threshold of target sample points from the set of sample points, and redirect the target sample points to The second target indoor sub-district, the second target indoor sub-district is an indoor sub-district that does not have high load characteristics among at least two indoor sub-districts; after optimizing the allocation, reacquire the network performance data of the indoor distribution system, and according to Determine whether the load balance of the indoor distribution system has been achieved; if the load balance of the indoor distribution system has not been reached, reselect the preset threshold target sample points from the sample point set, and redirect the target sample points to the second target The steps of dividing the indoor area into residential areas until the load balance of the indoor distribution system is achieved.

In a possible implementation manner, the determination module 502 is specifically used for at least one of the following:

If according to the re-acquired network performance data, it is determined that the first target indoor sub-district has high-load characteristics, and the second target indoor sub-district does not have high-load characteristics, then it is determined that the load balance of the indoor distribution system has not been reached; if according to the re-acquired network Performance data, determine that the first target indoor sub-district does not have high load characteristics, and the second target indoor sub-district has high load characteristics, then move the target sample point back to the first target indoor sub-district, and determine that the load of the indoor distribution system has not been reached Balanced; if it is determined that the first target indoor sub-district does not have high-load characteristics and the second target indoor sub-district does not have high-load characteristics according to the re-acquired network performance data, it is determined that the load balance of the indoor distribution system is reached; If it is determined that the first target indoor sub-district has high load characteristics and the second target indoor sub-district has high load characteristics, it is determined that the load balance of the indoor distribution system has been achieved.

In a possible implementation manner, the optimization module 504 is specifically configured to: select preset threshold target sample points from the sample point set after each preset time window, and redirect the target sample points to the second target room. district.

In a possible implementation manner, the determining module 502 is specifically configured to: determine the first indoor sub-district with high load characteristics according to the network performance data; determine the second indoor sub-district with poor user perception characteristics according to the user perception data; According to the first indoor sub-district and the second indoor sub-district, a first target indoor sub-district with load balancing optimization conditions is determined.

In a possible implementation manner, the determining module 502 is specifically configured to: determine that the cell downlink traffic is greater than or equal to the cell downlink traffic threshold, and the cell downlink PRB resource utilization rate is greater than or equal to the cell downlink PRB resource utilization threshold. The first room sub-division with high load characteristics.

In a possible implementation manner, the determination module 502 is specifically configured to: determine that the downlink traffic of the cell is greater than or equal to the threshold of the downlink traffic of the cell, and the number of RRC connected users of the cell is greater than or equal to the threshold of the number of RRC connected users of the cell as an indoor cell with a high load. Features of the first room subdivision.

In a possible implementation manner, the determination module 502 is specifically configured to: determine that the cell channel quality is less than or equal to the cell channel quality threshold, the number of cell freeze times is greater than or equal to the cell freeze frequency threshold, and the cell delay is greater than or equal to the cell delay threshold , the indoor cell whose cell average rate is less than or equal to the cell average rate threshold is the second indoor cell with poor user perception.

In a possible implementation manner, the load balancing optimization condition further includes: there is a second target indoor sub-district in the neighborhood of the first target indoor sub-district, and the second target indoor sub-district does not have high Indoor subdivision of load characteristics.

In a possible implementation manner, the determining module 502 is further configured to: if there is no second target indoor sub-district, end the process.

The device provided by the embodiment of the present disclosure can be used to execute the method of the foregoing embodiment, and its implementation principle and technical effect are similar, and will not be repeated here.

