CN117201310A - Network element capacity expansion method and device, electronic equipment and storage medium - Google Patents

Abstract

The disclosure provides a network element capacity expansion method, a device, electronic equipment and a storage medium, and relates to the technical field of communication. The method comprises the following steps: collecting service index data and resource index data of a target network element; inputting the service index data and the resource index data of the target network element into a pre-trained network element bearing capacity prediction model, and outputting the maximum bearing capacity when the target network element meets the preset condition; and if the maximum bearing capacity of the target network element exceeds the preset threshold, performing capacity expansion processing on the target network element. According to the method and the device, the service index data and the resource index data of the network element are monitored and analyzed in real time, the network element bearing capacity prediction model is used for analyzing and calculating the service index data and the resource index data of the network element which are collected in real time, the maximum bearing capacity of the target network element when the target network element meets the preset condition can be obtained in time, the problem of delay caused by manual monitoring data in the network element capacity expansion decision can be effectively avoided, and the accuracy and timeliness of the network element capacity expansion decision are improved.

Description

Network element capacity expansion method and device, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of communication, and in particular relates to a network element capacity expansion method, a device, electronic equipment and a storage medium.

Background

As the user data of operators gradually increases, the signaling link load between core network devices gradually increases, the signaling processing capacity of part of network elements is limited, network safety hidden danger is caused in the long term, when the network elements cannot process the current signaling in time, signaling storm may be formed, collapse of the whole core network is caused, and meanwhile, the limited processing capacity of the network elements is also a bottleneck for increasing the user quantity.

Therefore, the capacity expansion of the core network element is a technical problem to be solved urgently. The current capacity expansion scheme has postponement, capacity expansion is considered after the network element gives an alarm, and when the network element cannot process signaling in time, a signaling storm is possibly formed, and finally the whole core network is influenced.

It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The disclosure provides a network element capacity expansion method, a device, an electronic device and a storage medium, which at least overcome the problem of delay of network element capacity expansion in the related art to a certain extent.

Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.

According to one aspect of the present disclosure, there is provided a network element capacity expansion method, including: collecting service index data and resource index data of a target network element; inputting the service index data and the resource index data of the target network element into a pre-trained network element bearing capacity prediction model, and outputting the maximum bearing capacity when the target network element meets the preset condition; and if the maximum bearing capacity of the target network element exceeds the preset threshold, performing capacity expansion processing on the target network element.

In some embodiments, before inputting the service index data and the resource index data of the target network element into the pre-trained network element bearing capacity prediction model and outputting the maximum bearing capacity when the target network element meets the preset condition, the method further includes: acquiring sample data for constructing a network element bearing capacity prediction model, wherein the sample data comprises business index data and resource index data from one or more network elements; dividing the sample data into a training data set and a test data set; training the network element bearing capacity prediction model according to the training data set; and testing the trained network element bearing capacity prediction model according to the test data set.

In some embodiments, after obtaining the sample data that constructs the network element bearing capacity prediction model, the method further comprises: performing dimension reduction treatment on the sample data by using a Principal Component Analysis (PCA) method; and carrying out noise reduction and denoising treatment on the sample data subjected to the dimension reduction to obtain sample data meeting preset conditions.

In some embodiments, the traffic index data includes at least one of: the total number of 4G/5G users, the total number of long term evolution voice bearing VOLTE users, the registration success rate of the VOLTE users, the calling connection rate, the number of times of calling connection, the called connection rate and the number of times of called connection.

In some embodiments, the resource indicator data includes at least one of: network element memory occupancy rate, total network element memory idle amount, server occupancy rate, network connection number and bandwidth information.

In some embodiments, the target network element is a core network element.

According to another aspect of the present disclosure, there is also provided a network element capacity expansion device, including: the data acquisition module is used for acquiring service index data and resource index data of the target network element; the bearing capacity prediction module is used for inputting the service index data and the resource index data of the target network element into a pre-trained network element bearing capacity prediction model and outputting the maximum bearing capacity when the target network element meets the preset condition; and the network element capacity expansion module is used for carrying out capacity expansion processing on the target network element when the maximum bearing capacity of the target network element exceeds a preset threshold value.

