CN111885618B

CN111885618B - Network performance optimization method and device

Info

Publication number: CN111885618B
Application number: CN202010583061.0A
Authority: CN
Inventors: 蓝天明; 冯一真; 黄又平
Original assignee: Guangzhou Henghao Data Technology Co ltd
Current assignee: Guangzhou Henghao Data Technology Co ltd
Priority date: 2020-06-23
Filing date: 2020-06-23
Publication date: 2024-03-29
Anticipated expiration: 2040-06-23
Also published as: CN111885618A

Abstract

The application discloses a network performance optimization method and device, wherein the method comprises the following steps: arranging a performance optimization flow aiming at a target network based on a plurality of graphic assemblies, wherein the performance optimization flow comprises a plurality of task nodes which are represented by graphics corresponding to the graphic assemblies, and one task node represents one performance optimization step aiming at the target network; and calling at least one graphic component in the plurality of graphic components to execute a corresponding performance optimization step based on the execution logic of the performance optimization flow so as to execute optimization operation on the preset performance index of the target network when the preset optimization condition is met, thereby realizing the performance optimization of the target network. The method and the device disclosed by the application can automatically execute the performance optimization operation on the target network without too much depending on manual participation, so that the cost for optimizing the performance of the network equipment can be reduced, and the efficiency for optimizing the performance of the network equipment is improved.

Description

Network performance optimization method and device

Technical Field

The present disclosure relates to the field of communications technologies, and in particular, to a method and an apparatus for optimizing network performance.

Background

After the wireless communication network is established, network performance indexes need to be optimized continuously, for example, antenna direction angles, antenna transmitting power, base station capacity improvement and the like need to be adjusted continuously.

Currently, these metrics are optimized mainly by the skilled person. However, with the development of wireless communication technology, the number of wireless network devices (such as wireless base stations) is exploded, and the manner of manually optimizing the related indexes is high in cost and low in efficiency, so that the method is not applicable any more, and a performance optimization method with lower cost and higher efficiency is needed to be proposed.

It should be noted that the information disclosed in this background section is only for the purpose of increasing the understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person of ordinary skill in the art.

Disclosure of Invention

The embodiment of the application provides a network performance optimization method and device, which are used for reducing the cost of optimizing the performance of network equipment and improving the efficiency of optimizing the performance of the network equipment.

In a first aspect, an embodiment of the present application provides a network performance optimization method, including:

arranging a performance optimization flow aiming at a target network based on a plurality of graphic assemblies, wherein the performance optimization flow comprises a plurality of task nodes which are represented by graphics corresponding to the graphic assemblies, and one task node represents one performance optimization step aiming at the target network;

And calling at least one graphic component in the plurality of graphic components to execute a corresponding performance optimization step based on the execution logic of the performance optimization flow so as to execute optimization operation on the preset performance index of the target network when the preset optimization condition is met, thereby realizing the performance optimization of the target network.

In a second aspect, embodiments of the present application further provide a network performance optimization apparatus, including:

the flow arranging module is used for arranging and obtaining a performance optimization flow aiming at a target network based on a plurality of graphic assemblies, wherein the performance optimization flow comprises a plurality of task nodes which are represented by graphics corresponding to the graphic assemblies, and one task node represents one performance optimization step aiming at the target network;

and the performance optimization module is used for calling at least one graphic component in the plurality of graphic components to execute a corresponding performance optimization step based on the execution logic of the performance optimization flow so as to execute optimization operation on the preset performance index of the target network when the preset optimization condition is met, thereby realizing the performance optimization of the target network.

In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor and computer-executable instructions stored on the memory and executable on the processor, which when executed by the processor, perform the steps of the method as described in the first aspect above.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium for storing computer-executable instructions which, when executed by a processor, implement the steps of the method as described in the first aspect above.

The above-mentioned at least one technical scheme that this application embodiment adopted can reach following beneficial effect: the performance optimization flow aiming at the target network can be obtained by arranging the preset graphic components, and the corresponding performance optimization step can be automatically carried out by automatically calling at least one graphic component in the plurality of graphic components according to the execution logic of the performance optimization flow, so that the performance optimization operation is automatically carried out on the target network without too much depending on manual participation, the cost for optimizing the performance of the network equipment can be reduced, and the efficiency for optimizing the performance of the network equipment can be improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:

fig. 1 is a flow chart of a network performance optimization method according to an embodiment of the present application.

Fig. 2 is a schematic diagram of a network performance optimization flow provided in an embodiment of the present application.

Fig. 3 is a schematic diagram of another network performance optimization flow provided in an embodiment of the present application.

