Process model generation method, device and electronic equipment

Abstract

The present invention provides a process model generation method, device and electronic device, which establish a process template and label set corresponding to a target case; obtain the voice input instruction of each node, and convert the voice input instruction into a text instruction of each node into a calculation rule; obtain the original structured data corresponding to the target case, and parse the original structured data to obtain variable information, obtain the target label of the target variable information corresponding to each node from the label set, and configure the input parameters of the corresponding target variable information for each node based on the corresponding target label; calculate the input parameters of the corresponding target variable information for each node according to the corresponding calculation rule to obtain the calculation result, and generate the process model corresponding to the target case based on the process template and the calculation result. The present invention can alleviate the problem that the existing process model construction method is relatively cumbersome to operate and has high programming ability requirements for relevant personnel.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a process model generation method, device and electronic equipment. Background Art

[0002] For law firms, after receiving the materials provided by the client, they usually need to go through a series of process nodes before submitting the legal documents to the court, involving multiple roles such as business, operations, and lawyers. Since different cases usually have different processing procedures, it is necessary to generate a corresponding process model for each case separately so that the office automation platform can load the corresponding process model, which makes it easier for relevant personnel to perform business processing and approval of each process node through the office automation platform, thereby ensuring that each case is processed according to its corresponding processing flow. However, existing process platforms usually only support the provision of operation pages, and relevant personnel are required to manually perform a series of complex operations on the operation page to build a process model. The operation is cumbersome and requires high programming skills from relevant personnel. It is difficult to meet the needs of simply, efficiently, and flexibly building process models in frequently changing scenarios. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a process model generation method, device and electronic device to alleviate the problem that the existing process model construction method is relatively cumbersome to operate and requires high programming skills of relevant personnel.

[0004] In a first aspect, an embodiment of the present invention provides a process model generation method, the method comprising: establishing a process template and a label set corresponding to a target case; wherein the process template defines the flow direction of each node of the processing flow corresponding to the target case in the processing flow, and the label set comprises a first label representing each variable information corresponding to the target case; obtaining a voice input instruction of each node, and converting the voice input instruction into a text instruction of each node, and then converting the text instruction into a calculation rule of each node based on a mapping relationship between a pre-established text instruction and a calculation rule; obtaining original structured data corresponding to the target case, and using a preset rule engine to parse the original structured data to obtain each variable information; wherein the original structured data contains each variable information corresponding to the target case; obtaining a target label of the target variable information corresponding to each node from the label set, and configuring the input parameters of the corresponding target variable information for each node based on the corresponding target label; calculating the input parameters of the corresponding target variable information for each node according to the corresponding calculation rule to obtain the calculation result corresponding to each node, and generating a process model corresponding to the target case based on the process template and the calculation result corresponding to each node.

[0005] In a second aspect, an embodiment of the present invention further provides a process model generation device, the device comprising: a first establishment module, used to establish a process template and a label set corresponding to a target case; wherein the process template defines the flow direction of each node of the processing flow corresponding to the target case in the processing flow, and the label set includes a first label representing each variable information corresponding to the target case; a conversion module, used to obtain voice input instructions of each node, and convert the voice input instructions into text instructions of each node, and then convert the text instructions into calculation rules of each node based on a pre-established mapping relationship between text instructions and calculation rules; an acquisition module, used to obtain the target case The original structured data corresponding to the case is obtained, and a preset rule engine is used to parse the original structured data to obtain information of each variable; wherein the original structured data includes information of each variable corresponding to the target case; a first configuration module is used to obtain a target label of the target variable information corresponding to each node from the label set, and configure input parameters of the corresponding target variable information for each node based on the corresponding target label; a calculation module is used to calculate the input parameters of the corresponding target variable information for each node according to the corresponding calculation rules to obtain the calculation results corresponding to each node, and generate a process model corresponding to the target case based on the process template and the calculation results corresponding to each node.

[0006] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the process model generation method described in the first aspect above.

