Multi-module business process automatic splicing method and system

Abstract

The invention provides a multi-module business process automatic splicing method and system, and relates to the technical field of business process splicing, and the method comprises the steps: carrying out the modularization processing of business functions, obtaining a plurality of function module sets, and each function module set comprises a plurality of independent business function modules; performing function demand analysis based on the real-time service demand, and determining a target function demand; matching to obtain a plurality of standby function modules; compatibility and splicability analysis is carried out, N target function modules are obtained through screening, and N is larger than or equal to 0; carrying out business process analysis, and constructing a business flow chart containing a logic sequence and a dependency relationship; and based on the business flow chart, performing automatic splicing of the N target function modules to form a multi-module business flow. The technical problems that in the prior art, the development time is long and the flexibility is insufficient due to the fact that the corresponding process logic usually needs to be developed from the beginning for each new business requirement, and modular reuse is difficult to achieve are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of business process splicing, and particularly relates to a method and system for automatically splicing multi-module business processes. Background Art

[0002] In modern information systems, the design and implementation of business processes are one of the core links. Especially in complex and diverse business scenarios, such as email scanning and verification systems, production management systems, etc., how to efficiently define, manage, and execute business processes is a key issue. Traditional business process implementation methods usually rely on the following methods: First, the process logic is usually implemented by developers by writing a large amount of code, and specific business requirements need to be met through customized development; Second, when business requirements change, process management requires developers to manually modify and test new logic, which is complex and time-consuming; Third, processes with similar business logics usually require repeated code development, and it is difficult to achieve modular reuse. The above methods have exposed many deficiencies in practical applications. Especially when facing complex and changeable business requirements, traditional technologies are difficult to effectively meet the requirements of dynamic adjustment, flexible splicing, and rapid deployment. Summary of the Invention

[0003] The present application provides a method and system for automatically splicing multi-module business processes, aiming to solve the technical problem in the prior art that each new business requirement usually requires developing corresponding process logic from scratch, making it difficult to achieve modular reuse, resulting in long development time and insufficient flexibility.

[0004] In a first aspect disclosed by the present application, a method for automatically splicing multi-module business processes is provided. The method includes: performing modular processing on business functions to obtain a plurality of function module sets, where each function module set includes several independent business function modules; performing functional requirement analysis based on real-time business requirements to determine target functional requirements; based on the target functional requirements, traversing the plurality of function module sets to match and obtain a plurality of alternative function modules; performing compatibility and spliceability analysis on the plurality of alternative function modules, and screening to obtain N target function modules according to the analysis results, where N is greater than or equal to 0; parsing the business process according to the target functional requirements to construct a business process diagram including logical sequence and dependency relationships; based on the business process diagram, performing automatic splicing of the N target function modules to form a multi-module business process.

[0005] The second aspect disclosed in this application provides a multi-module business process automated splicing system. The system is used for the above-mentioned multi-module business process automated splicing method, and the system includes: a modular processing unit for modularly processing business functions to obtain multiple function module sets, where each function module set contains several independent business function modules; a function requirement analysis unit for performing function requirement analysis based on real-time business requirements to determine target function requirements; a spare function module acquisition unit for traversing the multiple function module sets based on the target function requirements to match and obtain multiple spare function modules; a spliceability analysis unit for performing compatibility and spliceability analysis on the multiple spare function modules, and screening to obtain N target function modules according to the analysis results, where N is greater than or equal to 0; a business process diagram construction unit for parsing the business process according to the target function requirements to construct a business process diagram including logical sequence and dependency relationships; an automated splicing unit for performing automated splicing of the N target function modules based on the business process diagram to form a multi-module business process.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects:

[0007] By modularizing business functions to form multiple function module sets, the flexibility and reusability of the system can be significantly improved. The modular business functions can operate independently, facilitating quick splicing and combination according to different business requirements. In this way, developers do not need to start from scratch for each new business, reducing the workload of repetitive development. They can directly combine existing modules, greatly shortening the development cycle. Moreover, the modular design enables the system to easily handle dynamic changes in business requirements and support rapid iteration of multiple scenarios. By analyzing functional requirements and traversing and matching alternative function modules, it is ensured that an appropriate module combination can be quickly found when business requirements change. In this way, module matching and splicing can be carried out immediately when requirements change, avoiding delays caused by business adjustments in traditional development. By selecting the optimal function modules based on actual requirements, the loading of redundant modules is avoided, improving the system operation efficiency. By analyzing the compatibility and spliceability of multiple alternative function modules, it can be ensured that the selected function modules can be seamlessly connected, reducing potential logical errors or conflicts. By parsing the target functional requirements, a business process diagram containing logical order and dependency relationships is automatically constructed to realize the visual management of business logic. This form of process diagram intuitively presents complex business logic, facilitating understanding and operation by developers and operation and maintenance personnel. In this way, non-technical personnel only need to adjust business logic based on the process diagram without delving into code implementation, thus reducing the dependence on technical background. And through the separate management of formal process diagrams and template process diagrams, flexible switching between multiple business scenarios is supported, ensuring the stability and consistency of system operation. Based on the constructed business process diagram, the splicing operation of multiple target function modules is automatically completed to form a multi-module business process. The automated splicing depends on the logical order and dependency relationships of the process diagram, avoiding errors that may be introduced by manual splicing. Moreover, the process setup and adjustment under different business scenarios can be quickly completed without repeating code development or conducting large-scale test verification.

