Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide the intelligent flow construction and dynamic optimization method and system based on low codes, which have the advantages of reducing the technical threshold of flow construction, realizing flow dynamic optimization and improving compliance control capability.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the intelligent flow construction and dynamic optimization method based on the low codes comprises the following steps:
Selecting a process category based on a preset process classification;
Generating an initial flow template based on the flow category, wherein the initial flow template is output in a visual mode and comprises an initial task node, an initial flow branch and an initial gateway;
the initial task node, the initial flow branch and the initial gateway are adjusted through visual dragging on the initial flow template to form a basic flow;
executing a basic flow and recording flow execution data;
analyzing the flow execution data based on a preset rule statistics module to generate a plurality of optimization suggestions;
The user selects an optimization scheme according to the optimization proposal and outputs an optimization flow;
and adjusting the initial flow template based on a preset flow adjustment strategy.
Further, a rule optimization auxiliary module is configured, including:
Constructing a flow operation database, wherein the flow operation database records historical operation flows and corresponding flow execution data, the historical operation flows comprise task nodes, flow branches and gateways of each historical operation flow, and the flow execution data comprise task node execution time length, processing data of node configuration personnel, selection frequency of the flow branches, gateway decision basis and execution results, total time consumption of the flows, rollback times and rollback comments;
Counting abnormal links based on a flow operation database and outputting, wherein the abnormal links reflect task nodes, flow branches or gateways which have influence on an execution result when being processed in the flow operation;
a user inputs a plurality of optimization targets, a plurality of optimization schemes are formulated according to abnormal links, and a plurality of optimization suggestions are generated based on a preset rule base;
And (3) manually scoring each optimization scheme, and outputting the optimization scheme with the highest score as an optimization suggestion.
Further, an optimization scheme verification policy is configured, and the optimization scheme verification policy is executed after the actual operation of the optimization flow, and includes:
The actual operation data after the operation of the optimization flow is continuously collected,
The actual operational data is compared with the corresponding simulated operational data,
And if the actual operation data accords with the expectations of the simulation operation data, writing the optimization scheme and the actual operation data into the flow operation database.
Further, the flow adjustment strategy includes:
Obtain the scoring information of the optimization scheme selected by the user history,
Forming a comprehensive scoring index according to scoring weights of users on different optimization targets,
And adjusting task nodes and flow path configuration in the initial flow template based on the comprehensive scoring index.
Further, a rule auxiliary construction step is configured, and the rule auxiliary construction step is synchronously executed when the initial flow template is visually dragged, and includes:
pre-extracting structural flow rules based on a system file to form a rule set;
when a user constructs a basic flow through dragging, comparing the current flow structure with constraint conditions in the rule set in real time;
if the current flow structure is detected to not meet the constraint conditions in the rule set, outputting prompt information and corresponding correction suggestions.
Further, a process initiation pre-audit step is configured, including:
Acquiring a history operation flow associated with the current operation flow, searching and corresponding flow history execution data in a flow operation database by taking the history operation flow as an index,
Acquiring high-frequency error points in the current operation flow according to the flow history execution data, wherein the high-frequency error points reflect links of which the error frequency exceeds a frequency threshold value in the history operation flow,
And outputting the high-frequency error point and giving corresponding system regulations.
Further, a form compliance verification step is configured, and the form compliance verification step is synchronously executed in the execution process of the basic flow, and includes:
automatically scanning form data, checking the integrity and format compliance of the form based on a preset field check rule;
acquiring historical approval form data, comparing filling contents of a current form with constraint conditions in a preset rule base, and identifying potential risk points, wherein the potential risk points are specifically the contents of corresponding system files violated after the current form is combined with the historical approval form;
And outputting risk prompt information based on the potential risk points.
Further, the form compliance verification step is configured with a dynamic path adjustment strategy, and when the existence of the potential risk point is detected, the corresponding task node is automatically added according to the specific content of the potential risk point and the real-time rule matching result.
Further, the initial flow template is configured with an initial template auxiliary generation strategy, including:
And receiving a flow template requirement instruction input by a user, extracting keywords from the flow template requirement instruction, comparing the keywords with the historical operation flow in similarity, calling an initial flow template corresponding to the historical operation flow with the highest similarity, and outputting the initial flow template.
