CN115348158A - Transaction full link analysis method and system based on banking non-standardized transaction message - Google Patents
Transaction full link analysis method and system based on banking non-standardized transaction message Download PDFInfo
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Abstract
The invention relates to the technical field of transaction tracking, in particular to a transaction full link analysis method and a system based on banking non-standardized transaction messages, which comprises flow acquisition, flow analysis and transaction series connection, wherein the invention does not need to carry out message standardized reconstruction on a non-standard system, saves the manpower and capital cost for system reconstruction, and avoids the service continuity risk possibly caused by the system reconstruction; secondly, comparing the similarity between the upstream and downstream messages by using a Jaccard similarity coefficient, and realizing the self-discovery of a non-standard transaction path in an unsupervised mode without manual intervention; in addition, three modes of conventional series connection, regular series connection of expert models and intelligent matching series connection are combined, the expert models can be used for performing characteristic matching under the scene that partial similarity matching cannot achieve a satisfactory effect, and the method is suitable for transaction series connection of most non-standardized messages.
Description
Technical Field
The invention relates to the technical field of transaction tracking, in particular to a transaction full-link analysis method and system based on banking non-standardized transaction messages.
Background
With the rapid development of the bank IT industry in recent years, the service scale is increased by orders of magnitude compared with the traditional mode, the business modes are various and complex, and accordingly, the bank business systems are diversified and the association among the systems is complex. Some complex business processes pass through several or even more than ten systems, and when problems occur in the business, a plurality of related business system responsible persons may be required to invest a large amount of labor and time to collaborate together to finally confirm the problems. How to effectively simplify the problem discovery process and quickly locate the root cause of the problem becomes a bottleneck in improving operation and maintenance efficiency, and is one of the important issues to be solved urgently by the bank IT operation and maintenance center at present.
At present, the operation and maintenance department of banking industry generally uses a transaction full link tracking system to realize the full life link tracking function of a single service. The system acquires and analyzes the application logs of all service systems in a row or service flow among the systems in real time to acquire and uniformly store the standardized transaction data of all transactions. The operation and maintenance personnel can search all the transactions in the transaction full-link tracking system through certain key identifiers to determine a single problem transaction. The transaction full link tracking system carries out global retrieval and transaction link assembly on the preorder and subsequent transactions containing the same serial number through a global unique identifier (transaction global serial number) in the standardized transaction message, obtains a complete transaction link of the transaction and a processing condition of the transaction in each application node in the transaction link, and therefore the rapid fault root cause positioning capability of the transaction of the cross-multi-application system is achieved.
However, there are three main aspects for link analysis based on transaction messages in the current middle and small banks, and firstly, based on historical reasons, there are large differences in message formats in many stock systems of banks; secondly, in order to ensure the rapid development of the medium and small banks, a plurality of systems are introduced by products, and the systems are more heterogeneous; thirdly, the global serial number is standard and difficult to fall to the ground, and most of the transactions are difficult to perform correlation analysis by adopting the unified serial number in reality. Therefore, the global serial number specification is difficult to fall to the ground in all business systems in the row, and the system for transaction full link analysis based on the global serial number can cause the condition of transaction link analysis open circuit, thereby bringing difficulty for operation and maintenance personnel to quickly position faults.
Disclosure of Invention
The invention aims to provide a transaction full link analysis method and a transaction full link analysis system based on banking non-standardized transaction messages, so as to solve the problems in the background technology.
The technical scheme of the invention is as follows: a transaction full link analysis method and system based on banking non-standardized transaction messages comprises the following steps:
s1, flow acquisition: mirroring the real-time transaction message flow among the production servers through an exchanger backboard mirroring function, filtering and converging the mirror flow of a plurality of production network exchangers through a network TAP (TAP), and uniformly converging the processed flow into a service flow pool of a system;
s2, flow analysis: the system uses an acquisition task distributor to excavate the traffic packets needed by the digging according to the needs from the traffic pool by using a traffic packet digging process PktMiner at a high speed and in parallel, and carries out the transaction traffic packets;
and S3, transaction series connection, namely, the transaction full link analysis system of the non-standardized transaction message is combined with three modes of conventional series connection, expert model regular series connection and intelligent matching series connection, so that the comprehensive acquisition of the transaction full link operation condition and the transverse positioning of the fault root cause are gradually realized.
