CN117931143A - Intelligent approval method, device, equipment and medium based on meta model - Google Patents
Intelligent approval method, device, equipment and medium based on meta model Download PDFInfo
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Abstract
The application provides an intelligent examination and approval method, device, equipment and medium based on a meta model, wherein an examination and approval stream of the meta model is constructed by responding to a construction instruction, the examination and approval stream of the meta model comprises an application node, an approval node and a gateway node, the application node comprises a first UserTask meta model, the approval node comprises a second UserTask meta model, the gateway node comprises a gateway meta model, the meta model examination and approval stream is started by responding to the input instruction, examination and approval contents are generated through the first UserTask meta model, an examination and approval result is generated according to the examination and approval contents, the gateway meta model and the second UserTask meta model, and the meta model examination and approval stream is constructed based on the meta model, so that the method is more convenient and faster compared with a BPM2.0 model, the cost is reduced, and the system integration deployment is facilitated.
Description
Technical Field
The present application relates to the field of computers, and in particular, to an intelligent approval method, apparatus, device, and medium based on a meta model.
Background
At present, the traditional approval flow method abstracts the approval flow process into a BPM2.0 standard model, and realizes the state flow and approval execution of the approval flow process by combining the state of a database storage related model with service data, while the BPM2.0 model abstraction is very complex and huge in quantity, and for a user, the learning cost and the use cost are relatively high. Meanwhile, the code volume of the whole approval flow engine is huge due to the BPM2.0 model, so that the system integration deployment is not facilitated, and certain flexibility is lacked when the system integration is performed with a third party system, and a higher technical threshold is needed.
Disclosure of Invention
The embodiment of the application provides an intelligent examination and approval method, device, equipment and medium based on a meta model, which are used for solving at least one problem existing in the related technology, and the technical scheme is as follows:
in a first aspect, an embodiment of the present application provides an intelligent approval method based on a meta-model, including:
Responding to a construction instruction, constructing a meta-model approval flow, wherein the meta-model approval flow comprises an application node, an approval node and a gateway node, the application node comprises a first UserTask meta-model, the approval node comprises a second UserTask meta-model, and the gateway node comprises a gateway meta-model;
Responding to an input instruction, starting the meta-model approval flow, and generating approval content through the first UserTask meta-model;
And generating an approval result according to the approval content, the gateway meta-model and the second UserTask meta-model.
In one embodiment, the generating approval content by the first UserTask-tuple model includes:
acquiring input application information through the first UserTask-element model;
according to the application information, constructing approval data in a preset format of field keywords and values corresponding to the field keywords;
And generating the approval content according to the approval data and the model ID of the first UserTask-membered model.
In one embodiment, when the gateway node includes parallel gateway sub-nodes and merge gateway sub-nodes, the generating the approval result according to the approval content, the gateway meta-model, and the second UserTask meta-model includes:
When the number of the approval nodes is multiple, determining a plurality of target approval nodes according to the model ID, and transmitting the approval contents to different target approval nodes through the gateway meta-model of the parallel gateway sub-nodes in parallel;
obtaining a plurality of sub-approval results according to the second UserTask th element models of different target approval nodes and the approval contents;
And merging the sub-approval results through a gateway meta-model of the merging gateway sub-node to generate the approval results.
In one embodiment, when the gateway node includes an inside and outside condition gateway node, the generating the approval result according to the approval content, the gateway meta-model, and the second UserTask meta-model includes:
determining an application type corresponding to the approval content according to a gateway meta-model of the internal and external condition gateway node and the approval content, wherein the application type comprises an external application and an internal application;
determining a target approval node according to the application type and the model ID;
and transmitting the approval content to a target approval node through the gateway meta-model of the internal and external condition gateway node so as to generate an approval result through a second UserTask meta-model of the target approval node.
In one embodiment, the obtaining a plurality of sub-approval results according to the second UserTask meta-model of the different target approval nodes and the approval content includes:
acquiring the approval content and the component type corresponding to the approval content on a display page through an approval flow engine;
Rendering the approval content according to the component type, and displaying the display pages in different target approval nodes;
and responding to different approval instructions of the display page to obtain a plurality of sub-approval results.
