Dynamic IP-VPN line automatic monitoring method, device and equipment and storage medium

Abstract

The application discloses a dynamic IP-VPN private line automatic monitoring method, device and equipment and a storage medium. The method comprises the following steps: acquiring ARP information of an access gateway BNG of an IP-VPN private line; obtaining comprehensive data of associated IP-VPN customer information based on the association relationship between the ARP information, OLT uplink information and comprehensive data customer information; performing dynamic analysis on customer interconnection equipment information based on the comprehensive data, and performing quantitative prediction on the next-day behavior data of the customer interconnection equipment based on a time sequence according to the dynamic analysis result to obtain next-day behavior prediction data; responding to a classification monitoring instruction constructed based on the next-day behavior prediction data, and performing index analysis based on the instruction response result to output corresponding IP-VPN customer early warning information according to an index abnormality determination result. The application can solve the problems of inaccurate resource system record information and information change caused by customer equipment adjustment, replacement and network adjustment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication technology, IP-VPN customer line technology, and particularly relates to a dynamic IP-VPN line automatic monitoring method, device, equipment and storage medium. BACKGROUND

[0002] IP-VPN service refers to a virtual enterprise IP private network constructed by using MPLS technology and a backbone network operated by a service provider, which is used to construct an enterprise internal private network for enterprise users. The IP-VPN service can realize internal data, voice, image and other multi-service communication between multiple branch offices of an enterprise, and provide various value-added services.

[0003] IP-VPN line refers to a provincial VPN line, that is, a BNG (Broadband Network Gateway) is used as an access gateway to meet the non-provincial VPN service demand, and the access mode is mainly GPON access (more than 90% of the use is GPON access). The end network mode mainly has two scenarios: 1) an IP-VPN customer has n interconnected devices connected to an ONU through a switch, each device is configured with an IP, and the gateway is directed to the BNG, and there is no independent CE, that is, there are multiple CE IP addresses (most use scenarios); 2) the customer interconnected devices are connected through a router, and the internal network is connected to the BNG through a unique router (CE) (a few use scenarios), as shown in FIG. 1. Figure 1

[0004] The current main method for monitoring the performance of the IP-VPN customer line is: (1) relying on the OLT / ONU / BNG and other device layer network management fault alarms to find problems and perform single dispatch maintenance processing. (2) finding problems through after-the-fact customer complaints, and checking and adjusting and processing accordingly by the maintenance technical personnel on site. (3) based on resource system record information (such as CE address, VPN instance, local network element, etc.), it is considered to log in to the BNG network element to perform performance testing.

[0005] ​The existing technology has the following problems: (1) according to the networking characteristics of the IP-VPN private line, the traditional DPI technology cannot identify the internal information, and the external dialing test technology cannot enter the customer VPN route, only relying on the device level fault alarm, the number of work orders is large, and the alarm work order cannot judge the actual impact of the customer business, that is, a large number of work order processing is out of touch with the actual perception of the customer. (2) The network problem is mainly found by the user, and there is a lack of active network bottleneck and problem monitoring and discovery capability. (3) The customer interconnection device IP (CE address) is allocated by the customer and used by the customer, and is limited by the customer device usage behavior (such as shutdown, offline, etc.), device adjustment, replacement and network adjustment. The interconnection IP actually used by the customer on site is often inconsistent with the record of the resource system, and the customer interconnection device IP recorded in the resource system is used for monitoring, performance analysis, fault location, etc. There will be misleading problems. (4) In addition, the resource system record information includes VPN instance name, local BNG name, CE address, etc. Affected by the pre-opening process or manual input maintenance problem, the accuracy is very low. The resource system record cannot be used as monitoring. SUMMARY

[0006] The present application aims to at least solve one of the technical problems in the related art to some extent.

[0007] To this end, the present application provides a dynamic IP-VPN private line automatic monitoring method, which periodically collects access gateway BNG ARP information, including VPN instance, customer interconnection device (CE) IP address, customer interconnection device (CE) MAC address, BNG interface, etc. The dynamic information of the interconnection device under the IP-VPN customer site is established and maintained, the inaccuracy of the resource system record information and the information change caused by the customer device adjustment, replacement and network adjustment are solved, the customer interconnection behavior analysis data is introduced, the misjudgment caused by the influence of the customer device usage behavior is avoided, and the automatic concurrent monitoring and problem judgment mechanism is provided. Ensure the accuracy of IP-VPN monitoring, improve the efficiency of monitoring and meet the timeliness requirements.

[0008] Another object of the present application is to provide a dynamic IP-VPN private line automatic monitoring device.

[0009] A third object of the present application is to provide a computer device.

[0010] A fourth object of the present application is to provide a non-transitory computer readable storage medium.

