NLP-based modular scheme rapid construction method and system

Abstract

The invention relates to the technical field of digital office scheme construction, in particular to an NLP-based modular scheme rapid construction method and system, comprising: constructing a dynamic case knowledge base and a modular scheme component library, the dynamic case knowledge base storing historical schemes with multi-dimensional classification tags, and the modular scheme component library storing the historical schemes with multi-dimensional classification tags; the modular scheme component library stores components classified according to scheme structures and corresponding adaptation rules thereof. According to the method, a dynamic case knowledge base and a modular scheme component library are constructed, semantic analysis of scheme requirements, accurate retrieval of cases and intelligent adaptation and combination of components are realized in combination with an NLP technology, and meanwhile, a basic library is continuously optimized through a feedback iteration mechanism; the problem that in the prior art, intelligent linkage adaptation between a modular assembly and a case knowledge base and between the modular assembly and a retrieval engine is lacked is effectively solved.

Description

Technical Field

[0001] This invention relates to the field of digital office solution construction technology, specifically to a method and system for rapid construction of modular solutions based on NLP. Background Technology

[0002] In digital office scenarios, solution writing is a crucial part of business operations, and intelligent solution writing platforms, by integrating knowledge bases, retrieval technologies, and modular components, are helping business personnel improve solution building efficiency, which has become an industry trend. These platforms typically rely on structured data management, semantic understanding, and component reuse technologies, enabling non-technical personnel to quickly build solutions and reducing reliance on professional writing skills.

[0003] The existing published patent CN118626057A, "Modular Development Method and System Based on Large Language Models," proposes to lower the application development threshold by generating applications through two queries to the large language model and a solidified process. This patent focuses on simplifying the application development process and achieving reuse by recording the process using a universally unique identifier. However, it does not optimize for the specific characteristics of the solution writing scenario. Its modular design emphasizes the solidification of the development process rather than the deep integration of solution components and case knowledge.

[0004] The current technical issues include a lack of intelligent linkage and adaptation mechanisms between modular components, the case knowledge base, and the search engine. Furthermore, when business users utilize the platform, retrieved historical cases are presented only as reference content. Components in the modular component library require manual selection and assembly. There is no automatic semantic association between component selection and the industry scenarios, customer types, or other characteristics of the retrieved cases. Additionally, component reuse is not adapted to incorporate successful experiences from the cases, causing business users to spend significant time matching components with cases. This not only reduces solution development efficiency but also easily leads to mismatches between components and required scenarios, impacting solution quality. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for rapid construction of modular solutions based on NLP. This solves the problems in existing technologies where modular components lack intelligent linkage and adaptation with case knowledge bases and search engines, requiring manual component selection, which leads to low solution construction efficiency and poor scenario adaptability.

[0006] To achieve the above objectives, the present invention provides a method for rapid construction of modular solutions based on NLP, comprising the following steps:

[0007] Step S1: Construct a dynamic case knowledge base and a modular solution component library, wherein the dynamic case knowledge base stores historical solutions with multi-dimensional classification tags, and the modular solution component library stores components classified according to solution structure and their corresponding adaptation rules;

[0008] Step S2: Receive the solution requirement description input by business personnel, parse the semantics of the solution requirement description through a multimodal retrieval engine, and perform retrieval by combining the classification tags in the dynamic case knowledge base, and output matching historical cases;

[0009] Step S3: Extract the features of the historical cases through the intelligent linkage adaptation module and convert them into component requirement parameters. Perform semantic matching between the component requirement parameters and the component adaptation rules in the modular solution component library to generate a preliminary adapted component list.

[0010] Step S4: Perform scene adaptability verification on the preliminary adapted component list, exclude mismatched components, and generate the final adapted component combination;

[0011] Step S5: Collect feedback from business personnel on the use of the final adapted component combination, and update the dynamic case knowledge base and the modular solution component library according to the feedback.

[0012] Furthermore, the implementation of step S1 includes the following specific steps:

[0013] Step S11: Add multi-dimensional category tags to historical solutions and record the content and iteration time of each solution update;

[0014] Step S12: Store the tagged historical solution data in a hierarchical structure to form the dynamic case knowledge base;

[0015] Step S13: Divide the components into framework, content, and data categories according to the solution structure, and extract the successful experiences from historical solutions into adaptation rules and usage instructions for each component;

[0016] Step S14: Store the categorized components and their adaptation rules to form the modular solution component library.

[0017] Furthermore, the implementation of step S2 includes the following specific steps:

[0018] Step S21: Process the solution requirement description based on the BERT model and extract core semantic information;

[0019] Step S22: Call the tag data of the dynamic case knowledge base to obtain the historical solution index of the corresponding tag;

[0020] Step S23: Connect the industry knowledge graph and filter the search results according to the context;

[0021] Step S24: Analyze the historical search and usage records of business personnel, generate personalized search recommendations, and output the historical cases with the highest matching degree.

