CN113286315B - Load balance judging method, device, equipment and storage medium - Google Patents
Load balance judging method, device, equipment and storage medium Download PDFInfo
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Abstract
The application provides a load balance judging method, a device, equipment and a storage medium, wherein the method obtains current network perception data of all users in a cell to be judged, wherein the current network perception data comprises current voice service data and current data service data; inputting current network perception data into a preset network perception discrimination model, and obtaining perception weights of all users in a preset network area according to the output of the preset network perception discrimination model; determining the number of the perception difference users in the cell to be judged according to a preset network perception threshold and perception weight; according to the number of the users with poor perception and the total number of the users of the cell to be judged, whether the cell to be judged needs to be subjected to load balancing or not is determined, the load condition of the cell can be judged and balanced more accurately, the condition of unbalanced load is effectively solved, network resource distribution is more reasonable, and network perception of users is improved.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, and a storage medium for load balancing determination.
Background
With the rapid development of wireless Communication Technology, the number of users in the fourth Generation Mobile Communication Technology (4G) and the fifth Generation Mobile Communication Technology (5G) is also increasing rapidly, the requirement for the network is higher and higher, and load balancing is required to meet the network requirement of each user.
The existing load balancing judgment method is to judge according to the network index of the base station side, and if the total network index collected by the base station side reaches the preset load balancing specified standard, the load balancing operation is performed on the user under the base station.
However, the existing load balancing method has a single method for judging whether the load is balanced, and the situation of unbalanced load cannot be effectively solved, so that network resources in an area are unreasonably distributed, and the user network perception is poor.
Disclosure of Invention
The application provides a load balancing judgment method, a load balancing judgment device, load balancing equipment and a storage medium, so that the technical problems that the network resource distribution in an area is unreasonable and the user network perception is poor due to the fact that the existing load balancing mode is single in load balancing judgment, and the load imbalance situation cannot be effectively solved are solved.
In a first aspect, an embodiment of the present application provides a load balancing determination method, including:
acquiring current network sensing data of all users in a cell to be judged, wherein the current network sensing data comprises current voice service data and current data service data;
inputting the current network perception data into a preset network perception discrimination model, and obtaining perception weights of all users in the preset network area according to the output of the preset network perception discrimination model;
determining the number of the perception difference users in the cell to be judged according to a preset network perception threshold value and the perception weight;
and determining whether the cell to be judged needs to carry out load balancing or not according to the number of the perception difference users and the total number of the users of the cell to be judged.
Here, before load balancing, in the embodiment of the present application, current network sensing data in a cell, including current voice service data and current data service data, is first obtained, sensing weight prediction is performed on a user of the current cell according to a preset network sensing discrimination model, then a sensing difference user number in the cell is determined according to a preset network sensing threshold and the sensing weight, and a load condition in the cell is determined according to the sensing difference user number and a total user number in the cell, in the embodiment of the present application, the two aspects of voice service and data service are combined, a real use condition of the user in a network interaction process is considered, the load condition prediction is performed by combining a carrying capacity of the voice service and a carrying capacity of the data service, a carrying condition of the cell service can be effectively determined, meanwhile, the embodiment of the present application, by combining a total number of users in the cell, the load condition can be determined according to a proportion, the cell load conditions can be judged and balanced more accurately, the condition of unbalanced load is effectively solved, network resource distribution is more reasonable, and user network perception is improved.
Optionally, before the inputting the current network awareness data into a preset network awareness discrimination model, the method further includes:
acquiring historical network perception data of users in the cell to be judged and historical perception weights corresponding to the historical network perception data, wherein the historical network perception data comprises historical voice service data and historical data service data;
and performing model training according to the historical network perception data and historical perception weights corresponding to the historical network perception data to obtain a preset network perception discrimination model.
Here, before predicting the sensing weight of the current network sensing data, the embodiment of the application first establishes a preset network sensing discrimination model to predict the sensing weight accurately and conveniently, performs the labyrinth training through the historical network sensing data of the users in the cell and the historical network sensing weight corresponding to the historical network sensing data, can obtain the accurate discrimination model, and in addition, the historical sensing data comprises historical voice service data and historical data service data, can judge the network sensing condition of the users in the current network according to different use requirements of the users on the internet data service and the voice service, avoids the phenomenon that the current network judges according to the network indexes of a base station side, can perform load balancing operation when the user network senses well, and can not perform load balancing operation when the user network senses poorly, the accuracy of judging whether the load is balanced or not is further improved, so that the network resource allocation is more reasonable, and the network perception of the user is improved.