It should be noted that it should be understood that the division of each module of the above device is only a division of logical functions, and may be fully or partially integrated into one physical entity or physically separated during actual implementation. And these modules can all be implemented in the form of calling software through processing elements; they can also be implemented in the form of hardware; some modules can also be implemented in the form of calling software through processing elements, and some modules can be implemented in the form of hardware. For example, the processing module can be a separate processing element, or it can be integrated in a chip of the above-mentioned device. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and a certain processing element of the above-mentioned device can Call and execute the functions of the above processing modules. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, and can also be implemented independently. The processing element mentioned here may be an integrated circuit with signal processing capability. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above method, for example: one or more specific integrated circuits (application specific integrated circuit, ASIC), or one or more microprocessors (digital signal processor, DSP), or, one or more field programmable gate arrays (field programmable gate array, FPGA), etc. For another example, when one of the above modules is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (central processing unit, CPU) or other processors that can call program codes. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

In the above embodiments, all or part of them may be implemented by software, hardware, firmware or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, all or part of the processes or functions according to the embodiments of the present disclosure will be generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website, computer, server or data center Transmission to another website site, computer, server, or data center by wired (eg, coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (eg, infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center integrated with one or more available media. The available medium may be a magnetic medium (for example, a floppy disk, a hard disk, or a tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)).

Fig. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. Exemplarily, the electronic device may be provided as a server or network device, such as a base station. Referring to FIG. 6 , the electronic device 600 includes a processing component 601, which further includes one or more processors, and a memory resource represented by a memory 602 for storing instructions executable by the processing component 601, such as application programs. The application program stored in memory 602 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 601 is configured to execute instructions to perform any one of the above method embodiments.

The electronic device 600 may also include a power supply component 603 configured to perform power management of the electronic device 600, a wired or wireless network interface 604 configured to connect the electronic device 600 to a network, and an input-output (I/O) interface 605 . The electronic device 600 can operate based on an operating system stored in the memory 602, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

The present disclosure also provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the processor executes the computer-executable instructions, the solution of the above load balancing optimization method is realized.

The present disclosure also provides a computer program product, including a computer program. When the computer program is executed by a processor, the solution of the above load balancing optimization method is realized.

The above-mentioned computer-readable storage medium, the above-mentioned readable storage medium can be realized by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.

An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium may be located in application specific integrated circuits (Application Specific Integrated Circuits, ASIC for short). Certainly, the processor and the readable storage medium may also exist in the load balancing optimization device as discrete components.

Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by program instructions and related hardware. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above-mentioned method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other various media that can store program codes.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments,

Those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the corresponding technical solutions The essence deviates from the scope of the technical solutions of the various embodiments of the present disclosure.

Claims (10)

1. A method for load balancing optimization, comprising:

acquiring network performance data and user perception data of an indoor distribution system, wherein the indoor distribution system comprises at least two indoor sub-cells;

Determining a first target indoor partition cell with a load balancing optimization condition according to the network performance data and the user perception data, wherein the load balancing optimization condition is an indoor partition cell with a high load characteristic and a user perception difference characteristic in the at least two indoor partition cells;

taking the coverage area of the indoor antenna corresponding to the first target indoor cell as a unit, and quantifying the first target indoor cell to obtain a target fragment;

load balancing optimization is carried out on the target fragments so as to achieve load balancing of the indoor distribution system;

the determining, according to the network performance data and the user perception data, a first target indoor partition cell with a load balancing optimization condition includes:

determining a first indoor partition with high load characteristics according to the network performance data;

determining a second indoor partition cell with a user perception difference characteristic according to the user perception data;

determining a first target cell with load balancing optimization conditions according to the first cell and the second cell;

the load balancing optimization for the target fragment to achieve the load balancing of the indoor distribution system comprises the following steps:

Determining the user terminal in the target fragment as a sample point set;

selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target cell, wherein the second target cell is a cell which does not have high-load characteristics in the at least two cells;

after optimizing distribution, re-acquiring network performance data of the indoor distribution system, and determining whether load balance of the indoor distribution system is achieved or not according to the re-acquired network performance data;

and if the load balance of the indoor distribution system is not achieved, reselecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target indoor partition cell until the load balance of the indoor distribution system is achieved.