In one embodiment, the building a metamodel approval stream in response to the build instruction includes:
responding to a first construction sub-instruction, establishing the application node, the approval node and the gateway node, and correspondingly generating model information of a first UserTask-element model, a second UserTask-element model and a gateway meta-model, wherein the model information comprises a model ID and a meta-model type identifier;
responding to the second construction sub-instruction, and generating a plurality of connection meta-models;
Constructing a meta-model approval stream according to the connecting meta-model, the application node, the approval node and the gateway node;
The connection meta-model characterizes the sequential relation of each meta-model in the meta-model approval flow, and the connection starting point and the connection ending point of the connection meta-model are model IDs of different meta-models.
In one embodiment, the method further comprises:
Acquiring node information of all child nodes of the application node from a memory according to the model information;
constructing an attribute SQL database execution language according to the node information;
And performing database persistence mapping according to the attribute SQL database execution language.
In a second aspect, an embodiment of the present application provides an intelligent approval apparatus based on a meta model, including:
The first response module is used for responding to the construction instruction to construct a meta-model approval flow, wherein the meta-model approval flow comprises an application node, an approval node and a gateway node, the application node comprises a first UserTask meta-model, the approval node comprises a second UserTask meta-model, and the gateway node comprises a gateway meta-model;
the second response module is used for responding to an input instruction, starting the meta-model approval flow and generating approval contents through the first UserTask meta-model;
And the generation module is used for generating an approval result according to the approval content, the gateway meta-model and the second UserTask meta-model.
In one embodiment, the first response module is further configured to:
Acquiring node information of all child nodes of the application node from a memory according to the model information;
Constructing an attribute SQL database execution language;
and performing database persistence mapping according to the attribute SQL database execution language, the model information of the application node and the node information.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory in which instructions are stored, the instructions being loaded and executed by the processor to implement the method of any of the embodiments of the above aspects.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program, which when executed implements a method in any one of the embodiments of the above aspects.
The beneficial effects in the technical scheme at least comprise:
The meta-model approval flow is built by responding to the building instruction, the meta-model approval flow comprises an application node, an approval node and a gateway node, the application node comprises a first UserTask meta-model, the approval node comprises a second UserTask meta-model, the gateway node comprises a gateway meta-model, the meta-model approval flow is started by responding to the input instruction, approval contents are generated through the first UserTask meta-model, approval results are generated according to the approval contents, the gateway meta-model and the second UserTask meta-model, and the meta-model approval flow is built based on the meta-model, so that the method is more convenient and faster than the BPM2.0 model, is beneficial to reducing cost and is convenient for system integration deployment.
The foregoing summary is for the purpose of the specification only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present application will become apparent by reference to the drawings and the following detailed description.
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In the drawings, the same reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily drawn to scale. It is appreciated that these drawings depict only some embodiments according to the disclosure and are not therefore to be considered limiting of its scope.
FIG. 1 is a flowchart illustrating steps of an intelligent approval method based on a meta-model according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a meta-model approval flow according to an embodiment of the present application;
FIG. 3 is a block diagram of an intelligent approval apparatus based on meta-model according to an embodiment of the present application;
fig. 4 is a block diagram of an electronic device according to an embodiment of the application.
Detailed Description
Hereinafter, only certain exemplary embodiments are briefly described. As will be recognized by those of skill in the pertinent art, the described embodiments may be modified in various different ways without departing from the spirit or scope of the present application. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.
Referring to fig. 1, a flowchart of a meta-model based intelligent approval method according to an embodiment of the present application is shown, where the meta-model based intelligent approval method may at least include steps S100-S300:
S100, building a meta-model approval stream in response to the building instruction.