[0007] A process model generation method, device and electronic device provided by an embodiment of the present invention establish a process template and label set corresponding to a target case; obtain the voice input instruction of each node, and convert the voice input instruction into a text instruction of each node, and then convert the text instruction into a calculation rule of each node based on a mapping relationship between a pre-established text instruction and a calculation rule; obtain the original structured data corresponding to the target case, and use a preset rule engine to parse the original structured data to obtain each variable information; obtain the target label of the target variable information corresponding to each node from the label set, and configure the input parameters of the corresponding target variable information for each node based on its corresponding target label; calculate the input parameters of the corresponding target variable information for each node according to the corresponding calculation rule to obtain the calculation results corresponding to each node, and generate the process model corresponding to the target case based on the process template and the calculation results corresponding to each node. By adopting the above technology, the process model can be quickly generated using the established process template and label set as well as the obtained original structured data and voice input instruction. The process model generation method is simple to operate and has low programming ability requirements for relevant personnel, and can meet the needs of simply, efficiently and flexibly building process models in frequently changing scenarios.

[0008] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0009] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 A schematic diagram of a process model generation method according to an embodiment of the present invention;

[0012] Figure 2 This is an example diagram of a process model generation method in an embodiment of the present invention;

[0013] Figure 3 It is a structural schematic diagram of a process model generating device in an embodiment of the present invention;

[0014] Figure 4 The figure is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] At present, existing process platforms usually only support the provision of operation pages, and relevant personnel are required to manually perform a series of complex operations on the operation pages to build a process model. The operation is cumbersome and requires high programming capabilities of relevant personnel. It is difficult to meet the needs of simply, efficiently and flexibly building process models in frequently changing scenarios.

[0017] Based on this, the present invention provides a process model generation method, device and electronic device, which can alleviate the problem that the existing process model construction method is relatively cumbersome to operate and requires high programming skills of relevant personnel.

[0018] To facilitate understanding of this embodiment, a process model generation method disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, the method may include the following steps:

[0019] Step S102, establishing a process template and label set corresponding to the target case.

[0020] The process template defines the flow direction of each node of the processing flow corresponding to the target case in the processing flow, and the label set includes a first label representing each variable information corresponding to the target case.

[0021] In actual application, an operation interface may be provided for users, so that users can define process template related parameters on the operation interface and define or select labels on the operation interface, thereby establishing a process template and a label set.

[0022] Step S104, obtaining the voice input instructions of each node, and converting the voice input instructions into text instructions of each node, and then converting the text instructions into calculation rules of each node based on the pre-established mapping relationship between the text instructions and the calculation rules.

[0023] In actual application, a voice input device can be provided to the user so that the user can perform voice input through the voice input device, and then the voice input device can obtain voice input instructions, so as to subsequently convert the obtained voice input instructions into text instructions and then into calculation rules.

[0024] Step S106, obtaining the original structured data corresponding to the target case, and using a preset rule engine to parse the original structured data to obtain information of each variable.

[0025] Among them, the original structured data contains the variable information corresponding to the target case.

[0026] Step S108, obtaining the target label of the target variable information corresponding to each node from the label set, and configuring the input parameters of the corresponding target variable information for each node based on the corresponding target label.

[0027] Step S110, for each node, the input parameters of the corresponding target variable information are calculated according to the corresponding calculation rules to obtain the calculation results corresponding to each node, and the process model corresponding to the target case is generated based on the process template and the calculation results corresponding to each node.

[0028] A process model generation method provided by an embodiment of the present invention establishes a process template and a label set corresponding to a target case; obtains the voice input instruction of each node, and converts the voice input instruction into a text instruction of each node, and then converts the text instruction into a calculation rule of each node based on a mapping relationship between a pre-established text instruction and a calculation rule; obtains the original structured data corresponding to the target case, and uses a preset rule engine to parse the original structured data to obtain each variable information; obtains the target label of the target variable information corresponding to each node from the label set, and configures the input parameters of the corresponding target variable information for each node based on its corresponding target label; calculates the input parameters of the corresponding target variable information for each node according to its corresponding calculation rule to obtain the calculation results corresponding to each node, and generates the process model corresponding to the target case based on the process template and the calculation results corresponding to each node. By adopting the above technology, the process model can be quickly generated using the established process template and label set as well as the obtained original structured data and voice input instruction. The process model generation method is simple to operate and has low programming ability requirements for relevant personnel, and can meet the needs of simply, efficiently and flexibly building process models in frequently changing scenarios.

