CN112308464B - Business process data processing method and device - Google Patents

Business process data processing method and device Download PDF

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CN112308464B
CN112308464B CN202011326959.6A CN202011326959A CN112308464B CN 112308464 B CN112308464 B CN 112308464B CN 202011326959 A CN202011326959 A CN 202011326959A CN 112308464 B CN112308464 B CN 112308464B
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business process
triples
triplet
process data
business
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CN112308464A (en
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孙海春
朱容辰
李欣
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PEOPLE'S PUBLIC SECURITY UNIVERSITY OF CHINA
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Abstract

The invention provides a business process data processing method and device. Wherein the method comprises the following steps: receiving a search term; searching from a pre-established business process model based on the search term, and determining a business process corresponding to the search term; wherein the business process model is built based on a plurality of triples obtained from the business process data. After receiving the search term, searching the held input from a business process model established based on a plurality of triples obtained from business process data, and determining a business process corresponding to the search term; in the mode, complicated business details can be separated, macroscopic understanding of the business process is provided, and a complete business process processing and application framework can be provided.

Description

Business process data processing method and device
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing business process data.
Background
The solidification and multiplexing of the business process have important significance in business intelligence and process management. Existing business process mining and curing techniques aim at extracting information from event logs associated with information systems, discovering business process models, and using the resulting models to perform verification and improvement work on business processes. The existing business processes depend on structured event logs, and in practical cases, the process files and expert experience case event information are carriers of the business processes.
The prior art has the following disadvantages: (1) The general method of the process mining of the business is to extract the structured event log from the original log generated by the enterprise information system, but the extracted process is usually very trivial and focused on the details, so that people cannot determine the macroscopic operation of the process, and meanwhile, similar processes are easy to be confused, and the action ambiguity of the user cannot be eliminated. (2) The other method is to summarize and generalize the business process and experience by an expert, but the method has lower efficiency, and simultaneously has a large amount of repeated labor, and the experience of the same type of business process is mutually overlapped. (3) At present, huge amounts of event information and expert experience information exist, but a proper database is lacked to realize efficient storage and business flow representation combining the field characteristics. (4) At present, business process knowledge lacks intelligent operations such as classification, fusion, reasoning and the like.
Disclosure of Invention
Therefore, the present invention aims to provide a business process data processing method and apparatus, so as to separate from complicated business details, provide macroscopic understanding of the business process, and provide a complete business process processing and application framework.
In a first aspect, an embodiment of the present invention provides a method for processing business process data, where the method includes: receiving a search term; searching from a pre-established business process model based on the search term, and determining a business process corresponding to the search term; wherein the business process model is built based on a plurality of triples obtained from the business process data.
In a preferred embodiment of the present invention, the business process model includes secure data and non-secure data.
In a preferred embodiment of the present invention, the business process model is established by: acquiring business process data; acquiring a plurality of triples from the business process data; the form of the triples is entity, relation and entity; calculating distances among the triples, and fusing the triples based on the distances; and constructing a business process model based on the fused three-dimensional structures.
In a preferred embodiment of the present invention, the step of obtaining the business process data includes: acquiring business process data from a business process sample obtained in advance in a crawler mode; the business process samples comprise web pages, pictures and texts; alternatively, business process data is imported from a sample database based on pre-written scripts.
In a preferred embodiment of the present invention, the step of obtaining a plurality of triples from the business process data includes: extracting at least one text chain from business process data based on word stock and natural language processing modes; a plurality of triplets is extracted from at least one text chain.
In a preferred embodiment of the present invention, the step of calculating the distances between the triples includes: based on the semantic network and the manner of word forests, the distances among the triples are calculated.
In a preferred embodiment of the present invention, the tuples include a first triplet and a second triplet; the step of fusing a plurality of triples based on distance comprises the following steps: and if the distance between the first triplet and the second triplet is smaller than the preset distance threshold value, fusing the first triplet and the second triplet.
In a preferred embodiment of the present invention, after the step of fusing the plurality of triples based on distance, the method further includes: and storing the fused triples in a business process database.
In a preferred embodiment of the present invention, after the step of fusing the plurality of triples based on distance, the method further includes: classifying the fused triples to obtain a classification result; the step of storing the fused triples in a business process database comprises the following steps: and storing the fused multiple triplets and the classification results corresponding to the triplets in a business flow database.