In one embodiment, the meta-model approval flow in fig. 2 is illustrated as an example, and in other embodiments, the meta-model approval flow may take other forms. Optionally, as shown in fig. 2, the meta-model approval flow includes a START node (START) 201, an application node (applicant) 202, approval nodes 203, 204, 205 (manager approval, department manager approval, and industry manager approval), gateway nodes (including parallel gateway child nodes 206 (parallel gateway-parallel), merge gateway child nodes 207 (parallel gateway-merge), inside and outside conditional gateway nodes (outside internal conditional gateway) 208, and END Node (END) 209; meanwhile, the meta-model approval stream further includes several wired meta-models 210 (not all labeled). It should be noted that, in some embodiments, the application node may be directly used as a start node, and the gateway node or the approval node may be used as an end node, where the start node and the end node are not set.
It should be noted that, when each node is constructed based on the meta-model, model information of the meta-model is correspondingly generated, including, but not limited to, a name, a model ID, a meta-model type identifier (MetaType), a meta-model service identifier (kined, which is not necessary for some meta-models, but is not necessary for some meta-models, where a representative field is used, a modeler may be assisted in communicating with a service person, and in a development stage, a code is passed to obtain a corresponding service value object with the information), and a partentid (a parent node ID of a service model tree where the model is located in the meta-model in the modeling process, and when the meta-model is retrieved in a memory, a dependency relationship of the meta-model may be defined). For example, the START node (START) 201 includes a START meta-model, the application node (applicant) 202 includes a first UserTask meta-model, each of the approval nodes includes a second UserTask meta-model, each of the second UserTask meta-models further includes an approval button (Action meta-model), the parallel gateway sub-node 206 and the gateway meta-model of the merging gateway sub-node 207 are PARALLELGATEWAY meta-models, the gateway meta-model of the internal and external condition gateway node 208 is Condition Gateway meta-models, the END Node (END) 209 includes an END meta-model, the connection meta-model 210 may include Forward links or cancer links, the connection meta-model characterizes a sequential relationship of each meta-model in the meta-model approval stream, and the connection START point and the connection END point of the connection meta-model are model IDs of different meta-models. For example, as shown in fig. 2, a connection start point of one of the connection meta-models 210 is a model ID of a meta-model of the internal and external condition gateway node 208, and a connection end point is a model ID of a meta-model of the approval node 203, in the meta-model approval flow, if a result of an application type obtained by the internal and external condition gateway node 208 is an external application, a next node reached at this time is the approval node 203. Therefore, the approval flow engine can know which node is the next node of the approval flow based on the model ID of the meta-model and the connection meta-model, so that the approval flow is smoothly carried out. Optionally, the wiring meta-model may revoke the wiring by Cancel wiring.
For example, the model information of the start node 201 may include:
for example, the model information of the application node 202 may include:
In the embodiment of the application, the two-dimensional array storage tree structure data is designed through ParentId fields. Providing performance support for accessing the data in the digital structure.
And S200, responding to an input instruction, starting a meta-model approval flow, and generating approval contents through a first UserTask-element model.
And S300, generating an approval result according to the approval content, the gateway meta-model and the second UserTask-element model.
The intelligent examination and approval method based on the meta model can be executed by an electronic control unit, a controller, a processor and the like of a terminal such as a computer, a mobile phone, a tablet, a vehicle-mounted terminal and the like, and also can be executed by a cloud server, for example, by a system in the cloud server.
According to the technical scheme, the meta-model approval flow is constructed in response to the construction instruction, the meta-model approval flow comprises an application node, an approval node and a gateway node, the application node comprises a first UserTask-element model, the approval node comprises a second UserTask-element model, the gateway node comprises a gateway meta-model, the meta-model approval flow is started in response to the input instruction, an intelligent approval flow is realized, approval content is generated through the first UserTask-element model, an approval result is generated according to the approval content, the gateway meta-model and the second UserTask-element model, and the meta-model approval flow is constructed based on the meta-model, so that the method is more convenient and faster than a BPM2.0 model, is beneficial to reducing cost and facilitates system integration deployment; and the system is more flexible, and reduces the technical threshold when being integrated with a third party system.
In one embodiment, step S100 includes steps S110-S130:
And S110, responding to the first construction sub-instruction, establishing an application node, an approval node and a gateway node, and correspondingly generating model information of a first UserTask-element model, a second UserTask-element model and a gateway element model.