[0011] To achieve the above object, the present application provides a dynamic IP-VPN private line automatic monitoring method, comprising:

[0012] Obtain the ARP information of the access gateway BNG of the IP-VPN private line;

[0013] comprehensive data obtained based on the association of the ARP information with OLT uplink information and comprehensive data customer information;

[0014] device behavior dynamic analysis of customer interconnection equipment information based on the comprehensive data, and time series-based quantitative prediction of customer interconnection equipment next-day behavior data according to the dynamic analysis result to obtain next-day behavior prediction data;

[0015] responding to the classification monitoring instruction constructed based on the next-day behavior prediction data, and performing index analysis based on the instruction response result to output corresponding IP-VPN customer warning information according to the index abnormality determination result.

[0016] The dynamic IP-VPN private line automatic monitoring method of the embodiment of the application can also have the following additional technical features:

[0017] In an embodiment of the application, the ARP information of the access gateway BNG in the IP-VPN private line is obtained, comprising:

[0018] accessing the BNG of the IP-VPN private line through a remote protocol to obtain a gateway connection result;

[0019] obtaining the ARP information of the BNG based on the gateway connection result and a preset output instruction.

[0020] In an embodiment of the application, after obtaining the ARP information of the access gateway BNG in the IP-VPN private line, the method further comprises:

[0021] extracting ARP key information in a preset time period;

[0022] performing a deduplication operation on the ARP key information to obtain a first data table according to the information deduplication result.

[0023] In an embodiment of the application, the ARP key information comprises multiple types of BNG name, IP-VPN customer interconnection equipment IP address, IP-VPN customer interconnection equipment MAC address, BNG interface, VPN instance name and inner and outer layer VLAN.

[0024] In an embodiment of the application, the comprehensive data obtained based on the association of the ARP information with OLT uplink information and comprehensive data customer information comprises:

[0025] obtaining metro network OLT uplink information and comprehensive data customer information;

[0026] Obtaining corresponding second data table and third data table based on the metro network OLT uplink information and the comprehensive data customer information respectively;

[0027] Obtaining fourth data table according to the association relationship of the first data table and the second data table;

[0028] Obtaining fifth data table of the comprehensive data of the associated IP-VPN customer information after obtaining the fourth data table and the association relationship of the third data table.

[0029] In an embodiment of the present application, the metro network OLT uplink information includes OLT name, BNG name and BNG interface; the comprehensive data customer information includes customer product number, customer name, OLT name and inner and outer layer VLAN.

[0030] In an embodiment of the present application, based on the comprehensive data, device behavior dynamic analysis is performed on customer interconnection equipment information to obtain dynamic analysis result, which includes:

[0031] Based on the fifth data table, the number of days of customer interconnection equipment is counted to obtain active rate statistical result;

[0032] According to the active rate statistical result, it is judged whether the IP address corresponding to the MAC address in the customer interconnection equipment information is changed, so as to obtain the dynamic analysis result of whether the latest IP address is used for updating according to the judgment result.

[0033] In an embodiment of the present application, in response to the classification monitoring instruction constructed based on the next day behavior prediction data, an instruction response result is obtained, which includes:

[0034] Based on the BNG name, the next day behavior prediction data is classified to obtain data classification result;

[0035] According to the data classification result, a classification monitoring instruction is constructed;

[0036] In response to the classification monitoring instruction, the connection between each BNG and the single BNG connection are correspondingly executed by using parallel mode and serial mode to obtain instruction response result.

[0037] In an embodiment of the present application, the parsed indicators include: packet loss rate, round-trip delay and jitter delay.

[0038] In order to achieve the above purpose, another aspect of the present application provides a kind of dynamic IP-VPN special line automatic monitoring device, including:

[0039] Gateway data acquisition module, for obtaining the ARP information of the access gateway BNG of IP-VPN special line;

[0040] Correlation data acquisition module, used for obtaining associated IP-VPN customer information after comprehensive data based on the association relationship between the ARP information and OLT uplink information and comprehensive data customer information;

[0041] Behavior data prediction module, used for performing device behavior dynamic analysis on customer interconnection equipment information based on the comprehensive data, and performing quantitative prediction on the next day behavior data of the customer interconnection equipment based on time sequence according to the dynamic analysis result to obtain next day behavior prediction data;

[0042] Instruction response early warning module, used for responding to the classification monitoring instruction constructed based on the next day behavior prediction data, and performing index analysis based on the instruction response result to output corresponding IP-VPN customer early warning information according to the index abnormality determination result

[0043] The dynamic IP-VPN special line automatic monitoring method and device provided by the embodiment of the application can periodically collect ARP information of an access gateway BNG, establish and maintain dynamic information of interconnection equipment under an IP-VPN customer site, introduce behavior analysis and behavior prediction data of customer interconnection equipment, and provide an automatic and concurrent monitoring and problem determination mechanism.