[0029] As a possible implementation, the above step S102 (i.e., establishing a process template and label set corresponding to the target case) may include: generating each node of the processing flow corresponding to the target case, and defining the flow direction of each node in the processing flow; generating a first label for each variable information corresponding to the target case based on a preset label database, and forming a label set with the obtained first labels; wherein a plurality of second labels are pre-stored in the preset label database.

[0030] Exemplarily, an operation interface can be provided to the user through an electronic device, and the user can perform a node adding operation of the processing flow corresponding to the target case on the operation interface. After the electronic device responds to the user's node adding operation, it generates a node of the processing flow corresponding to the target case and displays the node on the operation interface. The user can further select a node on the operation interface and perform a flow direction configuration operation on it. After the electronic device responds to the user's flow direction configuration operation on the selected node, it configures the flow direction of the selected node in the processing flow. A label database can be established in advance on the electronic device, and the label database already stores labels with some variable information. The user can perform operations on the operation interface provided by the electronic device to trigger the electronic device to directly obtain existing labels from the label database or add new labels to the label database, and then the electronic device is triggered by the user's operation on the operation interface to form a label set with the obtained existing labels and / or the added new labels.

[0031] As a possible implementation, each second label in the preset label database has a corresponding label code; wherein the label code may be a code (such as ID, name, etc.) for uniquely identifying the corresponding second label. Based on this, the above step of generating the first label of each variable information corresponding to the target case based on the preset label database may include:

[0032] Step 1: Determine the keywords of each variable information corresponding to the target case.

[0033] Continuing with the previous example, the user can select existing keywords or add new keywords on the operation interface provided by the electronic device according to the different variable information required for the case, so as to trigger the electronic device to determine the existing keywords selected by the user and / or the new keywords added by the user as the keywords of the variable information required for the case.

[0034] Step 2: For each variable information corresponding to the target case, the keyword of the variable information is used to match the second tag in the preset tag database. If the keyword of the variable information matches the corresponding second tag in the preset tag database successfully, the second tag with a tag code that successfully matches the keyword of the variable information is obtained from the preset tag database as the first tag of the variable information. If the keyword of the variable information does not match the corresponding second tag in the preset tag database successfully, a corresponding second tag is generated for the keyword of the variable information in the preset tag database, and a tag code is added to the generated second tag. Then, the second tag with a tag code corresponding to the keyword of the variable information is obtained from the preset tag database as the first tag of the variable information.

[0035] Continuing with the previous example, the user can select the corresponding keyword on the operation interface provided by the electronic device to trigger the electronic device to traverse the tag database to match the tag using each keyword selected by the user. For each keyword selected by the user, if there is a corresponding tag in the tag database that contains the keyword, the electronic device determines that the match is successful, and extracts the tag with the tag ID that is successfully matched in the tag database as the first tag. If there is no tag in the tag database that contains the keyword, the electronic device determines that the match is unsuccessful, and adds a new tag containing the keyword to the tag database, then adds a tag ID to the new tag, and extracts the new tag with the tag ID that is newly added to the tag database as the first tag.

[0036] As a possible implementation, the process model generation method may further include: using a preset rule engine to establish a mapping relationship between text instructions and calculation rules.

[0037] The rule engine may be QLExpress, Aviator, Drools, RuleBook, Easy Rules, etc., without limitation.

[0038] Taking QLExpress as an example, in QLExpress, a mapping relationship between text instructions and calculation rules can be established by customizing functions for text instructions as calculation rules, so that after QLExpress obtains text instructions converted from voice instructions input by the user, it can use the mapping relationship to map the obtained text instructions to the corresponding function for calculation.