As shown in FIG. 2, a user may operate in the system to generate build instructions, such as a first build sub-instruction and a second build sub-instruction. Optionally, the system establishes a START node (START) 201, an application node (applicant) 202, approval nodes 203, 204, 205 (manager approval, department manager approval, and industry manager approval), gateway nodes (including parallel gateway child nodes 206 (parallel gateway-parallel), merge gateway child nodes 207 (parallel gateway-merge), inside and outside conditional gateway nodes (outside internal conditional gateway) 208, and END Nodes (END) 209 in response to the first build sub-instruction, and generates model information of the corresponding meta-model in each node (as described above).
And S120, responding to the second construction sub-instruction, and generating a plurality of connection meta-models.
Optionally, as shown in FIG. 2, a number of wiring metamodels 210 are generated in response to the second build sub-instruction.
S130, constructing a meta-model approval stream according to the connecting meta-model, the application node, the approval node and the gateway node.
Optionally, the approval stream engine constructs a meta-model approval stream based on the contents of the connection meta-model, the application node, the approval node, the gateway node, the start node, the end node, and the like.
It should be noted that, the user may operate in a keyboard, a mouse, a touch screen, etc. in the system, so as to generate various instructions in the system, such as a construction instruction, an input instruction, etc.
In one embodiment, in step S200, a user (applicant) may input application information by manipulating to generate an input instruction, and the system initiates a meta-model approval stream in response to the input instruction. For convenience of description, the embodiment of the present application is described with reference to the examination and approval stream of leave-out, and other embodiments may be other examination and approval streams.
In one embodiment, when the meta-model approval flow is started, the approval flow engine calculates path information (a visualized model tree path) of each node and the meta-model, determines an execution sequence, and uses the determined execution sequence as analysis key information of the memory meta-model to perform aggregation information processing basis of dynamic content. For example, the path information may include:
Id Primary key information
-Kind node identification key
-Parentkey parent node id
Orientationkey Path to root node (made up of node ids by concatenation string "-")
In one embodiment, in step S200, generating approval content via the first UserTask-tuple model includes steps S210-S230:
s210, acquiring input application information through a first UserTask-element model.
For example, the applicant enters application information in a first UserTask meta-model of application node 202:
name of the leave-applicant information
Vacation: leave days
StartDay: leave-on date
EndDay: leave over date.
After inputting the application information, the first UserTask meta-model also provides the application button: the START node is connected and used as a starting node of the approval flow, and the button is withdrawn: the pre-application state may be withdrawn from the next node state to which it is connected, the hold button: the application may be saved as a pre-application state.
S220, according to the application information, constructing approval data in a preset format of the field keywords and values corresponding to the field keywords.
In the embodiment of the application, in order to transfer the context of the approval data in the approval flow, a preset format for construction of the approval data is set: the field key and the value corresponding to the field key, for example, the approval data are: [ { name: zhang San, vacation:3, startday:2023/12, endday:2023/12/14,
{ Name: zhang San vacation:1, startday:2023/12/16, endday: 2023/12/16).
Compared with the traditional mode (new application information), the method can avoid the unpacking and boxing processes in the business object processing process, thereby improving the processing performance.
And S230, generating approval content according to the approval data and the model ID of the first UserTask-membered model.
For example, assuming that the Model ID of the first UserTask meta-Model is/a/b/d, finally obtaining the business content of the context, that is, the approval content, where the approval content is a Model meta-Model, the Model meta-Model has a corresponding ModelID, and implementing the business and data decoupling through the basic data type of the Map structure. For example, the approval is:
{
A/b/d: [ { name: zhang San, vacation:3, startday:2023/12, endday:2023/12/14},
{ Name: zhang San vacation:1, startday:2023/12/16, endday:2023/12/16 })
ModelID2:[…]
}
In one embodiment, step S300 may include steps S310A-S330A:
And S310A, when the number of the approval nodes is multiple, determining a plurality of target approval nodes according to the model ID, and transmitting approval contents to different target approval nodes through the gateway meta-model of the parallel gateway sub-nodes.