Optionally, the performing model training according to the historical network perception data and the historical perception weight corresponding to the historical network perception data includes:
performing data preprocessing on the historical network perception data and the perception weight corresponding to the historical network perception data to obtain processed historical network perception data and processed historical perception weight;
and performing model training by taking the processed historical network perception data as input and the processed historical perception weight as output to obtain the preset network perception discrimination model.
Here, before model training is performed according to the historical network perception data and the perception weight corresponding to the historical network perception data, the embodiment of the present application further performs preprocessing on the data, where the preprocessing may be data cleaning to obtain data with a uniform format, so as to improve accuracy and convenience of feature training, the preprocessing may be feature engineering training to obtain feature data with more dimensions, so as to improve accuracy of feature training, and may also be other types of data preprocessing or a combination of different preprocessing modes, and the preprocessing on the data may improve training speed of the model and accuracy of the model weight, thereby further improving efficiency of load balancing judgment.
Optionally, the determining, according to a preset network sensing threshold and the sensing weight, the number of poor sensing users in the cell to be determined includes:
determining current network perception intervals of all the users according to a preset network perception threshold and the perception weight, wherein the current network perception intervals comprise perception difference intervals;
and determining the number of the users with the perception weight in the range of the perception difference region as the number of the perception difference users in the cell to be judged.
Here, when determining a poor perception user in a cell, the embodiment of the present application may determine different network perception intervals by presetting a network perception threshold, where the network perception interval includes the poor perception interval, where the preset network perception threshold may be determined according to an actual situation, where no specific limitation is made, if a perception weight of a user is within the poor perception interval, then the user may be determined to be the poor perception user, and the determination may be performed according to the interval, so that the poor perception user may be determined effectively and accurately, and the adjustment may be performed according to the actual situation, thereby adapting to perception requirements in different environments.
Optionally, the determining, according to the number of the sensing difference users and the total number of the users of the cell to be determined, whether the cell to be determined needs to perform load balancing includes:
determining the perception difference user proportion of the cell to be judged according to the perception difference user number and the total user number of the cell to be judged;
determining the load state of the cell to be judged according to the perception difference user proportion;
and determining whether the cell to be judged needs to carry out load balancing or not according to the load state.
Here, according to the embodiment of the application, whether load balancing needs to be performed in the cell is determined according to the proportion of the number of the users with poor sensing, and the total situation of the cell is considered, so that the load balancing is more reasonable and effective.
Optionally, the determining the load state of the cell to be determined according to the poor perception user ratio includes:
if the perception difference user proportion is larger than a first preset user proportion, determining that the load state of the cell to be judged is high;
if the perception difference user proportion is smaller than or equal to a first preset user proportion and larger than or equal to a second preset user proportion, determining that the load state of the cell to be judged is a common load;
and if the perception difference user proportion is smaller than a second preset user proportion, determining that the load state of the cell to be judged is low.
Here, the load condition of the cell is determined by combining different perception difference user ratios, different load balancing means can be performed on the cell according to different load conditions, and the load balancing mode is more flexible, accurate and effective.
In a second aspect, an embodiment of the present application provides a load balancing determination apparatus, including:
the system comprises an acquisition module, a judging module and a judging module, wherein the acquisition module is used for acquiring current network sensing data of all users in a cell to be judged, and the current network sensing data comprises current voice service data and current data service data;
the input module is used for inputting the current network perception data into a preset network perception discrimination model and obtaining the perception weights of all the users in the preset network area according to the output of the preset network perception discrimination model;
the first determining module is used for determining the number of the perception difference users in the cell to be judged according to a preset network perception threshold value and the perception weight;
and the second determining module is used for determining whether the cell to be judged needs to carry out load balancing according to the number of the sensing difference users and the total number of the users of the cell to be judged.
Optionally, before the input module inputs the current network awareness data to a preset network awareness discrimination model, the apparatus further includes:
the training module is used for acquiring historical network perception data of users in the cell to be judged and historical perception weights corresponding to the historical network perception data, wherein the historical network perception data comprises historical voice service data and historical data service data; and performing model training according to the historical network perception data and historical perception weights corresponding to the historical network perception data to obtain a preset network perception discrimination model.
