CN116523284A - Automatic evaluation method and system for business operation flow based on machine learning - Google Patents
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Abstract
The invention provides a machine learning-based automatic evaluation method and system for business operation flow, which are characterized in that event data elements such as business transactions, operations, system interactions and the like are extracted to form event attributes of operation paths and flows, original event attributes are learned, analyzed and modeled, including event time, action relevance, event ID, operation actions and the like are extracted, modeling is performed through a large number of machine learning, automatic evaluation of the operation flows is completed, a full-path diagram of all operation paths defined in a certain business frame, time and application and the like is provided, a machine learning algorithm is adopted to provide calculation basis of the full-path, the operation probability of each operation and each path under the full-path is analyzed and provided through unique marks such as operator attributes and business flow path attributes, finally modeling is completed through automatic fault tolerance threshold matching, and a path analysis diagram, an optimal path proposal and the like of an operator view are provided.
Description
Technical Field
The invention relates to a business system flow evaluation method, and belongs to the technical field of artificial intelligence.
Background
Currently, under the large background of informatization and data transformation, business operation processes of enterprises are more and more complex, crossed business paths are more and more, whether the operation process paths are reasonable and correct becomes more and more important along with more system and process requirements of continuous coding, and a business process analysis and evaluation method is introduced for scientific and systematic analysis and evaluation of business process implementation effects, which is proposed in a 'business process evaluation' (author Li Qingdan, release of 2018 in 25 th month in a letter network). The current analytical methods are as follows:
(1) And (5) value-added analysis. The operational rationality and potential problems of the process are analyzed using the object attributes of the model, especially the value coefficients of the activity. The analysis method can be used for analyzing the modeling of the existing business process and the effect after the business process is implemented. The incremental analysis is to measure the "bottleneck" activity of a process from the process perspective by evaluating three parameters of the activity: r (value coefficient), f (contribution), c (cost), and measuring the running effect of the activity. By "bottleneck" activities, we mean those critical activities that restrict the operation of a business process. During analysis, an index system of value-added analysis can be constructed by adopting methods such as a analytic hierarchy process and the like; then f values of all activities of the flow are compared through simulation operation, and the activities are divided into value-added activities, quasi-value-added activities and wasteful activities by combining the characteristics of the flow, so that bottleneck activities or problem activities of the flow are found out; finally, the deep cause of the problem is analyzed from the three aspects of the problem, the rule and the hypothesis.
(2) And (5) checking the correctness of the flow design. After the business process design is completed, feasibility analysis is carried out on the business process by means of related tools, collision and deadlock detection can be carried out by utilizing semantics of the Petri network, and the rationality and the correctness of the business process are verified.
(3) Evaluation of business process schemes. The evaluation of business processes includes two aspects: single index evaluation and comprehensive evaluation. The modeling type can be simulated and operated, so that key index data is obtained, and single index evaluation is realized by comparing and analyzing simulation results of different schemes. The evaluation of a complex business process is an evaluation problem with multiple inputs and multiple outputs, which cannot be solved by only relying on qualitative poverty. For comprehensive evaluation, some key index data of the simulation result can be extracted, and a plurality of performance indexes can be simultaneously inspected by adopting methods such as DEA (Data Envelopment Analysis) and data packet analysis to realize comprehensive evaluation. DEA is a decision-making method for evaluating the relative effectiveness between homogeneous departments or units, and is a powerful tool used in enterprise management to study boundary production functions with multiple inputs and multiple outputs.
Business process analysis and design provide a method and a way for process management personnel to understand and know the business process, and business process implementation realizes the conversion from a scheme to operation, so that a set of automatic evaluation technical mechanism for business process operation is needed. By means of automation technology evaluation, automation, full-quantification operation path evaluation and correct path recommendation can be performed on complex business logic and operation paths of a user, so that the user can effectively and rapidly evaluate whether self-managed business processes are reasonable and efficient in the automation and data process.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: an automatic evaluation method for business process operation is provided, which is used for automatically and fully quantifying the operation path evaluation and correct path recommendation of complex business logic and operation paths of users.