Fig. 4 is a schematic diagram of another network performance optimization flow provided in an embodiment of the present application.

Fig. 5 is a schematic structural diagram of a network performance optimization device according to an embodiment of the present application.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

In order to solve the problems of high cost and low efficiency of network performance optimization in the related art, the embodiment of the application provides a network performance optimization method and device. The network performance optimization method and device provided by the embodiment of the application can be applied to an operation and maintenance system of a communication network, the operation and maintenance system is provided with a visual operation interface, and in the visual operation interface, a network performance optimization flow can be arranged by dragging a graph corresponding to a preset graph component, and the like. The operation and maintenance system can be operated in an electronic device, such as a terminal device or a server device. In other words, the above method may be performed by software or hardware installed in a terminal device or a server device. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.

In the embodiment of the present application, the target network refers to any communication network that needs to perform network performance optimization, and the communication network may be in a city or in a provincial range. Network devices in the target network include, but are not limited to, one or more of base stations and cells.

Fig. 1 illustrates a network performance optimization method provided in an embodiment of the present application. As shown in fig. 1, the method may include the steps of:

step 101, arranging and obtaining a performance optimization flow aiming at a target network based on a plurality of graphic assemblies, wherein the performance optimization flow comprises a plurality of task nodes which are represented by graphics corresponding to the graphic assemblies, and one task node represents one performance optimization step aiming at the target network.

As described above, the method provided in the embodiment of the present application may be applied to an operation and maintenance system of a communication network, where the operation and maintenance system has a visual operation interface, and in the visual operation interface, a network performance optimization procedure may be arranged by dragging a graph corresponding to a preset graph component, so the step 101 may specifically include: and in the visual operation interface, receiving preset operations (such as operations of dragging, clicking, sliding and the like) of the graphics corresponding to the graphic components, and drawing to obtain a performance optimization flow aiming at the target network. The graphic components can be connected through connecting wires to form a orderly conditional network performance optimization flow, and when the network performance optimization flow is arranged, corresponding judgment conditions or parameters can be configured in the corresponding graphic components, which is equivalent to setting a self-healing strategy for network equipment in the network performance optimization flow.

It will be appreciated that a communication network is often complex, and that many network devices are involved, and therefore many ways of optimizing performance are required, and that by means of the above step 101, a plurality of network performance optimization flows adapted to different requirements can be arranged. It can be appreciated that expert experience of network performance optimization can be cured by way of flow programming; and because the network performance optimization is decoupled from the actual service, more network performance optimization strategies can be conveniently added, an operation and maintenance expert does not need to master programming skills, and the optimization conditions, operations and steps can be recorded in a mode similar to building blocks, so that an automatic task for optimizing the network performance is formed, the network performance optimization cost is reduced to the maximum extent, and the network performance optimization efficiency is improved.

In an embodiment of the present application, the plurality of graphic components may include, but are not limited to, at least two of the following graphic components: the system comprises a data access component, an index judging component, a file operating component, an instruction component, a database component, a general computing component, a data processing component, an AI component, a file operating component and the like.

The data access component is used for acquiring preset performance index data of the network equipment in the target network.

The index judging component is used for judging whether the preset performance index of the network equipment in the target network is abnormal or not.

The database component is used for accessing a performance index list which is stored in the database in advance and meets preset optimization conditions, and judging whether the preset performance index of the network equipment in the target network meets the preset optimization conditions or not based on the performance index list.

The file operation component is used for acquiring preset information of network equipment in a target network and determining the reason of the degradation of the preset performance index of the network equipment based on the preset information, wherein the preset information comprises at least one of configuration parameters, engineering parameters, alarm information and running state information.

And the general calculation component is used for screening network equipment needing to be optimized based on the data acquired by the data access component.

And the data processing component is used for processing the data acquired by the data access component.

And the AI component is used for predicting the preset performance index of the network equipment in the target network based on the historical data of the preset performance index and a preset AI prediction model of the network equipment in the target network. The preset AI prediction model is obtained by training historical data of preset AI algorithms and preset performance indexes of network equipment, and the preset AI algorithms can include, but are not limited to, long-Short-Term-memory networks (LSTM) and differential integration moving average autoregressive models (Autoregressive Integrated Moving Average model, ARIMA models) and the like.

And the instruction component is used for sending an instruction for executing the optimization operation to the network equipment in the target network.

And the report component is used for generating and displaying a performance optimization condition statistical report aiming at the target network.