Further, in S2, the traffic analysis includes analysis and matching, and the analysis includes: according to the message format between the service systems, converting the binary network flow bytes into formatted key and value key value pair message data; and the matching comprises matching the analyzed service messages according to the connection mode between the service systems to obtain a complete service message.
Further, in S3, the conventional concatenation includes: and adopting a plurality of self-defined serial identifications to serially connect.
Further, in S3, the expert model rule concatenation includes: operation and maintenance personnel can match rule series models through self-defining service scene full link and upstream and downstream transaction.
Further, in S3, the intelligent matching cascade includes: and the automatic series connection is realized through the mode of time sequence, clustering and mode identification of transaction messages.
Furthermore, for a single transaction message, the optimal matching mode of three matching modes of conventional series connection, expert model rule series connection and intelligent matching series connection is intelligently selected to carry out message series matching.
Further, the step of intelligently selecting the optimal matching mode to perform message serial matching includes:
s31, carrying out primary serial connection on the initial message by using conventional serial connection to match an initial transaction link;
s32, judging whether the link generated by the conventional serial connection is matched with an expert model in an expert model library; if so, matching the expert model;
s33, judging whether a link generated by the conventional serial connection is broken or not; if the open circuit exists, the link is indicated to have the non-standard message which does not contain the global serial number, and the intelligent matching series is used for carrying out automatic open circuit series matching so as to find the non-standard message.
Furthermore, the non-standard messages are intelligently matched and connected in series, a clustering and similarity matching algorithm is used, the downstream transaction messages with the highest similarity are automatically matched layer by layer from the breakpoint of the transaction link until the matched messages are successfully spliced to the downstream breakpoint to form a complete link, and the self-discovery of the non-standard transaction path is realized in an unsupervised mode.
A transaction full link analysis system based on banking non-standardized transaction messages comprises flow collection, flow analysis and transaction series connection.
Furthermore, the transaction full link analysis system compares the similarity between the upstream message and the downstream message by using a Jaccard similarity system, and for the upstream message A of the disconnection point, the system firstly screens out a result set { B }, wherein each message { B1 and B2.. Bn } in B combination conforms to B.SRCIP = A.DSTIP and B.SedTime < = (A.RecTime + theta), wherein theta is the estimated average processing time inside the A message receiving party system.
For the key/value set in each message Bn in the given message A and B sets, the Jaccard similarity coefficient is defined as the ratio of the size of all intersections in A and Bn to the size of the union of A and Bn:
compared with the prior art, the invention provides a transaction full link analysis method and a system based on banking non-standardized transaction messages by improvement, and the method and the system have the following improvements and advantages:
compared with the prior art, the invention does not need to carry out message standardization transformation on the non-standard system, saves the labor and capital cost for system transformation, and avoids the service continuity risk possibly caused by the system transformation; secondly, comparing the similarity between the upstream and downstream messages by using a Jaccard similarity coefficient, and realizing the self-discovery of a non-standard transaction path in an unsupervised mode without manual intervention; in addition, three modes of conventional series connection, regular series connection of expert models and intelligent matching series connection are combined, the expert models can be used for performing characteristic matching under the scene that partial similarity matching cannot achieve a satisfactory effect, and the method is suitable for transaction series connection of most non-standardized messages.
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The invention is further explained below with reference to the figures and examples:
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a block diagram of the architecture of the transaction concatenation of the present invention;
FIG. 3 is a block diagram of the intelligent matching architecture of the present invention.
Detailed Description
The present invention is described in detail below, and the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a transaction full link analysis method and a system based on banking non-standardized transaction messages through improvement, and the technical scheme of the invention is as follows:
as shown in fig. 1, a transaction full link analysis method and system based on banking non-standardized transaction messages includes the following steps:
s1, flow acquisition: the real-time transaction message flow between the production servers is mirrored through a switch backboard mirroring function, the mirror flow of a plurality of production network switches is filtered and converged through a network TAP, and the processed flow is uniformly converged into a service flow pool of the system;
s2, flow analysis: the system uses an acquisition task distributor to excavate required traffic packets from a traffic pool at a high speed and in parallel by using a traffic packet excavation process PktMiner as required, and carries out transaction traffic packets;
and S3, transaction series connection, namely, the transaction full link analysis system of the non-standardized transaction message is combined with three modes of conventional series connection, expert model regular series connection and intelligent matching series connection, so that the comprehensive acquisition of the transaction full link operation condition and the transverse positioning of the fault root cause are gradually realized.
