Service integration intelligent decision-making method and system based on credit-driven reinforcement learning closed loop

Abstract

The invention discloses a service integration intelligent decision-making method and system based on a credit-driven reinforcement learning closed loop, and the method comprises the following steps: S1, receiving the unstructured service demand input of a user, and generating a structured service task description containing at least one service module and a cooperative constraint condition thereof through mixed analysis processing; s2, initiating multiple rounds of dynamic bidding to a plurality of service providers to obtain dynamic service condition information, fusing the dynamic service condition information, user preference information and collaborative constraint conditions, performing calculation through a multi-target service optimization model of which the optimization weight can be dynamically adjusted, and generating and recommending at least one integrated service scheme to the user; and S3, responding to confirmation of the user to the integrated service scheme, starting and monitoring service execution, and automatically completing expense settlement according to a preset rule after the service execution is completed. Based on a pluggable field knowledge base and the same set of technical architecture taking a credit-driven reinforcement learning closed loop as a core, the system can be quickly adapted to various high-complexity and strong-collaboration service integration fields such as tourism, exhibition, enterprise travel, wedding celebration and medical rehabilitation travel planning by configuring different service modules and collaboration constraint conditions, and has the advantages of being high in practicability and high in practicability. And a set of unified technical architecture and solution which have high adaptability and can be reused is provided.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence, service computing, and decision support systems. Specifically, it relates to a service integration intelligent decision-making method and system based on a credit-driven reinforcement learning closed loop. This invention is particularly applicable to highly complex, collaborative, and personalized service integration scenarios such as tourism, exhibitions, corporate travel, and weddings. Background Technology

[0002] In highly complex and collaborative service integration fields such as tourism, conventions and exhibitions, and corporate travel, existing online platforms are undergoing a profound transformation from "information-based display" to "intelligent integration" as users' demands for personalized, one-stop services grow and technologies such as the Internet of Things and big data become more widespread. However, this transformation is facing profound constraints from the underlying technical architecture. Currently, most platforms are still in the rudimentary stage of "modular functional assembly," and their system architecture generally suffers from functional fragmentation in the three core links of "perception (demand understanding), decision-making (resource matching), and execution (performance assurance)," as well as a break in the flow of credit information, a key element throughout the entire service lifecycle, between different links (i.e., "credit decoupling"). This fragmentation and decoupling is not only a limitation of the capabilities of a single algorithm or model, but also a system-level, architectural defect, making it difficult for the system to achieve true personalization, adaptability, and continuous evolution. It has become a key bottleneck restricting the in-depth development of intelligent service integration.

[0003] I. Limitations of the Existing Technical Architecture Existing technologies mostly employ modular and phased processing logic, with each stage operating independently in terms of data, models, and logic, creating de facto "data silos" and "functional silos." While this architecture is efficient for simple, standardized services, its inherent limitations become glaringly apparent when faced with highly complex, multi-constrained, and highly personalized service integration tasks. 1. At the level of understanding (perception) of needs, there is a contradiction between superficial analysis and static cognition.

[0004] Current systems largely rely on keyword matching or general natural language processing models to parse user input. While they can extract explicit constraints (such as time, location, and price), they struggle to deeply understand the complex intentions, implicit preferences, and dynamically evolving service expectations expressed by users through multimodal interactions such as text, voice, and images. More importantly, the confidence assessment of parsing models is typically static or based on general statistics, lacking a dynamic, personalized calibration mechanism based on real-world feedback from users' historical behavior (such as subsequent satisfaction with service delivery). The inability to establish a calculable and feedback-based correlation between parsing results and final user satisfaction leads to stagnant system cognitive capabilities, preventing continuous evolution towards a deeper understanding of users through repeated use.

[0005] 2. At the resource matching and decision-making level, there is a contradiction between local optimization and global coordination.

[0006] Traditional service matching models often focus on cost optimization of single service elements (such as airfare and hotel). Their optimization objectives and decision-making logic are severely disconnected from the inherent spatiotemporal relevance, service quality thresholds, multi-party collaboration requirements, and dynamic market conditions of complex service integration. For example, in travel itinerary planning, optimizing airfare and hotel prices in isolation may overlook overall experience issues such as tight layover times and inconvenient accommodation. Furthermore, core preference parameters in the decision-making model (such as the weights of cost, quality, and risk) are usually preset or adjusted based on simple rules, failing to achieve deep integration and real-time linkage with credit data that dynamically reflects the historical reliability of service providers (service provider credit) and the consistency of user preferences (user credit). This makes matching decisions rigid in dynamic bidding, multiple constraints, and multi-objective trade-offs, making it difficult to achieve global Pareto optimality across dimensions such as cost, experience, and credibility.

[0007] 3. At the level of performance assurance and system evolution (execution and learning), there is a contradiction between ex-post evaluation and process disruption.

[0008] Existing platforms often lack transparency in their fulfillment processes, rely on slow and manual intervention for handling anomalies, and have cumbersome settlement procedures. Although some platforms have introduced credit systems such as user reviews and star ratings, this data is mostly presented as static, isolated, and post-hoc "labels." These are disconnected from core intelligent processes such as real-time demand analysis, dynamic bidding, and multi-objective optimization decision-making. Valuable fulfillment process status data and final user feedback cannot be systematically collected, structured, and transformed into effective reinforcement learning signals to be fed back to preceding stages in real-time, closed-loop fashion to drive the self-adjustment and evolution of analytical and decision-making models. Therefore, the system as a whole loses the ability to continuously learn autonomously and iterate performance based on real business closed-loop data, and its level of intelligence tends to solidify after deployment.

[0009] II. Root Cause of the Problem: The "Modular Assembly" Architecture and the "Credit Decoupling" Effect The root of the contradictions at these three levels lies in the long-standing "modular assembly" bias in this field. This bias assumes that an intelligent service integration platform can be built simply by combining functionally independent and heterogeneous "intelligent" modules (such as intelligent chatbots, price comparison search engines, online payment systems, and post-event evaluation modules). However, this architecture inherently leads to: The data flow is unidirectional, static, and heterogeneous: Modules (such as the demand analysis engine, price comparison engine, payment settlement system, and evaluation module) typically exchange processed result data (such as parsed keyword lists, final quotes, payment status, and five-star ratings) through predefined application programming interfaces (APIs). This data is heterogeneous in format (e.g., JSON, XML, database tables) and is transmitted unidirectionally and in batches, lacking a unified, fine-grained, and real-time synchronized core state variable. For example, real-time fluctuations in user preferences during an interaction cannot immediately affect the ongoing bidding strategy; a recent service provider fulfillment anomaly cannot be immediately fed back into the model confidence calculation for demand analysis of new users.

[0010] Credit information decoupling and state absence: Credit data, which serves as the core value carrier (such as user preference vectors calculated based on historical transactions and service provider reliability scores), is typically stored in separate databases or used as tags for offline analysis in existing systems. They lack a unified mathematical representation (such as the "credit representation vector" in this invention), and cannot function as a core state variable that can be calculated in real time, dynamically updated, and directly called and interpreted by the front-end decision-making model. This decoupling of credit information from business processes results in a state of "credit blindness" during system decision-making, hindering personalized intelligent decision-making based on dynamic credit assessment.

[0011] The system lacks an evolutionary closed loop: Due to the lack of a unified state variable and real-time feedback mechanism throughout the entire process, the system cannot form a reinforcement learning closed loop of "action-feedback-learning-optimization", and therefore does not have endogenous growth and self-adaptation capabilities.

[0012] III. Related Technological Attempts and Their Shortcomings In recent years, some studies have attempted to optimize each stage of service integration independently. For example, in demand analysis, deep semantic models (such as pre-trained Transformer-based models) are used to improve the accuracy of intent recognition (see related literature, Literature 1); in resource matching, multi-objective optimization algorithms are introduced to balance cost and service quality (see related literature, Literature 2); and in the fulfillment stage, blockchain technology is explored to achieve tamper-proof notarization of transaction data (see related literature, Literature 3). In addition, some platforms have attempted to build user profiles or supplier credit scoring systems (see related literature, Literature 4). However, these technological efforts share a common limitation: they all focus on the internal algorithm optimization of a single or adjacent stage, and are essentially still technological iterations under the "modular splicing" mindset. Specifically, the analytical model in Reference 1 has a static confidence assessment mechanism, which cannot be personalized based on subsequent user performance feedback; the multi-objective optimization model in Reference 2 has preset or manually adjusted weight parameters, failing to achieve real-time, automated, and deep coupling with dynamically changing credit data; the credit or evidence data in References 3 and 4 are mostly static labels added after the fact in the system, resulting in a "data flow disconnect" with the front-end real-time analysis and dynamic decision-making process, and thus cannot be transformed into reinforcement learning signals to drive continuous model evolution. Therefore, these existing technological attempts have failed, and have never attempted, to build an integrated solution at the system-level architecture level, with a unified credit data flow as the core driving force, capable of achieving closed-loop feedback and autonomous evolution throughout the "perception-decision-execution" process. For example: Simply improving the natural language understanding model cannot solve the problem of how to dynamically and personally calibrate the parsing confidence based on the user's historical feedback.

[0013] Simply adding a credit factor as a static weight to the matching model cannot achieve deep coupling and coordinated adjustment between credit data and dynamic bidding and real-time decision preferences.

[0014] Using blockchain only for post-event evidence storage fails to transform performance process data into learning signals that drive optimization of preceding stages in real time.

[0015] IV. The core technical problem to be solved by this invention In summary, the fundamental technical problem faced by existing service integration systems is not the performance bottleneck of a single algorithm module, but rather stems from system-level architectural flaws: 1. Functional fragmentation and data flow disruption throughout the entire "perception-decision-execution" process; 2. The "decoupling" of credit information throughout the entire chain prevents it from serving as a core signal flow to drive collaborative optimization and continuous evolution of the system.

