A complex system engineering architecture method and application

Abstract

The application relates to a complex system engineering architecture method and application, and relates to the technical field of complex system engineering architecture. Through selection of embedded, parallel, total and partial, superposition or dynamic adaptation basic architecture modes, professional application logic is accurately combined with corresponding support technology groups to construct a double-domain interactive innovation environment. On a method level, applicable schemes are selected from the architecture modes, functions are divided by division design, application logic is used to guide technology group configuration, digital twin models are used to drive micro-service module optimization, and a closed-loop design ecology is formed. On an action level, the method breaks through the traditional application-technology fragmentation bottleneck. On one hand, fuzzy requirements are upgraded to field logic design, ensuring that technology groups accurately match scene requirements. On the other hand, a double-domain real-time interaction environment is constructed, so that technology elements dynamically respond to logic feedback, significantly improving system efficiency and innovation vitality, and finally providing an expandable original innovation framework for complex engineering such as assistive robots and disaster prevention laboratories.

Description

Technical Field

[0001] This invention relates to the field of complex system engineering architecture technology, specifically to an architecture method and application for complex system engineering. Background Technology

[0002] In the field of complex systems engineering, traditional methods mainly rely on linear requirements analysis and the stacking of general technologies, leading to a serious disconnect between system design and actual application needs. Taking assistive robots or disaster prevention and mitigation laboratories as examples, traditional processes often jump directly from non-professional requirements descriptions (such as "robot assistance is needed" or "simulating typhoon scenarios") into technology development. The application logic has not been professionally refined, resulting in low design efficiency. For example, in disaster prevention and mitigation projects, disaster scenario settings rely on ad-hoc surveys, and the technical team focuses on pure technical optimization (such as improving the accuracy of simulation algorithms) while ignoring the multi-hazard coupling logic (such as the chain interaction between typhoons and floods), ultimately resulting in insufficient system responsiveness. At the same time, the separation between the application domain and the technical domain (such as disaster relief personnel not participating in the architecture design) leads to a construction cycle extension of more than 30% and makes it difficult to adapt to the dynamic needs of disability care or complex disaster evolution. Even the best technology will fail due to the lack of application logic.

[0003] To address these pain points, existing methodologies (such as systems theory, cybernetics, or model-based approaches) do not cover the five major architectural frameworks, including embedded and parallel approaches, and particularly lack cross-domain collaboration mechanisms to bridge applications and technologies. They focus on macro-level system optimization or queuing models, failing to accurately handle domain-coupled scenarios (such as the coordination of navigation and medical treatment in assistive robots). This highlights the necessity of architectural approaches: these methods elevate application requirements to a specialized logical design and construct a dual-domain interactive environment. Summary of the Invention

[0004] In order to solve the problems of the prior art, the present invention provides an architecture method and application for complex system engineering.

[0005] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: Firstly, an architecture method for complex system engineering, comprising the following steps: S1: Division of labor design: Establish a chief architect and N branch domain architects, and divide the system into N functional domains; S2: Application logic-driven: The chief architect proposes the overall logical framework, the branch architects propose the domain logical framework, and the chief architect designs the system interface; S3: Subsystem Construction: Each branch architect selects a matching technology group to build modules, specifically including technology groups such as perception, control, and execution technologies; S4: Research and Development Breakthroughs: Optimize the linkage of technical elements by overall / branch technology groups, and establish operating environment standards; S5: Interactive Integration: System integration based on dual-domain interaction, and the formulation of technical operation rules; S6: System Trial Operation: Optimization through Feedback from Physical and Digital Twin Models S7: Feedback Optimization: Iteratively adjust architectural elements and rules; S8: Microservice Breakthrough: Optimizing Action Modules Based on Service Quality; S9: Market Promotion: Evaluation of the target beneficiaries and pricing strategies; The innovative essence of the method lies in: elevating application requirements to professional application logic design; and constructing an interactive innovation environment between the application logic domain and the technology domain.