[0022] Furthermore, the implementation of step S3 includes the following specific steps:

[0023] Step S31: Receive the retrieved historical case data and extract the key points of industry adaptation and details of scenario requirements from the cases;

[0024] Step S32: Transform the extracted features into a standardized component requirement parameter vector. ,in Indicates the first Quantitative values ​​of each demand characteristic For feature dimensions;

[0025] Step S33: Call the component adaptation rule vector provided by the modular solution component library. ,in Indicates the first The component for the first The adaptation weight of each demand feature;

[0026] Step S34: Calculate the semantic matching score Components with scores higher than a preset threshold are selected to form the preliminary list of adapted components.

[0027] Further, in step S34, the preset threshold The calculation formula is ,in The mean of the matching scores for all components. Standard deviation This is the adjustment coefficient.

[0028] Furthermore, the implementation of step S4 includes the following specific steps:

[0029] Step S41: Based on the characteristics of the historical cases, construct a set of scenario constraints;

[0030] Step S42: Traverse each component in the preliminary adapted component list and check whether it meets the set of scene constraints.

[0031] Step S43: Remove components that do not meet the constraints, retain components that meet the constraints, and generate the final adapted component combination.

[0032] Furthermore, the implementation of step S5 includes the following specific steps:

[0033] Step S51: Record the business personnel's selection of solution components, adjustments, and evaluation of the solution's effectiveness after use, forming feedback data;

[0034] Step S52: Classify and process the feedback data, transmit it to the dynamic case knowledge base, and update the adaptation effect tags of historical cases;

[0035] Step S53: Transmit the feedback data to the modular solution component library to optimize the component adaptation rules and usage instructions.

[0036] Furthermore, in step S2, the collaborative filtering subunit of the multimodal retrieval engine constructs a user profile vector U based on the historical behavior data of business personnel, and calculates the similarity between the user profile and the case feature vector C. , where W is a learnable weight matrix used to generate personalized search recommendations.

[0037] Furthermore, the final combination of adaptation components is presented in a visual form, allowing business personnel to directly reuse or adjust it to complete the solution construction.

[0038] This invention also provides a modular approach based on NLP for rapid system construction, including:

[0039] A dynamic case knowledge base is used to store historical solutions with multi-dimensional classification tags;

[0040] A modular solution component library is used to store components categorized by solution structure and their corresponding adaptation rules;

[0041] A multimodal retrieval engine is used to parse the semantics of solution requirement descriptions and retrieve matching historical cases;

[0042] The intelligent linkage adaptation module is used to transform the characteristics of historical cases into component requirement parameters, perform semantic matching and adaptation verification with component adaptation rules, and generate the final adapted component combination.

[0043] The feedback iteration module is used to collect feedback and drive updates to the dynamic case knowledge base and the modular solution component library.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] This invention constructs a dynamic case knowledge base and a modular solution component library, combining NLP technology to achieve semantic parsing of solution requirements, accurate case retrieval, and intelligent adaptation and combination of components. Simultaneously, a feedback iteration mechanism continuously optimizes the basic library, effectively solving the problem of insufficient intelligent linkage and adaptation between modular components, case knowledge bases, and search engines in existing technologies. In practical applications, business personnel do not need professional solution writing skills or deep industry knowledge; they only need to input a concise description of their solution requirements to obtain matching historical cases and suitable component combinations, effectively lowering the barrier to solution writing. Intelligent component matching and scenario verification ensure a high degree of adaptation between solution content and requirement scenarios, reducing solution quality issues caused by inappropriate component selection and improving the professionalism and feasibility of the solution. Attached Figure Description

[0046] Figure 1 This is a flowchart of the method of the present invention;

[0047] Figure 2 This is a system structure diagram of the present invention. Detailed Implementation

[0048] 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.

[0049] Example 1

[0050] Please see Figure 1-2 This invention provides a method for rapidly constructing modular solutions based on NLP. This embodiment, combined with the application scenario of writing solutions in the enterprise service industry, details the implementation process of the method, including the following steps:

[0051] First, to achieve rapid solution construction, the basic library needs to be built. This involves constructing a dynamic case knowledge base and a modular solution component library. The dynamic case knowledge base stores historical solutions with multi-dimensional classification tags, while the modular solution component library stores components categorized by solution structure and their corresponding adaptation rules. For the construction of the dynamic case knowledge base, multi-dimensional classification tags are first added to historical solutions. These tag dimensions are based on the business characteristics of the enterprise service industry, covering key dimensions such as industry type, solution purpose, customer size, implementation cycle, and core needs. For example, industry type tags can include finance, education, manufacturing, and retail; solution purpose tags can be divided into digital transformation, marketing promotion, process optimization, and risk management; customer size tags are divided into micro, small, medium, and large enterprises based on the number of employees; implementation cycle tags are divided into short-term, medium-term, and long-term; and core needs tags are extracted based on specific business scenarios, such as cost control, efficiency improvement, compliance, and experience optimization. When adding tags to historical solutions, it is necessary to have professional business personnel and technical personnel collaborate to review them, ensuring consistency between the tags and the solution content. Simultaneously, each update and iteration of the solution should be recorded, including the updated modules, reasons for modification, and personnel involved, providing a basis for subsequent solution traceability and optimization. Subsequently, the tagged historical solution data is stored in a hierarchical structure using a distributed database for data management. The hierarchical logic is divided according to "industry type - solution purpose - customer scale," with each level further subdivided according to implementation cycle and core requirements. This hierarchical storage method improves the efficiency of subsequent retrieval, allowing business personnel to quickly locate historical cases that meet their needs.

