Network flow control method, device, equipment, computer storage medium and product

Abstract

The application discloses a network flow control method, device, equipment, computer storage medium and product. The network flow control method comprises the following steps: obtaining network traffic of a communication network; performing data analysis on the network traffic to obtain flow control data, wherein the flow control data is represented according to a flow control system relationship, and the flow control system relationship comprises a topological relationship of network elements formed by the network traffic; analyzing the flow control data according to the flow control system relationship to obtain a target network element in the network elements; performing optimal threshold detection on the target network element to obtain a test result; and adjusting a traffic threshold of the target network element according to the test result. The embodiment of the application greatly improves the network impact resistance.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of network communication, and particularly relates to a network flow control method, device, equipment, computer storage medium and product. BACKGROUND

[0002] As the core system in a communication network, a core network is responsible for user access control, mobility management and session management. Therefore, it is important to ensure the stability of the network under large-scale access by implementing end-to-end flow control of the core network.

[0003] The existing network flow control technology mainly realizes the access control capability of the network by configuring network flow control parameters. However, the existing flow control model is relatively fixed, and the flow control node and threshold do not have the ability to dynamically adjust, which cannot cope with the traffic changes in complex network environments, and the network has low impact resistance. SUMMARY

[0004] The embodiments of the present application provide a network flow control method, device, equipment, computer storage medium and product to solve the problem that the existing flow control technology cannot cope with the traffic changes in complex network environments and the network has low impact resistance.

[0005] In a first aspect, the embodiments of the present application provide a network flow control method, and the method comprises:

[0006] obtaining network traffic of a communication network;

[0007] performing data analysis on the network traffic to obtain flow control data, the flow control data being represented according to a flow control system relationship, the flow control system relationship including a topological relationship of network elements formed by the network traffic;

[0008] analyzing the flow control data according to the flow control system relationship to obtain a target related network element in the network elements;

[0009] performing best threshold detection on the target related network element to obtain a test result;

[0010] adjusting a traffic threshold of the target related network element according to the test result.

[0011] In a second aspect, the embodiments of the present application provide a network flow control device, and the device comprises:

[0012] an obtaining module configured to obtain network traffic of a communication network;

[0013] a first analysis module configured to perform data analysis on the network traffic to obtain flow control data, the flow control data being represented according to a flow control system relationship, the flow control system relationship including a topological relationship of network elements formed by the network traffic;

[0014] a second analysis module, configured to analyze the flow control data according to the flow control system relationship, to obtain a target related network element in the network element;

[0015] a detection module, configured to perform optimal threshold detection on the target related network element, to obtain a test result;

[0016] an adjustment module, configured to adjust a flow threshold of the target related network element according to the test result.

[0017] In a third aspect, an embodiment of the present application provides a terminal device, which comprises a processor and a memory storing computer program instructions.

[0018] The processor executes the computer program instructions to implement the network flow control method in the first aspect.

[0019] In a fourth aspect, an embodiment of the present application provides a computer storage medium, which stores computer program instructions. The computer program instructions are executed by a processor to implement the network flow control method in the first aspect.

[0020] In a fifth aspect, an embodiment of the present application provides a computer program product. Instructions in the computer program product are executed by a processor of an electronic device to enable the electronic device to perform the network flow control method in the first aspect.

[0021] The network flow control method provided by the embodiment of the present application analyzes the flow control data, finds a target related network element that has the greatest impact on the flow control data, and finally adjusts the flow threshold of the target related network element. The network flow control method realizes data analysis and dynamic adjustment of the flow control data and the flow control node, can adjust the flow control threshold in time according to the current network flow change, and improves the network impact resistance. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. For those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 is a flowchart of the network flow control method provided by the embodiment of the present application;

[0024] Figure 2 is a structural schematic diagram of the network flow control system provided by the embodiment of the present application;

[0025] Figure 3 is a structural schematic diagram of a flow control system tree in a specific application example;

[0026] Figure 4 is a structural schematic diagram of a network flow control device provided by an embodiment of the present application.

