Handling result path prediction method based on enterprise operation data and business process

Abstract

The invention discloses a transaction result path prediction method based on enterprise operation data and a business process, and the method comprises the steps: S1, business personnel elaborates all links, logic relations, existing branches and abnormal conditions of the business process in detail according to the actual demands of an enterprise, and serves as a basis for constructing a BPMN model in subsequent steps; s2, using a BPMN modeling tool to convert business process description provided by business personnel into an XML formatted file conforming to a BPMN standard; and S3, after the XML formatted file of the BPMN standard is obtained, the real process trend of the data is calculated by comparing the business process description in combination with the enterprise operation data. The scheme is based on a unified BPMN standard, and has wide applicability and high flexibility.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise process management, and in particular to a method for predicting a processing result path based on enterprise operation data and business processes. Background Art

[0002] With the continuous development of enterprise informatization and data-based management, enterprise operating data and business process data have become an important basis for enterprise decision-making. In order to make more effective use of these data and improve the accuracy and efficiency of system processing results, the industry has begun to explore methods to analyze and predict the path of system processing results based on enterprise operating data and business process definitions, aiming to provide enterprises with more accurate decision support and optimization suggestions through in-depth mining and analysis of enterprise data. In the context of the growing demand for enterprise informatization and data-based management, the continuous advancement of data processing and analysis technology, and the increasingly fierce market competition, there is an urgent need for a method to analyze and predict the path of system processing results based on enterprise operating data and business process definitions. Summary of the invention

[0003] In response to the above technical problems, the present invention provides a method for predicting the processing result path based on enterprise management data and business processes.

[0004] The present invention is implemented by adopting the following technical scheme: a method for predicting the processing result path based on enterprise operation data and business processes, comprising the following steps: Step S1: Business personnel elaborate on each link, logical relationship, existing branches and abnormal situations of their business process according to the actual needs of the enterprise, and use them as the basis for building a BPMN model in subsequent steps; Step S2: Using a BPMN modeling tool, convert the business process description provided by the business personnel into an XML formatted file that complies with the BPMN standard; Step S3: After obtaining the XML formatted file of the BPMN standard, the actual process direction of the data is calculated by combining the enterprise operation data and comparing it with the business process description.

[0005] Specifically, the step S2 XML formatted file conversion includes: encoding key information such as various links, logical relationships and data flows in the business process in a standardized XML format.

[0006] Specifically, the calculation of the actual flow trend of the data in step S3 is based on a process prediction algorithm, which conducts in-depth analysis and mining of the business operation data of the enterprise, identifies the flow path and state changes of the data in the business process, and takes into account the branches and abnormal situations in the business process.

[0007] Specifically, the process prediction algorithm inputs enterprise operation data in JSON format and a corresponding business process model in an XML file that complies with the BPMN specification.

[0008] Specifically, the process prediction algorithm includes the following steps: Step S31: Use the XML parser to obtain all nodes and paths of the business process model, and use the JSON parser to extract feature data from the enterprise operation data; Step S32: Based on the conditional expression on the process path, use the SpEL parser to compare and calculate with the feature data, and filter out the path whose feature data meets the corresponding conditional expression; Step S33: redrawing the flow chart based on the filtered path; Step S34: The redrawn flowchart obtains all paths from the start point to the end point of the process based on the depth-first search algorithm, and removes all nodes and paths that have not been passed; Step S35: Draw a new business process model with the remaining nodes and paths after the second screening to obtain a business process model that conforms to the business data of the enterprise.

[0009] Specifically, the node and path of step S31 include the two end nodes of the path and a conditional expression that complies with the SpringEL specification.

[0010] Specifically, the step S32 of screening the path is as follows: if the conditional expression does not exist on the path, it is preliminarily determined to be in compliance with the condition.