[0039] In the case where the rule engine adopts QLExpress, each calculation rule corresponds to a corresponding function, and the mapping relationship between text instructions and calculation rules can be established in the following manner: define one or more specific text instructions in QLExpress according to business needs, define one or more corresponding natural language instructions for each specific text instruction, and write mapping rules to associate each specific text instruction with its corresponding one or more natural language instructions, thereby establishing a mapping relationship between natural language instructions and specific text instructions; define a corresponding specific function for each specific text instruction in QLExpress, and write mapping rules in the form of QL expressions in QLExpress, the written mapping rules can associate each specific text instruction with its corresponding specific function, thereby establishing a mapping relationship between specific text instructions and specific functions; register these specific functions in the context of QLExpress so that these specific functions can be called for calculation using QL expressions. After receiving a natural language instruction converted from a voice instruction input by a user, QLExpress can use the mapping relationship between natural language instructions and specific text instructions to map the voice instruction into a corresponding text instruction, and then use QLExpress to use the mapping relationship between specific text instructions and specific functions to execute the corresponding QL expression to call the function corresponding to the text instruction in the QL expression for calculation.

[0040] Accordingly, in the above step S104, converting the text instructions into calculation rules for each node based on the pre-established mapping relationship between the text instructions and the calculation rules may include: for each node, using a preset rule engine to obtain the text instructions of the node, and using the preset rule engine to map the text instructions of the node into corresponding calculation rules based on the mapping relationship.

[0041] Continuing with the previous example, when a mapping relationship between text instructions and functions is established in QLExpress, QLExpress can collect the text instructions converted from the voice instructions of each node input by the user and use the established mapping relationship to map the text instructions of each node into a corresponding function for subsequent function calculation.

[0042] As a possible implementation, the rule engine may adopt QLExpress, and the original structured data may adopt structured table data; based on this, the above-mentioned process model generation method may also include: configuring a parser corresponding to each variable information for QLExpress, and configuring a parsing expression for the corresponding variable information for each parser.

[0043] Accordingly, in the above step S106, using a preset rule engine to parse the original structured data to obtain each variable information may include: using each parser configured by QLExpress to parse the structured table data according to the parsing expression of the corresponding variable information, so as to parse out each variable information corresponding to the target case.

[0044] Continuing with the previous example, in QLExpress, corresponding parsers can be configured for different variable information required for the case and corresponding QL expressions can be configured for each parser. After QLExpress obtains the structured table data (such as Excel tables, etc.) containing different variable information required for the case, QLExpress can use the corresponding parser to parse and calculate the obtained structured table data according to the corresponding QL expression to obtain the variable information required for the case.

[0045] As a possible implementation, configuring the input parameters of the corresponding target variable information for each node based on its corresponding target tag in the above step S108 may include: for each node, if the target variable information corresponding to the node includes construction period information, then configuring the input parameters of the construction period information corresponding to the node based on the first tag of the construction period information corresponding to the node is the construction period duration and construction period type.

[0046] The duration information can be used to determine whether the transaction processed by the corresponding node is overdue.

[0047] Continuing with the previous example, when Flowable is pre-configured as a process engine on an electronic device, the user can configure the duration of a node to be 2 days and the duration type of the node to be a standard working day on the operation interface provided by the electronic device. In response to the configuration operation, the electronic device configures the node flowable:dueDate="P2D" and flowable:dueDateType="workday" through Flowable.

[0048] Correspondingly, in the above step S110, the input parameters of the corresponding target variable information of each node are calculated according to the corresponding calculation rules to obtain the calculation results corresponding to each node, which may include: for each node, if the target variable information corresponding to the node includes construction period information, then the construction period duration and construction period type of the construction period information corresponding to the node are calculated according to the calculation rules corresponding to the node to generate the deadline corresponding to the node.

[0049] Continuing with the previous example, the electronic device may call the function corresponding to the corresponding duration information for the node configured with flowable:dueDate="P2D" and flowable:dueDateType="workday" through Flowable to perform calculations to convert the corresponding duration label into the deadline corresponding to the node.

[0050] As a possible implementation, generating a process model corresponding to the target case based on the process template and the calculation results corresponding to each node in the above step S110 may include:

[0051] Step A1, based on the flow direction of each node in the processing flow defined in the flow template, configure corresponding output parameters for each node.

[0052] Exemplarily, a preset process engine can be used to configure the corresponding output parameters for each node based on its flow direction. For example, the process engine uses Flowable, and for each node in the processing flow corresponding to the case, the output parameters of the node can be configured in Flowable according to the flow direction of the node, including the output result can only be true or false, the judgment conditions that the output result needs to meet, and the node ID of the next node to which the output result flows.