[0027] Figure 5 is a structural schematic diagram of a terminal device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0028] The features and exemplary embodiments of various aspects of the present application will be described below in detail, in order to make the purposes, technical solutions and advantages of the present application more clear and apparent, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, but not to limit the present application. The present application can be implemented without some of these specific details by those skilled in the art. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0029] It should be noted that, in this paper, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the elements defined by the statement "include" do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0030] In order to solve the problems in the prior art, the embodiments of the present application provide a network flow control method, device, equipment, computer storage medium and product. First, the framework to which the network flow control method provided by the embodiments of the present application can be applied is exemplified.

[0031] First, the network flow control method provided by the embodiments of the present application is introduced.

[0032] Figure 1 The flowchart of the network flow control method provided by an embodiment of the present application is shown. As shown in Figure 1 The method comprises:

[0033] Step 101, obtaining network traffic of a communication network;

[0034] Step 102: Perform data analysis on the network traffic to obtain flow control data. The flow control data is represented according to the flow control system relationship, which includes the topological relationship of network elements formed through the network traffic.

[0035] Step 103: Analyze the flow control data according to the flow control system relationship to obtain the target associated network element in the network element;

[0036] Step 104: Perform optimal threshold detection on the target associated network element to obtain the test result;

[0037] Step 105: Adjust the traffic threshold of the target associated network element based on the test results.

[0038] In step 101, network traffic can be obtained through the core network.

[0039] It should be noted that the network flow control method in this embodiment is applied to a network flow control system, such as... Figure 2 As shown, the network flow control system comprises four parts: the core network, flow control data processing nodes, flow control data analysis nodes, and system verification.

[0040] In step 102, the network traffic is analyzed to obtain flow control data. The flow control data is represented according to the flow control system relationship, which includes the topological relationship of network elements formed by the network traffic.

[0041] In this embodiment, flow control data can be obtained through flow control data processing nodes. Specifically, the flow control data nodes are responsible for extracting and synchronizing flow control data; they can extract flow control data from the core network to form flow control data based on the flow control hierarchy tree.

[0042] The above-mentioned flow control system relationships can be represented as a flow control system tree. For example... Figure 3 As shown, Figure 3 This is a schematic diagram of the flow control hierarchy tree, where MCS, SSA, and MME are all network elements. Network traffic flows according to the flow control hierarchy tree, for example, from MSC to SSA, or from MSC to STP, etc. Thus, the topology of network elements is formed through these flow relationships.

[0043] The aforementioned flow control data may include information such as the category of the network element, the flow control mechanism, parameter configuration, and the current traffic percentage. As an example, as shown in Table 1, Table 1 is the flow control data analysis table for the MME.

[0044]

[0045]

[0046] Table 1

[0047] In step 103, the flow control data is analyzed according to the flow control system relationship, and a target related network element in the network element is obtained.

[0048] In order to realize the management of the flow control data, the best flow control node needs to be found in the flow control system relationship, so as to adjust the best flow control node and achieve the purpose of controlling network flow. The flow control data can be analyzed by the flow control strategy analysis node, so as to obtain the target related network element.

[0049] In step 104, the best threshold of the target related network element is detected, and a test result is obtained.

[0050] In this embodiment, the best threshold detection is to detect whether the flow threshold of the target related network element reaches the first preset threshold. The first preset threshold can be set according to the flow proportion of the target related network element.

[0051] It should be noted that, as shown in Table 2, Table 2 is an optimal threshold detection table, which includes the best threshold detection result of the target related network element. The flow proportion of the network element is fixed, for example, the flow proportion of MME is 55%, and the flow proportion of SGSN is 50%.

[0052] As an example, when the target related network element includes MME, the first preset threshold can be set to 85, and if the detected flow threshold is 80, the test result is 0.95, indicating that the flow threshold of MME needs to be adjusted, and the adjustment coefficient can be set to +0.05, and the test result is adjusted to 1; when the target related network element includes SGSN, the first preset threshold can be set to 250, and if the detected flow threshold is 250, the test result is 1, indicating that the flow threshold of SGSN reaches the best and does not need to be adjusted, and the adjustment coefficient is 0.

[0053]

[0054] Table 2

[0055] In step 105, the flow threshold of the target related network element is adjusted according to the test result.