[0016] More importantly, the aforementioned "fragmentation" and "decoupling" are not merely technical model issues; they directly lead to a systemic failure of the service integration market at the business level. a) Market inefficiency: Due to the inability to achieve global dynamic optimization and credit-based intelligent matching, high-quality service providers find it difficult to highlight their reliability and collaborative value through technological means, while low-quality suppliers may disrupt the market through price wars, creating a "bad money drives out good" dilemma, which ultimately damages the health of the platform ecosystem and users' long-term trust.

[0017] b) High cost of personalized services: Every complex personalized request (such as customized travel, large-scale exhibitions) requires a lot of manpower to sort out the needs, coordinate suppliers, assemble solutions and monitor the process, making it impossible to provide high-end, high-value-added services on a large scale, and limiting the market to simple, standardized products.

[0018] c) Hindered innovation of business models: Due to the lack of automated and reliable control over the entire service process, the platform's business model has long been stuck in the primary stage of "information display and transaction commission", making it difficult to transform into providing high value-added services such as "intelligent integration, risk protection and experience optimization", and the value ceiling is within reach.

[0019] Therefore, the existing technical architecture has become a core bottleneck restricting the large-scale, intelligent, and highly reliable development of high value-added service industries such as tourism, conventions and exhibitions, corporate travel, and weddings.

[0020] There is an urgent need for a fundamental architectural innovation and methodological breakthrough to build a service-integrated intelligent decision-making system that is driven by a unified credit data flow, enabling closed-loop feedback and autonomous evolution throughout the entire process. This is precisely the core technical problem that this invention aims to solve and the source of its value. Summary of the Invention

[0021] The technical problem this invention aims to solve is that existing service integration systems suffer from structural fragmentation in their "perception-decision-execution" stages and disruptions in the flow of credit data across different stages. This results in shallow demand analysis, inefficient matching of resources with multiple constraints, high trust costs in the fulfillment process, and a lack of continuous evolution capabilities based on real business feedback. To address this, this invention provides a service integration intelligent decision-making method and system based on a credit-driven reinforcement learning closed-loop. The aim is to construct a closed-loop optimization architecture centered on a unified credit data flow that runs throughout the entire process, thereby achieving deep personalization, dynamic global optimization, and autonomous continuous evolution in the service integration process.

[0022] To address the aforementioned technical challenges, the core concept of this invention lies in introducing a unified and computable "credit representation vector." This vector serves as the core state variable and reinforcement learning signal throughout the entire service integration process, encompassing "demand analysis - resource matching - performance settlement." This allows for the construction of a self-evolving optimization architecture driven by real-time credit data flow and closed-loop feedback. This architecture fundamentally alters the static and isolated nature of credit data in traditional systems, enabling dynamic fusion and real-time driving of credit information across the entire business chain.

[0023] According to a first aspect of the present invention, a service integration intelligent decision-making method based on a credit-driven reinforcement learning closed loop is provided, characterized by comprising the following three steps that are sequentially connected and constitute a closed loop: S1: Receive unstructured service request input from users, and generate a structured service task description containing at least one service module and its collaborative constraints through hybrid parsing processing; wherein, the confidence level of the hybrid parsing processing is dynamically calibrated based on a credit representation vector that characterizes the consistency between the user's historical behavior and feedback.

[0024] S2: Based on the structured service task description, initiate multiple rounds of dynamic bidding with multiple service providers to obtain dynamic service condition information, and integrate the dynamic service condition information, user preference information, and collaborative constraints. Calculate using a multi-objective service optimization model with dynamically adjustable optimization weights to generate and recommend at least one integrated service solution to the user; wherein, the decision preference parameters for generating the integrated service solution are initialized or dynamically adjusted based on the credit representation vector and the historical credit data of each service provider.

[0025] S3: In response to the user's confirmation of the integrated service solution, initiate and monitor service execution, and automatically complete fee settlement according to preset rules after service execution is completed; wherein, based on the performance status data and user feedback data, a credit update signal is generated in real time; the credit update signal is used to update the user's credit representation vector and the historical credit data of each service provider; the updated credit representation vector is used as the core reinforcement learning signal, and is fed back in real time and dynamically to the hybrid parsing processing confidence calibration logic in step S1 and the multi-objective service optimization model decision preference parameter adjustment logic in step S2, thereby constructing a self-evolving service integration and optimization closed loop driven by credit data flow and running through the entire process of demand analysis, resource matching and performance settlement.

[0026] As a further optimization and improvement to the above core solution, the method of the present invention also includes one or more of the following preferred technical features. These features can be applied individually or in combination in specific embodiments to enhance the performance of the system in specific scenarios: 1. The credit representation vector is a dynamic vector representing the consistency between a user's historical behavior and feedback. As a core state variable, it is used to dynamically calibrate the confidence level of the hybrid analytical processing and / or to dynamically adjust the decision preference parameters of the multi-objective service optimization model. The historical credit data of the service providers refers to a dynamic evaluation index used to quantify their service reliability and collaborative capabilities, calculated based on at least one of the following: past performance records, user evaluations, anomaly handling response efficiency, and collaborative performance.

[0027] 2. The credit representation vector contains multiple independent dimensions, including at least a user preference consistency dimension, a performance feedback sensitivity dimension, and a price sensitivity dimension. Each dimension takes a continuous value between 0 and 1, where 0 represents completely insensitive or untrustworthy, and 1 represents highly sensitive or highly trustworthy. The user preference consistency dimension is used to calibrate the confidence level of the hybrid parsing process, and the performance feedback sensitivity dimension and the price sensitivity dimension are used to adjust the decision preference parameters of the multi-objective service optimization model.

[0028] 3. The credit representation vector, through continuous iteration of the reinforcement learning loop, forms a dynamic, implicit representation model of user service preferences and service provider performance reliability. The iteration cycle is updated in real-time or near real-time, and the convergence condition is that the change in the credit vector is less than a preset threshold ε in N consecutive iterations. This model can infer the evolution trend of user preferences and the changing trend of service provider reliability. The multi-objective service optimization model further integrates the evolution trend and the changing trend, and when generating the current integrated service solution, it simultaneously performs pre-matching and pre-optimization recommendation of related medium- and long-term service resources, thereby improving resource utilization efficiency and the continuity of user experience.

[0029] 4. The unstructured service requirement input is at least one of text, voice, image, or video; the hybrid parsing process includes converting the multimodal input into the structured service task description through a multimodal fusion parsing network.

[0030] 5. The multimodal fusion parsing network adopts a cross-modal attention fusion mechanism, which calculates the correlation weights between different modal features to achieve adaptive fusion of multimodal information and resolution of semantic conflicts. When using the cross-modal attention fusion mechanism for weighted fusion, the allocation of the fusion weights of each modal feature is dynamically calculated and adjusted based on the dimension representing the consistency of the user's historical modal preferences in the credit representation vector.

[0031] 6. The geographical boundary and time window parameters in the collaborative constraints are dynamically generated based on real-time traffic data and weather forecasts; the credit representation vector does not contain user personal identity information.

[0032] 7. The service module types and collaborative constraints defined in the method are predefined, configured, and dynamically extended through a pluggable domain knowledge base, thereby adapting to different complex service integration scenarios; the method is based on the same credit feedback closed-loop architecture and can be applied to cross-domain service integration scenarios including the following. a) Tourism service scenarios: The service modules include, for example, transportation booking, accommodation booking, attraction ticket booking, restaurant reservation, tour guide services, etc.; the collaborative constraints include, for example, constraints on the continuity of itinerary time, constraints on geographical location correlation, constraints on the access conditions of service providers, and constraints on budget, etc. b) Corporate travel service scenarios: The service modules include, for example, flight booking, hotel booking, ground transportation, visa application assistance, and conference venue rental; the collaborative constraints include, for example, corporate travel policy compliance constraints, budget department affiliation constraints, itinerary and personal schedule coordination constraints, and invoice and reimbursement format constraints. c) Exhibition service scenarios: The service modules include exhibition venue rental, booth design and construction, audiovisual equipment rental, attendee reception, and concurrent event planning; the collaborative constraints include strict time windows for setup and dismantling, collaborative constraints for on-site operations of multiple suppliers, and constraints on crowd density and safety regulations. d) Wedding service scenarios: The service modules include wedding venues, planning and coordination, photography and videography, wedding dresses and makeup, wedding banquet catering, wedding car rental, etc.; the collaborative constraints include strict temporal dependency constraints between multiple service links, overall visual and style consistency constraints, and on-site cooperation tightness constraints of key suppliers, etc. e) Medical rehabilitation itinerary planning service scenario: The service modules include, for example, medical institution appointments, rehabilitation center connections, accommodation for caregivers, and transportation arrangements for follow-up examinations; the collaborative constraints include, for example, rigid time windows for medical diagnosis and treatment, adaptive constraints on the patient's physical condition, and constraints on the movement and role responsibilities of caregivers. f) Service scenarios for cultural performances, sports events, and conferences: The service modules include, for example, event ticketing, venue / hall rental, equipment technical support, media promotion, on-site security, and guest reception; the collaborative constraints include, for example, rigid constraints on the event process timeline, on-site multi-supplier concurrent operation collaborative constraints, real-name or zoned management constraints for tickets, and public safety and emergency plan constraints. g) Festive service scenarios: The service modules include banquet catering, venue arrangement, master of ceremonies, performances and entertainment, video recording, gift planning, etc.; the collaborative constraints include constraints on the timing of the celebration process, the consistency of theme and atmosphere, constraints on guest movement and reception services, and constraints on contingency plans for emergencies, etc. 8. The multi-round dynamic bidding adopts reverse auction, forward auction, or combined auction modes, and includes a time window control mechanism for each round of bidding. After the time window of each round of dynamic bidding ends, based on all the bidding information in this round, a guidance signal is automatically generated and fed back to each participating service provider to guide the bidding participants to adjust their bidding strategies, so as to promote the overall bidding distribution to converge quickly towards the system optimization target. The guidance signal includes at least the percentile ranking information of the current bid in the overall bid, the statistical characteristics of the bid distribution, or the suggested bid adjustment direction for the next round calculated based on the game theory model. The multi-round dynamic bidding does not include manual intervention in the bid adjustment process. The content weight or sending priority of the guidance signal fed back to the service provider is adjusted differently based on the historical credit data of the service provider, so as to achieve priority guidance for high-credit service providers.