The intelligent flow construction and dynamic optimization method based on the low codes comprises the following steps:
the flow modeling engine is used for providing a visual dragging modeling interface of flow components such as nodes, paths, forms and the like;
The rule center module is used for maintaining a structured flow rule set and field compliance rules;
the data service module is used for collecting flow execution data in the flow operation process and synchronizing the flow execution data to the optimization suggestion generation module in real time;
the optimizing engine is used for analyzing the flow data based on a preset algorithm, generating optimizing suggestions and verifying effects;
the rule management module is used for converting the system text into executable compliance rules based on a preset structural standard;
and the intelligent auxiliary module is used for providing real-time assistance based on preset rules during process construction and approval. The invention has the beneficial effects that:
as can be seen from the above, the intelligent flow construction and dynamic optimization method and system based on the low codes provided by the application can be used for generating a basic flow through visual dragging and dynamically adjusting the flow structure based on the AI optimization model, so that the problems that the traditional OA system depends on professional technicians and the optimization period is long are solved, and the intelligent flow construction and dynamic optimization method and system based on the low codes have the advantages of reducing the technological threshold of flow construction, realizing dynamic optimization of the flow and improving the compliance control capability.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It will be understood that when an element is referred to as being "fixed to" another element, it can be directly on the other element or intervening elements may also be present. When a component is considered to be "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When an element is referred to as being "disposed on" another element, it can be directly on the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
In the prior art, an enterprise office automation system faces a technical bottleneck of process construction and optimization for a long time. Traditional systems rely on professional developers to modify the code level, which results in lengthy process adjustment periods and difficulty in adapting to rapidly changing business requirements. When the business process has efficiency bottleneck or compliance loopholes, an effective automatic analysis tool is lacking, and a manager needs to manually call the running log to perform problem positioning, so that the best optimization opportunity is missed. In the form approval link, approval personnel need to rely on personal experience completely to judge compliance, and potential risk points are easy to miss.
To solve the above problems, research and development teams observe that the low efficiency of process construction is due to the over-high technical threshold, while the optimized hysteresis is due to the lack of data-driven decision mechanism. By analyzing the enterprise flow management requirements, the visual construction tool can be found to reduce the technical dependence, and the real-time data acquisition and analysis can form an optimization basis. Further research finds that automatic compliance verification can be achieved by converting the system specification into the structural rule. Based on these findings, a technical route is formed that enables visual construction through a low code platform, dynamic optimization using operational data, and compliance assurance in conjunction with a rules engine.
Therefore, the application provides an intelligent flow construction and dynamic optimization method based on low codes, please refer to fig. 1, which comprises selecting flow categories based on preset flow classification, generating an initial flow template output in a visual mode based on the flow categories, wherein the template comprises initial task nodes, flow branches and gateways, forming a basic flow through a visual drag adjustment template component, executing the flow and recording execution data, analyzing the data through a rule statistics module to generate optimization suggestions, outputting the optimization flow according to the optimization scheme selected by a user, and adjusting the initial template based on the flow adjustment strategy. The preset flow classification is according to the business field, such as purchasing, personnel, finance, approval level, such as department level/company level, and flow complexity, such as tree classification system of single node/multi-branch division, including purchasing class, expense reimbursement class, personnel transaction class, etc., and the user selects matching class through classification catalog or label.
The process classification selection refers to dividing the process types according to the service scene, and can be realized by adopting a tree classification catalog or a label system to ensure the targeted generation of the process templates. The initial flow template generation refers to calling a preset template library according to the classification result, and can be realized by adopting a template matching algorithm, and the editable flow framework is displayed through a visual interface. The visual drag adjustment refers to modifying a flow structure through graphical operation, and can be realized by adopting an HTML5 drag-and-drop API, so that non-technicians are allowed to directly modify node relations and path logic. The process execution data record refers to an operation log obtained when the process runs, and can be specifically realized by adopting a buried point technology, and key indexes such as time consumption of a task, path selection frequency and the like are captured. The rule statistics module is a built-in data analysis engine, and can be realized by adopting an association rule mining algorithm to identify low-efficiency links and compliance risks in the process. The flow regulation strategy refers to a mechanism for dynamically updating the template, and can be realized by adopting a version control technology, and the template configuration is automatically iterated according to the optimization result.
Specifically, when the enterprise needs to newly create a purchase approval process, an operator first selects a "purchase class" process in the classification catalog. The system automatically generates an initial template containing standard nodes such as purchasing application, price comparison examination and approval, contract examination and approval and the like. And adding supplier qualification inspection nodes in a dragging mode, and adjusting the inspection path to be in a parallel countersign mode. After the flow is in online operation, the system records that the average time consumption of the contract approval node is abnormal. The rule statistics module analyzes and discovers that the node is repeatedly modified due to lack of the overhead audit, and accordingly an optimization suggestion for adding the overhead audit node is generated. After the user confirms, the system automatically updates the initial template configuration, and the follow-up newly-built flow defaults to contain the optimized node.