Step 102, based on the execution logic of the performance optimization flow, invoking at least one graphic component in the plurality of graphic components to execute a corresponding performance optimization step, so as to execute an optimization operation on a preset performance index of the target network when a preset optimization condition is met, and realize performance optimization of the target network.

The optimization operation may include, but is not limited to, adjustment of preset parameters, where the preset parameters include configuration parameters of network devices (such as cells or base stations), engineering parameters, and the like, the configuration parameters may include minimum access level, transmit power, and the like, and the engineering parameters may include data such as base station ID, azimuth angle, inclination, longitude and latitude (base station position), and the like.

The following describes, by way of two examples, a performance optimization procedure arranged based on step 101 and execution logic based on the performance optimization procedure, a procedure of calling at least one graphic component of the plurality of graphic components to execute a corresponding performance optimization step, so as to execute an optimization operation on a preset performance index of the target network when a preset optimization condition is satisfied, thereby realizing performance optimization of the target network.

It should be noted that, according to the network performance optimization method provided in the embodiment of the present application, different network performance optimization flows for repairing different network devices may be arranged through the step 101, and the method is not limited to the two examples described below, so as to implement automatic optimization of the same or different network performances for different network devices.

Example 1

Fig. 2 shows an example of performance optimization steps involved in a performance optimization procedure programmed for a network device (e.g., bad cell) with degraded performance. In this example, the above-mentioned multiple graphic components may include one or more of a data access component, a database component, an index judgment component, a file operation component, an instruction component, and a report component, as shown in fig. 2, the performance optimization procedure for the network device with performance degradation may include the following steps, that is, the above-mentioned step 102 may include:

step 201, start.

Step 202, a data access component is invoked to acquire performance data of network equipment in a target network in a target period.

The target period may be a period of a specified duration from the current time onward or backward, also referred to as the current period, for example, assuming that the current time is 8:00 on the 6 th month of 2020 and the specified duration is 24 hours, the target period may be a period of 8:00 on the 19 th month of 2020 to 8:00 on the 20 th month of 2020, or the target period may be a period of 8:00 on the 20 th month of 2020 to 8:00 on the 21 th month of 2020.

In step 202, performance data of all network devices in the target network may be acquired, or performance data of only a part of the specified network devices may be acquired. Accordingly, the performance data may include statistics of the obtained preset performance indicators of the network device, where the preset performance indicators may include, but are not limited to, one or more of the following indicators: the radio resource control (Radio Resource Control, RRC) set up success rate, evolved radio access bearer (Evolved Radio Access Bearer, E-RAB) drop rate, S1 handover success rate, X2 handover success rate, etc., and the performance data of interest under different policies are different and are not listed.

Step 203, invoking an index judging component to judge whether a preset performance index of the network equipment is deteriorated or not based on the performance data; if so, step 204 is performed, otherwise, step 209 is performed.

It can be appreciated that the manner of determining whether it is degraded is different for different preset performance indicators. Taking the RRC connection success rate as an example, if it falls below a preset threshold (e.g., 90%), the RRC connection success rate is considered to be degraded.

Optionally, if step 203 determines that the preset performance index of the network device is degraded, the following steps may be further performed: and calling a database component, accessing a performance index list which is stored in the database in advance and meets preset optimization conditions, judging whether the preset performance index meets the preset optimization conditions or not based on the performance index list, and executing step 204 again when the judgment result of the step is yes.

Step 204, calling a file operation component, obtaining preset information of the network equipment, and determining a reason for degradation of the preset performance index of the network equipment based on the preset information.

The preset information includes at least one of configuration parameters, engineering parameters, alarm information and operation state information, and details about the configuration parameters and the engineering parameters are described above, and the operation state information may include whether normal, activated, deactivated, and the like.

In one example, the cause of the degradation of the preset performance index of the network device may be determined based on the alarm information, for example, if the RRC connection success rate of the network device is reduced within a target period and the hardware of the network device alarms within the target period, it may be considered that the hardware failure affects the user access, thereby resulting in the reduction of the RRC connection success rate.

In another example, the cause of the degradation of the preset performance index of the network device may be determined based on the configuration parameter and/or the engineering parameter, for example, if the RRC connection success rate of the network device is reduced within the target period and the configuration parameter and/or the engineering parameter of the network device are changed within the target period, it may be considered that the change of the configuration parameter and/or the engineering parameter causes the reduction of the RRC connection success rate.

It will be appreciated that a determination rule for determining a cause of degradation of a preset performance index of a network device based on preset information may be preset, and the cause of degradation of the preset performance index is located according to the rule, where the above two examples are two exemplary rules, which are not listed herein.