[0033] 9. When there are multiple service modules with spatiotemporal or service logic dependencies in the structured service task description, a joint bidding invitation is first initiated to the service provider combination that can provide cross-module collaborative pricing; wherein, the priority of the service provider combination is identified based on a credit collaboration network constructed according to historical collaborative performance records.

[0034] 10. The optimization objectives of the multi-objective service optimization model in step S2 include at least the total service cost, overall service matching degree, comprehensive credit score of participating service providers, and constraint violation penalty value; the comprehensive credit score is calculated based on the historical credit data of each service provider; the optimization weights of the multi-objective optimization model are dynamically adjusted through a reinforcement learning model, and can be specifically tuned based on specific dimension values ​​in the credit representation vector or the average credit data of service providers by setting trigger conditions.

[0035] 11. The method further includes: receiving a scheme adjustment instruction input by a user in natural language form, parsing the instruction into a structured modification of at least one service parameter in the structured service task description using natural language processing technology, and re-triggering the dynamic bidding and scheme generation process based on the modified task description.

[0036] 12. Before the service is executed, a virtual simulation of the integrated service solution is performed based on digital twin technology, and a risk assessment and solution fine-tuning are carried out based on the simulation results; during the simulation, abnormal event injection is simulated, and the risk threshold is adjusted according to the user credit representation vector.

[0037] 13. Step S3 further includes sub-steps for intelligent performance monitoring, trusted evidence storage, and dynamic settlement: T1: During service execution, performance status data is collected in real time through the network of performance status information collection devices and the application interface of the service provider, and the performance status data is analyzed in real time using an anomaly detection algorithm. T2: When an abnormal event that meets the preset triggering conditions is identified, the response logic associated with the abnormal event type is automatically executed, and the key information of the abnormal event and the response action is generated into a hash value and stored in the decentralized trusted evidence storage network; wherein, the response logic is automatically executed according to the terms of the smart contract encoded in the decentralized trusted evidence storage network; T3: After the service is completed, automatic settlement is triggered based on the trusted evidence information in the decentralized trusted evidence storage network that indicates that the service has been completed in compliance with regulations; wherein, during settlement, the commission is automatically allocated based on the dynamic commission model encoded in the smart contract, and the calculation of the dynamic commission model refers at least to the performance data of this order and the historical credit data of the service provider.

[0038] 14. The network of performance status information collection devices includes edge computing nodes for local anomaly detection and preliminary response; the mapping relationship between the anomaly event type and the response logic is dynamically optimized based on the service provider's historical credit data and the user's credit representation vector.

[0039] 15. The historical credit data of the service provider is updated using a sliding window weighted average algorithm based on a time decay factor, so that the impact of recent performance on credit data has a greater weight than that of long-term performance.

[0040] 16. In step S3, the step of feeding back the updated credit representation vector and historical credit data as the core reinforcement learning signal is executed automatically and immediately without human intervention after the credit data is updated, so as to ensure the low-latency closed-loop transmission of the credit-driven signal in the service integration process.

[0041] 17. The reinforcement learning signal transmission link supports a hybrid update mode of online learning and offline batch learning.

[0042] 18. The credit representation vector is updated using a federated learning framework, which optimizes the credit model parameters collaboratively among multiple participants while protecting the data privacy of users and service providers.

[0043] 19. It also includes system model iterative optimization steps: collecting the complete data chain for each service integration process: original user requirements → parsing results → bidding process → solution selection → fulfillment status → user feedback; based on the data chain, constructing a set of evaluation metrics for assessing the model performance at each stage; using the data chain as training samples, periodically fine-tuning the requirement parsing model, the price prediction model, and the multi-objective service optimization model; selectively deploying the optimized model version based on the evaluation results of the optimized model version; the set of evaluation metrics includes at least one or more of the following: parsing confidence accuracy, bidding convergence speed, and fulfillment anomaly handling success rate, wherein the weight of each metric is dynamically allocated based on the credit representation vector.

[0044] 20. Support two-way dynamic negotiation between users and service providers. At least one of the following can be dynamically set based on the user's credit representation vector and the service provider's historical credit data: upper limit of negotiation rounds, strength of guidance strategy, and negotiation constraints.

[0045] According to a second aspect of the present invention, a service integration intelligent decision-making method based on a credit-driven reinforcement learning closed loop is provided, characterized in that it is used to quickly optimize the decision model during the initial deployment of a service integration system or when facing new users, comprising the following steps: U1: Receives unstructured service request input from users and generates a structured service task description containing at least one service module and its collaborative constraints through hybrid parsing processing; U2: Based on the structured service task description, multiple rounds of dynamic bidding are initiated with multiple service providers to obtain dynamic service condition information. This dynamic service condition information, user preference information, and collaborative constraints are then integrated and calculated using a multi-objective service optimization model with dynamically adjustable optimization weights. At least one integrated service solution is generated and recommended to the user. The decision preference parameters for generating the integrated service solution are initialized based on an initial credit representation vector and historical credit data from each service provider. When historical credit data is unavailable, a collaborative filtering-based credit vector initialization method is used. In the initial service loops, incremental learning and a credit decay factor are employed to accelerate the optimization of the decision model. U3: In response to the user's confirmation of the integrated service solution, initiate and monitor service execution, and automatically complete fee settlement according to preset rules after service execution is completed; wherein, based on the performance status data and user feedback data, a credit update signal is generated in real time; the credit update signal is used to update the user's credit representation vector and the historical credit data of each service provider; the updated credit representation vector is used as the core reinforcement learning signal, and is fed back in real time and dynamically to the hybrid parsing processing confidence calibration logic in step S1 and the multi-objective service optimization model decision preference parameter adjustment logic in step S2, thereby constructing a self-evolving service integration and optimization closed loop driven by credit data flow and running through the entire process of demand parsing, resource matching and performance settlement.

[0046] According to a third aspect of the present invention, a service integration intelligent decision-making system for implementing any of the methods described above is provided, characterized in that it comprises: at least one server node connected via a communication network to a user terminal, a service provider terminal, a network of performance status information collection devices, and a decentralized trusted evidence storage network; deployed on the server node: a demand parsing module, used to receive unstructured demands, parse them through a hybrid architecture and perform confidence calibration based on credit representation vectors, and generate a structured service task description; a dynamic bidding and decision-making module, used to initiate multi-round guided dynamic bidding, and generate an integrated service scheme based on a multi-objective service optimization model that dynamically adjusts decision preferences based on fused credit representation vectors and historical credit data; a performance monitoring and response module, used to monitor performance status through the performance status information collection device network, and perform anomaly detection and automatic response; a credit management and closed-loop feedback module, used to manage and update credit representation vectors; and a decentralized trusted evidence storage and settlement module, used to store key event information in the decentralized trusted evidence storage network, and trigger automatic settlement based on evidence verification; The credit management and closed-loop feedback module is configured to: generate a credit update signal in real time based on performance status data and user feedback data from the performance monitoring and response module, and use this signal to update the user's credit representation vector and the service provider's historical credit data; then, use the updated credit representation vector as the core reinforcement learning signal to feed back in real time and dynamically to the confidence calibration logic of the demand analysis module and the decision preference parameter adjustment logic of the dynamic bidding and decision-making module; thereby, the demand analysis module, dynamic bidding and decision-making module, performance monitoring and response module, credit management and closed-loop feedback module, and decentralized trusted storage and settlement module work together to realize the credit-driven reinforcement learning closed loop as described in claim 1.

[0047] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the method described in any of the preceding claims.

[0048] Beneficial effects Compared with existing technologies, the technical solution provided by this invention has the following significant and synergistic beneficial effects. Crucially, these effects do not stem from isolated algorithmic improvements to single components (such as demand analysis, resource matching, or credit evaluation) in existing "modular splicing" architectures, but are entirely rooted in and collectively demonstrate the novel and fundamental system architecture of the "credit-driven reinforcement learning closed loop" defined in claim 1. Specifically, its beneficial effects include: 1. Overcame the challenge of decoupling the static confidence level of the requirement analysis model from user feedback, and achieved a self-evolving deep requirement understanding. This invention proposes for the first time a technical approach to dynamically calibrate the confidence level of demand analysis based on a credit representation vector. By using the "credit representation vector"—a dynamic vector learned from users' historical behavior and performance feedback—as the core input, the confidence levels output by each module in the hybrid analysis process are weighted and corrected in real time and in a personalized manner. This transforms the traditional one-way, static analysis process into a two-way feedback process that is linked to long-term user satisfaction in real time. This mechanism enables the system to continuously optimize its understanding of users' implicit preferences and complex intentions based on the real feedback from each service loop, thus transforming the traditional one-way, static analysis process into a two-way feedback process that is linked to long-term user satisfaction in real time. This not only resolves the contradiction between "shallow analysis and static cognition" but also achieves continuous self-optimization of demand understanding from the source, significantly reducing the cost of rework and user complaints caused by misunderstandings of demands.

[0049] 2. It breaks through the limitations of local cost optimization and realizes global dynamic matching and decision-making driven by deep integration of credit data. This invention creatively constructs a real-time fusion mechanism for credit data and multi-objective optimization decision-making. In the decision-making stage (S2), the system not only initiates multiple rounds of dynamic bidding to obtain real-time market information, but more importantly, it deeply couples the "credit representation vector" reflecting real-time user preferences and the "historical credit data" representing service provider reliability as core parameters into the weight adjustment and solution generation logic of the multi-objective service optimization model. This allows the optimization objective to systematically evolve from the traditional isolated cost minimization to an integrated dynamic Pareto optimization of multiple dimensions, including cost, experience matching, service provider comprehensive creditworthiness, and satisfaction of collaborative constraints. This mechanism effectively resolves the contradiction between "local optimization and global collaboration" in the background technology, and in complex scenarios with multiple service providers and multiple constraints, it can automatically and intelligently generate more reliable integrated solutions that better meet the comprehensive value demands of users.

