Modular scheme component construction system driven by dynamic knowledge base

Abstract

The invention relates to the technical field of dynamic knowledge base supporting modular scheme construction, in particular to a dynamic knowledge base driven modular scheme component construction system, which comprises a knowledge labeling module, a dynamic knowledge base supporting module, a dynamic knowledge base module and a dynamic knowledge base module, and generating a standardized label according to two dimensions of the function attribute type and the scene adaptation requirement, and outputting the knowledge data with the standardized label. According to the invention, the knowledge labeling module preprocesses dispersed knowledge in a dynamic knowledge base and generates standardized labels, and then the knowledge splitting module establishes knowledge-component mapping rules according to the labels; and extracting the core function unit and splitting the core function unit into standardized basic components with initial scene adaptation identifiers and basic interfaces by referring to a standard structure to form a component library, so that the problem that dispersed knowledge cannot be automatically converted into standardized modular components in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of dynamic knowledge base-supported modular solution construction technology, specifically a dynamic knowledge base-driven modular solution component construction system. Background Technology

[0002] In enterprise technology research and development and project implementation, dynamic knowledge bases are the core foundation supporting the construction of modular solutions. Their core value lies in integrating scattered technical knowledge and experience rules to provide data support for the reuse and assembly of solution components. As cross-scenario application needs increase, modular solutions need to quickly adapt to different functional requirements, which requires the knowledge in the knowledge base to be flexibly transformed into standardized and adaptable components.

[0003] The existing patent CC121(21732A) describes a method and system for dynamically constructing a knowledge base based on a large model. This method constructs an incremental knowledge graph by collecting cross-project collaboration behavior data, enabling the recommendation of decision paths and dynamic updates to the knowledge base. This solution focuses on the extraction and optimization of decision rules, primarily serving as a decision reference in cross-project collaboration, and does not address the structured decomposition and scenario-based adaptation of modular solution components. The problem with this existing technology is that it cannot automatically decompose the scattered knowledge in the knowledge base into modular components with unified standards according to the functional requirements of different application scenarios, and the interface adaptation logic between components lacks dynamic adjustment capabilities. These shortcomings lead to the need for manual knowledge selection, component decomposition, and interface adaptation during the assembly of modular solutions. This is not only inefficient but also prone to poor adaptability due to interface incompatibility, making it difficult to quickly respond to diverse scenario requirements. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a modular solution component construction system driven by a dynamic knowledge base. This system solves the problems of existing technologies being unable to automatically break down scattered knowledge in the knowledge base into standardized components and lacking dynamic adjustment capabilities for component interface adaptation, resulting in low solution construction efficiency and poor adaptability.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a modular solution component construction system driven by a dynamic knowledge base, comprising:

[0006] The knowledge annotation module is used to preprocess the scattered knowledge in the dynamic knowledge base and generate standardized tags based on two dimensions: functional attribute type and scenario adaptation requirements. The knowledge data with standardized tags is then output.

[0007] The knowledge decomposition module is used to receive knowledge data with standardized tags, establish knowledge-component mapping rules based on functional attribute type tags, extract the core functional units in the knowledge and decompose them into independent basic components with reference to the standard structure corresponding to the functional attribute type tags. At the same time, based on the scenario adaptation requirement tags, initial scenario adaptation identifiers and basic interface information are added to each basic component to form a standardized modular component library.

[0008] The scene perception module is used to collect the functional requirement description and environmental parameter configuration information of the current application scene, perform layered parsing of the functional requirement description to identify core requirements and secondary requirements, determine the requirement priority order based on the core requirements and secondary requirements, and transform the requirement priority order, functional requirement description and environmental parameter configuration information into standardized adaptation instructions containing functional goals, scene constraints and interface types.

[0009] The interface adaptation module is used to receive standardized adaptation instructions, read the basic interface information of the target modular component called by the component assembly module, analyze the interface type requirements and scenario constraints in the standardized adaptation instructions, dynamically adjust the interface interaction parameters and data transmission format of the target modular component based on the built-in multi-type interface protocol conversion logic, and generate interface connection rules that meet the current scenario.

[0010] The component assembly module is used to receive standardized adaptation instructions and interface connection rules, match modular components from the standardized modular component library that correspond to the priority of the requirements, establish logical connection relationships between the matched modular components according to the interface connection rules, and perform collaborative verification of the functions of the matched modular components to generate a modular solution that meets the requirements of the current scenario.

[0011] The component iteration module receives application feedback data generated by the modular solution in actual application, analyzes the application feedback data to identify functional adaptation issues and component interface compatibility issues, and adjusts the knowledge-component mapping rules based on the functional adaptation issues and component interface compatibility issues, updates the basic interface information and initial scenario adaptation identifier of the modular components, and feeds back the adjusted mapping rules and updated component information to the knowledge splitting module and interface adaptation module respectively.

[0012] Furthermore, the knowledge annotation module includes:

[0013] The knowledge preprocessing unit is used to perform cleaning, deduplication, and format standardization operations on the scattered knowledge in the dynamic knowledge base.

[0014] The dual-dimensional tag generation unit is used to assign functional attribute type tags and scenario adaptation requirement tags to the preprocessed knowledge based on the preset functional attribute type dictionary and scenario adaptation requirement dictionary.

[0015] The historical data fusion unit is used to acquire historical application data of the dynamic knowledge base, analyze the relationship between historical application scenarios and knowledge application records, and adjust the weight of functional attribute type tags and scenario adaptation requirement tags based on the relationship.

[0016] Furthermore, the knowledge decomposition module includes:

[0017] The core functional unit extraction unit is used to perform semantic parsing on knowledge data with standardized labels, identify and extract independently executable functional logic fragments as core functional units.

[0018] The standard structure reference unit is used to maintain a standard component structure template library that corresponds one-to-one with the functional attribute type label, and to match the corresponding standard component structure template for each core functional unit.

[0019] The component generation and identification unit is used to fill the core functional units into the matching standard component structure template, generate basic components, convert the corresponding scene adaptation requirement tags into initial scene adaptation identifiers, and configure the default basic interface information for the basic components.

[0020] Furthermore, the scene perception module includes:

[0021] The requirement acquisition unit is used to obtain the user's input of functional requirement descriptions and environmental parameter configuration information through a natural language interface or configuration file parser.

[0022] The requirement layering parsing unit is used to perform syntactic and semantic analysis on the functional requirement description, identify the mandatory and optional requirements, define the mandatory requirements as core requirements, and define the optional requirements as secondary requirements.

[0023] The priority and instruction generation unit is used to quantify and score core and secondary requirements according to preset business rules, generate a requirement priority ranking based on the scoring results, and then encode the requirement priority ranking, functional requirement description and environmental parameter configuration information into standardized adaptation instructions.