[0056] In an embodiment, the step 105 further includes:

[0057] In the case that the test result is that the flow threshold of the target related network element is not the first preset threshold, an adjustment coefficient of the flow threshold is obtained;

[0058] According to the adjustment coefficient, the flow threshold is adjusted so that the flow threshold reaches the first preset threshold.

[0059] In the embodiment, as shown in Table 2, if the test result is not 1, it indicates that the traffic threshold of the target related network element is not the first preset threshold. Therefore, the adjustment coefficient can be determined according to the difference between the test result and 1. For example, when the test result is 0.95, the adjustment coefficient is +0.05; when the test result is 0.94, the adjustment coefficient is +0.06.

[0060] After the adjustment coefficient is determined, the traffic threshold of the target related network element is adjusted according to the adjustment coefficient until the traffic threshold reaches the first preset threshold.

[0061] In the embodiment, the network flow control method provided by the embodiment can analyze the flow control data, find the target related network element that has the greatest impact on the flow control data, and finally adjust the traffic threshold of the target related network element, so as to realize data analysis and dynamic adjustment of the flow control data and the flow control node, adjust the flow control threshold in time according to the current network traffic change, and improve the network impact resistance.

[0062] In an embodiment of the present application, the analyzing the flow control data according to the flow control system relationship to obtain the target related network element in the network element comprises:

[0063] According to the flow control system relationship, a plurality of first related network elements in the network element are determined, and the first related network element is an adjacent network element through which the network traffic flows.

[0064] According to the flow control data, the correlation information of the plurality of first related network elements is obtained by calculation.

[0065] According to the correlation information, the target related network element is screened out from the plurality of first related network elements.

[0066] In the embodiment, since the flow control system relationship represents the flow direction relationship of the network traffic between the network elements, the first related network element is two network elements having the flow direction relationship and being adjacent, including a first sub related network element and a second sub related network element. As an example, for example, Figure 3 the MSC and the SSA in the network element are the first related network elements, or the MSC and the STP are the first related network elements.

[0067] The association information includes support, promotion and confidence, wherein the support is the frequency of network traffic flowing through the first associated network element, i.e., the frequency of network traffic flowing through the first sub-associated network element and the second sub-associated network element; the promotion is the association direction of the first associated network element, i.e., the association direction between the first sub-associated network element and the second sub-associated network element, for example, flowing from the first sub-associated network element to the second sub-associated network element, or flowing from the second sub-associated network element to the first sub-associated network element; and the confidence is the association degree between the first sub-associated network element and the second sub-associated network element.

[0068] The target associated network element is the best flow control node, and by controlling the flow of the target associated network element, it can prevent too much or too little data transmission in the network, causing network congestion or low transmission efficiency.

[0069] In the embodiment, the target associated network element is intelligently found through the flow control system tree, and the best flow control node is selected to perform optimal threshold verification, thereby improving the overall flow control efficiency of the network.

[0070] In an embodiment of the present application, the calculating according to the flow control data to obtain the association information of the plurality of first associated network elements comprises:

[0071] For any first associated network element in the plurality of first associated network elements, a first ratio between the first number and the total flow is determined as the support between the first sub-associated network element and the second sub-associated network element.

[0072] In the embodiment, the flow control data includes the first number of network traffic flowing through the first sub-associated network element and the second sub-associated network element. The support can be calculated by the following expression:

[0073] Support(A,B)=M / K

[0074] Wherein, A represents the first sub-associated network element, B represents the second sub-associated network element, M represents the first number of simultaneously passing through the first sub-associated network element and the second sub-associated network element, K represents the total flow of network traffic, and Support(A,B) represents the support between the first sub-associated network element and the second sub-associated network element.

[0075] In an embodiment, in the case of the association information being promotion, the calculating according to the flow control data to obtain the association information of the plurality of first associated network elements comprises:

[0076] For any first associated network element in the plurality of first associated network elements, a second ratio between the second number and the total flow, and a third ratio between the third number and the total flow are obtained;

[0077] The product between the second ratio and the third ratio is calculated.

[0078] A ratio between the support and the product is determined as the lift between the first sub-associated network element and the second sub-associated network element.