[0050] 3. A fully automated, technology-backed system for ensuring contract fulfillment and reliable settlement has been established, significantly reducing collaborative trust costs. Addressing industry pain points such as low transparency in the performance process, delayed response to anomalies, cumbersome settlement, and high trust costs, this invention integrates and innovates intelligent performance monitoring, decentralized trusted evidence storage, and dynamic settlement sub-steps in the performance stage (S3). Real-time anomaly detection and initial response are achieved through a performance status information collection network and edge computing nodes; key performance events are automatically and immutably recorded and preset response logic and settlement rules are executed using decentralized trusted evidence storage networks (such as blockchain) and smart contracts. This technological system replaces the traditional trust-building model that relies on manual intervention and post-event negotiation with automated, transparent, and rule-based methods, significantly improving the performance certainty and settlement efficiency of complex service integration, and providing a reliable technical infrastructure for multi-service provider collaborative ecosystems.

[0051] 4. The technical architecture of the service integration system has been fundamentally restructured. For the first time, a reinforcement learning closed loop was proposed and implemented, using a 'credit representation vector' as the unified state variable and running through the entire 'perception-decision-execution' process. This is not only a complete subversion of the existing 'modular splicing' architecture, but also the first time that real-time fusion and closed-loop driving of credit data flow and business flow have been achieved at the system level, thereby giving rise to system-level endogenous continuous evolution and the emergence of intelligent capabilities.

[0052] The core of this invention's inventive contribution lies in its fundamental restructuring at the system architecture level. Unlike existing technologies that focus on "point-by-point optimization" of single stages, this invention creatively designs and introduces a unified, computable core state variable—the "credit representation vector"—as the sole reinforcement learning signal carrier that runs through and drives the entire process of "demand analysis (S1) → dynamic decision-making (S2) → performance feedback (S3)." Its innovative technical implementation is manifested in: transforming the traditionally static, isolated, and reactive performance status and user feedback into signals that dynamically update the credit representation vector in real time; and using this updated vector as a unified, low-latency reinforcement learning signal to automatically and automatically provide closed-loop feedback and directly affect the analytical confidence calibration logic of S1 and the decision preference parameter adjustment logic of S2 without human intervention.

[0053] This closed-loop architecture has brought about a qualitative leap: a) Unified State and Credit Integration: For the first time, the problem of "decoupling of credit information" has been solved, transforming credit from a static label into a dynamic core state that drives the optimization of the entire process.

[0054] b) Data flow integration and real-time feedback: For the first time, the data flow of the entire process of "perception-decision-execution" has been integrated, realizing millisecond-level real-time feedback based on real business closed loop and solving the problem of "data flow interruption".

[0055] c) Emergence of Endogenous Evolutionary Capabilities: For the first time, the system possesses an endogenous, adaptive, and continuous evolutionary capability based on real business flow data, employing a "action-feedback-learning-optimization" process. Its intelligence level no longer relies on external manual parameter tuning or module upgrades, but rather autonomously improves with each service loop. This marks the evolution of the service integration system from a "static, fragmented tool" to a "dynamic, self-evolving intelligent ecosystem hub." It not only solves the technical problems of "data silos" and "trust decoupling," but also drives the platform's business model leap from "information matching" to "intelligent service empowerment," providing a feasible technological foundation for the large-scale, high-value-added development of highly complex service integration.

[0056] Therefore, the deep personalization, global optimization, and high reliability achieved in each stage of demand analysis, resource matching, and performance settlement as described in points 1 to 3 above are not isolated technical improvements, but rather system-level capabilities that inevitably emerge from the reinforcement learning closed-loop architecture with the "credit representation vector" as the unified state variable. Through this fundamental architectural innovation, this invention systematically and thoroughly solves the three core architectural defects pointed out in the background art: "credit decoupling," "data flow disruption," and "lack of evolutionary closed loop."

[0057] 5. It has led to the development of trend prediction and forward-looking planning capabilities based on dynamic credit models, expanding the boundaries of system intelligence. Thanks to the continuous operation and iteration of the aforementioned reinforcement learning loop, the "credit representation vector" transcends its original credit recording function, evolving into a joint prediction model capable of dynamically and implicitly representing the evolution of user preferences and the trend of service provider reliability. This system not only responds to current needs but also uses the model to infer the evolutionary trends of user preferences and the changing trends of service provider reliability. Based on this, the multi-objective service optimization model can simultaneously perform intelligent pre-matching and recommendation of related medium- and long-term service resources when generating the current solution. This marks a leap in system capabilities from passive "responsive matching" to proactive "forward-looking planning," not only improving the seamless experience of individual services but also optimizing resource collaboration and allocation efficiency between the platform and ecosystem partners at the strategic level, demonstrating a significant extension of creativity.

[0058] 6. A highly scalable and scenario-adaptive unified technical architecture has been built. Through a pluggable domain knowledge base, the same credit-driven closed-loop core can be quickly adapted to multiple highly complex service sectors such as tourism, exhibitions, corporate travel, and weddings. This greatly reduces the marginal cost of multi-scenario deployment and provides a standardized and reusable technical engine for the platform to achieve cross-industry service integration, possessing significant economies of scale and ecosystem building potential.

[0059] The practicality of this invention is prominently reflected in the scalability and scenario adaptability of its architecture. Based on a pluggable domain knowledge base, the same technical architecture centered on a credit-driven reinforcement learning closed loop can quickly adapt to many highly complex and collaborative service integration fields, such as tourism, exhibitions, corporate travel, wedding planning, and medical rehabilitation itinerary planning, by configuring different service modules and collaborative constraints. This provides a highly adaptable and reusable unified technical architecture and solution to fundamentally solve the systemic problems faced by various high-value service integration fields, such as "user experience bottlenecks, high operating costs, and difficulties in ecosystem collaboration," as pointed out in the background technology, demonstrating broad industrial application prospects and large-scale promotion potential. Attached Figure Description

[0060] To more intuitively illustrate the technical solution and implementation process of the present invention, the present invention will be further described below in conjunction with the accompanying drawings: Figure 1 Overall Flowchart of a Credit-Driven Reinforcement Learning-Based Service Integration Intelligent Decision-Making Method This invention demonstrates the complete process from user input to closed-loop feedback, including demand analysis (S1), dynamic decision-making (S2), performance feedback (S3), and its credit-driven closed-loop connection.

[0061] Figure 2 Schematic diagram of credit-driven closed-loop architecture It demonstrates how the credit representation vector serves as a core state variable, flowing in real time between the three modules of demand analysis, dynamic decision-making, and performance feedback, and driving closed-loop self-evolution.

[0062] Figure 3 Schematic diagram of multimodal fusion analysis and credit calibration It demonstrates how multimodal inputs (text, speech, image, video) are adaptively fused through a cross-modal attention fusion mechanism, and the fusion weights are dynamically adjusted based on the credit representation vector to output a structured task description and calibrated confidence score.

[0063] Figure 4 Schematic diagram of multi-objective service optimization model and dynamic bidding guidance mechanism It demonstrates the dynamic bidding process, the objective composition of the multi-objective optimization model (such as total cost, matching degree, credit score, etc.), the dynamic weight adjustment mechanism, and the generation of credit-based differentiated guidance signals.

[0064] Figure 5 Intelligent performance monitoring and decentralized trusted evidence storage and settlement flowchart It showcases the entire process of performance monitoring, anomaly detection, automatic response, decentralized trusted evidence storage, smart contract-triggered settlement, and credit update. Detailed Implementation

[0065] The method and system described in this invention are particularly suitable for service integration fields with complex needs, high degree of personalization, strong collaboration among multiple modules, and strict requirements for the credibility of performance. Typical application scenarios include, but are not limited to: personalized customized tourism, compliant corporate travel management, full-case planning and execution of large-scale exhibitions and events, integrated wedding and celebration services, medical rehabilitation itinerary planning, and comprehensive service operation of cultural and sports events.

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the invention.

[0067] The core of this invention lies in constructing a reinforcement learning closed loop with a "credit representation vector" as the unified state variable, enabling continuous self-evolution throughout the service integration process. The following describes the implementation of this invention through various typical application scenarios and advanced functional examples.

[0068] Example 1 A complete demonstration of the intelligent tourism service integration process based on credit-driven reinforcement learning closed loop. 1. Scenario Setting: Multimodal Input Requirements and Credit Initialization Status for Family Trip to Sanya User A is a family user with multiple platform usage records. Their historical credit profile has been preliminarily established, with dimensions including: user preference consistency (0.85), performance feedback sensitivity (0.70), and price sensitivity (0.60). Each dimension's value is a normalized score ranging from 0 to 1, where 0.85 indicates high consistency, 0.70 indicates moderate sensitivity, and 0.60 indicates moderate price sensitivity. Service providers (such as travel agencies, hotels, and fleets) all possess credit scores calculated based on multi-dimensional data, including historical performance records, user reviews, and anomaly handling response efficiency, to quantify their service reliability and collaborative capabilities.

[0069] User A inputs their needs via a mobile app using a combination of voice and image: "I'm taking my two children (6 and 9 years old) to Sanya for 5 days next month during the National Day holiday. We want to stay at a family-friendly beach hotel, including airport transfers, and the attractions should be child-friendly, with a budget within a specific range." They also upload an image showing a beach, swimming pool, and children's playground as style reference. The system uses a WaveNet-based acoustic model and a BERT-based semantic error correction module in the speech-to-text process. Keywords relevant to family-friendly scenarios (such as "child-friendly" and "safety facilities") are enhanced through an attention mechanism to improve recognition accuracy in noisy environments. Images are processed using a pre-trained visual understanding model (such as CLIP) to extract scene tags like "beach leisure" and "family entertainment," and combined with OCR technology to recognize textual information (such as hotel signage and facility descriptions) in the images, forming a multimodal feature vector. This multimodal feature vector is then fused through a cross-modal attention network, where the modal weights are dynamically allocated based on the "historical modality preference consistency" dimension in the user's credit representation vector.

[0070] 2. Step S1 Implementation: Multimodal Fusion Analysis and Dynamic Confidence Calibration Based on Credit Representation Vector Speech is converted to text using ASR, text is used to extract intent and entities using BERT, and images are used to extract visual features using CNN.