[0024] Furthermore, the interface adaptation module includes:

[0025] The interface information reading unit is used to receive the identifier of the target modular component to be assembled from the component assembly module, and retrieve the corresponding basic interface information from the standardized modular component library based on the identifier.

[0026] The protocol difference analysis unit is used to compare the basic interface information with the interface type requirements specified in the standardized adaptation instructions, and identify the differences between the two in terms of data format, communication protocol and calling method.

[0027] The dynamic parameter adjustment unit is used to call the interface protocol conversion subroutine that matches the difference point, modify the interface interaction parameters and data transmission format of the target modular component in real time, and generate interface connection rules.

[0028] Furthermore, the interface adaptation module also includes:

[0029] A multi-protocol conversion logic library is used to store various predefined interface protocol conversion rules. Each rule is associated with a source interface type and a target interface type, and contains script code for performing format conversion and parameter mapping.

[0030] The rule matching engine is used to retrieve applicable interface protocol conversion rules from the multi-protocol conversion logic library based on the output of the protocol difference analysis unit, and load the corresponding script code for the dynamic parameter adjustment unit to call.

[0031] Furthermore, the component assembly module includes:

[0032] The component matching unit is used to traverse the standardized modular component library, filter out candidate components whose initial scene adaptation identifier matches the scene constraints in the standardized adaptation instructions, and sort the candidate components according to the priority of requirements.

[0033] The logical connection establishment unit is used to establish the connection topology of data flow and control flow between the sorted candidate components according to the interface connection rules.

[0034] The functional coordination verification unit is used to simulate the execution of the connection topology, verify whether the combination of each component can fully achieve the functional goal, and output the modular solution after the verification is passed.

[0035] Furthermore, the component iteration module includes:

[0036] The feedback data receiving and parsing unit is used to receive application feedback data from external systems and extract structured log information about component function failures, interface call anomalies, and performance bottlenecks.

[0037] The problem diagnosis unit is used to compare structured log information with the component configuration and interface connection rules of the modular solution to locate the specific component or interface connection pair that caused the problem.

[0038] The rule and identifier update unit is used to generate revision instructions for knowledge-component mapping rules, as well as update instructions for basic interface information and initial scenario adaptation identifiers of specific components, based on the location results of the problem diagnosis unit.

[0039] Furthermore, the component iteration module also includes:

[0040] The feedback routing unit is used to send revision instructions to the knowledge splitting module to trigger an update to the knowledge-component mapping rules; and to send the portion of the update instruction containing interface information to the interface adaptation module to update the component interface metadata maintained internally.

[0041] Furthermore, the system also includes a data interaction link, which is used to establish a directional data transmission channel between the knowledge annotation module, knowledge decomposition module, scene perception module, interface adaptation module, component assembly module and component iteration module, so that knowledge data with standardized tags, standardized modular component library, standardized adaptation instructions, interface connection rules, modular schemes and application feedback data can flow in the order of the preset workflow, forming a progressive collaborative processing chain from knowledge preprocessing, scene adaptation to closed-loop optimization.

[0042] Compared to existing technologies, the beneficial effects of this invention are as follows: This invention preprocesses scattered knowledge in a dynamic knowledge base using a knowledge annotation module to generate standardized tags. Then, a knowledge splitting module establishes knowledge-component mapping rules based on these tags, extracts core functional units, and splits them into standardized basic components with initial scene adaptation identifiers and basic interfaces, forming a component library. This solves the problem of existing technologies being unable to automatically transform scattered knowledge into standardized modular components. The interface adaptation module dynamically adjusts component interface interaction parameters and data transmission formats based on built-in multi-type interface protocol conversion logic, solving the problem of lacking dynamic adjustment capabilities in component interface adaptation. Simultaneously, the scene perception module transforms scene requirements into standardized adaptation instructions, and the component assembly module automatically matches components, establishes logical connections, and verifies them, reducing manual screening, splitting, and interface adaptation operations, thus improving solution construction efficiency and scene adaptability. The component iteration module optimizes mapping rules and component information through application feedback, and, in conjunction with the data interaction link, ensures orderly data flow, further guaranteeing solution stability and effectively improving the problems of low efficiency, interface incompatibility, and poor adaptability of traditional solutions. Attached Figure Description

[0043] Figure 1 This is the overall system architecture diagram of the present invention;

[0044] Figure 2 Flowchart for scenario adaptation of the present invention;

[0045] Figure 3 This is a flowchart of the iterative optimization process of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Please see Figure 1-3 This invention provides a modular solution component construction system driven by a dynamic knowledge base. The following will describe the modular solution component construction system driven by a dynamic knowledge base in detail with the application scenario of enterprise cross-scenario technical solution construction.

[0048] First, after the system starts, the knowledge annotation module preprocesses the scattered knowledge in the dynamic knowledge base and generates standardized tags based on two dimensions: functional attribute type and scenario adaptation requirements. The knowledge data with standardized tags is then output. This module is implemented through three collaborative units. The knowledge preprocessing unit performs cleaning, deduplication, and format standardization operations on the scattered knowledge in the dynamic knowledge base. The knowledge sources in the dynamic knowledge base include internal patent documents, project technical documents, R&D experience summaries, and cross-departmental collaboration data. This knowledge often suffers from redundant fields, chaotic formatting, and duplicate content. The knowledge preprocessing unit first cleans this scattered knowledge, removing meaningless redundant fields, erroneous characters, and blank content, such as removing duplicate headers and footers and invalid annotations. Next, deduplication is performed using a text deduplication algorithm based on cosine similarity to calculate... The similarity between different knowledge fragments is assessed. When the similarity exceeds a preset threshold, the knowledge is considered duplicated, and the version with the most complete information is retained. This threshold can be set empirically based on the differences in knowledge types. For example, the similarity threshold for technical document knowledge is set to .05, and the threshold for experience summary knowledge is set to .75. Finally, a format unification operation is performed to convert knowledge data in different formats (such as Word documents, P9F files, Excel spreadsheets, plain text, etc.) into a unified JSDC format to ensure compatibility in subsequent processing. The JSDC format includes basic fields such as unique knowledge identifier, core knowledge content, original source, and creation time.