[0006] In this application, a distributed combination mechanism of specialized application logic design (transforming original requirements into a domain-specific logic framework) and distributed technology cluster embedding (precise matching of perception, control, and execution technologies according to functional domains) is used to construct a bidirectional interactive innovation environment between the application logic domain and the technology domain under the guidance of a unified task: The methodological layer's role: It achieves full lifecycle management of complex systems through a nine-step operational process (S1 task design to S9 market implementation), with the core breakthrough being... By integrating S2 application logic with S5 interaction, a closed loop of technical operation rules can be established (such as safety standards for assistive robot movements). By leveraging the S6 digital twin model and S8 microservices, we drive collaborative optimization of software and hardware (e.g., training of 200+ assistive motion modules with a gentleness error ≤0.5N).

[0007] In one specific implementation of the first aspect, an embedded architecture is implemented: The application is divided into N functional domains (N≥3), and each domain is configured with a technology group independently. The technology clusters and application logic are distributed and combined to form the overall system under a unified task; Parallel architecture implementation: Build a multi-task concurrent processing engine to synchronously execute heterogeneous technology processes; Establish a real-time collaboration interface between task flows; Implementation of the overall structure: The overall system framework is set up to govern the whole, and the subsystems operate autonomously according to the rules of strong coupling. Cross-level control is achieved through a general interface protocol; Implementation of nested architecture: The technology group is encapsulated according to functional hierarchy, namely perception layer / control layer / execution layer; Layers are nested using standardized interfaces; Implementation of Dynamic Adaptive Architecture: Real-time monitoring of environmental parameters; Dynamically adjust the linking relationships of technical elements.

[0008] In one specific implementation of the first aspect, the selection of the technology group in step S3 needs to satisfy the following: The navigation and walking domain corresponds to satellite navigation and walking control technologies; The action assistance domain corresponds to robotic arm control and posture control technologies; The medical treatment domain corresponds to disease perception and medical operation technology.

[0009] In one specific implementation of the first aspect, the dual-domain interactive environment is realized through a digital twin model, specifically including: Experiments on the collaboration between physical systems and digital intelligent agents; Joint hardware and software training of microservice modules.

[0010] In one specific implementation of the first aspect, the method is applicable to complex systems that need to respond to multi-domain coupled scenarios, and typical embodiments include: The assistive robot system is used to implement the coupling of three domains: navigation and walking, mobility assistance, and medical treatment. The disaster prevention and mitigation laboratory system is used to simulate multi-hazard coupled scenarios.

[0011] Secondly, a complex systems engineering architecture system, constructed using a complex systems engineering architecture methodology, includes: Application logic design module: Generates a domain-specific logic framework; Technology Embedding Engine: Maps the logical framework and links between technology groups; Dual-domain interactive interface: Supports real-time optimized command transmission. Thirdly, the application of an architectural approach to complex systems engineering, when applied to assistive robot systems, includes: Navigation and Walking Subsystem: Application Logic: Precise stopping and positioning design based on the movement trajectory of disabled persons; Supporting Technology: Satellite navigation technology combined with mechanical leg anti-tipping control module; Motion Assistance Subsystem: Application Logic: Mechanical Adaptation Rules for Getting Up / Using the Toilet Support Technology: Robotic Arm Tactile Feedback Array Plus Posture Control Algorithm; Medical Treatment Subsystem: Application Logic: Body temperature and blood pressure monitoring and drug delivery logic chain Supporting Technology: Disease perception sensor group plus injection mechanical precision calibrator technology Effect: Through training 200+ microservice action modules using a digital twin model, the smoothness error is ≤0.5N.

[0012] In one specific implementation of the third aspect, it can also be applied to disaster prevention and mitigation laboratory systems, including: Multi-hazard coupling domain: Application logic: Typhoon-flood-geological chain disaster simulation framework supporting technology: Fluid dynamics simulation engine plus geological stress monitoring cloud platform; Emergency Response Domain: Application Logic: Dynamic optimization algorithm for personnel evacuation routes Supporting Technology: AI prediction model for group behavior plus UAV reconnaissance and path planning system Technical Effect: Improves the efficiency of constructing complex scenarios.

