CN116625324A - Water conservancy monitoring method based on digital twinning - Google Patents

Water conservancy monitoring method based on digital twinning Download PDF

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Publication number
CN116625324A
CN116625324A CN202310576673.0A CN202310576673A CN116625324A CN 116625324 A CN116625324 A CN 116625324A CN 202310576673 A CN202310576673 A CN 202310576673A CN 116625324 A CN116625324 A CN 116625324A
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data
water
water conservancy
monitoring
model platform
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孙吉涛
李鑫
潘雷
修奇
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Hangzhou Shengwei Zhizao Technology Co ltd
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Hangzhou Shengwei Zhizao Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C13/00—Surveying specially adapted to open water, e.g. sea, lake, river or canal
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/14—Rainfall or precipitation gauges
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use
    • Y02A20/152—Water filtration

Abstract

The invention discloses a water conservancy monitoring method based on digital twinning, in particular to the field of digital twinning, which comprises the steps of collecting hydrological water conservancy data through hardware equipment, constructing a digital twinning, namely an intelligent water conservancy model platform, and predicting operation parameters of a future time period; water conservancy monitoringThe cloud platform of the measurement system monitors the target area in real time by producing a data report or a graph through data acquired by hardware equipment of the target area; calculating the effective credibility of the intelligent water conservancy model platform according to the running state coverage rate, the twin component coverage rate, the real-time efficiency and the prediction accuracy rate of the intelligent water conservancy model platform; and according to the effective credibility G of the intelligent water conservancy model platform and a predefined effective credibility threshold G Threshold value Performing comparison and judgment, and performing early warning reminding according with expectations; therefore, the cloud platform of the water conservancy monitoring system responds to the dispatching staff to conduct field investigation on the early warning and reminding area, and precautionary measures are taken in advance.

