A function entity design method based on scene customization

Abstract

The application discloses a kind of function entity design methods based on scene customization. The method is first to the target application scene is modeled and demand quantification;Subsequently, based on the performance index and scene characteristics after quantification, automatically combine function module from modular configuration library, generate the morphology and function collaborative design scheme of at least one configurable entity;When needing multi-entity collaboration, formulate group collaborative strategy;After the function entity executes work, through the acquisition real-time feedback data, the design scheme and collaborative strategy are dynamically iterated and optimized.The application realizes the transformation of function entity design from static to dynamic, from general to special, and forms a new paradigm of function entity design with application scene demand as the core orientation.

Description

Technical Field

[0001] This invention relates to the field of functional entity design methods, and in particular to a scenario-based customized functional entity design method. Background Technology

[0002] Currently, the design paradigm for mechanical equipment largely follows the concepts of "technology-driven" or "platform universality." The design process begins with existing technology platforms or core components, defining the equipment by adding functions, and ultimately attempting to extend it to various application scenarios. This model has three major drawbacks: First, the equipment's functions and form are fixed at the design stage, making it difficult to accurately meet the unique constraints and needs of specific scenarios, leading to insufficient performance or wasted resources; second, the equipment lacks the ability to self-adjust and optimize based on environmental feedback during operation; and third, the collaborative strategies of multiple equipment systems are mostly pre-arranged statically, unable to cope with the dynamic changes in complex scenarios.

[0003] Despite significant advancements in modular design concepts and swarm intelligence algorithms, current technologies largely focus on module replacement within the equipment itself or multi-machine communication protocols. A complete methodology has yet to be developed, which takes scenario requirements as its starting point, reverse-engineers the integrated generation of equipment form and function, and iterates and optimizes it throughout its entire lifecycle. Therefore, this field urgently needs an innovative lean design approach to address the fundamental issue of deep adaptation between intelligent equipment and application scenarios. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing industrial equipment design methods and provide a scenario-based customized functional entity design method. This method completely transforms the design logic from "technology-driven" to "scenario-driven" and introduces a data closed-loop feedback mechanism, realizing the continuous evolution and self-optimization of functional entity design schemes.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A dynamic collaborative design method for functional entities based on scene-form-function coupling includes the following steps:

[0007] S1. Scenario Modeling and Demand Quantification: Digitally model the target application scenario, extract the characteristic parameters of the scenario, and quantify the operational requirements of the application scenario into one or more equipment performance indicators.

[0008] S2. Form-function co-design scheme generation: Based on the scene feature parameters and equipment performance indicators, through pre-established mapping rules, matching functional modules are called and combined from the pre-built modular configuration library to generate at least one form-function co-design scheme for equipment; wherein, the modular configuration library stores configuration information of multiple functional modules that encapsulate different enabling technologies and have standardized interfaces.

[0009] S3. Group Coordination Strategy Formulation: When an operation requires a group of equipment consisting of multiple pieces of equipment to be executed, a task allocation scheme and a collaborative operation strategy are formulated for the group of equipment based on the scene feature parameters and the operation objectives.

[0010] S4. Operation execution and data feedback: Based on the form and function collaborative design scheme and the collaborative operation strategy, control the equipment or equipment group to enter the application scenario to perform the operation, and collect the scenario feedback data and equipment operation data in real time during the operation process.

[0011] S5. Dynamic Iterative Optimization: The collected scenario feedback data and equipment operation data are compared with the expected equipment performance indicators, and the form and function collaborative design scheme and / or collaborative operation strategy are iteratively optimized based on the comparison results.

[0012] The specific steps in step S2 for generating a configurable entity's form and function co-design scheme include:

[0013] S21. Functional Projection: Decompose the quantified performance indicators of the functional entities into specific technical requirements for the perception, decision-making, and execution functional modules;

[0014] S22. Morphology Generation: Based on the specific technical requirements and the physical constraints in the scene feature parameters, select matching functional modules from the modular configuration library, perform three-dimensional spatial layout and structural optimization, and generate the physical morphology of the functional entity.

[0015] S23. Coupling Verification: Simulate and verify the generated morphological and functional co-design scheme in a virtual environment to ensure that it meets the performance indicators of all functional entities in the application scenario.

[0016] Compared with existing technologies, this invention provides a scenario-based customized functional entity design method, which has the following beneficial effects:

[0017] 1. Precise Design: Through a positive and quantitative mapping from scenario requirements to functional entity design, the final design solution is ensured to be highly compatible with the specific application scenario, improving the applicability and operational efficiency of functional entities from the design source;

[0018] 2. Dynamic Adaptation: Through the data closed loop of "design-deployment-feedback-optimization", the design scheme of functional entities is no longer a static blueprint, but a "living" scheme that can continuously evolve and be optimized according to actual operation data, and has the ability to cope with changes in the scenario;

[0019] 3. Efficient group collaboration: By combining the decision-making process of swarm intelligence with the form-function design of individual entities, entity clusters can consider collaboration from the design stage, thereby exhibiting more efficient and reliable overall performance at runtime. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention. Detailed Implementation

[0021] Example 1: Customized Design of Laser Additive Manufacturing Equipment for Microgravity Environment in Space

[0022] 1. Scenario Modeling and Requirement Quantification

[0023] Scenario Analysis: The environment inside a space station or dedicated experimental module is characterized by microgravity, high vacuum, large temperature differences, and confined space. Lightweight, high-strength aluminum or titanium alloy components need to be manufactured or repaired for the space station in orbit.

