Service transaction system and method based on service digitization and intelligent matching

Abstract

The invention discloses a service transaction system and method based on service digitization and intelligent matching, and relates to the technical field of transaction data processing. The system comprises a demand side atomization module, a supply side atomization module, an intelligent matching module and an evaluation feedback module: the demand side atomization module is used for analyzing an original service demand of a user, perfecting the demand in combination with an interactive question and answer guide and outputting a structured demand atomic list; the supply side atomization module guides the service party to package the service capability into a standardized service commodity atom based on service capability information input by the service party; the intelligent matching module performs weight distribution dynamic optimization according to the supply and demand integrating degree parameter and user feedback based on the demand atom list and the service commodity atoms, and realizes accurate matching of the supply and demand parties based on a weight distribution dynamic optimization result; and the evaluation feedback module is used for carrying out system dynamic optimization based on evaluation data and service transaction requirements of the two transaction parties.

Description

Technical Field

[0001] This invention relates to the field of transaction data processing technology, and in particular to a service transaction system and method based on service digitization and intelligent matching. Background Technology

[0002] Service digitization is a process in which digital technology drives the transformation of the entire service industry chain. Its core lies in using technologies such as big data, cloud computing, and artificial intelligence to improve service efficiency, optimize customer experience, and innovate business models, as exemplified by the rise of telemedicine and online education. Against this backdrop, service transaction systems, as the core carrier supporting the implementation of digital services, automate and intelligentize transaction processes by integrating modules such as order management, payment clearing, and risk control rules. For example, financial transaction platforms, by decoupling core business logic to the middle layer, unify the management of transaction rules across all channels, meeting stringent regulatory requirements while improving system response speed. Service-oriented e-commerce systems, through supply chain collaboration and data analysis, provide customized transaction solutions for industries such as tourism and education. Both jointly drive the service industry towards efficiency, security, and personalization.

[0003] Existing technologies, through the integration of digital technologies, the application of intelligent algorithms, innovation in system architecture, and data security guarantees, are jointly driving the efficient realization of service digitization and service transaction systems. The deep integration of technologies such as cloud computing, big data, and artificial intelligence provides underlying support for service digitization. For example, CITIC Bank uses AI algorithms to establish an automated marketing strategy library, greatly improving the timeliness of customer demand response. Intelligent algorithms play a core role in scenarios such as supply and demand matching and risk prediction, using machine learning to analyze supplier and buyer data to achieve intelligent recommendations, shortening the procurement cycle from weeks to days. Innovative technologies such as microservice architecture and blockchain build a resilient and secure foundation for transaction systems. For instance, DataCommerce Cloud uses a microservice architecture for modular deployment, supporting stable operation under high concurrency scenarios, while introducing blockchain technology to ensure the immutability of cross-border transaction contracts. Data encryption, access control, and other technologies ensure the security of transaction data.

[0004] For example, the digital integrated service platform based on big data and deep learning disclosed in patent application CN118134433A includes: a central control server, which is used to ensure the platform's computing and operation; and an e-commerce transaction platform, which is used to provide services for the sale of agricultural products.

[0005] For example, a digital innovation service platform published in patent application CN120297915A includes: a project management module, a dynamic resource pool module, a demand matching module, an innovation incubation module, a duplication detection module, a results display module, and a results transformation module. The project management module achieves seamless collaboration of design documents across software ecosystems through collaborative innovation work units, overcomes the limitations of traditional manual matching through intelligent demand analysis units, and combines the real-time update mechanism of the dynamic resource pool module with resource trading units. The innovation incubation module accelerates the project growth cycle. During project growth, the duplication detection module provides accurate early warning of infringement risks, and after the project results are incubated, the results display module provides intelligent promotion and comprehensive scoring.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: In existing technologies, due to the reliance on unstructured free text for service demand and supply descriptions, the limitation of matching algorithms to shallow keywords or single-dimensional collaborative filtering, and the lack of closed-loop data-driven transaction protection and optimization mechanisms, there are problems such as low matching accuracy, inability to adapt to complex service scenarios, uncontrollable transaction processes, and the inability of the system to continuously evolve. Summary of the Invention

[0007] This application provides a service transaction system and method based on service digitization and intelligent matching. It solves the problems in the prior art, such as low matching accuracy, inability to adapt to complex service scenarios, uncontrollable transaction process, and inability of the system to continuously evolve, due to the reliance on unstructured free text for service demand and supply descriptions, the limitation of matching algorithms to shallow keywords or single-dimensional collaborative filtering, and the lack of closed-loop data-driven transaction guarantee and optimization mechanisms. It realizes a fundamental transformation from the traditional non-standard, inefficient, and static service transaction model to a data-driven, accurate matching, and continuously evolving intelligent service transaction ecosystem.

[0008] This application provides a service transaction system based on service digitization and intelligent matching, including: a demand-side atomization module, a supply-side atomization module, an intelligent matching module, and an evaluation and feedback module. The demand-side atomization module receives and processes the user's original service request, parses it using a natural language processing module, and outputs a structured list of atomized requests after refining the request using an interactive question-and-answer wizard. The supply-side atomization module receives and processes service capability information input by the service provider, and guides the service provider to encapsulate service capabilities into standardized service product atoms using a service product editing module. The intelligent matching module is based on demand atoms... The system comprises a list of service atom units, which are dynamically optimized by weighting based on supply-demand fit parameters and user feedback. This dynamic optimization achieves precise matching between supply and demand, and the weighting adjustment means real-time weighting of the relative importance of each supply-demand fit parameter to adapt to different scenarios, user needs, and market changes. The evaluation and feedback module provides an extensible interface for optimizing transaction formats and a channel for system function iteration. It dynamically optimizes the system based on evaluation data from both parties and service transaction needs to adapt to various service transaction needs across the industry. This dynamic optimization includes dynamic optimization of the service atom library, matching algorithm, and system services.

[0009] This application provides a service transaction method based on service digitization and intelligent matching. It receives and processes original service requests input by users, parses these requests using a natural language processing module, and outputs a structured list of service requests after completing the requests with an interactive question-and-answer wizard. It also receives and processes service capability information input by service providers, guiding them to encapsulate service capabilities into standardized service product atoms through a service product editing module. Based on the list of service requests and service product atoms, it dynamically optimizes weight allocation according to supply-demand fit parameters and user feedback. This dynamic optimization achieves precise matching between supply and demand, meaning it adjusts the weight values ​​of each supply-demand fit parameter in real time to adapt to different scenarios, user needs, and market changes. Finally, it dynamically optimizes the system based on evaluation data from both parties and service transaction requests to adapt to various service transaction needs across the entire industry. This dynamic optimization includes dynamic optimization of the service atom library, matching algorithm, and system services.

[0010] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By structuring and atomizing the service needs and capabilities of both supply and demand sides, standardized demand atoms and service commodity atoms are formed. Based on this, a dynamic weight optimization algorithm that integrates multi-dimensional supply and demand matching parameters and real-time user feedback is used for precise matching. This constructs a highly intelligent and automated service docking engine, which greatly improves the accuracy and efficiency of matching. This achieves a fundamental transformation of service transactions from the traditional vague and inefficient matching model to a data-driven and precise matching model. Through continuous evaluation and feedback loops, the platform is driven to self-optimize and evolve, ultimately building a digital service transaction ecosystem that can dynamically adapt to various complex scenarios across the entire industry and has strong scalability and vitality.

[0011] 2. By combining natural language parsing, intent recognition, and interactive question-and-answer guidance based on domain knowledge graphs, the user's vague and unstructured original needs are gradually transformed into clearly defined, complete, and standardized structured needs atoms. This achieves a fundamental shift from subjective and ambiguous needs descriptions to objective, calculable, and accurately matchable data objects, laying a solid and reliable data foundation for subsequent efficient and intelligent matching.

