A microcosm closed-loop intelligent agent behavior data production system and method based on perturbation and bidirectional execution

Abstract

This invention discloses a system and method for producing physical behavior data of intelligent agents (AI individuals or robots) applicable to various types of physical operation scenarios. The system, centered on a scene perturbation unit and a bidirectional execution unit, continuously generates a large amount of non-repetitive, high-value intelligent agent behavior data through dynamic environmental changes and forward and reverse physical operations. Based on this, it constructs miniature physical scenes at scales of 1:5 to 1:20 and builds a 1:1 real-world scene as a calibration benchmark, achieving precise mapping between the miniature and real-world scenes in terms of spatial dimensions, movement paths, operational force, and action postures. This ensures that the data collected from the miniature scenes can be directly used for physical intelligent agents. The system can be expanded stepwise from a single scene to multi-scene clusters, comprehensive scenes, and even micro-community-level large-scale data production units, forming a 24 / 7 autonomous closed-loop data production system without human intervention. This invention can cover both conventional and extreme environments such as life services, industrial construction, warehousing and logistics, transportation, spaceport construction, lunar bases, and other planetary construction, effectively solving industry pain points such as high cost of acquiring training data for intelligent agents, insufficient realism, and difficulty in large-scale production.

Description

Technical Field

[0001] This invention relates to technologies for intelligent agent training, physical behavior data acquisition, automated scene control, scaling and real-world scene calibration, and particularly to an autonomous closed-loop intelligent agent data production system and method that combines scene perturbation and bidirectional execution with proportional scaling and 1:1 real-world benchmark calibration.

[0002] Neutral Technical Declaration: This invention pertains to a neutral technical solution for generating physical behavior data of artificial intelligence agents, providing only a systematic technical architecture for scene construction, dynamic perturbation, bidirectional execution, and cross-scale calibration. This invention does not limit the content of data collection or its application. Any entity using this invention's technical solution must strictly comply with national laws and regulations, industry standards, and public order requirements. The patent holder assumes no joint liability for the actual operation, data compliance, or commercial activities of the implementing party. Background Technology

[0003] Currently, when intelligent agents perform tasks such as construction, operation, and cleanup in physical scenarios, they generally require massive amounts of real physical behavior data to support training. Existing technologies have the following problems.

[0004] Static scenarios involve simple tasks, making it difficult to generate diverse and non-repetitive data.

[0005] Without a mechanism to dynamically change the environment, intelligent agents cannot adapt to complex real-world environments.

[0006] The lack of a unified 1:1 benchmark calibration between miniature scenes and real-world scenes makes the data ratio prone to errors and difficult to implement directly.

[0007] Existing data collection methods are costly and inefficient, making it impossible to achieve large-scale, autonomous, closed-loop, and continuous production.

[0008] To address this issue, the present invention proposes an autonomous closed-loop data production system that uses scene perturbation and bidirectional execution as its core, combined with proportional scaling and 1:1 real-scene calibration, to solve the above problems from the source. Summary of the Invention

[0009] This invention discloses a miniature scene autonomous closed-loop data production system and method for intelligent agents in physical operations, which solves the technical defects of existing technologies such as single static scene tasks, lack of dynamic environmental change mechanism, no unified benchmark calibration between miniature and real scenes, high data acquisition cost and difficulty in large-scale closed-loop production.

[0010] This invention uses scene perturbation combined with bidirectional execution as its core architecture. First, it achieves the autonomous generation of massive physical behavior data through dynamic perturbation and bidirectional operation execution. Then, it achieves large-scale expansion of data production capacity through proportional miniaturized scene construction. Finally, it completes full-domain parameter calibration based on a one-to-one real benchmark scene to ensure that the miniaturized generated data can be accurately transferred to real intelligent agents for use.

[0011] The entire system requires no manual intervention and can operate autonomously around the clock. It generates new work scenarios and tasks through continuous dynamic perturbation, with forward and reverse units completing physical tasks and simultaneously collecting behavioral data. Then, it uses a real benchmark scenario to uniformly calibrate the parameters of the miniature scenario, such as space, path, interaction intensity, and action posture, eliminating scaling errors. This invention's architecture is adaptable to various conventional physical work scenarios and intelligent agent simulation training, behavioral data accumulation, and work plan verification in extreme and special environments. The system comprises seven core functional units: the scenario perturbation unit is the core driver of the system, generating dynamic, non-repetitive, sudden, and abnormal work scenarios by adjusting the state of objects, environmental conditions, and task parameters, providing a continuous source of tasks for subsequent work units and ensuring the diversity of training data and the realism of the scenarios.

