CN112187932A - Intelligent monitoring and early warning method for small and medium reservoir dam based on edge calculation - Google Patents
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Abstract
The invention relates to an intelligent monitoring and early warning method for small and medium reservoir dams based on edge calculation, which comprises the following steps: the method comprises the steps that reservoir dam safety monitoring data are obtained through a monitoring sensor and a data acquisition terminal and are sent to an in-place edge gateway, the edge gateway conducts data missing detection and supplementary detection, and effective data are uploaded to a cloud service platform through mobile communication; and issuing the dam safety prediction model obtained by the cloud service platform training to the edge gateway, and drawing up an early warning index by the edge gateway according to the prediction model and carrying out in-situ seepage and deformation real-time early warning, so that the safety monitoring and early warning intellectualization level of the reservoir dam is improved. According to the invention, data processing is not required to be carried out in a central machine room in the dam region, the dam safety prediction early warning can be realized only through monitoring data and edge gateways, the working intensity of managers is reduced, and the safety early warning efficiency of the reservoir dam is improved.
Description
Technical Field
The invention relates to the field of reservoir dam safety monitoring and early warning and water conservancy informatization, in particular to an intelligent monitoring and early warning method for a small and medium-sized reservoir dam based on edge calculation.
Background
9.8 thousands seats of various existing reservoir dams in China, wherein the proportion of medium and small-sized reservoirs is 99.2 percent (the division of the reservoir grades is determined according to the water conservancy and hydropower engineering grade division and flood standard (SL 252-2017)), and the medium and small-sized reservoirs are mostly built in 50-70 years of the 20 th century, and because of the limitation of current historical influence and economic and technical conditions, a plurality of reservoirs have different degrees of risk problems, and immeasurable social and economic losses can be caused once the reservoirs are lost. Safety monitoring is an important means for evaluating the safety state of the dam and ensuring the safe operation of the dam.
At present, most of small and medium-sized reservoirs still carry out safety monitoring data acquisition and analysis work on the basis of a machine room management center, and the mode needs to build a special machine room and is provided with a computer, network equipment, constant-temperature and constant-humidity equipment, an electrostatic floor and the like, so that the mode is high in manufacturing cost, difficult to operate and maintain, limited in computing capacity, closed in data and incapable of circulating, and difficult to adapt to safety management requirements and development trends of small and medium-sized reservoir dams.
With the rapid development of modern information technology, the safety monitoring terminal is combined with the internet of things and cloud computing, so that the comprehensive perception and intelligent management of multi-source information can be realized, and a plurality of defects still exist: (1) it is difficult to guarantee real-time requirements. In the cloud computing model, data need to be uploaded to a cloud computing center for processing, the processing speed of the data is influenced by multiple factors such as network bandwidth, central computing capacity and total computing task amount, and timely response and safety early warning under the emergency condition of a reservoir are difficult to meet; (2) the communication network environment on the reservoir site is excessively dependent. Once the communication network is interrupted, the local data cannot be clouded, and real-time monitoring and early warning cannot be carried out on the reservoir dam.
Edge computing is used as a novel computing model for executing computing at the edge of a network, can effectively solve the problems existing in a cloud computing model, and has been successful in the fields of industrial intelligent manufacturing, unmanned driving, indoor positioning, video security and the like. The novel monitoring mode that middle-size and small-size reservoir safety monitoring combines the edge to calculate can compensate a great deal of not enough that traditional computer lab center and cloud calculated the mode and exist, avoids the excessive dependence to network environment to guarantee timely response and the safety precaution of emergency, provide new thinking and solution for automatic, intelligent modern safety monitoring technique.
Disclosure of Invention
The invention aims to provide an intelligent monitoring and early warning method for small and medium reservoir dams based on edge calculation, so as to improve the real-time in-situ monitoring and early warning capability of the small and medium reservoir dams and reduce the dependence on a network environment.
The invention relates to an intelligent monitoring and early warning method for small and medium reservoir dams based on edge calculation, which comprises the following steps:
the method comprises the steps of firstly, acquiring reservoir dam safety monitoring data through a monitoring sensor and a data acquisition terminal, sending the reservoir dam safety monitoring data to an in-place edge gateway, carrying out data missing detection and supplementary detection on the edge gateway, and uploading effective data to a cloud service platform through mobile communication.
And secondly, the cloud service platform performs deep data analysis and algorithm operation by means of strong computing service capacity of the cloud service platform, issues the trained dam safety prediction model to the edge gateway, and the edge gateway draws up an early warning index according to the prediction model and performs in-situ seepage and deformation real-time early warning, so that the safety monitoring and early warning intelligent level of the reservoir dam is improved.
In the technical scheme, the monitoring sensor mainly refers to a reservoir dam environment quantity, seepage and deformation data acquisition sensor.