[0012] 3. By combining natural language parsing, personalized template recommendations, and intelligent guided filling, the unstructured capability descriptions of service providers are efficiently and accurately transformed into standardized, atomized, and quality-controllable service product data units. This enables the digital transformation of the service supply side and lays a solid data foundation for building a precise, reliable, and scalable digital service market.

[0013] 4. By correlating and analyzing historical matching data with user feedback, and dynamically adjusting the weights of various supply and demand matching parameters, a self-learning and continuously optimizing intelligent matching core is constructed. This ensures that the matching standards always align with actual business results and market changes. Consequently, the matching results have been transformed from mechanical screening based on fixed rules to precise recommendations based on data, with business adaptability and evolutionary capabilities. Ultimately, this has built the platform's core competitive advantage and long-term viability. Attached Figure Description

[0014] Figure 1 A schematic diagram of the structure of a service transaction system based on service digitization and intelligent matching provided in this application embodiment; Figure 2 A flowchart illustrating the intelligent housekeeping service matching process of a service transaction system based on service digitization and intelligent matching, provided in this application embodiment; Figure 3 A flowchart illustrating the service atom generation process of a service transaction system based on service digitization and intelligent matching, provided in this application embodiment; Figure 4 A flowchart of the intelligent matching process for a service transaction system based on service digitization and intelligent matching, provided for embodiments of this application; Figure 5 A flowchart illustrating a service transaction method based on service digitization and intelligent matching, provided for embodiments of this application. Detailed Implementation

[0015] This application provides a service transaction system and method based on service digitization and intelligent matching. It addresses the problems in existing technologies, such as low matching accuracy, inability to adapt to complex service scenarios, uncontrollable transaction processes, and lack of continuous system evolution. These problems stem from the reliance on unstructured free text for service demand and supply descriptions, limitations of matching algorithms to shallow keywords or single-dimensional collaborative filtering, and the lack of closed-loop data-driven transaction protection and optimization mechanisms. The overall approach is as follows: Through demand-side and supply-side atomization modules, the system parses, guides, and encapsulates users' vague and unstructured service needs and service providers' scattered and descriptive service capabilities into a unified, clear, and machine-readable structured list of demand atoms and standardized service product atoms. The intelligent matching module, based on multi-dimensional supply-demand fit parameters and utilizing historical user feedback data, dynamically optimizes the weights of each parameter, achieving a deep understanding of service semantics and accurate matching adapted to specific scenarios. The evaluation and feedback module constructs a data-driven closed-loop optimization mechanism, enabling the system to continuously iterate and optimize its core components based on transaction feedback. This invention thus constructs a complete data closed loop from non-standard information input and intelligent, accurate matching to system self-evolution, fundamentally transforming service transactions from a traditional, inefficient matching model to a data-driven, accurately adapted, and continuously evolving intelligent ecosystem.

[0016] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0017] like Figure 1The diagram shows the structure of a service transaction system based on service digitization and intelligent matching provided in this application embodiment. The service atom library stores standardized service product atoms. These atoms are obtained by encapsulating service capability information input by the service provider through a service atomization engine. The service atomization engine is responsible for receiving and processing the service capability information input by the service provider, and guiding the service provider to encapsulate service capabilities into standardized service product atoms through a service product editing module. This includes steps such as extracting structured capability tags, dynamically recommending encapsulation templates, guiding the service provider to fill in fields, and real-time verification of information completeness and rationality. The requirement parsing engine receives and processes the original service requirements input by the user. The NLP processing unit performs semantic parsing on the original service requirement text, extracts key entities, identifies core service intent, and forms an initial requirement element set. The decision tree question-and-answer unit initiates interactive question-and-answer sessions based on the initial requirement element set to dynamically improve the requirements. By comparing with the service domain knowledge graph, missing and ambiguous information is identified, corresponding missing nodes are generated, and context-aware interactive multi-turn question-and-answer sessions are initiated with the user. The seven-dimensional matching algorithm and intelligent matching engine, based on the demand atomic list and service commodity atomics, dynamically optimize weight allocation by combining supply and demand matching parameters and user feedback to achieve accurate matching between supply and demand. The transaction guarantee engine ensures the smooth progress of the transaction process, including a fund custody system and a process monitoring system. The fund custody system is responsible for the safe custody of transaction funds, ensuring that funds are not misappropriated before the transaction is completed. The process monitoring system monitors the transaction process in real time, ensuring that the service is performed as agreed and promptly detecting and handling abnormal situations. The visualization display engine presents matching results, transaction progress, and other information to users in an intuitive way, improving the user experience. The evaluation feedback module collects evaluation data from both parties to the transaction, providing a basis for dynamic system optimization. Based on evaluation data and service transaction needs, the system dynamically optimizes the service atomic library, matching algorithm, and system services to adapt to the various service transaction needs of the entire industry.

[0018] The service transaction system based on service digitization and intelligent matching provided in this application includes: a demand-side atomization module, a supply-side atomization module, an intelligent matching module, and an evaluation and feedback module. The demand-side atomization module receives and processes original service requests input by users, parses these requests using a natural language processing module, and outputs a structured list of service requests after refining them with an interactive question-and-answer wizard. The supply-side atomization module receives and processes service capability information input by service providers, guiding them to encapsulate service capabilities into standardized service product atoms through a service product editing module. The intelligent matching module, based on big data and AI technology, dynamically optimizes the weight allocation based on the service request atomization list and service product atoms, according to supply-demand fit parameters and user feedback. This dynamic optimization achieves precise matching between supply and demand, with the weight allocation dynamically adjusting the relative importance of each supply-demand fit parameter in real time to adapt to different scenarios, user needs, and market changes. The evaluation and feedback module provides an extensible interface for optimizing transaction formats and iterating system functions. The system dynamically optimizes itself based on evaluation data and service transaction needs from both parties to adapt to various service transaction demands across the entire industry. This dynamic optimization includes improvements to the service atom library, matching algorithm, and system services. First, it continuously collects and structures the evaluation data generated by both parties after a transaction, as well as emerging service transaction demands captured through market analysis. Then, through data analysis models (such as trend analysis, correlation mining, and sentiment analysis), it extracts optimization insights into the service atom library (e.g., the accuracy of atom descriptions and tagging system), matching algorithm (e.g., supply-demand fit parameter weights and filtering rules), and system service functions (e.g., transaction processes and user interfaces). These insights are transformed into specific, executable optimization instructions, such as automatically calibrating or suggesting manual review and updates to service atom tags and pricing benchmarks, dynamically adjusting weight allocation strategies in the matching algorithm, and enabling or iterating new transaction agreement templates and functional plugins through configurable interfaces. This allows the system to proactively adapt to the dynamic needs of different industries and scenarios for cost reduction, efficiency improvement, and enhanced user experience, achieving self-evolution and precise adaptation of the service transaction ecosystem.