[0053] Step A2: Generate a process model based on the process template, the output parameters corresponding to each node, and the calculation results corresponding to each node.

[0054] Exemplarily, a preset process engine can be used to bind the process template with the output parameters and calculation results corresponding to each node to generate a process model. For example, since the flow direction of each node in the processing flow corresponding to the case has been defined in the process template, that is, the path position of each node is known, it is only necessary to bind the calculation result, output parameter and path position corresponding to the same node to determine the processing logic and flow direction of each node. In addition, since each process template can also have its own template parameters (such as template ID, template name, node ID, text mark, color mark, graphic mark, etc.), the result obtained by binding the process template with template parameters can be used as a process model.

[0055] For ease of understanding, the operation of the above process model generation method is described below by taking a specific application as an example.

[0056] See also Figure 2 As shown, the above process model generation method can be performed in the following operation modes:

[0057] Step 1: Create a process template, customize labels, and establish a label library.

[0058] Taking Business Process Modeling Notation (BPMN) as an example, BPMN mainly defines three basic elements: Activities (also known as tasks), Gateways (also known as routes), and Events. BPMN can be used to standardize the execution semantics and format of Flowable to describe the actual business process through standard graphics. Then, users can create process templates in Flowable in the form of standard graphics. The process template expresses the position of each process node in the overall process in the form of standard graphics and defines the flow direction of each process node in the form of routes.

[0059] Users can customize labels on the front-end interface, such as duration, case amount, case cycle, case type, required repayment interest rate, etc., and then establish a database as a label library to store customized labels. Before defining the duration, in order to realize the function of judging whether the transaction processed by the node is overdue, it is necessary to first formulate a basic work calendar based on statutory holidays, and then connect to the office automation interface to synchronize overtime, leave and other information to the work calendar; when defining the duration, users can configure the number of days and type of the duration on the front-end interface to trigger the background to set the respective values ​​of dueDate and dueDateType for the User Task node in the XML (ExtensibleMarkup Language) corresponding to BPMN. The background can also save the configured label parameters (such as dueDate and dueDateType of the duration) in the specified database, so that the required label parameters can be directly called from the database later.

[0060] After the tag library is established, you can also configure the corresponding tag processor for the specified tag, so that the backend can call the corresponding tag processor to process the specified tag. For example, the backend can obtain the specified tag from the tag library and match the obtained specified tag to the corresponding tag processor for processing. If the tag is a duration tag, you need to match the duration tag with the corresponding tag processor to convert the duration tag into the corresponding deadline so that the deadline can be parsed later.

[0061] In addition, each customized label needs to be compared with the existing labels in the label library before being added to the label library. If the existing labels in the label library match the customized labels, the matching labels can be directly stored in the customized process label table, and the ID of the corresponding process template and the ID of the corresponding node are associated with the labels stored in the process label table and saved. If the existing labels in the label library do not match the customized labels, the customized labels will be added to the label library, and the customized labels will be stored in the customized process label table, and the template ID of the corresponding process template and the node ID of the corresponding node will be associated with the labels stored in the process label table and saved. After the subsequent process is started, before each task starts, you can find the corresponding custom label information in the process label table based on the template ID and node ID, and get the corresponding label parameters from the database, so as to perform relevant calculations based on the task-related parameters and label parameters; for example, for a node task that needs to be judged whether it is overdue, before the node task starts, you can find the corresponding custom label information in the process label table based on the template ID and node ID associated with the duration, and get the corresponding dueDate and dueDateType values ​​from the database. Then, you can compare the work calendar with the start time of the node task and the current time to determine whether the transaction processed by the process node that executes the node task is overdue, and then perform subsequent operations. Here, the subsequent operation can be to send a message to the person responsible for the task and the corresponding supervisor, and reduce business risks through message reminders.

[0062] Step 2: Establish a mapping relationship between text instructions and calculation rules, convert the voice instructions input by the user into text instructions, and convert the text instructions into calculation rules.

[0063] QLExpress can be used to establish a mapping relationship between text instructions and functions. After the user inputs a voice instruction for a specified node, the voice instruction can be converted into a corresponding text instruction through the voice recognition engine. QLExpress can use the established mapping relationship to map the text instruction into a corresponding function as the calculation rule corresponding to the specified node.