[0008] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented in accordance with the content of the description. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0009] Figure 1 It is a schematic flowchart of the multi-module business process automated splicing method provided by an embodiment of this application.

[0010] Figure 2 It is a schematic structural diagram of the multi-module business process automated splicing system provided by an embodiment of this application.

[0011] Description of the drawing reference numerals: Modular processing unit 10, functional requirement analysis unit 20, spare function module acquisition unit 30, spliceability analysis unit 40, business process diagram construction unit 50, automatic splicing unit 60. Detailed implementation manners

[0012] By providing a multi-module business process automatic splicing method and system in an embodiment of the present application, the technical problems in the prior art that each new business requirement usually needs to develop the corresponding process logic from scratch, it is difficult to achieve modular reuse, resulting in long development time and insufficient flexibility are solved.

[0013] After introducing the basic principle of the present application, various non-limiting implementation manners of the present application will be specifically introduced below with reference to the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] Embodiment 1, as Figure 1 shown, the embodiment of the present application provides a multi-module business process automatic splicing method, and the method includes:

[0015] Perform modular processing on business functions to obtain a plurality of function module sets, where each function module set includes several independent business function modules.

[0016] According to the overall business requirements, disassemble the complex business process into multiple independent small function units. Each function unit represents the core part of a business process. These small function units are modular and independently executable. For example, in the business of scanning and checking letters, it can be disassembled into functions such as letter scanning, letter checking, and report generation. Through business process analysis, determine the input, output, dependency relationship, and execution order of each function module to ensure that each module has a clear function. Convert each disassembled function unit into a function module set. Each function module set includes several independent business function modules. These function module sets are combinations based on different business requirements. The functions of each module set can be executed independently or cooperate with other modules to complete more complex tasks.

[0017] Perform functional requirement analysis based on real-time business requirements to determine the target functional requirements.

[0018] Through the real-time data acquisition module, data from different sources is automatically collected, including user operation data, business load data, external environment data, etc. The collected data is preprocessed by the intelligent data analysis engine. The purpose of preprocessing is to remove noise data, fill in missing values, and standardize the data format. Based on the preprocessed data, the business environment is analyzed through the scenario recognition algorithm. Scenario recognition not only processes static data but also includes responses to the dynamic changes of data. For example, if it is detected that the business load is too heavy, the priorities of some functional modules need to be adjusted. Through the intelligent analysis model, the environmental data is deeply analyzed to extract specific functional requirements. For example, certain external conditions require the system to respond in real time, or certain user operations may trigger specific processes. According to the data analysis results, the target functional requirements required by the current business process are determined.

[0019] Based on the target functional requirements, traverse the multiple functional module sets to match and obtain multiple alternative functional modules.

[0020] Take the target functional requirements as the input and traverse multiple functional module sets. Each functional module set contains several independent business functional modules, and these modules are completely independent and cooperate with each other in some cases. The traversal method is to scan each functional module set one by one and check whether each module in it meets the current target functional requirements. During the traversal process, a function matching algorithm is used to determine which functional modules are suitable for the current requirements. For example, according to factors such as the input and output requirements of the module, the dependencies between modules, and the processing capabilities, suitable modules are matched. Through matching, multiple alternative functional modules are obtained. The matched alternative functional modules may not fully meet the target functional requirements, but they are replaceable modules that can perform some tasks of the target functional requirements under certain specific conditions.

[0021] Perform compatibility and spliceability analysis on the multiple alternative functional modules, and screen and obtain N target functional modules according to the analysis results, where N is greater than or equal to 0.