The invention adopts the following technical scheme for solving the technical problems:
the invention provides a machine learning-based business operation flow automatic evaluation method, which comprises the following steps:
s1, extracting event data elements to form original event attributes including operation paths and flows;
s2, analyzing and learning original event attributes, and extracting event time stamps, event IDs, action relevance and attribute information of operation actions; realizing attribute information event path association according to the mapping of the event attribute and the next operation action;
s3, establishing a model: modeling analysis is carried out on the business path of the interaction data of an operator or a system, and the probability of the next path is given based on an operation probability analysis method;
s4, model training: weighted average is carried out through a sample data increasing method, recommendation and actual path calculation are carried out through a decision tree model method, and the route probability value display of the operation path is given;
and S5, completing automatic evaluation of the business operation flow by using the trained model, and giving out a full path diagram of all operation paths defined under a certain condition.
The invention also provides a business operation flow automatic evaluation system based on machine learning, which comprises:
the data extraction module is used for extracting event data elements to form original event attributes including operation paths and flows;
the learning mapping module is used for analyzing and learning the original event attribute and extracting the event time stamp, the event ID, the action relevance and the attribute information of the operation action; realizing attribute information event path association according to the mapping of the event attribute and the next operation action;
the model building module is used for carrying out modeling analysis on the business path of the interaction data of an operator or a system and giving out the probability of the next path based on an operation probability analysis method;
the model training module is used for carrying out weighted average through a sample data increasing method, carrying out recommendation and actual path calculation through a decision tree model method, and giving out the route probability numerical value display of the operation path;
and the automatic evaluation module is used for completing the automatic evaluation of the business operation flow by using the trained model and providing a full path diagram of all operation paths defined under a certain condition.
The invention also proposes a computer-readable storage medium on which a computer program is stored which, when being executed by a processor, implements the steps of the method of the invention.
Finally, the invention proposes an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the specific steps of the method according to the invention when the computer program is executed.
Compared with the prior art, the invention adopts the technical means and has the following technical effects:
according to the invention, through multi-path and complex business event operation paths and action extraction, the establishment of an effective model is realized by inputting a path frame in advance as a training target point; feeding the model to a training model by a manual event path labeling method to realize effective path analysis and finally recommending an optimal operation path; meanwhile, the invention realizes code level logic labeling through a machine learning training model, and after a business path or technical logic is in error, the code is automatically modified into a correct code and is automatically covered, thereby realizing automatic fault tolerance of the path.
Drawings
Fig. 1 is a logic diagram of the whole of the present application.
Fig. 2 is a diagram of a basic technology logic framework in an embodiment of the present invention.
FIG. 3 is a logical view of a training model algorithm in an embodiment of the invention.
FIG. 4 is a logic diagram of an error path code fault tolerant algorithm in accordance with an embodiment of the present invention.
Description of the embodiments
In order to enable those skilled in the art to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions of the embodiments of the present application with reference to the drawings in the present embodiment. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
Examples
Referring to fig. 1 and 2, the invention provides a machine learning-based automatic evaluation method for a business process system, which comprises the following steps:
s1, extracting event data elements such as business transaction, business operation, business system interaction and the like to form original event attributes including operation paths and flows;
s2, analyzing and learning original event attributes, and extracting event time stamps, event IDs, action relevance and attribute information of operation actions; realizing attribute information event path association according to the mapping of the event attribute and the next operation action;
s3, establishing a model: modeling analysis is carried out on the business path of the interaction data of an operator or a system, and the probability of the next path is given based on an operation probability analysis method;
s4, model training: weighted average is carried out through a sample data increasing method, recommendation and actual path calculation are carried out through a decision tree model method, and the route probability value display of the operation path is given;
s5, completing automatic evaluation of the business operation flow by using the trained model, and giving out a full path diagram of all operation paths defined by a certain business frame, a certain time, an application and the like.
The invention extracts and realizes path mapping based on service extraction under an application frame and cross-application path extraction through multipath and complex service event operation paths and action extraction, and associates event paths according to attribute information such as time stamps, event IDs, mapping of next operation actions and the like.
In step S3, through unique marks including operator attributes and business flow path attributes, the operation probability of each operation and each path under the full path is analyzed and given, and finally modeling is completed through automatic fault tolerance threshold matching.
