Trusted commercial ecological agent based on AI large model multi-technology optimization integration

Abstract

The invention discloses a credible commercial ecological agent based on AI large model multi-technology optimization integration, and belongs to the crossing field of artificial intelligence and commercial information technologies. The intelligent agent adopts a dual-module collaborative architecture: a technology integration optimization module is used as a central scheduling core, and through an optimized strategy function (pi), an AI large model, a search engine, office software automation and a cloud storage memory system are dynamically coordinated, so that the technical shortages of information instantaneity, operation fluency and service continuity are systematically solved; the business application matching module serves as a business rule execution engine, integrates multi-dimensional logic adaptation, intelligent matching and anti-fraud credible engines, converts business processes such as intermediary agents and complex decisions into automatic technology tasks, and constructs a credible closed-loop transaction environment from supply and demand release to performance completion in the system. Through the above architecture, a new service normal form of "setting-to-walk" is realized: after a user completes one-time setting, the intelligent agent can automatically start and maintain a long-term service process, and market monitoring, intelligent matching and risk management and control are continuously carried out. According to the invention, a systematic technical solution is provided for credible and automatic application of the AI large model in a complex business scene.

Description

[Technical Field]

[0001] This invention relates to the intersection of artificial intelligence and business information technology, specifically to a trusted business ecosystem intelligent entity with an AI big model at its core. This entity systematically integrates three mature information technologies: search engines, office software automation, and cloud storage, and incorporates multi-dimensional logic adaptation, intelligent matching, and anti-fraud patent technologies. It constructs an intelligent entity with instant response, smooth interaction, and continuous memory capabilities. [Background Technology]

[0002] Currently, in the complex commercial application scenarios where internet technology serves the public, there are three major pain points:

[0003] ●First, traditional intermediary service models are inefficient and suffer from severe information asymmetry;

[0004] ●Secondly, general AI large-scale model applications have inherent defects such as knowledge lag, inability to operate physical software, and lack of long-term memory in dialogue.

[0005] ● Finally, existing platforms lack automated and reliable assurance mechanisms embedded throughout the entire business process. This results in cumbersome user operations, high decision-making costs, and significant transaction risks.

[0006] Therefore, there is an urgent need for a business decision-making and transaction matching system that can systematically integrate emerging AI technologies with mature information technologies, provide end-to-end automation, and embed trust guarantees. [Summary of the Invention]

[0007] The purpose of this invention is to overcome the aforementioned shortcomings and provide a reliable business ecosystem intelligent agent based on the optimization and integration of multiple technologies using a large AI model. This system achieves effective synergy between technology integration and business applications through an innovative "dual-module" architecture.

[0008] I. Technology Integration and Optimization Module (Ecological Optimization Intelligent Agent)

[0009] This module serves as the technical foundation and central scheduling core of the system. Through an optimized strategy function (π), it dynamically coordinates three mature technologies—AI large-scale model, search engine, office software automation, and cloud storage—to systematically address the three major technical shortcomings of AI agents in commercial applications: lack of environmental perception, operational execution gaps, and long-term state amnesia.

[0010] 1. Integration with search engines: Solving the "immediacy" problem. The intelligent agent transforms the user's demand for real-time information into structured search commands, calls the search API to obtain results, parses them, and integrates them into the answer to ensure the information is fresh.

[0011] 2. Integration with office software automation: Solving the "interaction fluency" problem. The intelligent agent translates user natural language commands into automated operations on office software (such as Excel and Word), enabling users to complete complex document processing directly through dialogue and achieving seamless human-computer interaction.

[0012] 3. Integration with cloud storage and vectorized memory systems: Solving the "service continuity" problem. The system establishes a dedicated memory for each user or project, maintaining context in long-term conversations through vector retrieval, completely avoiding "catastrophic forgetting".

[0013] II. Business Applications and Matching Module (AI Business Ecosystem)

[0014] This module builds upon the first module, forming a brand-new multilateral 1-1 real-time interactive service system and serving as the business rule execution engine. Its core features and technological innovations are as follows:

[0015] 1. Three embedded technology engines: The system integrates a "multi-dimensional logic adaptation engine" to handle complex constraint decisions, an "intelligent matching engine" to achieve dynamic supply and demand matching, and an "anti-fraud trust engine" covering the entire process to ensure transaction security.