Step 205, generating a performance optimization scheme based on the reasons.

The performance optimization scheme generated based on the reasons can determine configuration parameters and/or engineering parameters to be adjusted, adjustment amplitude, callback mechanism and the like according to time, space, software and hardware resources and the like. Wherein "time" represents the adjustment time (at what time to adjust); the space represents the position of the network equipment, whether the network equipment is in a dense area, an urban area or a suburban area, and the like; "hardware" refers to the number of antennas, network capacity, etc.; the callback mechanism is to adjust the callback mechanism back to the original state after the callback mechanism is adjusted for a prescribed time, so that the exception caused by excessive or too fast adjustment is avoided. The optimization of the performance can be realized by setting information association rules of time, space and software and hardware resources in the performance optimization scheme and adjusting according to the association rules. In general, one degradation cause may correspond to at least one performance optimization scheme, and when the degradation cause is identified, the optimization may be achieved by retrieving and executing the corresponding optimization scheme (which may be a script).

And 206, calling an instruction component, and sending an instruction for executing the optimization operation to the network equipment based on the performance optimization scheme so as to enable the network equipment to execute the optimization operation. After executing step 206, the process may be ended in step 209, or the process may be ended in step 207, so as to track the optimization effect.

The network device can execute the specific instructions/scripts of the optimization scheme, so as to achieve the purpose of optimizing the preset performance index.

Step 207, invoking an index judgment component, tracking the change of the preset performance index of the network equipment, and determining the optimization effect of the preset performance index of the network equipment based on the change; if the optimization is expected, then step 209 is performed; otherwise, step 208 is performed.

Step 208, the performance optimization scheme generated in step 205 is modified (adjusted or replaced), and then step 206 is executed according to the modified performance optimization scheme.

Step 209, end.

Optionally, before step 209, the performance optimization procedure shown in fig. 2 may further include: and calling a report component to generate and display a performance optimization condition statistical report aiming at the target network, wherein the performance optimization condition comprises at least one of the number of optimized network devices, the adjusted preset parameters, the adjustment trend of the preset parameters and the like.

The present example can draw a process of performance automatic analysis optimization of a network device (bad cell) with degraded performance using a unified graphical component, which can be parsed and automatically executed by a program. According to the process, the network performance optimization work is extremely complex, manual operation is easy to make mistakes, talent culture and cost are huge, automation of network performance optimization is realized by utilizing a process arrangement mode, experience processes can be clearly restored and optimized, experience can be rapidly accumulated, expert experience is gradually converted into digital productivity, and therefore network performance optimization cost is reduced, and network performance optimization efficiency is improved.

Example 2

Fig. 3 shows a schematic diagram of a performance optimization flow of automatic analysis optimization of traffic flow (abbreviated as traffic or traffic flow) of a network device (cell) in a long term evolution (Long Term Evolution, LTE) system, and fig. 4 shows performance optimization steps included in the performance optimization flow corresponding to fig. 3. The example is a process of automatically identifying the cell, associated parameters, alarms and other information of sudden decline of the total flow of the uplink and the downlink of the LTE, locating the reason thereof and adjusting the proper optimization parameters. When the tasks in the process are executed, the function of automatic analysis and optimization of the flow index of the corresponding cell can be realized, the traditional mode needs to build a system to realize the function, and the process arrangement method is also used, so that the system can be quickly and flexibly adjusted.

In example 2, the above-mentioned plurality of graphic components may include a database component, a general-purpose computing component, a data processing component, a file operation component, an instruction component, and a report component, and as shown in fig. 3, the traffic optimization procedure for a network device (cell) in LTE may include four phases: the data acquisition 31, the data processing 32, the data association analysis 33 and the performance adjustment optimization 34 specifically comprise the following task nodes:

node 300, start.

Node 301 sets the value of the target time variable (preset time), and when the preset time is reached, it proceeds to node 302.

Node 302 acquires traffic data of network devices in the target network in a first period, and then transfers to node 303.

The first period may be a period of time, such as one day, before the preset time, and in particular, the first period may be yesterday, assuming that the preset time is today.

Node 303 screens low-traffic network devices (network devices with traffic lower than a preset value) in the first period to obtain a first network device set, and unconditionally shifts to node 304.

And the node 304 and the data processing node screen out second network equipment with sudden drop of the flow from the first network equipment set based on a preset mode to form a second network equipment set.

The above-mentioned preset modes may include various modes, and two modes will be listed hereinafter, which are not described herein.

Node 305 determines that there is no traffic dip network device in the target network for the first period of time, and proceeds to node 315.