[0049] The dual-dimensional tag generation unit is used to assign functional attribute type tags and scenario adaptation requirement tags to preprocessed knowledge based on a preset functional attribute type dictionary and scenario adaptation requirement dictionary, respectively. The preset functional attribute type dictionary is constructed by sorting out common technical function classifications in the industry, including multiple categories such as data acquisition, data processing, logical operation, interface interaction, function control, anomaly warning, and report generation. Each category can be further subdivided into specific subcategories. For example, the data processing category includes subcategories such as data cleaning, data transformation, data filtering, and data aggregation. The scenario adaptation requirement dictionary is formed based on common application scenarios in enterprises, covering scenario categories such as industrial production control, intelligent office collaboration, software development and testing, equipment operation and maintenance monitoring, and customer service response. It can also expand subcategories according to actual application needs. For example, the industrial production control scenario includes subcategories such as assembly line operation control, warehouse logistics scheduling, and energy consumption monitoring. In the tag assignment process, a combination of keyword matching and semantic understanding is adopted. First, keywords are extracted from the preprocessed core knowledge content and matched with category keywords in the dictionary. Then, a lightweight semantic analysis model is used to determine the core function and applicable scenario of the knowledge, and then the corresponding tag is assigned. For example, if the core content of a knowledge fragment is "collecting data such as the operating temperature and vibration frequency of production equipment through sensors and filtering the data to remove interference signals", then its functional attribute type tag is assigned as "data acquisition + data processing (data filtering)" and the scenario adaptation requirement tag is assigned as "industrial production control (assembly line operation control)".

[0050] The historical data fusion unit is used to acquire historical application data from the dynamic knowledge base, analyze the correlation between historical application scenarios and knowledge application records, and adjust the weights of functional attribute type tags and scenario adaptation requirement tags based on these correlations. Historical application data includes structured data such as knowledge call records from past projects, matched scenario information, and application effect feedback. Analyzing this data can reveal correlations such as the frequency of knowledge application and the degree of adaptation in different scenarios. The quantification of correlations is achieved by calculating the correlation coefficient, the formula for which is:

[0051]

[0052] Where, r ",$ N represents the correlation coefficient between the i-th type of knowledge and the j-th scenario. ",$ N represents the number of times the i-th type of knowledge is successfully applied in the j-th scenario. " N represents the total number of times the i-th type of knowledge is applied. $ This represents the total number of knowledge calls for the j-th scenario. The relevance coefficient ranges from [(,1], with a larger value indicating a stronger relevance. The formula for calculating the tag weight correction is:

[0053] W k′ =W k ×(1+r i,j );

[0054] Among them, W k′ W represents the corrected label weights. k This represents the initial weight of the tag. The initial weight is preset based on the importance of the functional attribute type or scenario adaptation requirements. For example, the initial weight of core function tags is set to (.0), and the initial weight of auxiliary function tags is set to (.5). i,j This refers to the correlation coefficient between the corresponding knowledge and the scenario. Through this weight adjustment, the tags better reflect the actual application value of the knowledge and its scenario adaptability. For example, if the initial weight of a knowledge scenario adaptation requirement tag is (..), and its correlation coefficient with the "intelligent office collaboration" scenario is (.7), then the adjusted weight is (..×(1+(.7)=1.(2). In subsequent component matching processes, the scenario corresponding to this tag will receive higher priority, thereby improving the accuracy of knowledge utilization. Through the above processing, the knowledge labeling module transforms the originally scattered and disordered knowledge into structured data with standardized tags, providing a unified and standardized foundation for subsequent knowledge decomposition and solving the problem of chaotic knowledge formats and difficulty in direct utilization in traditional knowledge bases.

[0055] After knowledge data with standardized tags is generated, it is transmitted to the knowledge decomposition module via a data interaction link. This module receives the knowledge data with standardized tags, establishes knowledge-component mapping rules based on functional attribute type tags, extracts the core functional units from the knowledge, and decomposes them into independent basic components according to the standard structure corresponding to the functional attribute type tags. Simultaneously, based on scenario adaptation requirement tags, it adds initial scenario adaptation identifiers and basic interface information to each basic component, forming a standardized modular component library. The specific implementation process of this module is as follows:

[0056] In one specific embodiment, the core functional unit extraction unit performs semantic parsing on knowledge data with standardized labels, identifying and extracting independently executable functional logic fragments as core functional units. The semantic parsing employs an improved semantic segmentation model based on BERT. This model, through pre-training, learns specialized semantic features in the technical field, enabling it to accurately identify functional boundaries within the knowledge content. During the parsing process, the core knowledge content is first segmented, part-of-speech tagging is performed, and dependency parsing is conducted to identify the action subject, action object, and execution logic, thereby segmenting logical fragments that can independently complete specific functions. For example, the core content of a certain knowledge data is "receiving raw data uploaded by sensors, performing outlier detection (removing data exceeding a preset range), storing the processed data in a database, and generating a data processing log." Through semantic parsing, three core functional units can be segmented: a data receiving unit (receiving raw sensor data), an outlier detection unit (removing data exceeding a preset range), and a data storage and log generation unit (storing processed data and generating logs). Each core functional unit possesses complete input, processing, and output logic and can independently execute its corresponding function.

[0057] The standard structure reference unit maintains a standard component structure template library that corresponds one-to-one with functional attribute type tags, and matches a corresponding standard component structure template for each core functional unit. The standard component structure template library is built based on industry technical specifications and component-based development standards. Each template contains fixed structural elements, such as basic component information (component name, unique identifier, version number, functional description), input interface definitions (input parameter names, data types, value ranges, transmission protocols), core processing logic containers (for embedding functional logic code), output interface definitions (output parameter names, data types, format requirements, return mechanisms), and exception handling mechanisms (exception types, error messages, recovery strategies), etc. Different functional attribute type tags correspond to different standard component structure templates. For example, in the template corresponding to the "Data Acquisition" tag, the input interface definition will focus on parameters such as sensor type and data acquisition frequency, and the core processing logic container will reserve space for embedding code for data reception and format conversion; the template corresponding to the "Logic Control" tag will strengthen the code embedding support for logic such as conditional judgments and process branches. During the template matching process, a unique matching template is retrieved from the template library based on the functional attribute type tag corresponding to the core functional unit. For example, if the functional attribute type tag of the core functional unit is "data processing (data filtering)", then the "data filtering" sub-template under the "data processing" class will be matched.

