CN116934067A - Flow mining method, storage medium and equipment based on full-link monitoring data - Google Patents
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Abstract
The invention discloses a flow mining method, a storage medium and equipment based on full-link monitoring data. The method comprises the following steps: s1, acquiring data blood-edge links of each component, each index and each service of a service system from full-link monitoring data; s3, extracting a data flow direction relation of at least one pair of entities from each data blood edge link; s4, selecting a plurality of groups of entity groups with continuous data flow from the data flow relation of each pair of entities, and sequentially connecting the entities in each group in series to form a business flow link; s5, inquiring the metadata state of each node of each business process link, if the metadata state is invalid, removing the node, and generating the data flow relation between the front node and the rear node according to the data flow relation between the node and the front node and the rear node, so as to obtain the updated business process link. The data volume required to be processed by the method is not increased explosively, a large amount of calculation resources are not consumed, and a complete business process can be accurately mined.
Description
Technical Field
The present invention relates to the field of flow management technologies, and in particular, to a flow mining method, a storage medium, and a device based on full link monitoring data.
Background
The process mining means that the business process of the enterprise is mined by analyzing the business system data, the user operation behavior log, the business log and other data of the enterprise, so that the enterprise is helped to know the business process condition, the enterprise is convenient to optimize the business process, and the business process operation efficiency is improved.
There are two general process mining modes in the industry:
(1) The process mining is performed based on a frequent pattern mining algorithm. The frequent pattern mining algorithm can mine out a frequent item set and association rules among all frequent items in the frequent item set from the service data set, and accordingly a service flow is obtained. However, the data of the service data set increases with the occurrence of the service, the number of frequent item sets in the service data set increases exponentially, that is, the amount of data to be processed increases explosively, and after the service data set reaches a certain level, the required consumed computing resources increase greatly, and a large amount of computing resources are required to be consumed.
(2) The process mining is based on a process discovery algorithm. The process discovery algorithm understands the dependency relationship and execution sequence between activities in the log data through the log data, and deduces the business flow from the dependency relationship and the execution sequence. The process discovery algorithm requires that the log data contain the order relationships of all activities that may occur, i.e., that the order relationships of all activities be complete, so that the business process of the inference process is complete and accurate. However, in the actual running process of the service system, many log data do not include all the sequence relationships of the activities that may occur, only include part of the sequence relationships of the activities, and the service flow obtained by performing the process discovery algorithm according to the sequence mining may not be a complete service flow.
Disclosure of Invention
The invention aims to provide a flow mining method based on full-link monitoring data, a computer readable storage medium storing a computer program for realizing the method when being executed, and flow mining equipment capable of executing the method.
In order to achieve the above purpose, the present invention provides a process mining method based on full link monitoring data, comprising the following steps:
s1, acquiring data blood edge links of each component, each index and each service of a service system from full-link monitoring data corresponding to the service system to obtain a plurality of data blood edge links, wherein nodes in the data blood edge links are entities;
s2, carrying out data preprocessing on each data blood edge link;
s3, extracting at least one pair of entity data flow direction relations from each preprocessed data blood edge link by adopting a trained PCNNs convolutional neural network model;
s4, selecting a plurality of entity groups with coherent data flow directions from the obtained data flow direction relations of the entities, and sequentially connecting the entities in each entity group in series according to the data flow direction relations to form a business flow link;
s5, inquiring the metadata state of each node in each business process link, if the metadata state of the node is invalid, removing the node in the business process link, and generating the data flow relation between the front node and the rear node according to the data flow relation between the node and the front node and the rear node, so as to obtain the updated business process link.
Further, the method includes step S6, the node with the occurrence times exceeding the preset times in all the business process links is recorded as the key entity node so that the operation and maintenance personnel can maintain the key entity node in a key mode.
Further, the method includes step S7, associating a plurality of business process links with each other, wherein if the business process corresponding to one business process link fails and the node where the business process link fails is located is the overlapping node, the business corresponding to the business process link associated with the business process link is disconnected.
Further, the method comprises the following steps:
s8, respectively constructing corresponding business process models for each business process link;
s9, carrying out business process simulation by adopting a business process simulation engine according to each business process model to obtain operation performance data of each business process model so as to enable operation and maintenance personnel to optimize the business process model according to the operation performance data of each business process model, wherein the operation performance data comprises throughput, network delay time and resource utilization rate.
Further, the method comprises the step S10 of outputting operation performance data obtained by each business process model and simulation thereof through a visual chart.
Further, in step S2, the data preprocessing includes data cleansing, data dimension reduction, data normalization and data conversion.