[0052] In the construction of the modular solution component library, components are divided into framework, content, and data categories according to the solution structure. Framework components mainly contain the overall structural template of the solution, such as the fixed combination of modules like solution summary, requirements analysis, implementation plan, effect prediction, and risk assessment. Different industries and uses correspond to different framework templates. For example, the framework of a compliance solution in the financial industry should highlight modules such as compliance basis, existing problems, rectification measures, and compliance verification. Content components cover the specific descriptions under each module of the solution, including industry standard expressions, explanations of professional terms, summaries of successful experiences, and solutions to common problems. For example, in the "implementation plan" module of a marketing and promotion solution, content components may include channel selection suggestions, promotion schedule, and material preparation list. Data components are various data templates and analysis models used in the solution, such as market research data statistics templates, input-output analysis models, and trend prediction algorithm modules. Simultaneously, successful experiences from historical solutions are extracted into adaptation rules and usage instructions for each component. Adaptation rules clearly define the applicable scenarios for each component; for example, a certain marketing content component may only be applicable to online promotion scenarios in the retail industry. The usage instructions detail how to use the component, key modification points, and precautions, helping business personnel quickly get started using the component. Finally, the categorized components and their adaptation rules are stored in a component library, which is linked to a dynamic case knowledge base to achieve data interoperability and sharing.

[0053] After completing the basic library setup, the system moves into the solution requirement processing and case retrieval phase. It receives solution requirement descriptions input by business personnel, parses the semantics of these descriptions using a multimodal retrieval engine, and searches using classification tags from a dynamic case knowledge base, outputting matching historical cases. Business personnel input solution requirement descriptions through the system's interactive interface. Input can be in multiple modal formats, including text and voice. The system first processes voice input into text to ensure consistent parsing of requirement information. Subsequently, the solution requirement descriptions are processed based on the BERT model. Here, the BERT-base-uncased model is selected, as it exhibits excellent performance in semantic understanding tasks and can effectively extract core semantic information from text. Specifically, the requirement description text undergoes preprocessing operations such as word segmentation, part-of-speech tagging, and entity recognition. Then, the BERT model's encoder converts the text into a vector representation, thereby uncovering the core demands within the requirements, such as industry type, solution purpose, and core objectives.

[0054] The system retrieves historical solution indexes for corresponding tags by accessing tag data from a dynamic case knowledge base. Based on the core semantic information extracted by the BERT model, it matches the classification tags in the dynamic case knowledge base. For example, if the requirement description explicitly mentions "retail industry," "online marketing promotion," or "improving customer conversion rate," the system automatically matches the corresponding industry type tags, solution purpose tags, and core requirement tags. These tags then quickly locate relevant historical solution indexes in the knowledge base, reducing the search scope and improving search efficiency. Simultaneously, it associates with an industry knowledge graph to perform scenario-based filtering of search results. The industry knowledge graph integrates various entity information and their relationships within the enterprise service industry, including industry policies, business processes, technical standards, and customer characteristics. By analyzing the correlation between retrieved historical cases and the industry knowledge graph, cases that do not match the current requirement scenario are excluded. For example, if a historical case matches the tags but the marketing channel involved is prohibited by new industry regulations, the case will be filtered out, ensuring the scenario-appropriateness of the search results.

[0055] Furthermore, the collaborative filtering subunit of the multimodal retrieval engine constructs a user profile vector U based on the historical behavior data of business personnel, and calculates the similarity between the user profile and the case feature vector C. The system uses a learnable weight matrix, W, to generate personalized search recommendations. User historical behavior data includes past search keywords, viewed cases, used components, and modification / adjustment records. The system statistically analyzes this data to extract user preference characteristics. For example, a business professional who frequently handles marketing plans for the retail industry will have higher weights for retail and marketing-related features in their user profile. The weight matrix W is trained using a deep learning model. The training data consists of a large amount of user behavior data and case matching data accumulated by the system. The model parameters are optimized using a gradient descent algorithm to make the similarity calculation results more accurate. By combining tag retrieval, knowledge graph filtering, and personalized recommendations, the system ultimately outputs the historical cases with the highest matching degree, allowing business professionals to obtain reference cases that better suit their needs and reducing screening costs.

