CN111652468A - Business process generation method and device, storage medium and computer equipment - Google Patents
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Abstract
The invention discloses a method and a device for generating a business process, a storage medium and computer equipment, and relates to the technical field of computers. The method comprises the following steps: acquiring service flow requirement information, and extracting state node information, state transfer information and state extension information from the service flow requirement information; inputting the state node information and the state circulation information into a business process classification model to obtain state node configuration information; and generating a business process matched with the business process demand information according to the state node configuration information and the state expansion information. The method can feed back the service flow requirement very quickly, improves the multiplexing efficiency of the existing service flow and reduces the development cost of the service flow.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, a system, a storage medium, and a computer device for generating a business process.
Background
The flow business requirements are common requirements in the system development process. For example, when a system for interfacing with government departments is developed, due to different division of functions, different processes and different business scales of each government department, a plurality of business processes must be developed during the construction process of the system to meet the process requirements of the same business of different provincial and municipal government departments.
At present, when a large amount of business process development requirements are met, developers are difficult to quickly and effectively develop business processes meeting the requirements, but a large amount of manpower and material resources are wasted for process development and adjustment optimization, the multiplexing efficiency of the business processes is greatly reduced by the generation mode of the business processes, and meanwhile, the development cost of the business processes is increased.
Disclosure of Invention
In view of this, the present application provides a method, a system, a storage medium, and a computer device for generating a business process, and mainly aims to solve the technical problems of low multiplexing efficiency of the business process and high development cost of the business process.
According to a first aspect of the present invention, a method for generating a business process is provided, the method comprising:
acquiring service flow requirement information, and extracting state node information, state transfer information and state extension information from the service flow requirement information;
inputting the state node information and the state circulation information into a business process classification model to obtain state node configuration information;
and generating a business process matched with the business process demand information according to the state node configuration information and the state expansion information.
In one embodiment, the method further comprises: and collecting characteristic sample data of various business processes, and creating a business process classification model based on the gradient lifting tree according to the characteristic sample data of the various business processes.
In one embodiment, creating a gradient-boosting tree-based business process classification model according to feature sample data of a plurality of business processes includes: respectively constructing a first classification regression tree for each business process according to the characteristic sample data of the multiple business processes; respectively generating the prediction probability of each first classification regression tree by using a logistic regression function; obtaining a negative gradient value of each first classification regression tree according to the prediction probability of each first classification regression tree; respectively iterating for multiple rounds by using a gradient descent method according to the negative gradient value of each first classification regression tree to obtain multiple first lifting trees aiming at each business process; and respectively carrying out weighted summation on the first classification regression tree and the first lifting tree aiming at each business process to obtain a business process classification model based on the gradient lifting tree.
In one embodiment, inputting the state node information and the state flow information into a business process classification model to obtain the state node configuration information, includes: inputting the state node information and the state flow information into a business process classification model to obtain the prediction probability of each business process to which the state node information and the state flow information belong; comparing the predicted probability with a preset probability threshold; if the prediction probability is greater than the probability threshold, inquiring the class number of the business process corresponding to the prediction probability; and inquiring the state node configuration information corresponding to the class number in the database according to the class number of the service process.
In one embodiment, generating a service flow matched with the service flow requirement information according to the state node configuration information and the state extension information includes: acquiring state extension configuration information corresponding to the state extension information; adding the state expansion configuration information into the state node configuration information to obtain a service flow configuration table; and reading the service flow configuration table to generate the service flow matched with the service flow requirement information.
In one embodiment, the method further comprises: respectively establishing an execution time prediction model based on a gradient lifting tree for each business process according to the characteristic sample data of the multiple business processes; and inputting the state node information, the state circulation information and the state expansion information into a service process execution time prediction model corresponding to the state node configuration information to obtain an execution time prediction value of the service process.
In one embodiment, respectively creating an execution time prediction model based on a gradient lifting tree for each business process according to the feature sample data of the multiple business processes, including: dividing the feature sample data of various service flows according to the class numbers of the service flows to obtain the feature sample data of each service flow; respectively constructing a second classification regression tree aiming at the execution time of each business process according to the characteristic sample data of each business process; fitting the negative gradient value of the loss function in each second classification regression tree; respectively iterating for multiple rounds by using a gradient descent method according to the negative gradient value of each second classification regression tree to obtain multiple second lifting trees aiming at each service process execution time; and respectively carrying out weighted summation on the second classification regression tree and the second lifting tree aiming at the execution time of each business process to obtain an execution time prediction model of each business process.