[0019] The service transaction system based on service digitization and intelligent matching provided in this embodiment of the invention connects the various modules through a system bus to realize data flow and functional linkage. On the basis of supporting multi-language interaction, multi-currency settlement and cross-border service transaction compliance adaptation, it further optimizes the user registration and usage process, improves the system's user-friendliness and promotion potential, and realizes a more intelligent, intuitive and secure service transaction environment through module enhancement. The system also includes a user entry and identity management module and an enhanced functionality module. The user entry and identity management module features a convenient registration and login system, an intelligent identity verification system, and a user tiering and service system. The convenient registration and login system provides a simplified registration process, supporting multiple methods such as mobile phone number, email, and third-party accounts for quick registration, ensuring a flat platform structure and broad user access with no registration barriers, covering individual and enterprise users globally. The intelligent identity verification system, during user login, clearly distinguishes between demand and supply sides through guided selection or initial behavior analysis, dynamically presenting corresponding interfaces and functions to improve operational relevance. The user tiering and service system divides users into ordinary users and VIP users. Ordinary users enjoy all basic platform services, while VIP users can upgrade by submitting qualification certificates, accumulating credit, or subscribing to services, enjoying value-added services such as priority matching, exclusive insurance, and advanced visual displays. All users can browse all platform content, ensuring openness and inclusivity. The enhanced functionality module includes a transaction security module, a service visualization module, and a multi-channel communication module. The transaction security module is further divided into a fund custody unit, an insurance service unit, a government supervision unit, and an arbitration service unit. The fund custody unit employs a tiered custody and node release mechanism; the insurance service unit provides customized insurance coverage and works in conjunction with the intelligent matching module to achieve risk-based scenario-based recommendations; the government supervision unit provides third-party compliance supervision; and the arbitration service unit supports in-system arbitration or external professional institutions. The service visualization module adopts an intuitive display mode similar to e-commerce platforms (such as Taobao / JD.com), prioritizing the display of high-definition images, short video previews, 3D effects, or AR experience entrances for service products on the homepage and search results page, enhancing visual appeal and information acquisition efficiency. It supports visual comparison of demand and service atoms, scenario-based image generation, and immersive experiences, helping users intuitively assess service value. The multi-channel communication module integrates text, voice, video, and file transfer functions, spanning the entire process before, during, and after a transaction, supporting seamless communication between supply and demand parties.

[0020] For example, when the system is applied to intelligent home service matching, such as Figure 2The diagram shows the intelligent housekeeping service matching process of the service transaction system based on service digitization and intelligent matching provided in this application embodiment. After the user inputs the need for "deep cleaning of the whole house, with a focus on removing kitchen grease" through a mobile terminal, the NLP parsing engine first performs semantic analysis to identify the core needs. Then, it triggers the decision tree engine to conduct question-and-answer interaction. When the information completeness reaches a preset completeness threshold (70%), it further generates clarifying questions, such as "Do you need disinfection services?". After the user responds "Yes, especially in the bathroom," the decision tree engine outputs a structured list of needs. Subsequently, the matching engine uses a seven-dimensional matching algorithm (i.e., supply and demand matching parameters) to calculate the service providers that meet the needs and returns a list of the top 3 service providers. Finally, the transaction engine initializes the transaction contract and displays the matching results and transaction plan to the user, thereby completing the entire intelligent housekeeping service matching process.

[0021] In this embodiment, the system intelligently parses and encapsulates user service needs and capabilities into structured atomic lists and standardized product atoms through demand-side and supply-side atomization modules. This ensures that service products are presented concisely, professionally, and intuitively, and the visual display (such as images, videos, and AR experiences) similar to e-commerce platforms improves information acquisition efficiency. The user registration and data entry process is extremely simple and intelligent. Relying on natural language processing and interactive question-and-answer guides, the system proactively guides users to complete their information through big data and AI, reducing the operational burden. When human intervention is required, customer service can respond quickly through the integrated communication module, ensuring a seamless experience. The system always puts users first, continuously adapting to needs through intelligent matching and dynamic optimization, thereby attracting and retaining users from both the supply and demand sides, driving rapid user growth, and helping to quickly capture the market in the early stages. At the standardization level of service transaction sources, the demand-side atomization module uses natural language processing technology to semantically analyze users' original service requests, accurately extracting key entities such as service recipients and target deliverables. Combined with an interactive question-and-answer guide, it dynamically supplements missing information and clarifies ambiguous requests, ultimately outputting a structured list of demand atoms. This completely changes the traditional service transaction model where requests rely on free text descriptions that machines cannot accurately understand, significantly improving the structuring of request information and providing precise input standards for subsequent matching processes. Simultaneously, the supply-side atomization module guides service providers through a service product editing module to encapsulate fragmented service capabilities into standardized service product atoms containing service project names, skill descriptions, baseline working hours, price ranges, and quality acceptance standards. This transforms service supply from vague descriptions into quantifiable, verifiable, and comparable product forms, solving the problems of traditional service supply where capabilities cannot be concretely represented and service value is difficult to measure, thus standardizing service supply. The improved level of service delivery lays the foundation for information symmetry between the two ends of the service transaction. In terms of precise and dynamic matching of supply and demand, the intelligent matching module, based on big data and AI technology, uses a structured list of demand atoms and service commodity atoms as its foundation. It performs matching calculations by combining supply and demand fit parameters. More importantly, through a dynamic optimization mechanism of weight allocation, it adjusts the relative importance of each fit parameter in real time. For example, for urgent service needs, it automatically increases the weight of the time availability parameter; for high-budget professional service needs, it automatically increases the weight of skill fit and credit rating parameters. This effectively adapts to different industry scenarios (such as industrial equipment repair, domestic services, and enterprise technology development), different user demand types (such as cost-effectiveness-oriented, quality-oriented, and time-efficiency-oriented), and market changes (such as increasing the weight of price competitiveness when there is an oversupply of a certain type of service, and increasing the weight of service availability when there is a shortage). This significantly reduces the decision-making costs for demanders and the customer acquisition costs for suppliers, achieving a precise matching effect tailored to each individual.At the level of continuous evolution of the system ecosystem and adaptation to the entire industry, the evaluation and feedback module constructs a closed-loop mechanism for data collection, insight extraction, and optimization. On the one hand, it continuously collects evaluation data from both parties in transactions and emerging service transaction demands in the market. On the other hand, through data analysis models such as trend analysis, correlation mining, and sentiment analysis, it extracts optimization directions for the service atom library, matching algorithm, and system service functions from this information. For example, based on the ambiguity of service acceptance standards frequently mentioned in user reviews, it automatically calibrates the quality acceptance standard descriptions of service product atoms or suggests manual updates to the service atom label system; based on emerging industry services (such as new energy equipment operation and maintenance), it also optimizes the service atom library. Based on user feedback regarding simplified cross-border transaction processes, the system dynamically adjusts the weight of industry-specific skill parameters in the matching algorithm. Through configurable interfaces, it iterates the cross-border settlement plugin and compliance verification functions. This optimization mechanism enables the system to overcome the limitations of traditional service transaction systems, such as rigid functions and weak scenario adaptability. It achieves real-time updates to the service atomic library, self-iteration of the matching algorithm, and flexible expansion of system service functions. Ultimately, it adapts to all legitimate service transaction needs across primary, secondary, and tertiary industries, promoting the self-evolution of the service transaction ecosystem. This ensures the system maintains high efficiency, adaptability, and competitiveness amidst long-term market changes, providing technical support for the digital transformation of services across all industries.

[0022] Furthermore, the specific steps of the natural language processing module are as follows: Using named entity recognition technology, semantic parsing is performed on the original service requirement text to extract key entities, such as service recipients, target deliverables, technology stack, industry domain, and constraints; based on a deep learning model, the core service intent of the original service requirement text is identified, providing a framework for subsequent structuring; the key entities are associated and integrated with the categorized core service intents to form an initial set of requirements; based on the initial set of requirement elements, interactive question-and-answer sessions are initiated to dynamically refine the requirements; the finally confirmed requirement information is encapsulated into requirement atoms, which are structured data objects containing clearly defined fields, including but not limited to service content descriptions, expected deliverables, and preliminary acceptance criteria; the requirement atoms are output in a preset list format to form a structured list of requirement atoms.

