Method and apparatus for generating item processing flow, and electronic device and storage medium

Abstract

Provided in the present disclosure are a method and apparatus for generating an item processing flow, and an electronic device and a storage medium. The method for generating an item processing flow comprises: acquiring first information, which is input during a first operation event; parsing the first information by means of an artificial intelligence program, so as to generate processing flow information for a target item; and generating a processing flow for the target item on the basis of the processing flow information for the target item, wherein the first information is natural language information.

Description

Methods, apparatus, electronic devices, and storage media for generating task processing flows

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese Patent Application No. 202410509113.8, filed on April 25, 2024, entitled “Method, Apparatus, Electronic Device and Storage Medium for Generating Item Processing Flow”, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of computer technology, and more particularly to a method, apparatus, electronic device, and storage medium for generating event processing flows. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for generating event processing flows.

[0005] The following technical solution is adopted in this disclosure.

[0006] In some embodiments, this disclosure provides a method for generating a task processing flow, including:

[0007] Retrieve the first information input in the first operation event;

[0008] The first information is analyzed by an artificial intelligence program to generate processing flow information for the target matter;

[0009] Generate the processing flow for the target matter based on the processing flow information of the target matter;

[0010] The first piece of information is natural language information.

[0011] In some embodiments, this disclosure provides an apparatus for generating a task processing flow, comprising:

[0012] The acquisition unit is used to acquire the first information input in the first operation event;

[0013] The processing unit is used to parse the first information through an artificial intelligence program and generate processing flow information for the target matter;

[0014] The processing unit is further configured to generate a processing flow for the target matter based on the processing flow information of the target matter;

[0015] The first piece of information is natural language information.

[0016] In some embodiments, this disclosure provides an electronic device, including: at least one memory and at least one processor;

[0017] The memory is used to store program code, and the processor is used to call the program code stored in the memory to execute the above method.

[0018] In some embodiments, this disclosure provides a computer-readable storage medium for storing program code that, when run by a processor, causes the processor to perform the methods described above. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0020] Figure 1 is a flowchart of a method for generating a task processing flow according to an embodiment of this disclosure.

[0021] Figure 2 is a schematic diagram of the generation process structure data of an embodiment of this disclosure.

[0022] Figures 3 to 8 are schematic diagrams of a method for generating a process flow according to an embodiment of the present disclosure.

[0023] Figure 9 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0025] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0026] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0027] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0028] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0029] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0030] It should be understood that the various steps described in the method embodiments of this disclosure can be performed in sequence and / or in parallel. Furthermore, method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0031] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0032] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0033] It should be noted that the use of the word "a" in this disclosure is illustrative rather than restrictive, and those skilled in the art should understand that it should be understood as "one or more" unless otherwise expressly indicated in the context.

[0034] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0035] All user information obtained in this disclosure was obtained with the user's consent and authorization.

[0036] The solutions provided by the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings.

[0037] In business systems, such as approval systems, administrators spend a lot of time and effort creating process flows for tasks, and the process is inefficient. Ordinary staff also find it difficult to create process flows for tasks initially due to a lack of experience.

[0038] When generating workflows for specific tasks, such as those for approvals, tedious manual operations or reliance on professional personnel to write them are often required, resulting in low efficiency and a high risk of errors.

[0039] As shown in Figure 1, which is a flowchart of a method for generating a task processing flow according to an embodiment of the present disclosure, the method includes the following steps.

[0040] S11. Obtain the first information input in the first operation event.

[0041] In some embodiments, the method proposed in this disclosure can be used on a terminal or server. Specifically, it can be used to generate a processing flow for a target item in a transaction system on a terminal or server. The first operation event may include one or more operations, such as a user triggering a target control in the transaction system to bring up an information input area, where the user inputs first information via voice or text. The first information is natural language information, which is the language used by the user in daily life. The user can convey the first information by speaking or writing in their daily life. In some embodiments, the first information includes one or more of the following: a description of the type of the target item, a description of the branch judgment conditions, and a description of the handler. The first information describes which type of target item the user wants to create, such as a purchase approval, expense reimbursement approval, or resignation approval. The description of the branch judgment conditions can be a method for determining which branch of the processing flow to enter in the target item processing. For example, in purchase approval, the purchase amount can be used as the branch judgment condition. When the purchase amount is less than a certain value, the processing flow executes the first branch; when the purchase amount is not less than the value, the processing flow executes the second branch.