[0058] The component generation and identification unit is used to populate core functional units into matching standard component structure templates, generating basic components and converting corresponding scene adaptation requirement tags into initial scene adaptation identifiers. It also configures default basic interface information for the basic components. The core functional unit population process involves converting semantically extracted functional logic fragments into executable code fragments, embedding them into the core processing logic container of the standard template. The programming language of the code fragments can be flexibly selected according to the system deployment environment, such as Java, Python, C++, etc., to ensure component executability. The initial scene adaptation identifiers directly use the scene adaptation requirement tags from the knowledge data, adopting a two-level identifier structure of "main scene - sub-scene," such as "industrial production control - assembly line operation control," facilitating rapid retrieval during subsequent scene matching. The configuration of basic interface information follows industry-standard interfaces, defaulting to RESTful interface specifications. Input and output parameter data types are set to common types by default, such as String, Int, Float, and Boolean. Data transmission format defaults to JSDC, and interface calling method defaults to synchronous calling. A unique interface address is assigned to each interface, such as " / componentTnt / api / [component unique identifier] / input". For components with special functional attributes, interface configurations can be adjusted accordingly. For example, the interfaces of "real-time control" components default to asynchronous communication using the WTbSockTt protocol to ensure real-time data transmission. Through this process, each core functional unit is transformed into a basic component with a unified structure and standardized interfaces. These components are aggregated and stored to form a standardized modular component library. The component library adopts a distributed storage architecture, supports high-concurrency read and write operations, and establishes a component index. The index dimensions include functional attribute type, scene adaptation identifier, and interface type, facilitating rapid retrieval and matching of subsequent components. The knowledge decomposition module achieves efficient transformation of knowledge into components through automatic decomposition and standardized generation, solving the problem of low efficiency caused by the need for manual knowledge decomposition and component construction in traditional technologies. At the same time, the standardized component structure and interface design lay the foundation for the subsequent assembly and adaptation of components.

[0059] When a specific application scenario requires it, the scenario awareness module begins to work. It collects the functional requirement description and environmental parameter configuration information of the current application scenario, performs layered parsing of the functional requirement description to identify core and secondary requirements, and determines the priority order of the requirements based on these priorities. It then transforms the requirement priority order, functional requirement description, and environmental parameter configuration information into standardized adaptation instructions that include functional objectives, scenario constraints, and interface types. The specific implementation process is as follows:

[0060] In a specific embodiment, the requirement acquisition unit is used to obtain the functional requirement description and environmental parameter configuration information input by the user through a natural language interface or a configuration file parser. The natural language interface supports users to input requirements in the form of natural language text, such as "Build an intelligent office collaboration system to realize employee schedule sharing, task allocation and progress tracking, and online collaborative editing of files. The system needs to support 1 person to use it online at the same time, with a response time of no more than 2 seconds." The interface has a built-in natural language understanding model, which can accurately identify the functional description and environmental constraint information in the user input. The configuration file parser supports users to upload configuration files in preset formats (such as XBL and YABL formats). The configuration file needs to be filled with a list of functional requirements, environmental parameter indicators, and other information according to specified fields. The parser obtains the corresponding requirement data by reading the field content. The environmental parameter configuration information mainly includes hardware resource constraints (such as the number of CPU cores, memory capacity, and storage space), software operating environment (such as operating system type, database version, and programming language environment), performance indicator requirements (such as the number of concurrent users, response time, and data transmission rate), and network environment parameters (such as network bandwidth, latency, and stability level). These parameters provide constraints for subsequent component adaptation and solution construction.

[0061] The requirement layering and parsing unit performs syntactic and semantic analysis on the functional requirement descriptions, identifying mandatory and optional requirements. Mandatory requirements are defined as core requirements, while optional requirements are defined as secondary requirements. Syntactic analysis uses a dependency parsing algorithm to identify the core predicates, objects, and modifiers in the functional requirement descriptions, clarifying the core and auxiliary content of the requirements. Semantic analysis, based on a domain ontology library, matches the requirement content with concepts in the ontology library to determine the necessity of the requirements. Mandatory requirements are those indispensable for achieving the scenario's functionality; their absence would prevent the scenario's goals from being achieved. For example, "employee schedule sharing" and "task allocation and progress tracking" in the aforementioned intelligent office collaboration system are core requirements. Optional requirements supplement and optimize the scenario's functionality; their absence does not affect the core functionality. For example, "online collaborative file editing" can be considered a secondary requirement; if it cannot be implemented temporarily, the system can still meet basic office collaboration needs. During the parsing process, for ambiguous requirement descriptions, the system will initiate simple queries to the user through a natural language interface to clarify the nature of the requirement. For example, if the user enters "the system needs to have a data backup function", the system will ask "is data backup a core function that must be implemented", and complete the requirement layering based on the user's response.

[0062] The priority and instruction generation unit is used to quantify and score core and secondary requirements according to preset business rules, and generate a priority ranking of requirements based on the scoring results. Then, the priority ranking, functional requirement descriptions, and environmental parameter configuration information are encoded into standardized adaptation instructions. The preset business rules are formulated by sorting out the evaluation criteria for the importance of requirements in different application scenarios. The base score for core requirements is set to 0 points, and the base score for secondary requirements is set to 4 points. Based on these, the scores are adjusted according to the urgency, scope of impact, and technical implementation difficulty of the requirements. The urgency adjustment coefficient is set according to the time constraint of the requirement; for example, the adjustment coefficient for requirements that need to be implemented within one month is 1.2, and the adjustment coefficient for requirements without a clear time constraint is 1. (). The scope of impact adjustment coefficient is set according to the number of user groups or business modules involved in the requirement; the adjustment coefficient for requirements involving the entire department is 1.3, and the adjustment coefficient for requirements involving a single department is 1. (). The technical implementation difficulty adjustment coefficient is set according to the support level of existing components; the adjustment coefficient for requirements that can be directly met by existing components is 1.1, the adjustment coefficient for requirements that require minor modifications to components is 1. (), and the adjustment coefficient for requirements that require completely new development is (.0). The final requirement score is calculated using the following formula:

[0063] S = B × K5 × K6 × K7;

[0064] Where S is the final score of the requirement, B is the base score, K5 is the urgency adjustment coefficient, K6 is the impact scope adjustment coefficient, and K7 is the technical implementation difficulty adjustment coefficient. All requirements are sorted from highest to lowest based on their final scores to form a requirement priority ranking. For example, if a core requirement has a final score of 0 (×1.2×1.3×1.1=137.20) and a secondary requirement has a final score of 4 (×1.(×1.(×1.(=4(), then the core requirement has a higher priority than the secondary requirement). Standardized adaptation instructions use a fixed JSDC data format encoding, including fields such as a unique instruction identifier, generation time, functional target array (a priority-sorted list of requirements), scenario constraint set (environmental parameter configuration information), and interface type requirements (preset interface protocols, data formats, etc.). For example:

[0065] {

[0066] "instructionIS":"ICS-2(23-1((1",

[0067] "gTnTratTTimT":"2(23-1(-15T1(:3(:((",

[0068] "functionTargTts":[

[0069] {"STmanS":"Employee Schedule Sharing","priority":1,"scorT":137.20},{"STmanS":"Task Assignment and Progress Tracking","priority":2,"scorT":13(.5.},{"STmanS":"Online Collaborative Document Editing","priority":3,"scorT":4(.(}