[0023] In this embodiment, the natural language processing module employs a pre-trained BERT model combined with named entity recognition technology. The interactive question-answering wizard is implemented based on a decision tree algorithm and an LLM model. The output atomic list of requirements also includes service time requirements, geographical restrictions, budget ranges, and priority weights. Deep semantic decomposition is performed during requirement semantic parsing to accurately extract key entities such as service objects, target deliverables, technology stack (Java), industry domain, and constraints. This avoids information extraction bias caused by ignoring semantic relationships in traditional keyword matching, significantly improving the accuracy of key entity extraction and ensuring that no core requirement information is omitted or misjudged. Simultaneously, core service intent is identified based on deep learning models (such as the pre-trained BERT model), providing a clear framework for subsequent requirement structuring and avoiding deviations in requirement direction due to misjudgment of intent. The intent recognition accuracy remains stable above 92%, ensuring the correctness of requirement processing from the source. Regarding the completeness and accuracy of requirement information, after the module integrates key entities and core intents to form an initial set of requirement elements, it does not output them directly. Instead, it initiates interactive Q&A to address any missing or ambiguous information in the initial set. By dynamically asking follow-up questions, it guides users to supplement details, effectively resolving the problem of incomplete requirement information caused by insufficient professional knowledge or expression habits. This significantly reduces disputes arising from ambiguous requirements in subsequent transactions, such as the inability to meet business needs after system launch due to unclear concurrency levels. Regarding requirement structuring and subsequent support, the module encapsulates the finally confirmed requirement information into structured requirement atoms containing clearly defined fields such as service content description, expected deliverables, and preliminary acceptance criteria. It outputs a list of structured requirement atoms in the form of a preset checklist, completely changing the traditional situation where service requirements are described in free text, are unreadable by machines, and cannot be quantitatively compared. Structured demand atoms can be directly used as input data for the intelligent matching module, supporting AI algorithms to perform multi-dimensional quantitative matching of demand and supply without additional information conversion. At the same time, clear acceptance criteria provide verifiable evidence for the quality monitoring and dispute arbitration of the subsequent transaction guarantee module, forming an efficient collaborative link of demand processing, accurate matching, and transaction guarantee, and providing core technical support for service commodification and transaction standardization.

[0024] Furthermore, based on the initial set of requirement elements, the steps to initiate interactive question-and-answer sessions to dynamically refine the requirements include: comparing the initial set of requirement elements with a pre-defined service domain knowledge graph, which defines the core atomic dimensions required for complete service requirements and their relationships; identifying missing and ambiguous information in the initial set of requirement elements based on the comparison results, and generating corresponding missing nodes; initiating context-aware interactive multi-turn question-and-answer sessions to the user based on the missing and ambiguous information, where guided selection is provided for standardizable options and a text input interface is provided for personalized descriptions. Context-aware interactive multi-turn question-and-answer sessions refer to the dynamic adjustment and in-depth follow-up questions based on the user's answers to the previous questions, which can utilize the language understanding and generation capabilities of models such as LLM (Large Language Model) for intent recognition and multi-turn decision-making; and updating the requirement information in real time based on the user's answers. The system constructs a dynamically updated database of demand information. It structures and maps user responses in each round of Q&A according to key entity categories (service recipients, target outcomes, technology stack, industry sectors, constraints), synchronously linking them to the fields corresponding to the initial demand element set. If the user's response is a guided choice, the standardized data corresponding to the option is directly written into the demand information field. If the user's response is a personalized text description, the text is semantically extracted and standardized using an LLM model to generate machine-readable structured data, which is then updated to the demand information field. A snapshot of the demand information is generated after each update and stored on a blockchain notarization node to ensure traceability of the update process. When a missing node is completed or the Q&A reaches a preset number of rounds, the current round of Q&A ends, and the updated demand information is fed back to the user for confirmation. The system dynamically corrects the demand information based on the user's confirmation until it meets preset executable standards, such as JSON / XML format or a machine-readable and human-readable interface view.

[0025] In this embodiment, at the level of precise demand gap location, the present invention uses a preset service domain knowledge graph as a benchmark and compares the initial demand element set with it. This allows for the rapid identification of missing information (such as a user mentioning the development of an e-commerce website without specifying the budget range or launch time) and ambiguous information (such as only stating that the website has good performance without specifying the daily average visit threshold) in the initial demand, and generates corresponding missing nodes. This avoids gap omissions or misjudgments caused by reliance on customer service experience in traditional manual question answering, significantly improving the coverage of demand gap identification and ensuring accurate direction for demand improvement. At the level of context-aware question answering completion, the present invention initiates multiple rounds of question answering based on the located missing nodes, and the questions and answers have strong contextual relevance. Subsequent questions are dynamically adjusted based on the user's previous answers. At the same time, it provides guided selection for standardizable options, reducing user input costs and expression bias. For personalized descriptions, it provides a text input interface and performs semantic extraction and standardization transformation through an LLM model, ensuring both the flexibility of question answering and the standardization of information. This improves the efficiency of demand information completion and the semantic consistency of the completed information, avoiding information redundancy or repetition caused by questions and answers being out of context. At the level of demand information updates and traceability, this invention constructs a dynamic demand information update database. It structures and maps each user's response in each round according to key entity categories such as service recipients, target outcomes, technology stacks, industry sectors, and constraints, synchronously linking them to the fields corresponding to the initial demand element set. Guided selections are directly written into standardized data, and personalized text is converted by LLM to generate machine-readable structured data that is updated to the corresponding fields. Simultaneously, a demand information snapshot is generated after each update and stored on a blockchain notarization node, ensuring that the time, content, and operating entity of each information update are traceable. This avoids difficulties in determining responsibility due to demand information tampering or disputes in subsequent transactions, achieving traceability of the demand update process. At the final requirement fulfillment level, this invention sets a termination condition of completing missing nodes or reaching a preset number of Q&A rounds. After the termination, the updated requirement information must be fed back to the user for confirmation. If the user proposes corrections, the system will dynamically adjust until the requirement information meets the preset executable standard. This completely solves the problems of traditional requirement collection and requirement fulfillment lacking clear standards and user confirmation being merely a formality. It improves the executableness of the final requirement information and provides a clear, standardized, and usable core basis for subsequent requirement atomic encapsulation, precise supply and demand matching, and transaction security. This significantly reduces the risk of disputes caused by unclear requirements in subsequent stages.

[0026] like Figure 3The diagram shows a flowchart of the service atom generation process of a service transaction system based on service digitization and intelligent matching provided in this application embodiment. The specific steps of the service product editing module are as follows: Structured capability tags are extracted from the service capability information input by the service provider using natural language processing technology. These tags include key skill entities, project types, and tools or methodologies used, to construct a preliminary capability profile of the service provider. Based on the service type selected by the service provider and the preliminary capability profile, a service product atom encapsulation template is dynamically recommended. The template corresponds to a standardized service delivery unit and contains preset structured fields. The service delivery unit is a minimum service module with a defined scope, independent delivery value, and structure. This module is generated based on the predefined encapsulation template, contains the structured fields necessary to fully describe the service, and serves as a basic unit that can be individually retrieved, priced, combined, and delivered. The process is guided by... The service provider fills in each required field step by step according to the selected template to describe the service deliverables list and work scope boundaries. During the filling process, based on industry data and template specifications, intelligent recommendation options or auto-completion suggestions are provided for each field to assist in the encapsulation. The system checks the completeness (e.g., whether required fields are complete) and rationality (e.g., whether the price deviates significantly from the reasonable market range from the benchmark working hours and skill level) of the entered information in real time, and prompts the service provider to make corrections when anomalies are found. After the service provider completes all steps, a complete preview of the service product atom is generated for the service provider to confirm. In response to the service provider's confirmation operation, all input information is encapsulated into standardized service product atoms according to the predefined atomic data model and stored in the system service atom library. Service product atoms include, but are not limited to, service project name, skill description, benchmark working hours, price range, and clear quality acceptance standards.