[0044] To achieve the above object, the third aspect of the present application proposes a computer device, comprising: a processor and a memory; wherein the processor runs a program corresponding to the executable program code stored in the memory by reading the executable program code, to realize the dynamic IP-VPN special line automatic monitoring method as described in the first aspect of the embodiment.

[0045] To achieve the above object, the fourth aspect of the present application proposes a non-temporary computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the dynamic IP-VPN special line automatic monitoring method as described in the first aspect of the embodiment.

[0046] Additional aspects and advantages of the application will be described in part in the description that follows, and will become apparent from the description, or will be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0047] The above and / or additional aspects and advantages of the application will become apparent and be readily understood from the following description, taken in conjunction with the accompanying drawings, in which:

[0048] Figure 1 is a networking scenario schematic diagram;

[0049] Figure 2 is a flowchart of a dynamic IP-VPN special line automatic monitoring method according to an embodiment of the application;

[0050] Figure 3is a flow chart of still another dynamic IP-VPN private line automatic monitoring method according to an embodiment of the present application;

[0051] Figure 4 is a flow chart of another dynamic IP-VPN private line automatic monitoring method according to an embodiment of the present application;

[0052] Figure 5 is a flow chart of still another dynamic IP-VPN private line automatic monitoring method according to an embodiment of the present application;

[0053] Figure 6 is a schematic diagram of an IP-VPN customer monitoring instruction according to an embodiment of the present application;

[0054] Figure 7 is a schematic diagram of multiple interconnected devices connected through a switch according to an embodiment of the present application;

[0055] Figure 8 is a schematic diagram of devices under a customer connected through a router according to an embodiment of the present application;

[0056] Figure 9 is a structural schematic diagram of a dynamic IP-VPN private line automatic monitoring device according to an embodiment of the present application;

[0057] Figure 10 is a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0058] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0059] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative work should belong to the protection scope of the present application.

[0060] The dynamic IP-VPN private line automatic monitoring method, device, equipment and storage medium according to the embodiments of the present application will be described below with reference to the accompanying drawings.

[0061] Figure 2 is a flow chart of a dynamic IP-VPN private line automatic monitoring method according to an embodiment of the present application, as shown in Figure 2 the method includes but is not limited to the following steps:

[0062] S1, acquire ARP information of an access gateway BNG of an IP-VPN private line.

[0063] It can be understood that the IP-VPN private line of the present application refers to a provincial VPN private line, that is, a BNG (Broadband Network Gateway) is used as an access gateway.

[0064] In an embodiment of the present application, the ARP information of the access gateway BNG is acquired periodically.

[0065] In an embodiment of the present application, the ARP information of the access gateway BNG includes a VPN instance, a customer interconnection equipment (CE) IP address, a customer interconnection equipment (CE) MAC address, a BNG interface, and the like.

[0066] It can be understood that the present application introduces periodic ARP information acquisition, realizes dynamic information management of the interconnection equipment under the IP-VPN customer site, and can solve the problem of low analysis accuracy based on resource system record information (such as a CE address, a VPN instance, a local network element, and the like).

[0067] S2, obtain comprehensive data after association of IP-VPN customer information based on an association relationship between the ARP information and OLT uplink information and comprehensive data customer information.

[0068] It can be understood that this step associates IP-VPN customer information. That is, the access gateway BNG ARP information obtained in the previous step is used to perform data association by acquiring resource data and comprehensive data customer information.

[0069] It can be understood that the final comprehensive data after association of IP-VPN customer information is obtained by performing relationship association of related data in the data table through the data table corresponding to each data.

[0070] S3, perform device behavior dynamic analysis on customer interconnection equipment information based on the comprehensive data, and perform quantitative prediction based on a time sequence on next-day behavior data of the customer interconnection equipment according to the dynamic analysis result to obtain next-day behavior prediction data.

[0071] In an embodiment of the present application, the comprehensive data after association of IP-VPN customer information obtained according to step S2 is used to perform device behavior dynamic analysis on the customer interconnection equipment information (stored through a dictionary).

[0072] Further, the next-day behavior prediction of the IP-VPN customer interconnection equipment is performed according to the device behavior dynamic analysis result, and the next-day behavior prediction data is obtained by using a quantitative prediction method based on a time sequence.

[0073] It can be understood that the embodiment considers the behavior calculation analysis and behavior prediction (including customer interconnection device activity, next-day behavior prediction) of the IP-VPN customer interconnection device behavior, and can solve the misjudgment of monitoring caused by the influence of the use behavior of the customer interconnection device.