[0042] S12. The first information is analyzed by an artificial intelligence program to generate the processing flow information of the target matter.

[0043] S13. Generate the processing flow for the target item based on the processing flow information of the target item.

[0044] In some embodiments, step S13 may also be executed by an artificial intelligence program. The artificial intelligence program parses the first information and extracts or summarizes the processing flow information of the target matter, which is the matter that needs to be processed as described in the first information. The target matter may be an approval. A matter usually needs to be processed through multiple nodes according to the processing flow to be completed. The processing flow information may be the information required to generate the processing flow of the target matter, which may include, for example, the forms required for the target matter, the nodes in the processing flow, the personnel responsible for the nodes in the processing flow, and the branch judgment conditions of the branches in the processing flow. After obtaining the processing flow information of the target matter, the processing flow of the target matter is created in the matter system based on the processing flow information.

[0045] In some embodiments of this disclosure, an artificial intelligence program parses the natural language information input by the user as the first piece of information, thereby generating a processing flow for the target matter based on the parsed processing flow information. This reduces complexity and error rate, and improves efficiency. Users only need to verbally or in writing provide the system with the first piece of information in natural language to generate the processing flow for the target matter, reducing the difficulty for users in generating the processing flow. Users do not need to learn how to use the system, resulting in a better user experience.

[0046] In some embodiments of this disclosure, the processing flow information of the target matter includes: basic information of the target matter. Generating a processing flow for the target matter based on the processing flow information of the target matter further includes: associating the basic information of the target matter with the processing flow of the target matter.

[0047] In some embodiments, as shown in Figure 4, an interface for inputting initial information is displayed in the event system. In the event system, triggering a target control (which can be a control used to create a processing flow using artificial intelligence after being triggered) displays the interface shown in Figure 4. This interface has an information input area where the user can input text or voice. The interface also displays examples of inputtable information. Clicking on these examples, as shown in Figure 5, allows the user to input the example as initial information into the information input area. After the initial information is input, the artificial intelligence program parses it and begins generating the processing flow for the target event, as shown in Figure 6. Generating the processing flow for the target event may include steps such as basic information, form design, process design, and further settings. After parsing the initial information, the artificial intelligence program obtains the basic information of the target event (in this case, an approval event), which is included in the processing flow information. This basic information is then written into the basic information input area. In the interface for basic information of the approval item shown in Figure 6, the basic information includes the approval name, description, group, and who can submit the approval. The items marked with an asterisk "*" are required. As shown in Figure 6, after the artificial intelligence generates the basic information, it can be automatically written into the basic information input area to achieve association with the processing flow of the target item. In the interface for generating the processing flow of the target item, the sub-interface of the basic information can display the running status of the artificial intelligence program ("Generating basic information for you" in Figure 6) and also provides controls to regenerate the basic information and stop generating the basic information.

[0048] In some embodiments of this disclosure, an artificial intelligence program parses the first information to generate processing flow information for the target item, including: the artificial intelligence program generates form structure data for the target item by parsing the first information. Generating the processing flow for the target item based on the processing flow information includes: generating a form for the target item based on the form structure data and / or process structure data of the target item, and generating the processing flow for the target item based on the form of the target item, wherein the form structure data is data describing the form content in the form of the target item.

[0049] In some embodiments of this disclosure, the first information is parsed using an artificial intelligence program to generate processing flow information for the target matter. This includes: the artificial intelligence program generating process structure data for the target matter by parsing the first information. Generating the processing flow for the target matter based on the processing flow information includes: generating a flowchart for the target matter based on the process structure data, and generating the processing flow for the target matter based on the flowchart; wherein the process structure data is data describing the process components of the flowchart for the target matter.