According to a second aspect of the present invention, there is provided an apparatus for generating a business process, the apparatus comprising:
the system comprises a demand information acquisition module, a state node information acquisition module and a state transition information acquisition module, wherein the demand information acquisition module is used for acquiring service flow demand information and extracting state node information, state transition information and state extension information from the service flow demand information;
the configuration information query module is used for inputting the state node information and the state flow information into the business process classification model to obtain the state node configuration information;
and the business process generation module is used for generating the business process matched with the business process requirement information according to the state node configuration information and the state expansion information.
According to a third aspect of the present invention, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described method of generating a business process.
According to a fourth aspect of the present invention, there is provided a computer device, including a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for generating the business process when executing the program.
The invention provides a method, a system, a storage medium and computer equipment for generating a business process, which are characterized in that firstly, business process demand information is obtained, state node information, state transfer information and state extension information are extracted from the business process demand information, then, the extracted state node information and state transfer information are input into a business process classification model to obtain configuration information of each state node of the business process, and finally, the business process matched with the business process demand information is generated according to the state node configuration information and the extracted state extension information. According to the scheme, the business process demand information can be rapidly classified and predicted through the business process classification model, so that the developed state node configuration information which can be matched with new demands can be obtained, the personalized state extension information is arranged on the configuration information, the business process matched with the demand information can be accurately generated, through the mode, the business process demands can be fed back very rapidly, the multiplexing efficiency of the existing business process is improved, and the development cost of the business process is reduced.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flow chart illustrating a method for generating a business process according to an embodiment of the present invention;
fig. 2 is a flow chart illustrating another method for generating a business process according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram illustrating a generating apparatus of a business process according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of another apparatus for generating a business process according to an embodiment of the present invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
In an embodiment, as shown in fig. 1, a method for generating a business process is provided, which is described by taking an example that the method is applied to a computer terminal, and includes the following steps:
101. acquiring service flow requirement information, and extracting state node information, state circulation information and state extension information from the service flow requirement information.
The business process refers to a series of standardized steps set for achieving a certain business goal, and can embody the relation of 'what to do first and then what to do and who to do' in one work. The process provides a standardized program for the business, defines the work content and the responsible person of each node, and can ensure that the business is executed orderly and smoothly.
Further, in a complete business process, a plurality of state nodes and a flow relationship between the state nodes are usually included, and for a same business target, because the business process requirements of users are different, such as different function divisions, different handling processes and different business scales, a plurality of business processes are usually required to be designed to meet the requirements of a plurality of business processes to realize the same business target.
Specifically, after the service flow requirement information is acquired, state node information, state flow information, and state extension information may be extracted from the service flow requirement information, where the state node information may include information such as the number of state nodes, names of state nodes, and responsibility bodies of the state nodes; the state transition information may include information such as input parameters and output parameters of state nodes, transition conditions and transition paths between the state nodes, and the like; the state extension information comprises information of a preposed event, a postpositional event, a dynamic form, examination and approval configuration and the like of the state node. In this embodiment, the business process requirement information is usually subjected to certain preprocessing, so as to convert the requirement of the text type into structured and recognizable information or data.
102. And inputting the state node information and the state circulation information into a business process classification model to obtain the state node configuration information.
In this embodiment, the service process classification model may classify and analyze basic service process information such as state node information and state flow information, and after the state node information and the state flow information are input into the trained service process classification model, a plurality of prediction probability values may be obtained, where the prediction probability values respectively correspond to probabilities that the state node information and the state flow information belong to each service process, and thus, the service process classification model is actually a multi-classification model based on a gradient-spanning tree, and through the model, a type of a service process to be generated may be predicted.
Specifically, after the state node information and the state flow information are arranged into a form that the model can be input, the state node information and the state flow information can be input into a business process classification model to obtain a predicted probability value of each business process to which the state node information and the state flow information belong, then, each predicted probability value can be further compared with a preset probability threshold value to obtain a predicted passing rate of each business process to which the state node information and the state flow information belong, if one or more predicted probability values are larger than the set probability threshold value, a class number of the business process corresponding to the predicted probability can be inquired, the class number is input into a database storing business process configuration information, the state node configuration information corresponding to the class number is inquired, and the state node configuration information is returned to a user.