[0011] The beneficial effects of the present invention are as follows: the present invention, by using the standard BPMN business process modeling language, can accurately and correctly convert the business personnel's oral or written expressions of the business process into a digital, parsable XML file, thereby improving the transparency and traceability of the business process; a solution based on the SpEL expression language standard is adopted to cope with the technical challenge of matching specific data in the enterprise business data with the flowchart nodes and paths, thereby solving the matching problem between data and flowcharts, and improving the accuracy and efficiency of process execution, thereby ensuring that the enterprise business data can be correctly parsed and applied to the business process; the prediction method is based on the unified BPMN standard, which makes the method widely applicable and highly flexible, and provides the business party with visualized real process trend data. This innovative function provides unprecedented convenience for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0013] Figure 1 A schematic diagram of a process prediction algorithm in an embodiment of the present invention; Figure 2 A specific flow chart of an employee's company applying for leave in one embodiment of the present invention; Figure 3 A newly drawn flow chart in an embodiment of the present invention; Figure 4 A diagram showing the predicted real process trend in an embodiment of the present invention; Figure 5 It is a schematic diagram of the redrawing process in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0015] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0016] The following is combined with Figures 1 to 5 , some embodiments of the present invention are described in detail. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0017] The present invention proposes a method for predicting the processing result path based on enterprise operation data and business processes, comprising the following steps: Step S1: Business personnel elaborate on each link, logical relationship, existing branches and abnormal situations of their business process according to the actual needs of the enterprise, and use them as the basis for building a BPMN model in subsequent steps; Step S2: Using a BPMN modeling tool, convert the business process description provided by the business personnel into an XML formatted file that complies with the BPMN standard; Step S3: After obtaining the XML formatted file of the BPMN standard, the actual process direction of the data is calculated by combining the enterprise operation data and comparing it with the business process description.

[0018] In this embodiment, step S2 of converting the XML formatted file includes: encoding key information such as various links, logical relationships, and data flows in the business process in a standardized XML format.

[0019] In this embodiment, the calculation of the actual process trend of the data in step S3 is based on a process prediction algorithm, which conducts in-depth analysis and mining of the business data of the enterprise, identifies the flow path and state changes of the data in the business process, and takes into account the branches and abnormal situations in the business process.

[0020] In this embodiment, the process prediction algorithm inputs the enterprise operation data in JSON format and the corresponding business process model of the XML file that complies with the BPMN specification.

[0021] In this embodiment, the process prediction algorithm includes the following steps: Step S31: Use the XML parser to obtain all nodes and paths of the business process model, and use the JSON parser to extract feature data from the enterprise operation data; Step S32: Based on the conditional expression on the process path, use the SpEL parser to compare and calculate with the feature data, and filter out the path whose feature data meets the corresponding conditional expression; Step S33: redrawing the flow chart based on the filtered path; Step S34: The redrawn flowchart obtains all paths from the start point to the end point of the process based on the depth-first search algorithm, and removes all nodes and paths that have not been passed; Step S35: Draw a new business process model with the remaining nodes and paths after the second screening to obtain a business process model that conforms to the business data of the enterprise.

[0022] In this embodiment, the node and path in step S31 include the two end nodes of the path and a conditional expression that complies with the SpringEL specification.

[0023] In this embodiment, step S32 of screening the path is as follows: if the conditional expression does not exist on the path, it is preliminarily determined to be in compliance with the condition.

[0024] In another embodiment, the present invention is an innovative process path prediction method based on BPMN (Business Process Model and Notation) business process modeling language, which is specially designed to accurately predict the process path of enterprise business data. The specific implementation steps include: Step 1: Business personnel describe the business process. In this stage, business personnel elaborate on the various links, logical relationships, and possible branches and abnormal situations of their business processes according to the actual needs of the enterprise. These descriptions will serve as the basis for building the BPMN model in the subsequent steps.

[0025] Step 2: Convert to BPMN standard XML formatted file. Use BPMN modeling tools to convert the business process description provided by business personnel into an XML formatted file that complies with BPMN standards. In this step, key information such as each link, logical relationship, and data flow in the business process are encoded in a standardized XML format to ensure the accuracy and parsability of the business process.

[0026] Step 3: Calculate the real process direction. After obtaining the XML formatted file of the BPMN standard, further combine the business data and calculate the real process direction of the data by comparing it with the business process description. In this step, the self-developed algorithms and technical means are used to conduct in-depth analysis and mining of the business data to identify the flow path and state changes of the data in the business process. At the same time, possible branches and abnormal situations in the business process will also be considered to ensure the accuracy and comprehensiveness of the prediction results.

[0027] The core of the technical solution of the present invention lies in the process prediction algorithm, which is based on the business process model of the BPMN (Business Process Model and Notation) standard and combined with specific business business data to predict the real business process trend. The core idea is to use each real business data of the enterprise to make a detailed comparison with the business process model, so as to build a business model that matches the actual situation of the business data. In view of the uniqueness of each piece of business data of the enterprise, even different business instances under the same business type will have different process data. Therefore, the present invention determines the real business process trend data for each piece of business data through a precise comparison process.