For example, as shown in fig. 2, a plurality of approval nodes, such as approval nodes 204, 205, etc., may be included, and at this time, if the determination result of the internal and external conditional gateway node 208 is an internal application, a plurality of target approval nodes may be determined according to the model ID of the gateway meta-model of the internal and external conditional gateway node 208. Alternatively, the model ID of the gateway meta-model of the inside and outside condition gateway node 208 may be determined using the link meta-model 210 to determine the model ID of the link endpoint, for example, the ID of the second UserTask meta-model to the approval node 204, 205 may be determined to determine the approval node 204, 205 as the target approval node. The approval content is then transferred in parallel through the gateway metamodel of the parallel gateway sub-node 206 to different target approval nodes, such as to the approval nodes 204, 205.
S320A, obtaining a plurality of sub-approval results according to the second UserTask th yuan model and approval contents of different target approval nodes.
In the embodiment of the present application, the second UserTask meta-model has an approval button (Action meta-model), so that in the approval nodes 204, 205, the department manager and the industry approval manager can perform approval operation through the approval button (Action meta-model) to obtain a plurality of corresponding sub-approval results, which can be approval or rejection, for example.
Optionally, when the approval stream carries out context transfer, the approval stream engine acquires approval content and component types component corresponding to the approval content on the display page, wherein each item of data content in the approval content can have corresponding different components, and the components can be preset in advance. Then, the approval flow engine associates the approval content with the approval content according to the component type component, renders the approval content as basic information of rendering according to the component type component and the approval content, displays the display pages in different target approval nodes, and after the approval content is known in the display pages, performs approval operation through an approval button (an Action meta model) in a second UserTask meta model to generate an approval instruction, and the Action meta model of the target approval node responds to the approval instruction to obtain a plurality of corresponding sub-approval results.
S330A, merging the gateway meta-model sub-approval results of the gateway sub-nodes to generate approval results.
Alternatively, if all the sub-approval results are approval, at this time, all the sub-approval results are combined by combining the gateway meta-model of the gateway sub-node 207, so as to generate approval results, for example, the approval results are transmitted to the End node 209, and the approval results are returned to the application node 202 through the End meta-model. It will be appreciated that if any of the sub-approval results is rejected, the approval sub-results are returned to the application node 202 so that the applicant knows the final result of the approval.
It should be noted that in some embodiments, the approval node may be one, and the gateway node may not be set.
In one embodiment, step S300 may include steps S310B-S330B:
S310B, determining the application type corresponding to the approval content according to the gateway meta-model of the gateway node and the approval content of the internal and external conditions.
Optionally, the application type includes an external application and an internal application. For example, the gateway meta-model of the internal and external conditional gateway node 208 may determine whether the application type is an external application or an internal application based on the content of the internal, external lists, architecture, etc. set in advance, and match the name in the approval content, such as the applicant information, with the content set in advance.
S320B, determining a target approval node according to the application type and the model ID.
Optionally, if the application type is internal application, based on the principle of the model ID as in step S310A, the model ID of the gateway meta-model of the gateway node 208 with internal and external conditions is determined by combining the connection meta-model 210, so as to determine the model ID of the connection endpoint, and the node of the model ID is the target approval node. Similarly, if the application type is external, the model ID of the gateway meta-model of the gateway node 208 with the internal and external conditions can be determined by combining the connection meta-model 210, thereby determining the model ID of the connection endpoint, and finally determining the target approval node as the approval node 203.
S330B, transmitting the approval content to the target approval node through the gateway meta-model of the internal and external condition gateway node so as to generate an approval result through the second UserTask meta-model of the target approval node.
Specifically, the approval content is transmitted to the target approval node through the gateway meta-model of the internal and external conditional gateway node, so that an approval result is generated through the second UserTask meta-model of the target approval node. For example, through the approval button (Action meta-model) of the second UserTask meta-model, a department manager, industry approval manager, or manager may perform approval operations through the approval button (Action meta-model), and finally obtain approval results, such as approval or rejection.