2. The load balancing optimization method according to claim 1, wherein the determining whether the load balancing of the indoor distribution system is achieved according to the re-acquired network performance data comprises at least one of:

if the first target indoor partition cell is determined to have high load characteristics according to the re-acquired network performance data, and the second target indoor partition cell is determined to not have the high load characteristics, determining that the load balance of the indoor distribution system is not achieved;

If the first target indoor partition cell does not have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell has the high load characteristic, returning the target sample point to the first target indoor partition cell, and determining that the load balance of the indoor distribution system is not achieved;

if the first target indoor partition cell is determined to not have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell is determined to not have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved;

and if the first target indoor partition cell is determined to have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell is determined to have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved.

3. The load balancing optimization method according to claim 1, wherein the selecting a preset threshold number of target sample points from the set of sample points, redirecting the target sample points to a second target cell compartment, comprises:

and after each preset time window, selecting a preset threshold number of target sample points from the sample point set, and redirecting the first target sample points to the second target cell.

4. The method for load balancing optimization according to claim 1, wherein the network performance data includes a cell downlink traffic and a cell downlink physical resource block PRB resource utilization, and the determining the first indoor cell with the high load characteristic according to the network performance data includes:

and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold, and the cell with the cell downlink PRB resource utilization rate greater than or equal to the cell downlink PRB resource utilization rate threshold is a first cell with high load characteristics.

5. The load balancing optimization method according to claim 1, wherein the network performance data includes a cell downlink traffic and a cell radio resource control RRC connection user number, and the determining, according to the network performance data, the first indoor partition cell with the high load feature includes:

and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold, and the cell division cell with the cell RRC connection user number greater than or equal to the cell RRC connection user number threshold is a first cell division cell with high load characteristics.

6. The method of claim 1, wherein the user perceived data includes cell channel quality, cell stuck times, cell delay and cell average rate, and the determining the second compartmental cell with the user perceived difference feature according to the user perceived data comprises:

And determining that the cell channel quality is smaller than or equal to a cell channel quality threshold, the cell blockage times is larger than or equal to a cell blockage times threshold, the cell delay is larger than or equal to the cell delay threshold, and the cell division cell with the cell average rate smaller than or equal to the cell average rate threshold is a second cell division cell with the user perception difference characteristic.

7. A load balancing optimization method according to any one of claims 1 to 3, wherein the load balancing optimization conditions further include: and a second target indoor partition cell exists in the neighborhood of the first target indoor partition cell, and the second target indoor partition cell is an indoor partition cell which does not have high load characteristics in the at least two indoor partition cells.

8. A load balancing optimization apparatus, characterized in that the load balancing optimization apparatus comprises:

the indoor distribution system comprises at least two indoor subareas;

the determining module is used for determining a first target indoor partition cell with a load balancing optimization condition according to the network performance data and the user perception data, wherein the load balancing optimization condition is an indoor partition cell with a high load characteristic and a user perception difference characteristic in the at least two indoor partition cells;

The quantization module is used for quantizing the first target indoor partition cell by taking the indoor partition antenna coverage area corresponding to the first target indoor partition cell as a unit to obtain a target partition;

the optimizing module is used for carrying out load balancing optimization aiming at the target fragments so as to achieve the load balancing of the indoor distribution system;

the determining module is specifically configured to:

determining a first indoor partition with high load characteristics according to the network performance data;

determining a second indoor partition cell with a user perception difference characteristic according to the user perception data;

determining a first target cell with load balancing optimization conditions according to the first cell and the second cell;

the optimizing module is specifically configured to:

determining the user terminal in the target fragment as a sample point set;

selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target cell, wherein the second target cell is a cell which does not have high-load characteristics in the at least two cells;

after optimizing distribution, re-acquiring network performance data of the indoor distribution system, and determining whether load balance of the indoor distribution system is achieved or not according to the re-acquired network performance data;

And if the load balance of the indoor distribution system is not achieved, reselecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target indoor partition cell until the load balance of the indoor distribution system is achieved.

9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes the computer-executable instructions stored by the memory to implement the load balancing optimization method of any one of claims 1 to 7.

10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein computer executable instructions for implementing the load balancing optimization method according to any one of claims 1 to 7 when being executed by a processor.

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