Real-Time Optimization of Production Operations
Ongoing

Multi-Agent Collaborative Automation for the Full Precision-Motor Testing Pipeline

A multi-agent system integrating MCP tool invocation, memory, and skill orchestration upgrades motor test-and-tuning judgment — traditionally reliant on human expertise — into quantitative, automated decision-making

Multi-Agent System Industrial Intelligent Decision-Making Knowledge Automation Precision Motors
Multi-Agent Collaborative Automation for the Full Precision-Motor Testing Pipeline

Project Overview

Pass/fail judgment in conventional motor test-and-tuning depends heavily on individual expertise, so conclusions vary subjectively across time and personnel; only key scalar values and isolated screenshots are retained, while continuous transient waveforms go unrecorded, losing transient information.

Research Objectives

1

Robust parsing of multimodal test data and extraction of feature indicators

2

Codifying expert experience and evolving it into dynamic memory

3

High-precision quantitative decision-making and automated report generation

Methodology

A multi-agent collaborative framework (intent-aware routing, cross-modal parsing, intelligent multi-tool coordination) organizes the diagnostic pipeline via MCP tool invocation, Memory, Skills, and Hooks, with accumulated data and evaluation feedback continuously improving front-end accuracy.

Technical Approach: How It Works

  1. 1

    Digitizing the Test-and-Tuning Workflow

    The manual "sense-and-acquire → report generation → pass/fail judgment → loop tuning" workflow is decomposed into an orchestrable agent task graph, retaining continuous transient waveforms in full to eliminate the information loss of "screenshots plus scalars."

  2. 2

    Multi-Agent Collaborative Framework

    Three agent classes — intent-aware routing, cross-modal parsing, and multi-tool coordination — divide the work; MCP tool invocation, Memory, Skills, and Hooks together organize the diagnostic pipeline.

  3. 3

    Codifying and Feeding Back Expert Experience

    Veteran technicians' judgment experience is codified into reusable rules and memory; every judgment outcome is logged, evaluated, and fed back to the front end, continuously improving accuracy.

Figures: Methods & Results

Automated motor test-and-tuning workflow: sense — report — judge — tune
Automated motor test-and-tuning workflow: sense — report — judge — tune
Multi-agent quantitative analysis report interface
Multi-agent quantitative analysis report interface

Key Results

99.8% pass/fail judgment accuracy; 73% efficiency gain over manual judgment (person-day comparison)

Multimodal search and automated diagnosis now replace manual judgment

Quantitative analysis reports are generated automatically, with traceable conclusions

Real-Time Optimization of Production Operations

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