CN116090591A - Tunnel equipment facility state monitoring and early warning system based on machine learning - Google Patents
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Abstract
The invention relates to the technical field of equipment state monitoring and discloses a tunnel equipment facility state monitoring and early warning system based on machine learning, which comprises a system module assembly, wherein the system module assembly comprises an equipment data acquisition module, the equipment data acquisition module is connected with an equipment data preprocessing module, and the equipment data preprocessing module comprises a data classification module, processing modules of different characteristic parameters, a data cleaning module, a coefficient scaling module of the characteristic parameters and a correlation processing module among the characteristic parameters. According to the invention, rapid and stable uploading of different sensor data can be ensured through 5G transmission, the background uses the Internet of things management platform frame to realize standardized management of facility equipment in the tunnel, the machine learning technology can realize prediction of equipment state, early warning and processing of equipment failure are realized, the dynamic maintenance of important equipment facilities by workers is facilitated, and the service problem caused by equipment shutdown is reduced or avoided.
Description
Technical Field
The invention relates to the technical field of equipment state monitoring, in particular to a tunnel equipment facility state monitoring and early warning system based on machine learning.
Background
The tunnel is built with four large intelligent systems of fire-fighting ventilation water supply and drainage, strong electricity, illumination, weak electricity and the like, wherein the four intelligent systems comprise a fire-fighting system, a power monitoring system (water supply and drainage and ventilation), a UPS system, an illumination system, an iFix system, a central computer system, a frequency modulation broadcasting system, a traffic flow system, an unattended power monitoring system, a main cable dehumidification system, a health monitoring system and the like, so that the daily management requirement of the tunnel is met.
However, various informatization systems are various in equipment, ageing, damage, faults and the like can occur in equipment facilities in the daily use process, and because various informatization systems are independently developed, are independent of each other, lack a platform capable of carrying out comprehensive data acquisition and unified monitoring management, each set of system needs professional personnel to carry out operation and maintenance, lack a management platform capable of carrying out full life cycle monitoring and comprehensive equipment running state monitoring, each system only carries out data acquisition and monitoring, does not carry out relevant deepening processing on the data, and lack the capability of data analysis means and predicting the health state of the equipment facilities, therefore, the tunnel equipment facility state monitoring and early warning system based on machine learning is very necessary.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a tunnel equipment facility state monitoring and early warning system based on machine learning, which solves the problems that each set of system needs professional personnel to operate and maintain, lacks a management platform capable of full life cycle monitoring and comprehensive equipment operation state monitoring, only performs data acquisition and monitoring, does not perform relevant deep processing on the data, lacks data analysis means and the capability of predicting equipment facility health state, and cannot meet the requirements of people.
(II) technical scheme
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the tunnel equipment state monitoring and early warning system based on machine learning comprises a system module assembly, wherein the system module assembly comprises an equipment data acquisition module, the equipment data acquisition module is connected with an equipment data preprocessing module, the equipment data preprocessing module comprises a data classification module, a processing module for different characteristic parameters, a data cleaning module, a coefficient scaling module for characteristic parameters and a correlation processing module among characteristic parameters, the equipment data preprocessing module is connected with an algorithm model test module, the algorithm model test module comprises a linear regression model, a decision tree model, a neural network model and a random forest model, the algorithm model test module is connected with an algorithm model optimization verification module, the algorithm model optimization verification module is connected with a system model deployment operation module, the system module assembly comprises a communication module, the communication module comprises an internet of things lightweight gateway device, the internet of things lightweight gateway device is connected with an intelligent monitoring system for a tunnel, the equipment data preprocessing module is connected with an AI early warning module, the AI early warning module is based on an artificial intelligent machine learning architecture, the AI early warning module carries out analysis and prediction on state data of equipment needing to be focused by adopting a time sequence prediction algorithm based on a neural network, the AI early warning module is connected with the learning module and the learning model module, the system model optimization verification module comprises a data mining module, the system model deployment module is connected with the system model deployment module, the system module is used for data mining and the data mining method comprises a data mining module, and the data mining module is based on the data mining method, the data mining module obtains deep knowledge through machine learning or through a mathematical algorithm.
On the basis of the scheme, the information collected by the equipment data collection module comprises the current, the voltage, the temperature and the humidity of the equipment, and the information collected by the equipment data collection module also comprises historical state data with time information.
Further, the device data acquisition module is connected with an internet of things platform and a storage module, the internet of things platform stores the acquired data of the tunnel intelligent system into a database suitable for the type of the data of the internet of things, namely the storage module, and simultaneously opens an interface for acquiring the data in a related manner, and the data is published into a public cloud or a private cloud environment according to the instruction by utilizing available network connection.