[0064] Step three, obtain structured table data containing variable information required for the case, and parse the structured table data to obtain the variable information required for the case.

[0065] You can configure the parsers corresponding to different variable information and the QL expressions corresponding to different parsers in QLExpress. After obtaining the structured data required for the case, QLExpress can parse the structured data according to the corresponding QL expressions through the corresponding parsers to obtain the variable information required for the case. For example, the case structured data file (such as Excel file) provided by the bank will contain some fixed header information (such as amount, case commission cycle, exemption policy, etc.). QLExpress can obtain the parser that matches the file format according to the file format represented by the corresponding structured data file suffix to parse the structured data file to parse the required variable information.

[0066] Step 4: Generate a process model based on the process template, calculation rules and required variable information.

[0067] In Flowable, you can configure the output parameters of the process nodes according to the flow direction of the process nodes defined in the process template. The output parameters can be the output data type, output data content, judgment conditions before output, the node ID of the next process node to which the output data will flow, etc.; after using QLExpress to parse the required variable information, you can use QLExpress to perform calculations according to the corresponding functions based on the variable information corresponding to each process node to obtain the calculation results corresponding to each process node, and then use Flowable to bind the calculation results, output parameters and path positions corresponding to the same process node to the corresponding template ID to obtain the process model.

[0068] In addition, in order to increase the scalability of the system, the defined QL expressions can also be stored in the database, and the QL expressions can be replaced by the database in real time to achieve updates, thereby improving the flexibility of generating process models.

[0069] Based on the above process model generation method, the embodiment of the present invention also provides a process model generation device, see Figure 3 As shown, the device may include:

[0070] The first establishment module 302 is used to establish a process template and a label set corresponding to the target case; wherein the process template defines the flow direction of each node of the processing flow corresponding to the target case in the processing flow, and the label set includes a first label representing each variable information corresponding to the target case.

[0071] The conversion module 304 is used to obtain the voice input instructions of each node, and convert the voice input instructions into text instructions of each node, and then convert the text instructions into calculation rules of each node based on the pre-established mapping relationship between the text instructions and the calculation rules.

[0072] The acquisition module 306 is used to acquire the original structured data corresponding to the target case, and parse the original structured data using a preset rule engine to acquire information of each variable; wherein the original structured data includes information of each variable corresponding to the target case.

[0073] The first configuration module 308 is used to obtain the target label of the target variable information corresponding to each node from the label set, and configure the input parameters of the corresponding target variable information for each node based on the corresponding target label.

[0074] The calculation module 310 is used to calculate the input parameters of the corresponding target variable information for each node according to the corresponding calculation rules to obtain the calculation results corresponding to each node, and generate the process model corresponding to the target case based on the process template and the calculation results corresponding to each node.

[0075] By using the above-mentioned process model generation device, the process model can be quickly generated using the established process template and label set as well as the acquired original structured data and voice input instructions. This process model generation method is simple to operate and has low requirements on the programming ability of relevant personnel. It can meet the needs of simply, efficiently and flexibly constructing process models in frequently changing scenarios.

[0076] The above-mentioned first establishment module 302 can also be used to: generate each node of the processing flow corresponding to the target case, and define the flow direction of each node in the processing flow; generate the first label of each variable information corresponding to the target case based on the preset label database, and form the obtained first label into the label set; wherein, multiple second labels are pre-stored in the preset label database.

[0077] Each second tag in the above-mentioned preset tag database can correspond to a corresponding tag code; based on this, the above-mentioned first establishment module 302 can also be used to: determine the keywords of each variable information corresponding to the target case; for each variable information corresponding to the target case, use the keyword of the variable information to match the second tag in the preset tag database; if the keyword of the variable information successfully matches the corresponding second tag in the preset tag database, then obtain the second tag with a tag code that successfully matches the keyword of the variable information from the preset tag database as the first tag of the variable information; if the keyword of the variable information does not successfully match the corresponding second tag in the preset tag database, then generate a corresponding second tag for the keyword of the variable information in the preset tag database and add a tag code to the generated second tag, and then obtain the second tag with a tag code corresponding to the keyword of the variable information from the preset tag database as the first tag of the variable information.