In one implementation manner, the meta-model-based intelligent approval method of the embodiment of the present application may further include step S400, including steps S410 to S430:
s410, acquiring node information of all child nodes of the application node from the memory according to the model information.
Alternatively, in the meta-model approval flow, since there is an order of execution between nodes, a node that is in front of the order may be considered as a parent node of a node that is in rear of the order, for example, the start node 201 is a parent node of all nodes after the start node 201, the application node 202 is a parent node of all nodes after the application node 202, and conversely, all nodes after the application node 202 are child nodes of the application node 202. Thus, taking the application node 202 as an example, all children of the application node 202 may be looked up based on the model ID of the first UserTask meta-model of the application node 202: orientationkey like "$ { d.orientation }" ≡"AND PARENTKEY = $ { d.id }, and then obtaining node information of all child nodes. The node information includes, but is not limited to, model information in the node, approval content acquired by the node, approval sub-results (or approval results), and the like.
S420, constructing an attribute SQL database execution language according to the node information.
Alternatively, by cycling as:
For (node information)
Concatenation construction attribute SQL database execution language
End。
S430, performing database persistence mapping according to the attribute SQL database execution language.
And then, utilizing the constructed attribute SQL database execution language, converting the node information of the meta-model in each node into corresponding SQL sentence elements, and carrying out database persistence mapping on the data, thereby realizing the storage of the data in the approval stream.
According to the approval flow state management method, the corresponding approval flow state information is obtained through the approval flow service persistence database of the approval flow DB, the state of the approval flow is isolated from the state of the approval business object, multiplexing of the approval flow multi-approval object is achieved, and the isolation form is shown in a table 1 of a state table. In the state table, only service primary key information is stored, businessKey fields are used as associated fields with the service data table, so that the approval flow state is isolated from the service object data, namely the meta-model of each node, id is a meta-model ID, businessKey is a service primary key, name is a meta-model Name, userTaskID is a current approval node UserTaskID, flowCode is an execution node ID (action ID), and FlowStatus is a current approval state: normal/abnormal MetaType is a meta-model category identification.
TABLE 1
The approval flow authority management of the embodiment of the application converts the traditional DB authority management into an authority matching mechanism based on a memory by binding the authority by a meta-model. Wherein, the authority includes: the Model table access right, property field access right and Rule row data access right of the Model meta-Model are added with Policy access policies in the construction stage. After the meta-model is loaded into the memory, the corresponding authority information can be obtained through the access method (GetPolicy (model ID of the meta-model)) provided by Policy to perform service operation during service call.
Optionally, the rights element is defined as follows:
SG: range (organization)
Roller: role (business role)
Rule (dynamic injection into SQL affects business data operation)
Binding of the three elements is achieved in the memory (as meta-model attribute processing), SG and Rule are verified in the process of achieving memory meta-model to business processing, if the SG and the Rule pass through, rule is injected into corresponding SQL sentences, and authority verification and Rule injection are achieved.
According to the intelligent examination and approval method based on the meta-model, traditional examination and approval stream modeling is more abstract and generalized, and different from the traditional examination and approval stream object instantiation process, a single instance of the meta-model is resident in a memory, and the meta-model instance is packaged through a basic data type, so that the unpacking and boxing process in the model object instantiation process is reduced, and the code quantity is greatly reduced. Meanwhile, the examination and approval stream meta-model information is multiplexed from the memory dimension, so that an examination and approval stream engine based on meta-model driving is realized. In addition, the method of converting the front-end development of the traditional approval stream into the visual zero/low code is realized, the technical threshold of the front-end developer is greatly reduced, the development efficiency is greatly improved in the development quality, and the lead-in period of the front-end engineering is shortened.
Referring to FIG. 3, a block diagram of a meta-model based intelligent approval apparatus according to an embodiment of the present application is shown, which may include:
The first response module is used for responding to the construction instruction to construct a meta-model approval flow, wherein the meta-model approval flow comprises an application node, an approval node and a gateway node, the application node comprises a first UserTask meta-model, the approval node comprises a second UserTask meta-model, and the gateway node comprises a gateway meta-model;
The second response module is used for responding to the input instruction, starting a meta-model approval flow and generating approval contents through the first UserTask meta-model;
And the generation module is used for generating an approval result according to the approval content, the gateway meta-model and the second UserTask-membered model.