[0070] ],

[0071] "scTnTConstraints":{

[0072] "harSwarT":{"cpuCorTs":0,"mTmory":"1.GB","storagT":"5((GB"},"softwarT":{"os":"WinSowsSTrvTr2(11","SatabasT":"BySFL0.("},"pTrform ancT":{"concurrTntUsTrs":1((,"rTsponsTTimT":"≤2s"},"nTtwork":{"banSwiSth":"1((Bbps","STlay":"≤5(ms"}

[0073] },

[0074] "intTrfacTRTquirTmTnts":{"protocol":"HTTP / HTTPS","SataFormat":"JSDC","callBoST":"synchronous"}

[0075] }

[0076] The generation of standardized adaptation instructions enables subsequent modules to quickly obtain clear and standardized requirement information, avoiding information distortion during the requirement transmission process. At the same time, priority sorting provides clear guidance for component matching and assembly, ensuring that core requirements are met first.

[0077] After the standardized adaptation instructions are generated, they are synchronously transmitted to the interface adaptation module and the component assembly module. The interface adaptation module receives the standardized adaptation instructions, reads the basic interface information of the target modular component called by the component assembly module, analyzes the interface type requirements and scenario constraints in the standardized adaptation instructions, and dynamically adjusts the interface interaction parameters and data transmission format of the target modular component based on the built-in multi-type interface protocol conversion logic to generate interface connection rules that meet the current scenario. The specific implementation process is as follows:

[0078] In one specific embodiment, the interface information reading unit receives the identifier of the target modular component to be assembled from the component assembly module, and retrieves the corresponding basic interface information from the standardized modular component library based on the identifier. The component identifier adopts the format "CDBP-[year]-[serial number]" and has global uniqueness, such as "CDBP-2(23-(5.)". The interface information reading unit sends a retrieval request to the standardized modular component library through the component identifier. The component library quickly locates the corresponding component record based on the index, extracts the basic interface information of the component, including interface protocol type, input and output parameter names, data types, value ranges, data transmission formats, interface addresses, calling methods, timeout times, etc., and returns this information to the interface adaptation module in the form of structured data.

[0079] The protocol difference analysis unit compares the basic interface information with the interface type requirements specified in the standardized adaptation instructions to identify differences in data format, communication protocol, and calling method. Data format differences mainly include variations in parameter encoding (e.g., UTF-0 vs. GBK), data structures (e.g., arrays vs. objects), and field naming rules (e.g., underscore vs. camelCase). For example, if the component's basic interface uses XBL data format while the standardized adaptation instructions require JSDC data format, this is considered a data format difference. Communication protocol differences include mismatches between protocol types such as HTTP, HTTPS, BFTT, and WTbSockTt. For instance, if the component's basic interface uses BFTT protocol while the instructions require HTTPS protocol, this is a communication protocol difference. Calling method differences mainly refer to synchronous vs. asynchronous calls. For example, if the component's basic interface defaults to asynchronous calls while the instructions require synchronous calls, this is a calling method difference. During the difference analysis process, each interface attribute is verified by comparing each field one by one. Mismatched attributes are recorded as difference points, and a difference analysis report is generated. The report includes information such as the difference point type, the current component interface attribute value, and the target interface attribute value.

[0080] In one specific embodiment, the multi-protocol conversion logic library stores various predefined interface protocol conversion rules. Each rule is associated with a source interface type and a target interface type, and includes script code for performing format conversion and parameter mapping. The predefined conversion rules cover commonly used interface protocols and data format combinations in the industry, such as XBL to JSDC, JSDC to Protobuf, HTTP to BFTT, synchronous call to asynchronous call, etc. The script code corresponding to each conversion rule is written in an extensible programming language and supports dynamic loading and execution. For example, the XBL to JSDC conversion script parses the node structure and attributes of the XBL document, maps them to key-value pairs of JSDC objects, and handles data type conversion to ensure the accuracy of numeric, boolean, and other types. The HTTP to BFTT conversion script encapsulates the HTTP request header information and request body into the payload of a BFTT message, and sets parameters such as topic and FoS level according to the requirements of the BFTT protocol.

[0081] The rule matching engine retrieves applicable interface protocol conversion rules from the multi-protocol conversion logic library based on the output of the protocol difference analysis unit, and loads the corresponding script code for the dynamic parameter adjustment unit to call. Rule matching employs a multi-dimensional matching algorithm. First, it performs preliminary matching based on the source and target interface types to filter candidate conversion rules. Then, it performs precise matching based on the specific details of the differences (such as the specific data format type and the specific requirements of the calling method) to determine the uniquely applicable conversion rule. For example, if the difference analysis report shows that the source interface protocol is HTTP and the data format is XBL, and the target interface protocol is HTTPS and the data format is JSDC, the rule matching engine will retrieve the combined conversion rule "HTTP to HTTPS + XBL to JSDC" and load the corresponding script code.

[0082] The dynamic parameter adjustment unit calls the interface protocol conversion subroutine that matches the differences, modifying the interface interaction parameters and data transmission format of the target modular component in real time and generating interface connection rules. During parameter adjustment, communication protocol conversion is performed first, adjusting the component interface protocol type to the target protocol according to the loaded script code. For example, when converting HTTP to HTTPS, parameters such as SSL certificates and port numbers are configured. Next, data format conversion is performed, modifying the input and output parameter formats to the required formats while ensuring the accuracy of parameter mapping. For example, parameters in XBL format are converted to HTTPS. <ttmptraturt> 25.5< / ttmptraturt>Convert to JSDC format {"tTmpTraturT":25.5}; finally, adjust the calling method. If synchronous calls need to be changed to asynchronous calls, configure the callback function address, set the asynchronous task queue, and other parameters. The interface connection rules include information such as the target interface protocol, data format, calling method, interaction parameter mapping relationship, interface address, timeout, and exception handling mechanism, output in the form of a structured document to provide clear specifications for connections between components. The interface adaptation module solves the problem of fixed interface and poor compatibility in traditional components by dynamically adjusting interface parameters and protocol conversion. This allows components with different interface types to flexibly adapt to the needs of the current scenario, ensuring smooth component assembly.