[0071] A cross-modal attention mechanism is adopted to dynamically increase the fusion weight of image features based on the "historical modality preference consistency" dimension of user A.

[0072] The parsing model outputs a structured task description and confidence level. Based on user A's "preference consistency" dimension, a weighted smoothing is applied to generate a calibrated structured service task description.

[0073] 3. S2 Step Implementation: Multi-round dynamic bidding and credit-based multi-objective optimization decision-making Initiate a reverse auction to eligible service providers, set a certain time window for each round, and generate guiding signals (such as percentile ranking, suggestions for adjusting direction).

[0074] Detailed guidance signals will be sent to high-credit service providers first.

[0075] The multi-objective optimization model integrates total cost, overall matching degree, comprehensive credit score, and constraint violation penalty value. The weights are dynamically adjusted based on user A's "price sensitivity" and the average credit score of the bidding parties to generate multiple integrated solutions.

[0076] 4. S3 Step Implementation: Intelligent Performance Monitoring, Trusted Evidence Storage, and Real-time Credit Feedback Closed Loop By monitoring the performance status in real time through APIs and IoT devices, the edge node triggers an anomaly when it detects that "flight delays may cause the waiting time for airport pickup to exceed the set threshold".

[0077] The hash value of the abnormal event is stored on the blockchain, and the smart contract automatically executes the response logic (notification scheduling, prompting the user, and recording the response).

[0078] After the service is completed, dynamic settlement is triggered based on the evidence storage chain, and the commission rate fluctuates according to the performance of the service.

[0079] Based on performance data and user feedback, the system automatically updates credit within 30 seconds after the service is completed. The updated credit representation vector serves as the sole reinforcement learning signal and is fed back in real time to the analytical confidence calibration logic of S1 and the multi-objective optimization weight adjustment logic of S2 without human intervention, forming a closed loop.

[0080] 5. Summary of Results This embodiment fully demonstrates the three-step closed-loop process defined in claim 1, reflecting the full-process self-evolution capability of credit-driven analysis, bidding guidance, multi-objective optimization, performance verification and real-time feedback, and verifying the effectiveness of the closed-loop architecture.

[0081] Example 2 Policy compliance constraints and credit collaboration network applications in corporate travel service scenarios 1. Scene Description Employee B of a multinational company needs to travel from Beijing to Berlin to attend a conference and then to Paris for business negotiations. The company's policies include restrictions on flight class, hotel star rating, contracted suppliers, and invoice format.

[0082] 2. Key Implementation Points The system loads the "Corporate Travel" module from the domain knowledge base, automatically injecting corporate travel policy constraints such as "flight class must be economy or above," "hotel must be four-star or above," "must be selected from the list of contracted suppliers," and "invoice format must meet the requirements of the company's financial system." When parsing the requirements, the system transforms these policy constraints into hard constraints or penalty terms in a multi-objective optimization model. For example, if a user selects a non-contracted supplier, a "constraint violation penalty value" is added to the solution, lowering its recommendation priority.

[0083] The system constructs and maintains a credit collaboration network. This network calculates collaborative credit scores among service providers based on multi-dimensional data such as historical collaborative performance records, response time, and user reviews. When a strong spatiotemporal dependency is identified between "flight + hotel + ground transportation," the system prioritizes inviting service providers with a collaborative credit score ≥ 0.85 (out of 1.0) within the network to participate in joint bidding. This significantly improves the success rate of joint performance and simultaneously shortens anomaly response time.

[0084] It supports dynamic adjustments using natural language, such as re-triggering the process after a user changes the hotel location via voice.

[0085] 3. Closed-loop effect Automatic compliance verification, collaborative bidding to improve efficiency, and joint performance to improve credit collaborative network scores.

[0086] Example 3 Multi-supplier collaborative fulfillment and automatic anomaly response mechanism in exhibition service scenarios 1. Scene Description Technology exhibitions must complete setup, exhibition, and dismantling within a strict time window, involving coordination among multiple parties such as construction, equipment, power, and logistics.

[0087] 2. Key Implementation Points Deploy edge computing nodes to collect job progress in real time.

[0088] The system has pre-defined multi-level exception response smart contracts, for example: Level 1 anomaly (progress ≤ 20%): Automatically adjusts the time window for subsequent stages and sends an alert to the on-site supervisor; Level 2 anomaly (progress delay > 20% and ≤ 40%): Triggers the entry of a backup supplier and adjusts the commission allocation weight; Level 3 anomaly (progress delay > 40%): Activate the emergency plan, automatically notify the project director, and trigger the insurance claim process.

[0089] The contract response logic is dynamically adjusted based on the service provider's historical credit data. For example, the first-level abnormal threshold is relaxed to 25% for high-credit service providers.

[0090] Differentiated commission settlements are implemented based on actual collaborative performance.

[0091] 3. Closed-loop effect Automated scheduling effectively shortens setup time, automatic anomaly response reduces risk, and transparent and dispute-free evidence storage and settlement.

[0092] Example 4 Multimodal style analysis and visual consistency constraints in wedding service scenarios 1. Scene Description User C expressed their desire for a "forest-themed outdoor wedding" through text and pictures, requiring coordination with multiple service providers including venue, decorations, floral arrangements, photography, and catering.

[0093] 2. Key Implementation Points A cross-modal attention network fuses text and images to generate a "forest style vector" as a collaborative constraint.

[0094] In multi-objective optimization, style matching weights are dynamically adjusted based on the "preference consistency" dimension of user C.

[0095] The visual coordination of each step in the recommended solution is significantly improved.

[0096] 3. Closed-loop effect Precise style matching, unified visual experience, and enhanced credibility of service providers in niche markets.

[0097] Example 5 Multilingual parsing and cross-border collaborative constraint application in cross-border medical examination and treatment travel planning scenarios 1. Scene Description User J, a citizen of a European country, wishes to undergo specialized examinations and follow-up treatment at a top-tier hospital in China due to the long waiting times in their country's public healthcare system (typically 3-6 months). The user inputs their needs through the platform using English voice messages and uploaded scanned copies of their previous medical records: "I hope to complete a spinal MRI and specialist consultation within a short period. I plan to stay in China for several days (e.g., 7 days). I need an English report from the hospital, and assistance with translation, accommodation, and local transportation." The system uses a collaborative filtering algorithm to match the user with a high-credit user group that has previously served similar cross-border medical needs, initializing their "performance feedback sensitivity" dimension at 0.8 (above average), thus prioritizing service reliability and response speed in the solution generation.

[0098] 2. Key Implementation Points Multilingual and multimodal parsing: The system uses a hybrid parsing module to identify the type of examination (MRI), time window (within 1 month), duration of stay (7 days), and language service requirements (English report) in the user's English speech, and combines OCR technology to extract key medical history information from the medical record scan to generate a structured task description.

[0099] Cross-border collaborative constraints loading: Load cross-border specific constraints from the knowledge base of "medical rehabilitation itinerary planning", including: hospital international department reception qualifications, ability to issue English reports, visa invitation letter support, cross-border medical insurance connection, accommodation near hospitals, translation service qualifications, etc.

[0100] Core closed-loop driver: The system starts the closed-loop process based on the initial credit representation vector of user J (initialized through collaborative filtering).

[0101] Key sub-mechanism application: In the dynamic bidding process, the system prioritizes inviting service providers with high historical collaborative performance scores for the "hospital + translation + accommodation" combination to participate in joint bidding based on the credit collaboration network. The joint bidding mechanism enables the price convergence to increase rapidly.

[0102] Scenario Adaptability: In response to the unique constraints of cross-border medical scenarios such as "language services" and "visa support", the system loads and automatically injects optimized models from a pluggable domain knowledge base to ensure the compliance of the solution.

[0103] Performance optimization results: The final integrated solution has a significantly improved overall matching degree and user satisfaction, while meeting all rigid medical time windows.

[0104] Intelligent itinerary splicing: The system identifies strong temporal and geographical dependencies between "hospital appointment → translation and accompaniment → accommodation arrangement → city transportation" and prioritizes sending joint bidding invitations to service providers that can provide cross-module collaborative pricing.

[0105] Performance monitoring and evidence preservation: Performance progress is monitored in real time through multiple data sources, including hospital APIs, translation app location data, and hotel check-in systems. If inspection results are delayed, edge nodes trigger an anomaly response process, and the event is recorded on the blockchain and automatically notified to all parties.

[0106] Dynamic settlement and commission allocation: Upon completion of the service, the completion status of each step is verified based on the evidence storage chain, triggering automatic settlement. The commission rate fluctuates dynamically based on the service provider's performance efficiency (such as the timeliness of report issuance and translation on-time rate), incentivizing high-quality services.

[0107] 3. Closed-loop effect The system generates multiple integrated solutions in a short period of time, covering different hospital levels, accommodation standards and price ranges. The total time required for the optimal solution is significantly shorter than that required for users to coordinate on their own.

[0108] If a user's flight is delayed due to weather during the performance of the contract, the system will automatically adjust the airport pick-up and first-day reservation, and record the rights and responsibilities for the delay through smart contracts, and complete the settlement without disputes.

[0109] After a user completes a service and gives a high rating, the system updates the credit dimension values ​​such as "preference consistency" and "performance feedback sensitivity" in real time based on the performance data and user feedback. Based on the updated credit representation vector, the system proactively recommends continuous services such as remote consultation during the recovery period and cross-border drug delivery, forming a closed loop for cross-border medical services. This further verifies the adaptive and forward-looking planning capabilities of the credit-driven closed loop in cross-scenario continuous services.

[0110] Example 6 Virtual simulation and personalized risk assessment of service solutions based on digital twins 1. Scene Description We plan integrated solutions for complex business travel and meetings for corporate clients across multiple countries and locations, involving multiple senior executives.

[0111] 2. Key Implementation Points The system constructs a digital twin of the itinerary, based on historical flight punctuality data (e.g., a 15% delay probability for a certain route), venue availability, weather forecasts, and other information. It then incorporates abnormal scenarios such as "2-hour flight delay," "temporary venue closure," and "absence of key personnel," conducting 1000 Monte Carlo simulations. The system dynamically adjusts the risk acceptance threshold based on the user's "performance feedback sensitivity" dimension value. If the dimension value is greater than 0.8, the system will only recommend solutions with a risk incidence rate of less than 5%. If the dimension value is less than 0.3, the system can accept a solution with a risk occurrence rate of no more than 20% in exchange for cost optimization.