The data acquisition terminal can send the acquired data to the edge gateway in a wired or wireless mode according to the actual deployment situation in the field.
In the above technical solution, the edge gateway mainly includes a microprocessor module, a storage module, a power module, a multi-communication interface, a 4G communication module, and the like, wherein the microprocessor module is responsible for data processing and analysis and calculation; the storage module is responsible for data storage; the power supply module provides electric energy; the multi-communication interface is responsible for data interaction and mainly comprises a network interface, RS232 and RS485 serial ports and a USB interface; the 4G communication module is responsible for uploading the packaged data to a cloud platform and receiving the model and the rule issued by the cloud.
In the technical scheme, the cloud service platform is a business service system established on a private cloud service platform, and has the functions of safety monitoring data storage, calculation analysis, data display, model issuing and the like.
The dam safety prediction model can be expressed as
Wherein
An estimate is predicted for the seepage or deformation,
is a component of the upstream water level,
in order to be the rainfall component,
in order to be a component of the temperature,
is an aging component.
In the technical scheme, the concrete modeling process of the dam safety prediction model is as follows: firstly, calculating and selecting seepage or deformation influence factors through environmental quantity data such as water level, rainfall, air temperature and the like in front of a dam bank, then introducing a regression equation from large to small according to the significance degree of the influence factors on seepage or deformation monitoring quantity, and finally establishing a dam seepage or deformation prediction model.
In the technical scheme, the pre-warning index drawing is mainly determined by a confidence interval method, and the method mainly comprises the steps of obtaining a seepage or deformation predicted value YY according to a prediction model issued by a cloud (cloud service platform) and an actually measured environment quantity, and then calculating a standard deviation S of a historical seepage or deformation monitoring quantity, wherein the pre-warning index can be expressed as YY +/-3S. For seepage early warning, when the actual measured seepage pressure or seepage flow Y is more than YY +3S, early warning is carried out; and for the deformation early warning, when the actually measured deformation Y < YY-3S or Y > YY +3S, the early warning is carried out.
The invention relates to an intelligent monitoring and early warning method for small and medium reservoir dams based on edge calculation, which has the following advantages: the dam safety prediction early warning method has the advantages that data processing is not needed in a central machine room for dam area construction, dam safety prediction early warning can be achieved only through monitoring data and edge gateways, expenditure is saved by nearly 100 ten thousand yuan (including management room construction, purchase of a computer, an electrostatic floor, an uninterruptible power supply and constant temperature and humidity equipment), meanwhile, working intensity of managers is relieved, and safety early warning efficiency of the reservoir dam is improved.
The cloud end (cloud service platform) is responsible for establishing and issuing the prediction model, even if the local data cannot be uploaded to the cloud end due to interruption of network communication under special conditions, the edge gateway can still perform local safety early warning according to the prediction model issued in advance, and therefore the excessive dependence of cloud computing on the network communication is eliminated.
And the actually measured data are directly processed at the edge gateway, processing analysis and early warning are not needed through a cloud end, and the time delay of safety early warning of the reservoir dam is reduced so as to meet the timely response under the emergency condition.
Drawings
Fig. 1 is a schematic diagram of an intelligent monitoring and early warning method for a small and medium reservoir dam based on edge calculation.
In the figure: 1. an earth-rock dam; 2. a reservoir; 3. a water level gauge; 4.1. a first LoRa module; 4.2. a second LoRa module; 4.3. a third LoRa module; a first data acquisition terminal 5.1; a second data acquisition terminal 5.2; a third data acquisition terminal 5.3; 6. a serial port line; LoRa wireless transmission; 8. an air temperature meter; 9. a rain gauge; 10, a GNSS deformation monitoring station; a LoRa gateway; 12. an edge gateway; 13. an osmometer; 14. communication transmissions (including 4G transmissions, etc.); 15. communication base stations (including 4G base stations, etc.); 16. a flow meter; 17. a private cloud platform; the solid line part is serial port line transmission, and the dotted line part is LoRa wireless transmission.
Detailed Description
The embodiments of the present invention will be described in detail with reference to the accompanying drawings, which are not intended to limit the present invention, but are merely exemplary. While the advantages of the invention will be apparent and readily appreciated by the description.