In addition, modeling analysis is performed on the business path of the operator or system interaction data, which specifically includes:
(1) Prefabricating a path training model: setting a training range according to an application framework, and establishing an effective model by inputting a path framework in advance as a training target point;
(2) Complex path training model: through machine learning, a machine learning modeling method with characteristic sample size is created, event attribute numbers are prediction points, effective probability is calculated according to each event through continuous learning, and the effective path analysis is realized by feeding the effective probability to a training model through a manual event path labeling method, and finally an optimal operation path is recommended.
In step S4, the recommendation and actual path calculation are performed by a decision tree model method, and the method further includes implementing a code level fault tolerance mechanism of the existing path by a code mining algorithm. The code mining algorithm realizes code level logic labeling through a machine learning training model, and after a business path or technical logic is in error, the code is automatically modified into a correct code and is automatically covered, so that the automatic fault tolerance of the path is realized.
FIG. 3 is a logical view of a training model algorithm in an embodiment of the invention. The invention provides a prefabricated path training model method and a complex path training model method
The method for training the model by the prefabricated path comprises the following steps: the training range can be set according to the application framework, so that the repetition of samples and the invalid sample size are effectively reduced, and the effective model is built by inputting the path framework in advance as a training target point;
the complex path training model method comprises the following steps: the method mainly comprises the steps of creating a machine learning modeling method with characteristic sample size by machine learning, wherein event attribute numbers are prediction points, carrying out calculation basis of each event on effective probability by continuous learning, feeding to a training model by a manual event path labeling method, realizing effective path analysis, and finally recommending an optimal operation path;
decision trees and variants thereof are a class of algorithms that divide an input space into different regions, each region having independent parameters. The decision tree algorithm makes full use of the tree model, and the root node to a leaf node are classified path rules, and each leaf node symbolizes a judgment category. The method comprises the steps of dividing the samples into different subsets, performing segmentation recursion until each subset obtains the same type of samples, and obtaining the prediction category from the root node to the subtree and then from the subtree to the leaf node. The method has the characteristics of simple structure and higher data processing efficiency.
Referring to fig. 4, a logic diagram of an error path code fault tolerant algorithm in this embodiment is shown. The code mining algorithm path automatic fault-tolerant mechanism is mainly used for realizing the code level fault-tolerant mechanism of the existing path through the code mining algorithm. The technology is realized by training a model through machine learning, realizing code level logic labeling, automatically modifying codes into correct codes after errors occur in service paths or technical logic, and automatically covering the codes, thereby realizing automatic fault tolerance of paths.
Examples
The embodiment provides a business operation flow automatic evaluation system based on machine learning, which comprises:
the data extraction module is used for extracting event data elements to form original event attributes including operation paths and flows;
the learning mapping module is used for analyzing and learning the original event attribute and extracting the event time stamp, the event ID, the action relevance and the attribute information of the operation action; realizing attribute information event path association according to the mapping of the event attribute and the next operation action;
the model building module is used for carrying out modeling analysis on the business path of the interaction data of an operator or a system and giving out the probability of the next path based on an operation probability analysis method;
the model training module is used for carrying out weighted average through a sample data increasing method, carrying out recommendation and actual path calculation through a decision tree model method, and giving out the route probability numerical value display of the operation path;
and the automatic evaluation module is used for completing the automatic evaluation of the business operation flow by using the trained model and providing a full path diagram of all operation paths defined under a certain condition.
It should be noted that each module has corresponding functions and beneficial effects for executing each step of the method provided by the invention. Technical details not described in detail in this embodiment may refer to the method provided in the embodiment of the present invention, and are not described herein.
Examples
The embodiment of the invention also provides an electronic device which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor. It should be noted that, the flow of the execution of the computer program by the processor corresponds to the specific steps of the method provided by the embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may refer to the method provided in the embodiment of the present invention, and are not described herein.
Examples
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, implements the specific steps of the method provided by the embodiment of the invention. Technical details not described in detail in this embodiment may refer to the method provided in the embodiment of the present invention, and are not described herein.
In summary, by adopting the business process automatic evaluation method and system provided by the invention, users can effectively and rapidly evaluate whether the self-managed business process is reasonable and efficient in the automatic and data process.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.