[0050] In some embodiments, the processing flow of the target item requires the flow through various nodes in the flowchart via a form. Therefore, generating the processing flow of the target item requires generating a form and a flowchart for the target item. The flowchart may contain flow lines and nodes on the flow lines, with the personnel responsible for handling each node. If the flow lines have branches, the flowchart may also contain branch judgment conditions. The processing flow information includes form structure data and / or flow structure data. In some embodiments, the form structure data of the target item may describe the types of content in the form of the target item, such as fields and controls. The flow structure data may describe the flow lines, nodes, personnel, and branch judgment conditions of the flowchart of the target item. In this embodiment, users do not need to manually add or delete forms and flowcharts, reducing the operational difficulty for users and facilitating the creation of processing flows for target items with flowcharts and forms. In some embodiments, as shown in Figures 7 and 8, the form and flowchart of the target item are respectively displayed. The form and flowchart of the target item can be generated after generating basic information for the target item and writing it into the basic information input area.

[0051] In some embodiments of this disclosure, an artificial intelligence program is used to parse the first information to generate processing flow information for the target item, including: determining the fields in the form of the target item, or determining the controls in the form of the target item and the fields in the form of the target item, based on the content of the first information and preset rules. Generating the form of the target item based on the form structure data of the target item includes: arranging the fields in the form of the target item, or the fields and controls in the form of the target item, in a preset order to obtain the form of the target item.

[0052] In some embodiments, as shown in Figure 7, a form is created and displayed in a sub-interface of the form within the processing flow generation interface for the target item. This sub-interface can display the running status of the AI ​​program (“Generating a form for you” in Figure 7) and also provides controls for regenerating the form and stopping its generation. When generating the form, the AI ​​program can determine the form's composition based on the content of the first information and preset rules. Specifically, the content of the first information in Figure 5 can describe the form type (procurement) and the objects involved in the form (funds, with 10,000 as the dividing line). The preset rules can describe different types of forms and the fields and controls that will be called when different objects are involved in the form. As shown in Figure 7, in some embodiments, the form type is determined to be procurement based on the content of the first information, involving funds. Therefore, according to the preset rules, the fields within the form include: application description, budget item, procurement amount, and supplier category. The controls within the form are determined to be controls for adding related attachments. In some embodiments, the fields are attributes of input boxes, and the fields in the form describe the type of content input used in the corresponding input boxes. The preset order describes the positional order of different fields and controls. For example, it specifies the positional priority of each field and control, with higher priority fields placed at the top and lower priority fields arranged downwards. After obtaining the fields and controls in the form, they are arranged according to the preset order, thus determining the positional order of the input boxes and controls corresponding to each field within the form. For example, the default order is from top to bottom.

[0053] In some embodiments of this disclosure, the artificial intelligence program generates process structure data of the target matter by parsing the first information, including one or more of the following: generating a process framework of the target matter, generating the personnel handling the nodes in the process framework, or generating branch judgment conditions for the branch lines in the process framework.

[0054] In some embodiments, one, two, or all of the process framework, handlers, and branch judgment conditions can be generated from the process structure data. The flowchart of the target item can have either branch lines or no branch lines. When the flowchart of the target item has no branch lines, the process framework of the target item and the handlers for the nodes in the process framework can be generated. When the flowchart of the target item has branch lines, the process framework of the target item, the handlers for the nodes in the process framework, and the branch judgment conditions for the branch lines in the process framework can be generated. In some embodiments, as shown in Figure 3(a), the process framework is generated first, showing the process nodes and the process lines between them, but no approver is specified within each process node, and there are no branch judgment conditions at the branches of the process lines. As shown in Figure 3(b), the handlers for the process nodes in the process framework are generated, and the handlers can be written into the process nodes. If the process framework of the target item has branch lines, as shown in Figure 3(c), the judgment conditions for the branch lines of the process nodes are written (in Figure 7, leave exceeding three days and leave not exceeding three days are used as the branch judgment conditions for the two branch lines). It's important to note that when the process framework contains branches, the order of the personnel responsible for generating nodes and the branching criteria for branching branches is not fixed; they can occur simultaneously or sequentially, and either one can precede the other. By automatically supplementing personnel, the system ensures accurate assignment of tasks during the processing of the target matter, thereby improving the flexibility and usability of the process. Automatic analysis of process lines generates branching criteria, which are used to determine the flow direction based on different situations, automating complex processes. The data generated through these steps provides users with a clear and concise interface for handling the target matter.

[0055] In some embodiments of this disclosure, generating a process framework for a target item includes: generating a BPMN (Business Process Modeling and Notation) diagram based on first information; and converting the BPMN diagram into a programming language used by the item system for visualization within the item system.