Compared with the prior art, the traditional system modifies the flow and requires a developer to rewrite and approve the logic code, and the scheme realizes the flow structure adjustment through the visual operation and shortens the modification period from a few weeks to an hour level. The prior art lacks operation data analysis capability, and cannot actively find flow bottlenecks, and the scheme can automatically position low-efficiency links and propose an optimization scheme through buried point acquisition and rule analysis. In the aspect of compliance guarantee, the traditional system relies on manual check system files, and the scheme converts the specifications into structural rules to realize real-time check when the form is filled in.
By the technical scheme, non-technicians can independently complete flow design and adjustment, and dependence on professional development resources is remarkably reduced. By continuously collecting the operation data and generating the optimization suggestion, the dynamic improvement of the flow performance is realized. By combining the structured rule engine, the compliance risk caused by human negligence is effectively reduced, and the business process is ensured to always meet the latest system requirements.
The application further provides a scheme for configuring a rule optimization auxiliary module, please refer to fig. 2, which comprises the steps of constructing a flow operation database, recording a historical operation flow and corresponding flow execution data by the flow operation database, wherein the historical operation flow comprises task nodes, flow branches and gateways of each historical operation flow, the flow execution data comprises task node execution time, processing data of node configuration personnel, selection frequency of the flow branches, gateway decision basis and execution results, total time consumption of the flow, rollback times and rollback comments, counting abnormal links and outputting based on the flow operation database, the abnormal links reflect task nodes, the flow branches or the gateways which have influence on the execution results in the process of the flow operation, inputting a plurality of optimization targets by a user, formulating a plurality of optimization schemes according to the abnormal links, generating a plurality of optimization suggestions based on a preset rule base, manually grading each optimization scheme, and outputting the optimization scheme with the highest grading as the optimization suggestion. The preset rule base consists of structural rules, the rules are stored in a relational database in terms of condition-action logic pairs, such as if-else sentences, sources comprise enterprise system file analysis results and historical optimization experience precipitation, update contents of the latest system are automatically synchronized every week, the abnormal links reflect task nodes, flow branches or gateways which have influence on execution results in flow operation, the influence types comprise efficiency, the execution duration exceeds the average value of like nodes by 20%, the compliance is met, the constraint of the preset rule base is violated, the accuracy rollback times are more than or equal to 3 times, and any type is met, namely the abnormal is judged.
The process operation database refers to a database for storing historical process operation records, and can be specifically realized by adopting a relational database or a time sequence database, and is used for persistently storing structured data generated in the process of executing the process. The abnormal links refer to links which are identified through data analysis and affect the flow efficiency or result, and can be specifically realized by adopting a statistical analysis algorithm or a machine learning model and used for positioning flow bottlenecks or error high-occurrence points. The optimization target refers to an improvement direction expected to be achieved by a user, and the optimization target can be specifically achieved by receiving keywords or options input by the user through an interactive interface and is used for guiding the generation direction of an optimization scheme. The preset rule base refers to a rule set containing compliance constraint and optimization logic, and can be specifically realized by a rule engine or a decision tree model, and is used for automatically generating optimization suggestions meeting service specifications. The manual scoring refers to subjective evaluation of alternatives by users, and is specifically realized by collecting scoring results of the users on different dimensions through a scoring interface, and is used for comprehensively evaluating the actual feasibility of the optimization scheme.
Specifically, the process running database forms an analyzable process execution data set by collecting data such as task node execution duration, process branch selection frequency and the like of the historical process. The abnormal link identification module automatically screens out links with abnormal execution duration, over-high rollback frequency or missing decision basis based on a preset statistical threshold or a machine learning model. After a user inputs an optimization target through an interactive interface, the rule optimization auxiliary module combines the characteristics of the abnormal links and the compliance requirements in the rule base to generate a plurality of candidate optimization schemes. For example, when it is detected that the average processing time of an approval node exceeds a preset threshold, the system may suggest adding parallel approval paths or adjusting the node responsible person configuration. After all candidate schemes are scored by the user from the dimensions of efficiency improvement, resource consumption, compliance risk and the like, the scheme with the highest score is selected as the final optimization suggestion.
Compared with the prior art, the traditional OA system lacks of systematic analysis capability on historical operation data, the identification of abnormal links depends on artificial experience judgment, and the generation process of the optimization scheme has the problems of strong subjectivity and low response speed. The scheme can quickly locate the flow bottleneck and generate the data-driven optimization suggestion by constructing the flow operation database and the automatic analysis module, and simultaneously, the scheme meets the actual business requirement by combining with a manual scoring mechanism.