[0022] Perform compatibility analysis on multiple alternative functional modules. Specifically, first analyze the functional compatibility between alternative functional modules, that is, whether the functions of each module can work together and whether there are conflicts. For example, one module requires data input from an external system, while another module is a data provider, and it is necessary to check whether these modules can cooperate effectively; secondly, some modules may require specific hardware resources or system resources, such as memory, CPU, storage, etc. Therefore, it is necessary to check whether these alternative modules are compatible in terms of resources, specifically paying attention to whether the modules can run normally on the target hardware platform and whether the modules require independent resource pools.

[0023] Conduct spliceability analysis on multiple backup function modules. Specifically, conduct interface compatibility analysis, that is, analyze whether the input and output interfaces of the backup function modules match, that is, whether the output of each module can be the input of the next module, to ensure seamless connection of multiple modules in the system; conduct process splicing analysis, that is, analyze whether the process formed after module splicing is reasonable and whether it can meet the requirements of the entire business. Specifically, it is necessary to pay attention to the execution order between function modules and whether there are problems caused by improper sequence; further, in order to achieve the splicing of different function modules, it is necessary to analyze whether they can be connected through standardized interfaces, and it may be necessary to design an intermediate adaptation layer or conversion layer for some modules so that these modules can be combined as needed.

[0024] Based on the analysis results of compatibility and spliceability, screen out N target function modules that meet the target requirements. After screening, these modules meet the current business requirements, have good compatibility, and can be spliced smoothly, ensuring that the overall function and performance of the system meet the expectations.

[0025] Analyze the business process according to the target function requirements, and construct a business process diagram containing logical sequence and dependency relationship.

[0026] The target function requirements are the expectations for a certain business process, including the functions to be implemented, the requirements for input and output data, response time, interaction methods, etc. Analyzing the business process according to the target function requirements means transforming the entire business requirements into a series of operation steps and event flows. Each step involves the invocation or interaction of one or more function modules. According to the business process analysis results, construct a business process diagram containing logical sequence and dependency relationship. The business process diagram is a graphical representation, using nodes to represent each business step and arrows to represent the dependency relationship and execution order between tasks. Among them, if some tasks are executed in parallel, there will be branch points in the flowchart to represent parallel tasks, and multiple tasks can be carried out simultaneously.

[0027] Based on the business process diagram, conduct automated splicing of the N target function modules to form a multi-module business process.

[0028] According to the target function requirements and the business process diagram, map the nodes in each business process diagram to one or more target function modules. Each node represents a business operation, and it is necessary to ensure that the selected target function modules can complete this operation. Utilize the previously screened N target function modules to automatically determine which modules can meet the requirements of each step. Here, information such as the input and output interfaces, execution capabilities, and compatibility of the modules needs to be referred to. After the mapping relationship is clear, automatically splice the function modules together to form a complete multi-module business process. The splicing process will combine the modules in sequence according to the order and dependency relationships defined in the business process diagram to ensure the correctness of the data flow and control flow. Specifically, in the flowchart, the module splicing is carried out in the order indicated by the arrows. If a certain step depends on the output of the previous step, then connect the front and rear modules to ensure that the data can be correctly transmitted. For tasks executed in parallel, start multiple function modules simultaneously. These modules will independently run within the same time, execute their respective tasks, and merge or synchronize the results.

[0029] Finally, all function modules are successfully spliced according to the business process diagram to form a complete multi-module business process. This process is automatically executed to complete specific business requirements and can be further extended or optimized for the connections between modules as needed to meet business requirements.

[0030] Furthermore, the modular processing of business functions to obtain multiple function module sets, where each function module set contains several independent business function modules. The method includes:

[0031] Decompose the business process based on business functions to obtain multiple business process links; randomly extract the first business process link from the multiple business process links, analyze the functional requirements of the first business process link to obtain several first business functions; establish several first business function modules based on the mapping of the several first business functions, and combine the several first business function modules to obtain the first function module set; and so on, traverse the multiple business process links to obtain the multiple function module sets.

[0032] According to the overall business requirements, analyze the main functional points of the business. Business functions refer to specific operations or tasks that need to be executed to meet business requirements. Decompose the entire business process from beginning to end to identify multiple main links. Each link represents an independent part of the business, usually completing specific functional requirements and having logical or data dependencies with other links. Through the decomposition process, multiple business process links are finally obtained. These links can be relatively independent or interdependent. The purpose of decomposition is to transform a complex business process into multiple manageable small links for subsequent processing, optimization, and automated execution.

[0033] From the multiple business process steps obtained by disassembling, randomly select one as the first business process step for in-depth analysis. This step is an integral part of the entire process and represents any business operation. Random selection can ensure that different steps are processed in sequence and not fixed on a specific step. This approach helps to explore the functional requirements of different steps and provides broader support for the subsequent design of functional modules.