[0079] In the embodiment, the lift can be calculated by the following expression:

[0080] Lift(A, B) = Support(A, B) / (Support(A) * Support(B)) = (M / K) / ((N1 / K) * (N2 / K)) = (M*K) / (N1*N2);

[0081] Wherein, N1 represents the second number of network traffic flowing through the first sub-associated network element, N2 represents the third number of network traffic flowing through the second sub-associated network element, Support(A) represents the frequency of network traffic flowing through the first sub-associated network element, which can be represented by a second ratio between the second number and the total traffic, and Support(B) represents the frequency of network traffic flowing through the second sub-associated network element, which can be represented by a third ratio between the third number and the total traffic.

[0082] In an embodiment, in the case of the association information being the confidence, the calculating according to the flow control data to obtain the association information of the plurality of first associated network elements comprises:

[0083] Obtaining the traffic flow direction of each first associated network element in the plurality of first associated network elements;

[0084] In the case of the traffic flow direction being from the first sub-associated network element to the second sub-associated network element, a fourth ratio between the support and the second ratio is determined as the confidence;

[0085] In the case of the traffic flow direction being from the second sub-associated network element to the first sub-associated network element, a fifth ratio between the support and the third ratio is determined as the confidence.

[0086] In the embodiment, since the first associated network element is two network elements having a traffic flow relationship, the above traffic flow direction includes the first sub-association direction flowing to the second sub-association direction, and the second sub-association direction flowing to the first sub-association direction.

[0087] In the case of the traffic flow direction being from the first sub-associated network element to the second sub-associated network element, the confidence Confidence(A->B) can be calculated by the following expression:

[0088] Confidence(A->B) = Support(A, B) / Support(A) = M / N1;

[0089] In the case that the flow direction is from the second sub-associated network element to the first sub-associated network element, the confidence Confidence(B->A) can be calculated by the following expression:

[0090] Confidence(B->A)=Support(A,B) / Support(B)=M / N2

[0091] Wherein, Support(A,B) represents the support, Support(A) and M both represent the frequency of network flow through the first sub-associated network element, which can be represented by the second ratio between the second number and the total flow, Support(B) and N both represent the frequency of network flow through the second sub-associated network element, which can be represented by the third ratio between the third number and the total flow. Support(A,B) / Support(A) represents the fourth ratio, and Support(A,B) / Support(B) represents the fifth ratio.

[0092] In an embodiment of the present application, the target associated network element is filtered out from the plurality of first associated network elements according to the associated information, comprising:

[0093] In the case that the support of the first associated network element is greater than a third preset threshold, the promotion degree is greater than a fourth preset threshold, and the confidence is greater than a fifth preset threshold, the first associated network element is determined as the target associated network element.

[0094] In the case that the support is greater than the third preset threshold, it indicates that the network flow frequently flows through the first associated network element, i.e. the usage rate of the first associated network element is high, wherein the third preset threshold can be set by the person skilled in the art as needed, which is not limited in the present embodiment.

[0095] The fourth preset threshold can be 1. It should be noted that in the case that the promotion degree is greater than 1, the association relationship between the first sub-associated network element and the second sub-associated network element is a positive correlation relationship; in the case that the promotion degree is less than 1, the association relationship between the first sub-associated network element and the second sub-associated network element is a negative correlation relationship; in the case that the promotion degree is equal to 1, the association relationship between the first sub-associated network element and the second sub-associated network element is a non-influence relationship.

[0096] Finally, in the process of selecting the confidence greater than the fifth preset threshold, the first associated network element can be sorted in descending order according to the confidence, and then the first associated network element with the confidence greater than the fifth preset threshold is selected as the target associated network element, or the first associated network element with the top N highest confidence can also be selected as the target first associated network element.

[0097] In this embodiment, the first associated network element that meets the above conditions is determined as the target associated network element, so that when performing flow control, the flow control is directly performed on the target first associated network element, thereby improving the overall flow control efficiency of the network.

[0098] In another embodiment of this application, after adjusting the traffic threshold of the target associated network element based on the test result, the method further includes:

[0099] An adjustment strategy is generated based on the adjusted target associated network elements;

[0100] The adjustment strategy is then subjected to normative verification.