The present invention also provides a computer-readable storage medium having stored thereon an executable computer program that is executed to implement the full link monitoring data based flow mining method as described above.
The invention also provides a process mining device comprising a processor and a computer readable storage medium as described above, the processor executing a computer program in the storage medium to implement a process mining method based on full link monitoring data as described above.
Full link monitoring refers to monitoring of the entire data flow (i.e., data blood-edge links) from the origin, intermediate processing, and to final output of all data (including components, indexes, and services) in the business system, and the data recorded in full link monitoring is full link monitoring data, which includes the data blood-edge links of each data in the business system. The invention considers that the business process is usually accompanied with the generation and circulation of data, namely, the data blood-edge links reflect the links of the business process, so the invention adopts full-link monitoring data to carry out process mining, specifically, the data blood-edge links of each component, each index and each service in the business system are obtained from the full-link monitoring data according to the step S1, then the data flow direction relation among all entities is extracted through the step S3, and then the step S4 is executed to serially connect a plurality of entities with continuous data flow directions in sequence to form the business process links. During operation of the service system, the user may delete some data, for example, delete a table, where the deleted table may belong to a link node in the data blood-edge link, but the data blood-edge link in the full-link monitoring data still has a corresponding link node, which results in that some nodes in the service flow link formed in step S4 may be invalid. Therefore, the invention inquires the metadata state of each node in the business flow link through step S5, if the metadata state of the node is invalid, the node is removed from the business flow link, the data flow relation between the front node and the rear node is generated according to the data flow relation between the front node and the rear node, the optimized business flow link is obtained, and the accuracy and the effectiveness of the business flow link are ensured. The invention acquires the data blood-edge link based on the full-link monitoring data, and further extracts the data flow direction relation among all the entities from the data blood-edge link, thereby forming a complete business flow. Because one component/index/service corresponds to only one data blood-edge link, the data volume of the data blood-edge link is not increased along with the occurrence of the service like the service data, the data volume is generally stable, the explosion type growth is avoided, and a large amount of calculation resources are not required to be consumed.
Drawings
Fig. 1 is a flow diagram of a flow mining method based on full link monitoring data provided by the invention.
Detailed Description
The invention is further described in detail below in connection with the detailed description.
The embodiment provides a process mining device, which comprises a processor and a computer readable storage medium. The computer readable storage medium stores an executable computer program, and a processor of the flow mining device executes the computer program to implement the flow mining method based on the full link monitoring data as shown in fig. 1. The execution of the method is described below by way of specific examples.
In this embodiment, the full-link monitoring background server is used as the process mining device. The full-link monitoring background server (hereinafter referred to as background server) monitors the whole data flow link (namely, data blood-edge link) from the origin, the middle processing process and the final data of all data in the service system to form full-link monitoring data. In this embodiment, the generation and circulation of data are generally accompanied with the business process, that is, the link of the business process is reflected by the data blood edge link, so that the process mining is performed by adopting full-link monitoring data, and the specific process is as follows:
the background server firstly acquires all-link monitoring data of each component, each index and each service of the business system, and then acquires data blood-edge links of each component, each index and each service from the all-link monitoring data, wherein the components comprise reports, labels and the like, the indexes refer to measurement standard items for measuring, evaluating and monitoring specific targets or performances, and the services comprise an API (application program interface), a Restful interface and the like. Taking an a report displayed to a user at the front end of a business system as an example, wherein the a report is summarized by a b report, the b report is calculated according to a calculation formula xxx according to data of a c report, in the embodiment, "- - - - - - - -" is used for representing a data flow direction, brackets are used for marking corresponding data processing processes beside each "- - -" symbol, a data blood edge link of the a report is expressed as c- - - > (the calculation formula xxx) b- - > (summarized) a, a physical meaning corresponding to the data blood edge link is that the c report is calculated according to the calculation formula xxx to obtain the b report, and the b report is summarized to obtain the a report. After the background server acquires the data blood-edge links of each component, each index and each service, the background server performs data preprocessing on the data blood-edge links, and specifically includes:
(1) And (3) data cleaning, namely carrying out deletion value supplementing, abnormal value deleting, repeated value removing and noise smoothing on the data blood edge links by adopting related technologies.
(2) And (3) reducing the data dimension, namely reducing the dimension of the data blood-edge links by adopting a Chi-square test algorithm, and only retaining the characteristics highly related to target variables (data flow direction relations among different entities).
(3) Data normalization, which maps the characteristic values of data blood-edge links to a uniform numerical range through characteristic scaling (Feature scaling) to eliminate order-of-magnitude differences between different characteristics. Normalization, for example, is a common data normalization approach.