[0027] In this embodiment, at the service capability structure transformation level, the module uses natural language processing technology to accurately extract structured capability tags such as key skill entities (React framework), project type (corporate website development, e-commerce projects), and tools or methodologies used (Agile development) from the original service capability information input by the service provider. This constructs a preliminary capability profile of the service provider, avoiding the problems of disorganized service capability information and machine inability to recognize it in traditional free text descriptions. This significantly improves the structure extraction rate of service capability information, providing a clear capability foundation for the subsequent atomic encapsulation of service products. At the service product packaging standardization level, the module dynamically recommends suitable service product atomic packaging templates based on the service type and preliminary capability profile selected by the service provider. This template corresponds to a standardized service delivery unit (the smallest service module with a clear scope and independent delivery value) and includes preset structured fields to guide the service provider to fill in the necessary information such as the service deliverable list and work scope boundaries step by step, completely solving the problem of incomplete service descriptions and unclear boundaries caused by the lack of standardization awareness among service providers. At the same time, the template is designed based on common industry needs to ensure that different service providers follow a unified standard when packaging the same type of service, which significantly improves the standardization of service product atoms and provides a unified benchmark for service comparison and selection across service providers. Regarding the efficiency and accuracy of service information filling, the module provides intelligent recommendation options (such as recommending a reasonable range based on the industry average for similar services) or automatic completion suggestions (such as recommending acceptance indicators that conform to industry standards when filling in quality acceptance criteria) based on industry big data and template specifications during the service provider's field filling process. This significantly reduces the service provider's operating costs and professional threshold, and avoids subsequent transaction disputes caused by non-standard information filling. At the same time, it checks the completeness and rationality of the information in real time, and promptly prompts corrections when anomalies are found, ensuring that the final packaged service product atomic information is complete and the data is reasonable, reducing supply and demand mismatch or transaction disputes caused by information defects. At the level of service product atomic storage and reuse, after the service provider confirms and previews the information, the module encapsulates all information into standardized service product atoms according to a predefined atomic data model. These atoms contain core fields such as service project name, skill description, benchmark working hours, price range, and quality acceptance standards, and are stored in the system's service atomic library. These atoms can be retrieved, priced, combined, and delivered as independent units. This not only improves the reuse efficiency of the service provider's service capabilities but also provides high-quality data support for the system's subsequent intelligent matching based on atomic data. Ultimately, this realizes the transformation of service supply from abstract capabilities to standardized products, providing supply-side guarantees for the efficiency and standardization of service transactions.

[0028] Furthermore, based on the service type and preliminary capability profile selected by the service provider, the steps for dynamically recommending atomic encapsulation templates for service products include: receiving the service type selection instruction and the structured capability tags contained in the preliminary capability profile; matching the service type with the system's pre-set atomic encapsulation template library for service products to filter out candidate templates associated with the service type; comparing the structured capability tags with the required capability tag set of the candidate templates; generating a corresponding matching score as the matching degree based on the consistency between the structured capability tags and the required capability tags in terms of type and level; sorting the candidate templates in reverse order according to the matching degree to generate a dynamic recommendation template list; when the matching degree of all candidate templates in the recommendation template list is lower than the preset matching degree threshold, attaching an abnormal prompt message to the dynamic recommendation template list to indicate that there is a gap between the service provider's current capability profile and the typical requirements of the template.

[0029] In this embodiment, regarding the accuracy of template recommendation, the invention first receives the service type selected by the service provider and the structured capability tags in the preliminary capability profile. Using the service type as the core anchor, it matches the templates with a pre-built template library to filter out candidate templates directly related to the service type, avoiding the recommendation of templates unrelated to the service type and ensuring the accuracy of the recommendation direction from the source. Subsequently, it compares the structured capability tags with the capability tags required by the candidate templates (e.g., if the candidate template requires Java development and database design capabilities, it compares whether the service provider's capability tags contain the corresponding skills and skill levels), and generates a matching score based on the consistency of the tags in terms of type and level. This transforms capability suitability into a quantifiable matching indicator, avoiding the adaptation bias caused by subjective human judgment in traditional template recommendation. This significantly improves the fit between the recommended candidate templates and the service provider's capabilities, reducing the operational costs for the service provider to repeatedly adjust the templates due to mismatches with their own capabilities. Regarding the adaptability and guidance of template recommendations, this invention sorts candidate templates in reverse order based on their matching degree, generating a dynamic recommended template list. Service providers can directly select the template with the highest matching degree for encapsulation, eliminating the need for blindly filtering through a large number of templates and significantly improving template selection efficiency. Simultaneously, when the matching degree of all candidate templates is below a preset threshold, this invention will attach an anomaly warning message to the recommended list, clearly informing the service provider of the gap between their current capability profile and the typical requirements of the template. This avoids the risk of subsequent service delivery due to the service provider selecting an incompatible template and also points out the direction for capability improvement, forming a dual guiding role of template recommendation and capability diagnosis. This helps service providers more clearly understand the gap between their own capabilities and standardized service requirements, providing a clear reference for subsequent capability improvement or template adjustment. This invention ensures that the recommended service product atomic packaging templates not only match the business direction chosen by the service provider but also adapt to its actual service capabilities through precise type anchoring, quantitative tag comparison, and flexible sorting prompt mechanism. This effectively lowers the threshold for service providers to package standardized service products, improves packaging efficiency and quality, and provides capability diagnostic feedback for service providers. This lays the foundation for the standardized packaging of subsequent service product atoms and high-quality service delivery, further promoting the standardization process of service supply.

[0030] Furthermore, the steps to guide the service provider to fill in each required field step by step according to the selected template include: responding to the service provider's selection of the template, parsing the predefined set of structured fields and their dependencies in the template; dividing the set of structured fields into a sequence of field filling steps according to the dependencies; presenting the fields to be filled for the current step and their predefined filling specifications to the service provider in sequence according to the field filling step sequence; receiving and saving the field values ​​entered by the service provider for the fields to be filled in the current step; responding to the received completion instruction for the current step, automatically switching and presenting the fields to be filled for the next filling step according to the field filling step sequence; during the filling process, real-time validation of the field values ​​filled by the service provider based on preset inter-field logic rules; when it is determined that the field to be filled in a subsequent step depends on the field value of the previous step according to the dependency relationship, dynamically constraining the input value range of the field to be filled in the subsequent step using the field value confirmed in the previous step, and pre-constraining the input value range of the field to be filled in the subsequent step during the template design stage. The system sets explicit constraints on relational field pairs (such as "Project Type" and "Base Working Hours," "Skill Level" and "Price Range"). These rules are typically stored in the system's rule base or template metadata as a mapping table between "conditions" and "value sets." During user input, the system monitors changes in field values ​​in real time. Once a conditional precondition (such as "Skill Level: Senior Engineer") is confirmed, the rule engine is immediately triggered. The engine uses this field value as a query key to quickly match the corresponding conclusion in the rule base—the allowed valid value range for the subsequent field (such as "Price Range"). The system then immediately applies this valid value range to the corresponding input controls in the user interface. This can manifest as a dynamically updated dropdown list, input box prompts, a numerical slider's scale range, or real-time validation after input. Essentially, this transforms open-ended text input into a guided selection within a structured and compliant framework, thereby improving data quality and input efficiency at the source.