Node 306 obtains preset parameters and/or alarm information of the second network device, and unconditionally shifts to node 307.

Node 307, the data processing node, determines whether the preset parameters of the second network device have changed, and/or determines whether the second network device has an alarm, and unconditionally transitions to node 308.

Node 308, which saves the processing result of node 307, unconditionally shifts to node 309.

Node 309 determines an adjustable flow dip network device (second network device) in case of a change of the preset parameter of the second network device and/or in case of an alarm of the second network device, and unconditionally switches to node 310.

Node 310 generates a performance optimization scheme and an adjustment instruction for the second network device based on a preset rule, and unconditionally shifts to node 311.

The node 311 sends an adjustment instruction for the preset parameters of the second network device to the target network element, adjusts the preset parameters of the second network device, and unconditionally shifts to the node 312.

Node 312, save adjustment record, unconditionally go to node 313.

Node 313, end.

The specific steps included in the optimization flow shown in fig. 3 will be described below with reference to fig. 4, where, as shown in fig. 4, the optimization flow shown in fig. 3 includes the following steps:

step 401, start.

Step 402, in response to the setting operation, setting the value of the target time variable to a preset time.

The setting operation may be a setting operation performed by an operation staff or an expert on a visual interface of the operation and maintenance system, the preset time may be considered as a triggering condition for triggering a subsequent step of the process, and the preset time may be specific to year, month, day, hour and minute.

And step 403, when the preset time is reached, calling a data access component to acquire the flow data of the target network in the first period.

Step 404, invoking a general computing component to determine a first network device (first cell) in the target network, where the total traffic is less than or equal to a preset traffic (e.g. 100M) in the first period, based on traffic data of the target network in the first period, so as to obtain a first network device set.

Step 405, invoking a data processing component to screen out a second network device with sudden drop of the traffic from the first network device set based on a preset mode, so as to form a second network device set.

The above-mentioned preset modes may include various modes, and two modes are described below.

First, the above-mentioned preset mode includes: and calling a data access component to acquire flow data of the first network device in a second period (such as the last week), calling a data processing component to determine a second network device, in the first network device set, of which the flow in the first period exceeds a first preset amplitude relative to the average flow in the second period, so as to acquire the second network device set, wherein the second period is a historical period earlier than the first period, and the second period can be a week before the last day on the assumption that the first period is the last day.

For example, if, for a first network device, its yesterday's (first period) traffic is 50, its average traffic for the week (second period) before yesterday is 100, and the first preset amplitude is 30%, then the first network device's drop amplitude of yesterday's traffic (50) relative to the average traffic for the previous week (100) is 50%, exceeds the first preset amplitude, so it may be determined as a second network device with a sudden drop in traffic, and join the second set of network devices. The first preset amplitude may also be other values, as not limited herein.

Second, the above-mentioned multiple graphic components include AI components, and the above-mentioned preset mode includes: invoking a data access component to acquire flow data of the first network device in a third period; invoking an AI component to predict the predicted traffic of the first network device in the first period based on the traffic data of the first network device in the third period and a preset AI prediction model; and invoking a data processing component to determine a second network device, of which the descending amplitude of the real traffic of the first time period relative to the predicted traffic of the first time period exceeds a second preset amplitude, in the first network device set, so as to obtain a second network device set, wherein the third time period is a historical time period earlier than the first time period. The second preset amplitude may also be other values, as not limited herein.

For example, for a first network device, if its yesterday's (first period) traffic is 50, based on its yesterday's (third period) traffic data and a preset AI prediction model, it is predicted that the first network device's yesterday's predicted traffic is 100, and the second preset amplitude is 30%, then the first network device's real traffic (50) decreases by 50% relative to yesterday's predicted traffic (100) by more than the second preset amplitude, so it can be determined as a second network device whose traffic drops and join the second set of network devices.

The predicted traffic more reflects the traffic trend of a network device than the average traffic, and thus the second method can be a preferable method. In addition, in order to accurately determine whether the traffic of the first network device suddenly drops in the first period, the second period may not be too long, and the third period may not be too short, and generally, the length of the second period (e.g., one week) is smaller than the length of the third period (e.g., one month).

Step 406, determining whether the second network device set is empty, if yes, executing step 412, otherwise, executing step 407.

Step 407, calling a file operation component to acquire preset parameters and/or alarm information of the second network device.

Wherein the preset parameters include at least one of configuration parameters and engineering parameters.

Step 408, invoking a data processing component to determine whether a preset parameter of the second network device is changed, and/or to determine whether an alarm occurs at the second network device.

