CN112732423B - Process migration method, device, equipment and medium - Google Patents
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Abstract
The invention relates to the technical field of data synchronization, and discloses a process migration method, a device, equipment and a medium, wherein the method comprises the following steps: acquiring a target system, a flow set of a source system and a field migration list in a system flow migration request; performing domain semantic feature extraction on process document information and process nodes in each process through a domain design model, and identifying domain objects corresponding to each process; screening a to-be-migrated flow corresponding to a field object matched with the to-be-migrated field in the field migration list from the flow set; polymerizing the flows to be migrated of the objects in the same field to obtain a polymerization result; creating an entity data table in a target system; and reconstructing each target flow according to the flows to be migrated, all the entities, the entity data table, the value objects and the aggregation roots. The invention realizes the effect of accurately and reliably transferring the process to the target system and improves the efficiency and quality of the process transfer.
Description
Technical Field
The present invention relates to the field of data synchronization technology for big data, and in particular, to a method, an apparatus, a device, and a medium for process migration.
Background
With the rapid development of information technologies such as internet, cloud computing and internet of things, the flow data volume brought correspondingly also shows explosive growth, and the situation of flow migration inevitably exists due to hardware limitation or platform limitation of a system; secondly, the screened flows are copied and migrated, and because different development languages exist in different flows, the flows need to be manually converted into one language, and the development time is long; finally, the process after the copying and the migration is still huge, the process is complicated, the subsequent maintenance still has higher complexity, and the maintenance is inconvenient. Therefore, the existing flow migration scheme has the defects of large labor input workload, long development time and inconvenient maintenance, and the efficiency, the accuracy and the maintainability of the flow migration are seriously influenced.
Disclosure of Invention
The invention provides a process migration method, a device, computer equipment and a storage medium, which realize the purpose of migrating a process of a field to be migrated to a target system by using a field design model based on a field-driven design algorithm without manual screening, development and construction, greatly save the storage space of the process, achieve the effect of accurately and reliably migrating the process to the target system, and improve the efficiency and quality of the process migration.
A process migration method, comprising:
responding to a system flow migration request, and acquiring a flow set and a field migration list of a target system and a source system in the system flow migration request; the process set comprises a plurality of processes, and each process comprises process document information, process nodes and a process node relation;
inputting all the processes into a domain design model, performing domain semantic feature extraction on the process document information and the process nodes in each process through the domain design model, and identifying a domain object corresponding to each process;
screening the process corresponding to the field object matched with the field to be migrated in the field migration list from the process set, and determining the screened process as the process to be migrated;
aggregating the process node and the process node relation in the process to be migrated of the same domain object through the domain design model to obtain an aggregation result corresponding to the domain object; the aggregation result comprises an entity corresponding to the flow node, a value object associated with the entity and an aggregation root;
creating an entity data table associated with each of the entities at the target system according to the entities in each of the aggregation results and the value objects associated with the entities;
reconstructing each target flow corresponding to each flow to be migrated in the target system according to the flow to be migrated, the entity corresponding to the flow node in the flow to be migrated, the entity data table associated with the entity, the value object and the aggregation root, reconstructing all the target flows and determining that the system flow migration request is completed;
the aggregating the process node and the process node relationship in the to-be-migrated process of the same domain object through the domain design model to obtain an aggregation result corresponding to the domain object includes:
converging the relationship between the process nodes and the process nodes in the process to be migrated of the same field object through the field design model, and constructing a process node map;
and aggregating the process node maps through the field design model by using a Kanwer law algorithm to obtain an aggregation result corresponding to the field object.
A process migration apparatus, comprising:
the receiving module is used for responding to a system flow migration request and acquiring a flow set and a field migration list of a target system and a source system in the system flow migration request; the process set comprises a plurality of processes, and each process comprises process document information, process nodes and a process node relation;
the extraction module is used for inputting all the processes into a field design model, extracting field semantic features of the process document information and the process nodes in each process through the field design model, and identifying field objects corresponding to each process;
the matching module is used for screening the flow corresponding to the field object matched with the field to be migrated in the field migration list from the flow set and determining the screened flow as the flow to be migrated;
the aggregation module is used for aggregating the process node and the process node relation in the process to be migrated of the same field object through the field design model to obtain an aggregation result corresponding to the field object; the aggregation result comprises an entity corresponding to the flow node, a value object associated with the entity and an aggregation root;
a creating module configured to create an entity data table associated with each of the entities at the target system according to the entities in each of the aggregation results and the value objects associated with the entities;
a reconstruction module, configured to reconstruct, in the target system, each target flow corresponding to each to-be-migrated flow according to the to-be-migrated flow, the entity corresponding to the flow node in the to-be-migrated flow, and the entity data table, the value object, and the aggregation root associated with the entity, reconstruct all the target flows, and determine that the system flow migration request is completed;
the aggregation module is further to:
converging the flow node and the flow node relation in the flow to be migrated of the same field object through the field design model, and constructing a flow node map;
and aggregating the process node maps through the field design model by using a Kanwer law algorithm to obtain an aggregation result corresponding to the field object.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the flow migration method when executing the computer program.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned flow migration method.