[0034] Analyze the functional requirements of the first business process step. The goal of functional requirement analysis is to clarify the specific business requirements of this step, ensure that the functional requirements of each step are fully understood and can be transformed into technical implementation. Functional requirement analysis includes analyzing input and output requirements, processing logic, performance requirements, data dependencies, interaction requirements, etc. Through functional requirement analysis, further refine this business process step into several specific business functions. Each business function represents a specific operation or task in this step, that is, a subtask that needs to be completed within the system.

[0035] Based on the several first business functions parsed, map and establish several first business function modules. Each function module is designed according to specific business function requirements, is an independent executable unit, has specific input, output, and processing logic, and is responsible for implementing the specific operations required for the business function. Combine the several first business function modules established into a first function module set. Each function module set contains several independent function modules.

[0036] Continue to traverse the multiple business process steps disassembled. For each step, repeat the requirement analysis of the business functions of this step, map these functions to function modules, and finally combine them into a complete function module set. During the process of traversing all business process steps, create a corresponding function module set for each step. Finally, obtain multiple function module sets, and each function module set is associated with a specific business process step.

[0037] Furthermore, the method for performing functional requirement analysis based on real-time business requirements to determine target functional requirements includes:

[0038] Automatically collect business environment data through a real-time data collection module. Among them, the business environment data includes user operation data, business load data, and external environment data; perform data processing on the business environment data through an intelligent data analysis engine, and perform scenario recognition and requirement analysis to obtain the target functional requirements.

[0039] Deploy a real-time data collection module. The real-time data collection module is responsible for collecting real-time data from different sources. Specifically, it obtains business environment data through sensors, logging systems, network monitoring tools, etc., including user operation data, business load data, and external environment data. Among them, user operation data includes specific behavior data of users interacting with the system. For example, user login information, click behavior, form submission, search requests, etc. These data can help analyze user needs and operation habits and provide support for the selection of subsequent functional modules. Business load data refers to the load information generated during the execution of the business system, including server load, database response time, the number of processed requests, task complexity, etc. These data can reflect the performance status and load situation of the system and help evaluate whether the current system can carry new requirements. External environment data refers to external factors affecting the operation of the system, such as network environment, temperature, humidity, geographical location, device status, etc. External environment data is used to evaluate the stability and response ability of the system.

[0040] The intelligent data analysis engine cleans and preprocesses the data collected from different sources, including removing noisy data, filling in missing values, normalizing or standardizing the data, etc. The purpose of data preprocessing is to ensure data quality and provide high-quality input for subsequent analysis. For example, user operation data may contain duplicate records, and business load data may have errors, which all need to be corrected through data preprocessing steps.

[0041] Scenario recognition is a key process in intelligent data analysis. It identifies different scenarios occurring in the current business environment through data analysis. Each scenario represents a working state of the system or a specific manifestation of user needs. Requirement analysis extracts valuable information from the data and judges the functional requirements of the system in different scenarios. By comprehensively analyzing user behavior, load conditions, and the external environment, potential user needs are inferred. Based on the results of scenario recognition and requirement analysis, target functional requirements are generated. These requirements describe the specific functions that the system should perform.

[0042] Furthermore, the method of processing the business environment data through the intelligent data analysis engine, performing scenario recognition and requirement analysis, and obtaining the target functional requirements includes:

[0043] Activate the intelligent data analysis model of the intelligent data analysis engine. The intelligent data analysis model includes a first data analyzer and a second data analyzer. The first data analyzer is constructed based on a convolutional neural network, and the second data analyzer is constructed based on a long short-term memory network. Analyze the spatial features of the user operation data and business load data through the first data analyzer to obtain the spatial features of functional requirements. Analyze the time series features of the external environment data through the second data analyzer to obtain the time series features of functional requirements. Integrate the spatial features of functional requirements and the time series features of functional requirements to obtain the target functional requirements.

[0044] The intelligent data analysis engine is used to process a large amount of data collected from the business environment, can automatically trigger and select appropriate models for data analysis according to different requirements. The model contains multiple analyzers, and each analyzer has specific functions and applicable scenarios.

[0045] Among them, the first data analyzer is constructed based on a convolutional neural network. A convolutional neural network is a deep learning algorithm commonly used to process images and spatial data. Through multiple convolutional operations, it can effectively extract features from the original data. In this step, the first data analyzer uses the convolutional neural network to extract features from the spatio-temporal data collected from aspects such as user operation data and external environment data. Through the convolution kernel, the input data is convolved to extract valuable features. These features can help the model identify different business patterns or anomalies, and finally output the spatial features of functional requirements, describing the key patterns in the operation and load of the business system, such as which functions are frequently used, and the concentration of system load in different regions.