As a still further scheme of the invention, the system module assembly comprises a recording module, wherein the recording module automatically records an operation log, records fault information in the operation process in detail, and the system records error data in detail and pre-warns error input information.
On the basis of the scheme, the system model deployment operation module is connected with an execution module, and the execution module performs operations based on the data according to program design in a data processing stage.
Further, the communication module transmits through 5G.
As still further scheme of the invention, the time series-based prediction model generated by training can output state data predicted by the equipment facility for 10 days in the future through analysis of data before 10 days in the history, for evaluation of accuracy of the prediction model, correlation evaluation is carried out by adopting a prediction value and an actual value through an RMSE (root mean square error) algorithm, the data of the predicted equipment facility is compared with a preset equipment facility fault threshold in a correlation manner, and if the prediction value exceeds the equipment facility threshold, the possibility that the equipment facility will fail in a future day is judged.
On the basis of the scheme, the AI early warning module is embedded in the system platform, and the realization of the module function is based on the generation and deployment of a training model of an early warning algorithm.
The beneficial effects of the invention are as follows:
1. the invention can adapt to various proprietary communication protocols, solves the problem that different systems cannot be connected, acquires various real-time data of the tunnel intelligent system, realizes concurrent large data volume, and can be simple sensor data or complex real-time video stream data.
2. According to the method, historical state data with time information of equipment are collected, data preprocessing is conducted on the historical data based on a time sequence, characteristics of the data are analyzed, abnormal data are removed, the data are input into a neural network algorithm, the algorithm super-parameter variable is adjusted through repeated training of the algorithm, and finally an optimal prediction model is trained.
3. In the invention, the data is changed into the information tool through the data analysis module, the information is changed into the cognition tool through the data mining module, and the information obtained by data analysis is converted into effective prediction and decision.
4. According to the invention, rapid and stable uploading of different sensor data can be ensured through 5G transmission, the background uses the Internet of things management platform frame to realize standardized management of facility equipment in a tunnel, the machine learning technology can realize prediction of equipment state, early warning and processing of equipment failure are realized, dynamic maintenance of important equipment facilities by workers is facilitated, and service problems caused by equipment shutdown are reduced or avoided.
Drawings
Fig. 1 is a schematic diagram of a system frame structure of a tunnel equipment facility state monitoring and early warning system based on machine learning.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. It should be noted that the terms "mounted," "connected," and "disposed" are to be construed broadly, unless explicitly stated or defined otherwise, and that the particular meaning of such terms in this patent will be understood by those of ordinary skill in the art, as appropriate.
Example 1
Referring to fig. 1, a tunnel equipment facility state monitoring and early warning system based on machine learning comprises a system module assembly, wherein the system module assembly comprises an equipment data acquisition module, the equipment data acquisition module is connected with an equipment data preprocessing module, the equipment data preprocessing module comprises a data classification module, a processing module of different characteristic parameters, a data cleaning module, a coefficient scaling module of the characteristic parameters and a correlation processing module among the characteristic parameters, the equipment data preprocessing module is connected with an algorithm model test module, the algorithm model test module comprises a linear regression model, a decision tree model, a neural network model and a random forest model, the algorithm model test module is connected with an algorithm model optimization verification module, the algorithm model optimization verification module is connected with a system model deployment operation module, the system module assembly comprises a communication module, the communication module comprises an internet of things light-weight gateway device, the internet of things light-weight gateway device is connected with an intelligent monitoring system of a tunnel, various proprietary communication protocols can be adapted, the problem that the tunnel cannot be connected with different systems is solved, various real-time data of the tunnel intelligent system are obtained, large data volume is concurrent, the data can be simple sensor data or complex real-time video stream data, the equipment data preprocessing module is connected with an AI early-warning module, the AI early-warning module is based on an artificial intelligent machine learning architecture, the AI early-warning module adopts a neural network-based time sequence prediction algorithm to analyze and predict the state data of equipment facilities needing to be focused, the AI early-warning module is connected with a learning module, a training module and a prediction model module, the history state data of the equipment facilities with time information is acquired, the method comprises the steps of preprocessing data based on historical data of a time sequence, analyzing characteristics of the data, removing abnormal data, inputting the data into a neural network algorithm, repeatedly training the algorithm, adjusting algorithm hyper-parameter variables, and finally training an optimal prediction model, wherein the system module assembly comprises a machine learning data mining module, the machine learning data mining module comprises a data analysis module and a data mining module, the data analysis module acquires knowledge of data appearance based on a database through a statistical, calculation and sampling method, the data mining module acquires deep knowledge through machine learning or a mathematical algorithm, the data analysis module changes the data into information tools, the data mining module changes the information into cognitive tools, and the information obtained through data analysis is converted into effective prediction and decision.