[0056] In some embodiments, a BPMN diagram is first created based on the first information. BPMN is a tool for business process modeling and annotation. Then, the created BPMN diagram is converted into the programming language used by the event system, allowing it to be presented visually within the event system. This embodiment describes the process of modeling, converting, and visualizing the process framework of a target event. This allows for better understanding, analysis, and management of the process framework, and enables monitoring and control of the process within the event system, facilitating user viewing. As shown in Figure 8, after generating the process framework of the target event, the AI ​​program can write the personnel handling the nodes and branch judgment conditions into the process framework when generating the processing flow of the target event. This generates a flowchart of the processing flow of the target event and displays it in the flowchart sub-interface of the target event processing flow generation interface. This interface displays the running status of the AI ​​program (“Generating a process for you” in Figure 8) and also provides controls to regenerate the flowchart and stop generating the flowchart.

[0057] In some embodiments of this disclosure, the first information includes one or more of the following: a description of the type of the target matter, a description of the branch decision conditions, and a description of the handler. In some embodiments, the description of the type of the target matter indicates the category or type to which the target matter belongs. This helps us clarify the nature, characteristics, and requirements of the matter. The branch decision conditions provide specific descriptions of the corresponding decision conditions for different situations or branch paths that may occur in the processing flow. The description of the handler involves a description of the personnel responsible for handling the nodes in the processing flow of the target matter.

[0058] To better illustrate the method proposed in this embodiment, the following description is provided in conjunction with Figures 2 to 8. Figure 2 depicts the process of generating the target item. In this embodiment, the target item is an approval item as an example. As shown in Figures 2 and 3, the user first inputs text into the item system as the first information. Specifically, the user describes the approval to be created using natural language in the input box of the item system. Then, the artificial intelligence program begins to parse the user's input and determine the key requirements. As shown in Figure 6, the artificial intelligence program parses the content of the first information to obtain the basic information such as the name, description, and category of the approval item to be created. As shown in Figures 2 and 7, the artificial intelligence program parses the first information to determine the form structure data, which is used to generate the form. As shown in Figures 2 and 8, the artificial intelligence program parses the first information and combines it with the form structure data to generate a BPNM diagram, approver information (approver information is stored in the form of approver structure data), and branch judgment conditions. The BPMN diagram is converted into the programming language used by the item system, thereby generating visual node structure data (process framework). The node structure data, approver structure data, and branch judgment conditions are combined to generate a flowchart. The flowchart and the approval form are combined to generate the approval processing flow.

[0059] In some embodiments of this disclosure, when generating forms for target items using artificial intelligence, pre-set rules and sequences ensure a clear form structure and uniform format, improving the efficiency and manageability of the target item processing flow. In some embodiments of this disclosure, the flowchart is divided into three parts: the process framework, the personnel handling each node, and the branch judgment conditions. First, in the process framework generation stage, the overall structure and logic of the flowchart are established. Then, the AI ​​program automatically adds approvers and automatically fills in the required branch judgment conditions, reducing the complexity and error rate of generating the target item processing flow and improving generation efficiency. This step-by-step design makes the generation of the target item processing flow more systematic and efficient. In some embodiments of this disclosure, the AI ​​first uses a BPMN diagram to clearly display the structure and logic of the process framework. Then, it is converted into the programming language used by the item system, realizing the transformation from the conceptual level to the implementation level. This not only ensures the accuracy and completeness of the process framework but also improves the universality and applicability of the generated target item processing flow.

[0060] In some embodiments of this disclosure, forms are generated through artificial intelligence programs, resulting in forms with a unified format and structure, improved standardization and manageability, simplified form design, and reduced user burden. By dividing the flowchart generation process into three steps, flowchart construction becomes clearer and more controllable, reducing creation difficulty and increasing flexibility and customizability. By first generating a BPMN diagram and then converting it into a programming language, the automatic generation and conversion of the process framework is achieved, reducing manual intervention, lowering the complexity of creating the process framework, increasing automation, and ensuring the accuracy and consistency of the processing flow.

[0061] This disclosure also proposes an apparatus for generating a task processing flow, comprising:

[0062] The acquisition unit is used to acquire the first information input in the first operation event;

[0063] The processing unit is used to parse the first information through an artificial intelligence program and generate processing flow information for the target matter;

[0064] The processing unit is also used to generate a processing flow for the target item based on the processing flow information of the target item;

[0065] The first piece of information is natural language information.