(4) Data conversion, which sequentially performs logarithmic conversion, exponential conversion and barrelling (discretizing continuous values into different intervals) on the data blood-edge links.
Because some data blood-edge links of components/indexes/services may be only local in the business process, the data blood-edge links of components/indexes/services cannot represent a complete business process, and therefore, the present embodiment needs to analyze the relationships between entities in all the data blood-edge links, so as to comb out the complete business process. Thus, this embodiment requires the extraction of relationships between the various entities in the data blood-edge link. One of the typical applications of the PCNNs convolutional neural network model is to perform entity relationship extraction, so the present embodiment uses the PCNNs convolutional neural network model to extract the relationship between entities in the data blood-edge link. The data blood-edge link is represented in the full-link monitoring data in the form of an SQL sentence, so that in the embodiment, the SQL sentence (the SQL sentence contains metadata information of the entities and related operation information) is taken as input, and metadata of each entity and operation types (namely, data flow relation) between the entities are taken as output to form a group of training samples. After a large number of training samples are collected, a technician trains the PCNNs convolutional neural network model by adopting the training samples, so that the PCNNs convolutional neural network model has the capability of automatically extracting metadata of each entity in the SQL sentence and data flow direction relations among the entities according to the SQL sentence, and then the trained PCNNs convolutional neural network model is stored in a background server. After the background server finishes preprocessing the data blood-edge links, the trained PCNNs convolutional neural network model is adopted to extract entity pair (namely a pair of entities) relations of the data blood-edge links, namely the data flow relation of each pair of entities contained in each data blood-edge link. Taking the data blood edge link c— (calculation formula xxx) b- > (summary) a of the above report a as an example, where the report a, the report b, and the report c are all entities, and the data flow relationships of two pairs of entities extracted from the data blood edge link are c- > (calculation formula xxx) b, and b- > (summary) a, respectively, and as such, the data flow relationships of the pairs of entities contained in each data blood edge link are extracted, for example, the data flow relationships of the following pairs of entities are extracted: c- > (calculation formula xxx) b, b- > (summary) a, d- > (synchronization) c, e- > (simplification) d, o- > (screening condition xxx) p, p- > (calculation formula xxx) q, q- > (screening condition xxx) r, r- > (summary) s, m- > (averaging) n, l- > (screening) m, k- > (summary) l. The background server then selects three groups of data flow-coherent entity groups from the obtained data flow-direction relations of the entities, wherein the three groups of data flow-direction coherent entity groups are respectively as follows: (1) e- > (simplified) d, d- > (synchronized) c, c- > (calculation formula xxx) b, b- > (summarized) a; (2) o- > (screening conditions xxx) p, p- > (calculation formula xxx) q, q- > (screening conditions xxx) r, r- > (summary) s; (3) k- > (summary) l- > (screening) m, m- > (averaging) n. Then the background server connects each entity in each group of entities in series in sequence to form business process links, and three business process links are obtained, which are respectively: (1) e- > (simplified) d- > (synchronized) c- > (calculation formula xxx) b- > (summarized) a; (2) o- > (screening conditions xxx) p- > (calculation formula xxx) q- > (screening conditions xxx) r- > (summary) s; (3) k- > (summary) l- > (screening) m- > (averaging) n. Thus, a plurality of business process links are obtained. During operation of the service system, the user may delete some data, for example, delete a table, where the deleted table may belong to a link node in the data blood-edge link, but the data blood-edge link in the full-link monitoring data still has a corresponding link node, which results in that some nodes in the service flow formed in step S4 may be invalid. And if the metadata state of the node is invalid, namely that the node is invalid, the background server removes the node in the business flow link, and generates a data flow relation between the front node and the rear node according to the data flow relation between the node and the front node and the rear node, so as to obtain an updated business flow link. Taking the business process link (1) as an example, if the background server inquires that the metadata of the c report in the business process link (1) is in an invalid state, then removing the node c in the business process link (1), and generating a data flow relation d- > (synchronization+calculation formula xxx) b between the front node d and the rear node b according to the data flow relation d- > (synchronization) c, c- > (calculation formula xxx) b between the node c and the front node d and the rear node b, thereby obtaining the updated business process link (1): e- > (simplified) d- > (synchronization+calculation formula xxx) b- > (summary) a.
The background server respectively builds corresponding business process models for each business process link, and then adopts a business process simulation engine to simulate business processes according to each business process model to obtain operation performance data of each business process model, wherein the operation performance data comprises throughput, network delay time and resource utilization rate. The background server generates a flow mining report according to the flow mining report, and outputs operation performance data obtained by each business flow model and simulation thereof through a visual chart in the report. The operation and maintenance personnel check the flow mining report, analyze the defect that each business flow model can be optimized at present according to each business flow model and the operation performance data thereof in the report, optimize the business flow model accordingly, and adjust the business flow of the business system according to the optimized business flow model.