[0016] 2. **[Set Up and Go, No Waiting Required] Service Paradigm:** Users (demanders or suppliers) only need to complete a one-time requirement or capability setting and authorize the necessary data at the beginning of the service. Once set up, the system runs automatically 24 / 7, continuously monitoring, matching, and notifying users, without requiring continuous waiting or operation from the user.

[0017] 3. Closed-loop transaction matching within the system: The discovery of all business opportunities, supply and demand matching, negotiation, signing, payment and performance monitoring are all completed within the system, forming a credible and efficient closed-loop transaction environment.

[0018] III. Core Architecture: Dual-Module Collaborative System

[0019] 3.0 Integrated Creativity Analysis

[0020] The core innovation of the "dual-module collaborative system" described in this invention does not lie in individual components such as large AI models or search engines, but in the deep integration of these heterogeneous technology components with business rules through an innovative systemic architecture, resulting in a synergistic effect of "1+1>2". Specifically, this is reflected in:

[0021] 1. Creative Architecture: An "Ecological Optimization Intelligent Agent" was designed as a unified scheduling base. Its core is an optimized policy function (π), which solves the problem of unified adaptation and dynamic scheduling of different technical interfaces, protocols and data formats.

[0022] 2. Process Creativity: A closed-loop control logic of "mutual empowerment between technology foundation and business application" is proposed. The technology module provides the business module with real-time data, software operation, and memory retention capabilities; the business module defines task objectives and process constraints for the technology module, ensuring that technology calls are always optimized around business goals.

[0023] 3. Synergistic Effects: The above integration systematically addresses the three major technical shortcomings of "immediacy," "operability," and "continuity," and supports higher-level business application paradigms such as "set-and-go" and "closed-loop reliability," which cannot be achieved by a single technology or simple combination. This deep technical integration solution, designed to support new business models, is not obvious to those skilled in the art.

[0024] 3.1 Ecological Optimization Intelligent Agent (Technology Integration and Optimization Module)

[0025] The core innovation of this module lies in using a central scheduling core and an optimized policy function (π) to dynamically coordinate a large AI model with three mature technologies to build an intelligent decision-making base that can autonomously schedule and work collaboratively. This systematically addresses the three major technical shortcomings of large models in commercial applications: environmental perception, operation execution, and state persistence.

[0026] 3.1.1 Technology Integration and Optimization Framework

[0027] The scheduling problem of the ecological optimization agent can be modeled as a multi-objective optimization problem. Its core is optimizing a policy function (π), which...

[0028] Based on the user request R and the system state S, the system outputs a dynamic joint scheduling vector a = (a_m, a_s, a_o, a_f). Where:

[0029] • a_m represents the inference task configuration for the large AI model (parameter θ).

[0030] • a_s = (w_s, p_s) represents the scheduling decision for the search engine, where w_s is the call weight and p_s is the search strategy parameter.

[0031] ·a_o=(w_o,p_o) represents the scheduling decision for office automation, where w_o is the call weight and p_o is the operation sequence parameter.

[0032] ·a_f=(w_f,p_f) represents the scheduling decision for the cloud storage memory system, where w_f is the call weight and p_f is the retrieval strategy parameter.

[0033] In the scheduling vector a, the weight parameters (w_s, w_o, w_f) and policy parameters (p_s, p_o, p_f) of each sub-action are jointly optimized by the policy function π according to the real-time context. The goal is to maximize the overall system utility while satisfying strict performance constraints. This optimization problem is formally expressed as follows:

[0034] Objective function:

[0035] max_{θ, π}E_{(R, S)~D}[U(Response(R, S; θ, π))-λ·C(π)]

[0036] Constraints:

[0037] 1. Immediacy constraint: E[Latency(π_s(R,S))]≤τ1, where π_s is the part of the policy function involved in the search (a_s), and τ1 is the maximum allowable delay (e.g., 2.0 seconds).