[0074] S4, responding to the classification monitoring instruction constructed based on the next-day behavior prediction data, and performing index analysis based on the instruction response result to output corresponding IP-VPN customer warning information according to the index abnormality determination result.

[0075] In an embodiment of the application, the behavior dynamic information data of each interconnection device of each IP-VPN customer obtained according to step S3 is filtered to perform instruction construction monitoring on the behavior dynamic information data meeting the preset condition.

[0076] Further, the instruction execution result is analyzed, and the analysis indexes include packet loss rate, round-trip delay, and jitter delay, and the corresponding IP-VPN customer warning is output after being determined respectively.

[0077] It can be understood that the embodiment establishes an IP-VPN dedicated line active automatic detection and warning mechanism, restores the real perception of the customer through detection technology, ensures that the problem discovery is highly consistent with the actual perception of the user, and solves the problem that the past fault alarm means is single, the workload is large, and the actual perception of the customer is inconsistent.

[0078] According to the dynamic IP-VPN dedicated line automatic monitoring method of the embodiment of the application, the ARP information of the access gateway BNG of the IP-VPN dedicated line is obtained; the associated IP-VPN customer information after the comprehensive data of the associated relationship between the ARP information and the OLT uplink information and the customer information of the comprehensive data is obtained; the device behavior dynamic analysis is performed on the customer interconnection device information based on the comprehensive data, and the next-day behavior prediction data is obtained based on the time series quantitative prediction of the next-day behavior data of the customer interconnection device according to the dynamic analysis result; the classification monitoring instruction constructed based on the next-day behavior prediction data is responded, and the index analysis is performed based on the instruction response result to output the corresponding IP-VPN customer warning information according to the index abnormality determination result.

[0079] Figure 3 For a dynamic IP-VPN dedicated line automatic monitoring method of an embodiment of the application, as shown in Figure 3 the method comprises the following steps:

[0080] S201, the BNG of the IP-VPN dedicated line is accessed through a remote protocol to obtain a gateway connection result.

[0081] S202, the ARP information of the BNG is obtained based on the gateway connection result and a preset output instruction.

[0082] In one embodiment of the present application, the BNG network element can be logged in through remote SSH connection to obtain the gateway connection result.

[0083] In one embodiment of the present application, the ARP information of the BNG can be output and collected by using the instruction display arp all (for example, the Huawei BNG instruction).

[0084] S203, extracting ARP key information in a preset time period.

[0085] S204, performing a deduplication operation on the ARP key information to obtain a first data table according to the information deduplication result.

[0086] In one embodiment of the present application, the ARP data collected is extracted to obtain key information, including: BNG name (network element during login), IP ADDRESS (IP-VPN customer interconnection device IP address), MAC ADDRESS (IP-VPN customer interconnection device MAC address), INTERFACE (BNG interface), VPN-INSTANCE (VPN instance name), VLAN / CEVLAN (inner and outer layer VLANs).

[0087] In one embodiment of the present application, the data can be collected in four time periods (03:00, 09:00, 15:00, and 22:00) every day (to cover the terminals connected at different times as much as possible), and then the data obtained in the four time periods is deduplicated to form a data table A in a day dimension.

[0088] S205, obtaining metro network OLT uplink information and comprehensive data customer information.

[0089] S206, obtaining corresponding second and third data tables based on the metro network OLT uplink information and the comprehensive data customer information, respectively.

[0090] S207, obtaining a fourth data table according to the association between the first data table and the second data table.

[0091] S208, obtaining a fifth data table of the associated IP-VPN customer information and the comprehensive data according to the association between the fourth data table and the third data table.

[0092] In one embodiment of the present application, resource data is obtained: metro network OLT uplink information: OLT name, BNG name, BNG interface, and table B is obtained according to the metro network OLT uplink information.

[0093] In an embodiment of the present application, the customer information of the comprehensive data is acquired, including customer product number, customer name, OLT name, inner and outer layer VLAN, and a table C is obtained according to the customer information of the comprehensive data.

[0094] In an embodiment of the present application, the BNG name and BNG interface of table A are connected with the BNG name and BNG interface of table B by equal two-table association, and an intermediate table D is obtained.

[0095] In an embodiment of the present application, the OLT name and inner and outer layer VLAN of the intermediate table D are connected with the OLT name and inner and outer layer VLAN of table C by equal two-table association, and finally the comprehensive data after association of IP-VPN customer information is obtained every day, and the comprehensive data is shown in table 1:

[0096] Table 1

[0097]

[0098]

[0099] S209, device behavior dynamic analysis is performed on the customer interconnection equipment information based on the comprehensive data, and next day behavior prediction data is obtained by time series based quantitative prediction of next day behavior data of the customer interconnection equipment according to the dynamic analysis result.