The invention provides a process migration method, a device, computer equipment and a storage medium based on a domain design model, which are characterized in that a process set and a domain migration list of a target system and a source system in a system process migration request are obtained; performing domain semantic feature extraction on the process document information and the process nodes in each process through the domain design model, and identifying domain objects corresponding to each process; screening a process matched with the to-be-migrated field in the field migration list from the process set, and determining the process as the to-be-migrated process; aggregating the flows to be migrated of the objects in the same field to obtain an aggregation result; creating an entity data table at the target system according to the entities in each aggregation result and the value objects associated with the entities; according to the flows to be migrated, all the entities corresponding to the flow nodes in the flows to be migrated, the entity data table, the value objects and the aggregation roots associated with the entities, each target flow is reconstructed, therefore, the fields of each flow to be migrated are identified through field semantic feature extraction and identification, an aggregation result containing the entities, the value objects and the aggregation roots is obtained through aggregation, the entity data table is created and the target flows corresponding to the flows in one-to-one mode are reconstructed according to the aggregation result, the flow migration is completed, the affiliated fields of the flows are automatically identified, the flows of the fields to be migrated are migrated to a target system by using a field design model based on a field-driven design algorithm, manual screening and development and construction are not needed, the entities, the value objects and the aggregation roots are directly aggregated to generate the entities, the value objects and the aggregation roots, all the unified flows are integrated into a development language, the unified entity data table and the unified flow are created, the target flows are reconstructed, the storage space of the flows is greatly saved, the effect of accurately and reliably migrating the flows to the target system is achieved, and the efficiency and the quality of the flow migration are improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a schematic diagram of an application environment of a process migration method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a flow migration method according to an embodiment of the present invention;
FIG. 3 is a flowchart of step S20 of the flow migration method according to an embodiment of the present invention;
FIG. 4 is a flowchart of step S40 of the flow migration method in an embodiment of the present invention;
FIG. 5 is a flowchart of step S50 of the flow migration method according to an embodiment of the present invention;
FIG. 6 is a functional block diagram of a translation module of the process migration apparatus in an embodiment of the present invention;
FIG. 7 is a schematic diagram of a computer device in an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The process migration method provided by the invention can be applied to the application environment shown in fig. 1, wherein a client (computer device) communicates with a server through a network. The client (computer device) includes, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, cameras, and portable wearable devices. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers.
The migration method is executed by a client or a server.
In an embodiment, as shown in fig. 2, a flow migration method is provided, which mainly includes the following steps S10 to S60:
s10, responding to a system flow migration request, and acquiring a flow set and a field migration list of a target system and a source system in the system flow migration request; the process set comprises a plurality of processes, and each process comprises process document information, process nodes and a process node relationship.
Understandably, the target system, the source system and the field migration list are obtained through the system flow migration request, the target system is a constructed system platform used for migration, the target system already uses a platform system to be developed in a unified language, the source system includes the flow set, the source system is a system needing a migrated flow, an original flow developed by using different development languages exists in the source system, the flow set includes all flows operated in the source system, each flow includes flow document information, flow nodes and a flow node relationship, the flow document information is information related to the flow, the flow nodes are operation nodes on the flow of the flow, the flow node relationship is an association relationship between the flow nodes, the field migration list is a set of fields needing to be migrated, and the field migration list lists the fields to be migrated.
In an embodiment, as shown in fig. 3, before the step S10, that is, before the step of obtaining the flow set and the domain migration list of the target system and the source system in the system flow migration request in response to the system flow migration request, the method includes:
s101, all original flows of the source system are obtained.
Understandably, the original flow is a flow existing in the source system.
And S102, performing language type identification on each original flow through a flow chart construction model, and identifying a language type corresponding to each original flow.
Understandably, the flowchart building model is a model capable of identifying a language class for developing the original flow, for example, the language class includes C + + language, ASP language, delphi language, java language, and the like, the language class identification process is an identification process for marking a language class corresponding to a detected response request instruction as the language type corresponding to the original flow by acquiring an input interface of the original flow and inputting a request instruction of a different language class to the input interface, the language class identification process further includes a process for acquiring a main program code in each original flow and identifying by extracting a language format feature of the main program code, the language format feature is a feature of a writing format of various development languages, and a neural network model is introduced for extracting and identifying the language format feature.
Wherein, the main program code is a program code for starting to execute the original flow.
S103, acquiring an interpretation script corresponding to the language type corresponding to the original flow through a flow chart building model, interpreting the original flow through the acquired interpretation script corresponding to the original flow, and building the flow corresponding to the original flow according to a flow chart generating mode.