[0038] 2. Operational smoothness constraint: SuccessRate(π_o(R,S))≥γ2, where π_o is the part involving office automation (a_o), and γ2 is the minimum success rate (e.g., 0.95).

[0039] 3. Service continuity constraint: Recal1@K(π_f(R,S))≥η3, where π_f is the part involving memory retrieval (a_f), and η3 is the minimum recall rate (e.g., 0.90).

[0040] 4. Hardware-accelerated decision-making: For policy functions π that are determined to be computationally intensive subtasks (such as complex inference or multi-constraint programming), hardware offload instructions will be generated and dynamically scheduled to be executed by hardware acceleration support modules composed of FPGA / ASIC to meet the performance constraints of low latency and high success rate.

[0041] 3.1.2 Triple Integration Optimization Algorithm

[0042] Based on the above framework, the integration and optimization algorithm of each sub-technology is the specific implementation of the policy function π:

[0043] A. Search Engine Integration Optimization Algorithm

[0044] This algorithm is a concrete implementation of the policy function π_s, and its core is the optimization of dynamic weights w_s and search policy p_s. The algorithm determines the ratio of cache usage to real-time search (i.e., the allocation of w_s among cache sources and multiple external search APIs) through query classification and freshness prediction, and employs a confidence-based dynamic weighted fusion method to ensure the timeliness and accuracy of information. Its fusion formula reflects the dynamic nature of the weights w_s:

[0045] FinalAnswer=w_m*LLM_prior+∑_{i}(w_{s,i}*Confidence(r_i)*r_i)

[0046] Among them, w_m and w_{s, i} are dynamically determined by the strategy function based on the query context.

[0047] B. Office software integration and optimization algorithm

[0048] This algorithm is a concrete implementation of π_o in the policy function. Its core optimization decision lies in parsing the user's natural language instructions into a task graph and generating the optimal operation sequence based on task dependencies, parallelism, and resource constraints (p_o). For computationally intensive complex task graph optimization (such as large-scale typesetting or table calculations), the policy function can trigger a hardware offloading decision (d_h = 1), offloading the optimization computation process to the FPGA acceleration unit to minimize the completion time and satisfy the fluency constraint γ2.

[0049] C. Cloud Storage Integration and Optimization Algorithm

[0050] This algorithm is a concrete implementation of the policy function π_f. Its core optimization decision lies in employing a multi-stage retrieval strategy (vector retrieval, metadata filtering, and reordering) controlled by the p_f parameter to efficiently and accurately retrieve relevant historical information from the user's dedicated memory. Through dynamic context window management, it balances the breadth and relevance of memory, ensuring that the continuity constraint η3 is met.

[0051] 3.1.3 Overall Scheduling Optimization Algorithm

[0052] The global scheduler of the ecological optimization agent employs a multi-objective optimization strategy. "When the candidate strategy.requires_heavy_computation is true, the system evaluates the performance gain achieved through hardware acceleration support modules (such as FPGA computing cores). If hardware acceleration is enabled, the scheduler executes the bind_to_hardware_unit function to bind the computing task to a specific FPGA or ASIC hardware resource."

[0053] The following code demonstrates its core scheduling logic, illustrating how it comprehensively considers various resources, including hardware acceleration units:

[0054]

[0055]

[0056] 3.2.1 AI Business Ecosystem (Business Application and Matching Module)

[0057] This system constructs a closed-loop automated trading environment, with the core process shown below, ensuring that all business activities are completed and controlled within the system:

[0058] text

[0059]

[0060] The process is driven by the business rule execution engine, which translates each business stage into a series of call instructions to the underlying technology integration and optimization module and the internal dedicated engine, thereby achieving automated and technical execution of the process.

[0061] 3.2.2 Core features of the system's matchmaking mechanism:

[0062] 1. Unified identity and membership system: All participants (businesses and individuals) must complete unified registration and authentication within the system to build a trustworthy transaction foundation.