Optionally, the training module is specifically configured to:
performing data preprocessing on the historical network perception data and the perception weight corresponding to the historical network perception data to obtain processed historical network perception data and processed historical perception weight; and performing model training by taking the processed historical network perception data as input and the processed historical perception weight as output to obtain the preset network perception discrimination model.
Optionally, the first determining module is specifically configured to:
determining current network perception intervals of all the users according to a preset network perception threshold and the perception weight, wherein the current network perception intervals comprise perception difference intervals;
and determining the number of the users with the perception weight in the range of the perception difference region as the number of the perception difference users in the cell to be judged.
Optionally, the second determining module is specifically configured to:
determining the perception difference user proportion of the cell to be judged according to the perception difference user number and the total user number of the cell to be judged;
determining the load state of the cell to be judged according to the perception difference user proportion;
and determining whether the cell to be judged needs to carry out load balancing or not according to the load state.
Optionally, the second determining module is specifically configured to:
if the perception difference user proportion is larger than a first preset user proportion, determining that the load state of the cell to be judged is high;
if the perception difference user proportion is smaller than or equal to a first preset user proportion and larger than or equal to a second preset user proportion, determining that the load state of the cell to be judged is a common load;
and if the perception difference user proportion is smaller than a second preset user proportion, determining that the load state of the cell to be judged is low.
In a third aspect, an embodiment of the present application provides a load balancing determination device, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored by the memory to cause the at least one processor to perform the load balancing determination method as described above in the first aspect and various possible designs of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer-executable instruction is stored, and when a processor executes the computer-executable instruction, the load balancing determination method according to the first aspect and various possible designs of the first aspect is implemented.
In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method for determining load balancing according to the first aspect and various possible designs of the first aspect is implemented.
The method, the device, the equipment and the storage medium for judging the load balance, wherein before the load balance is carried out, the method firstly obtains current network perception data in a cell, including current voice service data and current data service data, predicts the perception weight of a user of the current cell according to a preset network perception distinguishing model, determines the perception difference user number in the cell according to a preset network perception threshold value and the perception weight, and judges the load condition in the cell according to the perception difference user number and the total user number in the cell, the embodiment of the application combines the two aspects of voice service and data service, considers the real use condition of the user in the network interaction process, combines the bearing capacity of the voice service and the bearing capacity of the data service to predict the load condition, and can effectively judge the bearing condition of the cell service, meanwhile, the embodiment of the application can determine the load condition according to the proportion by combining the total number of the users in the cell, can more accurately judge and balance the load condition of the cell, effectively solves the problem of unbalanced load, enables network resource allocation to be more reasonable, and improves the network perception of the users.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments of the present application, and for those skilled in the art, other drawings may be obtained according to these drawings without inventive labor.
Fig. 1 is a schematic diagram of a load balancing system according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a load balancing determination method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of another load balancing determination method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a load balancing determination apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a load balancing determination device according to an embodiment of the present application.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. The drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the disclosed concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terms "first," "second," "third," and "fourth," if any, in the description and claims of this application and the above-described figures are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The complexity of the network structure and the service diversification make the improvement of the user perception more and more complex, and the base station needs to perform network load balancing in order to meet the daily network requirements of the user, improve the network perception of the user and provide better use experience for the user.
The existing load balancing judgment method is to judge according to network indexes at the base station side, and if the indexes reach the specified standard of load balancing, corresponding load balancing operation can be performed even if the perception of a user under the base station is normal. The technical method has a single means, does not consider the real use condition of the user, and can increase the complexity of the network. On the contrary, when a user uses a terminal to connect a network, the user generally interacts with a data service and a voice service, and what is different is that the carrying capacities of base station cells for the data service and the voice service are different, and the same balancing standard is performed for people with different emphasis services, so that the current situation of load imbalance cannot be effectively solved, and unreasonable network resource allocation in an area is caused, and the network perception of the user is influenced.
In order to solve the above problems, embodiments of the present application provide a method, an apparatus, a device, and a storage medium for load balancing determination, where the method performs determination and operation of load balancing in a dimension perceived by a user, determines a network perception situation of the user in a current network according to different user requirements of the user on an internet service and a voice service, and determines whether a base station cell is in a load imbalance state according to the perception of the user, so as to perform balancing operation on an imbalance base station cell.
Optionally, fig. 1 is a schematic diagram of a load balancing system architecture provided in the embodiment of the present application. In fig. 1, the above architecture includes a base station 101, a cell 1010, a cell 1011, a cell 1012, a user equipment 10100, a user equipment 10101, and a user equipment 10102.