[0028] The algorithm is divided into 5 steps: extracting data, preliminary screening, redrawing the process, secondary screening, and processing results. The specific steps and input and output are described as follows Figure 1As shown. Input: business data (JSON format), corresponding business process model (XML file that complies with BPMN specification). Output: business process model that complies with the current business data. Steps include: Extract data: Use the XML parser to obtain all nodes and paths of the business process model (including the nodes at both ends of the path and conditional expressions that comply with the SpringEL specification), and use the JSON parser to extract feature data from the business operation data.

[0029] Preliminary screening: Based on the conditional expression on the process path, the SpEL parser is used to compare and calculate the feature data, and the path whose feature data meets the corresponding conditional expression is screened out (Note: if there is no conditional expression on the path, it is preliminarily judged to meet the condition).

[0030] Redraw the process: Redraw the flowchart based on the filtered paths (discarding the paths that have not been filtered).

[0031] Secondary screening: By redrawing the flowchart, all paths from the starting point to the end point of the process are obtained based on the depth-first search algorithm, and all nodes and paths that have not been passed are eliminated.

[0032] Processing result: Draw a new business process model with the remaining nodes and paths after the second screening to obtain a business process model that conforms to the business data of the enterprise.

[0033] In a specific embodiment, scenario: Zhang San, an employee of a company, submits a leave application, which includes the following information: the employee belongs to department A, and the leave duration is 1 day. The feature data that can be extracted are as follows: { "name": "Zhang San", "org": "a", "duration": 3 } The specific process of applying for leave at the company where the employee works is as follows: Figure 2 As shown, the first step is to obtain all process nodes and paths of the business process. In this case, the request process has a total of 10 process nodes, nodes 1 to 10; there are a total of 12 paths, paths 1 to 12, among which the paths with conditional expressions are configured as follows (the other paths are not configured with conditional expressions): Path 3: #{org=="A"} Path 4: #{org=="a"} Path 5: #{org!="a"&&org!="A"} Path 10: #{duration>3} Path 11: #{duration<=3} Step 2: Use the SpEL expression parser to calculate the conditional expression and characteristic data distribution through the conditional expression and characteristic data configured on the path. It can be obtained that path 4 and path 11 meet the above characteristic data, and paths 3, 5, and 10 do not meet the above characteristic data. Then the paths 1, 2, 4, 6, 7, 8, 9, 11, and 12 can be preliminarily screened out.

[0034] The third step is to redraw the flowchart based on the paths and process nodes selected in the previous step. In this case, the newly drawn flowchart is as follows Figure 3 shown.

[0035] The fourth step is to find the path from the start node to the end node through the newly drawn flowchart. The nodes it passes through will be regarded as valid nodes, and the paths it passes through will be regarded as valid paths. In this case, there is only one path from node 1 (start node) to node 10 (end node), which is: 1->2->3->5->7->8->10. These 7 nodes are regarded as valid nodes, and the corresponding paths 1, 2, 4, 7, 9, and 11 are regarded as valid paths.

[0036] The fifth step is to redraw the business process diagram based on the valid nodes and valid paths. The result is the actual process trend predicted by the current business data. In this case, the predicted actual process trend of the leave application process is as follows: Figure 4 shown.

[0037] Analysis of possible abnormal situations: The main abnormal situations currently occurring are that the business data of the enterprise does not match the corresponding business process or the business data of the enterprise is abnormal. It may be impossible to screen out an effective path during the second screening in the fourth step, resulting in the failure of the prediction algorithm. For example, in the above case, if the feature data extracted from the business data of the enterprise is: { "name": "Zhang San", "birth": "1994-01-16" } Then the feature data does not satisfy all paths configured in the conditional expression, resulting in the drawing of a flowchart when the process is redrawn in the third step, such as Figure 5 shown.

[0038] If a valid path from the start node (node ​​1) to the end node (node ​​10) is found during the fourth step of secondary screening, the process prediction algorithm becomes invalid.

[0039] The present invention uses the standard BPMN (Business Process Model and Notation) business process modeling language to accurately convert the business personnel's oral or written description of the business process into a digital, parsable XML file. As an internationally recognized standard modeling language, BPMN provides a wealth of graphical elements and symbols that can clearly depict the various links and logical relationships of the business process. This conversion not only improves the transparency and traceability of the business process, but also enables the business process to be understood and executed by computers, laying a solid foundation for realizing the automation and intelligence of the business process.