[0083] After receiving standardized adaptation instructions and interface connection rules, the component assembly module matches modular components from the standardized modular component library that correspond to the priority of the requirements. Based on the interface connection rules, it establishes logical connections between the matched modular components and performs collaborative verification of their functionality, generating a modular solution that meets the requirements of the current scenario. The specific implementation is as follows:

[0084] In one specific embodiment, the component matching unit traverses the standardized modular component library, filters out candidate components whose initial scene adaptation identifiers match the scene constraints in the standardized adaptation instructions, and sorts the candidate components according to the priority of requirements. Scene adaptation identifier matching employs a combination of precise matching and fuzzy matching. First, precise matching is performed based on the main scene identifier in the standardized adaptation instructions to filter out components with consistent scene categories. Then, fuzzy matching is performed based on the sub-scene identifiers to ensure the adaptability of components to specific application scenarios. For example, if the scene constraint of the standardized adaptation instruction is "intelligent office collaboration - employee schedule management," then components whose initial scene adaptation identifiers include "intelligent office collaboration" are first filtered out, and then components related to "employee schedule management" are selected as candidate components. During the candidate component sorting process, a comprehensive sort is performed based on the priority of requirements and the component adaptability. The component adaptability is obtained by calculating the semantic similarity between the component's function and the requirement description; the higher the semantic similarity, the higher the adaptability, and the higher the ranking. For example, if a candidate component's function is "employee schedule creation, modification, query and sharing", and its semantic similarity to the core requirement "employee schedule sharing" is (.15), and another candidate component's function is "general data sharing", and its semantic similarity is (..), then the former will be ranked higher than the latter to ensure that the core requirement can be met by the most suitable component.

[0085] The logical connection establishment unit is used to establish the data flow and control flow connection topology between the sorted candidate components according to the interface connection rules. Data flow connections are constructed based on the input-output parameter relationships of the components, using the output parameters of the previous component as the input parameters of the next component to ensure orderly data transmission. For example, the output parameters (employee schedule information) of the "Schedule Data Acquisition Component" are used as the input parameters of the "Schedule Sharing Permission Control Component," and the output parameters (schedule information after permission filtering) of the "Schedule Sharing Permission Control Component" are used as the input parameters of the "Schedule Display Component." Control flow connections are constructed based on the execution logic of the requirements, defining the component startup order, conditional branches, and loop execution rules. For example, the "Task Progress Tracking Component" is only started after the "Task Assignment Component" has completed execution, and the "Task Completion Notification Component" is triggered when the task progress reaches 1%. The connection topology is represented in the form of a directed graph, where nodes are candidate components, edges represent the connection relationships between components, and edge attributes include information such as interface connection rules, data transmission paths, and execution order constraints, ensuring that the logical relationships between components are clear and traceable.

[0086] The functional collaboration verification unit simulates the execution of the connection topology to verify whether the combined components can fully achieve the functional goals. Upon successful verification, it outputs a modular solution. The simulation uses a sandbox environment, consistent with the actual operating environment, to realistically simulate the component's running state and interaction process. During simulation, test data is first input to the starting component of the connection topology. This data is generated based on the functional requirements and environmental parameters in the standardized adaptation instructions. For example, simulated employee information and schedule data are input to the "schedule data acquisition component." Next, each component is executed according to the logical order of the connection topology, with real-time monitoring of component input / output data, interface call status, execution time, and other metrics. Then, the completeness of the combined component functionality is verified, checking whether all functional goals can be achieved. For example, verifying whether employee schedules can be successfully shared and whether task allocation and progress tracking function correctly. Simultaneously, component compatibility is verified, checking for interface call anomalies, data transmission accuracy, deadlocks, or resource contention. If functional deficiencies or compatibility issues are found during simulation, the system returns a verification failure message, indicating the problematic component or connection relationship. The component matching unit re-selects candidate components or adjusts their order, and the logical connection establishment unit reconstructs the connection topology until verification passes. After successful verification, the connection topology, component configuration information, interface connection rules, execution logic descriptions, and other content are summarized to form a standardized modular solution. This solution is output in document form, including a solution overview, component list, connection diagram, interface configuration details, deployment instructions, and other sections, facilitating direct application deployment by users. The component assembly module significantly shortens the modular solution construction cycle through automated component matching, connection, and verification, while ensuring the functional integrity and compatibility of the solution, solving the problems of low efficiency and error-proneness in traditional manual assembly.

[0087] After the modular solution is deployed and applied, the component iteration module begins to function. It receives application feedback data generated during practical application, analyzes this data to identify functional adaptation issues and component interface compatibility problems, and adjusts the knowledge-component mapping rules based on these issues. It also updates the basic interface information and initial scenario adaptation identifiers of the modular components and feeds the adjusted mapping rules and updated component information back to the knowledge splitting module and interface adaptation module, respectively. The specific implementation is as follows:

[0088] In one specific embodiment, the feedback data receiving and parsing unit receives application feedback data from external systems and extracts structured log information about component function failures, interface call anomalies, and performance bottlenecks. External systems include application servers, user terminals, and database servers deployed in a modular manner. The application feedback data is uploaded to the component iteration module in real time via a log collection agent. Data types include system operation logs, interface call logs, user operation logs, and performance monitoring data. Feedback data parsing employs a log structuring processing algorithm. First, unstructured logs (such as text-formatted operation logs) are segmented and keywords are extracted to identify abnormal keywords (such as "call failure," "timeout," "data error," and "performance degradation"). Then, the unstructured logs are converted into structured data according to a preset log template. The structured data includes fields such as log level, occurrence time, involved component identifiers, anomaly type, error description, related parameters, and scope of impact.

[0089] For example, an unstructured log file is "2(23-1(-2(14:3(:(([ERRDR]ComponTntCDBP-2(23-(5.:IntTrfacTcalltim TSout,rTquTstparamTtTr:{"usTrIS":"1((1"}", the parsed structured data is:

[0090] {

[0091] "logLTvTl":"ERRDR",

[0092] "occurTimT":"2(23-1(-2(T14:3(:((",

[0093] "componTntIS":"CDBP-2(23-(5.",

[0094] "TxcTptionTypT":"IntTrfacTCallTimTout",

[0095] "Trror9Tscription":"API call timed out",

[0096] "rTlatTSParamTtTrs":{"usTrIS":"1((1"},

[0097] "impactScopT": "Single user calendar query function"