[0056] After acquiring matching historical cases, the process moves to the component adaptation and composition stage. The intelligent linkage adaptation module extracts features from historical cases and transforms them into component requirement parameters. These parameters are then semantically matched with component adaptation rules in the modular solution component library to generate a preliminary list of adapted components. First, the retrieved historical case data is received, and industry adaptation points and scenario requirement details are extracted. Industry adaptation points are extracted based on the industry type of the case; for example, cases in the financial industry highlight compliance requirements and risk control, while cases in the education industry focus on policy compliance and ensuring teaching effectiveness. Scenario requirement details include the client's specific needs, implementation environment constraints, and the core objectives of the solution. For example, a process optimization solution for a manufacturing company might include production process simplification, labor cost control, and improved equipment utilization.

[0057] The extracted features are transformed into a standardized component requirement parameter vector. ,in Indicates the first Quantitative values ​​of each demand characteristic For feature dimensions. The value is determined based on industry characteristics and solution type, such as marketing solutions in the enterprise service industry. This can be set to 10, with corresponding requirements including channel adaptability, budget matching, timeframe fit, and consistency of expected results. (Quantitative value) The value range is [0,1], which is determined by the fuzzy comprehensive evaluation method. For example, for the channel adaptability feature, if the demand clearly requires online channels, the quantitative value of this feature is 0.9; if there are no special requirements for channels, the quantitative value is 0.5.

[0058] At the same time, the component adaptation rule vectors provided by the modular solution component library are invoked. ,in Indicates the first The component for the first The adaptation weight for each requirement feature. Adaptation weight The value range is [0,1]. It is determined using the analytic hierarchy process (AHP). Business and technical experts score the importance of each component for each requirement feature, and the final adaptation weight is obtained after a consistency check. For example, the adaptation weight of a certain online marketing content component for channel adaptability features... The adaptation weights for budget matching features This indicates that the component has stronger adaptability in terms of channel compatibility.

[0059] Calculate semantic matching score ,in This represents the dot product of the requirement parameter vector and the component adaptation rule vector. This represents the magnitude of the demand parameter vector. This represents the magnitude of the component adaptation rule vector. This formula measures the semantic matching degree between the component and the requirement by calculating the cosine similarity between two vectors; the higher the similarity, the more the component matches the requirement characteristics. Components with scores higher than a preset threshold are selected to form a preliminary list of adapted components. Preset Threshold The calculation formula is ,in The mean of the matching scores for all components. Standard deviation This is the adjustment coefficient. The value is dynamically adjusted based on industry needs and system performance. For example, in the initial stage of the enterprise service industry, It can be set to 0.8 to ensure that a sufficient number of candidate components are filtered out; as system data accumulates and is optimized, It can be adjusted to version 1.0 to improve the accuracy of component selection. Through this semantic matching process, intelligent association between components and requirement characteristics is achieved, avoiding the tedious operation of manually selecting components and improving the accuracy of component selection.

[0060] After the initial list of compatible components is generated, scenario compatibility verification is required. This verification process eliminates incompatible components and generates the final compatible component combination. Based on the characteristics of historical cases, a set of scenario constraints is constructed. These constraints stem from the implementation environment, industry standards, and customer needs. For example, for compliance-related solutions in the financial industry, scenario constraints include ensuring component content complies with the latest financial regulatory policies, that data sources have compliant qualifications, and that implementation processes meet risk control requirements. For solutions in the education industry, scenario constraints might include ensuring components comply with relevant regulations of the education department and that content descriptions are suitable for the target audience's cognitive level.

[0061] The system iterates through each component in the initial adapted component list, checking if it meets the set of scenario constraints. This check uses rule matching, comparing each component's attributes against the scenario constraints one by one. For example, if a component's content involves prohibited marketing methods, and the scenario constraints explicitly require "components to comply with current industry policies," then that component does not meet the constraints. Components that do not meet the constraints are removed, while those that do are retained, generating the final adapted component combination. During this process, the system automatically records the removed components and their unmet constraints, providing data support for subsequent component library optimization. The final adapted component combination is presented visually, using a tree structure to display the hierarchical relationship between components. Each component is labeled with its core function and adapted scenarios, allowing business personnel to directly reuse or adjust them to complete solution construction. This visual presentation reduces the operational difficulty for business personnel, enabling them to quickly understand the purpose and related logic of components, further improving solution construction efficiency.

[0062] Once the solution is built, user feedback needs to be collected to dynamically optimize the basic library. This involves collecting user feedback on the final adapted component combination and updating the dynamic case knowledge base and modular solution component library accordingly. Specifically, the system automatically records user choices regarding solution components, including whether components are reused, replaced, and the reasons for replacement; it also records component adjustments, including specific text changes, supplementary information, and deleted modules; and simultaneously, user feedback is submitted through the system's interactive interface, evaluating the solution's effectiveness based on factors such as component adaptability, content usability, framework rationality, and ease of implementation, thus generating comprehensive feedback data.