[0101] In this embodiment, the adjustment strategy can be verified through the system verification section in the above embodiments. Standardization verification includes the flow control target object, the range of matching parameters, and parameter standardization. As shown in Table 3, Table 3 is the standardization verification table. The target associated network elements can be verified sequentially according to the contents of Table 3, where the core network element name is the target associated network element name.

[0102]

[0103]

[0104] Table 3

[0105] In this embodiment, by performing standardization verification on the verification strategy, the accuracy of flow control can be guaranteed, and adjustment errors can be prevented.

[0106] like Figure 4 As shown in the figure, this application embodiment also provides a network flow control device 400, which includes:

[0107] Module 401 is used to acquire network traffic of the communication network;

[0108] The first analysis module 402 is used to perform data analysis on the network traffic to obtain flow control data. The flow control data is represented according to the flow control system relationship, which includes the topological relationship of network elements formed by the network traffic.

[0109] The second analysis module 403 is used to analyze the flow control data according to the flow control system relationship to obtain the target associated network element in the network element;

[0110] Detection module 404 is used to perform optimal threshold detection on the target associated network element and obtain the test result;

[0111] The adjustment module 405 is used to adjust the traffic threshold of the target associated network element according to the test results.

[0112] Optionally, the first analysis module 402 comprises:

[0113] a determination sub-module, configured to determine a plurality of first associated network elements in the network element according to the flow control system relationship, the first associated network element being an adjacent network element through which the network traffic flows;

[0114] a calculation sub-module, configured to calculate according to the flow control data to obtain associated information of the plurality of first associated network elements;

[0115] a screening sub-module, configured to screen a target associated network element from the plurality of first associated network elements according to the associated information.

[0116] Optionally, the calculation sub-module is specifically configured to:

[0117] determine, for any first associated network element in the plurality of first associated network elements, a first ratio between the first number and the total traffic as a support degree between the first sub-associated network element and the second sub-associated network element.

[0118] Optionally, the calculation sub-module further comprises:

[0119] a first acquisition unit, configured to acquire, for any first associated network element in the plurality of first associated network elements, a second ratio between the second number and the total traffic and a third ratio between the third number and the total traffic;

[0120] a calculation unit, configured to calculate a product between the second ratio and the third ratio;

[0121] a first determination unit, configured to determine a ratio between the support degree and the product as an improvement degree between the first sub-associated network element and the second sub-associated network element.

[0122] Optionally, the calculation sub-module further comprises:

[0123] a second acquisition unit, configured to acquire a traffic flow direction of each first associated network element in the plurality of first associated network elements;

[0124] a second determination unit, configured to determine, in a case where the traffic flow direction is from the first sub-associated network element to the second sub-associated network element, a fourth ratio between the support degree and the second ratio as the confidence degree;

[0125] a third determination unit, configured to determine, in a case where the traffic flow direction is from the second sub-associated network element to the first sub-associated network element, a fifth ratio between the support degree and the third ratio as the confidence degree.

[0126] Optionally, the screening submodule is specifically used for:

[0127] In a case where the support degree of the first related network element is greater than a third preset threshold, the promotion degree is greater than a fourth preset threshold, and the confidence degree is greater than a fifth preset threshold, the first related network element is determined as a target related network element.

[0128] Optionally, the adjusting module 405 comprises:

[0129] The acquisition submodule is configured to acquire an adjustment coefficient of the traffic threshold in a case where the inspection result is that the traffic threshold of the target related network element is not the first preset threshold.

[0130] The adjusting submodule is configured to adjust the traffic threshold according to the adjustment coefficient, so that the traffic threshold reaches the first preset threshold.

[0131] It should be noted that the network flow control device 400 is a device corresponding to the network flow control method applied to the server described above, and all implementation manners in the above method embodiments are applicable to the embodiments of the device, and the same technical effects can be achieved.

[0132] Figure 5 A hardware structure schematic diagram of a terminal device provided in an embodiment of the present application is shown.

[0133] The terminal device can include a processor 501 and a memory 502 storing computer program instructions.