The system architecture is only exemplary, one base station includes 3 sectors, each sector includes cells, the number of the cells is determined according to the situation, here, 3 cells are used as an illustration, and each cell in the system architecture includes a plurality of user equipments.
Specifically, in the implementation process, the load balancing of the base station 101 may be implemented by a server connected to the base station 101.
It is to be understood that the illustrated structure of the embodiments of the present application does not form a specific limitation to the load balancing system architecture. In other possible embodiments of the present application, the foregoing architecture may include more or less components than those shown in the drawings, or combine some components, or split some components, or arrange different components, which may be determined according to practical application scenarios, and is not limited herein. The components shown in fig. 1 may be implemented in hardware, software, or a combination of software and hardware.
In addition, the network architecture and the service scenario described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not constitute a limitation to the technical solution provided in the embodiment of the present application, and it can be known by a person skilled in the art that along with the evolution of the network architecture and the appearance of a new service scenario, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.
The technical scheme of the application is described in detail by combining specific embodiments as follows:
optionally, fig. 2 is a schematic flow chart of a load balancing determination method provided in the embodiment of the present application. The execution subject of the embodiment of the application may be a server, and the specific execution subject may be determined according to an actual application scenario. As shown in fig. 2, the method comprises the steps of:
s201: and acquiring current network perception data of all users in the cell to be judged.
The current network perception data comprises current voice service data and current data service data.
Optionally, the user information may be determined according to the user terminal under the base station within a certain period, and the network sensing data may further include network basic index data, including a network basic index, a data service index, and a voice service index of each user terminal under the base station.
S202: and inputting the current network perception data into a preset network perception discrimination model, and obtaining the perception weights of all users in a preset network area according to the output of the preset network perception discrimination model.
Optionally, the preset Network sensing discrimination model may be established for a plurality of classification algorithms, such as a Decision Tree (DT) classification algorithm, a naive Bayes (Navie Bayes, NB) classification algorithm, a Support Vector Machine (SVM) classification algorithm, a Neural Network (NN) classification algorithm, an XGBOOST classification algorithm, and the like, which is not specifically limited in the embodiment of the present application.
S203: and determining the number of the perception difference users in the cell to be judged according to the preset network perception threshold and the perception weight.
Optionally, determining the number of the perception difference users in the cell to be determined according to a preset network perception threshold and the perception weight, including: determining current network perception intervals of all users according to a preset network perception threshold and perception weights, wherein the current network perception intervals comprise perception difference intervals; and determining the number of the users with the perception weights in the perception difference range, wherein the number is the number of the perception difference users in the cell to be judged.
The preset network perception threshold value can be set according to requirements, and further, the user network perception threshold value can be set according to the requirements of users in the coverage area of the cell on data services and voice services. For example, if the users in the area use the network less frequently, for example, the area is a residential living area, a park, etc., the user network perception threshold may be set to 0.95 in a targeted manner; if the user in the area uses the network more frequently, for example, the area is a call dense area or a network dense area, the user network perception threshold value can be adjusted from 0.95 to 0.85 in a targeted manner, and a larger threshold value indicates that the user has a higher demand for the network, and the user network perception is more likely to be poor.
Specifically, according to a preset network perception threshold and a user network perception weight, determining a network perception interval corresponding to the network perception weight; the network-aware intervals may include: the perception interval of the user network is used for representing the mapping from the perception weight of the user network to the perception interval. For example, for a less frequently used area of a network such as a residential living area or a park in the above example, users with sensing thresholds of [0,0.2), [0.2,0.4), [0.4,0.6), [0.6,0.8), [0.8,1) may be mapped to a sensing interval of [ sensing good, sensing general, sensing bad ] respectively.
Here, when determining a poor perception user in a cell, the embodiment of the present application may determine different network perception intervals by presetting a network perception threshold, where the network perception interval includes the poor perception interval, where the preset network perception threshold may be determined according to an actual situation, where no specific limitation is made, if a perception weight of a user is within the poor perception interval, then the user may be determined to be the poor perception user, and the determination may be performed according to the interval, so that the poor perception user may be determined effectively and accurately, and the adjustment may be performed according to the actual situation, thereby adapting to perception requirements in different environments.
S204: and determining whether the cell to be judged needs load balancing or not according to the number of the users with poor perception and the total number of the users of the cell to be judged.