[0046] The second data analyzer is constructed based on a long short-term memory network. A long short-term memory network is a special type of recurrent neural network, especially suitable for processing and predicting time series data, capable of remembering long-term dependencies and effectively processing data with delays in the time dimension. In this step, the second data analyzer models the time series of business load data and user operations through the long short-term memory network, identifies the time dependencies in the data, and can accurately predict future business requirements or potential system bottlenecks by analyzing the change trends of the time series. Finally, it outputs the time series features of functional requirements, describing the potential impact of the external environment on the business.

[0047] By combining a convolutional neural network and a long short-term memory network, it is possible to process complex spatio-temporal data and time series data simultaneously. The convolutional neural network is good at extracting information from the spatial dimension, while the long short-term memory network performs excellently in time series analysis. The combination of the two can enhance the comprehensive analysis ability of the system, enabling the system to adapt to more types of data and complex business environments. After completing the data analysis, the intelligent data analysis engine combines the analysis results of the convolutional neural network and the long short-term memory network to obtain the final target functional requirements, providing a basis for the subsequent optimization of the functional modules.

[0048] Furthermore, before traversing the multiple function module sets based on the target functional requirements, the method includes:

[0049] Based on the spatial features of the functional requirements and the temporal features of the functional requirements, perform an urgency assessment of the business objectives to obtain an urgency assessment result; based on the urgency assessment result, use a priority scheduling algorithm to rank the business function modules in the multiple function module sets.

[0050] The evaluation indicators for the urgency assessment of business objectives include task importance, time sensitivity, current load status, and impact of environmental changes. Among them, task importance evaluates the importance of a function module for achieving the overall business objective, time sensitivity evaluates whether the task has strict time requirements, such as response time or deadline, current load status evaluates whether the function module is in a high-load or resource-constrained state, and impact of environmental changes evaluates whether the external environment requires the module to be executed first. Evaluate according to each evaluation indicator respectively, and then perform a weighted sum of the evaluation results to calculate the urgency score, and finally obtain the urgency assessment result, which includes the urgency score and priority category of each function module.

[0051] Use a priority scheduling algorithm, such as static priority scheduling, that is, sort by the urgency score from high to low, and select the module with the highest score to execute each time; or use dynamic priority scheduling, that is, update the urgency score of the module in real time and adjust the priority according to environmental changes. Based on the urgency assessment result and the scheduling algorithm, rank the multiple function modules by priority to form a priority queue, and high-priority modules enter the execution queue first, followed by medium- and low-priority modules. This process ensures the reasonable allocation of system resources and the priority achievement of key business objectives.

[0052] Furthermore, based on the target functional requirements, traversing the multiple function module sets to match and obtain multiple alternative function modules, the method includes:

[0053] Randomly extract a second set of functional modules from the multiple sets of functional modules; read the predetermined service characteristics, extract functional characteristics of several second service functional modules in the second set of functional modules based on the predetermined service characteristics to obtain several functional characteristic sets, and extract characteristics of the target functional requirement to obtain a functional requirement characteristic set; sequentially compare and analyze the functional requirement characteristic set with the several functional characteristic sets to obtain several characteristic similarities; based on the several characteristic similarities, extract the service functional modules that meet the characteristic similarity threshold as alternative functional modules.

[0054] Randomly extract a second set of functional modules from multiple sets of functional modules for in-depth analysis. The second set of functional modules contains several modules and further feature extraction and analysis are to be carried out.

[0055] The predetermined service characteristics describe the specific attributes of the target service function, including input requirements, output capabilities, runtime performance parameters, dependencies, and applicable scenarios. Based on the predetermined service characteristics, extract functional characteristics of several second service functional modules in the second set of functional modules. Specifically, important features are screened through feature selection and dimensionality reduction methods. For example, the principal component analysis method is used. The extracted functional characteristic set includes the main functions and performance indicators of the modules. In the same way, based on the predetermined service characteristics, extract characteristics of the target functional requirement to obtain a functional requirement characteristic set corresponding to the functional characteristic set.

[0056] The core of the comparative analysis lies in measuring the similarity of two characteristic sets. First, vectorize the functional requirement characteristic set and several functional characteristic sets, represent the characteristic sets in the form of vectors or matrices for easy similarity calculation, and then use a similarity calculation method. For example, use cosine similarity to measure the directional similarity of two vectors. Finally, calculate the weighted similarity according to different feature weights, record the similarity of each module with the target requirement characteristics, and obtain several characteristic similarities.