[0100] S210, a classification monitoring instruction is constructed in response to the next day behavior prediction data, and index analysis is performed based on the instruction response result to output corresponding IP-VPN customer early warning information according to the index abnormality determination result.

[0101] It should be noted that the specific implementation mode of steps S209-S210 can refer to the above embodiment, and will not be described here.

[0102] In the embodiment of the present application, the BNG of the IP-VPN private line is accessed by a remote protocol to obtain a gateway connection result; ARP information of the BNG is obtained based on the gateway connection result and a preset output instruction, ARP key information in a preset time period is extracted; the ARP key information is subjected to a deduplication operation to obtain a first data table according to an information deduplication result. Metro network OLT uplink information and comprehensive data customer information are obtained. A corresponding second data table and a third data table are obtained based on the metro network OLT uplink information and the comprehensive data customer information respectively. A fourth data table is obtained according to an association relationship between the first data table and the second data table. A fifth data table of associated IP-VPN customer information after comprehensive data is obtained according to an association relationship between the fourth data table and the third data table; device behavior dynamic analysis is performed on customer interconnection equipment information based on the comprehensive data, and quantitative prediction based on time series is performed on next-day behavior data of the customer interconnection equipment according to a dynamic analysis result to obtain next-day behavior prediction data; a classification monitoring instruction constructed based on the next-day behavior prediction data is responded, and index analysis is performed based on an instruction response result to output corresponding IP-VPN customer early warning information according to an index abnormality determination result. The present application is based on IP-VPN customer interconnection equipment dynamic information acquisition based on BNG ARP information collection, including VPN instances, customer interconnection equipment (CE) IP addresses, customer interconnection equipment (CE) MAC addresses, BNG interfaces, and the like, to further establish and maintain dynamic information of interconnection equipment under an IP-VPN customer site line, and to realize customer information association, solve the problem of inaccurate resource system record information, and solve the problem of information change caused by customer interconnection equipment adjustment, replacement, and network adjustment.

[0103] Figure 4 For another dynamic IP-VPN private line automatic monitoring method of the embodiment of the present application, as shown in the following formula (I), the method comprises the following steps: Figure 4

[0104] S310, ARP information of an access gateway BNG of an IP-VPN private line is obtained.

[0105] S320, comprehensive data after associated IP-VPN customer information is obtained based on an association relationship between the ARP information and OLT uplink information and comprehensive data customer information.

[0106] It should be noted that the specific implementation mode of steps S310-S320 can refer to the above embodiment, and will not be described here.

[0107] S330, the number of days of customer interconnection equipment is counted based on the fifth data table to obtain an active rate statistical result.

[0108] ​S340, judging whether the IP address corresponding to the MAC address in the client interconnection device information has changed according to the active rate statistical result, so as to obtain a dynamic analysis result of whether the latest IP address is used for updating according to the judgment result.

[0109] Specifically, the comprehensive data obtained after the associated IP-VPN client information is the fifth data table, and the device behavior dynamic analysis is performed on the "client interconnection device information (stored through a dictionary)".

[0110] In an embodiment of the application, the latest seven days are taken each time: the MAC addresses in each "client interconnection device information" are analyzed and calculated one by one: the number of days appearing in the latest seven days is counted, and the active rate in the latest seven days is calculated = the number of active days / 7, so as to obtain the active rate statistical result.

[0111] According to the latest data in the active rate statistical result, whether the IP address corresponding to the MAC address has changed is judged, and the latest IP address is used for updating if there is a change.

[0112] S350, according to the dynamic analysis result, the next day behavior data of the client interconnection device is quantitatively predicted based on time series to obtain next day behavior prediction data.

[0113] It can be understood that the application realizes the quantitative prediction method based on time series, considers that the actual data sequence presents a nonlinear increasing trend, and realizes the prediction method by adopting exponential smoothing (cubic exponential smoothing) in this embodiment, and the steps are as follows:

[0114] Let {y T} be the observation value of the time series, and the cubic exponential smoothing expression is as follows:

[0115]

[0116] Among them:

[0117] {y t} takes the historical data of 90 days after the current time as the observation value each time; including date sequence, MAC address and on-off line;

[0118] a is a static smoothing parameter, and 0

[0119] is a first exponential smoothing value, is a second exponential smoothing value, is a third exponential smoothing value, and the nonlinear prediction value of the t+m period is Among them,

[0120]

[0121]

[0122]

[0123] m is the prediction lead time (m = 1 in this embodiment, i.e. predicting the next day's behavior each time), A t , B t , C t are prediction parameters. The first, second and third exponential smoothing values are calculated in turn according to the exponential smoothing value calculation formula, and the coefficients A t , B t , C T of the non-linear prediction model are calculated, and F t+1 is obtained.