Optionally, determining whether the cell to be determined needs to perform load balancing according to the number of the perceived poor users and the total number of users of the cell to be determined, includes: determining the perception difference user proportion of the cell to be judged according to the perception difference user number and the total user number of the cell to be judged; determining the load state of a cell to be judged according to the perception difference user proportion; and determining whether the cell to be judged needs to carry out load balancing or not according to the load state.
Specifically, the total number of users and the number of users with poor perception under a cell are summarized by taking the cell used by a user as a reference, and the proportion of users with poor perception of the cell is calculated according to the following formula. Furthermore, the corresponding cells under the same sector of the base station with the same coverage are target cells which can be respectively used for load balancing operation. The calculation formula is as follows:
here, the embodiment of the application determines whether the cell needs to perform load balancing according to the proportion of the number of the perceived poor users, and considers the overall situation of the cell, so that the load balancing is more reasonable and effective.
Optionally, determining the load state of the cell to be determined according to the poor-perception user ratio includes:
if the perception difference user proportion is larger than a first preset user proportion, determining that the load state of the cell to be judged is high;
if the perception difference user proportion is smaller than or equal to a first preset user proportion and larger than or equal to a second preset user proportion, determining that the load state of the cell to be judged is general;
and if the perception difference user proportion is smaller than a second preset user proportion, determining that the load state of the cell to be judged is low.
In this step, the preset cell network sensing threshold may be set as required, and further, the cell network sensing threshold may be set according to the scene attribute of the area where the cell is located. For example, if the users in the region are not sensitive to the network usage, the cell network perception threshold can be set to a large value; if the users in the area are sensitive to network usage, the cell network awareness threshold can be set to a small value in a targeted manner. The smaller the threshold value is, the more sensitive the users in the area to the network perception is, and the larger the judgment influence of the perception difference user proportion on the load balance is. Specifically, the cell load state is determined according to a preset cell network perception threshold and a cell perception difference user proportion; the cell load state includes: the load is low, the load is general, and the load is high, and the cell load state is used for representing the mapping from the cell perception difference user proportion to the load state. For example, the cell perception difference user ratio may be mapped according to the cell load state, for example, for the user network perception sensitive area in the above example, users with the cell perception difference user ratio of [0,0.01), [0.01,0.5), [0.05,1) may be mapped to the load state interval of [ load low, load normal, load high ] respectively; for the user network perception insensitive area in the above example, the maximum values of the load low interval and the load general interval can be appropriately increased.
Specifically, according to the cell with unbalanced load, the user network perceives the weight to balance the high-weight user to other cells covered under the same sector. For example, for a cell with unbalanced load, users with high network perceived weight are preferentially screened, and users with high network demand are preferentially balanced to other cells covered by the same sector of the base station.
Here, the load condition of the cell is determined by combining different perception difference user ratios, different load balancing means can be performed on the cell according to different load conditions, and the load balancing mode is more flexible, accurate and effective.
Before load balancing, the embodiment of the application firstly obtains current network perception data in a cell, including current voice service data and current data service data, predicts the perception weight of a user in the current cell according to a preset network perception discrimination model, then determines the perception difference user number in the cell according to a preset network perception threshold value and the perception weight, and determines the load condition in the cell according to the perception difference user number and the total user number in the cell, the embodiment of the application combines two aspects of voice service and data service, considers the real use condition of the user in the network interaction process, combines the bearing capacity of the voice service and the bearing capacity of the data service to predict the load condition, can effectively determine the bearing condition of the cell service, and simultaneously, the embodiment of the application combines the total number of the users in the cell to determine the load condition according to proportion, the cell load conditions can be judged and balanced more accurately, the condition of unbalanced load is effectively solved, network resource distribution is more reasonable, and user network perception is improved.
Optionally, in the embodiment of the present application, a preset network perception discrimination model may be further pre-established to perform accurate and convenient perception weight prediction, and accordingly, fig. 3 is a schematic flow diagram of another load balancing determination method provided in the embodiment of the present application, as shown in fig. 3, the method includes:
s301: and acquiring current network perception data of all users in the cell to be judged.
The current network perception data comprises current voice service data and current data service data.
S302: historical network perception data of users in a cell to be judged and historical perception weights corresponding to the historical network perception data are obtained.
The historical network perception data comprises historical voice service data and historical data service data.
S303: and performing model training according to the historical network perception data and historical perception weights corresponding to the historical network perception data to obtain a preset network perception discrimination model.