[0057] The characteristic similarity threshold is the criterion for determining whether a module can be used as an alternative module. It can be set by referring to the similarity and result relationship of past tasks. For example, it is set to 0.8, that is, 80% similarity. Traverse the list of characteristic similarities, select the modules with similarity higher than the threshold into the alternative functional modules. Further, sort the selected modules in descending order of similarity, and give priority to the modules closest to the requirements, providing a basis for subsequent splicing and optimization.

[0058] Furthermore, the predetermined service characteristics include input requirements, output capabilities, runtime performance parameters, dependencies, and applicable scenarios.

[0059] The predefined business characteristics are key parameters for describing functional modules, mainly covering input requirements, output capabilities, runtime performance parameters, dependencies, and applicable scenarios. These characteristics can help evaluate the matching degree of business functional modules, thereby ensuring that the modules can meet specific business requirements. Among them, input requirements refer to the resources, data, or other input conditions required by the functional module during execution, used to evaluate whether the functional module can accept and process specific input data; output capabilities refer to the results, data, or response capabilities that the functional module can generate, used to ensure that the output of the functional module meets the target business requirements and can effectively interact with other modules; runtime performance parameters describe the performance of the module during operation, such as response time, processing delay, concurrent processing ability, CPU usage, etc., used to evaluate the operation efficiency and stability of the functional module under high load or complex scenarios; dependencies refer to the mutual dependencies and collaboration relationships between functional modules and other modules. Some modules may depend on external services or previous processing results. By analyzing the dependencies between modules, the module loading order can be effectively controlled and resource allocation can be optimized; applicable scenarios refer to the specific business scenarios or environments where the functional module is most suitable for application. These scenarios can be business types, workload types, specific user groups, etc. By defining the above predefined business characteristics, the applicability of each functional module and its role in the overall process can be accurately evaluated, thus providing decision-making support for the subsequent automatic splicing of modules and business process optimization.

[0060] In summary, the multi-module business process automatic splicing method provided by the embodiments of the present application has the following technical effects:

[0061] By modularizing business functions to form multiple function module sets, the flexibility and reusability of the system can be significantly improved. The modular business functions can operate independently, facilitating quick splicing and combination according to different business requirements. In this way, developers do not need to start from scratch for each new business, reducing the workload of repetitive development. They can directly combine based on existing modules, greatly shortening the development cycle. Moreover, the modular design enables the system to easily handle dynamic changes in business requirements and support rapid iteration of multiple scenarios. By analyzing functional requirements and traversing and matching alternative function modules, it is ensured that an appropriate module combination can be quickly found when business requirements change. In this way, module matching and splicing can be carried out immediately when requirements change, avoiding delays caused by business adjustments in traditional development. By selecting the optimal function modules based on actual requirements, the loading of redundant modules is avoided, improving the system operation efficiency. By analyzing the compatibility and spliceability of multiple alternative function modules, it can be ensured that the selected function modules can be seamlessly connected, reducing potential logical errors or conflicts. By parsing the target functional requirements, a business process diagram containing logical order and dependency relationships is automatically constructed to realize visual management of business logic. This form of process diagram intuitively presents complex business logic, facilitating understanding and operation by developers and operation and maintenance personnel. In this way, non-technical personnel only need to adjust business logic based on the process diagram without having to deeply understand the code implementation, thereby reducing the dependence on technical background. And through the separate management of formal process diagrams and template process diagrams, flexible switching between multiple business scenarios is supported, ensuring the stability and consistency of system operation. Based on the constructed business process diagram, the splicing operation of multiple target function modules is automatically completed to form a multi-module business process. The automated splicing depends on the logical order and dependency relationships of the process diagram, avoiding errors that may be introduced by manual splicing. Moreover, the process construction and adjustment under different business scenarios can be quickly completed without repeating code development or conducting large-scale test verification.

[0062] Embodiment 2, based on the same inventive concept as the multi-module business process automated splicing method in the foregoing embodiment, as Figure 2 shown, the embodiment of the present application provides a multi-module business process automated splicing system, and the system includes:

[0063] The modular processing unit 10 is used to modularize business functions to obtain multiple function module sets, where each function module set contains several independent business function modules; the function requirement analysis unit 20 is used to perform function requirement analysis based on real-time business requirements to determine the target function requirements; the spare function module acquisition unit 30 is used to traverse the multiple function module sets based on the target function requirements and match to obtain multiple spare function modules; the compatibility and spliceability analysis unit 40 is used to perform compatibility and spliceability analysis on the multiple spare function modules, and screen to obtain N target function modules according to the analysis results, where N is greater than or equal to 0; the business process diagram construction unit 50 is used to perform business process parsing according to the target function requirements and construct a business process diagram including logical sequence and dependency relationships; the automatic splicing unit 60 is used to perform automatic splicing of the N target function modules based on the business process diagram to form a multi-module business process.