A business process link may have one to a plurality of identical nodes (i.e., one to a plurality of overlapping nodes) with other business process links, and such nodes involving multiple business process links, once a problem occurs, affect multiple business process links, i.e., affect multiple businesses (one business process link corresponds to one business of a business system), should be subjected to important maintenance. Therefore, after obtaining all business flow links, the background server counts the number of the nodes related to all business flow links and the occurrence times of each node. A node appears in one business process link, its occurrence count is 1, appears in two business process links, its occurrence count is 2, and so on. The background server marks the nodes which appear more than 5 times (namely preset times) in all the business flow links as key entity nodes. Therefore, operation and maintenance personnel can carry out key maintenance on the key entity nodes, for example, operation and detection frequency is increased, operation and detection trigger sensitivity is improved, maintenance time limit requirements are improved, and the like. The background server also correlates a plurality of business process links with overlapped nodes, if one business process link fails and the node where the business process link is located is the overlapped node, the node is failed, then other businesses corresponding to the business process link related to the node are not processed, if the business system initiates the businesses, the business process link is failed, the background server judges whether the business process link is associated when the business process link corresponding to the business process link fails, if yes, the business system is disconnected, the business process link corresponding to the business process link is associated.
The invention acquires the data blood-edge link based on the full-link monitoring data, and further extracts the data flow direction relation among all the entities from the data blood-edge link, thereby forming a complete business flow. Because one component/index/service corresponds to only one data blood-edge link, the data volume of the data blood-edge link is not increased along with business like business data, the data volume is generally stable, the explosive growth is avoided, and a large amount of calculation resources are not consumed.
The above-described embodiments are provided for the present invention only and are not intended to limit the scope of patent protection. Insubstantial changes and substitutions can be made by one skilled in the art in light of the teachings of the invention, as yet fall within the scope of the claims.
Claims (8)
1. The process mining method based on the full-link monitoring data is characterized by comprising the following steps of:
s1, acquiring data blood edge links of each component, each index and each service of a service system from full-link monitoring data corresponding to the service system to obtain a plurality of data blood edge links, wherein nodes in the data blood edge links are entities;
s2, carrying out data preprocessing on each data blood edge link;
s3, extracting at least one pair of entity data flow direction relations from each preprocessed data blood edge link by adopting a trained PCNNs convolutional neural network model;
s4, selecting a plurality of entity groups with coherent data flow directions from the obtained data flow direction relations of the entities, and sequentially connecting the entities in each entity group in series according to the data flow direction relations to form a business flow link;
s5, inquiring the metadata state of each node in each business process link, if the metadata state of the node is invalid, removing the node in the business process link, and generating the data flow relation between the front node and the rear node according to the data flow relation between the node and the front node and the rear node, so as to obtain the updated business process link.
2. The process mining method based on full-link monitoring data according to claim 1, comprising the step s6 of recording nodes which occur more than a preset number of times among all the business process links as key entity nodes so that the operation and maintenance personnel can maintain key entity nodes with emphasis.
3. The flow mining method based on full link monitoring data according to claim 1, comprising step s7, associating a plurality of business flow links having overlapped nodes with each other, and if a business process corresponding to one business flow link fails and the node where the business process corresponding to the business flow link fails is an overlapped node, downloading a business corresponding to the business flow link associated with the business flow link.
4. The process mining method based on full link monitoring data according to claim 1, comprising:
s8, respectively constructing corresponding business process models for each business process link;
s9, carrying out business process simulation by adopting a business process simulation engine according to each business process model to obtain operation performance data of each business process model so as to enable operation and maintenance personnel to optimize the business process model according to the operation performance data of each business process model, wherein the operation performance data comprises throughput, network delay time and resource utilization rate.
5. The process mining method based on full link monitoring data according to claim 4, comprising step S10, outputting each business process model and the operation performance data obtained by simulation thereof through a visual chart.
6. The full link monitoring data based process mining method according to claim 1, wherein in step S2, the data preprocessing includes data cleansing, data dimension reduction, data normalization and data conversion.
7. A computer-readable storage medium having stored thereon an executable computer program, wherein the computer program is executed to implement the full link monitoring data based flow mining method of any of claims 1 to 6.
8. A flow mining apparatus comprising a processor and a computer readable storage medium as claimed in claim 7, the processor executing a computer program in the storage medium to implement the full link monitoring data based flow mining method as claimed in any one of claims 1 to 6.
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