According to another aspect of the present disclosure, there is also provided an electronic device including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the network element capacity expansion method of any of the above via execution of the executable instructions.

According to another aspect of the present disclosure, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the network element capacity expansion method of any one of the above.

According to another aspect of the present disclosure, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements the network element capacity expansion method of any one of the above.

The network element capacity expansion method, the device, the electronic equipment and the storage medium provided by the embodiment of the disclosure collect service index data and resource index data of a target network element; the service index data and the resource index data of the target network element are input into a pre-trained network element bearing capacity prediction model, and the maximum bearing capacity of the target network element when the target network element meets preset conditions is output; and when the maximum bearing capacity of the target network element exceeds a preset threshold, performing capacity expansion processing on the target network element. According to the embodiment of the disclosure, the problem of postponement caused by manually monitoring the data and then expanding the capacity of the network element is avoided, the real-time capacity expansion of the network element is realized, the data prediction is performed by using the network bearing capacity prediction model, the service index data and the resource index data of the network element are monitored and analyzed in real time, the data are input into the prediction model for calculation, the maximum bearing capacity of the target network element when the target network element meets the preset condition can be timely obtained, and the accuracy and timeliness of capacity expansion decision are improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.

Fig. 1 is a schematic diagram illustrating capacity expansion of a related art network element in an embodiment of the disclosure;

FIG. 2 illustrates a system architecture diagram of an application embodiment of the disclosed embodiments;

fig. 3 is a flowchart illustrating a network element capacity expansion method in an embodiment of the disclosure;

fig. 4 is a flowchart illustrating yet another network element capacity expansion method in an embodiment of the disclosure;

fig. 5 is a flowchart illustrating yet another network element capacity expansion method in an embodiment of the disclosure;

fig. 6 is a schematic diagram of a network element capacity expansion system in an embodiment of the disclosure;

fig. 7 shows a specific flow chart of network element capacity expansion in an embodiment of the disclosure;

fig. 8 is a schematic diagram of a network element capacity expansion device in an embodiment of the disclosure;

FIG. 9 shows a block diagram of an electronic device in an embodiment of the disclosure;

fig. 10 shows a schematic diagram of a computer-readable storage medium in an embodiment of the disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.

For ease of understanding, before describing embodiments of the present disclosure, several terms referred to in the embodiments of the present disclosure are first explained as follows:

as mentioned in the foregoing background, as operator user data increases, the increase in signaling link load between core network devices may indeed result in limited processing capacity of some network elements and may cause a series of problems including network security risks and signaling storms. For this case, a capacity-expanding core network element is a necessary solution. By increasing the processing power of the network element, the ever increasing user data and signalling load can be better handled. The manner in which the capacity is expanded may include adding hardware resources, performing network optimization and tuning to improve efficiency, or replacing existing devices with higher performance devices. Fig. 1 shows a diagram of a capacity expansion process of a network element in the related art, as shown in fig. 1, network management monitoring is performed on each network element under a monitoring module, indexes such as a CPU (central processing unit), a memory, a bandwidth, an MO (mobile object) (Mobile Originated, terminal initiated) success rate, a registration success rate and the like are observed, when the indexes show abnormality, an alarm is initiated, and when a deployment person finds the alarm, the network element is manually deployed, so that the capacity expansion of the network element is realized.

However, current dilatation schemes have a postponement. In general, the capacity expansion is considered after the network element has problems such as alarm or performance degradation, which may cause that the processing capacity cannot be increased in time before the signaling storm occurs, thereby causing damage to the whole core network.

In order to avoid such a situation, an embodiment of the present disclosure proposes a network element capacity expansion method, and a detailed description is given below of a specific implementation of the embodiment of the present disclosure with reference to the accompanying drawings.

Fig. 2 shows a schematic diagram of an exemplary application system architecture to which the network element capacity expansion method in the embodiments of the present disclosure may be applied. As shown in fig. 2, the system architecture may include a terminal device 201, a network 202, and a server 203.

The network 202 may be a wired network or a wireless network, and is a medium for providing a communication link between the terminal device 201 and the server 203.