[0064] Furthermore, the modular processing unit 10 includes:

[0065] The business process decomposition channel is used to decompose the business process based on business functions to obtain multiple business process links; the function requirement parsing channel is used to randomly extract the first business process link from the multiple business process links, perform function requirement parsing on the first business process link to obtain several first business functions; the module combination channel is used to map and establish several first business function modules based on the several first business functions, and combine the several first business function modules to obtain the first function module set; the module set acquisition channel is used to iterate through the multiple business process links in this way to obtain the multiple function module sets.

[0066] Furthermore, the function requirement analysis unit 20 includes:

[0067] The data acquisition channel is used to automatically acquire business environment data through a real-time data acquisition module, where the business environment data includes user operation data, business load data, and external environment data; the data processing channel is used to perform data processing on the business environment data through an intelligent data analysis engine, and perform scenario recognition and requirement analysis to obtain the target function requirements.

[0068] Furthermore, the data processing channel includes:

[0069] Analysis model activation node, used to activate the intelligent data analysis model of the intelligent data analysis engine, where the intelligent data analysis model includes a first data analyzer and a second data analyzer, the first data analyzer is constructed based on a convolutional neural network, and the second data analyzer is constructed based on a long short-term memory network; Spatial feature analysis node, used to perform spatial feature analysis on the user operation data and business load data through the first data analyzer to obtain functional requirement spatial features; Time series feature analysis node, used to perform time series feature analysis on the external environment data through the second data analyzer to obtain functional requirement time series features; Feature integration node, used to integrate the functional requirement spatial features and the functional requirement time series features to obtain the target functional requirements.

[0070] Furthermore, the spare function module acquisition unit 30 further includes:

[0071] Urgency assessment channel, used to perform urgency assessment of business objectives based on the functional requirement spatial features and the functional requirement time series features to obtain an urgency assessment result; Priority sorting channel, used to perform priority sorting on the business function modules in the multiple function module sets based on the urgency assessment result by using a priority scheduling algorithm.

[0072] Furthermore, the spare function module acquisition unit 30 includes:

[0073] Second function module set extraction channel, used to randomly extract a second function module set from the multiple function module sets; Feature extraction channel, used to read predetermined service features, perform functional feature extraction on several second service function modules in the second function module set based on the predetermined service features to obtain several functional feature sets, and perform feature extraction on the target functional requirements to obtain a functional requirement feature set; Comparative analysis channel, used to sequentially compare and analyze the functional requirement feature set with the several functional feature sets to obtain several feature similarities; Spare function module acquisition channel, used to extract the service function modules that meet the feature similarity threshold as spare function modules based on the several feature similarities.

[0074] Furthermore, the predetermined service features include input requirements, output capabilities, runtime performance parameters, dependencies, and applicable scenarios.

[0075] Through the foregoing detailed description of the multi-module business process automated splicing method in this specification, those skilled in the art can clearly know the multi-module business process automated splicing system in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for related parts, reference can be made to the description in the method part.

[0076] The foregoing description of the disclosed embodiments enables those skilled in the art to practice or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-module business process automated splicing method, characterized in that: The method comprises: Modularize the business functions to obtain multiple function module sets, where each function module set includes several independent business function modules; Conduct functional requirement analysis based on real-time business needs and determine target functional requirements; Based on the target functional requirement, traverse the multiple functional module sets and match and obtain multiple spare functional modules; Performing compatibility and splicing analysis on the multiple spare functional modules, and obtaining N target functional modules by screening according to the analysis results, where N is greater than or equal to 0; Analyze the business process according to the target functional requirements and construct a business process diagram containing logical sequence and dependency relationships; Based on the business process diagram, the N target functional modules are automatically spliced ​​to form a multi-module business process.

2. The multi-module business process automated splicing method according to claim 1, characterized in that: The business functions are modularized to obtain multiple function module sets, wherein each function module set includes a number of independent business function modules. The method includes: Decompose business processes based on business functions to obtain multiple business process links; Randomly extracting a first business process link from the multiple business process links, performing functional requirement analysis on the first business process link, and obtaining a plurality of first business functions; Establishing a plurality of first business function modules based on the plurality of first business function mappings, and combining the plurality of first business function modules to obtain a first function module set; In this way, the multiple business process links are traversed to obtain the multiple function module sets.