[0063] 2. Intelligent demand matching: Real-time intelligent matching based on multi-dimensional feature vectors, replacing traditional manual search and price comparison.

[0064] 3. Trusted Transaction Guarantee: Integrates anti-fraud and trusted mechanisms to monitor and control risks throughout the entire transaction process.

[0065] 4. Full-process digital management of services: From demand release to service delivery, acceptance and payment, all steps are recorded in the system to achieve standardized and traceable management.

[0066] 5. Dynamic Reputation System: Based on transaction performance and evaluations from both parties, the system dynamically updates the reputation scores of users and service providers to optimize the quality of subsequent matching.

[0067] 3.2.3 System-wide application of anti-fraud trust mechanisms

[0068] The system integrates a comprehensive anti-fraud and trust mechanism throughout the entire process. Its core integrated decision logic is as follows, demonstrating the collaborative working method of logic and probabilistic models:

[0069]

[0070] IV. Overall System Performance Optimization and Indicators

[0071] Through the integration of the above technologies and optimization of the architecture, especially the coordinated scheduling of software and hardware resources, this system has achieved a coordinated improvement in overall performance.

[0072] 4.1 Overall Optimization Effect Measurement

[0073] The overall system performance SPSP is a weighted normalized result of multiple key metrics MiMi (such as latency, success rate, satisfaction, security, and hardware energy efficiency):

[0074] SP=∑i=1nωi·Norm(Mi)∑i=1nωiSP=∑i=1nωi∑i=1nωi·Norm(Mi)

[0075] Where ωi represents the dynamic weight of each indicator. This model is used to quantitatively evaluate the system-level benefits brought about by the co-optimization of software and hardware.

[0076] 4.2 Key Performance Indicators

[0077] Based on the aforementioned architecture and algorithm, this system can achieve the following key performance indicators in typical application scenarios:

[0078]

[0079] 4.3 Optimization of Verification Methods

[0080] To verify the effectiveness of this system architecture and integrated optimization algorithm, the following rigorous testing and evaluation methods commonly used in the industry can be adopted:

[0081] 1. A / B testing framework: Set up an experimental group (using this integrated and optimized system) and a control group (using a traditional separate system or a single technical solution) to conduct comparative tests under the same business scenarios and loads.

[0082] 2. Multi-dimensional evaluation metrics: Test metrics cover technical performance (such as response latency, throughput, task success rate), business effectiveness (such as transaction conversion rate, user cost savings) and user experience (such as satisfaction rating CSAT, task abandonment rate).

[0083] 3. Statistical significance test: Perform statistical hypothesis testing (such as t-test) on the collected performance data to ensure that the performance improvement results are statistically significant (e.g., p-value < 0.05).

[0084] 4. Long-term stability and stress testing: Conduct 24 / 7 continuous operation tests to monitor system performance degradation and error rate; implement high-concurrency stress tests (such as simulating tens of thousands of users initiating service requests at the same time) to verify the system's elasticity and stability.

[0085] 5. Algorithm ablation experiment: By shutting down or replacing a core module of the system (such as disabling hardware acceleration or memory retrieval), the specific contribution of the module to the overall performance is quantitatively evaluated, thereby proving the necessity of the system design.

[0086] V. Implementation Methods and Examples

[0087] The "AI Business Ecosystem" is a new business model resulting from the integration and optimization of new technologies. The inventors believe that a more detailed and practical description of this new model is necessary. The following three typical embodiments provide a non-limiting explanation of the invention. It should be emphasized that the scope of protection of this invention is not limited thereto.

[0088] General prerequisites for service management

[0089] 1. All users (B / C, B / B, C / C) are registered as members in this system.

[0090] 2. The system uniformly provides AI-powered large-scale model-based automatic transaction matching services, automatically launching a new service paradigm of "set up and go, no waiting required".

[0091] 3. The system provides services and supervision for all members through an anti-fraud and trust mechanism, ensuring that transactions are conducted legally and in accordance with regulations.