In S2, the traffic analysis includes analysis and matching, and the analysis includes: according to the message format (SOAP, fixed length, json and the like) between service systems, converting binary network flow bytes into formatted key and value key value pair message data; the matching comprises matching the analyzed service messages according to the connection mode (synchronous/asynchronous, long/short connection and the like) between the service systems, so as to obtain a complete service message (request message + response message).
Wherein, in S3, the conventional concatenation comprises: a plurality of self-defined serial identifications (mainly transaction global serial numbers) are adopted for serial connection.
Wherein, in S3, the expert model rule concatenation includes: operation and maintenance personnel can match rule series models through self-defining service scene full link and upstream and downstream transaction.
Wherein, in S3, the intelligent matching series connection comprises: and the automatic series connection is realized through the mode of time sequence, clustering and mode identification of transaction messages.
The optimal matching mode of three matching modes of conventional series connection, expert model regular series connection and intelligent matching series connection is intelligently selected for single transaction messages to carry out message series matching.
As shown in fig. 2, the step of intelligently selecting the optimal matching mode to perform serial matching on the messages includes:
s31, performing primary serial connection on the initial message by using conventional serial connection to match an initial transaction link;
s32, judging whether the link generated by the conventional serial connection is matched with an expert model in an expert model library; if so, matching the expert model;
s33, judging whether a link generated by the conventional serial connection has an open circuit or not; if the open circuit exists, the link is indicated to have the non-standard message which does not contain the global serial number, and the intelligent matching series is used for carrying out automatic open circuit series matching so as to find the non-standard message.
As shown in fig. 3, the non-standard messages are intelligently matched in series, a clustering and similarity matching algorithm is used, the downstream transaction messages with the highest similarity are automatically matched layer by layer from the breakpoint of the transaction link until the matched messages are successfully spliced to the downstream breakpoint to form a complete link, and the self-discovery of the non-standard transaction path is realized in an unsupervised manner.
A transaction full link analysis system based on banking non-standardized transaction messages comprises flow collection, flow analysis and transaction series connection.
The transaction full link analysis system compares the similarity between upstream and downstream messages by using a 'Jaccard similarity coefficient (Jacchardsimilarycoefficient)', firstly analyzes and formats all single messages collected in a bypass mode, and decomposes a message body into a set of key and value formatting key value pairs. For the upstream message a at the disconnection point, the system firstly screens out a result set { B }, wherein each message { B1, B2.. Bn } in the combination of B conforms to b.srcip (source address) = a.dstip (destination address) and b.sedtime (message sending time) < = (a.rectime (message receiving time) + θ), wherein θ is estimated to be the average processing time in the system of the message receiving party a.
For the key/value set in each message Bn in the given message A and B sets, the Jaccard similarity coefficient is defined as the ratio of the size of all intersections in A and Bn to the size of the union of A and Bn:
the higher the Jaccard similarity factor, the more likely message B is a downstream message of A. The system iterates through the loop to automatically generate one or more possible closed-loop open-circuit paths
Compared with the prior art, firstly, the invention does not need to carry out message standardization transformation on a non-standard system, saves the labor and capital cost of system transformation, and avoids the service continuity risk possibly caused by system transformation; secondly, comparing the similarity between the upstream and downstream messages by using a Jaccard similarity coefficient, and realizing the self-discovery of a non-standard transaction path in an unsupervised mode without manual intervention; in addition, three modes of conventional series connection, regular series connection of expert models and intelligent matching series connection are combined, the expert models can be used for performing characteristic matching under the scene that partial similarity matching cannot achieve a satisfactory effect, and the method is suitable for transaction series connection of most non-standardized messages.