[0031] In this embodiment, regarding the logic and convenience of the filling process, the present invention first responds to the template selection operation of the service provider, parses the predefined structured field set and the dependencies between fields in the template, and divides the field set into an ordered sequence of filling steps accordingly, avoiding the service provider filling in the fields in an unordered manner; then, the fields to be filled and the filling specifications are presented in the order of the steps, and the service provider can complete the filling step by step according to the clear steps. At the same time, after completing each step, it can automatically switch to the next step without manually searching or switching fields, which greatly reduces the complexity of operation, improves the smoothness of the filling process, and reduces the interruption or omission of filling due to chaotic steps. Regarding the standardization and accuracy of data filling, this invention performs real-time validation of the field values ​​filled by the service provider based on preset inter-field logic rules during the filling process. It promptly detects and prompts non-compliant entries, preventing erroneous data from entering the subsequent encapsulation stage. More importantly, for fields with dependencies, this invention uses the field values ​​confirmed in the previous steps to dynamically constrain the input value range of subsequent fields. The constraint rules preset in the template design stage are called in real-time by the rule engine during the filling process. After the previous field is confirmed, the system immediately matches the valid value range of the subsequent field and guides the service provider to fill in the data through drop-down options, input prompts, and scale range restrictions. This transforms open text input into structured and compliant guided selection, avoiding filling deviations caused by the service provider's unfamiliarity with industry standards and reducing invalid or erroneous input. It ensures the accuracy and standardization of the filled data from the source, ensuring that the final generated service product atomic field data meets industry-standard requirements and system matching requirements. This invention, through the orderly planning of the filling steps, real-time verification of the filling process, and dynamic constraints on field values, not only reduces the operational threshold and professional knowledge requirements for service providers in field filling and improves filling efficiency, but more importantly, it ensures the filling quality of each necessary field. This enables the final packaged service product atoms to have complete, standardized, and accurate structured data, providing a reliable data foundation for the subsequent retrieval, matching, and transaction of service product atoms. It further strengthens the standardization level of the service supply side and promotes efficient collaboration across the entire service transaction chain.

[0032] Furthermore, the steps for dynamically optimizing weight allocation based on the demand atom list and service product atom, according to supply-demand fit parameters and user feedback, include: obtaining historical calculated values ​​for each supply-demand fit parameter based on historical demand atom and service product atom. These parameters include skill fit, geographical proximity, historical creditworthiness, price competitiveness, time availability, service style fit, and user preference matching. The steps also involve identifying service type tags in the demand atom list and calling the preset weight configuration template associated with those tags to obtain the initial weights for each supply-demand fit parameter; and collecting historical user feedback data associated with service product atom or service provider. The system generates a comprehensive user feedback score based on user feedback data. Statistical correlation analysis is used to calculate the correlation coefficient between the historical calculated values ​​of each supply-demand fit parameter and the comprehensive user feedback score. Based on the calculated correlation coefficients, a pre-defined weight increment coefficient mapping table is consulted to obtain the weight increment coefficient corresponding to each supply-demand fit parameter. This weight increment coefficient mapping table defines the mapping relationship between the correlation coefficient value range and the specific weight adjustment coefficient. The initial weights of the corresponding supply-demand fit parameters are then multiplied and corrected based on the weight increment coefficients. Finally, all parameter weights after the multiplication correction are normalized to obtain the supply-demand fit parameter weights.

[0033] In this embodiment, regarding the scenario adaptability of weight configuration, the present invention first obtains the historical calculated values ​​of each supply and demand matching parameter based on historical demand atoms and service product atoms, providing a data foundation for weight optimization; at the same time, it identifies the service type tags in the demand atom list, calls the associated preset weight configuration template to determine the initial weight, for example, for local housekeeping services, the initial weight will focus on geographical proximity and time availability, while for remote IT technology development, it will focus on skill matching and historical credit, ensuring that the initial weight matches the core needs of the service scenario, avoiding the problem that traditional unified weights cannot adapt to the differences in different service types, and ensuring the scenario rationality of weight configuration from the source. At the user-oriented level of weight optimization, this invention collects historical user feedback data associated with service products and service providers and generates a comprehensive score, transforming user satisfaction with service matching results into a quantifiable basis for optimization. Subsequently, through statistical correlation analysis, the correlation coefficient between the historical values ​​of each fit parameter and the user feedback score is calculated, accurately identifying the parameters that have a greater impact on user satisfaction. Based on a preset mapping table, the corresponding weight increment coefficient is obtained, and the initial weights are corrected by multiplication. The weights of parameters with high correlation are increased, while the weights of parameters with low correlation are appropriately reduced, so that the weight configuration is closely adjusted around the actual user feedback, avoiding subjective bias in weight optimization that deviates from user needs, and making the matching results more in line with the user's core demands for services. Regarding the dynamic adaptability and scientific nature of weight adjustment, this invention normalizes all parameter weights after weight correction to ensure the total weight conforms to the calculation logic. Simultaneously, it establishes an iterative weight optimization mechanism. As new demand atoms, service product atoms, and user feedback data accumulate, this invention continuously updates historical calculation values ​​and feedback scores, re-conducts correlation analysis and weight correction, enabling weight configuration to dynamically adapt to changes in market demand and user preferences. This avoids the problem of decreased matching efficiency caused by traditional fixed weights remaining unchanged for a long time. Overall, by integrating historical data, scenario characteristics, and user feedback, this invention ensures that the supply-demand fit parameter weights can accurately match the core needs of different service types, closely align with actual user satisfaction, and adjust in real time according to market and user demand changes. Ultimately, this significantly improves the accuracy of supply-demand matching and user acceptance, providing key technical support for improving service transaction efficiency and optimizing user experience.

[0034] Furthermore, the steps for generating a comprehensive user feedback score based on historical user feedback data include: raw feedback data includes numerical scores, textual evaluations, transaction completion rates, and user reselection rates; using a pre-trained sentiment analysis model, such as a model architecture employing Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), or Convolutional Neural Network (CNN), semantic understanding and sentiment classification are performed on the textual evaluation content, outputting a sentiment polarity score; the numerical scores, sentiment polarity scores, transaction completion rates, and user reselection rates are mapped to a unified measurement interval using a pre-defined standardization function; based on the service type of the service product atom, the associated indicator weight template is invoked, with the template assigning differentiated fusion weights to each feedback indicator; based on the indicator weight template, the normalized values ​​within the measurement interval are weighted and calculated to generate the comprehensive user feedback score.

[0035] In this embodiment, regarding the comprehensive integration and quantification of feedback data, the present invention first covers multiple sources of raw feedback data, including numerical ratings, textual evaluations, transaction completion rates, and user reselection rates. This breaks away from the one-sidedness of traditional evaluations that rely solely on single numerical ratings. For example, it not only incorporates users' direct ratings of the service but also reflects the stability of service execution through transaction completion rates and the long-term acceptance of the service through user reselection rates. In particular, for unstructured textual evaluations, a pre-trained sentiment analysis model is used for semantic understanding and sentiment classification, transforming users' subjective textual descriptions into quantifiable sentiment polarity scores. This avoids the problem of textual feedback being ignored because it cannot be directly involved in calculations, transforming all previously fragmented and diverse user feedback into fusionable quantitative data. This ensures that the ratings comprehensively reflect users' true experiences and attitudes towards the service. Regarding the standardization and objectivity of scoring calculation, this invention uses a pre-defined standardization function to map indicators of different dimensions and magnitudes, such as numerical scores, emotional polarity scores, transaction completion rates, and user reselection rates, to a unified measurement range. This eliminates scoring bias caused by differences in indicator units. For example, it avoids weight imbalance when directly integrating percentage data such as transaction completion rates with absolute scores such as numerical scores. This ensures that each type of feedback indicator plays a reasonable role in scoring calculation, rather than being overemphasized or weakened due to different data formats. From a computational logic perspective, this invention guarantees the objectivity and fairness of the scoring results, avoiding the distortion of results caused by the incomparability of indicator dimensions in traditional scoring. Regarding the scenario adaptability and accuracy of the scoring results, this invention does not use a uniform weight to merge standardized indicators. Instead, it calls associated indicator weight templates based on the service type to which the service product belongs. For example, for instant housekeeping services, higher weights are assigned to textual emotional feedback and transaction completion rate related to response speed, as these services emphasize execution efficiency and timeliness. For long-term technical maintenance services, the weights are increased for user reselection rate related to service stability and textual evaluation polarity scores related to technical capabilities, as these services rely more on long-term reliability and professionalism. This type of weight allocation allows the comprehensive score to accurately match the core concerns of users in different service scenarios, avoiding a one-size-fits-all weight design that would lead to a disconnect between the score and the actual service value judgment. The resulting comprehensive user feedback score not only comprehensively covers all dimensions of user feedback but also highlights the evaluation focus of different service types, truly reflecting users' core demands and satisfaction with specific types of services. This provides a precise and scenario-guided basis for subsequent optimization of supply and demand matching weights based on user feedback, making the matching results more in line with users' actual needs.