[0063] Feedback data is categorized and processed, then transmitted to a dynamic case knowledge base to update the adaptation effect tags of historical cases. Cases highly praised by business users and successfully applied without significant adjustments are tagged as "Excellent Adaptation"; cases requiring substantial adjustments to meet requirements are tagged as "Moderate Adaptation"; and cases with frequently replaced components or ultimately not adopted are tagged as "Poor Adaptation." These tag updates help to more accurately filter cases during subsequent searches, improving search quality. Simultaneously, feedback data is transmitted to a modular solution component library to optimize component adaptation rules and usage instructions. If multiple business users make the same adjustments to a component in the same scenario, it indicates that the component's adaptation rules have room for optimization. The system corrects the component's adaptation rules based on the adjustments, such as adjusting the weight values ​​in the component's adaptation rule vector. If business users report that a component's usage instructions are unclear, the instructions are supplemented and improved to enhance the component's usability.

[0064] By implementing the above process, intelligent linkage and adaptation between modular components, the case knowledge base, and the search engine are achieved, solving the problems of manual component selection and splicing and lack of automatic association with demand scenarios in existing technologies. Business personnel no longer need to spend a lot of time matching components and cases; they only need to input a description of their requirements to obtain accurately matched historical cases and adapted component combinations, significantly improving the efficiency of solution construction. At the same time, component selection is based on semantic matching and scenario constraint verification, ensuring a high degree of adaptation between components and demand scenarios, reducing component mismatch issues in the solution, and improving solution quality. In addition, the feedback and iteration mechanism allows the basic library to be continuously optimized. With the accumulation of usage data, the accuracy of case retrieval and the accuracy of component adaptation are continuously improved, further enhancing the system's practicality and adaptability.

[0065] The system portion of this embodiment is primarily implemented through a software architecture, including a dynamic case knowledge base, a modular solution component library, a multimodal retrieval engine, an intelligent linkage adaptation module, and a feedback iteration module. The modules interact with each other via API interfaces, and a microservice architecture ensures the system's flexibility and scalability. The dynamic case knowledge base and modular solution component library employ distributed storage technology, supporting the storage and efficient retrieval of massive amounts of data. The multimodal retrieval engine integrates NLP processing, tag retrieval, knowledge graph association, and collaborative filtering modules to achieve multi-dimensional case retrieval. The intelligent linkage adaptation module includes feature extraction, parameter transformation, semantic matching, and scene verification units to complete component adaptation and combination. The feedback iteration module is responsible for data collection, classification processing, and library update operations. All modules work collaboratively to ensure the stable operation and efficient response of the entire system.

[0066] Example 2

[0067] Please see Figure 2 The present invention also provides a modular solution rapid construction system based on NLP, including: a dynamic case knowledge base, a modular solution component library, a multimodal retrieval engine, an intelligent linkage adaptation module, and a feedback iteration module.

[0068] The dynamic case knowledge base adopts a distributed database architecture, storing historical solutions with multi-dimensional classification tags. It supports hierarchical data storage and fast retrieval, quickly returning matching case data based on search requests and receiving feedback data to update tags. The modular solution component library also uses distributed storage, classifying components by framework, content, and data categories. It stores components, their adaptation rules, and usage instructions, supporting fast component invocation and rule optimization.

[0069] The multimodal retrieval engine integrates an NLP processing module, a tag retrieval module, a knowledge graph association module, and a collaborative filtering module. The NLP processing module uses the BERT model to extract semantics from requirement descriptions; the tag retrieval module is responsible for obtaining case indexes by associating knowledge base tag data; the knowledge graph association module filters mismatched cases through industry knowledge graphs; and the collaborative filtering module generates personalized recommendations based on user historical behavior, all working together to achieve accurate case retrieval.

[0070] The intelligent linkage adaptation module includes a feature extraction unit, a parameter transformation unit, a semantic matching unit, and a scene verification unit. The feature extraction unit extracts industry adaptation points and scene requirement details from the retrieved cases. The parameter transformation unit converts these features into standardized component requirement parameter vectors. The semantic matching unit calculates the matching degree between the parameter vectors and the component adaptation rule vectors and filters components. The scene verification unit excludes mismatched components based on scene constraints and generates the final adapted component combination.

[0071] The feedback iteration module consists of a data collection unit, a classification and processing unit, and a library update unit. The data collection unit records the usage choices, content adjustments, and effect evaluations by business personnel. The classification and processing unit processes the feedback data into two categories: knowledge base updates and component library updates. The library update unit then transmits the processed data to the dynamic case knowledge base and the modular solution component library, respectively, to optimize case tags and component rules.

[0072] The system's operation follows the methodological implementation steps in an orderly manner. First, business personnel input their solution requirements through the system's interactive interface. After receiving the requirements, the system transmits them to the multimodal retrieval engine. The retrieval engine first performs semantic parsing on the requirement description, extracts core information, then associates it with tag data from the dynamic case knowledge base, combines industry knowledge graphs and user historical behavior to perform retrieval, and outputs matching historical cases.