[0134] Specifically, the processor 501 can include a central processing unit (CPU), or a specific integrated circuit (Application Specific Integrated Circuit, ASIC), or can be configured to implement one or more integrated circuits of the embodiments of the present application.

[0135] The memory 502 can include a mass storage for data or instructions. By way of example and not limitation, the memory 502 can include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 502 can include removable or non-removable (or fixed) media. Where appropriate, the memory 502 can be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, the memory 502 is a non-volatile solid-state memory.

[0136] In particular embodiments, the memory 502 can include read-only memory (ROM), random-access memory (RAM), magnetic disk storage mediums, optical storage mediums, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software that, when executed (by one or more processors), is operable to perform the operations described with reference to the methods according to an aspect of the present disclosure.

[0137] The processor 501 implements any one of the network flow control methods in the above embodiments by reading and executing computer program instructions stored in the memory 502.

[0138] In one example, the terminal device can further include a communication interface 503 and a bus 510. As shown, the processor 501, the memory 502, and the communication interface 503 are connected through the bus 510 and complete communication therebetween. Figure 5

[0139] The communication interface 503 is mainly used to realize the communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0140] The bus 510 includes hardware, software or both to couple components of the online data traffic billing device to each other in a known manner. By way of example, and not limitation, the bus can include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand (IB) interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination of two or more of these. Where suitable, the bus 510 can include one or more buses. Although particular buses are described and shown in the embodiments of the present application, the present application contemplates any suitable bus or interconnect.

[0141] In addition, in combination with the network flow control method in the above embodiments, the embodiments of the present application can provide a computer storage medium to implement. The computer storage medium has computer program instructions stored thereon; the computer program instructions are executed by the processor to implement any one of the network flow control methods in the above embodiments.

[0142] ​It is to be understood that the application is not limited to particular configurations and processes described herein and shown in the drawings. The detailed description is not to be taken as limiting the application. In the above embodiments, several specific steps are described and illustrated in order to provide a thorough understanding of the application. However, the application can be practiced with fewer or additional steps, and in a different order. The application is not limited to the described and illustrated embodiments.

[0143] The functions noted in the above structural block diagrams can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, and the like. When implemented in software, the elements of the application are program or code segments that are used to perform the required tasks. The program or code segments can be stored in a machine-readable medium, or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. A "machine-readable medium" includes any medium that can store or transfer information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. The code segments can be downloaded via computer networks such as the Internet, intranets, and the like.

[0144] It is also to be understood that the example embodiments described herein are based on a series of steps or apparatuses to describe some methods or systems. However, the application is not limited to the order of the steps described above, that is, the steps can be performed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be performed simultaneously.

[0145] The computer program instructions can also be loaded onto a computer, other programmable network control devices, or other devices to cause a series of operational steps to be performed on the computer, other programmable network control devices, or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable network control devices implement the functions / acts specified in the flowchart and / or block diagram block or blocks. Such processors can be, but not limited to, general purpose processors, special-purpose processors, special-purpose application processors, or field programmable logic arrays (FPLAs). It should also be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by dedicated hardware-based systems which perform the specified functions or acts, or combinations of hardware and software.

[0146] The above is merely specific implementation of the present application, and those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein again. It should be understood that the protection scope of the present application is not limited to this, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed in the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A network flow control method, characterized by, The method comprises: acquiring network traffic of a communication network; performing data analysis on the network traffic to obtain flow control data, the flow control data being represented according to a flow control system relationship, the flow control system relationship comprising a topological relationship of network elements formed by the network traffic; analyzing the flow control data according to the flow control system relationship to obtain a target related network element in the network elements; performing optimal threshold detection on the target related network element to obtain a test result; adjusting a traffic threshold of the target related network element according to the test result; the analyzing the flow control data according to the flow control system relationship to obtain a target related network element in the network elements comprises: determining a plurality of first related network elements in the network elements according to the flow control system relationship, the first related network elements being adjacent network elements through which the network traffic flows; each of the first related network elements comprising a first sub-related network element and a second sub-related network element; calculating the association information of the plurality of first related network elements according to the flow control data; the association information comprising a support degree, the support degree being a frequency of the network traffic flowing through the first related network elements; the association information comprising a promotion degree, the promotion degree being an association direction of the first related network elements; the association information comprising a confidence degree, the confidence degree being an association degree between the first sub-related network element and the second sub-related network element in the first related network elements; screening out a target related network element from the plurality of first related network elements according to the association information.