Step 409, generating a performance optimization scheme for the second network device based on the preset rule in case of a change of the preset parameters of the second network device and/or in case of an alarm of the second network device.

Optionally, when the preset parameters of the second network device are not changed, and when the second network device does not generate an alarm, the process goes to step 413 to end the process.

Step 410, a call instruction component sends an adjustment instruction for the preset parameters of the second network device to the target network element based on the performance optimization scheme, adjusts the preset parameters of the second network device, and then performs step 411 or step 413.

The target network element may be a gateway device that controls the second network device, or may be the second network device itself.

Step 411, call an index judgment component, track the traffic change of the second network device, and determine the performance optimization effect of the second network device based on the change, and then execute step 412 or step 413.

Step 412, invoking a database component to save an adjustment record of the preset parameters for the second network device for future reference.

Step 413, end.

Optionally, after step 412, before step 413, the flow shown in fig. 4 may further include: and calling a report component to generate and display a performance optimization condition statistical report aiming at the target network, wherein the performance optimization condition comprises at least one of the optimized number of second network devices, the adjusted preset parameters, the adjustment trend of the parameters and the like.

The service flow automatic analysis and optimization performance optimization flow of the network equipment (cell) in the LTE provided by the embodiment can be drawn into a functional flow chart of the LTE flow sudden drop cell automatic analysis and optimization by using a uniform graphical component, and the flow can be analyzed and automatically executed by a program. The process can be seen that the network optimization work is extremely complex, the manual operation is easy to make mistakes, talent culture and cost are huge, the automation of the network optimization is realized by utilizing a process arrangement mode, the experience process can be clearly restored and optimized, the experience can be rapidly accumulated, and the expert experience is gradually converted into the digital productivity.

In summary, according to the network performance optimization method provided by the embodiment of the application, the performance optimization flow aiming at the target network can be obtained by arranging the preset graphic components, and the corresponding performance optimization step can be automatically carried out by automatically calling at least one graphic component in the plurality of graphic components according to the execution logic of the performance optimization flow, so that the performance optimization operation is automatically carried out on the target network without too much depending on manual participation, thereby reducing the cost of optimizing the performance of the network equipment and improving the efficiency of optimizing the performance of the network equipment.

The foregoing describes a network performance optimization method provided in the embodiments of the present application, and corresponding to the foregoing network performance optimization method, the embodiments of the present application further provide a network performance optimization device, which is described below.

As shown in fig. 5, a network performance optimization apparatus provided in an embodiment of the present application may include a flow configuration module 501 and a performance optimization module 502.

The process scheduling module 501 schedules a performance optimization process for a target network based on a plurality of graphic components, wherein the performance optimization process comprises a plurality of task nodes represented by graphics corresponding to the graphic components, and one task node represents a performance optimization step for the target network.

As an example, the flow arranging module 501 may specifically receive, in the visual operation interface, a preset operation for graphics corresponding to the multiple graphic assemblies, and draw a performance optimization flow for the target network.

And the performance optimization module 502 invokes at least one graphic component in the plurality of graphic components to execute a corresponding performance optimization step based on the execution logic of the performance optimization flow, so as to execute an optimization operation on a preset performance index of the target network when a preset optimization condition is met, thereby realizing the performance optimization of the target network.

For examples of the network performance optimization procedure, please refer to the examples shown in fig. 2 and fig. 3 and fig. 4, and the description thereof will not be repeated here. It should be noted that, in the network performance optimizing apparatus provided in the embodiment of the present application, different network performance optimizing flows for repairing different network devices may be programmed by the flow programming module 501, and the method is not limited to the two examples, so as to implement automatic optimization of the same or different network performances for different network devices.

According to the network performance optimizing device, the performance optimizing flow aiming at the target network can be obtained by arrangement based on the preset graphic components, and the corresponding performance optimizing step can be automatically carried out by automatically calling at least one graphic component in the plurality of graphic components according to the execution logic of the performance optimizing flow, so that the performance optimizing operation is automatically carried out on the target network without too much dependence on manual participation, the cost for optimizing the performance of the network equipment can be reduced, and the efficiency for optimizing the performance of the network equipment is improved.

It should be noted that, since the network performance optimization device provided in the embodiment of the present application corresponds to the network performance optimization method provided in the embodiment of the present application, the description of the network performance optimization device in the present application is simpler, and reference is made to the description of the network performance optimization method hereinabove.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 6, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.

The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 6, but not only one bus or type of bus.

And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.