[0124] Further, through the aforementioned two behavior analyses, the IP-VPN customer site interconnection device behavior dynamic analysis data and the next day behavior prediction data can be obtained each day, and the output content includes: date, customer name, product number, VPN instance, BNG name, customer interconnection device information. The customer interconnection device information content is shown as follows:

[0125] {

[0126] 'Interconnection device 1': {'IP': '10.28.61.13', 'MAC': '84-d9-31-7f-03-01', 'active rate in the past seven days': 10%, 'next day behavior prediction': ['online']},

[0127] 'Interconnection device 2': {'IP': '10.28.61.14', 'MAC': 'd4-3a-65-09-39-6a', 'active rate in the past seven days': 80%, 'next day behavior prediction': ['offline']},

[0128] 'Interconnection device 3': {'IP': '10.28.61.15', 'MAC': '60-18-95-4e-d6-54', 'active rate in the past seven days': 50%, 'next day behavior prediction': ['online']},

[0129] …

[0130] 'Interconnection device n': {'IP': '10.28.61.16', 'MAC': '2c-56-dc-b5-c9-0f', 'active rate in the past seven days': 30%, 'next day behavior prediction': ['online']}.

[0131] S360, in response to the classification monitoring instruction constructed based on the next-day behavior prediction data, and based on the instruction response result, performing index analysis to output corresponding IP-VPN customer early warning information according to the index abnormality determination result.

[0132] It should be noted that the specific implementation of step S360 can refer to the above-mentioned embodiments, and will not be repeated here.

[0133] In the embodiment of the application, the ARP information of the access gateway BNG of the IP-VPN private line is obtained, the associated IP-VPN customer information after the comprehensive data based on the association relationship between the ARP information, the OLT uplink information and the comprehensive data customer information is obtained, the active rate statistical result is obtained by counting the number of days of the customer interconnection equipment based on the fifth data table, whether the IP address corresponding to the MAC address in the customer interconnection equipment information changes is determined according to the active rate statistical result, the dynamic analysis result of whether the latest IP address is used for updating is obtained according to the determination result, and the next-day behavior prediction data is obtained by performing quantitative prediction of the next-day behavior data of the customer interconnection equipment based on time series according to the dynamic analysis result. The IP-VPN customer interconnection equipment dynamic information obtained by association is collected based on the ARP cycle, the behavior data analysis and calculation and behavior prediction of each interconnection equipment of the IP-VPN customer are introduced (including the customer interconnection equipment activity), the dynamic monitoring method based on the behavior of the customer interconnection equipment is provided, and the false judgment caused by the influence of the use behavior of the customer interconnection equipment is solved. For example, the customer equipment is offline, which will cause the problem of false monitoring.

[0134] Figure 5 For another dynamic IP-VPN private line automatic monitoring method of the embodiment of the application, as shown in Figure 5 , the method comprises the following steps:

[0135] S410, obtaining ARP information of an access gateway BNG of an IP-VPN private line.

[0136] S420, obtaining comprehensive data after associated IP-VPN customer information based on the association relationship between the ARP information, the OLT uplink information and the comprehensive data customer information.

[0137] S430, performing device behavior dynamic analysis on customer interconnection equipment information based on the comprehensive data, and performing quantitative prediction of next-day behavior data of the customer interconnection equipment based on time series according to the dynamic analysis result to obtain next-day behavior prediction data.

[0138] It should be noted that the specific implementation of steps S410-S430 can refer to the above-mentioned embodiments, and will not be repeated here.

[0139] S440, classifying the next-day behavior prediction data based on the BNG name to obtain a data classification result.

[0140] S450, constructing a classification monitoring instruction according to the data classification result.

[0141] S460, responding to the classification monitoring instruction, respectively using a parallel mode and a serial mode to execute the classification monitoring instruction under each BNG connection and a single BNG connection to obtain an instruction response result.

[0142] Specifically, according to the above obtained dynamic information data of each interconnection device behavior of each IP-VPN customer, the instruction construction monitoring is performed on the condition that the "active rate in the past seven days" is greater than 0 and the next-day behavior prediction is "online".

[0143] In an embodiment of the present application, the classification and instruction construction are performed based on the "BNG name", and when the number of single BNG connection monitoring instructions exceeds 110 (15-minute monitoring granularity, and the timeout of each instruction is set to 8 seconds), a connection is added, each BNG is logged in through remote SSH, the monitoring instruction is executed in parallel mode under each BNG connection, and the monitoring instruction is executed in serial mode under a single BNG connection to perform 15-minute granularity cyclic monitoring, as shown in the following table 1. Figure 6

[0144] Further, the main parameters of the instruction construction are "VPN instance", "internet device IP address", and "BNG name" belonging to, one monitoring instruction for each internet device of the customer, and the instruction construction example is shown in the following table 2 (Huawei BNG instruction example).