[0078] The first configuration module 308 can also be used for: for each node, if the target variable information corresponding to the node includes duration information, then based on the first label of the duration information corresponding to the node, the input parameters of the duration information corresponding to the node are configured as duration duration and duration type. Correspondingly, the calculation module 310 can also be used for: for each node, if the target variable information corresponding to the node includes duration information, then according to the calculation rule corresponding to the node, the duration duration and duration type of the duration information corresponding to the node are calculated to generate the deadline corresponding to the node.

[0079] The rule engine can use QLExpress, and the original structured data can use structured table data, see Figure 3 As shown, the device method may also include:

[0080] The second configuration module 312 is used to configure the parser corresponding to each variable information for QLExpress, and configure the parsing expression of the corresponding variable information for each parser.

[0081] The acquisition module 306 can also be used to: use each parser configured by QLExpress to parse the structured table data according to the parsing expression of the corresponding variable information, so as to parse out each variable information corresponding to the target case.

[0082] See also Figure 3 As shown, the device method may also include:

[0083] The second establishing module 314 is used to establish a mapping relationship between text instructions and calculation rules using a preset rule engine.

[0084] The above conversion module 304 can also be used to: for each node, use a preset rule engine to obtain the text instruction of the node, and use the preset rule engine to map the text instruction of the node into a corresponding calculation rule based on the mapping relationship.

[0085] The above-mentioned calculation module 310 can also be used to: configure corresponding output parameters for each node based on the flow direction of each node defined in the process template in the processing flow; generate the process model based on the process template, the output parameters corresponding to each node and the calculation results corresponding to each node.

[0086] The calculation module 310 may also be used to: use a preset process engine to configure corresponding output parameters for each node based on its flow direction.

[0087] The calculation module 310 may also be used to: use a preset process engine to bind the process template with the output parameters and calculation results corresponding to each node to generate the process model.

[0088] The process model generation device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned process model generation method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0089] The embodiment of the present invention further provides an electronic device, such as Figure 4 As shown, it is a schematic diagram of the structure of the electronic device, wherein the electronic device includes a processor 41 and a memory 40, the memory 40 stores computer executable instructions that can be executed by the processor 41, and the processor 41 executes the computer executable instructions to implement the above-mentioned process model generation method.

[0090] exist Figure 4 In the illustrated embodiment, the electronic device further includes a bus 42 and a communication interface 43 , wherein the processor 41 , the communication interface 43 and the memory 40 are connected via the bus 42 .

[0091] Among them, the memory 40 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 43 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used. The bus 42 can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 42 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0092] The processor 41 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 41 or the instruction in the form of software. The above processor 41 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor 41 reads the information in the memory and completes the steps of the process model generation method of the above embodiment in combination with its hardware.

[0093] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0094] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that can be executed by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., various media that can store program codes.

[0095] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0096] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A process model generation method, characterized in that: The method comprises: Establishing a process template and a label set corresponding to the target case; wherein the process template defines the flow direction of each node of the processing process corresponding to the target case in the processing process, and the label set includes a first label representing each variable information corresponding to the target case; Acquire the voice input instruction of each node, and convert the voice input instruction into a text instruction of each node, and then convert the text instruction into a calculation rule of each node based on a pre-established mapping relationship between the text instruction and the calculation rule; Obtaining original structured data corresponding to the target case, and using a preset rule engine to parse the original structured data to obtain information of each variable; wherein the original structured data includes information of each variable corresponding to the target case; Obtaining a target label of the target variable information corresponding to each node from the label set, and configuring an input parameter of the corresponding target variable information for each node based on the target label corresponding to the node; The input parameters of the corresponding target variable information are calculated for each node according to the corresponding calculation rules to obtain the calculation results corresponding to each node, and the process model corresponding to the target case is generated based on the process template and the calculation results corresponding to each node.

2. The method according to claim 1, characterized in that Create a process template and label set corresponding to the target case, including: Generate each node of the processing flow corresponding to the target case, and define the flow direction of each node in the processing flow; A first label for each variable information corresponding to the target case is generated based on a preset label database, and the obtained first labels are used to form the label set; wherein a plurality of second labels are pre-stored in the preset label database.

