Monitoring method and device and storage medium

A technology for monitoring data and monitoring nodes, applied in measurement devices, instruments, etc., can solve the problems of single data source, error in monitoring results, and inability to make accurate predictions, and achieve the effect of reducing errors and accurate predictions

Inactive Publication Date: 2019-12-20
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AI-Extracted Technical Summary

Problems solved by technology

[0003] However, the current monitoring scheme still has the problems of single data source, larg...
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An embodiment of the invention discloses a monitoring method. The monitoring method comprises steps as follows: monitoring data of monitoring nodes in multiple target areas in a set time period are acquired; and the fitting relation between time points in the time period and the monitoring data corresponding to the time points is acquired on the basis of a machine learning algorithm, and a predication value of a target time point is acquired according to the fitting relation. As the monitoring data are acquired in the multiple target areas, the monitoring conditions in the target areas can bebetter reflected; and the fitting relation between the time points in the time period and the monitoring data corresponding to the time points is acquired on the basis of the machine learning algorithm, so that the monitoring data in the target time point can be more accurately predicted, and errors of the predication value can be reduced. Moreover, an embodiment of the invention also discloses amonitoring device and a storage medium.

Application Domain

Measurement devices

Technology Topic

Set up timeMonitoring data +4


  • Monitoring method and device and storage medium
  • Monitoring method and device and storage medium
  • Monitoring method and device and storage medium


  • Experimental program(1)

Example Embodiment

[0106] Example 1
[0107] See Figure 8 , which is a schematic diagram of the structural distribution of campus monitoring provided by an embodiment of the present disclosure. Among five monitoring sub-stations 82, each monitoring sub-station comprises a plurality of monitoring nodes 81, see Figure 9 , the monitoring node 81 includes an air monitoring module 101, a water body monitoring module 102, a temperature monitoring module 103, an acoustic wave monitoring module 104, a remote sensing monitoring module 105 and an underground infrared module 106, and the air monitoring module 101 is used for monitoring sulfur dioxide in the air , nitric oxide, hydrocarbons and floating dust, etc., the water body monitoring module 102 is used to monitor the types and concentrations of pollutants in the water body, the temperature monitoring module 103 is used to monitor temperature changes, and the sound wave monitoring module 104 is used to monitor noise sources, the remote sensing monitoring module 105 is used to take pictures of the environment and monitor environmental changes, and the underground infrared module 106 uses infrared sensors to monitor underground pipelines and monitor dredging conditions. The data information of the monitoring sub-station 82 is collected by the monitoring central station 85 and then processed in a centralized manner. See Figure 10 , the total monitoring station 85 includes a wireless transmission module 91 , an information processing module 92 , a PLC controller 93 , and an abnormal alarm module 94 . See Figure 11 , the campus environment monitoring method includes:
[0108] Step s1, the air monitoring module 101, the water body monitoring module 102, the temperature monitoring module 103, the sound wave monitoring module 104, the remote sensing monitoring module 105 through the monitoring nodes 81 of the four monitoring sub-stations 82 in the east, west, south, north and middle and the underground infrared module 106 respectively collect sulfur dioxide, nitrogen monoxide, hydrocarbons, floating dust data, pollutant types and concentration data, temperature data, noise data, environmental picture data, and underground pipeline dredging status data in the air within five days .
[0109] Step a2, the monitoring sub-station 82 sends the data acquired by the monitoring node 81 to the monitoring central station 85, and the monitoring central station 85 preprocesses the received monitoring data, including normalizing the data and Center translation processing.
[0110] Step a3, based on the preprocessed data within five days, use it as a training set to train the neural network to obtain the trained neural network model, that is, the time within the five-day period is obtained through training A fitting relationship between a point and the monitoring data corresponding to the time point.
[0111] Step a4, input the target time point, such as the time point two days after the current time point, into the neural network, and obtain the predicted value corresponding to the time point two days after the current time point according to the fitting relationship, where the prediction Values ​​can correspond to a forecast curve.
[0112] In step a5, the predicted value is displayed on the weather service platform 84 or the user terminal 87 .
[0113] Step a6, when the predicted average value is outside the set threshold, determine the monitoring node 81 and obtain the location information of the monitoring node 81, analyze and process the cause of the abnormality through the PLC controller, and the PLC controller controls the patrol inspection and no one is present. The machine searches the abnormal source nodes in the area, and in response to emergencies, the PLC controller controls the abnormal alarm module to give an alarm prompt, and dispatches the mobile emergency repair vehicle 86 or the inspection drone 83 to quickly deal with the abnormal situation and human intervention, such as Eliminate the failure of the monitoring node 81, take external intervention on the campus environment, etc.
[0114] In the embodiment of the present disclosure, by dividing the smart campus into five areas, the monitoring nodes are distributed in the area, and the air monitoring module, the water body monitoring module, the temperature monitoring module, the sound wave monitoring module, the remote sensing monitoring module and the underground infrared module are used to comprehensively monitor the smart campus. The environmental information of the campus nodes is sent to each monitoring sub-station, and is summarized at the main monitoring station. After processing, it is sent to the weather service platform to obtain the average value of the environment in each area of ​​the smart campus, which comprehensively reflects the monitoring data of the environment. Provide correct and comprehensive smart campus environment information; secondly, monitor various environmental information through the monitoring nodes all over the smart campus, the signal processing module processes the monitoring data, calculates the average value of the monitoring results in each area as a standard reference, and then The data of each node is compared with the standard reference. When the data differs greatly, the abnormal data is output, and the cause of the abnormality is analyzed and processed through the PLC controller. The PLC controller controls the inspection drone to search for the regional abnormal source node, and Timely mobilization of emergency repair vehicles to quickly deal with abnormal situations and human intervention can reduce the impact errors caused by individual environments on monitoring results, and at the same time avoid the possibility of environmental damage in time, which is conducive to protecting the environment; finally, based on the neural network, through the near The five-day environmental data is analyzed to obtain a fitting relationship, and predict the recent changes in the smart campus environment. The weather service platform sends the current and future environmental tweets to the user terminal according to the prediction, which is beneficial for people to make advance decisions. Make preparations to facilitate people's lives.


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Owner:国网河北省电力有限公司营销服务中心 +2

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