The information collected by the equipment data collection module comprises current, voltage, temperature and humidity of equipment, the information collected by the equipment data collection module also comprises historical state data with time information, the equipment data collection module is connected with an internet of things platform and a storage module, the internet of things platform stores the acquired data of the tunnel intelligent system into a database suitable for the type of the internet of things data, namely the storage module, an interface for related acquisition data is opened, the data is published to public cloud or private cloud environment according to an instruction by utilizing available network connection, the system module assembly comprises a recording module, the recording module automatically records operation logs, fault information in the operation process is recorded in detail, the system carries out detailed recording on the error data, and early warning is carried out on the error input information.
Example 2
Referring to fig. 1, a tunnel equipment facility state monitoring and early warning system based on machine learning comprises a system module assembly, wherein the system module assembly comprises an equipment data acquisition module, the equipment data acquisition module is connected with an equipment data preprocessing module, the equipment data preprocessing module comprises a data classification module, a processing module of different characteristic parameters, a data cleaning module, a coefficient scaling module of the characteristic parameters and a correlation processing module among the characteristic parameters, the equipment data preprocessing module is connected with an algorithm model test module, the algorithm model test module comprises a linear regression model, a decision tree model, a neural network model and a random forest model, the algorithm model test module is connected with an algorithm model optimization verification module, the algorithm model optimization verification module is connected with a system model deployment operation module, the system module assembly comprises a communication module, the communication module comprises an internet of things light-weight gateway device, the internet of things light-weight gateway device is connected with an intelligent monitoring system of a tunnel, various proprietary communication protocols can be adapted, the problem that the tunnel cannot be connected with different systems is solved, various real-time data of the tunnel intelligent system are obtained, large data volume is concurrent, the data can be simple sensor data or complex real-time video stream data, the equipment data preprocessing module is connected with an AI early-warning module, the AI early-warning module is based on an artificial intelligent machine learning architecture, the AI early-warning module adopts a neural network-based time sequence prediction algorithm to analyze and predict the state data of equipment facilities needing to be focused, the AI early-warning module is connected with a learning module, a training module and a prediction model module, the history state data of the equipment facilities with time information is acquired, the method comprises the steps of preprocessing data based on historical data of a time sequence, analyzing characteristics of the data, removing abnormal data, inputting the data into a neural network algorithm, repeatedly training the algorithm, adjusting algorithm hyper-parameter variables, and finally training an optimal prediction model, wherein the system module assembly comprises a machine learning data mining module, the machine learning data mining module comprises a data analysis module and a data mining module, the data analysis module acquires knowledge of data appearance based on a database through a statistical, calculation and sampling method, the data mining module acquires deep knowledge through machine learning or a mathematical algorithm, the data analysis module changes the data into information tools, the data mining module changes the information into cognitive tools, and the information obtained through data analysis is converted into effective prediction and decision.
The information collected by the equipment data collection module comprises current, voltage, temperature and humidity of equipment, the information collected by the equipment data collection module also comprises historical state data with time information, the equipment data collection module is connected with an internet of things platform and a storage module, the internet of things platform stores the acquired data of the tunnel intelligent system into a database suitable for the type of the internet of things data, namely the storage module, an interface for related acquisition data is opened, the data is published to public cloud or private cloud environment according to an instruction by utilizing available network connection, the system module assembly comprises a recording module, the recording module automatically records operation logs, fault information in the operation process is recorded in detail, the system carries out detailed recording on the error data, and early warning is carried out on the error input information.