Understandably, the flowchart building model includes a plurality of language types and the interpretation scripts corresponding to the language types one by one, the interpretation scripts are script tools which generate a flowchart corresponding to a source program code according to the source program code developed according to the language type corresponding to the interpretation scripts, the interpretation process is a process of analyzing all the source program codes in the original flow, analyzing all the flow nodes in the original flow, identifying an association relationship (i.e., a reference relationship between variables) between all the flow nodes in the original flow, associating and folding all the flow nodes in the original flow, and summarizing all the associated and folded flow nodes to generate the flowchart, and the source program code includes the main program code.
The invention realizes the purpose of obtaining all original flows of the source system; performing language type identification on each original flow through a flow chart construction model, and identifying a language type corresponding to each original flow; the method comprises the steps of obtaining an interpretation script corresponding to a language type corresponding to an original flow through a flow chart construction model, interpreting the original flow through the obtained interpretation script corresponding to the original flow, and constructing the flow corresponding to the original flow according to a flow chart generation mode, so that language type identification is carried out through the flow chart construction model, development languages of all the original flows are identified, interpretation scripts corresponding to the language types corresponding to all the original flows are obtained, interpretation is carried out on all the original flows, flows corresponding to the original flows one by one are constructed, the purposes that the flows of different development languages are interpreted and constructed into the flows with the flow chart formats are achieved, the workload of manual conversion is reduced, high-quality flows with the flow chart formats are provided for subsequent flow migration, the efficiency is improved, and the labor cost is reduced.
And S20, inputting all the flows into a field design model, extracting field semantic features of the flow document information and the flow nodes in each flow through the field design model, and identifying a field object corresponding to each flow.
Understandably, the field design model extracts the field semantic features of the process document information and the process nodes in each process, the field semantic features are features related to field objects and context semantic features and scene features between the field objects, the identification process comprises the steps of extracting the context semantic features and the scene features between the field objects from the process document information and the process nodes, carrying out context semantic identification on the context semantic features between the extracted field objects and carrying out context scene identification on the extracted context scene features, identifying a first identification result aiming at the context semantic identification and a second identification result aiming at the context scene identification, carrying out field boundary positioning according to the first identification result and the second identification result, and determining the identification process of the field objects corresponding to the process.
The process document information comprises a process requirement document and process scene information; the process requirement document is a document for developing a requirement class input by the process, namely the process requirement document comprises interface design of process nodes, execution sequence of the process nodes and a relationship between an upper process node and a lower process node, the process scene information comprises a scene of the process application, a scene object oriented to the process and a purpose or a target of realizing the process, the process scene information is information related to the scene of the process application, and the process nodes comprise process interfaces and node objects; the method comprises the steps that a domain design model is a model which is trained and completed based on a Domain Driven Design (DDD) algorithm and used for identifying a process flow the domain and carrying out process flow migration, a domain driven design concept is a tool which takes a uniform universal language as mutual training among the domains, main domain feature objects are found and dug out in the continuous training process, then the domain feature objects are designed into a domain model, the domain model is built by codes, the domain object represents the corresponding process flow domain, and the class of the domain object is a predefined domain name.
In an embodiment, as shown in fig. 4, in the step S20, performing, by the domain design model, domain semantic feature extraction on the process document information and the process nodes in each process, and identifying a domain object corresponding to each process includes:
s201, splicing the process demand document and the process interface in the process to obtain a first text object corresponding to the process, and combining the process scene information and the node object in the process to obtain a second text object corresponding to the process; the process document information comprises a process requirement document and process scene information; the process node includes a process interface and a node object.
Understandably, the splicing mode can be set according to requirements, preferably, the process interface is spliced before and after the process requirement document, the spliced process requirement document is subjected to embedding conversion to obtain the first text object, wherein the embedding conversion is used for dividing input text content into single words or words, each word or word is mapped and converted into a word vector corresponding to the word or word, a mapping relation is obtained by training an embedding model, all converted word vectors are subjected to feature vector combination to obtain a string of feature vector values, the string of feature vector values is determined as the first text object, meanwhile, the node object is inserted after each sentence in the process scene information, and the second text object is generated by combination.
The process document information comprises a process requirement document and process scene information; the process node includes a process interface and a node object, where the process interface is an interface used in the process node, for example: the process interface is an interface of a database for acquiring relevant information of an approval person, and the node object is an object oriented to the process node, that is, a set of input objects is included in the process node, for example: the node object includes the amount of money, time, and text information of each input field, etc., which are input in the flow node.
In an embodiment, in the step S201, that is, the splicing the process requirement document and the process interface in the process to obtain the first text object corresponding to the process, and meanwhile, combining the process scene information and the node object in the process to obtain the second text object corresponding to the process includes:
and S2011, splicing the process interfaces before and after the process requirement document, and performing embedding conversion to obtain the first text object corresponding to the process.