[0024] Digital modeling: Based on the space station's digital twin system, a precise three-dimensional model of the manufacturing unit is constructed, and constraints on the cabin space, power supply, and thermal management interfaces are integrated.

[0025] Demand Quantification: To manufacture a space telescope support structure with dimensions of 200mm×200mm×150mm in orbit, achieving mechanical properties of over 95% of those of terrestrial products.

[0026] Demand Quantification Indicators:

[0027] Forming accuracy: ±0.1mm.

[0028] Molten pool stability: No drift under microgravity, dimensional fluctuation <5%.

[0029] Power consumption limit: Peak power ≤ 2kW.

[0030] Powder treatment: 100% recyclable.

[0031] The entire process is unmanned and operates autonomously.

[0032] 2. Generation of Form-Function Co-design Scheme

[0033] Core manufacturing unit: It adopts a closed-loop powder supply module + low-power laser and high dynamic focusing lens + real-time monitoring module for molten pool vision and spectrum + space adaptive forming chamber, and outputs a compact, fully enclosed, multi-interface cubic equipment design and integration solution, which can directly connect to the standard interface of the space station experimental module.

[0034] Auxiliary Unit: Employs a small robotic arm and a multi-functional end effector. Outputs a lightweight, high-precision auxiliary robot design and integration solution for post-processing and online inspection.

[0035] 3. Formulation of group collaboration strategies

[0036] The system consists of a core manufacturing unit and an auxiliary robot forming a miniature swarm.

[0037] Task allocation: The core unit is responsible for the main manufacturing tasks; the auxiliary robot is responsible for installing the substrate before manufacturing, monitoring the optical signals of the molten pool in real time during manufacturing, cleaning the powder and performing preliminary dimensional inspection after manufacturing.

[0038] Collaborative Strategy: Establish a strict energy and heat management sequence. When the manufacturing unit is running at high power, the auxiliary robot is in a low-power standby state. The two work together through force feedback to complete precise docking operations.

[0039] 4. Task execution and data feedback

[0040] Data acquisition: Real-time acquisition of images of the molten pool, spectral signals of the molten zone, thickness and temperature of each layer, actual laser power, and vibration spectrum inside the chamber.

[0041] 5. Dynamic Iterative Optimization

[0042] Problem: The monitoring system detected that microgravity caused slight deformation of the molten pool during the manufacturing of certain structures, which poses a risk of defects.

[0043] Optimization: Feedback to the form-function co-design engine, online adjustment of the laser scanning path algorithm, automatic addition of remelting scanning strategy in this area, and fine-tuning of laser power parameters (5% improvement).

[0044] Results: Real-time monitoring data showed that the molten pool returned to stability, and subsequent metallographic analysis confirmed that the defects in the area were eliminated, and the overall performance of the component met the standards.

[0045] Example 2: Customized Design of High-Altitude On-Site Laser Additive Manufacturing Equipment for Wind Turbine Units

[0046] 1. Scenario Modeling and Requirement Quantification

[0047] Scenario Analysis: Inside the nacelle of an 80-meter-high wind turbine tower, space is limited, and there is continuous vibration, strong wind, and large fluctuations in temperature and humidity. The worn yaw gear needs to be repaired on-site.

[0048] Digital modeling: Using a drone equipped with a 3D scanner, a precise three-dimensional point cloud model of the area to be repaired is obtained, and environmental wind speed and vibration acceleration data are integrated.

[0049] Demand Quantification: In-situ repair of 200mm diameter yaw gears inside the nacelle of an 80-meter-high wind turbine tower. The repaired hardness is ≥HRC50 and the bonding strength is >500MPa.

[0050] Demand Quantification Indicators:

[0051] Wind resistance: Under wind pressure of level 6, the printhead positioning error is ≤0.2mm.

[0052] Dynamic response: Actively compensates for the unit's natural vibration frequency (2Hz).

[0053] Material adaptability: High-toughness iron-based or nickel-based alloy powders can be used.

[0054] Operational safety: No personnel are required to work at heights throughout the entire process.

[0055] 2. Generation of Form-Function Co-design Scheme

[0056] Mobile additive unit: It adopts a permanent magnet adsorption tracked chassis + high rigidity robotic arm + laser additive printing head + machine vision module to output a strong adsorption, vibration resistance and high mobility wall climbing robot integrated design solution;

[0057] Support Unit: A tethered drone and an in-cabin fixed platform are used as the material supply and power unit, and the mobile additive manufacturing unit is continuously supplied with materials and power through flexible pipelines.