In one embodiment, the first response module is further configured to:
acquiring node information of all child nodes of the application node from the memory according to the model information;
Constructing an attribute SQL database execution language;
And performing database persistence mapping according to the attribute SQL database execution language, the model information of the application node and the node information.
The functions of each module in each device of the embodiments of the present application may be referred to the corresponding descriptions in the above methods, and are not described herein again.
Referring to fig. 4, a block diagram of an electronic device according to an embodiment of the present application is shown, the electronic device including: memory 310 and processor 320, memory 310 stores instructions executable on processor 320, and processor 320 loads and executes the instructions to implement the meta-model based intelligent approval method of the above embodiments. Wherein the number of memory 310 and processors 320 may be one or more.
In one embodiment, the electronic device further includes a communication interface 330 for communicating with an external device for data interactive transmission. If the memory 310, the processor 320 and the communication interface 330 are implemented independently, the memory 310, the processor 320 and the communication interface 330 may be connected to each other and communicate with each other through buses. The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, peripheral interconnect (Peripheral ComponentInterconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 310, the processor 320, and the communication interface 330 are integrated on a chip, the memory 310, the processor 320, and the communication interface 330 may communicate with each other through internal interfaces.
An embodiment of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the meta-model-based intelligent approval method provided in the above embodiment.
The embodiment of the application also provides a chip, which comprises a processor and is used for calling the instructions stored in the memory from the memory and running the instructions stored in the memory, so that the communication equipment provided with the chip executes the method provided by the embodiment of the application.
The embodiment of the application also provides a chip, which comprises: the input interface, the output interface, the processor and the memory are connected through an internal connection path, the processor is used for executing codes in the memory, and when the codes are executed, the processor is used for executing the method provided by the application embodiment.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processor, digital signal processor (DIGITAL SIGNAL processing, DSP), application Specific Integrated Circuit (ASIC), field programmable gate array (fieldprogrammablegate array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc. A general purpose processor may be a microprocessor or any conventional processor or the like. It is noted that the processor may be a processor supporting an advanced reduced instruction set machine (ADVANCED RISC MACHINES, ARM) architecture.
Further, optionally, the memory may include a read-only memory and a random access memory, and may further include a nonvolatile random access memory. The memory may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may include a read-only memory (ROM), a Programmable ROM (PROM), an erasable programmable ROM (erasable PROM), an electrically erasable programmable EPROM (EEPROM), or a flash memory, among others. Volatile memory can include random access memory (random access memory, RAM), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available. For example, static random access memory (STATIC RAM, SRAM), dynamic random access memory (dynamic random access memory, DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous dynamic random access memory (doubledata DATE SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (ENHANCED SDRAM, ESDRAM), synchronous link dynamic random access memory (SYNCHLINK DRAM, SLDRAM), and direct memory bus random access memory (direct rambus RAM, DR RAM).
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with the present application are fully or partially produced. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. Computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another.
In the description of the present specification, a description referring to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Any process or method description in a flowchart or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process. And the scope of the preferred embodiments of the present application includes additional implementations in which functions may be performed in a substantially simultaneous manner or in an opposite order from that shown or discussed, including in accordance with the functions that are involved.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the methods of the embodiments described above may be performed by a program that, when executed, comprises one or a combination of the steps of the method embodiments, instructs the associated hardware to perform the method.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules described above, if implemented in the form of software functional modules and sold or used as a stand-alone product, may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that various changes and substitutions are possible within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. An intelligent approval method based on a meta model is characterized by comprising the following steps:
Responding to a construction instruction, constructing a meta-model approval flow, wherein the meta-model approval flow comprises an application node, an approval node and a gateway node, the application node comprises a first UserTask meta-model, the approval node comprises a second UserTask meta-model, and the gateway node comprises a gateway meta-model;
Responding to an input instruction, starting the meta-model approval flow, and generating approval content through the first UserTask meta-model;
And generating an approval result according to the approval content, the gateway meta-model and the second UserTask meta-model.