The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs, forms a network performance optimizing device on a logic level, and is specifically used for executing the following operations:

The method performed by the network performance optimization method disclosed in the embodiment shown in fig. 1 of the present application may be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.

Therefore, the electronic device executing the method provided by the embodiment of the present application may execute the methods described in the foregoing method embodiments, and implement the functions and beneficial effects of the methods described in the foregoing method embodiments, which are not described herein again.

The electronic device of embodiments of the present application may exist in a variety of forms including, but not limited to, the following devices.

(1) Mobile network devices, which are characterized by mobile communication capabilities and are primarily aimed at providing voice and data communication. Such terminals include smart phones (e.g., iPhone), multimedia phones, functional phones, and low-end phones, among others.

(2) Ultra mobile personal computer equipment, which belongs to the category of personal computers, has the functions of calculation and processing and generally has the characteristic of mobile internet surfing. Such terminals include PDA, MID and UMPC devices, etc., such as iPad.

(3) The server is similar to a general computer architecture in that the server is provided with high-reliability services, and therefore, the server has high requirements on processing capacity, stability, reliability, safety, expandability, manageability and the like.

(4) Other electronic devices with data interaction function.

The embodiments also provide a computer readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by an electronic device comprising a plurality of application programs, enable the electronic device to perform the network performance optimization method of the embodiment shown in fig. 1, and in particular to perform the following operations:

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It should be noted that, in the present application, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.

Claims

1. A method for optimizing network performance, the method comprising:

arranging and obtaining a cell performance optimization flow aiming at a target wireless communication network based on a plurality of graphic assemblies, wherein the cell performance optimization flow comprises a plurality of task nodes which are represented by graphics corresponding to the graphic assemblies, and one task node represents a performance optimization step aiming at the target wireless communication network;

based on the execution logic of the cell performance optimization flow, invoking at least one graphic component in the plurality of graphic components to execute a corresponding cell performance optimization step so as to execute optimization operation on a preset performance index of the target wireless communication network when a preset optimization condition is met, thereby realizing cell performance optimization of the target wireless communication network;

the plurality of graphic components includes:

The data access component is used for acquiring preset performance index data of network equipment in the target wireless communication network;

an index judging component, configured to judge whether a preset performance index of a network device in the target wireless communication network is abnormal; and

the file operation component is configured to obtain preset information of a network device in the target wireless communication network, determine a cause of degradation of the preset performance index of the network device based on the preset information, and generate a cell performance optimization scheme based on the cause, where the preset information includes at least one of configuration parameters, engineering parameters, alarm information and running state information.

2. The method of claim 1, wherein the scheduling, based on the plurality of graphical components, results in a cell performance optimization procedure for the target wireless communication network, comprising:

and in the visual operation interface, receiving preset operations of the graphics corresponding to the plurality of graphic assemblies, and drawing to obtain a cell performance optimization flow aiming at the target wireless communication network.

3. The method of claim 1, wherein the plurality of graphical components further comprises at least one of:

A database component for accessing a performance index list which is stored in a database in advance and meets preset optimization conditions, and judging whether preset performance indexes of network equipment in the target wireless communication network meet the preset optimization conditions or not based on the performance index list;

the general calculation component is used for screening network equipment needing to be optimized based on the data acquired by the data access component;

the data processing component is used for processing the data acquired by the data access component;

an AI component for predicting the preset performance index of a network device in the target wireless communication network based on historical data of the preset performance index and a preset AI prediction model of the network device in a current period of time;

an instruction component for sending an instruction to perform an optimization operation to a network device in the target wireless communication network;

and the report component is used for generating and displaying a cell performance optimization condition statistical report aiming at the target wireless communication network.

4. The method of claim 3, wherein the plurality of graphics components includes the data access component, the metrics determination component, the file manipulation component, and the instruction component;

Wherein the calling at least one graphic component of the plurality of graphic components to execute a corresponding cell performance optimization step, so as to execute an optimization operation on a preset performance index of the target wireless communication network when a preset optimization condition is satisfied, includes:

invoking the data access component to acquire cell performance data of network equipment in the target wireless communication network in a target period;

invoking the index judging component to judge whether the preset performance index of the network equipment is deteriorated or not based on the cell performance data;

invoking the file operation component under the condition that the preset performance index of the network equipment is degraded, acquiring preset information of the network equipment, and determining a reason for the degradation of the preset performance index of the network equipment based on the preset information, wherein the preset information comprises at least one of configuration parameters, engineering parameters, alarm information and running state information;

generating a cell performance optimization scheme based on the reasons;

and calling the instruction component, and sending an instruction for executing the optimization operation to the network equipment based on the cell performance optimization scheme so as to enable the network equipment to execute the optimization operation.