3. The multi-module business process automated splicing method according to claim 1, characterized in that: The method of performing functional requirement analysis based on real-time business requirements to determine target functional requirements includes: Automatically collect business environment data through a real-time data collection module, wherein the business environment data includes user operation data, business load data, and external environment data; The business environment data is processed by an intelligent data analysis engine, and scenario recognition and demand analysis are performed to obtain the target function requirements.

4. The multi-module business process automated splicing method according to claim 3, characterized in that: The method of processing the business environment data by an intelligent data analysis engine, performing scenario recognition and demand analysis, and obtaining the target function requirements includes: Activating an intelligent data analysis model of the intelligent data analysis engine, wherein the intelligent data analysis model includes a first data analyzer and a second data analyzer, the first data analyzer is constructed based on a convolutional neural network, and the second data analyzer is constructed based on a long short-term memory network; Performing spatial feature analysis on the user operation data and the business load data by the first data analyzer to obtain spatial features of functional requirements; Performing time series feature analysis on the external environment data by the second data analyzer to obtain time series features of functional requirements; The spatial characteristics of the functional requirements and the temporal characteristics of the functional requirements are integrated to obtain the target functional requirements.

5. The multi-module business process automated splicing method according to claim 4, characterized in that: Before traversing the plurality of function module sets based on the target function requirement, the method includes: Based on the spatial characteristics of the functional requirements and the temporal characteristics of the functional requirements, an urgency assessment of the business objectives is performed to obtain an urgency assessment result; Based on the urgency assessment result, a priority scheduling algorithm is used to prioritize the business function modules in the plurality of function modules.

6. The multi-module business process automated splicing method according to claim 1, characterized in that: The method of traversing the plurality of function module sets based on the target function requirement and matching to obtain a plurality of spare function modules comprises: Randomly extracting a second set of functional modules from the plurality of sets of functional modules; Reading a predetermined business feature, extracting function features of a plurality of second business function modules in the second function module set based on the predetermined business feature to obtain a plurality of function feature sets, and extracting features of the target function requirement to obtain a function requirement feature set; Comparing and analyzing the functional requirement feature set with the plurality of functional feature sets in sequence to obtain a plurality of feature similarities; Based on the several feature similarities, business function modules that meet the feature similarity threshold are extracted as standby function modules.

7. The multi-module business process automated splicing method according to claim 6, characterized in that: The predetermined business characteristics include input requirements, output capabilities, runtime performance parameters, dependencies, and applicable scenarios. 8.Multi-module business process automated splicing system, characterized by: For implementing the multi-module business process automatic splicing method according to any one of claims 1 to 7, the system comprises: A modular processing unit, used for modularizing the business functions to obtain a plurality of function module sets, wherein each function module set includes a plurality of independent business function modules; Functional requirement analysis unit, used to perform functional requirement analysis based on real-time business requirements and determine target functional requirements; A spare function module acquisition unit, configured to traverse the plurality of function module sets based on the target function requirement, and match and obtain a plurality of spare function modules; A splicing analysis unit, used to perform compatibility and splicing analysis on the plurality of spare function modules, and screen and obtain N target function modules according to the analysis results, where N is greater than or equal to 0; A business process diagram construction unit, used to parse the business process according to the target functional requirements and construct a business process diagram containing a logical sequence and dependency relationships; The automatic splicing unit is used to automatically splice the N target functional modules based on the business process diagram to form a multi-module business process.

9. The multi-module business process automation splicing system according to claim 8, characterized in that: The modular processing unit comprises: The business process decomposition channel is used to decompose the business process based on business functions and obtain multiple business process links; A function requirement analysis channel, used to randomly extract a first business process link from the multiple business process links, perform function requirement analysis on the first business process link, and obtain a plurality of first business functions; A module combination channel, used to establish a plurality of first business function modules based on the plurality of first business function mappings, and to combine the plurality of first business function modules to obtain a first function module set; The module set acquisition channel is used to traverse the multiple business process links and obtain the multiple function module sets.

10. The multi-module business process automation splicing system according to claim 8, characterized in that: The functional requirement analysis unit comprises: A data collection channel is used to automatically collect business environment data through a real-time data collection module, wherein the business environment data includes user operation data, business load data, and external environment data; The data processing channel is used to process the business environment data through an intelligent data analysis engine, and perform scenario recognition and demand analysis to obtain the target function requirements.

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