[0066] For embodiments of the apparatus, since they basically correspond to the method embodiments, relevant details can be found in the descriptions of the method embodiments. The apparatus embodiments described above are merely illustrative, and the modules described as separate modules may or may not be separate. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0067] The methods and apparatus of this disclosure have been described above based on embodiments and application examples. Furthermore, this disclosure also provides an electronic device and a computer-readable storage medium, which are described below.

[0068] Referring now to FIG9, a schematic diagram of the structure of an electronic device (e.g., a terminal device or a server) 800 suitable for implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The electronic device shown in the figure is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present disclosure.

[0069] Electronic device 800 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 801, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 802 or a program loaded from storage device 808 into random access memory (RAM) 803. RAM 803 also stores various programs and data required for the operation of electronic device 800. The processing device 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0070] Typically, the following devices can be connected to I / O interface 805: input devices 806 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 807 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 808 including, for example, magnetic tapes, hard disks, etc.; and communication devices 809. Communication device 809 allows electronic device 800 to communicate wirelessly or wiredly with other devices to exchange data. Although an electronic device 800 with various devices is shown in the figure, it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0071] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 809, or installed from a storage device 808, or installed from a ROM 802. When the computer program is executed by a processing device 801, it performs the functions defined in the methods of embodiments of this disclosure.

[0072] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0073] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol), and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0074] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0075] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods of the present disclosure.

[0076] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0077] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0078] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0079] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0080] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0081] According to one or more embodiments of this disclosure, a method for generating an event processing flow is provided, comprising: obtaining first information input in a first operation event;

[0082] The first information is analyzed by an artificial intelligence program to generate processing flow information for the target matter;

[0083] Generate the processing flow for the target matter based on the processing flow information of the target matter;

[0084] The first piece of information is natural language information.

[0085] According to one or more embodiments of this disclosure, a method for generating a task processing flow is provided, which uses an artificial intelligence program to parse the first information to generate processing flow information of the target task, including: the artificial intelligence program generates form structure data and / or process structure data of the target task by parsing the first information;

[0086] Generating a processing flow for the target item based on the processing flow information of the target item includes: generating a form for the target item based on the form structure data of the target item, and generating a processing flow for the target item based on the form of the target item; wherein, the form structure data is data describing the form content in the form of the target item.

[0087] According to one or more embodiments of this disclosure, a method for generating a task processing flow is provided. This method involves parsing first information using an artificial intelligence program to generate processing flow information for a target task. The method includes: the artificial intelligence program generating flow structure data for the target task by parsing the first information; and generating the processing flow for the target task based on the processing flow information, including: generating a flowchart for the target task based on the flow structure data, and generating the processing flow for the target task based on the flowchart. The flow structure data is data describing the flow composition of the flowchart for the target task.

[0088] According to one or more embodiments of this disclosure, a method for generating a task processing flow is provided, which uses an artificial intelligence program to parse the first information to generate processing flow information for a target task, including: determining fields in a form in the target task, or determining controls in a form in the target task and fields in a form in the target task, based on the content of the first information and preset rules;

[0089] Generating a form for the target item based on the form structure data of the target item includes: arranging the fields in the form of the target item, or the fields in the form of the target item and the controls in the form of the target item, in a preset order to obtain the form of the target item.

[0090] According to one or more embodiments of this disclosure, a method for generating a task processing flow is provided, wherein the artificial intelligence program generates flow structure data of a target task by parsing first information, including at least one of the following:

[0091] Generate a process framework for the target matter, generate the personnel responsible for handling the nodes in the process framework, or generate the branch judgment conditions for the branch lines in the process framework.

[0092] According to one or more embodiments of this disclosure, a method for generating a task processing flow is provided, which generates a flow framework for the target task, including:

[0093] Generate a BPMN (Business Process Modeling and Notation) diagram based on the first information;

[0094] The BPMN diagram is converted into the programming language used by the event system, so that it can be visualized in the event system.

[0095] According to one or more embodiments of this disclosure, a method for generating a task processing flow is provided, wherein the task processing flow information further includes: basic information of the target task;

[0096] The process of generating the processing flow of the target matter based on the processing flow information of the target matter also includes: associating the basic information of the target matter with the processing flow of the target matter.