103. And generating a business process matched with the business process demand information according to the state node configuration information and the state expansion information.
Specifically, configuration information corresponding to state extension information can be added to each state node of the state node configuration information through a business process configuration tool to obtain a complete business process configuration table, a complete business process can be generated by reading the business process configuration table, the business process can jump from one state node to another state node according to state circulation conditions, corresponding extension information can be displayed on each state node, a corresponding business interface is called to perform business operation, and a business operation result is returned. In this embodiment, the generated service flow may perform state transition according to a state node setting mode, a state transition mode, and a state extension information setting mode planned in the service flow requirement information, and match the service flow requirement.
The method for generating a service flow according to this embodiment includes obtaining service flow requirement information, extracting state node information, state transition information, and state extension information from the service flow requirement information, inputting the extracted state node information and state transition information into a service flow classification model to obtain configuration information of each state node of the service flow, and generating a service flow matched with the service flow requirement information according to the configuration information of the state node and the extracted state extension information. According to the method, the business process demand information can be rapidly classified and predicted through the business process classification model, so that the developed state node configuration information capable of being matched with new demands can be obtained, the personalized state extension information is arranged on the configuration information, the business process matched with the demand information can be accurately generated, through the mode, the business process demands can be fed back very rapidly, the multiplexing efficiency of the existing business process is improved, and the development cost of the business process is reduced.
Further, as a refinement and an extension of the specific implementation of the foregoing embodiment, in order to fully describe the implementation process of the present embodiment, a method for generating a business process is provided, as shown in fig. 2, the method includes the following steps:
201. and collecting characteristic sample data of various business processes, and creating a business process classification model based on the gradient lifting tree according to the characteristic sample data of the various business processes.
Specifically, feature data of the service flow may be extracted from the attribute of the service flow, or extracted from an operation log of the service flow, and the feature data of the service flow is sorted, and is subjected to corresponding processing such as data cleaning and structuring, so as to obtain service flow feature sample data. The service process characteristic sample data comprises information related to the service process, such as state node information, state flow information, state expansion information and the like.
Further, the training method for training the business process classification model according to the business process characteristic sample data may include the following steps: firstly, respectively constructing a first classification regression tree for each business process according to characteristic sample data of a plurality of business processes, then respectively generating the prediction probability of each first classification regression tree by using a logistic regression function, obtaining the negative gradient value of each first classification regression tree according to the prediction probability of each first classification regression tree, respectively iterating for multiple rounds by using a gradient descent method according to the negative gradient value of each first classification regression tree to obtain a plurality of first lifting trees for each business process, and finally respectively carrying out weighted summation on the first classification regression tree and the first lifting trees for each business process to obtain a business process classification model based on the gradient lifting trees.
In this embodiment, the process of creating the service flow classification model is actually a process of classifying and analyzing the service flow characteristic sample data, and after the service flow classification model is trained, one characteristic sample data is input into the model, so that a plurality of prediction probability values can be obtained, wherein the plurality of prediction probability values respectively correspond to the probability that the characteristic sample data belongs to each service flow, so that the service flow classification model actually belongs to a multi-classification model based on a gradient lifting tree, and the service flow to which the service flow characteristic data belongs can be predicted through the model.
202. Acquiring service flow requirement information, and extracting state node information, state circulation information and state extension information from the service flow requirement information.
Specifically, after the service flow requirement information is acquired, state node information, state flow information, and state extension information may be extracted from the service flow requirement information, where the state node information may include information such as the number of state nodes, names of state nodes, and responsibility bodies of the state nodes; the state transition information may include information such as input parameters and output parameters of state nodes, transition conditions and transition paths between the state nodes, and the like; the state extension information comprises information of a preposed event, a postpositional event, a dynamic form, examination and approval configuration and the like of the state node.
203. And inputting the state node information and the state circulation information into a business process classification model to obtain business state node configuration information.
Specifically, after the state node information and the state flow information are arranged into a form that the model can be input, the state node information and the state flow information can be input into a business process classification model to obtain a predicted probability value of each business process to which the state node information and the state flow information belong, then, each predicted probability value can be further compared with a preset probability threshold value to obtain a predicted passing rate of each business process to which the state node information and the state flow information belong, if one or more predicted probability values are larger than the set probability threshold value, a class number of the business process corresponding to the predicted probability can be inquired, the class number is input into a database storing business process configuration information, the state node configuration information corresponding to the class number is inquired, and the state node configuration information is returned to a user.