In a second aspect, an embodiment of the present invention further provides a service flow data processing apparatus, where the apparatus includes: the search term receiving module is used for receiving search terms; the business process determining module is used for searching from a pre-established business process model based on the search word and determining a business process corresponding to the search word; wherein the business process model is built based on a plurality of triples obtained from the business process data.
The embodiment of the invention has the following beneficial effects:
according to the business process data processing method and device provided by the embodiment of the invention, after the search word is received, the held input is searched in the business process model established based on a plurality of triples obtained from the business process data, so that the business process corresponding to the search word can be determined; in the mode, complicated business details can be separated, macroscopic understanding of the business process is provided, and a complete business process processing and application framework can be provided.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part will be obvious from the description, or may be learned by practice of the techniques of the disclosure.
The foregoing objects, features and advantages of the disclosure will be more readily apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a business process data processing method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for establishing a business process model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a framework of a business process experience curing and multiplexing engine for graph structure storage according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a business process data processing device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
At present, business process modeling is widely applied in the fields of office automation, industrial manufacturing and the like. The current process mining data sources are mainly log data, and the knowledge sources are single. Representative process mining algorithms include genetic algorithm-based process mining, log classification-based mining algorithms, and execution mode-based mining algorithms. These algorithms have advantages and disadvantages in terms of log integrity, control flow structure, noise handling, and model quality control. In the future, the processing of log data, the resolution of special control flow structures and the visualization of mining results are the development directions of flow mining research. Based on the above, the method and the device for processing business process data provided by the embodiment of the invention are based on general business process knowledge, and are used for researching and analyzing the problem to be solved, so that a business process experience solidification and multiplexing engine for graph structure storage is realized.
According to the embodiment of the invention, expert business processes are generalized to form an ontology model; based on the ontology model, completing rule merging and filtering; and (5) completing high-level semantic flow reasoning. The high-level ontology model is established, so that a log-based process mining environment is simplified, semantic meaning of log behaviors/actions is analyzed in stages, and automatic understanding of user action intention/elimination of user action ambiguity are facilitated. And finally, the storage and the representation of the business process are completed by utilizing the graph database, so that high-efficiency importing and high-visualization displaying can be supported.
For the sake of understanding the present embodiment, first, a detailed description is given of a business process data processing method disclosed in the present embodiment.
Example 1
Referring to the flow chart of a business process data processing method shown in fig. 1, the business process data processing method comprises the following steps:
step S102, receiving a search term.
The term in this embodiment may be a category of time, place, person, item, thing to be done, etc., and the term is not limited in this embodiment. If the user wants to search things to be done at a certain time, the user can input a search term of the time; if the user wants to input things which should be done at a certain place, the user can input the search term of the place; if the user wants to input a matter about a specific person, the specific person may be taken as a search term; if the user wants to search for items of a certain category, the category can be input as a search term; if the user wants to input what a step is to do next, part of the words of that step can be input as terms.
In addition, the types of the search terms mentioned in this embodiment may be other content, which is not limited herein. The search term in this embodiment may be a set of 1 or more words, or sentences.
Step S104, searching from a pre-established business process model based on the search term, and determining a business process corresponding to the search term; wherein the business process model is built based on a plurality of triples obtained from the business process data.
The business process model is established in advance according to a plurality of triples obtained from business process data, the business process data can be understood as a specific business process of each business, the main sources of the business process data can be cases, expert experiences, regulations, business processes and the like, and the business process data are divided into two types of internal data and external data, and the business process is divided into an external type and an internal type by the layer so as to ensure confidentiality of the internal process.
The triplets may be in the form of entity-relationship-entities, through which the relationship between each two entities may be clearly revealed. For example: the business process data is: employee A has gone to company X at 14 months 11 and has conducted on-the-fly communication with employee B, and from the business process data, at least 2 triples can be extracted, respectively: employee A-11 month 14 day-company X, employee A-on-the-face communication-employee B.
After the search term is obtained, the search term can be input into a pre-established business process model, the search result corresponding to the search term is searched, and the search result is displayed to the user, so that the user is helped to be familiar with or know the business process. The search result in this embodiment may include business process reasoning, business process search, business process question-answering, and business process decision.
According to the business process data processing method provided by the embodiment of the invention, after the search word is received, the held input is searched in the business process model established based on a plurality of triples obtained from the business process data, so that the business process corresponding to the search word can be determined; in the mode, complicated business details can be separated, macroscopic understanding of the business process is provided, and a complete business process processing and application framework can be provided.
