CN-122023922-A - Real-time monitoring and data analysis method and system for multi-source information fusion

CN122023922ACN 122023922 ACN122023922 ACN 122023922ACN-122023922-A

Abstract

The invention belongs to the technical field of equipment early warning, and particularly relates to a real-time monitoring and data analysis method and system for multi-source information fusion, wherein the method comprises the steps of realizing automatic identification and data acquisition, remote assistance and support, intelligent path planning and navigation functions in the inspection process of a chemical device so as to standardize the inspection process and improve the inspection efficiency and result accuracy; the method comprises the steps of establishing a chemical device image recognition model, collecting and preprocessing chemical device image data, performing model training by using a convolutional neural network deep learning model, deploying the model training into actual equipment, developing an abnormal characteristic extraction algorithm, combining an intelligent early warning and processing system to monitor the running state of the equipment in real time, quickly sending early warning and taking corresponding processing measures, adopting an abnormal detection and classification technology to realize an abnormal detection algorithm, developing an efficient alarm information transmission mechanism and an intelligent alarm information processing system, and establishing an alarm information feedback and closed-loop management mechanism to improve the accuracy and timeliness of abnormal detection.

Inventors

Li Gaijun
SHEN ZONGYUAN
ZHU YUHONG
XUE LI
ZHAO ZONGQIANG
WANG HAILONG
WANG GUANGXING
XU DONGHONG
SHI JIANHUA
CAO XIAODONG

Assignees

新疆圣雄氯碱有限公司
新疆中泰化学股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260203

Claims (10)

1. The real-time monitoring and data analysis method for multi-source information fusion is characterized by comprising the following steps of: acquiring multi-source state information from a patrol object; performing association fusion based on the multi-source state information to form comprehensive state data for representing the running state of the inspection object; And triggering abnormality judgment and generating a corresponding treatment instruction when the comprehensive state data meets a preset abnormal condition.
2. The method of claim 1, wherein the multi-source status information comprises patrol environment information, the method further comprising: And generating a patrol path based on the patrol environment information, and dynamically adjusting the patrol path according to the state change of the patrol object.
3. The method of claim 1, wherein the multi-source status information comprises image information of a patrol object, The image information is preprocessed and then used for extracting characteristic information for state judgment.
4. The method of claim 1, wherein the anomaly determination is accomplished based on a difference relationship between historical state data and current state data.
5. The method of claim 1, wherein the communicating and feeding back of the exception information includes recording and backtracking of exception handling results to update subsequent exception decision rules.
6. A system for real-time monitoring and data analysis of multi-source information fusion, comprising: The data acquisition module is used for acquiring multi-source state information of the inspection object; the fusion analysis module is used for carrying out association fusion on the multi-source state information to generate comprehensive state data; The abnormal triggering module is used for generating a disposal instruction when the comprehensive state data meets the abnormal condition; a feedback updating module for updating the abnormal judgment basis according to the treatment result, To form a closed loop system for anomaly identification and handling.
7. The system of claim 6, wherein the data acquisition module comprises an image acquisition unit for acquiring image information of the inspection object.
8. The system of claim 6, wherein the feedback update module is coupled to a defect library module for storing exception types and corresponding handling rules.
9. An anomaly alarm closed loop management module for a multi-source monitoring system, the module comprising: an abnormality information receiving unit configured to receive an abnormality determination result; A treatment instruction generation unit configured to generate an abnormality treatment instruction; a result feedback unit for receiving the treatment result and updating the abnormal treatment rule, To realize closed-loop management of abnormal alarm.
10. The module of claim 9, wherein the module is deployed at a server side and performs data interaction with a terminal device through a network.

Description

Real-time monitoring and data analysis method and system for multi-source information fusion Technical Field The invention belongs to the technical field of equipment early warning, but is not limited to the technical field of equipment early warning, and particularly relates to a method and a system for real-time monitoring and data analysis of multi-source information fusion. Background The intelligent interaction-based chemical process key equipment state early warning analysis and decision system has the following characteristics in the current development state and trend at home and abroad: The intelligent fault early warning and health management system for the data driven equipment is mainly applied to key equipment such as high-speed rails, airplanes, turbines, wind driven generators and the like, and relates to multiple aspects such as system modeling and simulation, state monitoring, fault early warning, reliability control and the like. Currently, most of the countries are in the traditional time domain and frequency domain analysis stage, and large foreign enterprises have begun to implement predictive maintenance. The global predictive maintenance solution market size reached 252 billions in 2019, and it was expected that 929 billions would be reached in 2026. The application of artificial intelligence in the chemical field mainly focuses on material research and development and synthesis, intelligent production scheduling and optimization, quality control and detection, predictive maintenance and equipment health management, safety risk assessment and early warning and the like. These applications help to improve production efficiency, reduce costs, and improve safety. Deep learning technology is widely applied to chemical process fault detection and diagnosis, including methods based on automatic encoders, deep belief networks, convolutional neural networks and recurrent neural networks. These methods have attracted considerable attention both in the academia and industry, but still face challenges such as data, modeling, and visualization. The data driving method is applied to the fault diagnosis of the chemical process, and the data driving method is used as a black box model and has great advantages in the fault diagnosis of the chemical process. At present, deep learning and ensemble learning are important research points of data driving methods. Research at home and abroad shows that the problem of chemical process can be effectively solved by combining and using various data driving methods. The application of the artificial intelligence in the chemical process control comprises the aspects of expert system, machine learning, natural language processing, computer vision, intelligent control and the like. The application of the techniques is helpful for realizing the automation, the intellectualization and the optimization control of the chemical production process. In summary, the intelligent interaction-based chemical process key equipment state early warning analysis and decision system is in a rapid development stage at home and abroad, and particularly in the application aspects of data driving and artificial intelligence technology. In the future, these techniques will be applied more deeply to various aspects of the chemical industry, improving the production efficiency and safety performance. Because most chemical production enterprises have the characteristics of inflammability, explosiveness, high temperature, high pressure and the like, and part of production processes contain highly corrosive and highly toxic substances, the requirements on safe operation and emergency treatment of the chemical production are higher. The occurrence rate of natural disasters and public accidents in China generally falls off in the 21 st century, but the accident control condition in the chemical industry is still serious. The problems of insufficient safety supervision, lagging production equipment and the like seriously affect the safety production in chemical enterprises, and serious safety accidents occur in chemical industry (dangerous chemicals). The inspection of the device is a critical work in the safety guarantee of chemical production, and aims to ensure the safe operation of chemical production facilities and the stability of production quality. In the inspection work, an inspector reaches a designated position through a preset plan to perform data acquisition, risk identification, hidden danger investigation and other work contents on an inspected object, so that the risk hidden danger and abnormal data existing in the enterprise safety production and equipment operation can be effectively ensured to be strictly controlled. This work involves the comprehensive inspection and monitoring of production equipment, process flows and environmental conditions. The inspection process comprises the steps of inspecting the appearance and the function of the equipment, ensuring the equipment