204. And generating a business process matched with the business process demand information according to the state node configuration information and the state expansion information.
Specifically, configuration information corresponding to state extension information can be added to each state node of the state node configuration information through a business process configuration tool to obtain a complete business process configuration table, a complete business process can be generated by reading the business process configuration table, the business process can jump from one state node to another state node according to state circulation conditions, corresponding extension information can be displayed on each state node, a corresponding business interface is called to perform business operation, and a business operation result is returned. In this embodiment, the generated service flow may perform state transition according to a state node setting mode, a state transition mode, and a state extension information setting mode planned in the service flow requirement information, and match the service flow requirement.
205. And respectively establishing an execution time prediction model based on the gradient lifting tree for each business process according to the characteristic sample data of the multiple business processes.
Specifically, the step of creating an execution time prediction model based on a gradient lifting tree for each business process comprises the following steps: firstly, dividing feature sample data of various service flows according to class numbers of the service flows to obtain feature sample data of each service flow, then respectively constructing a second classification regression tree aiming at each service flow execution time according to the feature sample data of each service flow, fitting a negative gradient value of a loss function in each second classification regression tree, respectively iterating for multiple rounds by using a gradient descent method according to the negative gradient value of each second classification regression tree to obtain multiple second lifting trees aiming at each service flow execution time, and finally respectively carrying out weighted summation on the second classification regression tree aiming at each service flow execution time and the second lifting trees to obtain an execution time prediction model of each service flow.
In this embodiment, the process of creating the execution time prediction model of the business process and the process of creating the business process classification model are different, the business process classification model is a multi-classification model based on a gradient lifting tree, the execution time prediction model of the business process is a regression model based on the gradient lifting tree, only one business process classification model needs to be created, a plurality of prediction probability values can be output, then the business process to which the business process characteristic data belongs is predicted, the execution time prediction model of the business process needs to be trained for each business process, the execution time prediction model of each business process only has one output value, and the output value is a predicted value of the execution time of the business process under the characteristic sample data. It should be noted that, for the same business process, operating in different places, the output feature data are different, and the execution time is also different, so the execution time prediction model of the business process can be used to predict the execution time of the same business process under different state node information, state transition information and state extension information.
206. And inputting the state node information, the state circulation information and the state expansion information into an execution time prediction model of the business process corresponding to the business process configuration information to obtain an execution time prediction value of the business process.
Specifically, in step 203, one or more service flows to which the state node information and the state flow information belong are predicted through the service flow classification model, and then, the execution time of each of the belonging service flows under the input state node information, state flow information, and state extension information can be further predicted through the execution time prediction model of the service flow.
Specifically, by the execution time of each of the business processes belonging to step 206 under the business process characteristic data, the business process with the smallest execution time prediction value can be selected as the most optimal business process from the business processes belonging to the plurality of business process characteristic data. In this embodiment, the business process with the smallest execution time prediction value may be used as the optimal business process.
Furthermore, a flow chart of the business process matched with the business process requirement information can be displayed through the display, and meanwhile, the execution time predicted value of the business process is displayed, so that a very intuitive impression is left for a user.
Further, as a specific implementation of the method shown in fig. 1 and fig. 2, this embodiment provides a device for generating a business process, and as shown in fig. 3, the device includes: a demand information obtaining module 31, a configuration information inquiring module 32 and a business process generating module 33, wherein:
the requirement information acquiring module 31 may be configured to acquire service flow requirement information, and extract state node information, state transition information, and state extension information from the service flow requirement information;
a configuration information query module 32, configured to input the state node information and the state flow information into the service process classification model to obtain state node configuration information;
and the service flow generation module 33 is configured to generate a service flow matched with the service flow requirement information according to the state node configuration information and the state extension information.
In a specific application scenario, as shown in fig. 4, the apparatus further includes a classification model creating module 34, configured to collect feature sample data of multiple service flows, and create a service flow classification model based on a gradient lifting tree according to the feature sample data of the multiple service flows.