[0012] The forward execution unit is used to complete constructive physical operations such as construction, assembly, tidying, replenishment, deployment, and repair, and collects effective behavioral data during the operation.

[0013] The reverse processing unit is used to perform reverse physical operations such as cleaning, recycling, removal, demolition, and disposal. It simultaneously collects corresponding disposal behavior data and forms a complete operation loop with the forward execution unit, thereby improving data output efficiency.

[0014] The data filtering unit automatically identifies and retains valid behavioral data, while eliminating erroneous actions, redundant states, and invalid disturbance data, thereby achieving data purification.

[0015] The proportionally scaled-down scene unit constructs a physically replicated scene at a fixed scale based on the real application environment, supports multiple copy replication, and realizes large-scale expansion of data production.

[0016] The 1:1 scale benchmark calibration module builds a benchmark scene consistent with the actual application environment, and performs unified calibration on the operating parameters of the entire miniature scene to ensure that the miniature data is error-free and adapted to the real intelligent agent.

[0017] The tiered, scaled-up units, following a progressive path from single-scenario to multi-scenario parallel processing, comprehensive scenarios, and cluster-level scenarios, enable industrialized, low-cost, batch data production.

[0018] The system's closed-loop operation process is as follows: the scene disturbance unit generates dynamically variable work tasks and environmental conditions; the forward execution unit completes constructive work and collects data; the reverse processing unit completes reverse processing work and collects data; after data filtering and archiving, the miniature scene undergoes periodic automatic parameter calibration based on the real benchmark scene; the scene disturbance is continuously iterated and updated, and the system runs in a loop to achieve uninterrupted data mass production; after the single scene operation is stable, the miniature unit is replicated to achieve step-by-step large-scale expansion. This invention's architecture is highly versatile and iterable, backward compatible with small, single-unit work scenarios, and upward expandable to large-scale integrated cluster scenarios, possessing multi-stage technology upgrade and industrial application expansion potential.

Claims

1. An autonomous closed loop agent physical behavior data production system, characterized by, include: At least one scene disturbance unit is configured to modify the environmental state, item attributes and task conditions to generate dynamic job tasks (repeatable). At least one forward execution unit is configured to perform constructive, incremental, regular, or assembly-type physical operations and collect valid behavioral data; At least one reverse processing unit is configured to perform reverse processing physical operations and collect valid behavioral data; The data filtering and storage module is configured to retain valid data and remove invalid data; The system features a proportionally scaled miniaturized scene unit, supporting scene construction at scales of 1:5 to 1:20 and large-scale multi-copy replication; a 1:1 real-scale benchmark calibration module for calibrating the scale and parameters of miniaturized scenes; and a system centered on scene perturbation and bidirectional execution to form an autonomous closed loop, enabling continuous production of intelligent agent physical behavior data without human intervention, and capable of producing intelligent agent physical behavior data on a large scale 24 / 7.

2. An autonomous closed-loop agent physical behavior data production method, characterized by, The process includes the following steps: a scene perturbation unit generates dynamically changing and non-repetitive tasks; a forward execution unit completes constructive physical operations and collects behavioral data; a reverse processing unit completes reverse physical operations and collects behavioral data; the system filters and retains valid behavioral data; based on a 1:1 real benchmark scene, data calibration is performed on miniature scenes at scales of 1:5 to 1:20, with calibration performed automatically on a regular basis; the perturbation, bidirectional execution, and data filtering processes are executed cyclically to achieve autonomous and uninterrupted data production; and the miniature scene unit is replicated to achieve multi-node parallel large-scale data production.

3. The system of claim 1, wherein: The system is applicable to physical operation scenarios that include forward construction and reverse processing logic, including but not limited to conventional and extreme environments such as retail, catering, community services, housekeeping, warehousing, factories, industrial construction, transportation, spaceport construction, lunar base and extraterrestrial base construction and maintenance.

4. The system according to claim 1, characterized in that: Forward execution, reverse processing, and scene disturbance functions can be implemented by independent intelligent agents or integrated by the same intelligent agent. The number of devices and deployment form do not affect the protection scope.

5. The system according to claim 1, characterized in that: The scene disturbance function can be implemented by an execution unit, an independent intelligent agent, a backend system, or a cloud unit. Any changes in the function deployment method fall within the protection scope of this invention.

6. The system according to claim 1, characterized in that: The scaled-down scenes are calibrated to a 1:1 real-world scale to ensure accurate mapping of spatial dimensions, motion paths, operational force, and action postures.

7. The method according to claim 2, characterized in that: Each step can be executed in parallel, interleaved, or asynchronously, and all steps that maintain the core architecture of "disturbance-driven - bidirectional execution - data calibration - closed-loop production" fall within the protection scope of this invention.