Optionally, performing model training according to the historical network perception data and historical perception weights corresponding to the historical network perception data, including:
performing data preprocessing on the historical network perception data and the perception weights corresponding to the historical network perception data to obtain processed historical network perception data and processed historical perception weights;
and taking the processed historical network perception data as input, taking the processed historical perception weight as output, and performing model training to obtain a preset network perception discrimination model.
Here, before model training is performed according to the historical network perception data and the perception weight corresponding to the historical network perception data, the embodiment of the present application further performs preprocessing on the data, where the preprocessing may be data cleaning to obtain data with a uniform format, so as to improve accuracy and convenience of feature training, the preprocessing may be feature engineering training to obtain feature data with more dimensions, so as to improve accuracy of feature training, and may also be other types of data preprocessing or a combination of different preprocessing modes, and the preprocessing on the data may improve training speed of the model and accuracy of the model weight, thereby further improving efficiency of load balancing judgment.
The method for establishing a classification model provided by the embodiment may include the following steps:
acquiring historical network perception data of users in an area, and establishing a user network interaction database; preprocessing according to the network basic index, the data service index and the voice service index data to obtain data with regular format; performing characteristic engineering according to the data service characteristics and the voice service characteristics with regular formats to obtain more-dimensional characteristic data; and establishing a user perception discrimination model based on a classification algorithm by taking the processed derived data as the characteristics of the training sample and taking the current network perception standard of the data service and the voice service as the labels of the training sample.
Optionally, the preprocessing the network basic index, the data service index and the voice service index data includes: and cleaning the data to obtain the data with a uniform format.
Specifically, data cleaning can be realized by field screening, repeated value filtering, null value filling, abnormal value detection and other methods.
Optionally, feature engineering may be performed according to the data service features and the voice service features that are formatted, so as to obtain feature data with more dimensions.
In this step, the performing of feature engineering on the data service features and the voice service features with regular formats includes: and performing feature combination and derivation on the data to obtain feature data with more dimensions.
Specifically, the feature engineering can be realized by means of feature pairwise combination, feature binning, feature discretization, time-dimension derivation of features and the like.
Optionally, the preset network sensing discrimination model may be a classification model obtained by acquiring a user sample in a network interaction database and training the user sample according to the user sample, where the user sample includes a large number of network basic indexes of users in a history base station cell, data service usage data, and voice service usage data. And establishing a perception discrimination model by taking the current network perception standard of user data service and voice service as a training sample label based on the obtained user sample as training sample characteristic data.
In one possible embodiment, XGBOOST is selected as the algorithmic model to train the data. In the training process, only one tree, namely one weak classifier, is trained at a time, and the final prediction result is the sum of all the trees. And the residual error is reduced as much as possible when the weak classifier is trained in each round, so that the answer is closer to the real answer. In the processing process of reducing the residual error, Taylor simplification of an objective function is carried out, a first-order derivative and a second-order derivative are led out, original traversal of a sample is converted into traversal of leaf nodes, so that the objective function is converted and solved, in the process of building the tree, a greedy strategy is adopted to build the tree from the root nodes layer by layer, and when the data volume is large, candidate split points are selected through an approximation algorithm to carry out optimization.
And establishing a plurality of regression trees by using the XGB OST algorithm by taking the processed derived data as the characteristics of the training samples and taking the current network perception standard of the data service and the voice service as the labels of the training samples. It should be noted that the training sample label may be the perception state of the user: the perception is good or poor, and corresponds to a classification result 1 or 0 in the model. Furthermore, in the model training process, the sum of the prediction results of the multiple regression trees represents the probability value that the sample belongs to the perceptually good standard, and in the model prediction process of a new sample, the perception weight of the user in the current network is the probability value.
S304: and inputting the current network perception data into a preset network perception discrimination model, and obtaining the perception weights of all users in a preset network area according to the output of the preset network perception discrimination model.
S305: and determining the number of the perception difference users in the cell to be judged according to the preset network perception threshold and the perception weight.
S306: and determining whether the cell to be judged needs to carry out load balancing or not according to the number of the sensing difference users and the total number of the users of the cell to be judged.