In a specific application scenario, the classification model creating module 34 may be specifically configured to respectively construct a first classification regression tree for each business process according to feature sample data of multiple business processes; respectively generating the prediction probability of each first classification regression tree by using a logistic regression function; obtaining a negative gradient value of each first classification regression tree according to the prediction probability of each first classification regression tree; respectively iterating for multiple rounds by using a gradient descent method according to the negative gradient value of each first classification regression tree to obtain multiple first lifting trees aiming at each business process; and respectively carrying out weighted summation on the first classification regression tree and the first lifting tree aiming at each business process to obtain the business process classification model based on the gradient lifting tree.
In a specific application scenario, the configuration information query module 32 is specifically configured to input the state node information and the state flow information into the service flow classification model, so as to obtain a prediction probability of each service flow to which the state node information and the state flow information belong; comparing the predicted probability with a preset probability threshold; if the prediction probability is greater than the probability threshold, inquiring the class number of the service process; and inquiring the state node configuration information corresponding to the class number in the database according to the class number of the service process.
In a specific application scenario, the business process generating module 33 is specifically configured to obtain state extension configuration information corresponding to the state extension information; adding the state expansion configuration information into the state node configuration information to obtain a service flow configuration table; and reading the service flow configuration table to generate the service flow matched with the service flow requirement information.
In a specific application scenario, as shown in fig. 4, the apparatus further includes: a prediction model creation module 35 and an execution time prediction module 36, wherein:
the prediction model creating module 35 is configured to create an execution time prediction model based on a gradient lifting tree for each business process according to the feature sample data of the multiple business processes;
and the execution time prediction module 36 is configured to input the state node information, the state flow information, and the state extension information into a service flow execution time prediction model corresponding to the service flow configuration information, so as to obtain an execution time prediction value of the service flow.
In a specific application scenario, the prediction model creating module 35 may be specifically configured to divide feature sample data of multiple service flows according to class numbers of the service flows to obtain feature sample data of each service flow; respectively constructing a second classification regression tree aiming at the execution time of each business process according to the characteristic sample data of each business process; fitting a negative gradient value of a loss function in each of the second classification regression trees; respectively iterating for multiple rounds by using a gradient descent method according to the negative gradient value of each second classification regression tree to obtain multiple second lifting trees aiming at each service process execution time; and respectively carrying out weighted summation on the second classification regression tree and the second lifting tree aiming at the execution time of each business process to obtain an execution time prediction model of each business process.
It should be noted that other corresponding descriptions of the functional units related to the apparatus for generating a service flow provided in this embodiment may refer to the corresponding descriptions in fig. 1 and fig. 2, and are not described herein again.
Based on the methods shown in fig. 1 and fig. 2, correspondingly, the present embodiment further provides a storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method for generating the business process shown in fig. 1 and fig. 2.
Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, and the software product to be identified may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, or the like), and include several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the method according to the implementation scenarios of the present application.
Based on the method shown in fig. 1 and fig. 2 and the embodiment of the generating apparatus of the business process shown in fig. 3 and fig. 4, in order to achieve the above object, this embodiment further provides an entity device for processing the business process, which may specifically be a personal computer, a server, a smart phone, a tablet computer, a smart watch, or other network devices, and the entity device includes a storage medium and a processor; a storage medium for storing a computer program; a processor for executing the computer program to implement the above-mentioned methods as shown in fig. 1 and fig. 2.
Optionally, the entity device may further include a user interface, a network interface, a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, and the like. The user interface may include a Display screen (Display), an input unit such as a keypad (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), etc.
Those skilled in the art will appreciate that the physical device structure of a business process provided in this embodiment is not limited to the physical device, and may include more or less components, or combine some components, or arrange different components.
The storage medium may further include an operating system and a network communication module. The operating system is a program for managing the hardware of the above-mentioned entity device and the software resources to be identified, and supports the operation of the information processing program and other software and/or programs to be identified. The network communication module is used for realizing communication among components in the storage medium and communication with other hardware and software in the information processing entity device.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus a necessary general hardware platform, and can also be implemented by hardware. By applying the technical scheme of the application, the business process classification model is created, the model is used for accurately analyzing the business process requirements, the type of the business process corresponding to the business process requirements can be accurately judged, the business process is timely inquired in the database and returned to a user for checking. Compared with the prior art, the method has the advantages that the method can provide very quick feedback for the business process requirements, improves the multiplexing efficiency of the existing business process, and reduces the development cost of the business process.