[0036] like Figure 4The diagram shows the intelligent matching flowchart of the service transaction system based on service digitization and intelligent matching provided in this application embodiment. The steps for achieving accurate matching between supply and demand based on the dynamic optimization results of weight allocation include: calculating the corresponding supply-demand fit parameter value for each pair of demand atoms and service product atoms; performing weighted fusion calculation based on the supply-demand fit parameter weight and the supply-demand fit parameter value to generate a comprehensive matching degree between demand atoms and service product atoms; sorting all candidate service product atoms in descending order according to the comprehensive matching degree; if the comprehensive matching degree is lower than the preset matching threshold, it is marked as no matching item; if there is a comprehensive matching degree not lower than the preset matching threshold, it is marked as no matching item. If a threshold is set, the maximum number of recommended service items and eligible service items are selected from the sorted list to generate a preliminary matching recommendation list. When any service item in the preliminary matching recommendation list is determined to be able to respond to multiple target demand lists simultaneously, a conflict coordination mechanism is triggered. The conflict coordination mechanism coordinates and determines the final matching object of the service item in the conflict scenario based on preset business rules. The final matching result after conflict coordination is published and notified through the interaction interface of both supply and demand parties. The final matching result also displays the key supply and demand fit parameters and overall matching degree on which the matching was achieved.

[0037] In this embodiment, regarding the accuracy and priority of the matching calculation, the present invention first calculates the supply-demand matching parameters such as skill fit and geographical proximity for each pair of demand atoms and service product atoms. Then, it combines the dynamically optimized parameter weights for weighted fusion to generate a comprehensive matching score. This means that parameters with higher weights will play a more dominant role in the comprehensive matching score, so that the matching results can closely match the core needs of the current service type and user feedback guidance, avoiding the problem of traditional one-size-fits-all matching ignoring key supply and demand demands. Subsequently, the candidate service product atoms are sorted in descending order according to the comprehensive matching score, and a preliminary matching list is selected by combining a preset threshold and the number of recommendations. This allows demanders to quickly obtain the service options that best match their needs without blindly filtering through a large number of irrelevant services, greatly improving the decision-making efficiency of demanders. At the same time, it helps high-quality service providers to reach target needs more accurately and reduce customer acquisition costs for service providers. Regarding the rationality of resource allocation and conflict resolution, this invention specifically sets up a conflict coordination mechanism. When a certain service product atom is determined to be able to respond to multiple demand atom lists at the same time, the final matching object will be determined based on preset business rules (such as prioritizing matching demanders with higher urgency and better historical performance records, or combining the service provider's own time availability and service capacity). This avoids multiple matching or resource waste caused by limited service resources, and ensures that service resources can be tilted towards more suitable demanders. This not only guarantees the service provider's service delivery quality (avoiding a decline in performance capacity due to taking on too many demands at the same time), but also protects the rights and interests of demanders (avoiding matching failure due to resource conflicts), thus achieving efficient and rational allocation of service resources. Regarding transparency and trust building in matching results, this invention, when publishing the final matching results, will display the key supply-demand fit parameters and overall match degree on which the match was based. For example, it explains to the demand side that the service product has a high skill fit, excellent historical credit rating, and a leading overall match degree; and to the service provider, it explains that the demand meets the price competitiveness and time availability parameters of its service capabilities. This allows both supply and demand sides to clearly understand the core basis for the match, avoiding the trust concerns caused by the black box operation of traditional matching. This transparent display not only reduces doubts about the matching results from both supply and demand sides, but also helps both parties to more clearly understand each other's fit points, laying a foundation of trust for the smooth conduct of subsequent service transactions. Through precise weighted calculation, reasonable sorting and filtering, intelligent conflict coordination, and transparent result disclosure, this invention fully transforms the dynamically optimized weight configuration into high-quality supply-demand matching results. This ensures that the match accurately meets the core needs of both supply and demand sides, achieves efficient utilization of service resources, and builds trust between supply and demand sides in the matching results. Ultimately, it promotes the rapid and smooth completion of service transactions, improves the operational efficiency of the entire service transaction ecosystem, and enhances user satisfaction.

[0038] like Figure 5The diagram shows a flowchart of a service transaction method based on service digitization and intelligent matching provided in this application embodiment. The method involves receiving and processing original service requests input by users, parsing these requests using a natural language processing module, and then outputting a structured list of service requests after completing the requests with an interactive question-and-answer wizard. It also involves receiving and processing service capability information input by service providers, guiding them to encapsulate service capabilities into standardized service product atoms using a service product editing module. Based on the list of service requests and service product atoms, dynamic optimization is performed on weight allocation according to supply-demand fit parameters and user feedback. This dynamic optimization achieves precise matching between supply and demand. The dynamic optimization of weight allocation means real-time adjustment of the weight values ​​of each supply-demand fit parameter during the matching process to adapt to different scenarios, user needs, and market changes. Finally, the system is dynamically optimized based on the evaluation data of both parties and the service transaction requests to adapt to various service transaction requests across the entire industry. This dynamic optimization includes dynamic optimization of the service atomic library, matching algorithm, and system services.

[0039] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0040] This invention is described with reference to the inventive drawings and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the inventive drawings and / or block diagrams, and combinations thereof, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the invention. Figure 1 This invention or a plurality of inventions and / or blocks Figure 1 A device that provides the functions specified in one or more boxes.

[0041] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in the present invention. Figure 1 This invention or a plurality of inventions and / or blocks Figure 1 The function specified in one or more boxes.

[0042] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the present invention. Figure 1 This invention or a plurality of inventions and / or blocks Figure 1 The steps of the function specified in one or more boxes.

[0043] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0044] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A service transaction system based on service digitization and intelligent matching, characterized in that, The method comprises a demand side atomization module, a supply side atomization module, an intelligent matching module and an evaluation feedback module. The demand side atomization module is configured to receive and process original service demand input by a user, analyze the original service demand of the user through a natural language processing module, and output a structured demand atom list after improving the demand through an interactive question and answer guide. The supply side atomization module is configured to receive and process service capability information input by a service provider, and guide the service provider to encapsulate the service capability as a standardized service commodity atom through a service commodity editing module. The intelligent matching module is configured to dynamically optimize the weight distribution based on the demand atom list and the service commodity atom according to the supply and demand matching degree parameters and user feedback, and realize accurate matching between the supply and demand based on the dynamic optimization result of the weight distribution. The evaluation feedback module provides an extensible transaction form optimization interface and a system function iteration channel, and dynamically optimizes the system based on the evaluation data and service transaction demand of the transaction parties to adapt to various service transaction demands in the industry.

2. The service transaction system based on service digitization and intelligent matching according to claim 1, characterized in that, The specific steps of the natural language processing module are as follows: Perform semantic analysis on the original service demand text and extract key entities. Identify the core service intent of the original service demand text to provide a framework basis for subsequent structuring. Associate and integrate the key entities with the classified core service intent to form an initial demand element set. Based on the initial demand element set, initiate an interactive question and answer to dynamically improve the demand. Encapsulate the final confirmed demand information as a demand atom, which is a structured data object containing explicit fields, including but not limited to service content description, expected delivery and preliminary acceptance criteria. Output the demand atom in a preset list form to form a structured demand atom list.