Before the perception weight prediction of the current network perception data is carried out, the preset network perception discrimination model is firstly established to carry out accurate and convenient perception weight prediction, the disorientation training is carried out through the historical network perception data of the users in the cell and the historical network perception weights corresponding to the historical network perception data, the accurate discrimination model can be obtained, in addition, the historical perception data comprises historical voice service data and historical data service data, the network perception condition of the users in the current network can be judged according to different use requirements of the users on internet data service and voice service, the phenomena that the current network judges according to network indexes at a base station side, load balancing operation is possibly carried out when the network perception of the users is good, and the load balancing operation is not carried out when the network perception of the users is not good are avoided, the accuracy of judging whether the load is balanced or not is further improved, so that the network resource allocation is more reasonable, and the network perception of the user is improved.
Fig. 4 is a schematic structural diagram of a load balancing determining apparatus according to an embodiment of the present application, and as shown in fig. 4, the apparatus according to the embodiment of the present application includes: an acquisition module 401, an input module 402, a first determination module 403 and a second determination module 404. The load balancing determination device may be the server itself, or a chip or an integrated circuit that implements the functions of the server. It should be noted here that the division of the obtaining module 401, the inputting module 402, the first determining module 403, and the second determining module 404 is only a division of logic functions, and the two may be integrated or independent physically.
The acquisition module acquires current network sensing data of all users in a cell to be judged, wherein the current network sensing data comprises current voice service data and current data service data;
the input module is used for inputting the current network perception data into a preset network perception discrimination model and obtaining the perception weights of all users in a preset network area according to the output of the preset network perception discrimination model;
the first determining module is used for determining the number of the poor perception users in the cell to be judged according to a preset network perception threshold value and perception weight;
and the second determining module is used for determining whether the cell to be judged needs to carry out load balancing according to the number of the users with poor perception and the total number of the users of the cell to be judged.
Optionally, before the input module inputs the current network awareness data to the preset network awareness discrimination model, the apparatus further includes:
the training module is used for acquiring historical network perception data of users in a cell to be judged and historical perception weights corresponding to the historical network perception data, wherein the historical network perception data comprises historical voice service data and historical data service data; and performing model training according to the historical network perception data and historical perception weights corresponding to the historical network perception data to obtain a preset network perception discrimination model.
Optionally, the training module is specifically configured to:
performing data preprocessing on the historical network perception data and the perception weights corresponding to the historical network perception data to obtain processed historical network perception data and processed historical perception weights;
and taking the processed historical network perception data as input, taking the processed historical perception weight as output, and performing model training to obtain a preset network perception discrimination model.
Optionally, the first determining module is specifically configured to:
determining current network perception intervals of all users according to a preset network perception threshold and perception weights, wherein the current network perception intervals comprise perception difference intervals;
and determining the number of the users with the perception weights in the perception difference interval range as the number of the perception difference users in the cell to be judged.
Optionally, the second determining module is specifically configured to:
determining the perception difference user proportion of the cell to be judged according to the perception difference user number and the total user number of the cell to be judged;
determining the load state of a cell to be judged according to the perception difference user proportion;
and determining whether the cell to be judged needs to carry out load balancing or not according to the load state.
Optionally, the second determining module is specifically configured to:
if the perception difference user proportion is larger than a first preset user proportion, determining that the load state of the cell to be judged is high;
if the perception difference user proportion is less than or equal to a first preset user proportion and greater than or equal to a second preset user proportion, determining that the load state of the cell to be judged is a general load;
and if the perception difference user proportion is smaller than the second preset user proportion, determining that the load state of the cell to be judged is low.
Fig. 5 is a schematic structural diagram of a load balancing determining device according to an embodiment of the present application, where the load balancing determining device may be a server. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not limiting to the implementations of the present application described and/or claimed herein.
As shown in fig. 5, the load balancing determination device includes: a processor 501 and a memory 502, the various components being interconnected using different buses, and may be mounted on a common motherboard or in other manners as desired. The processor 501 may process instructions executed within the load balancing determination device, including instructions for graphical information stored in or on a memory for display on an external input/output apparatus (such as a display device coupled to an interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. In fig. 5, one processor 501 is taken as an example.
The memory 502, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method of the load balancing determination apparatus in the embodiment of the present application (for example, as shown in fig. 4, the obtaining module 401, the input module 402, the first determining module 403, and the second determining module 404). The processor 501 executes various functional applications and data processing of the authentication platform by running non-transitory software programs, instructions and modules stored in the memory 502, that is, the method of implementing the load balancing determination device in the above method embodiments.
The load balancing determination device may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the load balancing determination apparatus, such as a touch screen, a keypad, a mouse, or a plurality of mouse buttons, a trackball, a joystick, or the like. The output device 504 may be an output device such as a display device of the load balancing determination device. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
The load balancing determination device in the embodiment of the present application may be configured to execute the technical solutions in the method embodiments of the present application, and the implementation principle and the technical effect are similar, which are not described herein again.