[0145] Table 2

[0146]

[0147]

[0148] S470, performing index analysis based on the instruction response result to output corresponding IP-VPN customer warning information according to the index abnormality determination result.

[0149] In the embodiment of the present application, the analysis is performed according to the execution result, and the analysis indexes include packet loss rate, round-trip delay, and jitter delay, which are respectively determined and output to output corresponding IP-VPN customer warning information.

[0150] Specifically, scenario 1: n interconnection devices of an IP-VPN customer are connected through a switch, and the gateways of the customer interconnection devices are all set to point to a BNG, as shown in the following figure 1. Figure 7 ​As shown, when n device indicators simultaneously appear abnormal and do not recover for 4 consecutive monitoring periods, it is determined that there is an access network problem and a warning is output, otherwise one of the n devices is normal and is considered to be a customer internal network problem.

[0151] Specifically, scenario 2: as shown, each device of the customer is connected through a router, and the router is used as a unique interface of the CE to the BNG. When abnormality occurs and does not recover for 4 consecutive monitoring periods, it is determined that there is an access network problem and a warning is output. Figure 8

[0152] In the embodiment of the application, ARP information of an access gateway BNG of an IP-VPN private line is acquired. Comprehensive data after associated IP-VPN customer information is obtained based on an association relationship between the ARP information, OLT uplink information and comprehensive data customer information. Device behavior dynamic analysis is performed on customer interconnection device information based on the comprehensive data, and time series-based quantitative prediction is performed on next-day behavior data of the customer interconnection device according to a dynamic analysis result to obtain next-day behavior prediction data. S440, the next-day behavior prediction data is classified based on a BNG name to obtain a data classification result. A classification monitoring instruction is constructed according to the data classification result. The connection between each BNG and the connection under a single BNG are correspondingly executed by using a parallel mode and a serial mode to execute the classification monitoring instruction to obtain an instruction response result in response to the classification monitoring instruction. S470, index analysis is performed based on the instruction response result to output corresponding IP-VPN customer warning information according to an index abnormality determination result. The method of the embodiment of the application applies an existing interface of the BNG network element, realizes the whole process output from ARP information acquisition, customer information association to monitoring instruction construction, concurrent execution to result judgment and warning, ensures the accuracy of IP-VPN monitoring, improves the efficiency of monitoring and meets the timeliness requirement.

[0153] To realize the above-mentioned embodiment, as shown in the figure, the embodiment further provides a dynamic IP-VPN private line automatic monitoring device 10, which comprises a gateway data acquisition module 100, an associated data acquisition module 200, a behavior data prediction module 300 and an instruction response warning module 400. Figure 9

[0154] The gateway data acquisition module 100 is used to acquire ARP information of an access gateway BNG of an IP-VPN private line.

[0155] The associated data acquisition module 200 is used to obtain comprehensive data after associated IP-VPN customer information based on an association relationship between the ARP information, OLT uplink information and comprehensive data customer information.

[0156] ​​The behavior data prediction module 300 is configured to perform dynamic analysis on the client interconnection device information based on the comprehensive data, and perform quantitative prediction on the next-day behavior data of the client interconnection device based on time series according to the dynamic analysis result to obtain next-day behavior prediction data.

[0157] The instruction response warning module 400 is configured to respond to the classification monitoring instruction constructed based on the next-day behavior prediction data, and perform index analysis based on the instruction response result to output corresponding IP-VPN client warning information according to the index abnormality determination result.

[0158] The dynamic IP-VPN special line automatic monitoring device according to the embodiment of the present application restores the real perception of the client through the detection technology, ensures that the problem discovery is highly consistent with the actual perception of the user, solves the problem that the past fault alarm means is single, the workload is large, and the actual perception of the client is inconsistent, solves the problem that the analysis accuracy is low based on the record information (such as the CE address, the VPN instance, and the local network element) of the resource system in the past, solves the problem of misjudgment of the monitoring caused by the influence of the use behavior of the client interconnection device, and ensures the accuracy of the IP-VPN monitoring, improves the efficiency of the monitoring, and meets the time efficiency requirement.

[0159] In order to realize the method of the above-mentioned embodiment, the present application further provides a computer device, as shown in the figure, the computer device 600 comprises a memory 601, a processor 602; wherein the processor 602 runs the program corresponding to the executable program code by reading the executable program code stored in the memory 601, so as to realize the steps of the method described above. Figure 10

[0160] In order to realize the above-mentioned embodiment, the present application further provides a non-transitory computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the method described in the above-mentioned embodiment.

[0161] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in combination with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above-mentioned terms is not necessarily for the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, the different embodiments or examples described in the present specification and the features of the different embodiments or examples can be combined and combined by those skilled in the art without contradiction.