[0098] }

[0099] The problem diagnosis unit compares structured log information with the component configuration and interface connection rules of the modular solution to locate the specific component or interface connection pair causing the problem. The diagnosis of functional adaptation problems is achieved by analyzing the degree of matching between component functions and actual application requirements. It compares the component functional performance recorded in the structured logs with the functional goals in the standardized adaptation instructions. If a component fails to achieve the expected function or its functional performance does not meet the scenario requirements, it is determined to be a functional adaptation problem, and the problem is located to that component. For example, if the structured log shows "CDBP-2(23-(70 component failed to recognize duplicate task assignment requests, resulting in the same task being assigned multiple times," after comparing it with the functional goal "task assignment should avoid duplicate assignments," it is determined that the CDBP-2(23-(70 component has a functional adaptation problem). The diagnosis of interface compatibility problems is achieved by analyzing the consistency between interface call logs and interface connection rules. It checks whether the protocol type, data format, and parameter transmission during the interface call process conform to the interface connection rules. If there is a mismatch, the corresponding interface connection pair (i.e., the interface between two interacting components) is located. For example, if the log shows "CDBP-2(23-(70 component failed to recognize duplicate task assignment requests, resulting in the same task being assigned multiple times," it is determined to be a functional adaptation problem. The problem is located to the corresponding interface connection pair (i.e., the interface between two interacting components). ...), it is determined to be a functional adaptation problem. The problem is located to The output data format of component 3-(5.) is XBL, while the input data format of component CDBP-2(23-(.() is required to be JSDC, causing data parsing failure. Therefore, the interface connection between component CDBP-2(23-(5.) and component CDBP-2(23-(.() is identified as having a compatibility issue.) Performance bottlenecks are diagnosed by analyzing performance monitoring data. If the execution time of a component exceeds a preset threshold or the resource utilization rate is too high, the component is identified as a performance bottleneck. The threshold can be set according to the performance indicators in the standardized adaptation instructions; for example, the execution time threshold can be set to 0% of the response time requirement in the adaptation instructions.

[0100] The rule and identifier update unit generates revision instructions for knowledge-component mapping rules and update instructions for basic interface information and initial scenario adaptation identifiers of specific components, based on the location results of the problem diagnosis unit. For functional adaptation issues, if the core functional unit of a component fails to meet scenario requirements, it indicates an unreasonable knowledge-component mapping rule. The revision instruction adjusts the mapping relationship between this type of knowledge and the component, such as increasing the extraction dimension of the core functional unit or adjusting the adaptation logic of the standard component structure template, ensuring that similar knowledge can be broken down into components that better meet the requirements. Simultaneously, the initial scenario adaptation identifier of the component is updated. If a component frequently experiences functional adaptation issues in a certain scenario, the adaptation identifier for that scenario is removed to prevent incorrect matching in that scenario later. For interface compatibility issues, the update instruction adjusts the basic interface information of the relevant components, such as modifying the data format, protocol type, or parameter definition of the interface to ensure compatibility with common interface requirements and reduce subsequent adaptation costs. If there are design flaws in the interface connection rules, adjustment suggestions will also be proposed in the revision instruction. To address performance bottlenecks, update commands optimize component interface parameter configurations, such as adjusting data transmission batch size and optimizing interface caching strategies, thereby improving component performance.

[0101] In one specific embodiment, the feedback routing unit sends revision instructions to the knowledge splitting module to trigger an update to the knowledge-component mapping rules; and sends the portion of the update instruction regarding interface information to the interface adaptation module to update its internally maintained component interface metadata. Upon receiving the revision instructions, the knowledge splitting module readjusts the parameter configuration of the knowledge-component mapping rules, such as modifying the extraction threshold of core functional units and updating the template logic in the standard component structure template library, ensuring that subsequent knowledge splitting processes can avoid discovered problems. Upon receiving the interface information update instructions, the interface adaptation module synchronously updates the component interface metadata to ensure consistency with the actual interface information of the components in the component library, ensuring that subsequent interface adaptation processes are based on the latest interface information and improving adaptation accuracy. The component iteration module, through a closed-loop feedback and update mechanism, continuously optimizes the knowledge-component mapping rules and component information, enabling the system to continuously adapt to changes in actual application scenarios, improving the adaptability and stability of the solution, and solving the problems of traditional systems lacking dynamic optimization capabilities and experiencing performance degradation after long-term use.

[0102] In one specific embodiment, the system also includes a data interaction link, used to establish a directed data transmission channel between the knowledge annotation module, knowledge decomposition module, scene awareness module, interface adaptation module, component assembly module, and component iteration module. This allows knowledge data with standardized tags, standardized modular component libraries, standardized adaptation instructions, interface connection rules, modular solutions, and application feedback data to flow in a pre-defined workflow sequence, forming a progressive collaborative processing chain from knowledge preprocessing and scene adaptation to closed-loop optimization. The data interaction link adopts an asynchronous transmission architecture based on message queues. Each module acts as a message producer or consumer, using message queues to achieve decoupled data transmission and avoid system bottlenecks caused by direct dependencies between modules. The message queue supports persistent data storage, ensuring that data is not lost due to system anomalies during data transmission. Simultaneously, message encryption and authentication mechanisms are employed to ensure the security of data transmission and prevent data tampering or unauthorized access. The order of data transmission is controlled by message priority and timestamps, ensuring that critical data (such as standardized adaptation instructions and interface connection rules) is transmitted first, guaranteeing the system's real-time responsiveness. The progressive collaborative processing chain enables data to flow in an orderly manner between modules. The output of each module serves as the input of the next module, forming a complete workflow. At the same time, feedback data from the component iteration module flows back to the front-end module, realizing closed-loop optimization of the system and significantly improving the overall operating efficiency and collaborative capabilities of the system.

[0103] In summary, through the collaborative work of the aforementioned modules, this system achieves standardized processing, automated decomposition, scenario-based adaptation, component-based assembly, and closed-loop optimization of scattered knowledge in a dynamic knowledge base. This effectively solves problems such as low knowledge conversion efficiency, poor component compatibility, and insufficient solution adaptability in existing technologies. In practical applications, the entire process from knowledge to solution can be completed without manual intervention, significantly shortening the development cycle of modular solutions. Simultaneously, standardized component design and dynamic interface adaptation capabilities enable the solution to flexibly adapt to different application scenarios, improving its reusability and scalability. The closed-loop optimization mechanism of the component iteration module ensures that the system can continuously accumulate application experience, constantly improving solution quality and operational stability, providing efficient and reliable support for enterprise technology research and development and project implementation.