Alternatively, the wireless network or wired network described above uses standard communication techniques and/or protocols. The network is typically the Internet, but may be any network including, but not limited to, a local area network (Local Area Network, LAN), metropolitan area network (Metropolitan Area Network, MAN), wide area network (Wide Area Network, WAN), mobile, wired or wireless network, private network, or any combination of virtual private networks. In some embodiments, data exchanged over a network is represented using techniques and/or formats including HyperText Mark-up Language (HTML), extensible markup Language (Extensible MarkupLanguage, XML), and the like. All or some of the links may also be encrypted using conventional encryption techniques such as secure sockets layer (Secure Socket Layer, SSL), transport layer security (Transport Layer Security, TLS), virtual private network (Virtual Private Network, VPN), internet security protocol (Internet Protocol Security, IPSec), etc. In other embodiments, custom and/or dedicated data communication techniques may also be used in place of or in addition to the data communication techniques described above.

The terminal device 201 may be a variety of electronic devices including, but not limited to, smart phones, tablet computers, laptop portable computers, desktop computers, smart speakers, smart watches, wearable devices, augmented reality devices, virtual reality devices, and the like.

Alternatively, the clients of the applications installed in different terminal devices 201 are the same, or clients of the same type of application based on different operating systems. The specific form of the application client may also be different based on the different terminal platforms, for example, the application client may be a mobile phone client, a PC client, etc.

The server 203 may be a server that provides various services, such as a background management server that provides support for devices operated by the user with the terminal apparatus 101. The background management server can analyze and process the received data such as the request and the like, and feed back the processing result to the terminal equipment.

Optionally, the server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs (Content Delivery Network, content delivery networks), basic cloud computing services such as big data and artificial intelligence platforms, and the like.

Those skilled in the art will appreciate that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative, and that any number of terminal devices, networks, and servers may be provided as desired. The embodiments of the present disclosure are not limited in this regard.

Under the system architecture, the network element capacity expansion method provided in the embodiments of the present disclosure may be executed by any electronic device having computing processing capability.

In some embodiments, the network element capacity expansion method provided in the embodiments of the present disclosure may be executed by a terminal device of the above system architecture; in other embodiments, the network element capacity expansion method provided in the embodiments of the present disclosure may be performed by a server in the system architecture described above; in other embodiments, the capacity expansion method provided in the embodiments of the present disclosure may be implemented by the terminal device and the server in the system architecture in an interactive manner.

Fig. 3 shows a flow chart of a network element capacity expansion method in an embodiment of the present disclosure, and as shown in fig. 3, the network element capacity expansion method provided in the embodiment of the present disclosure includes the following steps:

s302, collecting service index data and resource index data of a target network element.

It should be noted that the target network element may be any network element, and in the embodiment of the present disclosure, the target network element may be, but is not limited to, an AMF (Access and Mobility Management Function ) network element, an SMF (Session Management Function, session management function) network element, an MME (Mobility Management Entity ) network element, or the like.

In some embodiments of the present disclosure, the target network element may be a core network element, which is a key component of a communication network and is responsible for providing high-speed, reliable, and secure data transmission and routing services. The load-bearing capacity of the core network determines the performance and capacity of the overall network. With the increase of traffic, the increase of the number of users, the introduction of new technologies, etc., the core network element may suffer from insufficient bearing capacity. The capacity expansion processing of the core network element can improve the bearing capacity and performance of the core network and ensure the stable operation and high-quality service of the network. This is critical to meeting the ever-increasing communications demands and to providing a good quality user experience.

The service index data of the target network element refers to related index data for evaluating the current service of the target network element. Business index data in embodiments of the present disclosure may include, but is not limited to, the following:

1. total number of users: representing the total number of mobile subscribers connected to the target network element. In some embodiments, when the target network element is a network element of the 4G core network, the total number of users may be the total number of 4G mobile users connected to the target network element; in some embodiments, when the target network element is a network element of a 5G core network, the total number of users may be the total number of 5G mobile users connected to the target network element.

2. Total number of long term evolution voice bearer (Voice over Long Term Evolution, voLTE) users: representing the total number of users using Long Term Evolution (LTE) for voice communication at the target network element.