[0112] The risk response threshold is dynamically adjusted based on the enterprise customer's "susceptibility to performance feedback" dimension. For example, when the dimension value is >0.8, the risk threshold is reduced by 20%; when the dimension value is <0.3, the risk threshold is increased by 30%.

[0113] Identify and optimize high-risk processes (such as tight layover times).

[0114] 3. Closed-loop effect Proactive risk control, personalized risk thresholds to balance cost and experience, and optimized anomaly response logic library.

[0115] Example 7 Collaborative updates and privacy protection of cross-platform credit models under the federated learning framework 1. Scene Description The three travel platforms hope to improve the accuracy of their credit models through joint training, but they cannot directly share data.

[0116] 2. Key Implementation Points The system employs a horizontal federated learning architecture. Each participating platform locally adds noise to user credit data using differential privacy technology (ε=0.5) to train a local credit model (using a lightweight neural network). After each training round, each platform uploads its encrypted model gradients to a central aggregation server. The server uses the FedAvg algorithm to aggregate the gradients and generate a global model update. After the global model is distributed to each platform, the local models achieve collaborative evolution while protecting user privacy.

[0117] After the server aggregates gradients, it distributes global updates, and each platform updates its local model.

[0118] 3. Closed-loop effect Protect user privacy, improve model generalization ability, and promote overall industry intelligence.

[0119] Example 8 Fast initialization and incremental learning optimization of credit vectors in new user cold start scenarios 1. Scene Description New user D is using the platform for the first time to plan a "Weekend Shanghai City Culture Tour".

[0120] 2. Key Implementation Points Credit vectors are initialized using collaborative filtering, based on the average vector of similar user groups.

[0121] After the initial service, the vector is updated based on real feedback, and a credit decay factor is used to accelerate convergence.

[0122] The initial services will use incremental learning to quickly personalize the adaptation. 3. Closed-loop effect Optimized cold start experience, fast and personalized convergence, and strong system adaptability.

[0123] Example 9 End-to-end data-driven system model iterative optimization and A / B testing deployment process 1. Scene Description After the system has been running for a period of time, it has accumulated a large amount of closed-loop data, and we are ready to upgrade the analysis and optimization model.

[0124] 2. Key Implementation Points Extract massive amounts of complete data chains (requirements → analysis → bidding → solutions → fulfillment → feedback).

[0125] Construct a dynamic weighted evaluation index set (such as parsing accuracy, bidding convergence speed, and anomaly handling success rate).

[0126] After fine-tuning the model, A / B testing was conducted to compare the performance of V1 and V2. Once the optimization was confirmed, the model was fully deployed.

[0127] 3. Closed-loop effect Data-driven iteration, controllable deployment to reduce risks, and institutionalized continuous evolution.

[0128] Example 10 Service resource pre-matching and dynamic adjustment mechanism based on long-term preference evolution 1. Scene Description High-frequency users' preferences have shifted from "island vacations" to "in-depth cultural and historical tours," and the system identifies this trend and pre-matches resources accordingly.

[0129] 2. Key Implementation Points We analyzed user E's records over the past year to build a preference evolution model and predict future tendencies.

[0130] Simultaneously initiate pre-matching invitations to museums, guided tours, and other similar services when generating the current plan.

[0131] When a user actually initiates a request, pre-matched resources are directly retrieved to accelerate the generation of a solution.

[0132] 3. Closed-loop effect Improve user experience continuity, increase resource utilization, and enhance credit vector prediction capabilities.

[0133] Example 11 Extreme scenario handling for dynamically triggered credit weights in multi-objective optimization models 1. Scene Description User F has extremely low price sensitivity but extremely high sensitivity to performance feedback, and the average credit score of the bidding parties is below the safety threshold.

[0134] 2. Key Implementation Points Automatically adjust the cost weight and increase the credit weight.

[0135] We prioritize recommending service providers with high credit ratings, even if their prices are higher than the market average.

[0136] Verify the performance stability of high-credit portfolios through digital twin simulation.

[0137] 3. Closed-loop effect Even in extreme scenarios, personalized trade-offs are still achieved, significantly improving user satisfaction and forming a virtuous cycle of high credibility.

[0138] Example 12 Cross-scenario credit vector transfer and cold start acceleration mechanism 1. Scene Description User G already has a mature credit profile in the tourism scenario and is using the same platform for the first time in the wedding service scenario.

[0139] 2. Key Implementation Points Extract credit dimensions that can be transferred across scenarios (such as preference consistency and sensitivity to performance feedback).

[0140] By combining collaborative filtering with scene-specific dimensions, a credit vector for the wedding scene is initialized.

[0141] Based on the initial service feedback, updates are made quickly, and personalized convergence is achieved within multiple services.

[0142] 3. Closed-loop effect Cross-scenario credit reuse significantly improves the efficiency of cold start in new scenarios and enhances user retention.

[0143] Example 13 Intelligent Collaborative Application of Two-Way Dynamic Negotiation and Credit Adaptive Control in High-End Customized Services 1. Scenario setting and problem identification User H plans to organize a "multinational high-end business conference + team building" event, involving participants from multiple countries, high-standard conference venues, customized agendas, high-end catering and accommodation, and unique team building activities. The budget is high, and there are extremely stringent requirements for service quality, collaborative efficiency, and emergency response. Traditional negotiation models typically rely on repeated manual communication, which is inefficient and difficult to optimize globally in complex multi-service provider collaborations. This example aims to demonstrate how the system, in high-value, highly complex, and highly personalized service integration scenarios, achieves rapid, high-quality, and reliable solution generation and collaborative closed-loop through a two-way dynamic negotiation mechanism and credit-driven intelligent guidance.

[0144] 2. Strengthening Key Implementation Points and Technical Processes 2.1. Two-way negotiation triggering and credit adaptive control Negotiation Trigger and Admission: After submitting a request, User H can choose to "Start Negotiation". The system automatically determines that User H is a high-value, high-trust user based on their credit profile (e.g., high "performance feedback sensitivity" and low "price sensitivity"), allowing them to enter the two-way dynamic negotiation process.

[0145] Dynamic parameter settings: The maximum number of negotiation rounds is dynamically set: Based on user H's "performance feedback sensitivity" dimension (e.g., 0.85) and the average historical credit data of the main service provider combination (e.g., 0.90), the system automatically sets the maximum number of negotiation rounds to 5. If the user's credit or the service provider's credit is low, the maximum number of rounds may be reduced to 3 to control the complexity of the game and time cost.

[0146] The intensity of the guidance strategy is dynamically adjusted: For user H (high-credit user) and the service provider combination (high-credit service provider), the system adopts a high-intensity guidance strategy, including pushing detailed user preference analysis, providing multi-round personalized suggested price ranges, and offering in-depth strategy hints based on game theory. For participants with average credit, a standard-intensity guidance strategy is adopted, providing only basic ranking information.

[0147] Dynamic injection of negotiation constraints: Based on user H's "price sensitivity" dimension (e.g., 0.30, indicating low), the system automatically relaxes the price fluctuation range in the negotiation constraints (e.g., allowing the final price to be within 20% higher than the initial price), but at the same time strengthens the constraint that the "service provider's overall credit rating" must not be lower than the threshold of 0.85 to ensure that the quality is not reduced.

[0148] 2.2. Deepening Analysis of Multimodal Demands and Credit Calibration Users can not only input textual requirements but also upload multimodal materials such as images of past events, draft agendas, and participant profiles. The system uses a cross-modal attention fusion network to extract style and semantic vectors such as "high-end," "international," "technological," and "teamwork," which are then injected into the task description as collaborative constraints. The resolution confidence is dynamically calibrated based on the user's historical "preference consistency" dimension to ensure accurate understanding of requirements.

[0149] 2.3. Invitation to Service Provider Portfolio Driven by Credit Collaboration Network The system identifies a strong collaborative dependency among "meeting venue + equipment technical support + catering services + team building planning," and prioritizes selecting service provider combinations with high historical collaboration performance scores from the credit collaboration network to initiate joint bidding invitations. Joint bidding allows service provider combinations to submit overall package solutions and quotations, reducing subsequent coordination costs and improving solution consistency.

[0150] 2.4. Two-way generation of intelligent guidance signals and credit-differentiated push notifications Guiding service providers: After each round of price negotiation, the system generates suggested adjustment directions for the next round based on the current distribution of all bids and the preference dimension in the user's credit vector, and makes differentiated pushes based on the service provider's historical credit data (such as pushing detailed analysis to high-credit providers and basic information to general-credit providers).

[0151] User guidance: Based on the service provider's historical credit history and current pricing, the system generates visual guidance information such as "cost-effectiveness analysis," "credit risk assessment," and "recommended portfolio explanation" to assist users in decision-making. The level of detail and display priority of the guidance information are also dynamically adjusted based on the user's credit representation vector.

[0152] 2.5. Credit Incentives and Dynamic Weight Adjustment in Multi-Objective Optimization Models In the multi-objective service optimization model, "service provider's overall creditworthiness" is set as a high-weight objective, and a "collaborative pricing bonus" is added to encourage combined service providers to submit overall optimization solutions. When a user's "performance feedback sensitivity" dimension is identified as extremely high, the weights of "creditworthiness" and "matching degree" are automatically increased. The model supports real-time recalculation during the negotiation process, and the solution ranking is updated immediately after each new quote is received.

[0153] 2.6. Digital Twin Simulation and Risk Pre-Assessment During the negotiation process, the system initiates a digital twin virtual simulation based on the current optimal candidate solution, simulating the entire process and injecting abnormal events. The risk acceptance threshold is dynamically adjusted based on the user's "performance feedback sensitivity" dimension value; for example, for highly sensitive users, only solutions with a risk incidence rate of less than 5% in the simulation are recommended.