2. The network flow control method of claim 1, wherein, the flow control data comprises a first number of times of the network traffic flowing through the first sub-related network element and the second sub-related network element, and a total traffic of the network traffic; the calculating the association information of the plurality of first related network elements according to the flow control data comprises: for any first related network element in the plurality of first related network elements, determining a first ratio between the first number of times and the total traffic as the support degree between the first sub-related network element and the second sub-related network element.

3. The network flow control method of claim 2, wherein, the flow control data further comprises a second number of times of the network traffic flowing through the first sub-related network element, and a third number of times of the network traffic flowing through the second sub-related network element; the calculating the association information of the plurality of first related network elements according to the flow control data comprises: for any first related network element in the plurality of first related network elements, obtaining a second ratio between the second number of times and the total traffic, and a third ratio between the third number of times and the total traffic; calculating a product between the second ratio and the third ratio; determining a fourth ratio between the support degree and the product as the promotion degree between the first sub-related network element and the second sub-related network element.

4. The network flow control method of claim 3, wherein, the calculating the association information of the plurality of first related network elements according to the flow control data comprises: obtaining a traffic flow direction of each of the plurality of first related network elements; in a case where the traffic flow direction is from the first sub-related network element to the second sub-related network element, determining a fourth ratio between the support degree and the second ratio as the confidence degree; In a case where the flow direction is from the second sub-related network element to the first sub-related network element, a fifth ratio between the support degree and the third ratio is determined as the confidence degree.

5. The network flow control method of claim 4, wherein, The target-related network element is screened from the plurality of first-related network elements according to the correlation information. In a case where the support degree of the first-related network element is greater than a third preset threshold, the promotion degree is greater than a fourth preset threshold, and the confidence degree is greater than a fifth preset threshold, the first-related network element is determined as the target-related network element.

6. The network flow control method of claim 1, wherein, The traffic threshold of the target-related network element is adjusted according to the inspection result. In a case where the inspection result is that the traffic threshold of the target-related network element is not the first preset threshold, an adjustment coefficient of the traffic threshold is obtained. The traffic threshold is adjusted according to the adjustment coefficient, so that the traffic threshold reaches the first preset threshold.

7. A network flow control device, comprising: The apparatus comprises: The acquisition module is configured to acquire network traffic of a communication network. The first analysis module is configured to perform data analysis on the network traffic to obtain flow control data, the flow control data being represented according to a flow control system relationship, the flow control system relationship including a topological relationship of network elements formed by the network traffic. The second analysis module is configured to analyze the flow control data according to the flow control system relationship to obtain a target-related network element in the network elements. The detection module is configured to perform optimal threshold detection on the target-related network element to obtain an inspection result. The adjustment module is configured to adjust a traffic threshold of the target-related network element according to the inspection result. The second analysis module comprises: The determination submodule is configured to determine a plurality of first-related network elements in the network elements according to the flow control system relationship, the first-related network elements being adjacent network elements through which the network traffic flows; each first-related network element including a first sub-related network element and a second sub-related network element. The calculation submodule is configured to calculate according to the flow control data to obtain correlation information of the plurality of first-related network elements; the correlation information including a support degree, the support degree being a frequency of network traffic flowing through the first-related network elements; the correlation information including a promotion degree, the promotion degree being a correlation direction of the first-related network elements; and the correlation information including a confidence degree, the confidence degree being a correlation degree between the first sub-related network element and the second sub-related network element in the first-related network elements. The screening submodule is configured to screen a target-related network element from the plurality of first-related network elements according to the correlation information.

8. A terminal device, comprising: The device comprises a processor and a memory storing computer program instructions; The processor executes the computer program instructions to implement the network flow control method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by the processor to implement the network flow control method of any one of claims 1-6.

10. A computer program product, characterised in that, The computer product comprises a computer program, and the computer program is executed by the processor to implement the network flow control method of any one of claims 1-6.

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