[0073] Historical case data is then passed to the intelligent linkage adaptation module. The module first extracts case features and converts them into component requirement parameter vectors. Then, it calls the component adaptation rule vectors from the modular solution component library to calculate semantic matching degree and filter out a preliminary list of adapted components. Next, the module constructs a set of scenario constraints, verifies the preliminary adapted components, removes components that do not meet the constraints, generates the final adapted component combination, and presents it to business personnel in a visual form.

[0074] Business users construct solutions using component combinations and submit feedback. The feedback iteration module collects and categorizes this feedback data, updating the dynamic case knowledge base and modular solution component library accordingly, enabling the system to self-optimize. Throughout the process, modules interact with each other in real time via API interfaces, employing load balancing technology to ensure system response speed, supporting simultaneous operation by multiple users, and meeting the daily business needs of enterprise solution writing.

[0075] Example 3

[0076] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0077] This embodiment selects the scenario of "writing digital transformation solutions for small and medium-sized enterprises" in the enterprise service industry to verify the application of the modular solution rapid construction method and system based on NLP.

[0078] The application scenario is set as follows: Business personnel from an enterprise service company need to write a digital transformation plan for a small manufacturing enterprise with fewer than 50 employees. The core requirement is to optimize the production process and improve production efficiency through digital means, while controlling implementation costs. The implementation cycle is required to be completed within 3 months.

[0079] First, business personnel log into the NLP-based modular solution rapid construction system and input a solution requirement description in text form on the requirement input interface: "Write a digital transformation solution for a small manufacturing enterprise with fewer than 50 employees. The core objectives are to optimize production processes, improve production efficiency, control implementation costs, and implement within 3 months." After receiving the requirement description, the system starts its multimodal search engine for processing.

[0080] The search engine first performs semantic parsing of the requirement description based on the BERT-base-uncased model, extracting core semantic information: industry type is manufacturing, customer size is small business, solution purpose is digital transformation, core needs are optimizing production processes, improving production efficiency, controlling costs, and implementation cycle is short-term (within 3 months). Then, it calls the tag data from the dynamic case knowledge base, matches the tag combinations related to "manufacturing industry - digital transformation - small business - short-term implementation," and retrieves the corresponding historical solution index.

[0081] Simultaneously, the system connects to an enterprise service industry knowledge graph, which integrates information such as relevant policies, common implementation paths, suitable technical solutions, and key points for cost control in the manufacturing industry's digital transformation. Through knowledge graph correlation analysis, historical cases targeting large manufacturing enterprises, implementation cycles exceeding 6 months, or high-cost investments are filtered out to ensure the scenario-appropriateness of the search results. In addition, the collaborative filtering subunit constructs a user profile vector based on the business personnel's historical behavioral data. This business personnel has handled SME-related solutions multiple times in the past, and features such as "SME suitability" and "cost control" have high weights in the user profile. The system calculates the similarity between the user profile and the feature vectors of candidate cases, and finally outputs the three historical cases with the highest matching degree: "Digital Production Process Optimization Solution for a Small Machinery Manufacturing Enterprise," "Short-Term Digital Transformation Implementation Case for a Small Light Industry Manufacturing Enterprise," and "Low-Cost Digital Upgrade Solution for Small and Medium-Sized Manufacturing Enterprises."

[0082] After reviewing these three historical cases, the business personnel selected "Digital Production Process Optimization Solution for a Small Machinery Manufacturing Enterprise" as the primary reference case. The system then transmitted the case data to the intelligent linkage adaptation module. The module first extracted the characteristics of the case, including industry adaptation points (digital production process standards in the manufacturing industry, equipment adaptation requirements for SMEs, and short-term implementation priority ranking) and scenario requirements details (simplified production process, reduced labor costs, retrofitting and utilizing existing equipment, moderate implementation cost control, and completion of core module deployment within 3 months).

[0083] Next, the module transforms these features into a standardized component requirement parameter vector. The 10 characteristic dimensions are: industry adaptability, customer scale adaptability, implementation cycle adaptability, cost matching degree, production process optimization adaptability, efficiency improvement adaptability, equipment transformation adaptability, labor cost control adaptability, core module priority adaptability, and risk controllability. The quantitative values ​​of each characteristic are as follows: Determined through fuzzy comprehensive evaluation, for example, industry suitability. Customer size adaptability Implementation cycle adaptability Cost matching degree Production process optimization and adaptability Efficiency improvement and adaptability Equipment modification adaptability Human resource cost control adaptability Core module priority adaptability Risk controllability .

[0084] Subsequently, the system calls the component adaptation rule vectors in the modular solution component library. There are 42 components related to digital transformation in the component library, and each component corresponds to an adaptation rule vector. Taking the framework component "Short-term Digital Transformation Framework Template for Small Manufacturing Enterprises" as an example, its adaptation rule vector... Each adaptation weight The importance of the component for each requirement feature was determined by the analytic hierarchy process (AHP), with 5 business experts and 3 technical experts scoring the importance of the component. The final weight value was obtained after a consistency check.