[0058] 3. Formulation of group collaboration strategies

[0059] The system consists of one mobile additive manufacturing unit and one support unit.

[0060] Task allocation: The mobile additive manufacturing unit performs repair work; the support unit provides power and materials and acts as a communication relay.

[0061] Collaborative strategy: The mobile additive unit sends its own positioning and vibration data to the support unit in real time. The algorithm in the support unit predicts the vibration phase and performs look-ahead compensation for the printhead motion trajectory planning, so as to achieve dynamic and precise additive manufacturing.

[0062] 4. Task execution and data feedback

[0063] Operation execution: The mobile additive manufacturing unit crawls along the wall of the high-altitude wind turbine tower to the repair point and begins in-situ additive manufacturing.

[0064] Data acquisition: Real-time acquisition of the distance between the printhead and the substrate, the surface morphology of the deposited layer, the vibration spectrum of the unit, the ambient wind speed, and the deposition efficiency.

[0065] 5. Dynamic Iterative Optimization

[0066] Problem: Gusts of wind at high altitudes cause instantaneous changes in the robot's adhesion force, resulting in deviations in the printing path.

[0067] Optimization: Feedback is sent to the S3 swarm intelligence collaborative decision-making center to adjust the collaborative strategy in real time, instructing the additive manufacturing unit to suspend operation. At the same time, the motion control algorithm dynamically reconstructs the subsequent additive manufacturing path based on the prediction model to smooth out the deviation.

[0068] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A scenario-based customized functional entity design method, characterized in that, Includes the following steps: S1. Scenario Modeling and Demand Quantification: Digitally model the target application scenario, extract the characteristic parameters of the scenario, and quantify the operational requirements of the application scenario into one or more functional entity performance indicators. S2. Form-Function Co-design Scheme Generation: Based on the scene feature parameters and functional entity performance indicators, and through pre-established mapping rules, matching functional modules are called and combined from a pre-built modular configuration library to generate at least one form-function co-design scheme for a configurable entity. The modular configuration library stores configuration information for multiple functional modules encapsulated with different enabling technologies and standardized interfaces. The configurable entity refers to a physical object with a specific form and function, formed by selecting and combining functional modules from the modular configuration library; it can be, but is not limited to, mobile robots, fixed workstations, and special-purpose machine tools. S3. Group Collaboration Strategy Formulation: When a task requires execution by an entity cluster consisting of multiple functional entities, a task allocation scheme and collaborative operation strategy are formulated for the entity cluster based on the scenario feature parameters and the task objective; wherein, the entity cluster refers to a collection of multiple functional entities that work collaboratively to achieve a common task objective. S4. Job Execution and Data Feedback: Based on the form and function collaborative design scheme and the collaborative job strategy, control the functional entities or entity clusters to enter the application scenario to execute jobs, and collect scenario feedback data and functional entity operation data in real time during the job process. S5. Dynamic Iterative Optimization: The collected scenario feedback data and functional entity operation data are compared with the expected performance indicators of the functional entities, and the form and function collaborative design scheme and / or collaborative operation strategy are iteratively optimized based on the comparison results.

2. The scenario-based customized functional entity design method according to claim 1, characterized in that, The specific steps in step S2 for generating a configurable entity's form and function co-design scheme include: S21. Functional Projection: Decompose the quantified performance indicators of the functional entities into specific technical requirements for the perception, decision-making, and execution functional modules; S22. Morphology Generation: Based on the specific technical requirements and the physical constraints in the scene feature parameters, select matching functional modules from the modular configuration library, perform three-dimensional spatial layout and structural optimization, and generate the physical morphology of the functional entity. S23. Coupling Verification: Simulate and verify the generated morphological and functional co-design scheme in a virtual environment to ensure that it meets the performance indicators of all functional entities in the application scenario.

3. The scenario-customized functional entity design method according to claim 1, characterized in that, In step S3, the optimization objectives for formulating collaborative operation strategies for entity clusters include at least one of the following: maximizing task completion efficiency, minimizing total system energy consumption, or maximizing the robustness of entity clusters.

4. The scenario-customized functional entity design method according to claim 1, characterized in that, The iterative optimization in step S5 includes, but is not limited to: online updating of software parameters for the functional modules of functional entities or reallocating tasks for the entity cluster.

5. The scenario-customized functional entity design method according to claim 1, characterized in that, The enabling technologies encapsulated in the functional modules of the modular configuration library include at least two of the following: environmental perception technology, motion control technology, artificial intelligence algorithms, and energy management technology.

6. A scenario-customized functional entity design method according to any one of claims 1 to 5, characterized in that, The application scenarios include, but are not limited to: laser additive manufacturing in microgravity environments in space, high-altitude on-site additive manufacturing of wind turbines, additive manufacturing of large spare parts for marine engineering, and additive repair and remanufacturing of key braking components for high-speed railways.

7. A scenario-customized functional entity design method according to any one of claims 1 to 5, characterized in that, The functional entities include, but are not limited to: mobile robots, additive manufacturing equipment, or testing equipment.

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