VOLTE user registration success rate: the proportion of users who use the VOLTE technology for voice communication that successfully connect to the target network element during registration is measured.

4. Calling completing rate: representing the proportion of users connected to the target network element that initiated the calling call and successfully established the connection.

5. Number of caller turns on: indicating the number of calling calls successfully established by the user connected to the target network element.

6. Called call completing rate: representing the proportion of users connected to the target network element that received the call request and successfully established the connection.

7. Called number of turns on: indicating the number of called calls successfully established by the user connected to the target network element.

The resource index data of the target network element refers to related index data for evaluating the resources of the target network element. The resource indicator data may include, but is not limited to, the following:

1. network element memory occupancy rate: the method represents the current memory usage percentage of the target network element, and can be used for evaluating the utilization rate of the memory of the target network element and the potential memory pressure.

2. Total amount of network element memory idle: the total amount of the memory space currently available for the target network element is indicated and can be used for knowing the remaining condition of the memory resource of the target network element.

3. Server occupancy: representing the proportion of the task currently being performed by the server CPU where the target network element is located.

4. Network connection number: the number of network connections representing the current activity of the target network element can be used to monitor the connection load and connection status of the target network element to help evaluate network performance and capacity.

5. Bandwidth information: representing network bandwidth information used by the target network element.

S304, inputting the business index data and the resource index data of the target network element into a pre-trained network element bearing capacity prediction model, and outputting the maximum bearing capacity when the target network element meets the preset condition.

It should be noted that the network element load capacity prediction model may be any model for predicting the load capacity of the target network element. The network element bearing capacity prediction model estimates and predicts the maximum bearing capacity of the target network element under certain conditions by establishing a relation and an algorithm based on the service index data and the resource index data of the target network element. The network element load prediction model may be trained and constructed by monitoring and analyzing historical data of the target network element. In the model training process, the interaction and correlation between different indexes are captured by learning and analyzing a large amount of business index data and resource index data.

After the network element bearing capacity prediction model finishes training, the network element bearing capacity prediction model outputs the maximum bearing capacity of the target network element when the preset condition is met by analyzing the input currently acquired service index data and resource index data.

The carrying capacity is the carrying capacity of the network element, and the preset conditions required to be met by the embodiment of the disclosure can be the ensuring registration success rate and the completing rate. The registration success rate designates a preset condition of the registration success rate set by the target network element, and the call completing rate designates a preset condition of the call completing rate set by the target network element. The method and the device output the maximum bearing capacity of the target network element under the condition of meeting the registration success rate and the call completing rate, can ensure that the network element can be timely expanded under the scene of user proliferation, reduce the probability of network element overload and improve the user experience.

And S306, if the maximum bearing capacity of the target network element exceeds a preset threshold, performing capacity expansion processing on the target network element.

If the predicted maximum load exceeds the preset threshold, this indicates that the current configuration of the target network element has reached or exceeded the preset limit. In this case, the target network element is subjected to capacity expansion processing to increase the bearing capacity of the target network element, thereby meeting higher service requirements.

It can be seen from the foregoing that, in the network element capacity expansion method provided by the embodiment of the present disclosure, the maximum load capacity of the target network element is estimated by using the collected current data of the target network element and the target network element load capacity prediction model, and whether the capacity expansion processing needs to be performed is determined according to the preset threshold value, so that the current load capacity condition of the target network element can be more accurately estimated, the capacity of the target network element can be expanded in time, the capacity of the network element can be dynamically and timely expanded, the occurrence of hysteresis condition of the capacity expansion of the network element is avoided, overload operation of the network element is avoided according to the resource load condition, and stability and performance of the network are ensured. Meanwhile, the network manager can be helped to plan and manage network resources better, and capacity expansion preparation is made in advance so as to adapt to the requirement of future service growth.

It should be noted that, in the technical solution of the present disclosure, the acquiring, storing, using, processing, etc. of data all conform to relevant regulations of national laws and regulations, and various types of data such as personal identity data, operation data, behavior data, etc. relevant to individuals, clients, crowds, etc. acquired in the embodiments of the present disclosure have been authorized.