[0085] The system calculates the semantic matching score for each component. ,in , , After the calculation is complete, the mean of the matching scores for all components is calculated. Standard deviation adjustment coefficient The preset threshold is set to 0.9 (determined based on the adaptation needs of the solution for small and medium-sized enterprises in the manufacturing industry). Components with scores higher than 0.792 were selected, resulting in 18 initially adapted components, forming a preliminary adapted component list, including 3 framework components, 10 content components, and 5 data components.

[0086] Next, the intelligent linkage adaptation module constructs a set of scenario constraints based on the characteristics of historical cases. These constraints include: components must be compatible with the equipment infrastructure of small manufacturing enterprises; the implementation cycle must not exceed 3 months; cost investment must be within the budget of SMEs; content must cover the core aspects of digitalizing the production process; and the data model must support short-term effectiveness evaluation. The system iterates through the 18 initially adapted components, checking each one to ensure it meets these constraints. One framework component was removed because it included a long-term planning module (exceeding the 3-month implementation cycle); two content components involved digital technologies specific to large enterprises (unsuitable for small enterprises' equipment infrastructure); and one data component had an excessively long analysis cycle (not meeting short-term evaluation requirements). Finally, 14 components are retained, generating the final adapted component combination.

[0087] The system presents the component combination in a tree structure. The framework component is the "Short-term Digital Transformation Framework Template for Small Manufacturing Enterprises," which contains six sub-modules: Demand Analysis, Implementation Planning, Core Module Selection, Cost Budgeting, Risk Assessment, and Effect Prediction. Each sub-module corresponds to a content component, such as the "Pain Point Analysis of Production Processes for Small Manufacturing Enterprises" and "Digital Transformation Suitability Assessment" under the Demand Analysis module. Data components include "Cost Calculation Template for Digital Transformation of Small and Medium-sized Enterprises" and "Production Efficiency Improvement Effect Prediction Model." Business personnel can directly click on the components to view details and reuse them.

[0088] Based on the specific circumstances of this small manufacturing company, the business team made minor adjustments to the component combination. For example, in the "Core Module Selection" section, they replaced one component that was better suited to the company's existing equipment and supplemented the data related to the company's production scale. The remaining components were reused directly. After the solution was written, the business team submitted the solution and evaluated its effectiveness. The evaluation included feedback such as good component compatibility, clear framework logic, effective improvement in writing efficiency, and strong practicality of cost control-related content.

[0089] The feedback iteration module collects this usage feedback data, categorizes and processes it, and then transmits it to the dynamic case knowledge base and the modular solution component library. The dynamic case knowledge base updates the adaptation effect tag of "Digital Production Process Optimization Solution for a Small Machinery Manufacturing Enterprise" to "Excellent Adaptation" and records the adaptation advantages of this case in the short-term digital transformation scenario of a small manufacturing enterprise. The modular solution component library optimizes the adaptation rules for components replaced by business personnel, adds the adaptation condition of "adapting to specific equipment models of small manufacturing enterprises," and supplements the usage instructions for relevant content components, clarifying their applicable equipment scenarios.

[0090] Through this application verification, the method and system can quickly respond to the solution requirements of business personnel. Through intelligent retrieval, semantic matching, and scenario validation, it accurately generates suitable component combinations, significantly reducing the time business personnel spend selecting components and assembling solutions, while ensuring a high degree of fit between the solution content and the required scenario. The operation of the feedback iteration mechanism has further optimized the basic library, providing better support for writing solutions for similar scenarios in the future, fully demonstrating the practicality and adaptability of the technical solution.

[0091] In summary, this invention constructs a dynamic case knowledge base and a modular solution component library, and combines NLP technology to achieve semantic parsing of solution requirements, accurate case retrieval, and intelligent adaptation and combination of components. At the same time, it continuously optimizes the basic library through a feedback iteration mechanism, effectively solving the problem of the lack of intelligent linkage and adaptation between modular components and case knowledge bases and search engines in existing technologies.

[0092] In practical applications, business personnel do not need professional solution writing skills or in-depth industry knowledge. They only need to input a concise description of their solution requirements to obtain matching historical cases and suitable component combinations, effectively lowering the barrier to solution writing. Intelligent component matching and scenario validation ensure a high degree of fit between the solution content and the required scenario, reducing solution quality issues caused by inappropriate component selection and improving the professionalism and feasibility of the solution.

[0093] The dynamically optimized base library continuously improves search accuracy and component adaptability as usage data accumulates, enabling the system to adapt to the solution writing needs of different industries, customers of different sizes, and different demand scenarios, demonstrating excellent scalability and adaptability. The visual component combination presentation and simple operation process further enhance the user experience for business personnel, shorten the solution writing cycle, and provide efficient support for enterprise business operations, showing broad application prospects in multiple fields such as enterprise services, marketing promotion, and digital transformation.

[0094] 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.