Example 2
The embodiment of the invention also provides a method for establishing the business process model; the method is realized on the basis of the method of the embodiment; the method focuses on the specific implementation manner of establishing the business process model.
A flowchart of a method for creating a business process model as shown in fig. 2, the method for creating a business process model comprising the steps of:
step S202, obtaining business process data.
The specific business processes in this embodiment may refer to a schematic frame diagram of a business process experience curing and multiplexing engine stored in a graph structure shown in fig. 3, where business process data is derived from cases, expert experiences, regulations, office processes, and the like, and is classified into two types of confidential data and non-confidential data, and the layer classifies the business processes into two types of external (i.e. non-confidential data) and internal (i.e. confidential data) to ensure confidentiality of the internal processes.
Wherein, the business process data can be obtained by the following steps: acquiring business process data from a business process sample obtained in advance in a crawler mode; the business process samples comprise web pages, pictures and texts; alternatively, business process data is imported from a sample database based on pre-written scripts.
Crawling of network data (network data may include web pages, pictures and texts) may be performed by using a crawling manner, so as to obtain business process data, and scripts may be written, so that the business process data is imported from an internal database (i.e., a sample database).
Step S204, obtaining a plurality of triples from service flow data; the form of triples is entity, relationship, entity form.
As shown in fig. 3, the business process extraction may be implemented based on word stock, nlp (natural language processing), and the like, for example: extracting at least one text chain from business process data based on word stock and natural language processing modes; a plurality of triplets is extracted from at least one text chain.
The extraction of a triplet may also be referred to as relation extraction, where the main task of relation extraction is to extract two entities in a sentence and a relation between the entities given a piece of sentence text, so as to form a triplet (s, p, o), where s is a subject and represents a main entity, o is an object and represents a guest entity, and p is a prediction and represents a relation between the two entities. In general, (s, p, o) it is understood that "p of s is o". Of course, more than two entities in a sentence, and thus more than one relationship, are all that is required is to extract as many relationship entity pairs in the sentence as possible and correctly.
Specifically, several sentences (text chains) may be extracted from a large piece of text (i.e., business process data), followed by several pairs (triples) of relational entities from the several sentences (text chains).
In step S206, distances between the triples are calculated, and the triples are fused based on the distances.
After extracting the triples, as shown in fig. 3, the triples need to be fused, such as fusing approximate triples, which may be referred to as business process experience fusion. Business process experience fusion comprises instance and concept fusion, and is realized by using a method of hownet (semantic network) and word forest and the like, calculating jaccard (Jaccard) distance and the like, for example: based on the semantic network and the manner of word forests, the distances among the triples are calculated.
For any two triples (e.g., a first triplet and a second triplet), the following can be fused: and if the distance between the first triplet and the second triplet is smaller than the preset distance threshold value, fusing the first triplet and the second triplet.
First, the jaccard distance between the first triplet and the second triplet can be calculated, and if the calculated jaccard distance is smaller than a preset distance threshold, the first triplet and the second triplet are close in content and can be fused.
As shown in fig. 3, the fused triples may be stored in a business process database, for example: and storing the fused triples in a business process database, namely storing business process experiences in FIG. 3.
Business process experience storage may use mongo db (database based on distributed file storage), ES (distributed full text search database), neo4j (network oriented database) to implement hybrid storage and map instances into a process ontology model. MongoDB is not a relational database, but has the characteristics of a plurality of relational databases, has a storage structure, is simple and convenient to read and write sentences, and has an obvious hierarchical structure inside. The system of this embodiment is mostly a text, semi-structured representation in mongo db. In the embodiment, the ES search engine is used for indexing the flow, so that the retrieval efficiency is improved when the method is used, and the internal Chinese word segmentation plug-in and the inverted index mode are greatly convenient for application of the method provided by the embodiment. Neo4j stores the flows in the form of triples of (entity-relation-entity), and works such as flow reasoning, flow clustering and the like are realized.
In addition to using a database to store, the present embodiment may also categorize the fused triples, for example: classifying the fused triples to obtain a classification result; and the classification result obtained by classification is also stored in a database, for example: and storing the fused multiple triplets and the classification results corresponding to the triplets in a business flow database. As shown in fig. 3, the classification of the present embodiment may implement fine-grained classification of transaction types, scenes, times, places.