Understandably, the process of splicing the front and back is inserting the content of the process interface before the process requirement document, inserting the content of the process interface after the process requirement document, determining the process of splicing the inserted process requirement document, dividing the embedded document into single words or words according to the input text content, mapping each word or word into a word vector corresponding to the word or word, obtaining the mapping relation through training the embedded model, combining feature vectors of all the converted word vectors to obtain a string of feature vector values, and determining the string of feature vector values as the first text object.
S2012, inserting the node object after each sentence in the process scene information, and merging the second text objects corresponding to the process.
Understandably, the merging process is to insert the node object behind each punctuation mark in the process scene information, and then insert a separator behind the node object, so that the operation process of inserting the node object behind each sentence is realized, and the second text object is merged and generated.
The method and the device realize splicing the process interface before and after the process requirement document and carry out embedding conversion to obtain the first text object corresponding to the process; and inserting the node object after each sentence in the process scene information, and combining the second text object corresponding to the process, thereby realizing a method for combining a process interface and a process requirement document, providing a data base for subsequent context semantic recognition, and combining the process scene information and the node object, providing a data base for subsequent context scene recognition, improving the recognition accuracy and quality, and finally improving the recognition accuracy of the subsequent field object.
S202, performing context semantic recognition on the first text object through a first text recognition model in the field design model to recognize a first recognition result, and performing long-context scene recognition on the second text object through a second text recognition model in the field design model to recognize a second recognition result.
Understandably, the first text recognition model realizes that a first recognition result model is obtained by recognizing the context semantic features in the first text object and according to the extracted context semantic features, the node category result is recognized, and the service field recognition is performed on the recognized node category result, the first text recognition model is a trained language model, the context semantic features are the features related to the node category in the context correlation between two sentences, the node category result is the weight distribution of the recognized categories of each service, the node category is the category of the service, and the flow nodes are added in the first text object, so that the flow nodes are combined in the context of the flow demand document, the recognition accuracy of the first recognition result of the first text recognition model is improved, the node categories can be recognized more easily, and the recognition efficiency of the first recognition result is improved.
Understandably, the second text recognition model recognizes a scene category result according to the extracted context scene features, and performs scene field recognition on the recognized scene category result to obtain a model of a second recognition result, the second text recognition model is a trained language model, the context scene features are features related to the scene categories and related to the context between two sentences, the scene categories are preset applied categories of scenes, the scene category result comprises at least one scene category and a prediction probability corresponding to the scene category, namely the probability distribution of the recognized categories of each scene, and the node objects are added to the second text object in order to combine the node objects in the context of the process scene information, so that the recognition accuracy of the second recognition result of the second text recognition model is improved, the scene categories can be recognized more easily, and the recognition efficiency of the second recognition result is improved.
The field design model comprises the first text recognition model and the second text recognition model, the first recognition result is a probability distribution result of each field determined from the service field dimension according to the node type result, and the second recognition result is a probability distribution result of each field determined from the scene field dimension according to the scene type result.
In an embodiment, in the step S202, performing context semantic recognition on the first text object through a first text recognition model in the domain design model, and recognizing a first recognition result includes:
s2021, extracting context semantic features in the first text object through the first text recognition model; the first text recognition model is a Bi-LSTM based language model.
Understandably, the first text recognition model is a model built on the basis of a Bi-LSTM network structure, the Bi-LSTM network structure model comprises a forward LSTM model and a backward LSTM model, bidirectional context semantics are captured through the bidirectional LSTM model, bidirectional context semantics are performed on the first text object through the first text recognition model, and semantic features of the context are extracted, wherein the context semantics features are features related to node classes and are in semantic aspects of context association between two sentences.
S2022, identifying a node type result corresponding to the first text object according to the extracted context semantic features through the first text identification model.
Understandably, arranging the extracted context semantic features through the first text recognition model, outputting the context semantic features into a service feature vector, outputting a one-dimensional service full-connection feature vector through a full-connection layer in the first text recognition model, and performing weighted multiplication on the vector in the corresponding service full-connection feature vector through the weight corresponding to each node category so as to recognize the node category result.
The node type result includes at least one node type and a weight corresponding to the node type, that is, the node type result is a weight distribution of the identified type of each service, the node type result indicates a weight predicted in the field of each service, and the range of the weight is 0 to 1.
S2023, performing service field recognition on the node type result through the first text recognition model to obtain the first recognition result corresponding to the first text object.
Understandably, the service domain identification is performed for the domain of the service dimension to which the process node belongs, that is, the probability distribution situation of the domain of each service dimension is predicted through the weight distribution in the node type result, and may also be understood as the probability distribution obtained by aggregating the node types in the domains of the same service dimension in the node type result.
The method realizes the extraction of the context semantic features in the first text object through the first text recognition model; the first text recognition model is a Bi-LSTM-based language model; identifying a node category result corresponding to the first text object according to the extracted context semantic features through the first text identification model; the node type result is subjected to service field recognition through the first text recognition model to obtain the first recognition result corresponding to the first text object, so that the context semantic features in the first text object are extracted through the first text recognition model based on the Bi-LSTM language model, the node type can be recognized more easily by using a service field recognition method to automatically recognize the first recognition result of the first text object, the recognition efficiency of the first recognition result is improved, manual recognition is reduced, and the recognition accuracy and efficiency are improved for follow-up.