[0095] 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 method for rapid construction of modular solutions based on NLP, characterized in that, Includes the following steps: Step S1: Construct a dynamic case knowledge base and a modular solution component library, wherein the dynamic case knowledge base stores historical solutions with multi-dimensional classification tags, and the modular solution component library stores components classified according to solution structure and their corresponding adaptation rules; Step S2: Receive the solution requirement description input by business personnel, parse the semantics of the solution requirement description through a multimodal retrieval engine, and perform retrieval by combining the classification tags in the dynamic case knowledge base, and output matching historical cases; Step S3: Extract the features of the historical cases through the intelligent linkage adaptation module and convert them into component requirement parameters. Perform semantic matching between the component requirement parameters and the component adaptation rules in the modular solution component library to generate a preliminary adapted component list. Step S4: Perform scene adaptability verification on the preliminary adapted component list, exclude mismatched components, and generate the final adapted component combination; Step S5: Collect feedback from business personnel on the use of the final adapted component combination, and update the dynamic case knowledge base and the modular solution component library according to the feedback.

2. The method for rapid construction of modular solutions based on NLP according to claim 1, characterized in that, The implementation of step S1 includes the following specific steps: Step S11: Add multi-dimensional category tags to historical solutions and record the content and iteration time of each solution update; Step S12: Store the tagged historical solution data in a hierarchical structure to form the dynamic case knowledge base; Step S13: Divide the components into framework, content, and data categories according to the solution structure, and extract the successful experiences from historical solutions into adaptation rules and usage instructions for each component; Step S14: Store the categorized components and their adaptation rules to form the modular solution component library.

3. The method for rapid construction of modular solutions based on NLP according to claim 1, characterized in that, The implementation of step S2 includes the following specific steps: Step S21: Process the solution requirement description based on the BERT model and extract core semantic information; Step S22: Call the tag data of the dynamic case knowledge base to obtain the historical solution index of the corresponding tag; Step S23: Connect the industry knowledge graph and filter the search results according to the context; Step S24: Analyze the historical search and usage records of business personnel, generate personalized search recommendations, and output the historical cases with the highest matching degree.

4. The method for rapid construction of modular solutions based on NLP according to claim 1, characterized in that, The implementation of step S3 includes the following specific steps: Step S31: Receive the retrieved historical case data and extract the key points of industry adaptation and details of scenario requirements from the cases; Step S32: Transform the extracted features into a standardized component requirement parameter vector. ,in Indicates the first Quantitative values ​​of each demand characteristic For feature dimensions; Step S33: Call the component adaptation rule vector provided by the modular solution component library. ,in Indicates the first The component for the first The adaptation weight of each demand feature; Step S34: Calculate the semantic matching score Components with scores higher than a preset threshold are selected to form the preliminary list of adapted components.

5. The method for rapid construction of modular solutions based on NLP according to claim 4, characterized in that, In step S34, the preset threshold The calculation formula is ,in The mean of the matching scores for all components. Standard deviation This is the adjustment coefficient.

6. The method for rapid construction of modular solutions based on NLP according to claim 1, characterized in that, The implementation of step S4 includes the following specific steps: Step S41: Based on the characteristics of the historical cases, construct a set of scenario constraints; Step S42: Traverse each component in the preliminary adapted component list and check whether it meets the set of scene constraints. Step S43: Remove components that do not meet the constraints, retain components that meet the constraints, and generate the final adapted component combination.

7. The method for rapid construction of modular solutions based on NLP according to claim 1, characterized in that, The implementation of step S5 includes the following specific steps: Step S51: Record the business personnel's selection of solution components, adjustments, and evaluation of the solution's effectiveness after use, forming feedback data; Step S52: Classify and process the feedback data, transmit it to the dynamic case knowledge base, and update the adaptation effect tags of historical cases; Step S53: Transmit the feedback data to the modular solution component library to optimize the component adaptation rules and usage instructions.

8. The method for rapid construction of modular solutions based on NLP according to claim 1, characterized in that, In step S2, the collaborative filtering subunit of the multimodal retrieval engine constructs a user profile vector U based on the historical behavior data of business personnel, and calculates the similarity between the user profile and the case feature vector C. , where W is a learnable weight matrix used to generate personalized search recommendations.

9. The method for rapid construction of modular solutions based on NLP according to claim 1, characterized in that, The final combination of adaptation components is presented in a visual form, allowing business personnel to directly reuse or adjust it to complete the solution construction.

10. A modular solution rapid construction system based on NLP, used to implement the modular solution rapid construction method based on NLP as described in any one of claims 1 to 9, characterized in that, include: A dynamic case knowledge base is used to store historical solutions with multi-dimensional classification tags; A modular solution component library is used to store components categorized by solution structure and their corresponding adaptation rules; A multimodal retrieval engine is used to parse the semantics of solution requirement descriptions and retrieve matching historical cases; The intelligent linkage adaptation module is used to transform the characteristics of historical cases into component requirement parameters, perform semantic matching and adaptation verification with component adaptation rules, and generate the final adapted component combination. The feedback iteration module is used to collect feedback and drive updates to the dynamic case knowledge base and the modular solution component library.

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