2. The meta-model based intelligent approval method of claim 1, wherein: the generating approval content through the first UserTask meta-model comprises the following steps:
acquiring input application information through the first UserTask-element model;
according to the application information, constructing approval data in a preset format of field keywords and values corresponding to the field keywords;
And generating the approval content according to the approval data and the model ID of the first UserTask-membered model.
3. The meta-model based intelligent approval method of claim 2, wherein: when the gateway node includes parallel gateway sub-nodes and merged gateway sub-nodes, the generating an approval result according to the approval content, the gateway meta-model and the second UserTask meta-model includes:
When the number of the approval nodes is multiple, determining a plurality of target approval nodes according to the model ID, and transmitting the approval contents to different target approval nodes through the gateway meta-model of the parallel gateway sub-nodes in parallel;
obtaining a plurality of sub-approval results according to the second UserTask th element models of different target approval nodes and the approval contents;
And merging the sub-approval results through a gateway meta-model of the merging gateway sub-node to generate the approval results.
4. A meta-model based intelligent approval method according to claim 2 or 3, characterized in that: when the gateway node includes an internal and external conditional gateway node, the generating an approval result according to the approval content, the gateway meta-model and the second UserTask meta-model includes:
determining an application type corresponding to the approval content according to a gateway meta-model of the internal and external condition gateway node and the approval content, wherein the application type comprises an external application and an internal application;
determining a target approval node according to the application type and the model ID;
and transmitting the approval content to a target approval node through the gateway meta-model of the internal and external condition gateway node so as to generate an approval result through a second UserTask meta-model of the target approval node.
5. A meta-model based intelligent approval method according to claim 3, characterized in that: the obtaining a plurality of sub-approval results according to the second UserTask th yuan model of different target approval nodes and the approval contents comprises:
acquiring the approval content and the component type corresponding to the approval content on a display page through an approval flow engine;
Rendering the approval content according to the component type, and displaying the display pages in different target approval nodes;
and responding to different approval instructions of the display page to obtain a plurality of sub-approval results.
6. A meta-model based intelligent approval method according to any of claims 1-3, characterized in that: the responding to the construction instruction, constructing the meta-model approval stream comprises:
responding to a first construction sub-instruction, establishing the application node, the approval node and the gateway node, and correspondingly generating model information of a first UserTask-element model, a second UserTask-element model and a gateway meta-model, wherein the model information comprises a model ID and a meta-model type identifier;
responding to the second construction sub-instruction, and generating a plurality of connection meta-models;
Constructing a meta-model approval stream according to the connecting meta-model, the application node, the approval node and the gateway node;
The connection meta-model characterizes the sequential relation of each meta-model in the meta-model approval flow, and the connection starting point and the connection ending point of the connection meta-model are model IDs of different meta-models.
7. The meta-model based intelligent approval method of claim 6, wherein: the method further comprises the steps of:
Acquiring node information of all child nodes of the application node from a memory according to the model information;
constructing an attribute SQL database execution language according to the node information;
And performing database persistence mapping according to the attribute SQL database execution language.
8. An intelligent approval device based on a meta model, which is characterized by comprising:
The first response module is used for responding to the construction instruction to construct a meta-model approval flow, wherein the meta-model approval flow comprises an application node, an approval node and a gateway node, the application node comprises a first UserTask meta-model, the approval node comprises a second UserTask meta-model, and the gateway node comprises a gateway meta-model;
the second response module is used for responding to an input instruction, starting the meta-model approval flow and generating approval contents through the first UserTask meta-model;
And the generation module is used for generating an approval result according to the approval content, the gateway meta-model and the second UserTask meta-model.
9. An electronic device, comprising: a processor and a memory in which instructions are stored, the instructions being loaded and executed by the processor to implement the method of any one of claims 1 to 7.
10. A computer readable storage medium having stored therein a computer program which when executed implements the method of any of claims 1-7.
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