Claims (10)
1. The automatic evaluation method for the business operation flow based on machine learning is characterized by comprising the following steps:
s1, extracting event data elements to form original event attributes including operation paths and flows;
s2, analyzing and learning original event attributes, and extracting event time stamps, event IDs, action relevance and attribute information of operation actions; realizing attribute information event path association according to the mapping of the event attribute and the next operation action;
s3, establishing a model: modeling analysis is carried out on the business path of the interaction data of an operator or a system, and the probability of the next path is given based on an operation probability analysis method;
s4, model training: weighted average is carried out through a sample data increasing method, recommendation and actual path calculation are carried out through a decision tree model method, and the route probability value display of the operation path is given;
and S5, completing automatic evaluation of the business operation flow by using the trained model, and giving out a full path diagram of all operation paths defined under a certain condition.
2. The automated evaluation method for business operation flow based on machine learning according to claim 1, wherein the step S1 is to extract event data elements including business transactions, business operations, and business system interactions.
3. The automated evaluation method of business operation flow based on machine learning according to claim 1, wherein in step S2, path mapping is implemented based on business extraction under an application framework and cross-application path extraction.
4. The automated evaluation method for business operation flow based on machine learning according to claim 1, wherein in step S3, the modeling is completed by analyzing and giving the action probability of each operation and each path under the full path through the unique mark including the operator attribute and the business flow path attribute, and finally by automated fault tolerance threshold matching.
5. The automated evaluation method for business operation flow based on machine learning according to claim 1, wherein in step S3, modeling analysis is performed on a business path of operator or system interaction data, specifically comprising:
(1) Prefabricating a path training model: setting a training range according to an application framework, and establishing an effective model by inputting a path framework in advance as a training target point;
(2) Complex path training model: through machine learning, a machine learning modeling method with characteristic sample size is created, event attribute numbers are prediction points, effective probability is calculated according to each event through continuous learning, and the effective path analysis is realized by feeding the effective probability to a training model through a manual event path labeling method, and finally an optimal operation path is recommended.
6. The automated evaluation method for business operation flow based on machine learning according to claim 1, wherein in step S4, recommendation and actual path calculation are performed by a decision tree model method, and further comprising implementing a code level fault tolerance mechanism of an existing path by a code mining algorithm.
7. The automatic evaluation method for business operation flow based on machine learning according to claim 6, wherein the code mining algorithm realizes code level logic labeling through a machine learning training model, and after errors occur in business paths or technical logic, the codes are automatically modified into correct codes and automatically covered, so that automatic fault tolerance of the paths is realized.
8. A machine learning based business process automation evaluation system, comprising:
the data extraction module is used for extracting event data elements to form original event attributes including operation paths and flows;
the learning mapping module is used for analyzing and learning the original event attribute and extracting the event time stamp, the event ID, the action relevance and the attribute information of the operation action; realizing attribute information event path association according to the mapping of the event attribute and the next operation action;
the model building module is used for carrying out modeling analysis on the business path of the interaction data of an operator or a system and giving out the probability of the next path based on an operation probability analysis method;
the model training module is used for carrying out weighted average through a sample data increasing method, carrying out recommendation and actual path calculation through a decision tree model method, and giving out the route probability numerical value display of the operation path;
and the automatic evaluation module is used for completing the automatic evaluation of the business operation flow by using the trained model and providing a full path diagram of all operation paths defined under a certain condition.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed by the processor.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117435582A (en) * | 2023-10-11 | 2024-01-23 | 广东美尼科技有限公司 | Method and device for capturing and processing ERP temporary data |
| CN117726237A (en) * | 2024-02-07 | 2024-03-19 | 四川大学华西医院 | Instant evaluation method, instant evaluation device, computer equipment and readable storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117435582A (en) * | 2023-10-11 | 2024-01-23 | 广东美尼科技有限公司 | Method and device for capturing and processing ERP temporary data |
| CN117435582B (en) * | 2023-10-11 | 2024-04-19 | 广东美尼科技有限公司 | Method and device for capturing and processing ERP temporary data |
| CN117726237A (en) * | 2024-02-07 | 2024-03-19 | 四川大学华西医院 | Instant evaluation method, instant evaluation device, computer equipment and readable storage medium |
| CN117726237B (en) * | 2024-02-07 | 2024-05-10 | 四川大学华西医院 | Instant evaluation method, instant evaluation device, computer equipment and readable storage medium |
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