Through the technical scheme, the method and the device can effectively solve the problems that data analysis is insufficient and optimization suggestions lack objective basis in the traditional process optimization process. The abnormal links are automatically identified, a multidimensional optimization scheme is generated, the process optimization period is obviously shortened, meanwhile, the service suitability problem possibly generated by pure algorithm recommendation is avoided by introducing a manual scoring mechanism, so that the finally selected optimization scheme meets the data analysis conclusion and can meet the personalized requirements of actual service scenes.
The application further provides an optimization scheme verification strategy, when the optimization flow is actually operated, the optimization scheme verification strategy is executed, referring to fig. 3, the optimization scheme verification strategy comprises continuously collecting actual operation data after the operation of the optimization flow, comparing the actual operation data with corresponding simulation operation data, and if the actual operation data accords with the expectation of the simulation operation data, writing the optimization scheme and the actual operation data into a flow operation database.
The optimization scheme verification strategy refers to a mechanism for verifying the effect of the implemented process optimization scheme, and can be specifically realized by adopting an automatic monitoring module and a data comparison algorithm, so that the actual effect of the optimization scheme is ensured to accord with expectations. The actual running data refers to an execution record generated in a real service scene by the optimized process, and can be obtained specifically through a buried point acquisition and log analysis technology and used for reflecting the actual running state of the optimization scheme. The simulated operation data refers to a predictive operation index generated based on historical data and an algorithm model, and can be specifically generated by adopting a machine learning model or a flow simulation tool for establishing an expected benchmark of the optimization effect. The process operation database refers to a knowledge base for storing historical process data and an optimization scheme, and can be specifically realized by adopting a distributed database architecture, and is used for accumulating optimization experience and providing data support for subsequent process iteration.
Specifically, after the optimization flow is in online operation, the system automatically starts a data acquisition module, and operation indexes such as execution duration, path selection frequency and the like of each node of the flow are captured in real time. The acquired actual operation data is processed by the data cleaning module and then is subjected to field level comparison with the simulated operation data which is generated by the flow simulation engine in advance. And when the deviation value of the key index in the actual operation data and the simulation data is in a preset threshold range, judging that the verification of the optimization scheme is passed. The verification-passed optimization scheme and the corresponding operation data thereof are automatically archived to a flow operation database and used as a reference basis for the subsequent flow optimization. The whole verification process is triggered by a preset automatic rule, and a step of manually intervening data comparison and result judgment is not needed.
Compared with the prior art, the traditional flow optimization scheme lacks a systematic verification mechanism after implementation, often depends on manual sampling inspection or post statistical analysis, and has the problems of long verification period and insufficient sample coverage. By establishing an automatic verification mechanism, the scheme realizes real-time monitoring and objective evaluation of the optimization effect, avoids subjective errors of human judgment, and obviously shortens the verification feedback period.
Through the technical scheme, the problems of lag verification and lack of data support after the implementation of the traditional flow optimization scheme are solved, and closed loop verification of the optimization effect is realized. In an actual business scene, after an enterprise implements a new purchasing approval process optimization scheme, the system can automatically verify whether the approval time after optimization reaches an expected shortening target, and convert the verified optimization scheme into a reusable process template, so as to provide a reliable data basis for the follow-up similar process optimization.
The application further provides a flow regulation strategy which comprises the steps of obtaining scoring information of an optimization scheme selected by a user history, forming a comprehensive scoring index according to scoring weights of the user on different optimization targets, and adjusting task nodes and flow path configuration in an initial flow template based on the comprehensive scoring index.
The optimization scheme scoring information selected by the user history refers to a data set for quantitatively evaluating the actual effect of the implemented optimization scheme by the user, and the data set can be specifically realized by adopting a five-level scoring system or a numerical interval scoring method and is used for reflecting the actual effect of the optimization scheme in the dimensionalities of efficiency improvement, compliance improvement and the like. The scoring weight refers to the relative importance of different optimization targets in the overall evaluation system, and can be calculated by adopting a hierarchical analysis method or an entropy weight method, and is used for quantifying the difference of attention degrees of users on targets such as cost control, approval aging and the like. The comprehensive scoring index refers to a multidimensional evaluation parameter formed by weighting calculation, and can be realized by adopting a linear weighting model or a fuzzy comprehensive evaluation algorithm, and is used for converting scattered scoring information into a global optimization basis for quantitative comparison. The task node and flow path configuration adjustment refers to dynamic modification of elements such as approval links, branch conditions and the like in the flow Cheng Moban, and particularly, a flow mining technology can be adopted to identify low-efficiency nodes, and the approval path is reconfigured by combining a grading result.