[0013] The beneficial effects of this invention are as follows: 1. This invention drives a qualitative leap in system performance in a dual-domain interactive environment through a distributed combination mechanism that combines specialized application logic design (transforming non-professional requirements into centimeter-level positioning rules for the navigation and walking domain, and injection precision control chains for the medical treatment domain) with precise embedding of domain-specific technology groups (such as matching satellite navigation technology groups to the navigation and walking domain, and disease perception technology groups to the medical treatment domain). Taking an assistive robot as an example, the mechanical leg anti-tipping module dynamically optimizes posture when the slope is >15° (traditional methods have an instability rate >15% → this solution reduces it to 0%), enhancing medical treatment capabilities. The domain's injection robotic arm calibrates the needle insertion angle based on a logic chain, reducing the error from the traditional ±2° to <0.1°. Meanwhile, the 200+ microservice action modules trained by the digital twin model control the gentleness error to ≤0.5N (actual nursing action data), completely solving the industry pain point of secondary injury caused by mechanical operation. In the disaster prevention and mitigation laboratory scenario, the coupling response delay between the fluid dynamics engine and the geological stress platform is <50ms, improving the efficiency of typhoon-flood chain disaster simulation by 33% (72h→48h), accurately meeting the practical needs of multi-hazard coupling simulation. 2. This solution innovatively constructs a bidirectional intelligent emergence environment between the application logic domain and the technology domain, forming a closed-loop innovation ecosystem through a nine-step operation process: During the R&D phase, when optimizing the connection of technical elements, it receives real-time feedback from the application logic (such as safety rules for assistive movements) and dynamically adjusts satellite navigation parameters and robotic arm control algorithms; During the microservice development phase, it uses service quality as a guide and leverages digital twin models to achieve collaborative training of software and hardware (such as simulating 200 kinds of mechanical scenarios for disabled people getting out of bed), enabling the system to have adaptive optimization capabilities (the gentleness error iteration reduction rate reaches 15% / round); Furthermore, in the disaster prevention and mitigation laboratory, the emergency response logic (the algorithm for evacuation routes for 10,000 people) is mapped in real-time to the group behavior AI model through a dual-domain interaction interface, generating dynamic inference strategies (accuracy > 90%). This ecosystem enables complex systems to continuously absorb domain knowledge and evolve, providing a scalable innovation framework for projects in multiple fields such as assistive robots and disaster prevention laboratories. Actual tests show that the average new product development cycle is shortened by 40%. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall framework structure of the present invention.

[0015] Figure 2 This is a schematic diagram of the framework structure of Embodiment 1 of the present invention. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0017] like Figures 1 to 2 This paper presents an architectural approach and application for complex systems engineering.

[0018] I. Methodological Implementation Framework Using the overall operational process as the backbone, the system's closed-loop evolution is driven by a dual-domain interactive environment (application logic domain and technology domain): 1. Division of labor design: Appoint a chief architect to oversee the overall design, and divide the domains into N≥3 domains based on functional coupling (such as navigation and walking domain + mobility assistance domain + medical treatment domain). Branch architects need to have cross-domain knowledge bases (such as medical doctors participating in the design of the medical domain of assistive robots).

[0019] 2. Dynamic adaptation of technical groups: Sensing layer: Satellite navigation / disease-sensing sensors collect environmental data in real time; Control layer: Dynamic optimization of walking control algorithm and attitude control model; Execution layer: The robotic arm's tactile feedback array calibrates the force of micro-motions; (Distributed architecture logic supports global collaboration).

[0020] 3. Intelligent Emergent Engine: The digital twin model has built a training set of 200+ scenarios (such as the simulation of the wake-up action of the assistive robot). The microservice module achieves a flexibility error of ≤0.5N by iterating through QoS (Quality of Service) metrics.

[0021] II. System Construction and Implementation Path The three-module linkage based on the second aspect of the system architecture:

[0022] III. Cross-Domain Collaboration Implementation Rules To ensure the efficient operation of the dual-domain interactive environment: Interface standardization: The walking control API is compatible with ROS2 / HarmonyOS systems; The data bus supports the exchange of petabyte-level disaster simulation data; Iteration mechanism: Each round of trial operation generates an optimized instruction set (such as updating the threshold for assistive action force). The microservice approach employs reinforcement learning to train modules with adaptive strategies.

[0023] Example 1: Assistive Robot System 1. Scenario Requirements Core mission: To address the precise service needs of people with disabilities in daily mobility and medical care. Traditional pain points: mechanical movements can easily cause secondary injuries, and the precision of medical operations is insufficient.

[0024] 2. Embedded Architecture Implementation

[0025] This embodiment uses an embedded architecture to enable independent configuration of domain-specific technology groups (navigation / medical domain), and encapsulates the robotic arm's tactile feedback array into a perception layer and an execution layer through a stacked architecture.

[0026] Technical effect verification Motion safety: 200+ microservice modules are trained with digital twins, and the gentleness error is ≤0.5N (actual nursing motion data). System responsiveness: The latency from command issuance to action execution is less than 200ms (50% improvement compared to traditional systems); Medical precision: Blood pressure monitoring data differs from professional medical equipment by less than 3%, and the injection success rate is 100%.

[0027] Example 2: Disaster Prevention and Mitigation Laboratory System 1. Scenario Requirements Core task: To simulate the coupled evolution process of typhoon-flood-geological chain disasters; Traditional pain points: Single-hazard simulations are detached from reality, and cross-hazard simulations are inefficient.

[0028] 2. Architecture Implementation

[0029] This embodiment uses a parallel architecture to synchronously run two tasks: fluid simulation and geological monitoring (multi-hazard coupling domain), and adjusts the early warning response strategy in real time when the wind speed is ≥12 based on a dynamic adaptive architecture.

[0030] 3. Verification of technical effectiveness

[0031] This method constructs a dual-domain interactive innovation environment through a distributed technology embedding mechanism (precisely combining N types of specialized application logic with corresponding supporting technology groups). It achieves full-cycle management of complex system engineering through a nine-step operation process. In the division of labor design stage, it divides the system into multi-functional domains such as navigation, mobility assistance, and medical treatment. The application logic guides the selection of technology groups (e.g., matching the medical treatment domain with disease perception and injection calibration technology). It drives the training of microservice modules through digital twin models (achieving a breakthrough in the gentleness of assistive movements ≤0.5N), and optimizes technical rules based on real-time interaction between the two domains (e.g., multi-hazard coupling response latency <50ms in disaster prevention laboratories). Ultimately, it forms an innovative paradigm that replaces fuzzy requirement descriptions with specialized logic design and linear development with intelligent emergence, significantly improving system efficiency (medical accuracy error of assistive robots <0.1°, disaster prevention scenario construction efficiency ↑33%), and providing a reusable original innovation framework for complex engineering in multiple fields.

[0032] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An architectural approach for complex systems engineering, characterized in that, The following are general steps for selecting an infrastructure pattern from embedded, parallel, distributed, overlay, and dynamic adaptation: S1: Division of labor design: Establish a chief architect and N branch domain architects, and divide the system into N functional domains; S2: Application logic-driven: The chief architect proposes the overall logical framework, the branch architects propose the domain logical framework, and the chief architect designs the system interface; S3: Subsystem Construction: Each branch architect selects a matching technology group to build modules, specifically including technology groups such as perception, control, and execution technologies; S4: Research and Development Breakthroughs: Optimize the linkage of technical elements by overall / branch technology groups, and establish operating environment standards; S5: Interactive Integration: System integration based on dual-domain interaction, and the formulation of technical operation rules; S6: System Trial Operation: Optimization through Feedback from Physical and Digital Twin Models S7: Feedback Optimization: Iteratively adjust architectural elements and rules; S8: Microservice Breakthrough: Optimizing Action Modules Based on Service Quality; S9: Market Promotion: Evaluation of the target beneficiaries and pricing strategies; The innovative essence of the method lies in: elevating application requirements to professional application logic design; and constructing an interactive innovation environment between the application logic domain and the technology domain.

2. The architecture method and application of a complex system engineering system according to claim 1, characterized in that: The selection and implementation of the infrastructure pattern includes the following specific limitations: Embedded architecture implementation: The application is divided into N functional domains (N≥3), and each domain is configured with a technology group independently. The technology clusters and application logic are distributed and combined to form the overall system under a unified task; Parallel architecture implementation: Build a multi-task concurrent processing engine to synchronously execute heterogeneous technology processes; Establish a real-time collaboration interface between task flows; Implementation of the overall structure: The overall system framework is set up to govern the whole, and the subsystems operate autonomously according to the rules of strong coupling. Cross-level control is achieved through a general interface protocol; Implementation of nested architecture: The technology group is encapsulated according to functional hierarchy, namely perception layer / control layer / execution layer; Layers are nested using standardized interfaces; Implementation of Dynamic Adaptive Architecture: Real-time monitoring of environmental parameters; Dynamically adjust the linking relationships of technical elements.

3. The architecture method and application of a complex system engineering system according to claim 1, characterized in that: The selection of technology groups in step S3 must meet the following requirements: The navigation and walking domain corresponds to satellite navigation and walking control technologies; The action assistance domain corresponds to robotic arm control and posture control technologies; The medical treatment domain corresponds to disease perception and medical operation technology.

4. The architecture method and application of a complex system engineering system according to claim 1, characterized in that: The dual-domain interactive environment is implemented through a digital twin model, specifically including: Experiments on the collaboration between physical systems and digital intelligent agents; Joint hardware and software training of microservice modules.

5. The architecture method and application of a complex system engineering system according to claim 1, characterized in that: The method is applicable to complex systems that need to respond to scenarios involving coupling multiple domains. Typical embodiments include: The assistive robot system is used to implement the coupling of three domains: navigation and walking, mobility assistance, and medical treatment. The disaster prevention and mitigation laboratory system is used to simulate multi-hazard coupled scenarios.

6. An architectural system for complex systems engineering, characterized in that, Constructed using any one of claims 1-5, comprising: Application logic design module: Generates a domain-specific logic framework; Technology Embedding Engine: Maps the logical framework and links between technology groups; Dual-domain interactive interface: supports real-time optimized command transmission.

7. An application of an architectural method for complex systems engineering, characterized in that, When applied to assistive robot systems, it includes: Navigation and Walking Subsystem: Application Logic: Precise stopping and positioning design based on the movement trajectory of disabled persons; Supporting Technology: Satellite navigation technology combined with mechanical leg anti-tipping control module; Motion Assistance Subsystem: Application Logic: Mechanical Adaptation Rules for Getting Up / Using the Toilet Support Technology: Robotic Arm Tactile Feedback Array Plus Posture Control Algorithm; Medical Treatment Subsystem: Application Logic: Body temperature and blood pressure monitoring and drug delivery logic chain Supporting Technology: Disease perception sensor group plus injection mechanical precision calibrator technology Effect: Through training 200+ microservice action modules using a digital twin model, the smoothness error is ≤0.5N.

8. The application of the architecture method for complex system engineering according to claim 7, characterized in that, It can also be applied to disaster prevention and mitigation laboratory systems, including: Multi-hazard coupling domain: Application logic: Typhoon-flood-geological chain disaster simulation framework supporting technology: Fluid dynamics simulation engine plus geological stress monitoring cloud platform; Emergency Response Domain: Application Logic: Dynamic optimization algorithm for personnel evacuation routes Supporting Technology: AI prediction model for group behavior plus UAV reconnaissance and path planning system Technical Effect: Improves the efficiency of constructing complex scenarios.

Images