[0040] A solution based on the SpEL (Spring Expression Language) expression language standard is used to address the technical challenge of matching specific data in enterprise business data with flowchart nodes and paths. With its powerful expression evaluation capabilities and support for complex objects and collections, SpEL can flexibly define data matching rules and process execution logic based on the specific characteristics of enterprise data. This innovative application not only solves the matching problem between data and flowcharts, but also improves the accuracy and efficiency of process execution, ensuring that enterprise business data can be correctly parsed and applied to business processes.

[0041] The developed prediction method is based on the unified BPMN standard, which makes the method widely applicable and highly flexible. Whether it is manufacturing, service or financial industries, as long as the business process can be abstracted as a BPMN model, the prediction method can be applied to it. In addition, the prediction method adopts an in-depth intervention method, that is, without changing the existing business processes and data structures of the enterprise, it realizes real-time monitoring and prediction of business processes by embedding specific prediction modules. This method not only ensures the continuity and stability of the normal business of the enterprise, but also enables the prediction results to be fed back to business personnel in a timely manner to help them make more informed decisions.

[0042] This innovative function provides business parties with visualized real process trend data, which provides unprecedented convenience for users. Through the visual interface, business personnel can intuitively see the real-time trend and status of the business process, so as to predict the subsequent development of the business process in advance. This predictive ability enables business personnel to adjust business processes in a timely manner according to business trends, optimize resource allocation, and improve business efficiency. At the same time, the real process trend data also provides business personnel with valuable risk warning information. Through the analysis and mining of historical data, business personnel can identify potential risk points in the business process and take corresponding measures in advance to avoid and respond. This risk warning mechanism not only helps to reduce the operational risks of enterprises, but also enhances the competitiveness and market position of enterprises.

[0043] For the aforementioned embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily required by the present application.

[0044] The above embodiments describe the basic principles and main features of the present invention and the advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the changes and modifications made by those skilled in the art shall be within the scope of protection of the appended claims of the present invention without departing from the spirit and scope of the present invention.

Claims

1. A processing result path prediction method based on enterprise operation data and business processes, characterized in that: The following steps are involved: Step S1: Business personnel elaborate on each link, logical relationship, existing branches and abnormal situations of their business process according to the actual needs of the enterprise, and use them as the basis for building a BPMN model in subsequent steps; Step S2: Using a BPMN modeling tool, convert the business process description provided by the business personnel into an XML formatted file that complies with the BPMN standard; Step S3: After obtaining the XML formatted file of the BPMN standard, the actual process direction of the data is calculated by combining the enterprise operation data and comparing it with the business process description.

2. The method for predicting the processing result path based on enterprise operation data and business process according to claim 1, characterized in that: The step S2 XML formatted file conversion includes: encoding key information such as each link, logical relationship and data flow in the business process in a standardized XML format.

3. The method for predicting the processing result path based on enterprise operation data and business process according to claim 1, characterized in that: The calculation of the actual flow trend of the data in step S3 is based on a process prediction algorithm, which conducts in-depth analysis and mining of the business data of the enterprise, identifies the flow path and state changes of the data in the business process, and takes into account the branches and abnormal situations in the business process.

4. The method for predicting the processing result path based on enterprise operation data and business process according to claim 3, characterized in that: The process prediction algorithm inputs enterprise operation data in JSON format and a corresponding business process model in an XML file that complies with the BPMN specification.

5. The method for predicting the processing result path based on enterprise operation data and business process according to claim 4, characterized in that: The process prediction algorithm comprises the following steps: Step S31: Use the XML parser to obtain all nodes and paths of the business process model, and use the JSON parser to extract feature data from the enterprise operation data; Step S32: Based on the conditional expression on the process path, use the SpEL parser to compare and calculate with the feature data, and filter out the path whose feature data meets the corresponding conditional expression; Step S33: redrawing the flow chart based on the filtered path; Step S34: The redrawn flowchart obtains all paths from the start point to the end point of the process based on the depth-first search algorithm, and removes all nodes and paths that have not been passed; Step S35: Draw a new business process model with the remaining nodes and paths after the second screening to obtain a business process model that conforms to the business data of the enterprise.

6. The method for predicting the processing result path based on enterprise operation data and business process according to claim 5, characterized in that: The nodes and paths in step S31 include the two end nodes of the path and conditional expressions that comply with the SpringEL specification.

7. The method for predicting the processing result path based on enterprise operation data and business process according to claim 5, characterized in that: The step S32 of screening the path is specifically as follows: if there is no conditional expression on the path, it is preliminarily determined to be in compliance with the condition.

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