Specifically, after the user completes the selection and implementation of the optimization scheme, the system continuously collects the evaluation data of the scheme in actual operation. For example, in a purchase approval scenario, a user may give a higher score for an optimization scheme that shortens the approval period, while a lower score for a cost control class scheme. The scoring weight module automatically identifies that the user is more concerned with the timeliness index by analyzing historical operation data of the user. The comprehensive score index generation module resets the timeliness weight to 0.7 and the cost control weight to 0.3 to form a personalized evaluation system. The flow regulation engine carries out iterative optimization on the flow template based on the index, for example, the parallel approval nodes arranged in the high-scoring scheme are popularized and applied to the similar flows, and meanwhile, the budget rechecking frequency related to the low-scoring scheme is reduced.
Compared with the prior art, the traditional flow management system lacks dynamic learning ability for user preference, and flow optimization mainly depends on manual experience judgment. The static scoring model in the prior art cannot distinguish target priorities under different service scenes, so that the matching degree of an optimization scheme and actual requirements is low. According to the scheme, a quantifiable comprehensive scoring system is established, so that the process template can be continuously improved according to real feedback of a user, and personalized process configuration is realized on the premise of ensuring compliance of a system.
Through the technical scheme, the technical problem that the optimization direction of the traditional flow management system is disjointed with the actual demand of the user is solved. By quantitatively analyzing the attention degree of the user to different optimization targets, the process adjustment strategy can be accurately matched with service requirements, for example, compliance is guaranteed preferentially in a cost reimbursement process, and timeliness is emphasized in a purchase approval process. The dynamic adjustment mechanism effectively improves the adaptability of the flow template and the execution effect of the optimization scheme, and avoids the problems of subjective deviation and hysteresis caused by manual adjustment.
The application further provides a rule auxiliary construction step, wherein the rule auxiliary construction step is synchronously executed when the visualized dragging is carried out on the initial flow template, the rule auxiliary construction step comprises the steps of extracting structured flow rules in advance based on a system file to form a rule set, comparing constraint conditions in the current flow structure and the rule set in real time when a user drags and constructs a basic flow, and outputting prompt information and corresponding correction suggestions if the constraint conditions in the rule set are not met by the current flow structure.
The structured flow rule refers to a standardized flow requirement analyzed from an enterprise system document, and specifically, a natural language processing technology can be adopted to extract key flow elements, such as approval levels, monetary thresholds, department authorities and the like, so as to form a rule base identifiable by a machine. Constraint conditions refer to compliance requirements that must be met when a process is constructed, for example, a purchase request of a specific amount must pass through a financial department audit node, and a text system can be converted into a logic judgment condition through a rule engine. The prompt information and the correction proposal refer to an automatically generated positioning problem description and adjustment scheme when the flow structure violation is detected, for example, when the necessary approval link is detected to be absent, approval nodes inserted into the designated roles can be recommended.
Specifically, when a user drags a task node to construct a flow through a visual interface, the system background synchronously runs a rule checking mechanism. For example, when a purchase approval process is set up, if a user does not set a financial review node in the process, and the system rule requires that the purchase amount exceeds a set threshold, financial review must be performed, and at this time, the system immediately triggers an early warning prompt. The early warning information can specifically indicate the missing node type and the violated system clause number, and an operation suggestion for inserting the financial auditing node is given. The user can directly click to confirm the added node according to the prompt, or manually adjust the flow structure until all constraint conditions are met.
Compared with the prior art, the traditional OA system lacks real-time rule checking capability in the process design stage, and a designer can only rely on memory or manual check of system documents, so that key approval links are easily omitted. For example, when a cross-department collaboration process is built, process breakpoints often occur because of unfamiliarity with other department systems, and these problems are often only discovered when the process is actually running, resulting in a large amount of rework. By means of automatic rule analysis and real-time verification, the scheme can intercept illegal operations in real time in the process construction stage, and error processes are prevented from entering an execution link.
Through the technical scheme, the real-time compliance guarantee in the process design process is realized, and the process defect caused by human negligence is effectively reduced. For example, when a complex process involving multi-department collaboration is built, the system can automatically identify missing countersign nodes or override operations, ensuring that the process structure is fully matched with the system requirements. The dynamic verification mechanism remarkably reduces the workload of subsequent flow adjustment and exception handling, so that the flow of the low-code platform construction has flexibility and meets the compliance requirement.
The application further provides a process initiating pre-checking step, which comprises the steps of obtaining a history operation process related to a current operation process, searching corresponding process history execution data in a process operation database by taking the history operation process as an index, obtaining high-frequency error points in the current operation process according to the process history execution data, reflecting links of the error frequency exceeding a frequency threshold in the history operation process, outputting the high-frequency error points and giving out corresponding system regulations.
