Abnormal data detection method and system for vehicle-mounted mobile pump station

By clustering and quantifying the anomaly probability of real-time vibration and pressure fluctuation data of vehicle-mounted mobile pump stations, and dynamically adjusting the model, the problems of low anomaly detection accuracy and poor fault early warning reliability of vehicle-mounted mobile pump stations in complex environments are solved, and accurate anomaly identification and rapid fault early warning are achieved.

CN122346701APending Publication Date: 2026-07-07SHANDONG SANYUAN IND CONTROL AUTOMATION CO LTD
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Patent Information

Application Number
CN202610491087.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-14
Publication Date
2026-07-07

AI Technical Summary

Technical Problem

In existing technologies, vehicle-mounted mobile pump stations have low accuracy in detecting anomalies and poor reliability in fault warning in complex environments. They are difficult to distinguish between internal equipment anomalies and external disturbances, leading to frequent misjudgments and missed judgments.

Method used

Clustering algorithms are used to group and aggregate real-time vibration and pressure fluctuation data. Support vector machines and random forest algorithms are combined to quantify anomaly probabilities. The grouping model is dynamically adjusted through a real-time feedback mechanism to achieve accurate identification of equipment status and fault warning.

Benefits of technology

It enables accurate identification and rapid location of equipment anomalies, reduces false alarms and missed alarms, and improves the reliability and adaptability of fault warning.

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Abstract

The invention relates to the technical field of industrial data processing, and discloses a vehicle-mounted mobile pump station abnormal data detection method and system.The method comprises the steps that real-time vibration parameters and pressure fluctuation data are obtained, and preliminary feature vectors are obtained through preprocessing; clustering the preliminary feature vectors to obtain grouped data clusters; training a classifier to obtain a decision boundary, and obtaining an abnormal probability score through probability mapping; if so, analyzing the feature contribution degree and matching the mapping dictionary to obtain a preliminary abnormal label; performing cross validation and confidence evaluation by fusing historical environmental factor data to obtain refining abnormal source classification; grouping parameters are adjusted in combination with real-time feedback, and an optimized data grouping model is obtained; and performing abnormal quantitative analysis on the real-time data flow by using the optimization model, and if the attribution probability exceeds the early warning limit, generating a fault early warning signal and triggering blocking logic. According to the method, high-precision anomaly detection and real-time early warning of the vehicle-mounted mobile pump station in a complex environment can be realized.
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