An embodiment of the present application further provides a computer-readable storage medium, where a computer-executable instruction is stored in the computer-readable storage medium, and when the computer-executable instruction is executed by a processor, the computer-executable instruction is used to implement any one of the load balancing determination methods described above.
An embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program is configured to implement any one of the load balancing determination methods described above.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is only a logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (8)
1. A method for judging load balance is characterized by comprising the following steps:
acquiring current network sensing data of all users in a cell to be judged, wherein the current network sensing data comprises current voice service data and current data service data;
inputting the current network perception data into a preset network perception discrimination model, and obtaining perception weights of all users in the preset network area according to the output of the preset network perception discrimination model;
determining the number of the perception difference users in the cell to be judged according to a preset network perception threshold value and the perception weight;
determining whether the cell to be judged needs to carry out load balancing according to the number of the sensing difference users and the total number of the users of the cell to be judged;
determining whether the cell to be judged needs to perform load balancing according to the number of the perception difference users and the total number of the users of the cell to be judged, including:
determining the perception difference user proportion of the cell to be judged according to the perception difference user number and the total user number of the cell to be judged;
determining the load state of the cell to be judged according to the perception difference user proportion;
and determining whether the cell to be judged needs to carry out load balancing or not according to the load state.
2. The method of claim 1, wherein prior to said inputting the current network-aware data into a preset network-aware discriminant model, further comprising:
acquiring historical network perception data of users in the cell to be judged and historical perception weights corresponding to the historical network perception data, wherein the historical network perception data comprises historical voice service data and historical data service data;
and performing model training according to the historical network perception data and historical perception weights corresponding to the historical network perception data to obtain a preset network perception discrimination model.
3. The method according to claim 2, wherein the performing model training according to the historical network perception data and the historical perception weights corresponding to the historical network perception data comprises:
performing data preprocessing on the historical network perception data and the perception weight corresponding to the historical network perception data to obtain processed historical network perception data and processed historical perception weight;
and performing model training by taking the processed historical network perception data as input and the processed historical perception weight as output to obtain the preset network perception discrimination model.
4. The method according to any one of claims 1 to 3, wherein the determining the number of the poor-sensing users in the cell to be determined according to a preset network sensing threshold and the sensing weight includes:
determining current network perception intervals of all the users according to a preset network perception threshold and the perception weight, wherein the current network perception intervals comprise perception difference intervals;
and determining the number of the users with the perception weight in the range of the perception difference region as the number of the perception difference users in the cell to be judged.
5. The method according to claim 1, wherein the determining the load status of the cell to be determined according to the poor-perception user ratio comprises:
if the perception difference user proportion is larger than a first preset user proportion, determining that the load state of the cell to be judged is high;
if the perception difference user proportion is smaller than or equal to a first preset user proportion and larger than or equal to a second preset user proportion, determining that the load state of the cell to be judged is a common load;
and if the perception difference user proportion is smaller than a second preset user proportion, determining that the load state of the cell to be judged is low.
6. A load balancing determination device, comprising:
the system comprises an acquisition module, a judging module and a judging module, wherein the acquisition module is used for acquiring current network sensing data of all users in a cell to be judged, and the current network sensing data comprises current voice service data and current data service data;
the input module is used for inputting the current network perception data into a preset network perception discrimination model and obtaining the perception weights of all the users in the preset network area according to the output of the preset network perception discrimination model;
the first determining module is used for determining the number of the perception difference users in the cell to be judged according to a preset network perception threshold value and the perception weight;
a second determining module, configured to determine whether the cell to be determined needs to perform load balancing according to the number of the poor sensing users and the total number of users of the cell to be determined;
the second determining module is specifically configured to:
determining the perception difference user proportion of the cell to be judged according to the perception difference user number and the total user number of the cell to be judged;
determining the load state of the cell to be judged according to the perception difference user proportion;
and determining whether the cell to be judged needs to carry out load balancing or not according to the load state.
7. A load balancing determination device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of load balancing determination as claimed in any one of claims 1 to 5.
8. A computer-readable storage medium, wherein a computer-executable instruction is stored in the computer-readable storage medium, and when the computer-executable instruction is executed by a processor, the computer-readable storage medium is configured to implement the load balancing determination method according to any one of claims 1 to 5.
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