Step S208, constructing a business process model based on the fused triples.
After the hybrid storage is implemented using MongoDB, ES, neo4j, the instances can be mapped into a process ontology model, resulting in a business process model. After the business process model is obtained, as shown in fig. 3, a business process experience application can be implemented, that is, the user implements interaction with the system at the layer, the user inputs or sends a request, and the system then sequentially transmits information upwards from the bottom layer. Based on graph database reasoning, technology such as natural language understanding develops four applications of business process searching, decision making, question answering and reasoning, and provides ideas for experience communication, data sharing, detection early warning and decision making processes.
According to the method provided by the embodiment, when a user performs a specific business process, the user can rely on the engine to learn the upper layer body of the business, so that the user can understand the whole business process logic; meanwhile, the graph database returns the follow-up business for carrying out knowledge reasoning operation, provides follow-up business process reference for the user, and is convenient for the user to make a decision; for mass data sources, automatic processing can be realized, and efficient data management is realized.
Now, a specific example is given, a public security organization in a certain place builds a business process model, three scenes of a crowd handling work class, a case handling process class and a investigation and battle method class are determined first, and a subclass body is defined based on the three scenes, for example, subclasses of the case handling process class are criminal case processes, civil case processes and the like, while subclasses of the criminal case processes are subjected to cases, case standing, investigation, criminal penalty execution and the like, and the like.
And then, extracting the data such as the working case handling process, the public security technical and tactics and the like, and storing the data in a diagram database in a triplet form. For example, the public security video investigation technique is: firstly, a monitoring video of the local circle for 1 km is called, then video collision in different areas is carried out, the sound and sound image of the suspected person are found, finally, the places are connected, and the moving track of the suspected person is estimated. Based on the technical and tactical method, the triplet extraction work can be performed for each sentence. The chain is stored in a knowledge graph in a triplet form (calling monitoring-video collision-searching body shadow) (searching body shadow-location connection-presumption track) after the work of flow fusion, entity disambiguation and the like is completed.
The method provided by the embodiment of the invention has the characteristics of simple and quick data import, various and visual display forms and the like based on the knowledge graph, can complete operations such as flow reasoning, flow fusion, flow disambiguation and the like, and is convenient for various applications based on flow knowledge.
The method for solidifying and multiplexing the business process experience stored in the graph structure and the business process processing and application framework are disclosed, the business process experience solidifying and multiplexing method is novel, the business process ontology layer is provided, complicated business details are separated, macroscopic understanding of the business process is provided, and the functions of process reasoning, process fusion and the like can be realized. And the method has a complete business process processing and application framework.
Example 3
Corresponding to the above method embodiment, the embodiment of the present invention provides a business process data processing device, as shown in fig. 4, which is a schematic structural diagram of the business process data processing device, and the business process data processing device includes:
a search term receiving module 41 for receiving a search term;
a business process determining module 42, configured to determine a business process corresponding to the search term, based on the search term searching from a pre-established business process model; wherein the business process model is built based on a plurality of triples obtained from the business process data.
After receiving the search word, the business process data processing device provided by the embodiment of the invention searches the service process model built by the held input based on a plurality of triples obtained from the business process data, and can determine the business process corresponding to the search word; in the mode, complicated business details can be separated, macroscopic understanding of the business process is provided, and a complete business process processing and application framework can be provided.
The business process model includes secure data and non-secure data.
The device also comprises a business process model building module for: acquiring business process data; acquiring a plurality of triples from the business process data; the form of the triples is entity, relation and entity; calculating distances among the triples, and fusing the triples based on the distances; and constructing a business process model based on the fused three-dimensional structures.
The business process model building module is used for: acquiring business process data from a business process sample obtained in advance in a crawler mode; the business process samples comprise web pages, pictures and texts; alternatively, business process data is imported from a sample database based on pre-written scripts.
The business process model building module is used for: extracting at least one text chain from business process data based on word stock and natural language processing modes; a plurality of triplets is extracted from at least one text chain.
The business process model building module is used for: based on the semantic network and the manner of word forests, the distances among the triples are calculated.
The triplets comprise a first triplet and a second triplet; the business process model building module is used for: and if the distance between the first triplet and the second triplet is smaller than the preset distance threshold value, fusing the first triplet and the second triplet.
The business process model building module is further used for: and storing the fused triples in a business process database.