In some embodiments of the present disclosure, as shown in fig. 4, before inputting the service index data and the resource index data of the target network element into the pre-trained network element bearing capacity prediction model, and outputting the maximum bearing capacity when the target network element meets the preset condition, the method further includes the following steps:

s402, obtaining sample data for constructing a network element bearing capacity prediction model, wherein the sample data comprises business index data and resource index data from one or more network elements;

s404, dividing sample data into a training data set and a test data set;

s406, training the network element bearing capacity prediction model according to the training data set;

and S408, testing the trained network element bearing capacity prediction model according to the test data set.

It should be noted that, the sample data is a data set for constructing the network element bearing capacity prediction model, and the specific construction process is divided into a training process and a test verification process, so that the obtained sample data is correspondingly divided into a training data set and a test data set, wherein the training data set is used for training the network element bearing capacity prediction model, and the test data set is used for testing and verifying the trained network element bearing capacity prediction model, so that the maximum bearing capacity output by the network element bearing capacity prediction model is more accurate.

It should be further noted that, the sample data is from service index data and resource index data of one or more network elements, and it should be understood that these service index data and resource index data are historical data of corresponding network elements, and the neural network model is trained according to these historical data to obtain a network element bearing capacity prediction model, which can better adapt to the characteristics and changes of actual data, so as to improve the accuracy and precision of prediction.

In some embodiments of the present disclosure, the network element load prediction model is trained from a support vector regression (Support Vector Regression, SVR) model. Support vector regression is a regression method based on a support vector machine (Support Vector Machine, SVM) algorithm, which fits data by finding an optimal hyperplane and predicts based on the distance of discrete sample points on the hyperplane from the hyperplane. SVR is applicable to nonlinear relationships and is capable of processing high-dimensional data.

By training using a Support Vector Regression (SVR) model, the load bearing capacity of the network element can be more accurately predicted by means of the characteristics and strong fitting capacity of the model. In the training process, the model learns a model capable of carrying out regression prediction on the bearing capacity of the target network element according to the historical business index data and the resource index data.

In some embodiments of the present disclosure, as shown in fig. 5, after obtaining the sample data for constructing the network element bearing capacity prediction model, the method further includes the following steps:

s502, performing dimension reduction processing on the sample data by using a principal component analysis (Principal Component Analysis, PCA) method;

s504, carrying out noise reduction and denoising treatment on the sample data after the dimension reduction to obtain sample data meeting preset conditions.

It should be noted that principal component analysis (Principal Component Analysis, PCA) is a dimension reduction method that reduces the dimension of data by finding the principal features in the original data and converting them into a set of principal components of lower dimensions. By reducing the dimensionality of the data, redundant feature information can be removed while retaining the primary features that have a greater contribution to the data change, thereby reducing the complexity of the data set.

In the network element capacity expansion method provided by the embodiment of the disclosure, the purpose of performing the dimension reduction processing on the sample data by using the PCA method is to reduce the computational complexity and improve the efficiency of model training and prediction. By selecting an appropriate number of principal components, the dimensions of the sample can be reduced while maintaining the principal characteristics of the data.

Furthermore, in the sample data after the dimension reduction, there may be some noise or abnormal value, which may adversely affect the performance of the model. Therefore, the accuracy and stability of model prediction can be further improved by carrying out noise reduction on the sample data after dimension reduction.

In some embodiments of the present disclosure, taking the network element capacity expansion system diagram disclosed in fig. 6 and the network element capacity expansion specific flowchart disclosed in fig. 7 as examples, a specific exemplary explanation is made for implementation of the embodiments of the present disclosure:

as shown in fig. 6, the network element capacity expansion system includes: a monitoring module 601, a calculating module 602 and a deployment module 603.

The functions realized by the modules comprise the following steps:

the monitoring module 601 is mainly used for monitoring service index data and system resource index data of the core network element. The system resource index comprises a server memory, a CPU and bandwidth information.

The calculation module 602 mainly constructs a network element bearing capacity prediction model based on an SVR algorithm according to historical data of each network element, and calculates the maximum bearing capacity of the network element according to the network element bearing capacity prediction model on the premise that the core network element operates normally.