5. The method of claim 4, wherein invoking at least one of the plurality of graphical components to perform the corresponding cell performance optimization step to perform an optimization operation on a preset performance indicator of the target wireless communication network when a preset optimization condition is met, further comprises:

and after the network equipment executes the optimization operation, invoking the index judgment component, tracking the change of the preset performance index of the network equipment, and determining the optimization effect of the preset performance index of the network equipment based on the change.

6. The method of claim 5, wherein invoking at least one of the plurality of graphical components to perform the corresponding cell performance optimization step to perform an optimization operation on a preset performance indicator of the network device when a preset optimization condition is met, further comprises:

and if the preset performance of the network equipment is not optimized based on the optimization effect, correcting the cell performance optimization scheme, executing the calling instruction graphic assembly again after the correction, and sending an instruction for executing the optimization operation to the network equipment based on the cell performance optimization scheme so as to enable the network equipment to execute the optimization operation.

7. The method of claim 3, wherein the plurality of graphics components includes the data access component, the general purpose computing component, the data processing component, the file manipulation component, and the instruction component;

wherein the invoking the at least one of the plurality of graphic assemblies to perform a corresponding performance optimization step to perform an optimization operation on a preset performance index of the target wireless communication network when a preset optimization condition is satisfied includes:

when reaching the preset time, calling the data access component to acquire the flow data of the target wireless communication network in a first period;

invoking the general computing component to determine a first network device in the target wireless communication network, wherein the total flow of the first network device is smaller than or equal to a preset flow in the first period, based on the flow data of the target wireless communication network in the first period, so as to obtain a first network device set;

invoking the data processing component to screen out second network equipment with sudden drop of flow from the first network equipment set based on a preset mode to form a second network equipment set;

when the second network equipment set is not empty, calling the file operation assembly to acquire preset parameters and/or alarm information of the second network equipment, wherein the preset parameters comprise at least one of configuration parameters and engineering parameters;

Invoking the data processing component to determine whether the preset parameters of the second network device are changed and/or determine whether an alarm occurs to the second network device;

generating a cell performance optimization scheme for the second network equipment based on a preset rule under the condition that the preset parameters of the second network equipment are changed and/or under the condition that the second network equipment gives an alarm;

and calling the instruction component, sending an adjustment instruction for preset parameters of the second network equipment to a target network element based on the performance optimization scheme, and adjusting the preset parameters of the second network equipment.

8. The method of claim 7, wherein the step of determining the position of the probe is performed,

the preset mode comprises the following steps: invoking the data access component to acquire flow data of the first network device in a second period; invoking the data processing component to determine a second network device, in the first network device set, of which the descending amplitude of the flow in the first period relative to the average flow in the second period exceeds a first preset amplitude, so as to obtain a second network device set, wherein the second period is a historical period earlier than the first period;

Or,

the plurality of graphic components include the AI component, and the preset manner includes: invoking the data access component to acquire flow data of the first network device in a third period; invoking the AI component to predict the predicted traffic of the first network device in the first period based on the traffic data of the first network device in the third period and a preset AI prediction model; and calling the data processing component to determine a second network device, of which the descending amplitude of the real traffic of the first time period relative to the predicted traffic of the first time period exceeds a second preset amplitude, in the first network device set, so as to obtain a second network device set, wherein the third time period is a historical time period earlier than the first time period.

9. The method of claim 7, wherein invoking at least one of the plurality of graphical components to perform the corresponding cell performance optimization step to perform an optimization operation on a preset performance indicator of the target wireless communication network when a preset optimization condition is met, further comprises:

and after the preset parameters of the second network equipment are adjusted, the index judging component is called, the flow change of the second network equipment is tracked, and the performance optimizing effect of the second network equipment is determined based on the change.

10. A network performance optimization apparatus, the apparatus comprising:

the flow arranging module is used for arranging and obtaining a cell performance optimizing flow aiming at a target wireless communication network based on a plurality of graphic assemblies, wherein the cell performance optimizing flow comprises a plurality of task nodes which are represented by graphics corresponding to the graphic assemblies, and one task node represents a performance optimizing step aiming at the target wireless communication network;

the cell performance optimization module is used for calling at least one graphic component in the graphic components to execute a corresponding cell performance optimization step based on the execution logic of the cell performance optimization flow so as to execute optimization operation on a preset performance index of the target wireless communication network when a preset optimization condition is met, thereby realizing cell performance optimization of the target wireless communication network;

the plurality of graphic components includes: