Collaborative optimization system and method based on composite optimization space

Abstract

The invention provides a collaborative optimization system and method based on a composite optimization space, and belongs to the technical field of complex system optimization and efficiency evaluation. The system comprises a dynamic routing component, a plurality of functional components, an execution and feedback component and a closed loop learning component. The dynamic routing component routes the request to the functional component according to the context information; the closed-loop learning component constructs a composite optimization space containing routing strategy parameters of the dynamic routing component and internal configurable parameters of the functional component at the same time, generates a quantitative evaluation result based on effect feedback data, and performs joint optimization in the composite optimization space by adopting an automatic tuning algorithm; and based on a single global performance evaluation value formed by the same effect feedback data set collected in the same execution period, cooperatively generating a routing strategy updating signal and a functional component parameter updating signal in the unified optimization process. According to the method, the collaborative effect of the architecture is quantitatively verified by constructing the isolated optimization baseline system and calculating the collaborative efficiency gain coefficient K.

Description

Technical Field

[0001] This invention belongs to the field of complex system optimization and performance evaluation technology, specifically relating to a system architecture and method that achieves dynamic routing and functional execution linkage optimization by constructing a composite optimization space of routing policy parameters and configurable parameters within functional components. The method and system aim to provide a unified and quantifiable technical foundation for numerous engineering and technical fields requiring multi-component collaborative optimization, such as industrial manufacturing, fintech, energy and power, healthcare, government governance, intelligent transportation, compiler optimization, and network control. Background Technology

[0002] In the field of complex system optimization, existing technical solutions generally adopt a component-isolated optimization paradigm. With the popularization of cloud-native architecture, achieving autonomous system optimization has become key to improving performance. Existing automated optimization solutions have systemic limitations in their architecture and can be categorized into two types: the first type is optimization systems based on pre-configured static rules, whose execution paths and outputs are fixed and cannot adapt to dynamic environments; the second type is intelligent optimization systems that introduce machine learning models, but these typically only optimize a single functional component, or treat the optimization of routing strategies and the optimization of parameters within functional components as two independent processes.

[0003] In-depth analysis reveals that the architectural separation in existing technologies leads to the following technical problems: the optimization of routing strategies and the optimization within functional components are disconnected in terms of parameter space, optimization objectives, and evaluation cycle. Routing strategy parameters and configurable parameters within functional components reside in different optimization spaces and are optimized independently based on different evaluation metrics. This architectural separation results in a bottleneck in the overall system performance, making it difficult to adaptively balance multiple performance metrics in complex environments.

[0004] The existing architectural separation leads to: capability gaps (limited optimization space for individual components), optimization friction (conflicting goals), challenges in trust and control (lack of unified verification), cold start problems (difficulty in adapting new components), and the trap of local optima (inability to discover the global optimum). Another prominent problem with existing technologies is the inability to effectively distinguish between local optimization and true architectural synergy when evaluating the effectiveness of architectural innovations. This results in: a) the inability to differentiate whether performance improvements stem from individual component optimization or true architectural synergy; b) a lack of comparable standards for synergistic gains across different systems; and c) difficulty in accurately assessing and convincingly demonstrating the commercial value and technological superiority of architectural innovations.

[0005] This invention is based on a deep understanding of the architectural separation problem in existing technologies: by constructing a composite optimization space of routing policy parameters and configurable parameters within functional components, and performing joint optimization based on the same quantitative evaluation results, it fundamentally solves the problem of fragmented optimization at the architectural level. The core lies in deeply integrating routing policies and functional optimization, achieving coordinated optimization during the unified optimization process, and generating a synergistic effect.

[0006] The fundamental technical obstacle to the separation of existing technical architectures Through in-depth analysis, the applicant found that the prevalent use of component-isolated optimization paradigms in existing technologies stems from a long-standing and unresolved technical obstacle: an inherent time-scale contradiction exists between the immediacy requirement of routing decisions and the long-term requirements of functional component parameter optimization. Routing decisions need to be completed within milliseconds or seconds, while parameter optimization of functional components (especially complex machine learning models) often requires minutes or even hours of data accumulation and training. To resolve this contradiction, existing technologies have been forced to separate the two architecturally, forming independent optimization loops and objectives, leading to the aforementioned optimization inefficiencies and global optimum traps. This invention, by introducing a "composite optimization space" and a "unified optimization process based on the same quantitative evaluation result," reconstructs the optimization signal generation mechanism at the architectural level, providing a novel technical path to solve this fundamental technical obstacle.

[0007] It is important to note that the applicant is aware of existing technologies such as multi-agent task scheduling and tool invocation frameworks designed to handle complex tasks. However, the core design goal and optimization direction of these existing solutions lie in extending model capabilities to complete complex tasks or improving task execution efficiency and success rate. The optimization logic of their schedulers or routers is independent of the internal optimization of the task execution units, with separate objectives. This differs significantly from the core architectural concept of "systematic collaborative optimization based on a unified parameter space and the same performance objective" that this invention aims to solve. The architectural innovation of this invention provides a general infrastructure support for achieving reliable, secure, and controllable system runtime governance in mission-critical domains.

[0008] This invention addresses the following technical problems in the prior art by constructing a composite optimization space: (1) the separation between routing strategy optimization and functional component optimization in the parameter space; (2) the bottleneck of overall system performance caused by inconsistent optimization objectives; and (3) the cold start adaptability problem when adding new functional components. The resulting technical effects include: (1) achieving the coordinated evolution of routing strategies and functional component parameters; (2) improving the overall system performance in complex environments; and (3) accelerating the system integration and optimization process of new functional components. Summary of the Invention

[0009] (I) Core inventive concept: The core inventive concept of this invention is to build a general collaborative optimization infrastructure, which maps routing policy parameters and configurable parameters within functional components to a unified composite optimization space, and performs joint search based on the same quantitative evaluation result to achieve collaborative evolution of the two.

[0010] The present invention solves the corresponding technical problems through the following core technical means: Technical problems: Routing optimization and functional optimization are disconnected; Technical solution: Construct a composite optimization space containing two types of parameters; Technical effect: Achieve unified exploration of the parameter space; Technical problems: Inconsistent optimization objectives lead to internal friction; Technical solution: Unified optimization based on the same quantitative evaluation results; Technical effect: Ensure consistency of optimization direction; Technical problems: The system is difficult to adaptively evolve; Technical solution: Closed-loop learning-driven collaborative evolution mechanism; Technical effect: Achieve continuous optimization of system capabilities.

[0011] Technical Path of Composite Optimization Space Construction The "composite optimization space" is constructed through the following technical path: the closed-loop learning component automatically identifies all adjustable routing policy parameters of the dynamic routing component and the internal configurable parameters of all functional components in the functional component set through parameter discovery and metadata aggregation mechanisms; then, through parameter vectorization and space mapping mechanisms, the above heterogeneous parameters are uniformly represented as a multi-dimensional vector, thereby forming a unified mathematical space that can be explored by automated tuning algorithms. The core of this technical path lies in achieving unified discovery, unified representation, and unified management of parameters scattered across various components of the system, laying the foundation for subsequent joint optimization.

[0012] Efficient methods for constructing composite optimization spaces The efficient construction of the composite optimization space is achieved through the following technical approaches: dynamically identifying all configurable parameters in the system using an automatic parameter discovery mechanism; uniformly describing the attributes and constraints of heterogeneous parameters through metadata aggregation technology; reducing the dimensionality of the optimization space using feature selection and dimensionality reduction techniques; and supporting an incremental update mechanism to adapt to dynamic changes in the system. This construction process ensures manageability and searchability of the optimization space while maintaining the semantic integrity of the parameters.

[0013] The system's breakthrough improvement lies in constructing a composite optimization space for routing policy parameters and configurable parameters within functional components through a closed-loop learning component. Based on the same quantitative evaluation results, an automated tuning algorithm performs a joint search within this composite space. In a unified optimization process, a first optimization signal for updating the routing policy of the dynamic routing component and a second optimization signal for updating the configurable parameters within the functional components are collaboratively generated. This linkage optimization mechanism between "routing" and "learning" constitutes an integrated architecture capable of collaborative evolution, fundamentally solving the existing technical deficiency of "separation between routing optimization and functional optimization."

[0014] The linkage optimization mechanism is achieved through a technical path whereby a closed-loop learning component collaboratively generates two types of optimization signals based on the same quantitative evaluation result during the unified optimization process, ensuring that the routing strategy update and the functional component parameter update are consistent in their objectives and coupled in their mechanisms.

[0015] This invention represents a paradigm shift in system architecture: from "optimizing individual components in independent parameter spaces" to "optimizing the dynamic collaborative relationships between components in a unified composite optimization space." The optimization philosophy of existing technologies lies in "optimizing each individual functional component" (i.e., "reductionism"), hoping to achieve overall excellence through local superiority. However, the core insight upon which this invention is based is that in complex systems, the collaborative relationships and connections between components are as important as, or even more important than, the components themselves. Therefore, the optimization philosophy of this invention shifts to "optimizing the dynamic collaborative relationships between components" (i.e., "systems theory"). This paradigm shift is achieved by introducing a "closed-loop learning component" as a dedicated "relationship optimizer," configured to manage and optimize the interaction between "dynamic routing components" and "functional components" in a unified composite parameter space based on the same quantitative evaluation results. It is this fundamental shift in architectural thinking that enables the technical effect of global collaborative evolution of the system.

[0016] The architectural innovation of this invention lies in proposing for the first time a technical paradigm that coordinates routing strategy optimization and functional component parameter optimization under a unified framework. By constructing a composite optimization space and a unified optimization process based on the same quantitative evaluation results, it fundamentally solves the systemic technical problem of fragmented optimization in the prior art.

[0017] It is important to note that the "functional component" protected by this invention is a highly abstract and inclusive concept, its core being the ability to receive input and generate execution results. Its specific implementation may include a rule engine based on symbolic logic, a traditional machine learning model based on statistical learning, a prediction model based on deep neural networks, an agent with autonomous communication and collaboration capabilities, and other automated execution units that can conform to the context interface and execution interface specifications defined in this invention. The core value of this invention lies in providing a unified collaborative management, scheduling, and evolution platform for these heterogeneous and constantly evolving functional capabilities. The implementation forms of the functional components include, but are not limited to, rule engines, machine learning models, and agents, whose common technical features are having internally configurable parameters and conforming to the interface specifications defined in this invention, and being able to receive requests and generate execution results.

[0018] Definition of core terms Co-evolution: The dynamic routing strategy and functional component parameters evolve together based on the same performance feedback; Based on the same quantitative evaluation result: A single global evaluation value formed by the same effect feedback dataset within the same period; Unified optimization process: A technical framework for collaboratively generating two types of optimization signals based on the same evaluation result; Effect feedback data: A multi-dimensional system state data set collected from the execution environment; The technical constraint of "based on the same quantitative evaluation result" is clearly defined. It should be particularly clarified that "based on the same quantitative evaluation result" in this invention refers to the closed-loop learning component calculating a single global performance evaluation value (i.e., the quantitative evaluation result) in one go based on the same effect feedback dataset collected within the same execution period, and simultaneously optimizing the routing strategy parameters and the configurable parameters inside the functional components based on this single evaluation value.

[0019] The specific implementation paths for this technical constraint include: Consistency of data sources: The generation of both the first and second optimization signals is based on the exact same performance feedback dataset; Uniqueness of the calculation process: A single global performance evaluation value is formed through a one-time calculation, rather than calculating multiple local evaluation values ​​separately; Uniqueness of optimization objectives: The updates to routing policy parameters and configurable parameters within functional components are all aimed solely at optimizing this single evaluation value; Coordination of signal generation: The two types of optimization signals are generated collaboratively within the same optimization framework, ensuring consistency in the optimization direction.

[0020] "The phrase 'within a unified optimization process' refers to the closed-loop learning component generating the first optimization signal and the second optimization signal within a coherent and goal-consistent optimization framework, based on the same quantitative evaluation result formed from the same effect feedback data collected within the same execution cycle. The core of this process lies in the unity of the optimization objectives (both serving to optimize the quantitative evaluation result) and the synergy of the optimization process (the generation of the two types of signals is interconnected and interdependent). Typical implementation methods include, but are not limited to: a technical path that places routing strategy parameters and configurable parameters within functional components in a composite space for joint optimization, or an alternating optimization strategy that optimizes one type of parameter while fixing another. As long as the core characteristics of unified objectives and synergistic process are met, regardless of whether the optimization signals are generated synchronously or alternately in time, they should be considered to fall within the scope of the 'unified optimization process.'" The phrase "mandatory application of the same optimization objective" means that, during the optimization process, the closed-loop learning component structurally excludes the possibility of optimization based on different or local objective functions, and mandates that the updates to routing strategies and the internal configuration parameters of functional components must be solely aimed at optimizing the single, global quantitative evaluation result.

[0021] Robust Collaborative Technical Approach: To ensure the robustness of the collaborative evolution process and avoid optimization direction oscillations caused by noise or short-term fluctuations in performance feedback data, the unified optimization process can integrate a robust design technical approach. The core of this approach lies in introducing a mechanism for sensing and smoothing historical performance trends, as well as an adaptive constraint mechanism for parameter update magnitudes, when generating optimization signals based on the quantitative evaluation results. This technical approach ensures that the evolution of routing strategies and functional component parameters is smooth and gradual, maintaining the overall stability of the system even in dynamically changing environments, thereby guaranteeing the reliability of collaborative optimization in long-term operation.

[0022] The "cooperative evolution" is manifested in the following technical path: the first optimization signal changes the routing strategy, affecting the input distribution and load of each functional component; the second optimization signal changes the internal configuration parameters of the functional component, improving its ability and efficiency in processing specific requests; this two-way promoting closed-loop mechanism is carried out in a unified composite optimization space, ensuring the consistency of the optimization direction and fundamentally solving the "global optimization challenge". The technical effect of this invention is that by using linked dual optimization based on the same quantitative evaluation result, it overcomes the technical defects of the adaptability of routing strategies and functional components in dynamic environments, thereby achieving a synergistic improvement in system self-consistency and reliability.

[0023] (II) Further systematic explanation of the "unified optimization process" The phrase "within a unified optimization process" is the core architectural mechanism for achieving collaborative evolution in this invention. The essence of its "unity" lies in its systematic and holistic nature. This process is not a simple combination of two independent optimization tasks, but rather a solution to a global system optimization problem through a specific technical path. Its core discriminative feature is: 1. Globality and Uniqueness of Objective: The entire optimization process serves only one global objective—optimizing the same quantitative evaluation result based on the same performance feedback dataset. This objective is the sole criterion for evaluating the value of any parameter changes (whether routing strategy parameters or internal parameters of functional components). The architecture eliminates the possibility of establishing different, or even conflicting, local objective functions for routing optimization and functional component optimization.

[0024] 2. Coupling and Synergy of Optimization Signals: The generation mechanisms of the first and second optimization signals are mutually coupled and interdependent. Under the "joint optimization" path, this coupling is explicit and synchronous; under the "alternating optimization" path, this coupling is implicit and alternating. Specifically, when optimizing routing strategy parameters, the composition and value of its objective function (i.e., the quantitative evaluation result) intrinsically depend on the functional component's capability profile determined by the configurable parameters within the currently fixed functional component. Optimizing the routing strategy is precisely about finding the optimal request allocation scheme under this specific capability profile. Conversely, when optimizing the configurable parameters within a functional component, the composition and value of its objective function also intrinsically depend on the request input distribution determined by the currently fixed routing strategy parameters. Optimizing functional component parameters is precisely about performing the most targeted capability tuning under this specific input distribution. Therefore, each alternation is not an independent restart, but a collaborative step forward to achieve the same global goal based on the new system state established by the previous optimization step. The optimization of these two types of parameters is like two sides of the same coin, together constituting a complete collaborative evolution strategy.

[0025] 3. Mandatory Architecture: The "unified optimization process" is enforced by the dedicated architecture of the "closed-loop learning component." This component is designed to perform this overall optimization, and there are no bypass paths in the system architecture that allow routing strategies and functional component parameters to be optimized independently based on different objectives.

[0026] In summary, as long as the closed-loop learning component generates a first optimization signal and a second optimization signal that are coupled in mechanism within a unified framework based on the same quantitative evaluation result, regardless of whether these two types of signals are output synchronously or alternately, they should be considered as achieving a "unified optimization process" and falling within the protection scope of this invention. The "alternating optimization" of this invention is fundamentally different from the "independent parallel optimization" described in the background art; the latter lacks a unified goal and a coupling mechanism, and cannot generate system-level synergistic effects.

[0027] Technical Performance Verification: Through actual deployment testing, this invention demonstrates significant improvements over existing technologies in multiple dimensions, including overall system performance, resource utilization, recovery speed from abnormal scenarios, and multi-objective optimization balance. To objectively quantify the synergistic effects of this invention's architecture, the system provides a method for verifying synergistic performance. This method constructs a standardized, isolated optimization baseline reference system, providing an objective and reproducible quantitative standard for evaluating synergistic gains.

[0028] The synergistic effect is achieved through a technical approach of constructing a composite optimization space and performing unified optimization based on the same quantitative evaluation results. Specifically, it is manifested in the synergistic improvement of the system on multiple heterogeneous performance indicators, and is quantitatively verified by the synergistic performance gain coefficient K.

[0029] Technical Implementation Path of the Collaborative Performance Verification Step: It should be noted that the collaborative performance verification step, as an optional technical means, has the core value of providing an objective and quantifiable verification method for evaluating the synergistic effect generated by the linkage optimization mechanism. The technical implementation of this step does not constitute a necessary limitation on the core architecture of this invention. Any technical solution that achieves the collaborative evolution of routing strategies and functional component parameters in a unified optimization process based on the same quantitative evaluation result, regardless of whether it includes the collaborative performance verification step, should fall within the protection scope of this invention.

[0030] The synergistic efficiency gain coefficient \(K\) is calculated using the following formula: \[ K = \frac{\text{Global\_Performance} - \max(\text{Isolated\_Performance\_A}, \text{Isolated\_Performance\_B}, \text{Isolated\_Performance\_C})}{\max(\text{Isolated\_Performance\_A}, \text{Isolated\_Performance\_B}, \text{Isolated\_Performance\_C})} \] The construction of the isolated optimization baseline system satisfies the following conditions to ensure the fairness and scientific nature of the comparison: using the same training and test datasets; employing the same hardware and software environment; undergoing sufficient optimization iterations until convergence; and using the same performance evaluation metrics and weight configurations.

[0031] This verification method fully demonstrates that the linkage optimization mechanism of the present invention generates a true system-level synergistic effect (i.e., K > 0) that transcends the simple superposition of any local optimization. Accordingly, the method also includes a synergistic performance verification step, used to quantitatively verify the synergistic effect generated by the linkage optimization mechanism. This step constructs an evaluation model, compares the global performance index under linkage optimization with the local performance index under isolated optimization baseline, calculates the synergistic performance gain, and verifies the effectiveness of the synergistic evolution.

[0032] (III) Technical Solution Based on the above core concept, in a first aspect, the present invention provides a collaborative optimization method based on a composite optimization space. The method includes the following steps: a dynamic routing step: using a dynamic routing component, the request is routed to one of multiple pre-configured functional components that are distinct in their functional characteristics, based on the request's context information; a function execution step: the routed functional component executes the corresponding function and outputs the execution result; a closed-loop learning step: collecting post-execution effect feedback data and generating a quantitative evaluation result based on the effect feedback data; subsequently, using a dedicated closed-loop learning component, a composite optimization space is constructed that simultaneously includes the routing strategy parameters of the dynamic routing component and the internal configuration parameters of the functional component. Based on the single, global quantitative evaluation result, the same optimization objective is forcibly applied, and in a unified optimization process, a first optimization signal for updating the routing strategy of the dynamic routing component and a second optimization signal for updating the internal configuration parameters of the functional component are generated in a linked manner within the composite optimization space.

[0033] The composite optimization space is constructed by mapping routing policy parameters and configurable parameters within functional components to a unified parameter space. In this space, the two types of parameters together constitute an optimizable parameter vector, which is jointly searched using an automated tuning algorithm. The composite optimization space is constructed by uniformly representing routing policy parameters and configurable parameters within functional components as parameter vectors that the optimization algorithm can process, thus forming a unified parameter space searchable by the automated tuning algorithm.

[0034] On the other hand, this invention provides a general collaborative optimization system architecture that can be widely applied to various technical systems requiring multi-component collaborative optimization. The architecture includes: a dynamic routing component for routing requests to multiple functional components based on context information; multiple functional components, each with internally configurable parameters; and a closed-loop learning component for constructing a composite optimization space containing routing strategy parameters and the internally configurable parameters of the functional components. Based on execution effect feedback data, an automated tuning algorithm is used for joint search to collaboratively generate routing strategy update signals and functional component parameter update signals.

[0035] Furthermore, this invention provides a method for quantitative verification of system synergistic effectiveness. This method constructs multiple standardized isolated optimization baseline systems as reference systems, providing an objective means of evaluating the synergistic effects of various synergistic optimization systems. Correspondingly, this invention also provides a synergistic optimization system based on a composite optimization space. The system adopts a modular architecture, specifically including: a dynamic routing component, a set of functional components, an execution and feedback component, and a closed-loop learning component. The closed-loop learning component is configured to: construct a composite optimization space that simultaneously includes the routing strategy parameters of the dynamic routing component and the internal configurable parameters of the functional components; and perform a joint search in the composite optimization space using an automated tuning algorithm; based on the same quantitative evaluation result, collaboratively output updates to the routing strategy of the dynamic routing component and updates to the internal configurable parameters of the functional components in a unified optimization process; wherein, the dynamic routing component is configured to route requests to the corresponding functional components in the functional component set; the functional component set is configured to issue the functional output to the execution and feedback component; the execution and feedback component is configured to provide the effect feedback data to the closed-loop learning component; and the closed-loop learning component is configured to output the updated routing strategy to the dynamic routing component and the updated internal configurable parameters of the functional components to the functional component set.

[0036] (iv) Optimal Solution In a preferred embodiment of the present invention, the plurality of pre-configured functional components are distinguished from each other in terms of functional characteristics, including deployment location; wherein, the plurality of pre-configured functional components include at least a first functional component and a second functional component; the first functional component is deployed on the edge side and is used to process requests with real-time requirements higher than a first preset threshold; the second functional component is deployed on the non-edge side and is used to process requests with real-time requirements lower than a second preset threshold. As a typical implementation, the non-edge side may include the cloud, local data center, etc.

[0037] (v) Declaration of Architectural Abstraction and Argumentation of Independence It is particularly important to emphasize that the "composite optimization space" constructed by this invention represents a general and independently implementable system optimization architecture paradigm. Any technical system that contains a configurable routing component and at least one functional component with internally adjustable parameters can unify the originally fragmented optimization process and achieve system-level collaborative evolution by introducing the logical architecture, data flow relationships, and interface specifications defined in this invention.

[0038] Justification of the Architectural Claims The "architecture" protected by claim 23 of this invention, and the "system" protected by claim 16, represent different levels of technical contributions with substantial distinctions. The "architecture" protects a neutral design paradigm and specification system decoupled from specific implementation technologies. It defines standardized interface specifications and mandatory collaborative optimization mechanisms, defining the interaction protocols and data flow relationships that logical units must follow to achieve the objectives of this invention. Its technical features lie in the standardization of interfaces and the mandatory uniformity of the optimization process, rather than any specific implementation code, deployment model, or communication protocol. Protecting the "architecture" aims to prevent competitors from circumventing patent protection through simple technology stack replacement or component reorganization. The "system," on the other hand, is one or more specific physical or software implementation instances of this architecture. It embodies how a working entity can be built based on this architectural blueprint using specific programming languages, network protocols, and hardware facilities. The relationship between the two is similar to that between a "TCP / IP protocol suite" (architecture) and "a network application (such as a browser) developed according to the TCP / IP protocol" (system). The protocol itself (architecture) and the application (system) implemented based on the protocol are two different levels of inventions, each of which can be protected independently.

[0039] The technical features of the architecture are embodied in the collaborative optimization mechanism enforced by the interface specification. Any system that conforms to this specification can generate the aforementioned synergistic effect, and the architecture itself constitutes a complete technical solution. The rationality of the claims regarding the architecture is based on the fact that the architecture, through mandatory interface specifications and technical constraints, provides a complete technical framework for building collaborative optimization systems. Any system implemented according to this architecture, regardless of its specific technical implementation details, can generate the aforementioned synergistic effect based on the composite optimization space, which reflects the technical value and innovation of the architecture itself.

[0040] Interface Specification Technical Content Explanation The interface specifications in the architecture form the technical foundation for implementing the collaborative optimization mechanism, including: The first interface specification defines the data interaction requirements for the context information input and routing decision output of the dynamic routing logic unit; the second interface specification defines the data interaction requirements for the request input and execution result output of the functional logic unit; and the third interface specification defines the mechanism requirements for the closed-loop learning logic unit to construct a composite optimization space and output optimization signals. These interface specifications collectively constitute mandatory technical constraints, ensuring that any system implemented according to this architecture can generate the aforementioned collaborative effect based on the composite optimization space.

[0041] It should be noted that the "cooperative generation" described in this invention is manifested in the following technical path: the first optimization signal and the second optimization signal are generated based on the same quantization evaluation result, within the same optimization framework, and jointly serve to optimize the same objective function. This technical path eliminates the possibility of optimization based on different or local objective functions, structurally ensuring the consistency of the two types of optimization signals in terms of the objective. The "cooperative generation" includes, but is not limited to, a time-synchronized output method, and also includes a technical implementation that alternates output in a coherent optimization loop through an alternating optimization strategy. Attached Figure Description

[0042] Figure 1 This is a block diagram of the logical architecture of the collaborative optimization system of the present invention.

[0043] Figure 2 This is a flowchart of the routing decision-making process of the dynamic routing component.

[0044] Figure 3 It is a complete workflow diagram for achieving system optimization.

[0045] Figure 4 It is a flowchart of the internal processing of a unified multi-level functional component architecture.

[0046] Figure 5 This is a flowchart of the automated parameter tuning process.

[0047] Figure 6 This is a schematic diagram of the linkage optimization mechanism between dynamic routing and closed-loop learning. Detailed Implementation

[0048] The following detailed description, through exemplary embodiments and in conjunction with the accompanying drawings, illustrates the technical implementation path of a collaborative optimization method and system based on a composite optimization space according to the present invention. It should be understood that these embodiments are merely illustrative of the technical solution of the present invention and are not intended to limit it. It is particularly important to note that the core of the present invention lies in a general system optimization architecture paradigm, the wide applicability of which stems from the abstractness and universality of this architecture. Those skilled in the art will understand that the collaborative optimization architecture described in this invention is also applicable to, but not limited to, the following fields: collaborative optimization of production control parameters in industrial manufacturing; construction of dynamic risk assessment and adjudication processes in financial technology; and coordinated scheduling of signal control and path planning in intelligent transportation. Any technical solution that collaboratively updates routing strategies and configurable parameters within functional components during a unified optimization process based on the same quantitative evaluation result should fall within the protection scope of the present invention. It should be particularly noted that the specific embodiments described in this specification are merely illustrative of the implementation paths of the technical solution of the present invention in different application scenarios and should not be construed as an undue limitation on the protection scope of the present invention. Any technical solution that achieves coordinated evolution of routing strategies and configurable parameters within functional components during a unified optimization process based on the same quantitative evaluation results, regardless of its specific application field, functional component implementation form, or technical implementation details, should fall within the protection scope of this invention.

[0049] System architecture implementation independence explanation: It should be noted that the core technical feature of the architecture of this invention lies in the logical collaboration relationship and data flow between various functional components based on a composite optimization space. This logical architecture has high flexibility in physical implementation, and its technical path does not depend on a specific deployment topology. Instead, it achieves linkage optimization by constructing a unified optimization space for routing policy parameters and configurable parameters within functional components.

[0050] Architectural abstraction declaration: Figure 1 The "connections," "connections to," or "input / output" relationships between the components shown in the accompanying figures should be interpreted as logical coupling relationships of data flow or control flow. Their physical implementation includes, but is not limited to: direct function calls, inter-process communication (IPC), remote procedure calls via network protocols, asynchronous message passing via message middleware, or data interaction via shared databases. Any technical solution that implements the logical architecture and data flow relationships defined in this invention, regardless of its specific communication mechanism or deployment topology, should fall within the protection scope of this invention.

[0051] System Architecture Overview ( Figure 1 ) Dynamic routing component: routing decisions based on context information; Functional component set: multiple functional component instances with distinct characteristics; Execution and feedback component: instruction execution and multi-dimensional data acquisition; Closed-loop learning component: quantitative evaluation and collaborative optimization signal generation; The four components are connected through specific data streams to achieve coordinated optimization of dynamic routing and closed-loop learning.

[0052] Component collaboration interface: Composite parameter space construction: Automated discovery of parameter metadata; Unified performance data collection: Standardized alignment of multi-source indicators; Collaborative signal distribution: Atomic transactions and version consistency.

[0053] Technical path principles for interface data formats To achieve protocol independence in the architecture, the data exchange of the technical interfaces follows these technical path principles: 1. Parameter metadata description path: A self-describing parameter metadata technical path is adopted, enabling the closed-loop learning component to dynamically discover and parse the characteristics of the configurable parameters of each component; 2. Performance data standardization path: A technical path based on a unified spatiotemporal benchmark is used to achieve the correlation and fusion of multi-dimensional performance indicators; 3. Optimized signal transmission path: A versioned transaction protocol technical path is used to ensure the coordination and consistency of parameter updates. The core feature of the data format technical path is its protocol independence; any specific data format that implements the above data exchange principles should fall within the protection scope of this invention.

[0054] Technological path expansion of functional component forms The functional components are highly inclusive in terms of technical implementation, and their technical paths include, but are not limited to: 1. Single-module decision-making logic path: a decision-making unit based on a single algorithm or rule set; 2. Multi-module collaborative path: a composite decision-making unit that collaborates through multiple internal processing modules; 3. Multi-agent collaborative path: a collaborative decision-making unit that is achieved through multiple autonomous agents based on task decomposition, dynamic coordination, and result fusion technical paths.

[0055] The technical features of the multi-agent cooperative path are as follows: Complex decision requests are decomposed into interrelated sub-tasks by a task decomposition engine; professional decision-making units are dynamically organized based on capability matching by an intelligent agent coordinator; and intelligent integration and conflict resolution of multi-source outputs are achieved by a result fusion module. Any functional component that implements the above technical path should fall within the protection scope of this invention.

[0056] Declaration of variability of technical elements Those skilled in the art should understand that the specific technical elements mentioned in the foregoing and various embodiments have highly replaceable implementation paths, which reflects the universality and inclusiveness of the core architecture of this invention. For example, the resource rate mapping table in the "real-time resource cost estimation path" can be replaced by a dynamic inquiry path based on real-time market bidding, or a fixed cost parameter calling path based on long-term contracts. The collection path of the "effect feedback data" can be based on the system's native telemetry data, external probe injected data, or data aggregation paths provided by third-party monitoring services. The "automatic tuning algorithm" covers any path that can search in the parameter space and lead to a better solution, including but not limited to gradient-based, model-based, or derivative-free optimization paths. Any implementation scheme that follows the logical architecture, data flow relationship, and collaborative optimization mechanism defined in this invention and adopts a functionally equivalent technical path should fall within the protection scope of this invention.

[0057] Technical Path of Cold Start and Gradual Synergy To address the cold start challenge when adding new functional components to the system, the proposed collaborative optimization architecture employs a progressive exploration and knowledge transfer approach. During system initialization or the introduction of a new component, the closed-loop learning component is configured to allocate higher exploration weights to regions with unknown parameters within the composite optimization space, and heuristically guides routing strategies based on the performance metadata of existing functional components. This approach ensures that even in the absence of historical data, the system can quickly establish an initial fit between new components and routing strategies through guided exploration, and smoothly transition to a comprehensive collaborative optimization phase as feedback data accumulates, thereby effectively avoiding phased declines in system performance.

[0058] The technical path to achieve coordinated optimization is ensured through the following data flow and signal flow connections: the dynamic routing component provides routing decisions for the set of functional components; the execution and feedback component provides multi-dimensional effect feedback data for the closed-loop learning component; the closed-loop learning component constructs a composite optimization space and performs joint search, collaboratively outputting optimization signals to the dynamic routing component and the set of functional components; the request routing between the dynamic routing component and the set of functional components, as well as the functional output distribution between the set of functional components and the execution and feedback component, constitute the complete execution technical path.

[0059] System Deployment Architecture The collaborative optimization system supports multiple deployment modes: 1. Centralized deployment: All components are deployed in the same cluster and communicate through an internal network, suitable for optimization scenarios within a single organization; 2. Distributed deployment: Dynamic routing components and functional components can be deployed across regions and communicate encrypted through a dedicated network or public network, suitable for multi-regional collaborative scenarios; 3. Hybrid cloud deployment: Sensitive functional components are deployed in a private cloud, and compute-intensive components are deployed in a public cloud, with data exchange achieved through a security gateway.

[0060] System Deployment and Scalability Implementation like Figure 1 The core components in the illustrated logical architecture can preferably be encapsulated as independent functional units in their physical implementation, specifically: 1. Independent Deployment: To achieve the technical path of this invention, each core functional component is built as an independent, deployable service unit, communicating through well-defined standardized interfaces to ensure the technical implementation of the linkage optimization mechanism. 2. Horizontal Scaling: The system achieves horizontal scaling through automated elastic scaling technology: automatically adjusting the number of service instances based on resource utilization or business metrics to ensure the stable operation of the linkage optimization mechanism under different loads. 3. Stateful Service Processing: For services that need to maintain state, a state data persistence technology is adopted: storing state data in an external distributed storage system, enabling stateless design of business logic and ensuring the scalability and reliability of the system architecture.

[0061] Component structure implementation: The dynamic routing component, in its physical implementation, may include logical modules such as request reception, context analysis, routing decision, and result output. The closed-loop learning component, in its physical implementation, may include logical modules such as data collection, parameter optimization, and update distribution.

[0062] Dynamic Routing and Functional Components Explained: The technical approach of the system to perform routing decisions through dynamic routing components is to collaboratively evaluate multiple contextual information, achieve intelligent scheduling based on pre-configured routing strategies, and ensure that requests are routed to the most appropriate functional components.

[0063] To achieve seamless integration of heterogeneous functional components, the dynamic routing component uses a standardized context information interface technology path: defining a set of core, domain-independent context attribute dimensions, any functional component that conforms to this interface specification can be automatically discovered, parsed, and scheduled by the system.

[0064] The routing strategy executed by the dynamic routing component is highly flexible in its implementation, and can be selected and combined according to the system's requirements for intelligence, real-time performance, and interpretability. For example, its implementation includes, but is not limited to, the following types: 1. Threshold-based rule set technology path: routing is achieved through a series of pre-configured judgment rules. This path has fast response and strong interpretability.

[0065] 2. Weighted decision-making model technology approach: Assign weights or priorities to different contextual information, and use mathematical models such as weighted scoring or priority ranking algorithms to achieve collaborative evaluation and nonlinear trade-offs of multi-dimensional objectives, and calculate the comprehensive score of routing to each functional component.

[0066] 3. Machine Learning Model Technology Path: Contextual information is used as a feature vector and input into a machine learning model, which directly outputs the routing target. This model can be continuously trained and optimized using historical routing decisions and performance feedback data provided by a closed-loop learning component.

[0067] The dynamic routing component achieves collaborative evaluation by executing pre-configured routing policy technical paths, such as threshold-based judgment rule sets.

[0068] After receiving a request, the dynamic routing component executes the following technical path: it performs a collaborative evaluation of various contextual information and routes the request to the most appropriate functional component based on the evaluation results.

[0069] The result of the routing decision and the context information on which it is based serve as key metadata, which is associated with the subsequent effect feedback data to form the training data basis for the closed-loop learning components to perform linked optimization.

[0070] Closed-loop learning and collaborative optimization mechanism like Figure 3 As shown, the complete technical path to achieve system optimization is demonstrated, and the process embodies a complete autonomous closed loop from perception, execution to learning.

[0071] The technical approach for quantifying the execution effect in the closed-loop learning step is as follows: by monitoring the system status data after the instruction is executed, multiple quantifiable performance indicators are calculated based on a predefined evaluation model to construct a unified quantitative evaluation result.

[0072] "In the closed-loop learning process, the quantitative evaluation of execution performance ensures the constraint of 'the same quantitative evaluation result' through the following technical paths: 1. Uniformity of data collection: Collect a complete performance feedback dataset within the same execution cycle; 2. Uniqueness of evaluation calculation: Perform a unified calculation on the complete dataset in one go based on a predefined global evaluation model to form a single quantitative evaluation result; 3. Consistency of optimization application: Based on this single evaluation result, collaboratively generate routing strategy update signals and functional component parameter update signals during the same optimization process." The "effect feedback data" that forms the basis of the quantitative evaluation is a collection of multi-dimensional data collected by the execution and feedback components. Its technical approach follows the construction principles of multi-dimensionality, quantifiability, and causal correlation: 1. Technical implementation of the multi-dimensional principle: Data collection covers performance efficiency dimensions (system response latency, throughput, error rate), resource economics dimensions (computational resource utilization, financial costs estimated based on resource rates), business objective dimensions (conversion rate, user experience score), and system stability dimensions (service instance health status, failure rate). 2. Quantifiability principle: All data exists in numerical, Boolean, or categorizable label form for processing by mathematical models. 3. Technical implementation of the causal correlation principle: Through correlation mechanisms such as timestamps and transaction IDs, a traceable causal relationship is established between a specific "functional output" and the resulting system state changes.

[0073] The quantitative evaluation function of the closed-loop learning component generates a unified evaluation conclusion based on this multi-dimensional feedback dataset and through a predefined or learned mapping function technique.

[0074] Performance and real-time requirements The optimization process of the closed-loop learning component is designed to meet real-time requirements, including: low-latency routing decisions, configurable parameter update cycles, timely performance feedback data collection, and a model training mechanism adapted to the amount of data, supporting incremental learning to adapt to dynamic environments.

[0075] The closed-loop learning step employs an automated optimization algorithm to periodically optimize key system parameters with the goal of minimizing the comprehensive loss function (L(\Theta)) constructed based on quantitative indicators of execution performance. The comprehensive loss function (L(\Theta)) is constructed through the following technical path: based on multiple heterogeneous indicators such as response time, execution accuracy, resource consumption cost, service anomaly rate, and frequency of manual intervention, weight coefficients are dynamically configured and weighted according to different business objectives.

[0076] As a preferred embodiment of the present invention, the "resource consumption cost" indicator is estimated in real time through the following technical path: the system maintains a pre-configured resource rate mapping table; when a functional component generates a functional output containing resource specification requirements, the system parses the requirements and queries the resource rate mapping table to calculate the estimated financial cost of executing the functional output. This real-time estimated resource cost is provided to the dynamic routing component as a reference factor for routing decisions through a context information interface.

[0077] As a preferred embodiment of the present invention, the comprehensive loss function (L(\Theta)) is constructed by fusing multiple heterogeneous performance indicators. An exemplary implementation unifies multiple indicators into an optimizable scalar objective through weighted combination, for example, by using linear weighted summation: L(\Theta) = w_1 \cdot \text{Latency} + w_2 \cdot \text{Cost} + w_3 \cdot \text{ErrorRate} - w_4 \cdot \text{Accuracy}. Where \text{Latency} (response time), \text{Cost} (resource cost), \text{ErrorRate} (error rate), and \text{Accuracy} (execution accuracy) are normalized indicators calculated from the performance feedback data, and w_1 to w_4 are weighting coefficients dynamically configured according to business objectives. However, it should be understood that the construction of the comprehensive loss function is not limited to this example, and various mathematical forms can be used to fuse multiple indicators.

[0078] To achieve financial estimation and routing applications of resource costs, the system adopts the following technical approach: 1. Real-time resource cost estimation technology approach: The system maintains a resource rate mapping table to store financial cost information corresponding to various cloud computing resources; when a functional component generates functional output, the system parses the resource specification requirements in the output, queries the resource rate mapping table, and calculates the estimated financial cost.

[0079] The resource cost estimation is achieved through a resource specification-performance-cost mapping table maintained by the system. This mapping table establishes a quantitative relationship between resource operations and system efficiency based on historical operating data, providing a technical cost-efficiency assessment basis for routing decisions. At the same time, the mapping table stores the unit time cost parameters of various computing resources. The system automates the technical assessment of execution costs by parsing the resource requirement specifications output by the parsing function and querying the mapping table.

[0080] The specific technical paths include: 1. Data acquisition and modeling path: Constructing a quantitative correlation model of resource specifications, performance indicators and cost parameters based on historical operating data; 2. Real-time evaluation path: Automating the estimation of execution costs by parsing the resource requirement specifications output by the parsing function and combining them with the technical parameters in the mapping table; 3. Decision support path: Feeding the cost-effectiveness evaluation results as technical parameters to the dynamic routing component to support routing decisions based on multi-objective trade-offs.

[0081] 2. Application of resource cost in routing decision-making: The dynamic routing component receives the estimated resource cost at the context information input end, configures the routing strategy with cost-related decision rules, and when the estimated cost exceeds a certain threshold, the request will be routed to an alternative functional component with lower cost, thus realizing an intelligent trade-off between execution performance and economy.

[0082] 3. Application of resource cost in closed-loop learning: The feedback data collected by the closed-loop learning component includes the actual resource cost incurred after instruction execution; when constructing the comprehensive loss function (L(\Theta)), resource cost is included as an explicit optimization term in the function construction. By minimizing (L(\Theta)), the system is driven to select and optimize more cost-effective execution paths and parameters.

[0083] The automated parameter tuning process in the closed-loop learning step is as follows: Figure 5 The technical path shown executes a complete optimization workflow.

[0084] The closed-loop learning step achieves coordinated optimization through the following technical path: an automated tuning algorithm is used to periodically optimize the key parameters of the system with the goal of minimizing the comprehensive loss function (L(\Theta)) constructed based on the quantitative indicators of execution performance.

[0085] The comprehensive loss function (L(\Theta)) is constructed through the following technical path: based on multiple heterogeneous performance indicators, a unified global execution quality measurement standard is formed by combining the technical path of weight configuration.

[0086] The parameter space requiring optimization is achieved through a composite space construction technique: it simultaneously includes routing strategy parameters from dynamic routing components and internal configurable parameters from functional components, forming a unified optimization target space. Routing strategy parameters may include threshold parameters for determining high real-time performance and high risk, while internal configurable parameters from functional components may include arbitration thresholds and model weight parameters from decision fusion.

[0087] By periodically executing optimization processes, the system automatically and collaboratively adjusts routing strategies and functional components within a composite parameter space based on unified feedback data, achieving coordinated optimization between the two.

[0088] Implementation methods of the unified optimization process To achieve the "unified optimization process" of claim 1 and its dependent claims, the system adopts the following technical path: Based on the same quantitative evaluation result, the closed-loop learning component constructs a composite optimization space containing routing strategy parameters and configurable parameters within the functional components, and generates a first optimization signal and a second optimization signal that are coupled to each other by adopting a joint optimization or alternating optimization technical path.

[0089] Specific Implementation Examples of Automated Tuning Algorithms Example 1: An optimization technique based on a probabilistic model, comprising: randomly sampling initial points in a composite optimization space; modeling the comprehensive loss function using a probabilistic model; selecting the next evaluation point through a sampling function; and iterating until convergence.

[0090] Example 2: An optimization technique based on population iteration, comprising: initializing a population containing parameters; evaluating the fitness of individuals in the population; selecting superior individuals; generating a new population through mutation and crossover; iterating until convergence.

[0091] The automated optimization algorithm includes, but is not limited to, optimization based on probabilistic models and optimization based on population iteration. Its core feature is that it can perform parameter search in a composite optimization space to optimize the objective function.

[0092] As a further refinement of the aforementioned "unified optimization process," when the functional component being optimized is a rules engine, the system achieves automated adjustment of the rules engine through the following technical path: 1. Rule effect tracking technology path: The system maintains an effect indicator file for each rule in the rule engine, and establishes a mapping relationship between the file and the effect feedback data through an association mechanism to track multi-dimensional indicators such as rule triggering frequency, execution success rate after triggering, and causal correlation between rule triggering and final effect.

[0093] 2. Rule discovery and generation technology path: Based on the effect feedback data, the system automatically identifies successful execution patterns and negative patterns that lead to execution failures that are not covered by existing rules through pattern recognition technology; and automatically generates candidate rules and puts them into the candidate rule pool using built-in rule templates or symbolic regression algorithms.

[0094] 3. Rule Priority and Confidence Optimization Techniques: The priority parameters (execution order) and confidence parameters (execution weight) of each rule in the rule engine are incorporated into the internal configurable parameters of the functional components; the closed-loop learning component uses an automated tuning algorithm to jointly or alternately optimize these parameters in the composite optimization space to maximize the optimization effect of the comprehensive loss function.

[0095] 4. Rule set evolution and testing technology path: The closed-loop learning component periodically performs rule set evolution, selects excellent rules from the candidate rule pool through the effect evaluation technology path, and injects them into the formal rule engine through the canary release technology path; for existing rules whose effect indicators are consistently lower than the elimination threshold, the rule set is continuously optimized through the automatic disabling or archiving technology path.

[0096] Regarding the specific implementation of the unified optimization process, this invention provides a variety of exemplary but not restrictive implementation methods: (I) Joint optimization technology path based on composite parameter space: In this embodiment, the closed-loop learning component maps the routing strategy parameters and the configurable parameters inside the functional components to a unified optimization space by constructing a composite parameter space; and uses an automated tuning algorithm to perform a global search in this composite space to find the parameter combination that minimizes the comprehensive loss function, thereby discovering the synergistic effect between the routing strategy and the capabilities of the functional components.

[0097] (ii) Alternating optimization technique based on fixed parameters: As another technical implementation path, the closed-loop learning component adopts the alternating optimization technique path and performs the following steps: In the first optimization cycle, the configurable parameters inside the functional component are fixed and the routing strategy parameters are optimized. In the subsequent second optimization cycle, the routing strategy parameters are fixed and the configurable parameters inside the functional component are optimized. The two types of parameters are coordinated and evolved through the alternating iterative technique path.

[0098] It should be noted that the aforementioned joint optimization and alternating optimization technical paths are typical, not exhaustive, implementations of the 'unified optimization process' of this invention. The core of this invention lies in achieving coupled optimization of routing strategy parameters and configurable parameters within functional components based on the same quantitative evaluation result, within a unified technical framework. Specific implementations of different technical paths should all fall within the protection scope of this invention.

[0099] Regarding the technical approach for quantitative evaluation, the closed-loop learning component is calculated based on a comprehensive loss function. This function is constructed through the following technical approach: integrating multiple heterogeneous performance indicators such as execution accuracy (or false positive / false negative rate), average response time, and resource consumption cost estimated based on resource operation instructions. Through the weight configuration technical approach, an intelligent trade-off is achieved between execution quality, response speed, and economic benefits.

[0100] Optimization process fault tolerance mechanism The system ensures the stability of the optimization process through the following technical approaches: 1. Rollback mechanism: When parameter updates cause system performance to degrade beyond a threshold, the system automatically rolls back to the previous stable version; 2. Safety boundary: Physical constraint boundaries are set for critical parameters to prevent the optimization algorithm from generating dangerous values; 3. Multi-version verification: New parameters are only applied to production traffic after being verified in shadow mode; 4. Anomaly detection: Anomalies in the optimization process are monitored in real time, and abnormal optimization tasks are stopped in a timely manner.

[0101] Logical Argumentation of Synergistic Effect This invention generates a synergistic effect through the technical path of the aforementioned linkage optimization mechanism. Its internal logic achieves a unified optimization process by placing routing strategies and functional components in a common composite parameter space for joint exploration. Based on the same global objective function, the technical path discovers and utilizes the complementary capabilities that cannot be reached by the two under independent optimization.

[0102] Synergistic effect quantification K = \frac{\text{Global\_Performance} - \max(\text{Isolated\_Performance\_Baselines})}{\max(\text{Isolated\_Performance\_Baselines})} K > 0 verifies system-level synergistic effects; the larger the value, the more significant the synergistic gain.

[0103] The coefficient K calculated through this technical approach remains positive, providing quantitative evidence that the system produces a synergistic effect that goes beyond the simple superposition of local optimizations.

[0104] Baseline system construction technology path The construction of the multiple isolated optimization baseline systems is achieved through the following technical paths, which may include: Baseline System A construction path: fixing the internal configurable parameters of all functional components and optimizing only the routing policy parameters of the dynamic routing component; Baseline System B construction path: fixing the routing policy parameters of the dynamic routing component and optimizing only the internal configurable parameters of each functional component; Baseline System C construction path: allowing simultaneous optimization of routing policy parameters and internal parameters of functional components, but the two are performed independently based on different local objective functions without unified coordination.

[0105] The quantitative verification path for the system's collaborative performance gain is to verify the collaborative performance gain coefficient K. The system adopts the following quantitative verification technical path: Benchmark establishment technical path: In the non-collaborative optimization mode of the system, by optimizing the configurable parameters inside each functional component, the local performance index that each functional component can achieve at its optimal operating point is determined.

[0106] Global measurement technical approach: After enabling the linkage optimization mechanism of this invention, the global performance index that the system can achieve based on the same comprehensive goal is measured by constructing a composite optimization space.

[0107] System calculation technology approach: Substitute the baseline measurement value and the global measurement value into the calculation formula of the synergistic efficiency gain coefficient K, and calculate the specific synergistic gain value through this technology approach.

[0108] The core of this verification technique lies in objectively verifying the synergistic effect of the system's architectural innovation by using quantitative calculations to compare the global performance under coordinated optimization with the optimal local performance under isolated optimization. Those skilled in the art can use this technique to complete the system's calculations and verification.

[0109] Synergistic effect verification technology path The verification of the synergistic evolution is achieved by constructing an isolated optimized baseline system, comparing the performance indicators of each system under the same workload, and quantitatively verifying it using the synergistic performance gain coefficient K. The specific technical path is detailed in the following paragraphs. Those skilled in the art can use this technical path to verify the synergistic effect in their specific application scenarios.

[0110] The quantitative verification of the synergistic performance is achieved through the following specific technical path: The system can optionally be configured with multiple isolated optimization baseline systems, such as baseline system A (optimizing only routing strategies), baseline system B (optimizing only internal parameters of functional components), and baseline system C (optimizing routing and functional components independently and in parallel). Under the same workload, the optimal local performance index of each baseline system is measured and compared with the global performance index of the interconnected optimization system of this invention. Quantitative verification is completed using the calculation formula for the synergistic performance gain coefficient K. This verification can serve as an optional technical means for system performance evaluation and monitoring.

[0111] To ensure the fairness and scientific rigor of the collaborative performance verification, the construction of the isolated optimization baseline system follows the following standardized comparison principles: 1. Data Consistency Principle: The baseline system and the linked optimization system use the same source and partitioned training and test datasets to ensure the uniformity of optimization and evaluation benchmarks. 2. Environment Consistency Principle: All comparison systems are deployed and tested in the same hardware computing platform and basic software stack environment to eliminate the interference of infrastructure differences on performance indicators. 3. Optimization Sufficiency Principle: The optimization process of each baseline system continues until its key performance indicators no longer show statistically significant improvements over multiple consecutive iterations, at which point it is considered sufficiently optimized and converged. 4. Evaluation Consistency Principle: The same performance evaluation index system and weight configuration are used to quantitatively evaluate all systems.

[0112] The quantitative verification of the synergistic performance is achieved through the following specific technical path: the system optionally configures at least three isolated optimization baselines—baseline system A (optimizing only routing strategies), baseline system B (optimizing only internal parameters of functional components), and baseline system C (optimizing routing and functional components independently and in parallel). Under the same workload, the optimal local performance index of each baseline system is measured and compared with the global performance index of the linkage optimization system of this invention. Quantitative verification is completed using the calculation formula of the synergistic performance gain coefficient K. This verification can serve as an optional technical means for system performance evaluation and monitoring.

[0113] The isolated optimization baseline system can include various types, such as: Baseline System A: Fixes the internal configurable parameters of all functional components, and only optimizes the routing policy parameters of the dynamic routing component; Baseline System B: Fixes the routing policy parameters of the dynamic routing component, and only optimizes the internal configurable parameters of each functional component; Baseline System C: Allows simultaneous optimization of routing policy parameters and internal parameters of functional components, but the two are performed independently based on different local objective functions without unified coordination.

[0114] The core inventive concept of this invention—the linkage optimization of dynamic routing and closed-loop learning—its inherent synergistic enhancement mechanism can be most intuitively demonstrated through Figure 6.

[0115] As shown in Figure 6, this figure illustrates the technical path for achieving coordinated optimization: the closed-loop learning component acts as the optimization hub, generating quantitative evaluation results based on the same effect feedback data. Through the technical path of constructing a composite optimization space, it collaboratively outputs the first optimization signal and the second optimization signal to the dynamic routing component and the set of functional components during the unified optimization process. Through the technical path of signal coupling, a self-evolving integrated architecture is formed, realizing the coordinated evolution of routing strategies and configurable parameters within functional components.

[0116] General Architecture Implementation To demonstrate the universality of the architecture of this invention, its application framework in general technical systems is described below: 1. System Composition: The dynamic routing component receives input requests and distributes them to multiple functional components according to pre-configured routing policies; 2. Composite Optimization Space: Contains the selection threshold parameters for routing policies and the internal configurable parameters of each functional component; 3. Closed-Loop Learning: Based on execution performance feedback data (such as system throughput, response time, and resource consumption), an automated tuning algorithm performs a joint search in the composite space; 4. Collaborative Validation: By constructing multiple baseline systems (such as optimizing only routes, optimizing only functional parameters, and independent parallel optimization), the collaborative performance gain coefficient K is calculated.

[0117] This embodiment demonstrates that the architecture is applicable to any technical system that includes routing and parameter optimization components.

[0118] It should be noted that the "functional components" described in this invention are specifically embodied in the following ways: in the field of compiler optimization, they include, but are not limited to, loop optimizers and register allocators; in the field of network control, they include, but are not limited to, congestion controllers and packet schedulers; and in the field of databases, they include, but are not limited to, query optimizers and data partition managers. All these functional components conform to the functional component interface specifications defined in this invention.

[0119] Multi-domain application examples To demonstrate the versatility of the architecture of this invention, its application example in the field of compiler optimization is briefly described below: In this application, the dynamic routing component acts as a compiler strategy selector; the multiple functional components may include different types of functional components, such as a loop optimizer (for optimizing loop-related parameters) and a register allocator (for optimizing register allocation strategies); the composite optimization space simultaneously includes routing strategy parameters and the internal configurable parameters of each functional component; the closed-loop learning component optimizes the above parameters uniformly based on the performance feedback of the compiled code execution, achieving synergistic optimization of compilation speed and code execution efficiency, and verifying the synergistic gain by comparing with an isolated optimization baseline.

[0120] Application Examples of Cloud Computing Resource Scheduling System In resource scheduling system application scenarios, the dynamic routing component routes requests to different processing strategies (functional components) based on workload characteristics and system constraints; the composite optimization space includes both the decision parameters of the routing strategy and the internal configuration parameters of each processing strategy; the closed-loop learning component optimizes the above parameters in a unified manner based on execution performance feedback, thereby achieving coordinated optimization of system performance and resource utilization.

[0121] Further explanation of key system technologies: 1. The automated tuning algorithm is implemented through parameter search and optimization, employing machine learning algorithms such as Bayesian optimization, evolutionary algorithms, and sequence model-based optimization. Those skilled in the art can choose the appropriate algorithm implementation method based on the specific needs of the scenario. 2. The health status monitoring mechanism is implemented through the following technical paths: configuring an active detection strategy or passively receiving status information, and determining the component failure status based on the statistical characteristics of continuous monitoring responses. 3. The configuration synchronization mechanism ensures consistency and synchronization of configuration status with other nodes after a faulty node recovers through state consistency maintenance. 4. The version control mechanism manages model versions through structured identification and management; the canary release strategy adopts a gradual increase in traffic, ensuring the security and controllability of the release process by increasing the traffic of new versions in stages.

[0122] Reliability assurance mechanism The system achieves autonomous recovery from edge node failures through the following technical paths: detecting the disconnection status of edge nodes through a health status monitoring mechanism; the dynamic routing component uses a request rerouting technique to reroute requests to other available edge nodes or non-edge sides; when a faulty node recovers, the system confirms node stability through a recovery verification process and automatically performs configuration synchronization and health checks to ensure that the recovered node maintains consistency with other nodes; the configuration synchronization includes, but is not limited to, the synchronous update of service configuration information, routing policy parameters, and functional component parameters.

[0123] Fault Node Management Implementation Example As a technical approach to achieve autonomous fault recovery, the system employs a gradual traffic recovery strategy to re-manage nodes. The specific technical path is as follows: After a faulty edge node passes a health check, the system load balancer reintroduces it into the service cluster with a preset low initial traffic weight; the system automatically performs configuration synchronization operations, synchronizing the latest service configuration, routing policy parameters, and functional component parameters to the recovered node; through continuous performance monitoring, the system collects performance indicators such as node response latency, error rate, and resource utilization in real time; based on the monitoring data, a dynamic traffic weight adjustment technique is used to gradually increase the node's traffic load until it can stably handle normal load.

[0124] In the closed-loop learning process, the system ensures the safe and reliable updating of internal configuration parameters of functional components through the following technical paths: a model version control mechanism is used to structurally identify and manage the model, for example, semantic version control specifications can be used to identify model versions, and the version number can be updated based on interface compatibility and functional changes; optimized models are deployed to the production environment through a small-volume canary release technique; and a performance anomaly detection mechanism is automatically triggered to restore the system to a stable version when performance anomalies are detected.

[0125] The model version management is deeply integrated with the closed-loop learning process: version information is fed back to the dynamic routing component as part of the context information, which is used to make routing decisions based on model version compatibility; at the same time, version history is stored in association with the corresponding performance indicators, providing the closed-loop learning component with data on the effect of version evolution, and guiding the direction of subsequent model optimization.

[0126] Specific implementation of the model security release mechanism The model version management and secure release mechanism ensures the reliability of model updates through the following technical paths: 1. Anomaly detection technical path: The system implements performance anomaly detection based on the technical path of pre-set indicators. During the gray release, the system continuously compares the performance data of the new version and the stable version under the same traffic. Through the performance indicator comparison technical path, the system monitors key indicators such as the average response time and prediction accuracy of model inference. When the indicators show significant deterioration within the preset threshold range, the automatic rollback mechanism is triggered.

[0127] 2. Canary Release Technical Path: Canary release is achieved through a system load balancer or a dedicated traffic management service. The initial low traffic weight is used to control the initial traffic reception of the new version model. Based on continuous performance monitoring, the stability of the new version is verified within a preset observation period. The traffic load is gradually increased through a phased traffic increment technique to complete the version replacement.

[0128] It should be noted that the above technical path illustrates the implementation method of the technical solution of the present invention. Those skilled in the art will understand that the threshold parameters for performance anomaly judgment, the initial traffic ratio for canary release, and the traffic increment strategy can all be adjusted based on actual business needs through parameter configuration technical path, without departing from the core architecture of the present invention.

[0129] The core of this invention lies in using the technical path of model version management, canary release, and automatic rollback as an organic component of the closed-loop learning process, thereby achieving the system's self-consistency and safe and reliable operation.

[0130] The aforementioned model version management and secure release mechanism is a crucial guarantee for ensuring that the coordinated optimization process can proceed continuously and stably in the production environment without causing system service degradation due to a single undesirable update. It controls the risks of the optimization process through technical means, allowing the "co-evolution" of routing strategies and functional component parameters to proceed boldly within a safe and controllable boundary.

[0131] Definition of Functional Output: It should be specifically noted that the "functional output" achieves system optimization through the following technical path: generating specific executable technical instructions based on the capabilities and characteristics of functional components and the target execution environment. This technical implementation path encompasses various technical forms such as the generation of executable instructions, the construction of control instruction sets, or the output of policy configuration files. The core technical feature of the functional output lies in issuing executable instructions to the target system or infrastructure management platform through the system instruction execution interface, ultimately serving the technical realization of system optimization goals.

[0132] Core Mechanism Abstract Implementation To clarify the core mechanism and technical path of this invention, the following abstract embodiment is considered: the system pre-configures multiple functional components that are distinct in their functional characteristics; the dynamic routing component performs initial routing based on the context information of the request; the closed-loop learning component forms a unified quantitative evaluation result by observing the global effect after the function execution; based on this single result, within a unified optimization cycle, the closed-loop learning component collaboratively generates a first optimization signal for updating the routing strategy of the dynamic routing component and a second optimization signal for updating the internal configurable parameters of the functional components by constructing a composite optimization space; the first optimization signal achieves routing optimization by adjusting the router parameter trade-off strategy, and the second optimization signal improves execution capability by adjusting the internal configurable parameters of the functional components; this technical path achieves the synergistic evolution of the routing strategy and the capabilities of the functional components, thereby improving system performance.

[0133] Example 1: Self-sustaining cost governance of cloud platforms Problem: Unexpected automatic scaling leads to soaring costs; Solution: 1. Intelligent routing: When costs exceed a threshold, routing to optimized components; 2. Collaborative optimization: Adjusting parameters collaboratively based on multi-dimensional feedback; 3. Effect verification: Achieving a balance between cost savings and stability; Technical effect: Shifting from passive monitoring to proactive governance architecture.

[0134] The limitations of existing technologies are manifested in the following technical paths: In the system resource governance scenario, multiple functional components focus on different optimization objectives; the dynamic routing component routes requests to the most suitable functional component based on the real-time system status; the closed-loop learning component, based on multi-dimensional performance feedback, collaboratively optimizes the routing strategy and the internal parameters of each functional component to achieve intelligent trade-offs between multiple objectives, but lacks a cost consideration mechanism; the two systems are disconnected in terms of functional architecture and data flow, making it impossible to achieve systemic optimization.

[0135] This invention is achieved through the following technical path: 1. Dynamic routing technical path: When the estimated cost of a resource creation request exceeds a preset threshold, the dynamic routing component uses the cost threshold to determine the technical path and routes the request to a functional component C that has the ability to balance cost and performance, replacing the default stability priority logic B.

[0136] 2. Collaborative Optimization Technology Path: The closed-loop learning component, based on a multi-dimensional feedback dataset encompassing cost, stability, and processing time, generates two types of optimization signals through a unified optimization process: First optimization signal generation technology path: Dynamically adjusting the router's cost threshold parameters. Through a business cycle-aware technology path, the threshold is lowered during off-peak periods to trigger cost optimization, and raised during peak periods to ensure stability. Second optimization signal generation technology path: Optimizing the internal configurable parameters of functional component C. Through a model enhancement technology path, the ability to identify low-cost alternatives is improved, including recommending economical instances or adjusting parameters such as automatic scaling step size. Synergistic effect technology path: The system achieves cost savings through a self-consistent execution technology path, demonstrating a continuous optimization trend. This technology path realizes an architectural shift from passive cost monitoring to proactive cost governance, autonomously finding the optimal balance point among multiple objectives through a collaborative optimization mechanism.

[0137] System Collaborative Performance Verification Technology Path The effectiveness of the path evaluation technology is verified through the following experiments: the system of this invention is deployed and compared with a baseline system using a non-cooperative optimization architecture; the operating data under the same workload is collected, and the technical path is quantitatively analyzed based on the comprehensive loss function and the cooperative performance gain coefficient K; through this technical path, the cooperative gain effect of the system on heterogeneous performance indicators such as resource cost and execution accuracy can be verified.

[0138] Preferred Implementation Example 2: Cloud-Edge Collaborative Intelligent IoT Implementation Example This embodiment demonstrates the application of the present invention in a cloud-edge collaborative distributed environment through the following technical path: the system constructs a collaborative architecture of edge-side and non-edge-side functional components, utilizing the computing power of the non-edge side and the low-latency response of the edge side. Taking industrial IoT predictive performance scenarios as an example, the system functions are implemented through a technical path of real-time monitoring of device status and execution of functions when anomalies are detected.

[0139] The limitations of existing technologies are manifested through the following technical paths: centralized execution on non-edge sides faces the technical constraint of network transmission latency, affecting the real-time control window; independent execution on the edge side is limited by the technical constraint of device computing power, making it impossible to run complex diagnostic models. This fragmentation of capabilities at the architectural level leads to technical bottlenecks in the system's real-time performance and execution accuracy.

[0140] Invention solution: 1. Dynamic routing technology path: The dynamic routing component distributes requests based on multi-dimensional context information such as data processing real-time requirements, model complexity, and network status, and uses intelligent routing strategies.

[0141] In a distributed computing environment, multiple functional components are deployed at different computing levels. The dynamic routing component routes requests to the corresponding functional components at the appropriate level based on the real-time requirements and computational complexity of the requests. The closed-loop learning component, based on the feedback of execution results, collaboratively optimizes the routing decision boundary and the processing capabilities of each level of functional components, and distributes them to the first functional component deployed at the edge. This component adopts a lightweight model or rule engine technical architecture to ensure millisecond-level response.

[0142] High computational complexity request processing technology path: For non-real-time, high computational complexity requests such as device lifetime prediction and large-scale data trend analysis, the router distributes them to the second functional component deployed on the non-edge side through the non-edge side routing technology path; this component adopts a complex large model technical architecture to achieve in-depth analysis and planning.

[0143] High-risk request handling technology path: For high-risk requests such as those with low confidence or conflicting conclusions between edge and non-edge side models, the router distributes them to a third functional component for final adjudication through a human-machine collaborative routing technology path.

[0144] 2. Collaborative Optimization Technique Path: Based on multi-dimensional feedback datasets encompassing execution accuracy, response latency, and resource consumption, the closed-loop learning component generates two types of optimization signals through a unified optimization process: The first optimized signal generation technology path is to dynamically adjust the router's execution boundary parameters and dynamically adjust the high real-time threshold through a network condition adaptive technology path; and to make decisions on task localization processing strategies based on the remaining computing resources, based on the edge device resource monitoring technology path.

[0145] The second optimized signal generation technology path is to collaboratively optimize the accuracy of the large model on the non-edge side and the speed of the lightweight model on the edge side. Through the technology path of model compression and optimization, the accuracy of the edge model is continuously improved by utilizing the labeled data on the non-edge side. Based on the technology path of edge execution effect feedback, the feature extraction parameters of the non-edge side model are adjusted in reverse.

[0146] Automated Adjustment Implementation Examples of the Rules Engine To illustrate the technical path of rule adjustment as described in claim 7, this embodiment demonstrates the entire process of how the internal rules of the rule engine are automatically adjusted by the closed-loop learning component when the cloud resource cost optimization scenario is used as a functional component.

[0147] In the technical implementation of the cloud resource management functional component set, the rule-based functional component (Component_R) handles the execution of patterned resource scaling through a technical path of pre-configured rule sets. The initial rule set includes: Rule R1 (high load scaling): IF [performance metric A] exceeds [high load threshold] for [time window T1] THEN Execute scaling (number of instances N1); Rule R2 (low load scaling): IF [performance metric A] is lower than [low load threshold] for [time window T2] THEN Execute scaling (number of instances M1); The system discovers through the effect tracking mechanism that Rule R2 is frequently triggered during peak business hours, leading to increased service latency.

[0148] The closed-loop learning component uses this feedback data to perform a unified optimization process. The second optimized signal generation technology path: Optimize the internal configurable parameters of rule R2 by adjusting the low-load threshold or extending the time window T2, enabling the rule to execute a more conservative scaling-down strategy during peak periods. The first optimized signal generation technology path: Optimize the routing strategy parameters of the dynamic routing component by using a time-aware routing approach to route core service scaling-down requests to functional components with a cost-stability trade-off model during morning peak periods, replacing the basic Component_R. This technology path demonstrates how, within the architecture of this invention, the rule engine transforms from a statically configured, parameter-optimized approach into an autonomous component that evolves collaboratively with other functional components.

[0149] Intelligent Task Scheduling Implementation Examples To demonstrate the technical implementation path and application effects of this invention in the field of task scheduling, this embodiment describes the construction of an intelligent task scheduling system. In this system, the dynamic decision router serves as the intelligent task scheduler, and the multiple functional components differ in their processing capabilities. The dynamic routing component performs intelligent routing based on task characteristics and system status. The closed-loop learning component collaboratively optimizes the routing strategy and the internal configuration parameters of each functional component based on task execution performance.

[0150] 1. System Configuration and Technical Path Task Requests: The system handles various types of computing task requests, which differ and potentially conflict in terms of real-time requirements, computational complexity, and resource cost sensitivity. Heterogeneous Executors: The system pre-configures multiple executors that complement each other in terms of deployment location (e.g., cloud, edge), computing architecture (e.g., CPU, GPU), resource guarantee levels, and cost structures. Dynamic Routing Technology: The intelligent task scheduler receives task requests and the aforementioned multi-dimensional context information. Through its internal collaborative evaluation mechanism (e.g., based on weighted decision models or machine learning models), it intelligently weighs conflicting objectives in real time and routes tasks to the most suitable executor.

[0151] 1. Closed-loop learning and collaborative optimization technical approach The closed-loop learning module, acting as the system optimization hub, executes the following technical path: **Composite Optimization Space Construction:** Constructing a unified optimization space that simultaneously includes the scheduler's routing strategy parameters (such as thresholds for balancing real-time performance and cost) and the internal configurable parameters of each executor (such as batch size and resource allocation weights for computational tasks). **Unified Optimization Process:** Based on feedback data such as actual task execution performance (e.g., response time and success rate) and system resource consumption, an automated tuning algorithm performs a joint search within the composite space. **Linked Signal Output:** During this unified optimization process, a first optimization signal and a second optimization signal are output synchronously. **First Optimization Signal (Routing Strategy Update):** Dynamically adjusting the router's collaborative evaluation logic. For example, by adaptively adjusting the weights or decision thresholds of different context dimensions, the routing strategy prioritizes success rates during peak periods and optimizes resource costs during off-peak periods. **Second Optimization Signal (Executor Parameter Update):** Collaboratively optimizing the internal configuration of each executor. For example, optimizing the concurrent allocation strategy of computational resources for executors handling high-throughput tasks, or adjusting the scheduling priority parameters of the task queue for executors handling real-time tasks.

[0152] 1. Technical Effects Through the aforementioned synergistic optimization approach of "routing" and "learning," the system achieves: Cooperative evolution: The scheduler's routing strategy and the executor's processing capabilities evolve together based on the same global objective. Performance breakthrough: The system can automatically discover and utilize the hidden complementarity between executor characteristics and task requirements, thereby achieving a synergistic performance gain (K>0) relative to any static or independent optimization strategy across multiple heterogeneous metrics such as global throughput, average response latency, and overall resource cost.

[0153] Implementation Conclusion: This embodiment demonstrates that applying the core architecture of this invention to the field of task scheduling can solve the problem of intelligent routing and resource collaborative optimization under multi-dimensional goal conflicts through a clear technical path, thereby achieving a breakthrough in the overall system performance.

[0154] Intelligent Code Development Examples In intelligent code development application scenarios, the collaborative optimization architecture described in this invention is implemented through the following technical path: the dynamic routing component is configured as a code generation strategy scheduler, which routes the request to one of multiple pre-configured functional components based on the context information of the code development request (including but not limited to code complexity, real-time requirements, and risk level); the multiple functional components are different from each other in terms of functional characteristics, including a code generation component (which generates code snippets based on a large language model), a code security scanning component (which detects security vulnerabilities based on static analysis technology), and a code performance analysis component (which identifies performance bottlenecks and generates optimization suggestions).

[0155] The construction technology path of the composite optimization space covers the following parameter dimensions: routing strategy parameters of the dynamic routing component (including code generation model selection threshold and real-time routing threshold), internal configurable parameters of the code generation component (including prompt word template configuration and generation length limit parameters), internal configurable parameters of the code security scanning component (including vulnerability scoring threshold and false positive rate control parameters), and internal configurable parameters of the code performance analysis component (including performance benchmark threshold and resource consumption upper limit configuration).

[0156] The closed-loop learning component generates quantitative evaluation results based on the feedback dataset generated by the same code submission event. Its data acquisition technology path covers multiple dimensions of indicators: code functional correctness indicators (including compilation pass rate and unit test coverage), code security indicators (including the number of vulnerabilities identified in static security scans and the number of compliance violations), and code performance indicators (including runtime performance and resource utilization on benchmark sets). Based on this same quantitative evaluation result, the closed-loop learning component adopts an automated tuning algorithm in the unified optimization process, performing a joint search in the composite optimization space to collaboratively generate a first optimization signal and a second optimization signal. The first optimization signal is used to update the routing strategy parameters of the dynamic routing component (including adjusting the priority threshold and risk control parameters of code generation routes), and the second optimization signal is used to collaboratively update the internal configurable parameters of each functional component (including optimizing the model selection logic of the code generation strategy, adjusting the sensitivity threshold of security scans, and the benchmark configuration of performance analysis).

[0157] This embodiment, through the aforementioned technical path, achieves the coordinated evolution of the code generation routing strategy and the internal parameters of each functional component, improving development efficiency while ensuring code quality, and without unduly limiting the specific algorithm implementation or deployment environment. Any technical solution that achieves coordinated optimization of routing strategies and functional parameters in a unified optimization process based on the same quantitative evaluation result falls within the protection scope of this invention.

[0158] Industrial Application Verification: The technical path demonstrated in this embodiment shows significant technical advantages in the following typical application scenarios: automated code generation for enterprise-level software development, security and compliance checks for open-source component integration, code migration for legacy system modernization, performance optimization for cross-platform application development, etc. Any technical solution that achieves the coordinated evolution of code generation routing strategy and internal parameters of each functional component in a unified optimization process based on the same quantitative evaluation result reflects the technical advancement and practical value of this invention in the field of intelligent software development.

[0159] Implementation Examples of Security Governance for Large Language Models In the application scenario of large language model security governance, the collaborative optimization architecture described in this invention is implemented through the following technical path: the dynamic routing component is configured as an LLM request classifier, which routes the request to one of multiple pre-configured functional components based on the context information of the input request (including but not limited to business scenario characteristics, risk level and real-time environment status); the multiple functional components are different from each other in terms of functional characteristics, including a prompt word preprocessor (used to dynamically generate security enhancement prompt templates based on a pre-configured security policy rule base), multiple LLM inference engines with different security policies (each engine is parameterized for a specific risk scenario or business domain), and an output postprocessor (used to perform multi-level verification and security control on the LLM output).

[0160] The construction technology path of the composite optimization space covers the following parameter dimensions: routing strategy parameters of the dynamic routing component (including request classification threshold and risk routing priority parameters), internal configurable parameters of each LLM inference engine (including inference temperature parameters and generation length limit parameters), internal configurable parameters of the output post-processor (including verification threshold parameters and security filtering strength parameters), and configuration parameters of the risk assessment model (including weight parameters and policy rule parameters).

[0161] The closed-loop learning component generates quantitative evaluation results based on the same decision-making effect feedback dataset. Its data acquisition technology path covers multi-dimensional indicators: manually annotated dialogue quality score data, violation rate statistics triggered by automatic security filters, end-to-end response latency performance indicators, and business indicator data (including user satisfaction and task completion rate). Based on this same quantitative evaluation result, the closed-loop learning component adopts an automated tuning algorithm technology path in the unified optimization process, performs joint search in the composite optimization space, and collaboratively generates a first optimization signal and a second optimization signal: the first optimization signal is used to update the routing strategy parameters of the dynamic routing component (including adjusting the request classification threshold and risk routing logic), and the second optimization signal is used to collaboratively update the internal configurable parameters of each functional component (including optimizing the inference parameters of the LLM inference engine, adjusting the verification threshold of the post-processor, and configuring the risk assessment model).

[0162] This embodiment, through the aforementioned technical path, achieves the coordinated evolution of LLM request routing strategies and security governance parameters, adaptively balancing dialogue fluency while ensuring output security and compliance. Any technical solution that achieves coordinated optimization of routing strategies and functional parameters in a large language model security governance scenario based on the same quantitative evaluation results and during a unified optimization process falls within the protection scope of this invention.

[0163] Industrial Application Value: The technical path demonstrated in this embodiment provides a reliable security governance infrastructure for the large-scale application of large language models in the following key areas: compliant dialogue systems in the financial customer service field, professional consultation systems in the medical and health field, personalized learning systems in the education and tutoring field, and intelligent auxiliary systems in the content creation field. Any technical solution that achieves the co-evolution of LLM request routing strategies and security governance parameters in a unified optimization process based on the same quantitative evaluation results demonstrates the architectural technical advantages of this invention in general artificial intelligence governance scenarios.

[0164] Multi-agent cooperative evolution implementation examples In multi-agent system application scenarios, the collaborative optimization architecture described in this invention is implemented through the following technical path: the dynamic routing component is configured as an intelligent task allocator, which decomposes and routes the request to one of multiple pre-configured functional components based on the context information of the complex decision request (including but not limited to task characteristics, system load status, and real-time resource constraints); the multiple functional components are specifically autonomous agents with autonomous decision-making and mutual communication capabilities, which are different from each other in terms of functional characteristics, including a task management agent (responsible for task decomposition and coordination), a cluster of professional capability agents (containing multiple agent units with different professional capabilities), and a coordination agent (responsible for managing the collaborative relationship between agents).

[0165] The construction technology path of the composite optimization space covers the following parameter dimensions: routing strategy parameters of the dynamic routing component (including task decomposition granularity control parameters, routing matching rule weights and agent team building strategy parameters), internal decision parameters of each agent (including exploration and utilization balance parameters, cooperation relationship weight matrix, trust update mechanism parameters), and system-level coordination parameters (including communication protocol configuration parameters and resource allocation strategy parameters).

[0166] The closed-loop learning component generates quantitative evaluation results based on the overall system performance feedback dataset. Its data acquisition technology covers multiple dimensions: task completion efficiency indicators (including number of steps completed and time consumption), resource utilization efficiency indicators (including computing resources and communication resources consumed), collaboration efficiency indicators (including communication protocol success rate and sub-goal collaborative completion rate), and decision quality indicators (including accuracy and confidence). Based on this same quantitative evaluation result, the closed-loop learning component adopts an automated tuning algorithm in the unified optimization process, performing a joint search in the composite optimization space to collaboratively generate a first optimization signal and a second optimization signal. The first optimization signal is used to update the routing strategy parameters of the dynamic routing component (including optimizing the task decomposition strategy and agent selection mechanism), and the second optimization signal is used to collaboratively update the internal decision parameter configuration of each agent (including balancing exploration and utilization strategies and adjusting the trust and collaboration weights between agents).

[0167] This embodiment, through the aforementioned technical path, achieves the coordinated evolution of task allocation strategies and agent decision parameters, significantly reducing system internal friction while improving overall decision-making efficiency, without unduly limiting specific agent architectures or communication protocols. Any technical solution that achieves coordinated optimization of task allocation strategies and agent parameters in a multi-agent system scenario based on the same quantitative evaluation result and in a unified optimization process falls within the protection scope of this invention. Industrial Applicability: The technical approach demonstrated in this embodiment can be widely applied in the following fields: distributed production scheduling systems in intelligent manufacturing, multi-agent traffic control systems in smart cities, collaborative decision analysis systems in financial risk control, distributed device management systems in the Internet of Things environment, etc. Any technical solution that achieves the collaborative evolution of multi-agent task allocation strategies and individual decision parameters in a unified optimization process based on the same quantitative evaluation results falls within the protection scope of this invention.

[0168] Synergistic effect verification examples To specifically illustrate the synergistic effect verification technique described in claim 21, and as a practical guide for the aforementioned exemplary quantitative verification embodiments, this embodiment demonstrates how to quantitatively verify the synergistic effect in a cloud resource cost optimization scenario. By deploying the system of this invention and conducting comparative tests with three baseline systems, key indicators such as resource cost and execution accuracy are collected under the same workload, and the synergistic efficiency gain coefficient K is calculated. Experimental results show that the system of this invention achieves significant cost savings while ensuring service quality, and the K value remains consistently positive, verifying the synergistic effect generated by the linkage optimization mechanism.

[0169] Exemplary quantitative verification examples To illustrate how to implement the aforementioned synergistic effectiveness verification process and verify the synergistic effect principle and technical potential of the present invention, this embodiment constructs the following illustrative model through theoretical analysis and reasonable inference: 1. System configuration and comparison benchmark: Experimental Group (This Invention): Deploys the linkage optimization system described in this invention, with the closed-loop learning component employing an alternating optimization technique. Control Group A (Route Optimization Only): Fixes the internal configurable parameters of all functional components, optimizing only the routing strategy parameters of the dynamic routing component. Control Group B (Functional Component Optimization Only): Fixes the routing strategy parameters of the dynamic routing component, optimizing only the internal configurable parameters of each functional component. Control Group C (Independent Parallel Optimization): Allows simultaneous optimization of routing strategy parameters and functional component internal parameters, but both are performed independently based on different local objective functions, without unified coordination.

[0170] 2. Evaluation Scenarios and Metrics: Test scenario: Simulate cloud resource cost governance scenario and handle resource scaling execution requests.

[0171] Key metrics: The comprehensive loss function (L(Theta)) is used as a unified performance measurement standard, with the following weights: w1 = 0.4 (response time), w2 = 0.3 (resource cost), w3 = 0.2 (error rate), and w4 = 0.1 (execution accuracy). The smaller the value, the better the overall system performance.

[0172] 3. Parameter Setting and Theoretical Derivation: Based on the theoretical analysis of the performance limits of typical systems in this field under the "component isolation optimization paradigm," a set of reasonable and representative theoretical parameters is set for this illustrative model. Analysis and inference suggest that, after sufficient optimization, the convergence value of the comprehensive loss function (L(Theta)) of each control group system is expected to reach: Control group A (routing optimization only): (L(\Theta)) = 0.28; Control group B (functional component optimization only): (L(\Theta)) = 0.31; Control group C (independent parallel optimization): (L(\Theta)) = 0.25 (this is the best baseline performance) Theoretical Analysis and Inference: This invention, through its proposed "linkage optimization" architecture, can overcome the aforementioned theoretical performance limitations of the "component isolation optimization paradigm." Based on its core mechanism of constructing a composite optimization space and performing unified optimization based on the same quantitative evaluation results, it is reasonable to infer that the system of this invention can improve the global performance to (L(\Theta)) = 0.15.

[0173] 4. Example calculation results: Based on the theoretically inferred values ​​above, the synergistic performance gain coefficient K is calculated (using the optimal baseline control group C as a benchmark): K = \frac{\text{Global\_Performance\_Metric} - \text{Isolated\_Performance\_C}}{\text{Isolated\_Performance\_C}} = \frac{0.25 - 0.15}{0.25} = 40% Example Conclusion: This exemplary calculation is intended to illustrate and demonstrate the principles and potential of the present invention. The theoretical result quantitatively reveals that the architecture of the present invention can generate significant synergistic gains (K > 0) and is expected to overcome the performance bottlenecks of existing technological paradigms. Those skilled in the art will understand that the "synergistic gain" trend and underlying mechanism revealed by the present invention are stable and reproducible; specific values ​​may vary depending on the actual scenario, but its relative advantages and technological advancements are certain.

[0174] 6. Verification Path Description: Those skilled in the art can implement this invention through the aforementioned technical paths (such as constructing a composite optimization space, employing automated tuning algorithms, etc.), and can design benchmark tests according to their specific application scenarios to actually observe and verify the synergistic gain generated by this invention. This exemplary calculation fully demonstrates the specific implementation process of the synergistic performance verification step. Those skilled in the art can implement this verification method in their specific application scenarios based on this teaching to objectively quantify and confirm the synergistic effect generated by the system of this invention. The value of this embodiment lies in determining the existence and significance of the physical phenomenon of synergistic gain (K>0), rather than limiting its specific numerical value.

[0175] It should be noted that the specific values ​​given in the above embodiments (such as K=40%) are merely illustrative examples based on theoretical models and reasonable inferences, intended to clearly demonstrate the technical path and calculation logic of the synergistic effectiveness verification process, and to prove the objective existence and significance of the synergistic gain (K>0) technical effect. Those skilled in the art will understand that in practical application scenarios, the specific value of the synergistic gain coefficient K will vary depending on system configuration, workload, and business objectives. However, the technical path of "achieving synergistic evolution through constructing a composite optimization space" and the resulting synergistic gain trend disclosed in this invention are stable and reproducible. Any implementation scheme based on the same technical path should fall within the protection scope of this invention.

[0176] Terminology Definition Index Functional components: Functional units implemented through technical paths that receive context input and generate execution results. Their technical implementation includes logical judgment paths based on rule engines, inference paths based on machine learning models, generation paths based on large language models, or collaborative execution paths based on intelligent agents.

[0177] Internal configurable parameters refer to the set of variables within a functional component that can change in response to external optimization signals, thereby affecting its execution behavior or performance. Technical implementation paths include adjusting the weights of machine learning models, and the priority and confidence levels of rule engines.

[0178] Unified optimization process: By constructing a composite optimization space, based on the same quantitative evaluation results, the process of collaboratively generating the first optimization signal and the second optimization signal using joint search or alternating iteration techniques; this technical path ensures the coupled optimization of routing strategy parameters and configurable parameters within functional components.

[0179] Collaborative Evolution: Through a unified optimization loop, the routing strategy parameters of the dynamic routing component and the internal configurable parameters of the functional component are based on the same performance feedback, forming a co-evolutionary process that is mutually coupled, mutually dependent, and jointly driven, promoting and adapting to each other. This technical path is manifested in the first optimization signal changing the input distribution and the second optimization signal improving the processing capability, with the two forming a positive feedback loop.

[0180] Structured decision interface: refers to the technical interface used to receive external input and transform expert decisions into executable output parameters of the system. Its implementation methods include, but are not limited to, form-based input, decision tree selection, parameter adjustment interface, etc., to ensure that external decisions can be accurately parsed and executed by the system.

[0181] Synergy effect verification technology path: By constructing a comparative baseline system and calculating the synergy efficiency gain coefficient K, a technical method is used to quantitatively verify the synergistic effect of the system due to architectural innovation.

[0182] Synergistic performance verification: This refers to the technical step of constructing an isolated optimization baseline system, comparing the performance indicators under linked optimization and isolated optimization, and using the synergistic performance gain coefficient K for quantitative calculation to verify the synergistic effect of the system.

[0183] Contextual information refers to the set of real-time system status data available during routing decisions, including but not limited to request metadata (source, priority, latency requirements), system resource status (CPU / memory utilization, network bandwidth), functional component health status (response time, error rate, confidence level), and environmental factors (geographical location, network topology).

[0184] Industrial applicability: This invention achieves autonomous system performance optimization in the field of IT intelligent operation and maintenance (AIOps), self-healing of equipment status in the field of industrial Internet of Things, adaptive risk assessment in the field of financial risk control, and automated traffic scheduling in the field of intelligent transportation by constructing a composite optimization space. This technical approach provides engineering implementation solutions for highly reliable automated execution in various fields.

[0185] Technical essence of the embodiments: Those skilled in the art should understand that although the above embodiments involve different application fields and technical scenarios, their technical essence all embodies the core architecture of the present invention: by constructing a composite optimization space of routing policy parameters and configurable parameters within functional components, the synergistic evolution of the two is achieved in a unified optimization process based on the same quantitative evaluation result. The technical value of this architecture lies in its domain-independent universality and scalability.

[0186] Summary of Technical Contributions: In summary, the core technical contribution of the collaborative optimization architecture based on a composite optimization space provided by this invention lies in proposing a general system design paradigm that is not limited by specific application domains or functional component implementation forms. This paradigm, by decoupling the collaborative optimization mechanism from specific business logic, provides a reusable foundational technical solution for building intelligent autonomous systems with continuous evolution capabilities. The scope of protection of this invention covers all technical solutions that achieve collaborative evolution of routing strategies and configurable parameters within functional components during a unified optimization process, based on the same quantitative evaluation results.

[0187] The specification supports the following statement: All contents of this specification, including but not limited to the technical field, background art, invention description, description of drawings, specific embodiments, and various examples, provide full, complete, and consistent support for the technical solutions claimed in the claims. In particular, all technical features and combinations thereof in independent claims 1-3 and dependent claims 4-24 are supported by clear implementation paths, embodiments, and terminology definitions in the specification.

[0188] Declaration of technical independence: The core of the protection of this invention lies in its system architecture and the technical path of linkage optimization. The implementation of this architecture and path does not depend on any specific technology platform, programming language, communication protocol or commercial software.

[0189] Specifically: 1. Deployment Independence: The system components can be deployed on any physical node, virtual machine, or container with computing capabilities, and their deployment topology does not affect the realization of the technical effects of this invention. 2. Communication Protocol Independence: Components are logically coupled through standard interfaces, and their physical implementation can adopt function calls, inter-process communication (IPC), remote procedure calls (RPC), message brokers, or any other feasible communication mechanism. 3. Algorithm Implementation Independence: The "automatic tuning algorithm" covers any algorithm that can search and optimize within a parameter space, such as model-based optimization, evolutionary algorithms, and reinforcement learning strategies, and is not limited to a specific algorithm implementation. 4. Functional Component Form Independence: The implementation form of the "functional components" includes, but is not limited to, rule engines, machine learning models, large language models, or intelligent agents. Any functional unit that conforms to the context interface and execution interface specifications defined in this invention can be included in this architecture.

[0190] Any technical solution that implements the logical architecture, data flow relationship, and linkage optimization technical path defined in this invention, regardless of the specific underlying technical implementation method it adopts, shall fall within the protection scope of this invention.

Claims

1. A collaborative optimization method based on a composite optimization space, characterized in that, Includes the following steps: Dynamic routing steps: Using a dynamic routing component, the request is routed to one of several pre-configured functional components based on the context information of the request; Function execution steps: The routed functional component executes the corresponding function and outputs the execution result; Closed-loop learning steps: Collect post-execution effect feedback data and generate quantitative evaluation results based on the effect feedback data; The closed-loop learning step involves constructing a composite optimization space that includes both the routing policy parameters of the dynamic routing component and the internal configurable parameters of the functional component. An automated tuning algorithm is then used to perform joint optimization in the composite optimization space, with the unified goal of optimizing the quantitative evaluation results. In a unified optimization process, a first optimization signal for updating the routing policy of the dynamic routing component and a second optimization signal for updating the internal configurable parameters of the functional component are generated collaboratively. The phrase "based on the same quantitative evaluation result" refers to joint optimization based on a single global performance evaluation value formed by the same effect feedback dataset collected within the same execution cycle. The first optimization signal and the second optimization signal originate from the optimization of the same objective function and are interdependent and mutually influential in their generation mechanisms.

2. The method according to claim 1, characterized in that, The generation of the first optimization signal and the second optimization signal are coupled and interdependent in mechanism to ensure that the routing strategy and the configurable parameters inside the functional components achieve verifiable co-evolution.

3. The method according to claim 1, characterized in that, The dynamic routing component makes routing decisions by executing pre-configured routing policies, and the context information it relies on includes one or more of the following: request metadata, system resource status, functional component health status, and environmental factors.

4. The method according to claim 1, characterized in that, The plurality of functional components are distinguished from each other in their deployment locations; wherein, the plurality of functional components include at least a first functional component and a second functional component, the first functional component being deployed on the edge side for processing requests with real-time requirements higher than a first preset threshold; the second functional component being deployed on the non-edge side for processing requests with real-time requirements lower than a second preset threshold; wherein, the first preset threshold is higher than the second preset threshold.

5. The method according to claim 1, characterized in that, The multiple functional components are distinct from each other in terms of the applicable risk level range; among them, the multiple functional components include a third functional component, which is used to process requests with a risk level higher than a preset risk threshold, and to receive external input through a structured decision interface, and to convert external decisions into system-executable output parameters.

6. The method according to claim 1, characterized in that, The multiple functional components are distinct from each other in terms of their functional characteristics; among them, the multiple functional components include a rule engine based on symbolic logic and a prediction component based on a machine learning model.

7. The method according to claim 1, characterized in that, In the closed-loop learning step, updating and optimizing the internal configuration parameters in the functional components includes: updating the weight parameters of the machine learning model through the second optimization signal, and / or adjusting the priority and confidence parameters of the rules in the rule engine.

8. The method according to claim 1, characterized in that, In the closed-loop learning step, an automated optimization algorithm is used to periodically optimize the key system parameters in the composite optimization space with the goal of minimizing the comprehensive loss function constructed based on the effect feedback data; the comprehensive loss function is constructed based on a weighted average of multiple heterogeneous performance indicators.

9. The method according to claim 8, characterized in that, The comprehensive loss function is constructed by weighting multiple heterogeneous performance indicators among response time, resource cost, error rate, and execution accuracy.

10. The method according to claim 1, characterized in that, When quantifying the performance of the closed-loop learning step, resource cost is used as one of the evaluation indicators. The resource cost is estimated in real time based on the financial impact of resource operations and is fed back to the dynamic routing component as contextual information for subsequent routing decisions.

11. The method according to claim 1, characterized in that, The unified optimization process is achieved through the following technical paths: placing the routing policy parameters of the dynamic routing component and the internal configurable parameters of the functional component in a composite optimization space for joint optimization, or by fixing one type of parameters while optimizing another type of parameters through an alternating optimization strategy; both technical paths are based on the same quantitative evaluation result, and finally collaboratively output the first optimization signal and the second optimization signal.

12. The method according to claim 11, characterized in that, The unified optimization process is achieved by placing the routing policy parameters of the dynamic routing component and the internal configurable parameters of the functional component into a composite optimization space for joint optimization, wherein a model-based optimization algorithm or evolutionary algorithm is used to perform global parameter search in the composite space.

13. The method according to claim 4, characterized in that, It also includes an autonomous recovery step for edge node failures; a health status monitoring mechanism is used to detect edge node failures that carry the first functional component; the dynamic routing component reroutes requests that were originally routed to the failed edge node to other available edge nodes or upgrades them to the second functional component for processing; when the failed edge node recovers, configuration synchronization and health checks are automatically performed, and it is reintegrated into the service cluster after it is confirmed to be stable.

14. The method according to claim 1, characterized in that, The closed-loop learning steps include model version management and a secure release mechanism; the model version management uses semantic version control for updated machine learning models; the secure release mechanism includes deploying the optimized model to the production environment using a canary release strategy, and automatically reverting to a stable version when performance anomalies are detected.

15. The method according to claim 1, characterized in that, The output of the functional components includes executable instructions, control instruction sets, or policy configuration files.

16. A collaborative optimization system based on a composite optimization space, used to implement the method of claim 1, characterized in that, include: The dynamic routing component is configured to route requests based on context information; A collection of functional components, containing multiple functional components to perform corresponding functions based on the routed request; The execution and feedback component is configured to execute function outputs and collect effect feedback data; The closed-loop learning component is configured to perform quantitative evaluation based on performance feedback data; in, The dynamic routing component is configured to route requests to the appropriate functional component in the set of functional components; The set of functional components is configured to issue functional outputs to the execution and feedback components; The execution and feedback component is configured to provide the effect feedback data to the closed-loop learning component; The closed-loop learning component is configured as follows: A composite optimization space is constructed that includes both the routing policy parameters of the dynamic routing component and the internal configurable parameters of the functional component. An automated tuning algorithm is used to perform joint optimization in the composite optimization space. Based on the same quantitative evaluation result, in a unified optimization process, updates to the routing policy are collaboratively output to the dynamic routing component, and updates to the internal configurable parameters of the functional component are output to the functional component set. The phrase "based on the same quantitative evaluation result" refers to joint optimization based on a single global performance evaluation value formed from the same effect feedback dataset collected within the same execution cycle.

17. The system according to claim 16, characterized in that, The set of functional components includes at least a first functional component and a second functional component; wherein, the first functional component is deployed on the edge side to process requests with real-time requirements higher than a preset threshold; and the second functional component is deployed on the non-edge side to process requests with real-time requirements lower than the preset threshold.

18. The system according to claim 17, characterized in that, The set of functional components also includes a third functional component, which is used to process requests with a risk level higher than a set risk threshold, and to receive external input through a human-computer interaction interface to generate or confirm the functional output.

19. The system according to claim 16, characterized in that, The dynamic routing component, functional component set, execution and feedback component, and closed-loop learning component can be deployed and horizontally scaled as independent functional units.

20. The method according to claim 1, characterized in that, The unified optimization process is achieved through an alternating optimization strategy that fixes one type of parameter while optimizing another type of parameter. The alternating optimization strategy is executed according to the following technical path: in the first optimization cycle, the internal configurable parameters of the functional component are fixed and the routing policy parameters of the dynamic routing component are optimized. In the subsequent second optimization cycle, the routing policy parameters are fixed and the internal configurable parameters of the functional component are optimized. The two types of parameters are coordinated and evolved through alternating iteration.

21. The method according to claim 1, characterized in that, It also includes a collaborative performance verification step, which is an optional technical means used to quantitatively evaluate the collaborative effect generated by the linkage optimization mechanism. The collaborative performance verification step is carried out by constructing multiple isolated optimization baseline systems for comparative verification. The isolated optimized baseline system includes: The first baseline system fixes the internal configurable parameters of the functional components and optimizes only the routing policy parameters of the dynamic routing components; The second baseline system fixes the routing policy parameters of the dynamic routing component and optimizes only the internal configurable parameters of the functional component; The third baseline system performs independent and parallel optimization of the routing policy parameters and the internal configurable parameters.

22. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 15, 20 to 21.

23. A collaborative optimization system architecture based on a composite optimization space, characterized in that, The architecture described is a set of specifications that define the logical relationships between components, data flow interfaces, and collaborative optimization mechanisms. Any system implemented in accordance with this specification includes: A dynamic routing logic unit that conforms to the first interface specification is used to route requests to one of multiple functional logic units based on context information; Multiple functional logic units that conform to the second interface specification, each unit having internal configurable parameters; Closed-loop learning logic units conforming to the third interface specification are used for: (a) Construct a composite optimization space that simultaneously includes the routing policy parameters of the dynamic routing logic unit and the internal configurable parameters of the functional logic unit; (b) Generate quantitative evaluation results based on performance feedback data; (c) An automated tuning algorithm is used to perform a joint search in the composite optimization space. Based on the same quantitative evaluation result, a first optimization signal for updating the routing policy parameters and a second optimization signal for updating the internal configurable parameters are generated collaboratively in a unified optimization process. Among them, the first, second and third interface specifications jointly require that the closed-loop learning logic unit must, based on the same quantitative evaluation result, collaboratively generate a first optimization signal for updating the routing policy parameters and a second optimization signal for updating the internal configurable parameters in a unified optimization process. Furthermore, the first interface specification, the second interface specification, and the third interface specification together constitute a technical constraint system, which mandates that each logical unit follow a standardized data exchange and collaborative optimization mechanism to ensure that the system generates a synergistic effect based on a composite optimization space.

24. The collaborative optimization system based on the architecture described in claim 23, characterized in that, The system is used to construct a collaborative optimization system selected from the following systems: intelligent transportation system, cloud computing resource scheduling system, IT operation and maintenance system, compiler optimization system, or network control system.

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