Loading quantity calibration system based on cloud platform big data and excavator

By leveraging cloud platform big data technology and utilizing Kalman filtering and neural network optimization of weight coefficients to achieve a dynamic calibration mechanism, the problem of error accumulation and poor adaptability caused by environmental interference in the loading operation of engineering machinery by pure vision algorithms has been solved, thus achieving high-precision and stable loading quantity calibration.

CN120808230APending Publication Date: 2025-10-17潍柴(青岛)智慧重工有限公司 +1
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Patent Information

Application Number
CN202510881363.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-27
Publication Date
2025-10-17

AI Technical Summary

Technical Problem

In existing technologies, pure vision algorithms are susceptible to the accumulation of computational errors caused by environmental interference and have poor cross-scene adaptability in engineering machinery loading operations.

Method used

By introducing a dynamic calibration mechanism through big data from the cloud platform, the Kalman filter algorithm is used to fuse the excavator's operation video stream and sensor data. The weight coefficients are optimized through a neural network model, and dynamic calibration is performed by combining historical operation data and real-time sensor feedback to correct the visual algorithm parameters in real time.

Benefits of technology

It effectively eliminates error accumulation caused by environmental interference, improves calibration accuracy and cross-scenario adaptability, and enhances the robustness and reliability of the system.

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Abstract

The invention provides a loading quantity calibration system based on cloud platform big data and an excavator, and relates to the technical field of engineering machinery metering and calibration, and the system comprises a data collection module which is used for obtaining an excavator operation video stream N vision, sensor data [delta] N real-time and historical operation data [delta] N history; the cloud platform data processing module is used for fusing the multi-source data, dynamically calibrating the loading quantity and outputting a calibration result; and the feedback execution module is used for issuing the calibration result to the vehicle control unit and correcting the visual algorithm parameters in real time. According to the loading quantity calibration system based on the cloud platform big data and the excavator, through multi-source data fusion, dynamic weight adjustment and the cloud platform technology, the defects of a pure vision algorithm in the prior art are effectively overcome, high-precision and real-time calibration of the loading quantity is achieved, and the loading quantity calibration system has the advantages of being high in robustness, high in expandability and low in delay.
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