The business process model building module is further used for: classifying the fused triples to obtain a classification result; the business process model building module is further used for: and storing the fused multiple triplets and the classification results corresponding to the triplets in a business flow database.
The business process data processing device provided by the embodiment of the invention has the same technical characteristics as the business process data processing method provided by the embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
Example 4
The embodiment of the invention also provides electronic equipment for running the business process data processing method; referring to fig. 5, an electronic device includes a memory 100 and a processor 101, where the memory 100 is configured to store one or more computer instructions, and the one or more computer instructions are executed by the processor 101 to implement the business process data processing method described above.
Further, the electronic device shown in fig. 5 further includes a bus 102 and a communication interface 103, and the processor 101, the communication interface 103, and the memory 100 are connected through the bus 102.
The memory 100 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and at least one other network element is implemented via at least one communication interface 103 (which may be wired or wireless), and may use the internet, a wide area network, a local network, a metropolitan area network, etc. Bus 102 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 5, but not only one bus or type of bus.
The processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 101 or instructions in the form of software. The processor 101 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processor, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 100 and the processor 101 reads information in the memory 100 and in combination with its hardware performs the steps of the method of the previous embodiments.
The embodiment of the invention also provides a computer readable storage medium, which stores computer executable instructions that when being called and executed by a processor, cause the processor to implement the above business process data processing method, and the specific implementation can be referred to the method embodiment and will not be described herein.
The computer program product of the business process data processing method and apparatus provided in the embodiments of the present invention includes a computer readable storage medium storing program codes, where the instructions included in the program codes may be used to execute the method in the foregoing method embodiment, and specific implementation may refer to the method embodiment and will not be described herein.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus and/or the electronic device described above may refer to the corresponding process in the foregoing method embodiment, which is not described in detail herein.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (8)

1. A business process data processing method, the method comprising:
receiving a search term;
searching from a pre-established business process model based on the search term, and determining a business process corresponding to the search term; wherein the business process model is established based on a plurality of triples obtained from business process data;
the business process model is established by the following steps:
acquiring the business process data;
acquiring a plurality of triples from the business process data; the form of the triples is entity, relation and entity;
calculating distances among a plurality of triples, and fusing the triples based on the distances;
constructing the business process model based on the fused triples;
the triples comprise a first triplet and a second triplet;
a step of fusing a plurality of said triples based on said distances, comprising:
and if the distance between the first triplet and the second triplet is smaller than a preset distance threshold value, fusing the first triplet and the second triplet.
2. The method of claim 1, wherein the business process model contains secure data and non-secure data.
3. The method of claim 1, wherein the step of obtaining the business process data comprises:
acquiring the business process data from a business process sample obtained in advance in a crawler mode; the business process samples comprise web pages, pictures and texts;
alternatively, the business process data is imported from a sample database based on pre-written scripts.
4. The method of claim 1, wherein the step of obtaining a plurality of said triples from said business process data comprises:
extracting at least one text chain from the business process data based on word stock and natural language processing modes;
extracting a plurality of triplets from at least one text chain.
5. The method of claim 1, wherein the step of calculating the distance between a plurality of said triples comprises:
and calculating the distance between a plurality of triples based on the semantic network and the word forest mode.
6. The method of claim 1, wherein after the step of fusing a plurality of the triples based on the distance, the method further comprises:
and storing the fused triples in a business process database.
7. The method of claim 6, wherein after the step of fusing a plurality of the triples based on the distance, the method further comprises:
classifying the fused triples to obtain a classification result;
the step of storing the fused triples in a business process database comprises the following steps:
and storing the fused triplets and the classification results corresponding to the triplets in a business flow database.
8. A business process data processing apparatus, the apparatus comprising:
the search term receiving module is used for receiving search terms;
the business process determining module is used for searching from a pre-established business process model based on the search word and determining a business process corresponding to the search word; wherein the business process model is established based on a plurality of triples obtained from business process data;
the business process model building module is used for: acquiring the business process data; acquiring a plurality of triples from the business process data; the form of the triples is entity, relation and entity; calculating distances among a plurality of triples, and fusing the triples based on the distances; constructing the business process model based on the fused triples;
the triples comprise a first triplet and a second triplet;
and the business process model building module is used for fusing the first triplet and the second triplet if the distance between the first triplet and the second triplet is smaller than a preset distance threshold value.
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