The flow operation database is a structured database for storing historical flow operation records, and can be specifically realized by adopting a relational database or a time sequence database, and is used for persistently storing operation tracks such as task node states, approval records, time-consuming data and the like generated in the execution process of the flow. The database provides a data basis for error pattern analysis by establishing an associative mapping of flow versions to execution data.
The high-frequency error point refers to a link of repeated error identified by the statistical analysis historical process execution data, and specifically, the frequency of error occurrence can be counted by adopting a sliding window algorithm, and the judgment can be performed by combining a preset frequency threshold. For example, an approving node is marked as a high frequency error point when it has more than 5 timeout or rollback operations in the last 30 process instances.
The rule matching means that the high-frequency error points are associated with relevant rule terms of the rule files, and particularly, a natural language processing technology can be adopted to extract keywords from the rule texts, so that a mapping relation library of error types and rule terms is established. When a high-frequency error point is detected, the corresponding system basis is automatically called through matching the error characteristic keywords.
Specifically, in the process initiation stage, the system automatically retrieves the historical operation instance corresponding to the current process template, and identifies the task node or path branch with the error occurrence rate exceeding the preset threshold by analyzing the data such as the approval log, the rollback record and the like stored in the process operation database. For example, in a purchase approval process, the system discovers that the contract auditing link has more than 20% of supplementary material requirements in the history process, i.e. determines that the link is a high-frequency error point. And then, the system invokes the purchase contract auditing specification clause from the system management module and pushes the specific bill of materials requirement and auditing standard to the flow initiator.
Compared with the prior art, the traditional OA system lacks an active early warning mechanism for a historical error mode when a process is initiated, and approval personnel often need to repeatedly process the same type of form errors. According to the scheme, through establishing an automatic analysis mechanism of flow operation data, potential risk links can be actively identified in a flow starting stage, and a specific guide is provided by combining a system specification, so that the repeated occurrence of errors in a newly-built flow is avoided.
Through the technical scheme, the error-prone links can be identified in advance and the compliance guide can be pushed in the process initiation stage, so that the approval interruption probability caused by form filling errors or process design defects is effectively reduced, the process rollback times and the communication cost are reduced, and meanwhile, the compliance guarantee of the process design is enhanced through the instant association of system clauses.
The application further provides a form compliance checking step, which is configured, and the form compliance checking step is synchronously executed in the execution process of a basic flow, and comprises the steps of automatically scanning form data, checking the integrity and format compliance of a form based on a preset field checking rule, acquiring historical approval form data, comparing filling contents of a current form with constraint conditions in a preset rule base, identifying potential risk points, specifically, violating contents of corresponding system files after the current form is combined with the historical approval form, and outputting risk prompt information based on the potential risk points.
The preset field verification rule refers to a predefined logic condition set for verifying the compliance of the form field, and specifically can be implemented by adopting regular expression matching, data type verification or business rule logic judgment, so as to ensure that the form is complete in filling and the data format accords with the specification. The historical approval form data refer to form examples of completed approval flows and associated approval result data stored in a database, and can be obtained through a database query interface specifically and used for providing a historical reference basis for compliance judgment of a current form. The potential risk points refer to abnormal situations that conflict exists between the content of the current form and the historical approval records and the requirements of the system, and can be specifically identified by logically reasoning constraint conditions in a rule engine and a preset rule base on the field values of the form, so as to find out the compliance risk caused by form filling errors or rule understanding deviation.
Specifically, in the execution process of the expense reimbursement flow, when the applicant submits the electronic reimbursement form, the system automatically scans fields such as expense type, amount and invoice number in the form to verify whether the necessary filling item is missing or the format of the amount is wrong. For example, if it is detected that the invoice number is not filled in the "year-department-serial number" format, the correction is immediately prompted. Meanwhile, the system calls the approval record of the similar expense in the past three months of the applicant, and when the current reimbursement amount is found to exceed the historical average value by a certain proportion through comparison, the potential risk of unfilled explanation is identified by combining with the provision of additional explanation about abnormal amount in the expense management system. At this time, the system generates prompt information containing specific risk descriptions and system clause references, pushes the prompt information to an approver interface, and automatically adds financial review nodes in an approval path.
Compared with the prior art, the traditional OA system relies on the approval personnel to manually check the contents of the form, and is difficult to quickly find format errors or historical data conflicts, and the scheme automatically checks the form basic compliance through preset rules, dynamically identifies potential risks by combining with the historical approval data, and achieves pre-alarm of risks. In the prior art, the approval personnel can find the problems of repeated reimbursement and the like only by manually turning over the historical documents, and the scheme automatically associates the historical data through the rule engine, so that the risk identification efficiency is remarkably improved.