3. The service transaction system based on service digitization and intelligent matching according to claim 2, characterized in that, The step of initiating an interactive question and answer to dynamically improve the demand based on the initial demand element set comprises the following steps: Compare the initial demand element set with a preset service field knowledge graph, which defines the core atomic dimensions required for complete service demand and their associated relationships. Identify missing information and fuzzy information in the initial demand element set based on the comparison result, and generate corresponding missing nodes. Based on the missing information and fuzzy information, initiate an interactive multi-round question and answer with context awareness to the user, wherein guided selection is provided for standardized options and a text input interface is provided for personalized descriptions. Update the demand information in real time based on the user's answers. When the missing nodes are completed or the question and answer reaches the preset number of rounds, end the round of question and answer, feed back the updated demand information to the user for confirmation, and dynamically correct based on the user confirmation result until the demand information meets the preset executable standard.

4. The service transaction system based on service digitization and intelligent matching according to claim 1, characterized in that, The specific steps of the service commodity editing module are as follows: extracting structured capability tags from the service capability information input by the service provider to construct a preliminary capability profile of the service provider; based on the service type selected by the service provider and the preliminary capability profile, dynamically recommending a service commodity atomic encapsulation template, the template corresponding to a standardized service delivery unit and containing preset structured fields; guiding the service provider to fill in each required field according to the selected template in steps to describe the service delivery item list and the working range boundary; in the filling process, providing intelligent recommendation options or automatic completion suggestions for each field based on industry data and template specifications; checking the completeness and rationality of the filled information in real time and prompting the service provider to correct when there are abnormalities; after the service provider completes all steps, generating a complete preview of the service commodity atom for confirmation; in response to the confirmation operation of the service provider, encapsulating all input information into a standardized service commodity atom according to a predefined atomic data model, and storing it in the system service atom library, the service commodity atom including but not limited to service item name, skill description, benchmark man-hours, price range, and explicit quality acceptance standards.

5. The service transaction system based on service digitization and intelligent matching according to claim 4, characterized in that, The step of dynamically recommending a service commodity atomic encapsulation template based on the service type selected by the service provider and the preliminary capability profile includes: receiving the service type selection instruction and the structured capability tags contained in the preliminary capability profile; matching the service type with the system-preset service commodity atomic encapsulation template library to filter out candidate templates associated with the service type; comparing the structured capability tags with the required capability tag set of the candidate templates; generating a matching score as the matching degree according to the consistency degree of the structured capability tags and the required capability tags in type and level; sorting the candidate templates in reverse order according to the matching degree to generate a dynamic recommendation template list; when the matching degrees of the candidate templates in the recommendation template list are all below a preset matching degree threshold, attaching abnormal prompt information to the dynamic recommendation template list.

6. The service transaction system based on service digitization and intelligent matching according to claim 4, characterized in that, The step of guiding the service provider to fill in each required field according to the selected template includes: in response to the service provider's selection operation on the template, parsing the structured field set predefined by the template and its dependency relationship; dividing the structured field set into a field filling step sequence according to the dependency relationship; according to the field filling step sequence, presenting the service provider with the to-be-filled field corresponding to the current step and its predefined filling specification in sequence; receiving and saving the field value input by the service provider for the to-be-filled field in the current step; in response to the completion instruction received for the current step, automatically switching and presenting the to-be-filled field corresponding to the next filling step according to the field filling step sequence; in the filling process, real-time checking the field value filled by the service provider based on preset inter-field logical rules; when it is determined according to the dependency relationship that the to-be-filled field of a subsequent step depends on the field value of a previous step, dynamically constraining the input value range of the to-be-filled field of the subsequent step using the field value confirmed in the previous step.

7. The service transaction system based on service digitization and intelligent matching according to claim 1, characterized in that, The step of dynamically optimizing the weight allocation according to the demand atom list and the service commodity atom based on the supply-demand matching degree parameters and user feedback comprises: Based on the historical demand atoms and service commodity atoms, the historical calculation values of each supply-demand matching degree parameter are obtained, and the supply-demand matching degree parameters include skill matching degree, geographical location proximity, historical credit degree, price competitiveness, time availability, service style matching degree, and user preference matching degree; The service type label of the demand atom list is identified, and a preset weight configuration template associated with the label is called to obtain the initial weight of each supply-demand matching degree parameter; Historical user feedback data associated with the service commodity atom or service party is collected, and a user feedback comprehensive score is generated based on the user feedback data; The correlation coefficients between the historical calculation values of each supply-demand matching degree parameter and the user feedback comprehensive score are calculated respectively; According to the calculated correlation coefficients, a preset weight increment coefficient mapping table is queried to obtain the weight increment coefficient corresponding to each supply-demand matching degree parameter, and the weight increment coefficient mapping table defines the mapping relationship between the correlation coefficient value interval and the specific weight adjustment coefficient; The initial weight of the corresponding supply-demand matching degree parameter is multiplied and corrected based on the weight increment coefficient; All parameter weights after multiplication correction are normalized to obtain the supply-demand matching degree parameter weight.

8. The service transaction system based on service digitization and intelligent matching according to claim 7, characterized in that, The step of generating a user feedback comprehensive score based on historical user feedback data comprises: The original feedback data includes numerical scores, text evaluations, transaction completion rates, and user reselection rates; The text evaluation content is subjected to semantic understanding and sentiment classification, and a sentiment polarity score is outputted; The numerical scores, sentiment polarity scores, transaction completion rates, and user reselection rates are respectively mapped to a unified measurement interval through a preset standardization function; According to the service type to which the service commodity atom belongs, an index weight template associated with the type is called, and the template assigns differentiated fusion weights to each feedback index; Based on the index weight template, the normalized values in the measurement interval are weighted and calculated to generate a user feedback comprehensive score.

9. The service transaction system based on service digitization and intelligent matching according to claim 7, characterized in that, The step of realizing accurate matching between the supply and demand parties based on the weight allocation dynamic optimization result comprises: For each pair of demand atom and service commodity atom, the corresponding supply-demand matching degree parameter value is calculated respectively; Based on the supply-demand matching degree parameter weight and the supply-demand matching degree parameter value, a weighted fusion calculation is performed to generate the comprehensive matching degree between the demand atom and the service commodity atom; According to the comprehensive matching degree, all candidate service commodity atoms are sorted in descending order; According to the preset matching threshold and the upper limit of the recommended number, the service commodity atoms that meet the conditions are selected from the sorted list to generate a preliminary matching recommendation list; When any service commodity atom in the preliminary matching recommendation list is determined to be able to respond to multiple target demand atom lists simultaneously within the same time period, a conflict coordination mechanism is triggered, and the conflict coordination mechanism coordinates and determines the final matching object of the service commodity atom in the conflict scenario according to the preset business rules. The final matching result processed by the conflict coordination is published and notified through the interactive interface of the supply and demand parties. In the final matching result, the key supply-demand matching degree parameter value and the comprehensive matching degree on which the matching is achieved are associated and displayed.

10. A service transaction method based on service digitization and intelligent matching, applied in the service transaction system based on service digitization and intelligent matching as claimed in claims 1-9, characterized in that, The specific steps include: Receiving and processing the original service demand input by the user, analyzing the original service demand of the user through the natural language processing module, and outputting the structured demand atom list after improving the demand through the interactive question and answer guide. Receiving and processing the service capability information input by the service provider, and guiding the service provider to encapsulate the service capability as a standardized service commodity atom through the service commodity editing module; Based on the demand atom list and the service commodity atom, the weight allocation dynamic optimization is dynamically optimized according to the supply-demand matching degree parameter and user feedback, and the precise matching of the supply and demand parties is realized based on the weight allocation dynamic optimization result, which means that the weight value of each supply-demand matching degree parameter in the supply-demand matching process is adjusted in real time to adapt to different scenarios, user demand and market changes; Based on the evaluation data and service transaction demand of the transaction parties, the system is dynamically optimized to adapt to various service transaction demands in all industries, and the system dynamic optimization includes dynamic optimization of the service atom library, the matching algorithm and the system service.

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