In particular, the system model deployment operation module is connected with an execution module, the execution module is used for executing operation according to the program design based on the data, the communication module is used for transmitting through 5G, the rapid and stable uploading of different types of sensor data can be guaranteed through 5G transmission, the background is used for realizing the standardized management of facility equipment in a tunnel by using an Internet of things management platform frame, the machine learning technology can be used for predicting the state of the equipment, the early warning and processing of equipment faults are realized, the time sequence-based prediction model generated by training can be used for outputting the state data predicted for the equipment facilities in the future 10 days through the analysis of the data before the 10 days, the prediction model is used for evaluating the accuracy of the prediction model by adopting a prediction value and an actual value through an RMSE (root mean square error) algorithm, the predicted equipment facility data is compared with a preset equipment fault threshold in a correlation mode, if the prediction value exceeds the equipment fault threshold value, the situation that faults possibly occur in a future day is judged, the dynamic maintenance of important equipment facilities is facilitated for workers, the service problem caused by equipment faults is reduced or avoided, the early warning module is embedded in the system, and the system deployment module is used for realizing the early warning model based on the function deployment algorithm.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (8)
1. The tunnel equipment facility state monitoring and early warning system based on machine learning comprises a system module assembly, and is characterized in that the system module assembly comprises an equipment data acquisition module, the equipment data acquisition module is connected with an equipment data preprocessing module, the equipment data preprocessing module comprises a data classification module, a data cleaning module, a coefficient scaling module of characteristic parameters and a correlation processing module among the characteristic parameters, the equipment data preprocessing module is connected with an algorithm model test module, the algorithm model test module comprises a linear regression model, a decision tree model, a neural network model and a random forest model, the algorithm model test module is connected with an algorithm model optimization verification module, the algorithm model optimization verification module is connected with a system model deployment operation module, the system module assembly comprises a communication module, the communication module comprises an internet of things gateway device, an intelligent monitoring system of an internet of things lightweight gateway device butt-joint tunnel, the equipment data preprocessing module is connected with an AI early warning module, the AI module is based on an artificial intelligent machine learning architecture, the state data of equipment needing attention is predicted by adopting a time sequence prediction algorithm based on the neural network, the AI early warning module is connected with the algorithm model test module, the data mining module is connected with the data mining module, the data mining module is used for acquiring the data mining module, the data mining module is based on the data mining module, the data mining method, the data mining module obtains deep knowledge through machine learning or through a mathematical algorithm.
2. The machine learning based tunnel equipment status monitoring and early warning system of claim 1, wherein the information collected by the equipment data collection module comprises current, voltage, temperature and humidity of the equipment, and the information collected by the equipment data collection module further comprises historical status data with time information.
3. The machine learning-based tunnel equipment facility state monitoring and early warning system according to claim 1, wherein the equipment data acquisition module is connected with an internet of things platform and a storage module, the internet of things platform stores the acquired data of the tunnel intelligent system into a database suitable for an internet of things data type, namely the storage module, and simultaneously opens an interface for acquiring the data by opening an available network connection, and issues the data into a public cloud or private cloud environment according to an instruction.
4. The machine learning based tunnel equipment facility state monitoring and early warning system according to claim 1, wherein the system module assembly comprises a recording module, the recording module automatically records operation logs, fault information occurring in the operation process is recorded in detail, the system records error data in detail, and early warning is carried out on error input information.
5. The machine learning based tunnel equipment facility state monitoring and early warning system according to claim 4, wherein the system model deployment operation module is connected with an execution module, and the execution module performs operations based on the above data according to a program design in a data processing stage.
6. The machine learning based tunnel equipment status monitoring and early warning system of claim 5, wherein the communication module transmits via 5G.
7. The machine learning based tunnel facility status monitoring and early warning system according to claim 1, wherein the time series based prediction model generated by training can output status data predicted for the facility 10 days in the future through analysis of data before 10 days in the history, for evaluation of accuracy of the prediction model, the prediction value and the actual value are used for relevant evaluation through RMSE (root mean square error) algorithm, the data of the predicted facility is compared with a preset facility failure threshold, and if the prediction value exceeds the facility threshold, it is judged that a failure may occur in a future day.
8. The machine learning based tunnel equipment facility state monitoring and early warning system of claim 7, wherein the AI early warning module is embedded in a system platform, and the implementation of the module function is based on the generation and deployment of a training model of an early warning algorithm.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116401128A (en) * | 2023-06-06 | 2023-07-07 | 四川观想科技股份有限公司 | Big data-based information operation and maintenance management system |
| CN117499424A (en) * | 2023-08-23 | 2024-02-02 | 云南云岭高速公路交通科技有限公司 | Tunnel water fire control data acquisition monitoring system |
| CN117666505A (en) * | 2023-12-07 | 2024-03-08 | 江苏保捷精锻有限公司 | Automatic control system of bearing production equipment |
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2022
- 2022-09-29 CN CN202211202938.2A patent/CN116090591A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116401128A (en) * | 2023-06-06 | 2023-07-07 | 四川观想科技股份有限公司 | Big data-based information operation and maintenance management system |
| CN116401128B (en) * | 2023-06-06 | 2023-08-08 | 四川观想科技股份有限公司 | Big data-based information operation and maintenance management system |
| CN117499424A (en) * | 2023-08-23 | 2024-02-02 | 云南云岭高速公路交通科技有限公司 | Tunnel water fire control data acquisition monitoring system |
| CN117666505A (en) * | 2023-12-07 | 2024-03-08 | 江苏保捷精锻有限公司 | Automatic control system of bearing production equipment |
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