Deployment module 603: the monitoring module 601 sends the bearing capacity of the network element to the deployment module 603 in real time, the deployment module 603 calculates a threshold value according to the calculation module 602 to judge whether the network element is overloaded, and if so, the network element is deployed to realize the capacity expansion of the target network element.

As shown in fig. 7, the monitoring module 601 mainly includes two parts: monitoring service indexes and resource indexes;

monitoring service indexes: 4G/5G user total number, volte user registration success rate, calling call completing rate, calling number of times, called number of times, etc.;

monitoring resource indexes: memory occupancy rate of each network element, total memory idle amount, CPU occupancy rate of a target server, network connection number and the like;

and (3) reducing the dimension of the collected historical data by adopting PCA (principal component analysis), and keeping some most important characteristics of the data with high dimension, and removing noise and unimportant characteristics, thereby achieving the aim of improving the data processing speed.

The processed data is divided into a training data set and a test data set according to 7:3, a support vector regression model SVR (Support Vector Regression) is constructed, and a Gaussian kernel function is selected as the kernel function of the SVR.

Training a network model by utilizing a training data set, training a network element bearing capacity prediction model suitable for predicting the maximum bearing capacity of the network element, calculating the maximum bearing capacity of the network element on the premise of ensuring the registration success rate and the call completing rate by the network element bearing capacity prediction model, calculating the threshold value of the network element and pushing the threshold value to a deployment module.

The monitoring module collects data in real time and sends the data to the deployment module, and the deployment module judges whether the network element needs capacity expansion or not through a threshold value.

According to the embodiment of the disclosure, the maximum bearing capacity of each network element is calculated by utilizing the SVM model on the premise of ensuring the registration success rate and the call completing rate according to the historical data index collected by each network element, so that the network element can be timely expanded in a scene of user surge, the probability of network element overload on the existing network is reduced, and the user experience is improved.

Based on the same inventive concept, the embodiments of the present disclosure also provide a network element capacity expansion device, as described in the following embodiments. Since the principle of solving the problem of the embodiment of the device is similar to that of the embodiment of the method, the implementation of the embodiment of the device can be referred to the implementation of the embodiment of the method, and the repetition is omitted.

Fig. 8 is a schematic diagram of a network element capacity expansion device in an embodiment of the disclosure, where, as shown in fig. 8, the device includes:

a data acquisition module 801, configured to acquire service index data and resource index data of a target network element;

the load capacity prediction module 802 is configured to input service index data and resource index data of a target network element into a pre-trained network element load capacity prediction model, and output a maximum load capacity when the target network element meets a preset condition;

and the network element capacity expansion module 803 is configured to perform capacity expansion processing on the target network element when the maximum bearing capacity of the target network element exceeds a preset threshold.

In some embodiments of the present disclosure, the load-bearing capacity prediction module 802 is further configured to obtain sample data for constructing a network element load-bearing capacity prediction model, where the sample data includes traffic index data and resource index data from one or more network elements; dividing the sample data into a training data set and a test data set; training the network element bearing capacity prediction model according to the training data set; and testing the trained network element bearing capacity prediction model according to the test data set.

In some embodiments of the present disclosure, the load-bearing capacity prediction module 802 is further configured to perform a dimension reduction process on the sample data by using a principal component analysis PCA method; and carrying out noise reduction and denoising treatment on the sample data subjected to the dimension reduction to obtain sample data meeting preset conditions.

It should be noted that the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in the above method embodiments. It should be noted that the modules described above may be implemented as part of an apparatus in a computer system, such as a set of computer-executable instructions.

Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.

An electronic device 900 according to such an embodiment of the present disclosure is described below with reference to fig. 9. The electronic device 900 shown in fig. 9 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.

As shown in fig. 9, the electronic device 900 is embodied in the form of a general purpose computing device. Components of electronic device 900 may include, but are not limited to: the at least one processing unit 910, the at least one storage unit 920, and a bus 930 connecting the different system components (including the storage unit 920 and the processing unit 910).