In an embodiment, in the step S202, performing context scene recognition on the second text object through a second text recognition model in the domain design model, and recognizing a second recognition result includes:
s2024, extracting scene features of the second text object through the second text recognition model; the second text recognition model is a Bert-based recognition model.
Understandably, the second text recognition model is a model constructed on the basis of a Bert network structure, the model of the Bert network structure is a model trained by jointly adjusting a bidirectional Transformer algorithm in all layers, and performing word sense vector conversion and labeling on texts in the second text object, the context scene features are extracted from the second text object through the first text recognition model, and the context scene features are features related to scene categories and related to contextual relevance between two sentences. S2025, performing context association degree recognition according to the extracted scene features through the second text recognition model, and recognizing a scene category result corresponding to the second text object.
Understandably, performing relevance identification on the extracted context scene features through the second text recognition model, performing relevance analysis on the context-related characters identified as contexts to analyze a scene feature vector, performing relevance analysis on the scene span or the scene dimension between the contexts, outputting a one-dimensional scene full-connection feature vector through a full-connection layer in the second text recognition model, and identifying the node category result through full-connection.
The scene type result comprises at least one scene type and a prediction probability corresponding to the scene type, namely the scene type result is the probability distribution of the types of the identified scenes, the scene type result shows the probability value predicted in the field of the scenes, and the range of the probability value is 0% -100%.
S2026, carrying out scene field recognition on the scene classification result through the second text recognition model to obtain a second recognition result corresponding to the second text object.
Understandably, the scene domain identification is performed for the domain of the scene dimension to which the flow node belongs, that is, the probability distribution situation of the domain of each scene dimension is predicted through the probability distribution in the scene classification result, and may also be understood as the probability distribution obtained by aggregating the scene classifications in the domains of the same scene dimension in the scene classification result.
The scene feature extraction of the second text object is realized through the second text recognition model; the second text recognition model is a recognition model based on Bert; identifying the context association degree according to the extracted scene features through the second text identification model, and identifying a scene category result corresponding to the second text object; and performing scene field recognition on the scene category result through the second text recognition model to obtain a second recognition result corresponding to the second text object, so that the context scene characteristics in the second text object are extracted through the second text recognition model based on the recognition model of the Bert, the second recognition result of the second text object is automatically recognized by applying a scene field recognition method, manual recognition is reduced, the process nodes are recognized through scene dimensions, and the accuracy and the efficiency of recognition are improved for the follow-up process.
S203, performing field boundary positioning on the first recognition result and the second recognition result through the field design model, and determining the field object corresponding to the process.
Understandably, the domain boundary is located to convert the first recognition result and the second recognition result into a numerical range of one dimension, and then locate a service central point corresponding to the first recognition result and a scene central point corresponding to the second recognition result in a domain polygon, and comprehensively locate domain objects according to the distances between the service central point and the scene central point and the boundaries of the respective domain objects, for example: the numerical range is 1 to 10, all the field objects form a polygon with the same point as the total number of all the field objects, each point represents a value of 10 of the field object, the center of the polygon is a value of 0 of each field object, namely, the median of the first recognition result is a value of 1 corresponding to the field of a service, the median of the second recognition result is a value of 100% corresponding to the field of a scene, one field object corresponds to the field of a service and the field of a scene, points corresponding to the fields of each service are positioned in the formed polygon according to the distribution condition of the first recognition result, then the points are connected into a service-class polygon, the center of the service-class polygon is taken as the service center point of the first recognition result, points corresponding to the fields of each scene are positioned in the formed polygon according to the distribution condition of the second recognition result, then the points are connected into a scene-class polygon, the center point of the scene-class polygon is taken as the scene center point of the second recognition result, the distance between the service field objects and the field object is taken as the final flow ratio of the overlapping of the field objects, and the final flow of the scene center points of the objects are determined.
The method and the device realize that a first text object corresponding to the process is obtained by splicing the process requirement document and the process interface in the process, and simultaneously, the process scene information and the node object in the process are combined to obtain a second text object corresponding to the process; performing context semantic recognition on the first text object through a first text recognition model in the field design model to recognize a first recognition result, and performing long-context scene recognition on the second text object through a second text recognition model in the field design model to recognize a second recognition result; and carrying out field boundary positioning on the first recognition result and the second recognition result through the field design model, and determining the field object corresponding to the process, so that the context semantic and the context scene are respectively recognized through the first text recognition model and the second text recognition model, and the field of the process is determined through the field boundary positioning of two dimensions, so that the affiliated field of the automatic recognition process is realized, and the process in the subsequent field to be migrated is migrated.