[0097] According to one or more embodiments of this disclosure, a method for generating a task processing flow is provided, wherein the first information includes one or more of the following: a description of the type of the target task, a description of the branch judgment conditions, and a description of the handler.

[0098] According to one or more embodiments of this disclosure, an apparatus for generating a task processing flow is provided, comprising:

[0099] The acquisition unit is used to acquire the first information input in the first operation event;

[0100] The processing unit is used to parse the first information through an artificial intelligence program and generate processing flow information for the target matter;

[0101] The processing unit is further configured to generate a processing flow for the target matter based on the processing flow information of the target matter;

[0102] The first piece of information is natural language information.

[0103] According to one or more embodiments of the present disclosure, an electronic device is provided, including: at least one memory and at least one processor;

[0104] The at least one memory is used to store program code, and the at least one processor is used to call the program code stored in the at least one memory to execute the method described in any one of the above.

[0105] According to one or more embodiments of the present disclosure, a computer-readable storage medium is provided for storing program code that, when executed by a processor, causes the processor to perform the methods described above.

[0106] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0107] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0108] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for generating a task processing flow, comprising: Retrieve the first information input in the first operation event; The first information is analyzed by an artificial intelligence program to generate processing flow information for the target matter; Generate the processing flow for the target matter based on the processing flow information of the target matter; The first piece of information is natural language information.

2. The method according to claim 1, wherein, The artificial intelligence program parses the first information to generate processing flow information for the target item, including: the artificial intelligence program parses the first information to generate form structure data for the target item; The process flow for the target item is generated based on the processing flow information of the target item, including: generating a form for the target item based on the form structure data of the target item, and generating a processing flow for the target item based on the form of the target item; The form structure data refers to the form content data in the form describing the target item.

3. The method according to claim 1, wherein parsing the first information using an artificial intelligence program to generate processing flow information for the target matter includes: The artificial intelligence program generates process structure data for the target matter by parsing the first information; Generating a processing flow for the target matter based on the processing flow information of the target matter includes: generating a flowchart of the target matter based on the flow structure data of the target matter, and generating a processing flow for the target matter based on the flowchart of the target matter; The process structure data refers to the process composition data that describes the flowchart of the target matter.

4. The method according to claim 2, wherein, The artificial intelligence program generates form structure data of the target item by parsing the first information, including: determining the fields in the form of the target item, or determining the controls in the form of the target item and the fields in the form of the target item, based on the content of the first information and preset rules; Generating a form for the target item based on the form structure data of the target item includes: arranging the fields in the form of the target item, or the fields in the form of the target item and the controls in the form of the target item, in a preset order to obtain the form of the target item.

5. The method according to claim 3, wherein the artificial intelligence program generates process structure data of the target matter by parsing the first information, comprising at least one of the following: Generate a process framework for the target matter, generate the personnel responsible for handling the nodes in the process framework, or generate the judgment conditions for each execution branch in the process framework.

6. The method of claim 5, wherein generating the process framework for the target matter comprises: Generate a BPMN (Business Process Modeling and Notation) diagram based on the first information; The BPMN diagram is converted into the programming language used by the event system, so that it can be visualized in the event system.

7. The method according to claim 1, wherein, The processing flow information for the target matter also includes: basic information about the target matter; The process of generating the processing flow of the target matter based on the processing flow information of the target matter also includes: associating the basic information of the target matter with the processing flow of the target matter.

8. The method according to claim 1, wherein, The first information includes one or more of the following: a description of the type of the target matter, a description of the branch judgment conditions, and a description of the person handling the matter.

9. An apparatus for generating a task processing flow, comprising: The acquisition unit is used to acquire the first information input in the first operation event; The processing unit is used to parse the first information through an artificial intelligence program and generate processing flow information for the target matter; The processing unit is further configured to generate a processing flow for the target matter based on the processing flow information of the target matter; The first piece of information is natural language information.

10. An electronic device, comprising: At least one memory and at least one processor; The at least one memory is used to store program code, and the at least one processor is used to call the program code stored in the at least one memory to execute the method of any one of claims 1 to 8.

11. A computer-readable storage medium for storing program code that, when executed by a processor, causes the processor to perform the method of any one of claims 1 to 8.

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