With reference to the accompanying drawings: the invention relates to an intelligent monitoring and early warning method for small and medium reservoir dams based on edge calculation, wherein a water level meter 3 is arranged in a reservoir area 2 of an upstream reservoir and transmits measurement data to a first data acquisition terminal 5.1; the air thermometer 8 and the rain gauge 9 are arranged at the top of the earth-rock dam 1, and transmit the measurement data to the second data acquisition terminal 5.2; the data of the first data acquisition terminal 5.1 are transmitted to the LoRa gateway 11 through the first LoRa module 4.1, the data of the second data acquisition terminal 5.2 are transmitted to the LoRa gateway 11 through the second LoRa module 4.2, the flowmeter 16 is arranged at a downstream dam foot, and the measurement data of the flowmeter 16 are transmitted to the LoRa gateway 11 through the third LoRa module 4.3;
the osmometer 13 and the GNSS deformation monitoring station 10 are arranged on a downstream dam slope, the measured data of the osmometer 13 is transmitted to the edge gateway 12 through the third data acquisition terminal 5.3, and the measured data of the GNSS deformation monitoring station 10 is directly transmitted to the edge gateway 12; the edge gateway 12 simultaneously receives the data in the LoRa gateway 11, transmits information to the communication base station 15 (including but not limited to a 2G base station, a 3G base station, a 4G base station, a 5G base station, etc.) through the communication transmission 14 (including but not limited to a 2G transmission, a 3G transmission, a 4G transmission, a 5G transmission, etc.), and uploads the data to the private cloud platform 17 through the communication base station 15; the private cloud platform 17 can issue the prediction model to the edge gateway 12 at the same time, and the edge gateway 12 draws up an early warning index according to the prediction model to perform in-situ seepage and deformation real-time early warning.
Examples
The medium reservoir homogeneous earth dam in China has the maximum dam height of 31m, the dam top length of 185m and the width of 6.0 m. In order to master the working state of the dam in real time and realize real-time early warning of the dam site, the intelligent monitoring and early warning method for the small and medium-sized reservoirs based on edge calculation is adopted to carry out development work, safety monitoring data of the dam of the reservoir are obtained through a monitoring sensor and a data acquisition terminal and are sent to the site edge gateway 12, the edge gateway 12 carries out data missing detection and reanalysis according to preset rules, and effective data are uploaded to the private cloud platform 17 through 4G communication. The private cloud platform 17 performs deep data analysis and algorithm operation by means of strong computing service capability, and the data analysis and the algorithm operation are the prior art; and (3) issuing the trained dam safety prediction model to the edge gateway 12, and drawing up an early warning index by the edge gateway 12 according to the prediction model and carrying out real-time early warning on in-situ seepage and deformation.
The monitoring sensor mainly senses dam safety monitoring data in real time, and the sensor installed in the embodiment mainly comprises: the monitoring system comprises environmental quantity monitoring sensors (comprising a water level meter 3, an air temperature meter 8 and a rain gauge 9), seepage monitoring sensors (an osmometer 13 and a flow meter 16) and a GNSS deformation monitoring station 10, wherein the water level meter 3 is arranged at an upstream reservoir area, the air temperature meter 8 and the rain gauge 9 are arranged at the top of a dam, the osmometer 13 and the GNSS deformation monitoring station 10 are arranged at a downstream dam slope, and the flow meter 16 is arranged at a downstream dam foot.
The data acquisition terminal be used for data real-time collection, can adopt wired or wireless mode to send the data collection to edge gateway 12 according to the actual conditions in place, wherein wired mode indicates that serial port line 6 connects, wireless mode indicates that to send for loRa gateway 11 through the loRa module, again by loRa gateway 11 through serial port line 6 connection edge gateway 12.
In the embodiment, the data of the water level meter 3, the air temperature meter 8, the rain gauge 9 and the flow meter 16 are all connected to a data acquisition terminal through serial port lines 6 and then are sent to an edge gateway 12 in a wireless mode; the osmometer 13 and the GNSS deformation monitoring station 10 are directly connected to the edge gateway 12 through the serial port line 6.
The edge gateway 12 is arranged on a downstream dam slope, is used for data preprocessing, storage, transmission, analysis calculation and early warning, and mainly comprises a microprocessor module, a storage module, a power supply module, a multi-communication interface, a 4G communication module and the like, wherein the microprocessor module is responsible for data processing, analysis calculation and the like; the storage module is responsible for data storage; the power supply module provides electric energy; the multi-communication interface is responsible for data interaction and mainly comprises a network interface, RS232 and RS485 serial ports and a USB interface; the 4G communication module is responsible for uploading the packaged data to a cloud platform and receiving the model and the rule issued by the cloud.
The preset rule refers to that 30 minutes are spent at 8 am every day, the edge gateway 12 automatically checks the data acquisition condition of 8 points in the morning, if the data is missing, re-acquisition is carried out every 3 minutes until all sensors have data, and after 10 times of re-acquisition, an alarm is given if the data is not acquired yet.
The cloud service platform is a business service system established on the private cloud platform 17 based on a B/S architecture, and has the functions of safety monitoring data storage, calculation analysis, data display, model issuing and the like.