S30, screening the process corresponding to the field object matched with the field to be migrated in the field migration list from the process set, and determining the screened process as the process to be migrated.
Understandably, the field object matched with the field to be migrated is screened out from the flow set, the flow corresponding to the matched field object is searched, the searched flow is determined as the flow to be migrated, and the flow to be migrated is the flow to be migrated.
The field migration list comprises a plurality of preset fields to be migrated.
S40, aggregating the process nodes and the process node relation in the process to be migrated of the same field object through the field design model to obtain an aggregation result corresponding to the field object; the aggregation result comprises an entity corresponding to the flow node, a value object associated with the entity, and an aggregation root.
Understandably, converging the flow nodes and the flow node relationship in the to-be-migrated flow of the same domain object through the domain design model, and constructing a flow node map, wherein the converging process is to converge the same flow nodes to one flow node, the flow node comprises the process of the flow node relationship associated with the original flow node, and the constructing process is to associate all the converged flow nodes according to all the flow node relationships to form a mesh flow node map; and then, aggregating the process node maps by using a Kanwei law algorithm to obtain an aggregation result corresponding to the field object, wherein the aggregation result comprises an entity corresponding to the process node, a value object associated with the entity and an aggregation root.
The Congway law algorithm is an algorithm that similar process nodes and process node relations are combined to generate a new process node, the new process node can include functions of an original process node and the original process node relation, the new process node is determined to be an entity, the process node relation after aggregation is determined to be an aggregation root, and the process nodes contained in the entity are determined to be value objects.
In an embodiment, as shown in fig. 5, in the step S40, that is, the aggregating the relationship between the process node and the process node in the to-be-migrated process of the same domain object through the domain design model to obtain an aggregation result corresponding to the domain object includes:
s401, converging the relationship between the process nodes and the process nodes in the process to be migrated of the same field object through the field design model, and constructing a process node map.
Understandably, the converging is to separate the process nodes in the to-be-migrated process of the same domain object from all the process nodes, and converge the relationship between the two separated process nodes, so as to construct the process node graph of the domain object, thereby constructing the process node graphs of all the domain objects.
S402, aggregating the process node maps through the domain design model by using a Kanwei law algorithm to obtain the aggregation result corresponding to the domain object.
Understandably, the conway law algorithm is an algorithm that combines similar process nodes and process node relations under the same process node map to generate a new process node, the new process node can include functions of an original process node and the original process node relation, the new process node is determined as an entity, the aggregated process node relation is determined as an aggregation root, the process nodes included in the entity are determined as value objects, and the aggregation result includes an entity corresponding to the process node, the value objects associated with the entity and the aggregation root.
The method and the system realize the convergence of the process nodes and the process node relationship in the process to be migrated of the same field object through the field design model, and construct a process node map; and aggregating the process node maps through the field design model by using a Kangwei law algorithm to obtain an aggregation result corresponding to the field object, thus aggregating the processes of the same field object through the field design model to construct a process node map, aggregating the process node maps by using the Kangwei law algorithm to output the aggregation result, reducing the cost of manual aggregation, automatically aggregating the relationships between each process node and each process node into an entity, a value object and an aggregation root, saving the capacity of a target process, greatly compressing the occupied space of the process, and unifying a construction platform and a development language.
S50, according to the entities in the aggregation results and the value objects related to the entities, an entity data table related to the entities is created in the target system.
Understandably, the entity comprises an entity name, an entity storage data format and an entity interface, the entity data table conforming to the entity is created in the target system, the table name of the data table is associated with the entity name, the field in the data table corresponds to the value object associated with the entity, the database structure stored in the entity data table is the same as the entity storage data format, and the entity data table can communicate through the entity interface.
S60, according to the flows to be migrated, the entities corresponding to the flow nodes in the flows to be migrated, the entity data table, the value objects and the aggregation roots associated with the entities, reconstructing each target flow corresponding to each flow to be migrated in the target system, reconstructing all the target flows and determining that the system flow migration request is completed.
Understandably, each process node in the to-be-migrated process is replaced by the corresponding entity, the entity data table corresponding to the entity is associated, and the process node relationship is replaced by the value object and the aggregation root in a scene manner, that is, different value objects correspond to different relationships of the aggregation root, so that the target process corresponding to the to-be-migrated process is reconstructed, the process function realized by the target process is the same as that of the to-be-migrated process, and all the target processes are unified by one development language, so that the subsequent operation and maintenance are facilitated.
The method and the device realize that the flow set and the field migration list of the target system and the source system in the system flow migration request are obtained by responding to the system flow migration request; inputting all the processes into a domain design model, extracting domain semantic features of the process document information and the process nodes in each process through the domain design model, and identifying domain objects corresponding to each process; screening the flow corresponding to the field object matched with the field to be migrated in the field migration list from the flow set, and determining the screened flow as the flow to be migrated; aggregating the flow node and the flow node relation in the to-be-migrated flow of the same domain object through the domain design model to obtain an aggregation result corresponding to the domain object; creating an entity data table associated with each entity in the target system according to all the entities in the aggregation result corresponding to each field object and the value objects associated with the entities; reconstructing each target flow corresponding to each flow to be migrated in the target system according to the flow to be migrated, all the entities corresponding to the flow nodes in the flow to be migrated, the entity data table associated with the entity, the value object and the aggregation root, and reconstructing all the target flows to determine that the system flow migration request is completed.