The previous description is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A transaction full link analysis method based on banking non-standardized transaction messages is characterized by comprising the following steps: the method comprises the following steps:
s1, flow acquisition: mirroring the real-time transaction message flow among the production servers through an exchanger backboard mirroring function, filtering and converging the mirror flow of a plurality of production network exchangers through a network TAP (TAP), and uniformly converging the processed flow into a service flow pool of a system;
s2, flow analysis: the system uses an acquisition task distributor to excavate the traffic packets needed by the digging according to the needs from the traffic pool by using a traffic packet digging process PktMiner at a high speed and in parallel, and carries out the transaction traffic packets;
and S3, transaction series connection, namely, the transaction full link analysis system of the non-standardized transaction message needs to combine three modes of conventional series connection, regular series connection of expert models and intelligent matching series connection, so that the comprehensive acquisition of the transaction full link operation condition and the transverse positioning of fault root cause are gradually realized.
2. The transaction full link analysis method based on banking non-standardized transaction messages according to claim 1, characterized in that: in S2, the traffic analysis includes analysis and matching, and the analysis includes: according to the message format between the service systems, converting the binary network flow bytes into formatted key and value key value pair message data; and the matching comprises matching the analyzed service messages according to the connection mode between the service systems to obtain a complete service message.
3. The transaction full link analysis method based on banking non-standardized transaction messages according to claim 1, characterized in that: in S3, the conventional concatenation includes: and adopting a plurality of self-defined serial identifications to serially connect.
4. The transaction full link analysis method based on banking non-standardized transaction messages according to claim 1, characterized in that: in S3, the expert model rule concatenation includes: operation and maintenance personnel can match rule series models through self-defining service scene full link and upstream and downstream transaction.
5. The transaction full link analysis method based on banking non-standardized transaction messages according to claim 1, characterized in that: in S3, the intelligent matching series connection comprises: and the messages are automatically connected in series in a mode of time sequence, clustering and pattern recognition of transaction messages.
6. The transaction full link analysis method based on banking non-standardized transaction messages according to claim 1, characterized in that: and for a single transaction message, intelligently selecting the optimal matching mode from three matching modes of conventional series connection, expert model regular series connection and intelligent matching series connection to carry out message series matching.
7. The transaction full link analysis method based on banking non-standardized transaction messages according to claim 6, characterized in that: the step of intelligently selecting the optimal matching mode to carry out message serial matching comprises the following steps:
s31, carrying out primary serial connection on the initial message by using conventional serial connection to match an initial transaction link;
s32, judging whether a link generated by the conventional serial connection matches an expert model in an expert model library; if so, matching the expert model;
s33, judging whether a link generated by the conventional serial connection is broken or not; if the open circuit exists, the link is indicated to have the non-standard message which does not contain the global serial number, and the intelligent matching series is used for carrying out automatic open circuit series matching so as to find the non-standard message.
8. The transaction full link analysis method based on banking non-standardized transaction messages according to claim 1, characterized in that: and (3) carrying out intelligent matching series connection on the non-standard messages, using a clustering and similarity matching algorithm, automatically matching the downstream transaction messages with the highest similarity layer by layer from the breakpoint of the transaction link until the matched messages are successfully spliced to the downstream breakpoint to form a complete link, and realizing self-discovery of the non-standard transaction path in an unsupervised mode.
9. A transaction full link analysis system based on banking non-standardized transaction messages is characterized in that: the transaction full link analysis system comprises flow collection, flow analysis and transaction series connection.
10. The transaction full link analysis system based on banking non-standardized transaction messages according to claim 9, characterized in that: the transaction full link analysis system compares the similarity between an upstream message and a downstream message by using a Jaccard similarity system, for an upstream message A of a breaking point, the system firstly screens a coincidence result set { B }, each message { B1 and B2.. Bn } in B combination conforms to B.SRCIP = A.DSTIP and B.SedTime < = (A.RecTime + theta), wherein theta is estimated to be the internal average processing time of a message receiving party system.
For a key/value set in each message Bn in a given message A and B set, the Jaccard similarity coefficient is defined as the ratio of the size of all intersections in the A and the Bn to the size of the union of the A and the Bn:
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