The dam safety prediction model is characterized in that seepage or deformation influence factors are calculated and selected through environmental quantity data such as water level, rainfall, air temperature and the like in front of a dam reservoir, a regression equation is introduced from large to small according to the contribution degree of the influence factors to seepage or deformation monitoring quantity, F significance test is carried out, and if the influence factors are significant, the regression equation is introduced; and searching for a factor with the minimum contribution degree from the factors introduced currently, performing F significance test, and if the factor is not significant, removing the factor from the regression equation. Through repeated introduction and elimination, a dam seepage and deformation prediction model is finally established, a closed loop is formed, and influence factors can be continuously updated.
The early warning index drawing is mainly determined by a confidence interval method, and the method mainly comprises the steps of firstly obtaining a seepage or deformation predicted value YY according to a prediction model issued by a cloud platform and an actually measured environment quantity, and then calculating a standard deviation S of a historical seepage or deformation monitoring quantity, wherein the early warning index can be expressed as YY +/-3S. For seepage safety early warning, early warning is carried out when the actual measured seepage pressure or seepage flow Y is more than YY + 3S; and for the deformation safety early warning, when the actually measured deformation Y < YY-3S or Y > YY +3S, the early warning is carried out.
Other parts not described in detail are prior art.
Claims (7)
1. The intelligent monitoring and early warning method for the medium and small reservoir dam based on the edge calculation is characterized by comprising the following steps:
firstly, acquiring reservoir dam safety monitoring data through a monitoring sensor and a data acquisition terminal, sending the reservoir dam safety monitoring data to an in-place edge gateway, carrying out data missing detection and supplementary detection on the edge gateway, and uploading effective data to a cloud service platform through mobile communication;
and secondly, the cloud service platform performs data analysis and algorithm operation according to the computing service capacity, issues the trained dam safety prediction model to the edge gateway, and the edge gateway draws up an early warning index according to the prediction model and performs in-situ seepage and deformation real-time early warning, so that the safety monitoring and early warning intelligent level of the reservoir dam is improved.
2. The intelligent monitoring and early warning method for the small and medium reservoir dam based on the edge calculation, as claimed in claim 1, is characterized in that: the monitoring sensor mainly refers to a reservoir dam environment quantity, seepage and deformation data acquisition sensor;
and the data acquisition terminal transmits the acquired data to the edge gateway in a wired or wireless mode according to the actual deployment situation in the field.
3. The intelligent monitoring and early warning method for the small and medium reservoir dam based on the edge calculation, as claimed in claim 1, is characterized in that:
the edge gateway mainly comprises a microprocessor module, a storage module, a power supply module, a multi-communication interface and a 4G communication module;
the microprocessor module is responsible for data processing and analysis and calculation;
the storage module is responsible for data storage;
the power supply module provides electric energy;
the multi-communication interface is responsible for data interaction and mainly comprises a network interface, RS232 and RS485 serial ports and a USB interface;
and the communication module is responsible for uploading the packaged data to a cloud platform and receiving the model and the rule issued by the cloud.
4. The intelligent monitoring and early warning method for small and medium reservoir dams based on edge calculation, according to claim 3, is characterized in that: the cloud service platform is a business service system established on a private cloud service platform and has the functions of safety monitoring data storage, calculation analysis, data display and model issuing.
5. The intelligent monitoring and early warning method for the small and medium reservoir dam based on the edge calculation, as claimed in claim 1, is characterized in that:
the dam safety prediction model is as follows:
wherein
Predicting an estimate for seepage or deformation;
6. The intelligent monitoring and early warning method for small and medium reservoir dams based on edge calculation, according to claim 5, is characterized in that: the modeling process of the dam safety prediction model is as follows: firstly, calculating and selecting seepage or deformation influence factors through water level, rainfall and air temperature environment quantity data in front of a dam bank, then sequentially introducing regression equations from large to small according to the significance degree of the influence factors on seepage or deformation monitoring quantity, and finally establishing a dam seepage or deformation prediction model.
7. The intelligent monitoring and early warning method for the small and medium-sized reservoir dam based on the edge calculation, as claimed in claim 5 or 6, is characterized in that: the early warning index drawing is mainly determined by a confidence interval method, and the method mainly comprises the steps of firstly obtaining a seepage or deformation predicted value YY according to a prediction model issued by a cloud and an actual measurement environment quantity, and then calculating a standard deviation S of historical seepage or deformation monitoring quantity, wherein the early warning index can be expressed as YY +/-3S;
for seepage early warning, when the actual measured seepage pressure or seepage flow Y is more than YY +3S, early warning is carried out;
and for the deformation early warning, when the actually measured deformation Y < YY-3S or Y > YY +3S, the early warning is carried out.
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