Therefore, the method and the device realize the purpose that the process set and the field migration list of the target system and the source system in the system process migration request are obtained; performing domain semantic feature extraction on the process document information and the process nodes in each process through the domain design model, and identifying a domain object corresponding to each process; screening the process corresponding to the field object matched with the field to be migrated in the field migration list from the process set, and determining the process as the process to be migrated; aggregating the flows to be migrated of the same domain object through the domain design model to obtain an aggregation result; creating an entity data table at the target system according to all the entities in each aggregation result and the value objects associated with the entities; according to the flow to be migrated, all entities corresponding to the flow nodes in the flow to be migrated, the entity data table, the value objects and the aggregation root associated with the entities, each target flow is reconstructed, therefore, the fields of the flows to be migrated are identified through field semantic feature extraction and identification, the flows in the same field are aggregated through a field design model, an aggregation result containing the entities, the value objects and the aggregation root is obtained through aggregation, the entity data table is created and the target flows corresponding to the flows one by one are reconstructed according to the aggregation result, flow migration is completed, the affiliated fields of the flows are automatically identified, the flows of the fields to be migrated are migrated to a target system through a field design model based on a field-driven design algorithm, manual screening and development construction are not needed, the entities, the value objects and the aggregation root are directly generated through aggregation, all the flows are unified into a development language, the entity data table is created, and the target flows are reconstructed, the storage space of the flows is greatly saved, the effect of accurately and reliably migrating the target system is achieved, and the efficiency and the quality of migration are improved.
In an embodiment, a process migration apparatus is provided, and the process migration apparatus corresponds to the process migration method in the above embodiments one to one. As shown in fig. 6, the flow migration apparatus includes a receiving module 11, an extracting module 12, a matching module 13, an aggregating module 14, a creating module 15, and a reconstructing module 16. The functional modules are explained in detail as follows:
a receiving module 11, configured to respond to a system flow migration request, and acquire a flow set and a field migration list of a target system and a source system in the system flow migration request; the process set comprises a plurality of processes, and each process comprises process document information, process nodes and process node relations;
an extraction module 12, configured to input all the processes into a domain design model, perform domain semantic feature extraction on the process document information and the process nodes in each process through the domain design model, and identify a domain object corresponding to each process;
the matching module 13 is configured to screen the flow corresponding to the domain object matched with the domain to be migrated in the domain migration list from the flow set, and determine the screened flow as the flow to be migrated;
the aggregation module 14 is configured to aggregate, through the domain design model, the process nodes and the process node relationship in the to-be-migrated process of the same domain object to obtain an aggregation result corresponding to the domain object; the aggregation result comprises an entity corresponding to the process node, a value object associated with the entity and an aggregation root;
a creating module 15, configured to create, in the target system, an entity data table associated with each entity according to the entity in each aggregation result and the value object associated with the entity;
a reconstructing module 16, configured to reconstruct, in the target system, each target flow corresponding to each to-be-migrated flow according to the to-be-migrated flow, the entity corresponding to the flow node in the to-be-migrated flow, and the entity data table, the value object, and the aggregation root associated with the entity, reconstruct all the target flows, and determine that the system flow migration request is completed.
For specific limitations of the process migration apparatus, reference may be made to the above limitations of the process migration method, which is not described herein again. The modules in the flow migration apparatus may be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a process migration method.
In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the flow migration method in the above embodiments is implemented.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the flow migration method in the above embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (9)
1. A process migration method, comprising:
responding to a system flow migration request, and acquiring a flow set and a field migration list of a target system and a source system in the system flow migration request; the process set comprises a plurality of processes, and each process comprises process document information, process nodes and a process node relation;
inputting all the processes into a domain design model, extracting domain semantic features of the process document information and the process nodes in each process through the domain design model, and identifying domain objects corresponding to each process;
screening the flow corresponding to the field object matched with the field to be migrated in the field migration list from the flow set, and determining the screened flow as the flow to be migrated;
aggregating the process node and the process node relation in the process to be migrated of the same domain object through the domain design model to obtain an aggregation result corresponding to the domain object; the aggregation result comprises an entity corresponding to the flow node, a value object associated with the entity and an aggregation root;
creating an entity data table associated with each entity at the target system according to the entity in each aggregation result and the value object associated with the entity;
reconstructing each target flow corresponding to each flow to be migrated in the target system according to the flow to be migrated, the entity corresponding to the flow node in the flow to be migrated, the entity data table associated with the entity, the value object and the aggregation root, reconstructing all the target flows and determining that the system flow migration request is completed;
the aggregating the relationship between the process node and the process node in the to-be-migrated process of the same domain object through the domain design model to obtain an aggregation result corresponding to the domain object includes:
converging the flow node and the flow node relation in the flow to be migrated of the same field object through the field design model, and constructing a flow node map;
and aggregating the process node maps through the field design model by using a Kanwer law algorithm to obtain an aggregation result corresponding to the field object.