[0154] 2.7. On-chain evidence storage and smart contract protection Key events such as price negotiations, user selection, and service provider commitments are recorded in real-time using hash values ​​stored on a decentralized, trusted evidence storage network. Default clauses and dynamic commission mechanisms are encoded in the smart contract to ensure the immutability and automatic execution of the negotiation process.

[0155] 3. The closed-loop effect and value become apparent. Improved negotiation efficiency and intelligence: By setting negotiation parameters (number of rounds, guidance strength, constraints) based on credit, high-end customized solutions that traditionally require weeks of communication can be quickly converged within 3-5 rounds of negotiation within the system, generating multiple reliable solutions, resulting in a significant improvement in efficiency.

[0156] Enhanced solution quality and collaboration: The credit collaboration network and joint bidding mechanism ensure high quality in terms of style consistency and process integration.

[0157] A positive cycle in the credit ecosystem: users with high credit scores and service providers obtain better bargaining conditions and guidance resources, which reflects their credit value and further incentivizes high-quality behavior within the ecosystem.

[0158] System self-evolution enhancement: The negotiation and performance data of this round are fed back to the credit vector and collaborative network in real time, continuously optimizing the subsequent negotiation guidance strategy and combination recommendation logic, realizing the full-process closed-loop self-evolution as described in claim 1.

[0159] Example 14 Computer-readable storage medium embodiments—system software deployment and execution carriers This embodiment provides a computer-readable storage medium for implementing the service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop described in any of the foregoing embodiments. This storage medium carries all or part of the software program implementing the technical solution of this invention and serves as the physical carrier for system deployment, operation, and maintenance.

[0160] 1. Scene Description A service integration platform operator needs to deploy the intelligent decision-making method of the present invention on a cloud server cluster and provide services to users through a client application. The operator stores a computer program implementing the method of any one of claims 1 to 22 in a computer-readable storage medium for loading and execution by a server processor.

[0161] 2. Key Implementation Points 2.1. Media Type and Storage Content Solid-state drives (SSDs) are used as computer-readable storage media, on which computer programs containing the following functional modules are stored: Requirements analysis module program code; Dynamic bidding and decision-making module program code; Performance monitoring and response module program code; Credit management and closed-loop feedback module program code; Decentralized trusted evidence storage and settlement module program code.

[0162] 2.2. Program Execution Flow When the server processor reads and executes the computer program from the SSD, the following steps are performed: Receive unstructured service requests from users via the client; Based on the credit representation vector, the analytical confidence level is dynamically calibrated to generate a structured task description; Initiate multiple rounds of dynamic bidding and integrate credit data to generate integrated service solutions; Monitor the performance process, use blockchain to record key events and trigger automatic settlement; The credit representation vector is updated based on performance feedback, and then fed back to the analysis and decision-making stages in real time in a closed loop.

[0163] 2.3. Deployment and Update Mechanism The operator distributes the program stored on the network to various server nodes, supporting remote updates and version iterations. System supports: Incremental updates and hot patch deployment minimize service interruptions; Multiple versions coexisting and A / B testing ensure the stability of new versions; Containerized deployment improves system elasticity and resource utilization.

[0164] 3. Closed-loop effect The computer-readable storage medium provided in this embodiment achieves the following technical effects: 3.1. High portability and standardized deployment of the method: The medium encapsulates the complete credit-driven closed-loop logic, supporting rapid deployment in various hardware and cloud environments and reducing integration complexity.

[0165] 3.2. System Operation Consistency and Intelligent Assurance: All server nodes execute the same program version to ensure that the credit-driven reinforcement learning closed loop runs consistently across the entire platform, ensuring the intelligent and self-evolving capabilities of the service integration process.

[0166] 3.3. Reliable technical carrier for commercialization: It provides a stable, maintainable and iterable software carrier for the large-scale application of the method, which meets the protection scope defined in claim 24 and has practical commercial promotion value.

[0167] 4. Typical application scenarios Tourism service platform: Deploy the program on public cloud servers to provide personalized intelligent travel itinerary planning services for a massive number of users; Corporate Travel Management System: Integrates the program into the enterprise's private cloud to achieve automated execution of travel policies and credit-based resource matching; Exhibition service companies: Deploy systems on local server clusters to support intelligent management of the entire process of large-scale exhibition projects; Wedding planning platform: Deployed on a hybrid cloud architecture, enabling credit collaboration and automatic settlement of smart contracts across multiple regions and service providers.

[0168] Summary of the Implementation Effects and Architectural Advantages of this Invention 1. Summary of System Architecture Advantages Closed-loop self-evolution: Credit flow and business flow are integrated in real time throughout the entire process, and the system has the ability to continuously optimize itself.

[0169] Credit-driven: Transforming credit into a computable vector to drive personalized and reliable service matching.

[0170] Scenario Adaptability: Quickly adapts to multiple domains through a pluggable knowledge base, offering strong scalability.

[0171] Secure and trustworthy: Integrating blockchain and federated learning ensures data immutability and user privacy.

[0172] Intelligent guidance and game theory: Achieving rapid convergence and high-quality matching in bidding and negotiation.

[0173] 2. Prospects for Industrial Application and Promotion Value This invention represents a new paradigm for intelligent service integration, which can drive industries such as tourism, conventions and exhibitions, enterprise services, and weddings to upgrade from "information matching" to "intelligent service empowerment," enhance the platform's ecosystem control, service provider incentives, and user experience, and has broad social and economic value.

Claims

1. A service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop, characterized in that, Includes the following steps: S1: Receive unstructured service request input from users, and generate a structured service task description containing at least one service module and its collaborative constraints through hybrid parsing processing; wherein, the confidence level of the hybrid parsing processing is dynamically calibrated based on a credit representation vector that characterizes the consistency between the user's historical behavior and feedback. S2: Based on the structured service task description, initiate multiple rounds of dynamic bidding with multiple service providers to obtain dynamic service condition information, and integrate the dynamic service condition information, user preference information, and collaborative constraints. Calculate using a multi-objective service optimization model with dynamically adjustable optimization weights to generate and recommend at least one integrated service solution to the user; wherein, the decision preference parameters for generating the integrated service solution are initialized or dynamically adjusted based on the credit representation vector and the historical credit data of each service provider. S3: In response to the user's confirmation of the integrated service solution, initiate and monitor service execution, and automatically complete fee settlement according to preset rules after service execution is completed; wherein, based on the performance status data and user feedback data, a credit update signal is generated in real time; the credit update signal is used to update the user's credit representation vector and the historical credit data of each service provider; the updated credit representation vector is used as the core reinforcement learning signal, and is fed back in real time and dynamically to the hybrid parsing processing confidence calibration logic in step S1 and the multi-objective service optimization model decision preference parameter adjustment logic in step S2, thereby constructing a self-evolving service integration and optimization closed loop driven by credit data flow and running through the entire process of demand analysis, resource matching and performance settlement.

2. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, The credit representation vector is a dynamic vector representing the consistency between a user's historical behavior and feedback. As a core state variable, it is used to dynamically calibrate the confidence level of the hybrid analytical processing and / or to dynamically adjust the decision preference parameters of the multi-objective service optimization model. The historical credit data of the service providers refers to a dynamic evaluation index used to quantify their service reliability and collaborative capabilities, calculated based on at least one of the following: past performance records, user evaluations, anomaly handling response efficiency, and collaborative performance.

3. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 2, characterized in that, The credit representation vector contains multiple independent dimensions, including at least a user preference consistency dimension, a performance feedback sensitivity dimension, and a price sensitivity dimension. Each dimension takes a continuous value between 0 and 1, where 0 represents completely insensitive or untrustworthy, and 1 represents highly sensitive or highly trustworthy. The user preference consistency dimension is used to calibrate the confidence level of the hybrid parsing process, and the performance feedback sensitivity dimension and the price sensitivity dimension are used to adjust the decision preference parameters of the multi-objective service optimization model.

4. A service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop as described in claim 1 or 2, characterized in that, The credit representation vector, through continuous iteration of the reinforcement learning loop, forms a dynamic, implicit representation model of user service preferences and service provider performance reliability. The iteration cycle is updated in real time or near real time, and the convergence condition is that the change in the credit vector is less than a preset threshold ε in N consecutive iterations. This model can infer the evolution trend of user preferences and the change trend of service provider reliability. The multi-objective service optimization model further integrates the evolutionary and changing trends. When generating the current integrated service solution, it simultaneously performs pre-matching and pre-optimization recommendations for related medium- and long-term service resources, thereby improving resource utilization efficiency and the continuity of user experience.

5. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, The unstructured service requirement input is at least one of text, voice, image, or video; the hybrid parsing process includes converting the multimodal input into the structured service task description through a multimodal fusion parsing network. The multimodal fusion parsing network adopts a cross-modal attention fusion mechanism, which calculates the correlation weights between different modal features to achieve adaptive fusion of multimodal information and resolution of semantic conflicts; when using the cross-modal attention fusion mechanism for weighted fusion, the allocation of the fusion weights of each modal feature is dynamically calculated and adjusted based on the dimension representing the consistency of the user's historical modal preferences in the credit representation vector.

6. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, The geographical boundary and time window parameters in the collaborative constraints are dynamically generated based on real-time traffic data and weather forecasts; the credit representation vector does not contain user personal identity information; the service module types and collaborative constraints defined in the method are predefined, configured, and dynamically extended through a pluggable domain knowledge base, thereby adapting to different complex service integration scenarios; the method is based on the same credit feedback closed-loop architecture and can be applied to cross-domain service integration scenarios including at least one of the following. a) Tourism service scenario: The service modules include, for example, transportation booking, accommodation booking, attraction ticket booking, restaurant reservation, and tour guide service; the collaborative constraints include, for example, itinerary time continuity constraints, geographical location correlation constraints, service provider access conditions constraints, and budget constraints. b) Corporate travel service scenarios: The service modules include, for example, flight booking, hotel booking, ground transportation, visa application, and conference venue rental; the collaborative constraints include, for example, corporate travel policy compliance constraints, budget department affiliation constraints, itinerary and personal schedule coordination constraints, and invoice and reimbursement format constraints. c) Exhibition service scenarios: The service modules include exhibition venue rental, booth design and construction, audiovisual equipment rental, attendee reception, and concurrent event planning; the collaborative constraints include strict time windows for setup and dismantling, collaborative constraints for on-site operations with multiple suppliers, and constraints on crowd density and safety regulations. d) Wedding service scenarios: The service modules include wedding venues, planning and coordination, photography and videography, wedding dresses and makeup, wedding banquet catering, and wedding car rental; the collaborative constraints include strict temporal dependency constraints between multiple service links, overall visual and style consistency constraints, and on-site cooperation tightness constraints of key suppliers. e) Medical rehabilitation itinerary planning service scenario: The service modules include, for example, medical institution appointments, rehabilitation center connections, accommodation for caregivers, and transportation arrangements for follow-up examinations; the collaborative constraints include, for example, rigid time window constraints for medical diagnosis and treatment, adaptive constraints for patients' physical condition, and constraints on the movement and role responsibilities of caregivers. f) Service scenarios for cultural performances, sporting events, and conferences: The service modules include, for example, event ticketing, venue / hall rental, equipment technical support, media promotion, on-site security, and guest reception; The collaborative constraints include rigid constraints on the activity process timeline, collaborative constraints on concurrent operations of multiple suppliers on site, constraints on real-name or zoned management of tickets, and constraints on public safety and emergency plans. g) Festive service scenarios: The service modules include, for example, banquet catering, venue setup, emceeing, performances and entertainment, video recording, and gift planning; the collaborative constraints include, for example, constraints on the timing of the celebration process, constraints on the consistency of the theme and atmosphere, constraints on guest movement and reception services, and constraints on contingency plans for emergencies.

7. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, The multi-round dynamic bidding adopts reverse auction, forward auction, or combined auction modes, and includes a time window control mechanism for each round of bidding. After the time window of each round of dynamic bidding ends, based on all the bidding information in this round, a guidance signal is automatically generated and fed back to each participating service provider to guide the bidding participants to adjust their bidding strategies, so as to promote the overall bidding distribution to converge quickly towards the system optimization target. The guidance signal includes at least the percentile ranking information of the current bid in the overall bid, the statistical characteristics of the bid distribution, or the suggested bid adjustment direction for the next round calculated based on the game theory model. The multi-round dynamic bidding does not include manual intervention in the bid adjustment process. The content weight or sending priority of the guidance signal fed back to the service provider is adjusted differently based on the historical credit data of the service provider, so as to give priority to high-credit service providers.

8. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, When there are multiple service modules with spatiotemporal or service logic dependencies in the structured service task description, joint bidding invitations are first sent to the service provider combination that can provide cross-module collaborative pricing; wherein, the priority of the service provider combination is identified based on a credit collaboration network constructed according to historical collaborative performance records.

9. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, The optimization objectives of the multi-objective service optimization model in step S2 include at least total service cost, overall service matching degree, comprehensive credit score of participating service providers, and constraint violation penalty value; wherein, the comprehensive credit score is calculated based on the historical credit data of each service provider; the optimization weights of the multi-objective optimization model are dynamically adjusted through a reinforcement learning model, and the dynamic adjustment of the optimization weights in the multi-objective service optimization model is subject to specific triggering conditions; when the price sensitivity dimension value in the user's credit representation vector exceeds a preset threshold, the optimization weight corresponding to the total service cost is increased; when the average historical credit data of the participating service providers is lower than a preset threshold, the optimization weight corresponding to the comprehensive credit score is increased.

10. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, Also includes: The system receives a scheme adjustment instruction input by the user in natural language form, parses the instruction into a structured modification of at least one service parameter in the structured service task description using natural language processing technology, and re-triggers the dynamic bidding and scheme generation process based on the modified task description.

11. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, Before the service is executed, a virtual simulation of the integrated service solution is conducted based on digital twin technology, and a risk assessment and solution fine-tuning are performed based on the simulation results. The simulation process involves injecting abnormal events and adjusting the risk threshold based on the user's credit representation vector.

12. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, The S3 step also includes sub-steps for intelligent performance monitoring, trusted evidence storage, and dynamic settlement: T1: During service execution, performance status data is collected in real time through the network of performance status information collection devices and the application interface of the service provider, and the performance status data is analyzed in real time using an anomaly detection algorithm. T2: When an abnormal event that meets the preset triggering conditions is identified, the response logic associated with the abnormal event type is automatically executed, and the key information of the abnormal event and the response action is generated into a hash value and stored in the decentralized trusted evidence storage network; wherein, the response logic is automatically executed according to the terms of the smart contract encoded in the decentralized trusted evidence storage network; T3: After the service is completed, automatic settlement is triggered based on the trusted evidence information in the decentralized trusted evidence storage network that indicates that the service has been completed in compliance with regulations; wherein, during settlement, the commission is automatically allocated based on the dynamic commission model encoded in the smart contract, and the calculation of the dynamic commission model refers at least to the performance data of this order and the historical credit data of the service provider; The network of performance status information collection devices includes edge computing nodes for local anomaly detection and preliminary response; the mapping relationship between the anomaly event type and the response logic is dynamically optimized based on the service provider's historical credit data and the user's credit representation vector.

13. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, The update of the service provider's historical credit data adopts a sliding window weighted average algorithm based on a time decay factor, which makes the impact of recent performance on credit data greater than that of long-term performance.

14. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, In step S3, the step of feeding back the updated credit representation vector and historical credit data as core reinforcement learning signals is executed automatically and instantly without human intervention after the credit data is updated, so as to ensure low-latency closed-loop transmission of credit-driven signals in the service integration process.

15. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, The reinforcement learning signal transmission link supports a hybrid update mode of online learning and offline batch learning. The update of the credit representation vector adopts a federated learning framework, which optimizes the credit model parameters collaboratively among multiple participants while protecting the data privacy of users and service providers.

16. The service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop according to claim 1, characterized in that, It also includes system model iterative optimization steps: collecting the complete data chain for each service integration process: user's original needs → parsing results → bidding process → solution selection → fulfillment status → user feedback; based on the data chain, constructing a set of evaluation indicators to assess the model performance of each stage; using the data chain as training samples, periodically fine-tuning the demand parsing model, the price prediction model, and the multi-objective service optimization model; based on the evaluation results of the optimized model version, selectively deploying the optimized model version; the set of evaluation indicators includes at least one or more of the following: parsing confidence accuracy, bidding convergence speed, and fulfillment anomaly handling success rate, wherein the weight of each indicator is dynamically allocated based on the credit representation vector.

17. A service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop as described in claim 1 or 7, characterized in that, It supports two-way dynamic negotiation between users and service providers, with at least one of the following: upper limit of negotiation rounds, strength of guidance strategy, and negotiation constraints, dynamically set based on user credit representation vector and service provider historical credit data.

18. A service integration intelligent decision-making method based on credit-driven reinforcement learning closed loop, characterized in that, To quickly optimize decision-making models during the initial deployment of a service integration system or when facing new users, the following steps are included: U1: Receives unstructured service request input from users and generates a structured service task description containing at least one service module and its collaborative constraints through hybrid parsing processing; U2: Based on the structured service task description, multiple rounds of dynamic bidding are initiated with multiple service providers to obtain dynamic service condition information. This dynamic service condition information, user preference information, and collaborative constraints are then integrated and calculated using a multi-objective service optimization model with dynamically adjustable optimization weights. At least one integrated service solution is generated and recommended to the user. The decision preference parameters for generating the integrated service solution are initialized based on an initial credit representation vector and historical credit data from each service provider. When historical credit data is unavailable, a collaborative filtering-based credit vector initialization method is used. In the initial service loops, incremental learning and a credit decay factor are employed to accelerate the optimization of the decision model. U3: In response to the user's confirmation of the integrated service solution, initiate and monitor service execution, and automatically complete fee settlement according to preset rules after service execution is completed; wherein, based on the performance status data and user feedback data, a credit update signal is generated in real time; the credit update signal is used to update the user's credit representation vector and the historical credit data of each service provider; the updated credit representation vector is used as the core reinforcement learning signal, and is fed back in real time and dynamically to the hybrid parsing processing confidence calibration logic in step S1 and the multi-objective service optimization model decision preference parameter adjustment logic in step S2, thereby constructing a self-evolving service integration and optimization closed loop driven by credit data flow and running through the entire process of demand parsing, resource matching and performance settlement.

19. A service integration intelligent decision-making system based on credit-driven reinforcement learning closed loop, characterized in that, include: At least one server node is connected to user terminals, service provider terminals, a network of performance status information collection devices, and a decentralized trusted evidence storage network through a communication network; Deployed on the server node: The requirement parsing module receives unstructured requirements, parses them using a hybrid architecture, and performs confidence calibration based on credit representation vectors to generate a structured service task description. The dynamic bidding and decision-making module is used to initiate multiple rounds of guided dynamic bidding and generate integrated service solutions based on a multi-objective service optimization model that dynamically adjusts decision preferences according to the fused credit representation vector and historical credit data. The performance monitoring and response module is used to monitor the performance status through the network of the performance status information acquisition equipment, and to perform anomaly detection and automatic response. The credit management and closed-loop feedback module is used to manage and update the credit representation vector; The decentralized trusted evidence storage and settlement module is used to store key event information in the decentralized trusted evidence storage network and trigger automatic settlement based on evidence verification. The credit management and closed-loop feedback module is configured to: generate a credit update signal in real time based on performance status data and user feedback data from the performance monitoring and response module, and use this signal to update the user's credit representation vector and the service provider's historical credit data; then, use the updated credit representation vector as the core reinforcement learning signal to feed back in real time and dynamically to the confidence calibration logic of the demand analysis module and the decision preference parameter adjustment logic of the dynamic bidding and decision-making module; thereby, the demand analysis module, dynamic bidding and decision-making module, performance monitoring and response module, credit management and closed-loop feedback module, and decentralized trusted storage and settlement module work together to realize the credit-driven reinforcement learning closed loop as described in claim 1.

20. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the processor performs the method as described in any one of claims 1 to 18.

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