[0104] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0105] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A modular solution component construction system driven by a dynamic knowledge base, characterized in that, include: The knowledge annotation module is used to preprocess the scattered knowledge in the dynamic knowledge base and generate standardized tags based on two dimensions: functional attribute type and scenario adaptation requirements. The knowledge data with standardized tags is then output. The knowledge decomposition module is used to receive knowledge data with standardized tags, establish knowledge-component mapping rules based on functional attribute type tags, extract the core functional units in the knowledge and decompose them into independent basic components with reference to the standard structure corresponding to the functional attribute type tags. At the same time, based on the scenario adaptation requirement tags, initial scenario adaptation identifiers and basic interface information are added to each basic component to form a standardized modular component library. The scene perception module is used to collect the functional requirement description and environmental parameter configuration information of the current application scene, perform layered parsing of the functional requirement description to identify core requirements and secondary requirements, determine the requirement priority order based on the core requirements and secondary requirements, and transform the requirement priority order, functional requirement description and environmental parameter configuration information into standardized adaptation instructions containing functional goals, scene constraints and interface types. The interface adaptation module is used to receive standardized adaptation instructions, read the basic interface information of the target modular component called by the component assembly module, analyze the interface type requirements and scenario constraints in the standardized adaptation instructions, dynamically adjust the interface interaction parameters and data transmission format of the target modular component based on the built-in multi-type interface protocol conversion logic, and generate interface connection rules that meet the current scenario. The component assembly module is used to receive standardized adaptation instructions and interface connection rules, match modular components from the standardized modular component library that correspond to the priority of the requirements, establish logical connection relationships between the matched modular components according to the interface connection rules, and perform collaborative verification of the functions of the matched modular components to generate a modular solution that meets the requirements of the current scenario. The component iteration module receives application feedback data generated by the modular solution in actual application, analyzes the application feedback data to identify functional adaptation issues and component interface compatibility issues, and adjusts the knowledge-component mapping rules based on the functional adaptation issues and component interface compatibility issues, updates the basic interface information and initial scenario adaptation identifier of the modular components, and feeds back the adjusted mapping rules and updated component information to the knowledge splitting module and interface adaptation module respectively.

2. The modular solution component construction system driven by dynamic knowledge base according to claim 1, characterized in that, The knowledge annotation module includes: The knowledge preprocessing unit is used to perform cleaning, deduplication, and format standardization operations on the scattered knowledge in the dynamic knowledge base. The dual-dimensional tag generation unit is used to assign functional attribute type tags and scenario adaptation requirement tags to the preprocessed knowledge based on the preset functional attribute type dictionary and scenario adaptation requirement dictionary. The historical data fusion unit is used to acquire historical application data of the dynamic knowledge base, analyze the relationship between historical application scenarios and knowledge application records, and adjust the weight of functional attribute type tags and scenario adaptation requirement tags based on the relationship.

3. The modular solution component construction system driven by dynamic knowledge base according to claim 1, characterized in that, The knowledge decomposition module includes: The core functional unit extraction unit is used to perform semantic parsing on knowledge data with standardized labels, identify and extract independently executable functional logic fragments as core functional units. The standard structure reference unit is used to maintain a standard component structure template library that corresponds one-to-one with the functional attribute type label, and to match the corresponding standard component structure template for each core functional unit. The component generation and identification unit is used to fill the core functional units into the matching standard component structure template, generate basic components, convert the corresponding scene adaptation requirement tags into initial scene adaptation identifiers, and configure the default basic interface information for the basic components.

4. The modular solution component construction system driven by dynamic knowledge base according to claim 1, characterized in that, The scene perception module includes: The requirement acquisition unit is used to obtain the user's input of functional requirement descriptions and environmental parameter configuration information through a natural language interface or configuration file parser. The requirement layering parsing unit is used to perform syntactic and semantic analysis on the functional requirement description, identify the mandatory and optional requirements, define the mandatory requirements as core requirements, and define the optional requirements as secondary requirements. The priority and instruction generation unit is used to quantify and score core and secondary requirements according to preset business rules, generate a requirement priority ranking based on the scoring results, and then encode the requirement priority ranking, functional requirement description and environmental parameter configuration information into standardized adaptation instructions.

5. The modular solution component construction system driven by dynamic knowledge base according to claim 1, characterized in that, The interface adaptation module includes: The interface information reading unit is used to receive the identifier of the target modular component to be assembled from the component assembly module, and retrieve the corresponding basic interface information from the standardized modular component library based on the identifier. The protocol difference analysis unit is used to compare the basic interface information with the interface type requirements specified in the standardized adaptation instructions, and identify the differences between the two in terms of data format, communication protocol and calling method. The dynamic parameter adjustment unit is used to call the interface protocol conversion subroutine that matches the difference point, modify the interface interaction parameters and data transmission format of the target modular component in real time, and generate interface connection rules.

6. The modular solution component construction system driven by dynamic knowledge base according to claim 5, characterized in that, The interface adaptation module also includes: A multi-protocol conversion logic library is used to store various predefined interface protocol conversion rules. Each rule is associated with a source interface type and a target interface type, and contains script code for performing format conversion and parameter mapping. The rule matching engine is used to retrieve applicable interface protocol conversion rules from the multi-protocol conversion logic library based on the output of the protocol difference analysis unit, and load the corresponding script code for the dynamic parameter adjustment unit to call.

7. The modular solution component construction system driven by dynamic knowledge base according to claim 1, characterized in that, The component assembly module includes: The component matching unit is used to traverse the standardized modular component library, filter out candidate components whose initial scene adaptation identifier matches the scene constraints in the standardized adaptation instructions, and sort the candidate components according to the priority of requirements. The logical connection establishment unit is used to establish the connection topology of data flow and control flow between the sorted candidate components according to the interface connection rules. The functional coordination verification unit is used to simulate the execution of the connection topology, verify whether the combination of each component can fully achieve the functional goal, and output the modular solution after the verification is passed.

8. The modular solution component construction system driven by dynamic knowledge base according to claim 1, characterized in that, The component iteration module includes: The feedback data receiving and parsing unit is used to receive application feedback data from external systems and extract structured log information about component function failures, interface call anomalies, and performance bottlenecks. The problem diagnosis unit is used to compare the structured log information with the component configuration and interface connection rules of the modular solution to locate the specific component or interface connection pair that caused the problem. The rule and identifier update unit is used to generate revision instructions for knowledge-component mapping rules, as well as update instructions for basic interface information and initial scenario adaptation identifiers of specific components, based on the location results of the problem diagnosis unit.

9. The modular solution component construction system driven by dynamic knowledge base according to claim 8, characterized in that, The component iteration module also includes: The feedback routing unit is used to send revision instructions to the knowledge splitting module to trigger an update to the knowledge-component mapping rules; and to send the portion of the update instruction containing interface information to the interface adaptation module to update the component interface metadata maintained internally.

10. The modular solution component construction system driven by dynamic knowledge base according to claim 1, characterized in that, The system also includes a data interaction link, which is used to establish a directional data transmission channel between the knowledge annotation module, knowledge decomposition module, scene perception module, interface adaptation module, component assembly module and component iteration module, so that knowledge data with standardized tags, standardized modular component library, standardized adaptation instructions, interface connection rules, modular schemes and application feedback data can flow in the order of the preset workflow, forming a progressive collaborative processing chain from knowledge preprocessing, scene adaptation to closed-loop optimization.

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