2. The process migration method according to claim 1, wherein before acquiring the process set and the domain migration list of the target system and the source system in the system process migration request in response to the system process migration request, the method comprises:
acquiring all original processes of the source system;
performing language type identification on each original flow through a flow chart construction model, and identifying a language type corresponding to each original flow;
acquiring an interpretation script corresponding to the language type corresponding to the original flow through a flow chart building model, interpreting the original flow through the acquired interpretation script corresponding to the original flow, and building the flow corresponding to the original flow according to a flow chart generating mode.
3. The process migration method according to claim 1, wherein performing, by the domain design model, domain semantic feature extraction on the process document information and the process nodes in each of the processes to identify a domain object corresponding to each of the processes comprises:
splicing the flow demand document and the flow interface in the flow to obtain a first text object corresponding to the flow, and simultaneously combining the flow scene information and the node object in the flow to obtain a second text object corresponding to the flow; the process document information comprises a process requirement document and process scene information; the process node comprises a process interface and a node object;
performing context semantic recognition on the first text object through a first text recognition model in the field design model to recognize a first recognition result, and performing long-context scene recognition on the second text object through a second text recognition model in the field design model to recognize a second recognition result;
and performing field boundary positioning on the first recognition result and the second recognition result through the field design model, and determining the field object corresponding to the process.
4. The process migration method according to claim 3, wherein the splicing the process requirement document in the process with the process interface to obtain a first text object corresponding to the process, and combining the process scene information in the process with the node object to obtain a second text object corresponding to the process comprises:
splicing the process interface before and after the process requirement document, and carrying out embedding conversion to obtain the first text object corresponding to the process;
and inserting the node object after each sentence in the process scene information, and combining the second text object corresponding to the process.
5. The process migration method according to claim 3, wherein said performing context semantic recognition on said first text object through a first text recognition model in said domain design model to identify a first recognition result comprises:
extracting context semantic features in the first text object through the first text recognition model; the first text recognition model is a Bi-LSTM-based language model;
identifying a node category result corresponding to the first text object according to the extracted context semantic features through the first text identification model;
and performing service field identification on the node type result through the first text identification model to obtain a first identification result corresponding to the first text object.
6. The process migration method according to claim 5, wherein the identifying a second recognition result by performing context scene recognition on the second text object through a second text recognition model in the domain design model comprises:
scene feature extraction is carried out on the second text object through the second text recognition model; the second text recognition model is a recognition model based on Bert;
identifying the context association degree according to the extracted scene features through the second text identification model, and identifying a scene category result corresponding to the second text object;
the scene type result comprises at least one scene type and a prediction probability corresponding to the scene type;
and carrying out scene field recognition on the scene category result through the second text recognition model to obtain a second recognition result corresponding to the second text object.
7. A process migration apparatus, comprising:
the receiving module is used for responding to a system flow migration request and acquiring a flow set and a field migration list of a target system and a source system in the system flow migration request; the process set comprises a plurality of processes, and each process comprises process document information, process nodes and a process node relation;
the extraction module is used for inputting all the processes into a field design model, performing field semantic feature extraction on the process document information and the process nodes in each process through the field design model, and identifying a field object corresponding to each process;
the matching module is used for screening the flow corresponding to the field object matched with the field to be migrated in the field migration list from the flow set and determining the screened flow as the flow to be migrated;
the aggregation module is used for aggregating the flow node and the flow node relation in the to-be-migrated flow of the same field object through the field design model to obtain an aggregation result corresponding to the field object; the aggregation result comprises an entity corresponding to the flow node, a value object associated with the entity and an aggregation root;
a creating module, configured to create, in the target system, an entity data table associated with each of the entities according to the entities in each of the aggregation results and the value object associated with the entity;
a reconstruction module, configured to reconstruct, in the target system, each target flow corresponding to each to-be-migrated flow according to the to-be-migrated flow, the entity corresponding to the flow node in the to-be-migrated flow, and the entity data table, the value object, and the aggregation root associated with the entity, reconstruct all the target flows, and determine that the system flow migration request is completed;
the aggregation module is further to:
converging the flow node and the flow node relation in the flow to be migrated of the same field object through the field design model, and constructing a flow node map;
and aggregating the process node maps through the field design model by using a Kangwei law algorithm to obtain the aggregation result corresponding to the field object.
8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the process migration method of any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, in which a computer program is stored, wherein the computer program, when being executed by a processor, implements the process migration method according to any one of claims 1 to 6.
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