By the technical scheme, the problems of lag in compliance inspection and dependence on manual experience in risk discovery of the form in the traditional flow approval are solved, real-time verification and risk early warning in the form filling stage are realized, and flow rollback caused by form errors is avoided. Meanwhile, through associating historical approval data with the system rules, composite risks which are difficult to find in single form inspection, such as repeated application, abnormal amount fluctuation and the like, can be identified, and accuracy of approval decision making and risk control capability are improved.
The application further provides that the form compliance verification step is configured with a dynamic path adjustment strategy, and when the potential risk points are detected, corresponding task nodes are automatically added according to the specific content of the potential risk points and the real-time rule matching result.
The potential risk points refer to the content of the corresponding system file violated after the current form is approved by the combination history of the form, and specifically, the potential risk points can be realized by adopting a natural language processing technology to carry out semantic analysis on form fields and carrying out logic judgment by combining a rule engine. This feature is used to identify form data combinations that may violate preset compliance requirements. The real-time rule matching refers to instant comparison of the current form data with constraint conditions in a preset rule base, and can be realized by adopting a regular expression matching or decision tree algorithm. This feature ensures that risk decisions are based on the latest compliance criteria. The dynamic path adjustment strategy is to automatically generate a remedial measure node according to the risk type, and can be specifically realized by dynamically inserting an approval link or a supplementary material node through an API (application program interface) of a flow engine. This feature enables intelligent reconstruction of the flow path to cope with burst risk.
Specifically, in the process of executing the purchasing application flow, when the condition that the supplier qualification of a certain purchasing order is associated with the historical blacklist is detected, the system automatically triggers a risk early warning mechanism. And determining that the order has qualification inspection defects by matching the admission rules of suppliers in the purchase management system in real time. At this time, the process engine automatically inserts a legal review node in the original approval chain, and requires the legal department to conduct special review on the background of the provider. The newly added nodes synchronously generate matched examination form templates and push the examination form templates to a specified examination person workbench.
Compared with the prior art, the traditional system can only interrupt the flow or return to modify when the form problem is found, and can not automatically adjust the subsequent processing path according to the risk property. According to the scheme, through a linkage mechanism of rule matching and flow reconstruction, a risk disposal link can be accurately positioned on the premise of maintaining flow continuity, and efficiency loss caused by flow interruption is avoided.
Through the technical scheme, the method and the device effectively solve the problem of stiffness in handling the qualified risks in the conventional approval process. After detecting the potential risk, the system can automatically adapt an optimal disposal path, such as supplementary material submission, special approval link insertion or multi-department countersignature mechanism start, so that risk disposal and business flow are in seamless connection. The method not only guarantees the rigidity requirement of system execution, but also avoids response delay caused by manual intervention, and remarkably improves the risk control efficiency under complex business scenes.
The application further provides that the initial flow template is configured with an initial template auxiliary generation strategy, which comprises the steps of receiving a flow template requirement instruction input by a user, extracting keywords from the flow template requirement instruction, comparing the keywords with the historical operation flow, calling the initial flow template corresponding to the historical operation flow with the highest similarity, and outputting the initial flow template.
The flow template requirement instruction refers to a flow construction requirement of a user through natural language or structured form input, and can be specifically realized by adopting a text input box and a drop-down selection component, and is used for defining core elements of a flow to be constructed. Keyword extraction refers to identifying key business elements from instructions through natural language processing technology, and can be realized by combining a word segmentation algorithm with a domain dictionary, so that user requirements can be accurately captured. The similarity comparison refers to calculating the matching degree of the current requirement and the historical flow through an algorithm, and can be specifically realized by combining a cosine similarity algorithm with a semantic vector model and used for rapidly positioning the reusable template. The historical operation flow refers to a verified flow instance stored in a database, and can be stored in a flow version snapshot form, and the historical operation flow comprises structural data such as node configuration, path rules and the like and is used for providing a reliable template subjected to practical inspection.
Specifically, when a user needs to newly build a service flow, the system receives a flow construction requirement containing elements such as a service scene, an approval link and the like through an interactive interface. After the input text is subjected to semantic analysis, the key business elements are extracted as standardized labels. The system performs multidimensional matching on the labels and the historical process metadata stored in the process database, and screens out the historical process template with the highest matching degree by calculating the weighted value of the semantic similarity and the structural similarity. After the template is subjected to compliance verification, the template is automatically loaded to a visual modeling interface to serve as an initial construction basis.
In some embodiments, when the user inputs a "purchase order approval process" requirement, the system automatically matches the process templates with up-to-standard dimension similarity, such as the purchase amount interval and the supplier type, in the historical process library. If multiple candidate templates exist, a visual contrast view may be provided to assist in user selection.