Those skilled in the art will appreciate that the figures are merely schematic representations of one preferred implementation scenario and that the blocks or flow diagrams in the figures are not necessarily required to practice the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario may be distributed in the devices in the implementation scenario according to the description of the implementation scenario, or may be located in one or more devices different from the present implementation scenario with corresponding changes. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above application serial numbers are for description purposes only and do not represent the superiority or inferiority of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of the present application, but the present application is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present application.
Claims (10)
1. A method for generating a business process, the method comprising:
acquiring service flow demand information, and extracting state node information, state circulation information and state extension information from the service flow demand information;
inputting the state node information and the state circulation information into a business process classification model to obtain state node configuration information;
and generating a business process matched with the business process demand information according to the state node configuration information and the state expansion information.
2. The method of claim 1, further comprising:
collecting characteristic sample data of various business processes, and creating a business process classification model based on a gradient lifting tree according to the characteristic sample data of the various business processes.
3. The method according to claim 2, wherein said creating a gradient-boosting tree-based business process classification model according to the feature sample data of said plurality of business processes comprises:
respectively constructing a first classification regression tree for each business process according to the characteristic sample data of the multiple business processes;
respectively generating the prediction probability of each first classification regression tree by using a logistic regression function;
obtaining a negative gradient value of each first classification regression tree according to the prediction probability of each first classification regression tree;
respectively iterating for multiple rounds to obtain multiple first lifting trees for each business process by using a gradient descent method according to the negative gradient value of each first classification regression tree;
and respectively carrying out weighted summation on the first classification regression tree and the first lifting tree aiming at each business process to obtain a business process classification model based on the gradient lifting tree.
4. The method of claim 3, wherein the inputting the state node information and the state flow information into a business process classification model to obtain state node configuration information comprises:
inputting the state node information and the state flow information into the business process classification model to obtain the prediction probability of each business process to which the state node information and the state flow information belong;
comparing the prediction probability with a preset probability threshold;
if the prediction probability is larger than the probability threshold, inquiring the class number of the business process corresponding to the prediction probability;
and inquiring the state node configuration information corresponding to the class number in a database according to the class number of the service flow.
5. The method according to claim 4, wherein the generating a business process matching the business process requirement information according to the state node configuration information and the state extension information comprises:
acquiring state extension configuration information corresponding to the state extension information;
adding the state expansion configuration information into state node configuration information to obtain a service flow configuration table;
and reading the service flow configuration table to generate a service flow matched with the service flow requirement information.
6. The method of claim 5, further comprising:
respectively establishing an execution time prediction model based on a gradient lifting tree for each business process according to the characteristic sample data of the multiple business processes;
and inputting the state node information, the state circulation information and the state expansion information into a service process execution time prediction model corresponding to the state node configuration information to obtain an execution time prediction value of the service process.
7. The method according to claim 6, wherein said creating an execution time prediction model based on a gradient lifting tree for each business process according to the feature sample data of said plurality of business processes comprises:
dividing the characteristic sample data of the various business processes according to the class numbers of the business processes to obtain the characteristic sample data of each business process;
respectively constructing a second classification regression tree aiming at the execution time of each business process according to the characteristic sample data of each business process;
fitting a negative gradient value of a loss function in each of the second classification regression trees;
respectively iterating for multiple rounds to obtain multiple second lifting trees aiming at the execution time of each business process by utilizing a gradient descent method according to the negative gradient value of each second classification regression tree;
and respectively carrying out weighted summation on the second classification regression tree and the second lifting tree aiming at each service process execution time to obtain an execution time prediction model of each service process.
8. An apparatus for generating a business process, the apparatus comprising:
the system comprises a demand information acquisition module, a state node information acquisition module and a state transition information acquisition module, wherein the demand information acquisition module is used for acquiring service flow demand information and extracting state node information, state transition information and state extension information from the service flow demand information;
the configuration information query module is used for inputting the state node information and the state flow information into a business process classification model to obtain state node configuration information;
and the business process generation module is used for generating the business process matched with the business process demand information according to the state node configuration information and the state expansion information.
9. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, realizing the steps of the method of any one of claims 1 to 7.
10. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 7 when executed by the processor.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113240387A (en) * | 2021-05-11 | 2021-08-10 | 浪潮软件股份有限公司 | Electronic certificate business circulation model and device |
| CN113435859A (en) * | 2021-07-12 | 2021-09-24 | 建信金融科技有限责任公司 | Letter processing method and device, electronic equipment and computer readable medium |
| CN113570333A (en) * | 2021-07-21 | 2021-10-29 | 北京东方通科技股份有限公司 | Process design method suitable for integration |
| CN114357029A (en) * | 2022-01-04 | 2022-04-15 | 工银瑞信基金管理有限公司 | Method, device, equipment, medium and program product for processing service data |
| CN115422414A (en) * | 2022-10-11 | 2022-12-02 | 广州盛祺信息科技股份有限公司 | Visual configuration method for approval process |
| CN115471141A (en) * | 2022-11-02 | 2022-12-13 | 成都飞机工业（集团）有限责任公司 | Business process cycle management and control method, device, equipment and medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103617047A (en) * | 2013-12-16 | 2014-03-05 | 北京中电普华信息技术有限公司 | Method, device and processor for developing business processes |
| CN106022568A (en) * | 2016-05-09 | 2016-10-12 | 福建南威软件有限公司 | Workflow processing method and apparatus |
| CN107103059A (en) * | 2017-04-14 | 2017-08-29 | 上海众开信息科技有限公司 | Flow drawing generating method and device based on financial affair work flow |
| CN107886238A (en) * | 2017-11-09 | 2018-04-06 | 金航数码科技有限责任公司 | A kind of business process management system and method based on mass data analysis |
| CN110032571A (en) * | 2019-04-18 | 2019-07-19 | 腾讯科技（深圳）有限公司 | Business flow processing method, apparatus, storage medium and calculating equipment |
| CN110390496A (en) * | 2019-09-18 | 2019-10-29 | 浙江华云信息科技有限公司 | A kind of workflow design method that adaptation business is complicated and changeable with tissue |
| CN110610346A (en) * | 2019-08-07 | 2019-12-24 | 北京航空航天大学 | Intelligent office automation system workflow instance time prediction analysis |
| CN110659937A (en) * | 2019-09-20 | 2020-01-07 | 鞍钢集团矿业有限公司 | Gradient-lifting-tree-based improved supplier quantitative scoring prediction algorithm |
-
2020
- 2020-04-27 CN CN202010342101.2A patent/CN111652468A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103617047A (en) * | 2013-12-16 | 2014-03-05 | 北京中电普华信息技术有限公司 | Method, device and processor for developing business processes |
| CN106022568A (en) * | 2016-05-09 | 2016-10-12 | 福建南威软件有限公司 | Workflow processing method and apparatus |
| CN107103059A (en) * | 2017-04-14 | 2017-08-29 | 上海众开信息科技有限公司 | Flow drawing generating method and device based on financial affair work flow |
| CN107886238A (en) * | 2017-11-09 | 2018-04-06 | 金航数码科技有限责任公司 | A kind of business process management system and method based on mass data analysis |
| CN110032571A (en) * | 2019-04-18 | 2019-07-19 | 腾讯科技（深圳）有限公司 | Business flow processing method, apparatus, storage medium and calculating equipment |
| CN110610346A (en) * | 2019-08-07 | 2019-12-24 | 北京航空航天大学 | Intelligent office automation system workflow instance time prediction analysis |
| CN110390496A (en) * | 2019-09-18 | 2019-10-29 | 浙江华云信息科技有限公司 | A kind of workflow design method that adaptation business is complicated and changeable with tissue |
| CN110659937A (en) * | 2019-09-20 | 2020-01-07 | 鞍钢集团矿业有限公司 | Gradient-lifting-tree-based improved supplier quantitative scoring prediction algorithm |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113240387A (en) * | 2021-05-11 | 2021-08-10 | 浪潮软件股份有限公司 | Electronic certificate business circulation model and device |
| CN113435859A (en) * | 2021-07-12 | 2021-09-24 | 建信金融科技有限责任公司 | Letter processing method and device, electronic equipment and computer readable medium |
| CN113570333A (en) * | 2021-07-21 | 2021-10-29 | 北京东方通科技股份有限公司 | Process design method suitable for integration |
| CN114357029A (en) * | 2022-01-04 | 2022-04-15 | 工银瑞信基金管理有限公司 | Method, device, equipment, medium and program product for processing service data |
| CN115422414A (en) * | 2022-10-11 | 2022-12-02 | 广州盛祺信息科技股份有限公司 | Visual configuration method for approval process |
| CN115471141A (en) * | 2022-11-02 | 2022-12-13 | 成都飞机工业（集团）有限责任公司 | Business process cycle management and control method, device, equipment and medium |
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