[0092] All transactions within the system are technically implemented and controlled throughout the entire process by the commercial ecosystem intelligent agent of this invention, through its built-in multi-dimensional logic adaptation engine, intelligent matching engine, and anti-fraud trusted engine. These engines work together to ensure the intelligence, accuracy, and security of transactions.

[0093] As a new business model, the system also includes a dispute resolution and performance guarantee module based on smart contracts. This module can automatically initiate a predefined negotiation or arbitration process based on the stored evidence data when the agreed conditions of the transaction are not met, providing a technical rights protection mechanism for both parties to the transaction.

[0094] Example 1: Intelligent Recruitment Matching (Optimized Intermediary Agency Services)

[0095] (I) Existing Difficulties

[0096] 1. Job seekers browse multiple recruitment websites, repeatedly comparing various positions, salaries, locations, and other criteria, often hesitating and wavering. Statistics show that job seekers spend an average of 80 hours comparing application requirements.

[0097] 2. The psychological torment of job hunting. Choosing a good job and environment that suits you is very important, and waiting and agonizing lead to widespread anxiety.

[0098] (II) Solution

[0099] 1. Register as a member of the "AI Business Ecosystem", set your resume and job application intentions / conditions and confirm submission.

[0100] 2. The system enables the "Set Up and Go, No Waiting" service. All time and effort spent on searching / comparing is automatically matched by the system. Leveraging its powerful search and intelligent matching technologies, the system conducts a comprehensive 24 / 7 search and comparison of suitable positions.

[0101] 3. The system will push the search and comparison results to job seekers (via SMS reminder) at the agreed time. A definite result will be given to the member regardless of whether a result is found.

[0102] (III) Service Effectiveness

[0103] The AI ​​Business Ecosystem leverages its powerful technological capabilities to maximize the ability of its members to find and compare recruitment agencies that match job seekers' qualifications. What used to take 80 hours is now reduced to 2 minutes. The matching accuracy rate is over 85%. The fatigue, anxiety, and troubles that job seekers previously experienced are completely eliminated.

[0104] (iv) Description of technical features

[0105] In this system, the technical implementation path of this service is as follows:

[0106] 1. Intelligent Agent Scheduling: Ecosystem optimization intelligent agents use search engines to obtain real-time job information and use office automation interfaces to parse resumes and job descriptions.

[0107] 2. Core Matching: The AI ​​business ecosystem automatically and periodically invokes a multi-dimensional logical adaptation and intelligent matching engine to quantitatively compare job seekers' skills, experience, expectations, and other multi-dimensional feature vectors with job requirement vectors, dynamically calculating the matching degree. Its intelligent matching core employs a dynamic weighting algorithm and private domain data source technology to ensure matching accuracy and data security.

[0108] 3. Transaction and Risk Control: The entire process from initial communication to electronic signing is completed within the system, and a reliable anti-fraud mechanism verifies and monitors the information and transaction behavior of both parties in real time.

[0109] Example 2: Intelligent Decision-Making System for Home Renovation (Complex Condition Decision-Making Service)

[0110] (I) Existing Difficulties

[0111] 1. Information asymmetry: Consumers have little understanding of the quality and price of building materials.

[0112] 2. Too many details: From design, construction, and procurement to supervision, it involves many different professions and trades. Most consumers do not understand the professional standards and cannot determine the quality of each matter.

[0113] 3. The biggest problem consumers face when it comes to home renovation is that it's exhausting and costly, yet they often fail to achieve the desired results.

[0114] (II) Solution

[0115] 1. The AI ​​Business Ecosystem uses advanced technology to help consumers select the best service team throughout the entire process and assist them in completing compliant contracts with service providers.

[0116] 2. Conduct technical scanning and screening of the goods provided by the purchaser.

[0117] 3. Conduct compliance reviews of quality, price, and time limits, and strictly control the procedures and formalities for changes.

[0118] (III) Service Effectiveness

[0119] The AI ​​Business Ecosystem utilizes multi-dimensional logic adaptation technology to help owners complete construction scientifically and effectively on schedule, significantly improving satisfaction with the solutions, for example, by about 40%.