Compared with the prior art, the traditional system requires a user to manually construct a flow from a blank canvas, and the scheme realizes quick start of the flow construction through intelligent matching of the history template. In the prior art, manual retrieval history flow is relied on, and the scheme automatically recommends the optimal template through an algorithm, so that the flow design period is obviously shortened.
Through the technical scheme, the method and the device effectively solve the problems of low template multiplexing rate and poor construction efficiency of the traditional flow construction system. By automatically matching the historical best practice template, repeated design workload is reduced, and meanwhile, the newly-built process is ensured to accord with the existing service specification. The user can quickly generate a compliant initial flow frame without professional flow design experience, and the technical threshold of flow construction is obviously reduced.
The application further provides an intelligent flow construction and dynamic optimization system based on low codes, referring to fig. 4, which comprises a flow modeling engine, a rule center module, a data service module, an optimization engine, a rule management module and an intelligent auxiliary module. The system comprises a flow modeling engine, a rule center module, a data service module, an optimization suggestion generation module, a rule management module and an intelligent auxiliary module, wherein the flow modeling engine is used for providing a visual drag modeling interface of flow components such as nodes, paths and forms, the rule center module is used for maintaining a structured flow rule set and field compliance rules, the data service module is used for collecting flow execution data in a flow operation process and synchronizing the flow execution data to the optimization suggestion generation module in real time, the optimization engine is used for analyzing the flow data based on a preset algorithm to generate an optimization suggestion and verifying effect, the rule management module is used for converting a system text into an executable compliance rule based on a preset structured standard, and the intelligent auxiliary module is used for providing real-time assistance based on the preset rule during flow construction and approval.
The flow modeling engine is a core component supporting flow design through a graphical interface, and can be realized by adopting a drag-and-drop editor based on a browser, and the technical threshold is reduced by presetting standardized flow elements. The rule center module refers to a database system for storing and managing flow constraint conditions, and can be specifically realized by adopting a relational database in combination with a rule engine, so that the automatic matching of rule requirements during flow construction is ensured. The data service module is a middleware responsible for process operation data acquisition and transmission, and can be realized by combining a message queue with a data pipeline technology, so that real-time synchronization and persistent storage of operation data are realized. The optimization engine is a calculation module for generating a flow optimization proposal based on data analysis, and can be realized by combining a machine learning algorithm with a business rule base, and the effectiveness of an optimization scheme is verified through simulation. The rule management module is a processing unit for converting a natural language system into a machine executable rule, and can be specifically realized by combining a natural language processing technology with a knowledge graph to realize the structural analysis of the system clause. The intelligent auxiliary module is an interactive component for providing real-time guidance in the process of flow operation, and can be specifically realized by combining a rule matching engine with a context sensing technology, and compliance prompts are actively pushed at key nodes.
Specifically, in the process construction stage, the process modeling engine provides a visual interface for service personnel to directly drag the process elements, and the rule management module synchronously converts related system documents into structural rules and stores the structural rules into the rule center module. When the user configures the form fields, the intelligent auxiliary module automatically invokes the field compliance rules in the rule center module to verify in real time. In the process of flow operation, the data service module continuously collects data such as task execution time length, path selection frequency and the like, and the optimization engine identifies flow bottlenecks and generates optimization suggestions based on the data. In the approval link, the intelligent auxiliary module dynamically marks potential risk points by comparing historical approval data with the contents of the current form. When the process path is detected to deviate from the preset rule, the rule center module triggers an alarm and recommends a correction scheme, and the optimization engine performs simulation verification on the adjusted process.
Compared with the prior art, the traditional OA system requires professional developers to write codes to realize flow configuration, and the system enables business personnel to directly participate in flow design through a visual modeling engine, so that technical dependence is greatly reduced. The existing system lacks real-time rule checking capability, and the system realizes automatic conversion and execution of the system specification through a rule management module, so that the process construction stage is ensured to meet the compliance requirement. The traditional scheme is difficult to discover the flow defects in operation in time, and the system realizes continuous analysis of operation data by means of the data service module and the optimization engine, so that flow configuration can be dynamically optimized.
Through the technical scheme, the method solves the problems that the traditional OA system flow construction depends on coding, compliance verification is lagged, data support is lacking in optimization and the like. Business personnel can quickly build a compliance flow without technical background, and the system specification is directly integrated into a flow design link through automatic analysis, so that the compliance risk caused by artificial omission is avoided. The real-time collection and analysis of the operation data provide objective basis for flow optimization, so that the flow efficiency can be continuously improved along with the business development. In the approval link, the system actively recognizes form filling abnormality and path deviation, and effectively reduces approval delay and operation errors.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.