Wherein the storage unit stores program code that is executable by the processing unit 910 such that the processing unit 910 performs steps according to various exemplary embodiments of the present disclosure described in the above-described "exemplary methods" section of the present specification. For example, the processing unit 910 may perform the following steps of the method embodiment described above: collecting service index data and resource index data of a target network element; inputting the service index data and the resource index data of the target network element into a pre-trained network element bearing capacity prediction model, and outputting the maximum bearing capacity when the target network element meets the preset condition; and if the maximum bearing capacity of the target network element exceeds the preset threshold, performing capacity expansion processing on the target network element.

The storage unit 920 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 9201 and/or cache memory 9202, and may further include Read Only Memory (ROM) 9203.

The storage unit 920 may also include a program/utility 9204 having a set (at least one) of program modules 9205, such program modules 9205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

The bus 930 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 900 may also communicate with one or more external devices 940 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 900, and/or any devices (e.g., routers, modems, etc.) that enable the electronic device 900 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 950. Also, electronic device 900 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 960. As shown, the network adapter 960 communicates with other modules of the electronic device 900 over the bus 930. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 900, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.

In particular, according to embodiments of the present disclosure, the process described above with reference to the flowcharts may be implemented as a computer program product comprising: and the computer program realizes the network element capacity expansion method when being executed by the processor.

In an exemplary embodiment of the present disclosure, a computer-readable storage medium, which may be a readable signal medium or a readable storage medium, is also provided. Fig. 10 illustrates a schematic diagram of a computer-readable storage medium in an embodiment of the present disclosure, where a program product capable of implementing the method of the present disclosure is stored on the computer-readable storage medium 1000 as illustrated in fig. 10. In some possible implementations, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.

More specific examples of the computer readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

In this disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Alternatively, the program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

In particular implementations, the program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.

From the description of the above embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.

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 disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general 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.

Claims (10)

1. A network element capacity expansion method, comprising:

collecting service index data and resource index data of a target network element;

inputting the business index data and the resource index data of the target network element into a pre-trained network element bearing capacity prediction model, and outputting the maximum bearing capacity when the target network element meets preset conditions;

and if the maximum bearing capacity of the target network element exceeds a preset threshold, performing capacity expansion processing on the target network element.

2. The network element capacity expansion method according to claim 1, wherein before inputting the service index data and the resource index data of the target network element into a pre-trained network element capacity prediction model and outputting the maximum capacity when the target network element meets a preset condition, the method further comprises:

acquiring sample data for constructing a network element bearing capacity prediction model, wherein the sample data comprises business index data and resource index data from one or more network elements;

dividing the sample data into a training data set and a test data set;

training the network element bearing capacity prediction model according to the training data set;

and testing the trained network element bearing capacity prediction model according to the test data set.

3. The network element capacity expansion method according to claim 2, wherein after obtaining the sample data constructing the network element capacity prediction model, the method further comprises:

performing dimension reduction treatment on the sample data by using a Principal Component Analysis (PCA) method;

and carrying out noise reduction and denoising treatment on the sample data subjected to the dimension reduction to obtain sample data meeting preset conditions.

4. The network element capacity expansion method according to claim 1, wherein the network element capacity prediction model is trained according to a support vector regression SVR model.

5. The network element capacity expansion method according to claim 1, wherein the service indicator data includes at least one of: the total number of 4G/5G users, the total number of long term evolution voice bearing VOLTE users, the registration success rate of the VOLTE users, the calling connection rate, the number of times of calling connection, the called connection rate and the number of times of called connection.

6. The network element capacity expansion method according to claim 1, wherein the resource index data includes at least one of: network element memory occupancy rate, total network element memory idle amount, server occupancy rate, network connection number and bandwidth information.

7. The network element capacity expansion method according to claim 1, wherein the target network element is a core network element.

8. A network element capacity expansion device, comprising:

the data acquisition module is used for acquiring service index data and resource index data of the target network element;

the bearing capacity prediction module is used for inputting the service index data and the resource index data of the target network element into a pre-trained network element bearing capacity prediction model and outputting the maximum bearing capacity when the target network element meets the preset condition;

and the network element capacity expansion module is used for carrying out capacity expansion processing on the target network element when the maximum bearing capacity of the target network element exceeds a preset threshold value.

9. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the network element capacity expansion method of any of claims 1 to 7 via execution of the executable instructions.

10. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the network element expansion method of any of claims 1 to 7.