[0120] (iv) Description of technical features

[0121] In this system, the technical implementation path of this service is as follows:

[0122] 1. Intelligent Agent Scheduling: After the homeowner sets their renovation needs, the system operates automatically. The ecosystem-optimized intelligent agent uses a search engine to query building material prices and reviews, and leverages office automation to generate renderings and budget sheets.

[0123] 2. Core Decision Making and Matching: The AI ​​business ecosystem utilizes a multi-dimensional logic adaptation engine to generate optimized solutions under multiple constraints such as budget, style, and schedule. It then connects designers and construction teams through an intelligent matching engine. The core computation for handling complex decisions with multiple constraints is achieved through hardware acceleration support modules, leveraging FPGA-accelerated multi-dimensional logic adaptation technology to obtain high-performance computing support.

[0124] 3. Transaction and Risk Control: All steps from contract signing to acceptance are completed and recorded within the system, and are monitored throughout by a reliable anti-fraud mechanism to ensure that consumers receive satisfactory service.

[0125] Example 3: Personal Guided Tour Service (CC Innovative Service)

[0126] (I) Future Trends

[0127] 1. As large-scale model technology matures, the number of clerical positions in enterprises will decrease, and society will need to create new forms of employment.

[0128] 2. The government will vigorously promote tourism consumption among the public, which can both boost the economy and solve employment problems.

[0129] 3. To enable the people to enjoy the benefits of development and improve their quality of life.

[0130] (II) Current Dilemma

[0131] 1. Service fees are unreasonable, and hotel accommodation prices are too high during peak tourist season.

[0132] 2. Local guide services lacking technical oversight cannot guarantee the quality of tourism services.

[0133] 3. People need local guides who are both affordable and provide enthusiastic service.

[0134] (III) Solution

[0135] 1. C / C local guide services first need to address service attitude and quality standards. The "AI Business Ecosystem" utilizes private domain exclusive data source technology to ensure the credibility and quality supervision of local guide services.

[0136] 2. The AI ​​Business Ecosystem can plan the best value-for-money travel routes and services for users based on their budget and travel preferences, and use intelligent matching technology to select reputable and trustworthy local guides for users.

[0137] 3. The "AI Business Ecosystem" will regulate and supervise the services and transaction agreements between the two parties to ensure that the local guide services can make tourists' eating, accommodation, transportation, playing and shopping satisfactory.

[0138] (iv) Description of technical features

[0139] In this system, the technical implementation path of this service is as follows:

[0140] 1. Risk Control and Scheduling: When a tourist posts a travel request, the system first activates an anti-fraud and trust mechanism to verify their identity. The ecosystem optimization intelligent agent uses a search engine to integrate real-time tourism information, plan travel routes and service items, and moderate discussions between the two parties regarding service standards.

[0141] 2. Core Matching and Matchmaking: The AI ​​business ecosystem automatically uses multi-dimensional logical adaptation and intelligent matching engines to match and evaluate the service items and prices of both parties, facilitating a contract signing when an agreement is reached. Its personalized matching comprehensively utilizes multi-dimensional logical adaptation and intelligent matching technologies.

[0142] 3. Transaction and Performance: The system manages the entire process of service and payment, ensuring that both parties can successfully complete the transaction.

[0143] In all the above embodiments, the ecological optimization agent, through its optimization scheduling capabilities, ensures the smooth collaboration between the AI ​​big model and the three mature technologies, thereby providing solid technical support for automated and reliable transaction matching in the AI ​​business ecosystem.

[0144] VI. Functions and Significance of the Invention

[0145] Based on the service philosophy of "technology for people," this invention optimizes and integrates the unique one-to-one real-time human-computer interaction service of AI large-scale models with existing mature technologies to create an "AI Business Ecosystem" with ecological optimization capabilities. It provides a technological solution to address the consumer decision-making difficulties caused by information asymmetry and technical knowledge barriers.

[0146] 1. Technical aspects: It has created a feasible path for the deep integration of large AI models and mature information technologies, enabling them to serve real business scenarios with "immediacy, operability, and continuity".