[0162] ​Furthermore, the terms "first", "second", "third", "fourth", "fifth" and "sixth" are used herein for descriptive purposes only and are not to be construed as indicating or implying relative importance or a significant nature of so described technical features. It is to be understood that a technical feature described with the "first", "second", "third", "fourth", "fifth" or "sixth" can implicitly or explicitly include at least one of the technical features described with the "first", "second", "third", "fourth", "fifth" or "sixth". In the description of the present application, the meaning of "a plurality" is at least two, for example, two, three, etc., unless otherwise specifically defined.

Claims

1. A method for dynamic IP-VPN leased line automatic monitoring, characterized in that, The method comprises: obtaining ARP information of an access gateway BNG of an IP-VPN private line; based on the association relationship between the ARP information, OLT uplink information and comprehensive data customer information, obtaining associated IP-VPN customer information after comprehensive data; based on the comprehensive data, performing device behavior dynamic analysis on customer interconnection device information, and based on the dynamic analysis result, performing quantitative prediction on the next day behavior data of the customer interconnection device based on time series to obtain next day behavior prediction data; in response to the classification monitoring instruction constructed based on the next day behavior prediction data, and based on the instruction response result, performing index analysis to output corresponding IP-VPN customer early warning information according to the index abnormality determination result.

2. The method of claim 1, wherein, The method comprises: accessing the BNG of the IP-VPN private line through a remote protocol to obtain a gateway connection result; based on the gateway connection result and a preset output instruction, obtaining the ARP information of the BNG.

3. The method of claim 2, wherein, After obtaining the ARP information of the access gateway BNG in the IP-VPN private line, the method further comprises: extracting ARP key information in a preset time period; performing a deduplication operation on the ARP key information to obtain a first data table according to the information deduplication result.

4. The method of claim 3, wherein, The ARP key information includes BNG name, IP-VPN customer interconnection device IP address, IP-VPN customer interconnection device MAC address, BNG interface, VPN instance name and inner and outer layer VLAN.

5. The method of claim 4, wherein, Based on the association relationship between the ARP information, OLT uplink information and comprehensive data customer information, obtaining associated IP-VPN customer information after comprehensive data, comprising: obtaining metropolitan area network OLT uplink information and comprehensive data customer information; based on the metropolitan area network OLT uplink information and comprehensive data customer information respectively, obtaining corresponding second data table and third data table; obtaining a fourth data table according to the association relationship between the first data table and the second data table; obtaining a fifth data table of associated IP-VPN customer information after comprehensive data according to the association relationship between the fourth data table and the third data table.

6. The method of claim 5, wherein, The metropolitan area network OLT uplink information includes OLT name, BNG name and BNG interface; the comprehensive data customer information includes customer product number, customer name, OLT name and inner and outer layer VLAN.

7. The method of claim 6, wherein, Based on the comprehensive data, performing device behavior dynamic analysis on customer interconnection device information to obtain a dynamic analysis result, comprising: based on the fifth data table, counting the number of days of customer interconnection devices to obtain an active rate statistical result; determining whether the IP address corresponding to the MAC address in the customer interconnection device information has changed according to the active rate statistical result, to obtain a dynamic analysis result of whether to use the latest IP address for updating according to the determination result.

8. The method of claim 7, wherein, In response to the classification monitoring instruction constructed based on the next day behavior prediction data, obtaining an instruction response result, comprising: based on the BNG name, classifying the next day behavior prediction data to obtain a data classification result; constructing a classification monitoring instruction according to the data classification result; The classification monitoring instruction is executed in parallel and in series to obtain an instruction response result.

9. The method of claim 1, wherein, The parsed indicators include a packet loss rate, a round-trip delay and a jitter delay.

10. A dynamic IP-VPN private line automatic monitoring apparatus, characterized by comprising: The method comprises the steps of: a gateway data acquisition module configured to acquire ARP information of an access gateway BNG of an IP-VPN private line; an association data acquisition module configured to obtain associated IP-VPN customer information based on an association relationship between the ARP information, OLT uplink information and comprehensive customer information; a behavior data prediction module configured to perform dynamic analysis on customer interconnection equipment information based on the comprehensive data, and to perform quantitative prediction on next-day behavior data of the customer interconnection equipment based on a time sequence according to a dynamic analysis result to obtain next-day behavior prediction data; an instruction response early warning module configured to respond to a classification monitoring instruction constructed based on the next-day behavior prediction data, and to perform indicator analysis based on an instruction response result to output corresponding IP-VPN customer early warning information according to an indicator abnormality determination result.

11. A computer device, comprising: The method comprises the steps of: The program is executed by the processor to implement the dynamic IP-VPN private line automatic monitoring method.

12. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the dynamic IP-VPN private line automatic monitoring method.

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