3. The method according to claim 2, characterized in that Each second tag in the preset tag database has a corresponding tag code; Generate a first label of each variable information corresponding to the target case based on a preset label database, including: Determine the keywords of each variable information corresponding to the target case; For each variable information corresponding to the target case, the keyword of the variable information is used to match the second tag in the preset tag database. If the keyword of the variable information matches the corresponding second tag in the preset tag database successfully, the second tag with a tag code that successfully matches the keyword of the variable information is obtained from the preset tag database as the first tag of the variable information. If the keyword of the variable information does not match the corresponding second tag in the preset tag database successfully, the corresponding second tag is generated for the keyword of the variable information in the preset tag database, and the tag code is added to the generated second tag. Then, the second tag with a tag code corresponding to the keyword of the variable information in the preset tag database is used as the first tag of the variable information.

4. The method according to claim 1, characterized in that: Configure the input parameters of the corresponding target variable information for each node based on its corresponding target label, including: For each node, if the target variable information corresponding to the node includes duration information, then based on the first label of the duration information corresponding to the node, the input parameters of the duration information corresponding to the node are configured as duration length and duration type; For each node, the input parameters of the corresponding target variable information are calculated according to the corresponding calculation rules to obtain the calculation results corresponding to each node, including: For each node, if the target variable information corresponding to the node includes duration information, the duration and duration type of the duration information corresponding to the node are calculated according to the calculation rule corresponding to the node to generate the deadline corresponding to the node.

5. The method according to claim 1, characterized in that The rule engine uses QLExpress, the original structured data uses structured table data, and the method further includes: Configure the parser corresponding to each variable information for QLExpress, and configure the parsing expression of the corresponding variable information for each parser; The original structured data is parsed using a preset rule engine to obtain information about each variable, including: Each parser configured by QLExpress parses the structured table data according to the parsing expression of its corresponding variable information to parse out each variable information corresponding to the target case.

6. The method according to claim 1 or 5, characterized in that: The method further comprises: A preset rule engine is used to establish a mapping relationship between text instructions and calculation rules; The text instructions are converted into calculation rules for each node based on a pre-established mapping relationship between the text instructions and the calculation rules, including: For each node, a preset rule engine is used to obtain the text instruction of the node, and the preset rule engine is used to map the text instruction of the node into a corresponding calculation rule based on the mapping relationship.

7. The method according to claim 1, characterized in that Generate a process model corresponding to the target case based on the process template and the calculation results corresponding to each node, including: Based on the flow direction of each node defined in the process template in the processing flow, configure corresponding output parameters for each node; The process model is generated based on the process template, the output parameters corresponding to each node, and the calculation results corresponding to each node.

8. The method according to claim 7, characterized in that Based on the flow direction of each node defined in the process template in the processing flow, corresponding output parameters are configured for each node, including: Use the preset process engine to configure the corresponding output parameters for each node based on its flow direction; Generating the process model based on the process template, the output parameters corresponding to each node and the calculation results corresponding to each node includes: The process template is bound to the output parameters and calculation results corresponding to each node using a preset process engine to generate the process model.

9. A process model generation device, characterized in that: The device comprises: A first establishment module is used to establish a process template and a label set corresponding to the target case; wherein the process template defines the flow direction of each node of the processing process corresponding to the target case in the processing process, and the label set includes a first label representing each variable information corresponding to the target case; A conversion module, used to obtain the voice input instruction of each node, and convert the voice input instruction into a text instruction of each node, and then convert the text instruction into a calculation rule of each node based on a pre-established mapping relationship between the text instruction and the calculation rule; An acquisition module, used to acquire original structured data corresponding to the target case, and parse the original structured data using a preset rule engine to acquire information of each variable; wherein the original structured data includes information of each variable corresponding to the target case; A first configuration module is used to obtain a target label of the target variable information corresponding to each node from the label set, and configure input parameters of the corresponding target variable information for each node based on the corresponding target label; The calculation module is used to calculate the input parameters of the corresponding target variable information for each node according to the corresponding calculation rules to obtain the calculation results corresponding to each node, and generate the process model corresponding to the target case based on the process template and the calculation results corresponding to each node.

10. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 8.

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