[0147] 2. Business Perspective: By implementing the new service paradigm of "set up and go, no waiting required" and intelligent matching within the system, transaction efficiency has been greatly improved, user participation costs have been reduced, and the service experience has been reshaped.

[0148] 3. Trust level: By deeply embedding anti-fraud mechanisms into the process, the risks caused by information asymmetry are systematically reduced, and a reliable digital transaction environment is built.

[0149] 4. Social level: It provides infrastructure for optimizing the intermediary industry, standardizing complex services, and marketizing individual skills, which helps improve resource allocation efficiency and promote new types of employment.

[0150] ●In summary, the core contribution of this invention lies in two levels of "optimization and integration": First, through policy function-driven scheduling, the optimization and integration of AI large models and multiple mature information technologies at the capability level are realized, overcoming the technical shortcomings of single models; Second, through the business rule execution engine, the optimization and integration of the above-mentioned integrated technical capabilities and diversified business processes at the application level are realized, constructing a business ecosystem intelligent body that supports the new paradigm of "set-and-go, trusted closed loop".

[0151] This invention provides a systematic technical solution for building a reliable and automated business service infrastructure using complex AI technologies.

Claims

1. A trusted business ecosystem intelligent agent, characterized in that, It includes a technology integration and optimization module and a business application matching module; The technology integration and optimization module is configured as a central scheduling core, which dynamically generates and executes a joint scheduling action sequence for AI large model, search engine, office software automation and cloud storage memory system based on user requests and system status through an optimized strategy function (π). The business application matching module, built on the technology integration and optimization module, is configured as a business rule execution engine to translate business processes, including at least intermediary agency, complex condition decision-making, and person-to-person services, into specific technical tasks executed by the technology integration and optimization module, and complete a closed-loop transaction from supply and demand release to fulfillment feedback within the system. The optimization objective of the strategy function (π) is to maximize the overall system utility while satisfying constraints on immediacy, operational fluency, and service continuity.

2. The trusted business ecosystem intelligent agent according to claim 1, characterized in that, The objective function of the technology integration and optimization module is: max_{θ, π}E_{(R, S)~D}[U(Response(R, S; θ, π))-λ·C(π)] Where θ is the large model parameter, π is the policy function, U(·) is the comprehensive utility function, C(π) is the resource cost, and λ is the regularization coefficient.

3. The trusted business ecosystem intelligent agent according to claim 1 or 2, characterized in that, The technology integration and optimization module also includes a hardware acceleration support unit, which includes a programmable gate array (FPGA) and / or an application-specific integrated circuit (ASIC) for offloading and accelerating tasks involving complex logic calculations or large-scale matching calculations in the policy function (π).

4. The trusted business ecosystem intelligent agent according to claim 1, characterized in that, The business application matching module implements the "set-and-go" service paradigm in the following way: after the user completes a one-time intent setting, the user intent is structured and stored in a dedicated memory; the business rule execution engine automatically creates and maintains a long-running service process according to the setting type; this process periodically triggers the technology integration and optimization module to obtain the latest information, perform matching calculations, and automatically notify the user when the conditions are met.

5. The trusted business ecosystem intelligent agent according to claim 1, characterized in that, The trusted business ecosystem intelligent agent is specifically implemented to provide intelligent recruitment matching services; wherein, the technology integration and optimization module is responsible for crawling and parsing real-time job information and user resumes; the intelligent matching engine in the business application matching module periodically matches vectorized resumes with job requirements and automatically promotes the system transaction process from initial communication to electronic signing.

6. A commercial service method based on the integration of multiple technologies using a large AI model, characterized in that, Performed by a trusted business ecosystem intelligent agent as described in any one of claims 1-5, the method includes: Technology integration and scheduling steps: Receive user requests, and through the strategy function (π) in the technology integration and optimization module, make decisions and schedule AI large models, search engines, office software automation and cloud storage memory systems to collaboratively generate responses or execute operations; Business process execution steps: Through the business application matching module, predefined business process rules are transformed into a series of call instructions to the technology integration and optimization module, so as to automatically complete supply and demand matching, trusted transactions and fulfillment management within the system. (End)

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