Description

Water conservancy monitoring method based on digital twinning
Technical Field
The invention relates to the technical field of digital twinning, in particular to a water conservancy monitoring method based on digital twinning.
Background
As is well known, china is a river and lake in many countries. With the rapid development of the mass economy, the hydraulic engineering plays an increasingly large role in the mass economy, and the flood prevention problem is the most influenced, the most dominant and the most extensive in the hydraulic engineering; in recent years, various flood news bursts exist, and a river and south storm event with the deepest printing image has to pay attention to flood prevention of hydraulic engineering.
The existing water conservancy monitoring system is mainly used for monitoring water conservancy operation conditions of rivers, lakes and reservoirs, timely reflects hydrologic characteristics of various water areas, is convenient for relevant departments to make arrangement, and prevents flood disasters and accidents; the method has the advantages of fast data checking, automatic overrun alarming, automatic recording and uploading and the like. The user can check the detailed data in the computer or the mobile phone without reaching the water level station, and the method is convenient, quick and accurate.
However, when the water conservancy monitoring system is actually used, more defects still exist, such as the existing water conservancy monitoring system can only timely prevent the water conservancy monitoring system, and cannot predict and conduct precautionary measures in advance, although the water conservancy monitoring system is more convenient and timely compared with the traditional monitoring system, delay still exists in the actual prevention efficiency, flood disaster accidents are imperative in the minute once happening, economic loss weight is caused when the flood disaster accidents are light in one second later, casualties are caused, and national economy development and social stability are seriously affected.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a digital twin-based digital systemThe hydraulic monitoring method comprises the steps of collecting hydrological hydraulic data through hardware equipment, constructing a digital twin body, namely an intelligent hydraulic model platform, and predicting operation parameters of a future time period; the cloud platform of the water conservancy monitoring system monitors the target area in real time through a data production data report or graph acquired by hardware equipment of the target area; calculating the effective credibility of the intelligent water conservancy model platform according to the running state coverage rate, the twin component coverage rate, the real-time efficiency and the prediction accuracy rate of the intelligent water conservancy model platform; and according to the effective credibility G of the intelligent water conservancy model platform and a predefined effective credibility threshold G Threshold value Performing comparison and judgment, and performing early warning reminding according with expectations; therefore, the cloud platform of the water conservancy monitoring system responds to the dispatching staff to conduct field investigation on the early warning and reminding area, precautionary measures are taken in advance, and the problems in the background technology are solved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
step S1, marking an area where a water conservancy monitoring system is located as a target monitoring area, dividing the target monitoring area into monitoring subareas according to an equal area dividing mode, and numbering the monitoring subareas in the target monitoring area as 1,2, n;
step S2, corresponding water level data, water quality data, water flow speed data, water temperature data and actual geographical hardware equipment condition data are collected in real time for 24 hours according to the rain gauge, the water quality sensor, the flowmeter, the temperature sensor and the monitoring camera arranged in each monitoring subarea of each target monitoring area, and the data are transmitted to a cloud platform of a water conservancy monitoring system through a wireless network, wherein the transmission frequency is accurate to one second;
step S3, calculating the average value, the standard deviation, the variation coefficient and the deviation coefficient of the obtained water level data, water quality data, water flow velocity data and water temperature data through a water conservancy monitoring system cloud platform;
s4, automatically updating data in a second unit to record hydrological meteorological data by producing a data report or a graph through water level data, water quality data, water flow speed data, average value, standard deviation, variation coefficient and deviation coefficient calculated by a water conservancy monitoring system cloud platform;
s5, constructing a data twin body according to the target monitoring area range, the hardware facilities, the acquired water level data, the acquired water quality data, the acquired water flow velocity data and the acquired water temperature data, and recording the data twin body as an intelligent water conservancy monitoring model platform, wherein the intelligent water conservancy monitoring model platform is a subsystem of a water conservancy monitoring system cloud platform;
s6, according to the intelligent water conservancy monitoring model platform, simulation test and verification are carried out on relevant parameters of the rain gauge, the water quality sensor, the flowmeter and the temperature sensor through modification, and the running state coverage rate, the twin component coverage rate, the real-time efficiency and the prediction accuracy rate of the intelligent water conservancy monitoring model platform are calculated;
s7, calculating the effective credibility of the intelligent water conservancy model platform according to the running state coverage rate, the twin component coverage rate, the real-time efficiency and the prediction accuracy rate of the intelligent water conservancy model platform, and predefining an effective credibility threshold G Threshold value Performing comparison and judgment; wherein G is Threshold value Is a constant;
s8, according to the effective credibility G of the intelligent water conservancy model platform and a predefined effective credibility threshold G Threshold value Performing comparison and judgment; wherein G > G Threshold value When the intelligent water conservancy model platform is in accordance with the expectation, the effective credibility G of the intelligent water conservancy model platform is shown, the predicted operation parameters of the intelligent water conservancy model platform can be trusted, and the intelligent water conservancy model platform can carry out corresponding early warning reminding according to the predicted operation parameters; g is less than or equal to G Threshold value When the intelligent water conservancy model platform is in a state of being in a state of not meeting the expectation, the effective credibility G of the intelligent water conservancy model platform is indicated, and the predicted operation parameters of the intelligent water conservancy model platform cannot be trusted;
and S9, according to the expected effective credibility of the intelligent water conservancy model platform, scheduling workers to conduct on-site investigation on the early warning and reminding area through the water conservancy monitoring system cloud platform to take precautionary measures in advance, and producing a data report or graph through data acquired by hardware equipment of the target area to monitor the target area in real time.
In a preferred embodiment, the target monitoring area is divided into monitoring subareas according to an equal area division manner, wherein the monitoring subareas need to be more than three times, and hardware devices including a rain gauge, a water quality sensor, a flowmeter, a temperature sensor and a monitoring camera are arranged in each monitoring subarea of each target monitoring area.
In a preferred embodiment, the specific collection mode of the 24-hour real-time collection is as follows: according to the rain gauge, the water quality sensor, the flowmeter, the temperature sensor and the monitoring camera of each monitoring subarea of the target monitoring area, respectively acquiring water level data, water quality data, water flow speed data, water temperature data and actual geographical hardware equipment conditions through a wireless network, and respectively recording the water level data, the water quality data, the water flow speed data, the water temperature data and the actual geographical hardware equipment conditions as A i ，B i ，C i ，D i ，E i Where i=1, 2,..n, i denotes the i-th sub-region number.
In a preferred embodiment, the calculation formula for calculating the average value of the obtained water level data, water quality data, water flow velocity data and water temperature data is as follows:
wherein X represents an average value, xi comprises water level data, water quality data, water flow rate data and water temperature data, and lambda is represented as an influence factor.
In a preferred embodiment, the calculation formula for performing standard deviation calculation on the acquired water level data, water quality data, water flow rate data and water temperature data is as follows:
where S represents the standard deviation and Xi includes water level data, water quality data, water flow rate data, water temperature data, and λ represents an influence factor.
In a preferred embodiment, the calculation formula for calculating the variation coefficient of the obtained water level data, water quality data, water flow velocity data and water temperature data is as follows:
where CV represents the coefficient of variation, S represents the standard deviation, X represents the mean, and λ represents the influence factor.
In a preferred embodiment, the specific processing procedure of calculating the deviation coefficient of the acquired water level data, water quality data, water flow velocity data and water temperature data is as follows:
according to the average value and standard deviation of the water level data of each monitoring subarea, the calculation formula of the bias coefficient is obtained as follows:
where SK is expressed as a bias coefficient, S is expressed as standard deviation, < >>And (3) representing weight factors, wherein Xi comprises water level data, water quality data, water flow rate data and water temperature data, and lambda is represented as an influence factor.
In a preferred embodiment, the specific operation state coverage rate and twin component coverage rate of the intelligent water conservancy monitoring model platform are calculated as follows:
according to the relevant parameters of the modified rain gauge, the water quality sensor, the flowmeter and the temperature sensor, the twin component coverage rate calculation formula of the intelligent water conservancy model platform is obtained by evaluation:
wherein Z is expressed as a coverage rate of a twin component, k1, k2, k3 and k4 are respectively expressed as a weight factor corresponding to a rain gauge, a water quality sensor, a flowmeter and a temperature sensor, hi is expressed as a twin component of the rain gauge, mi is expressed as an actual component of the rain gauge, ui is expressed as a twin component of the water quality sensor, vi is expressed as an actual component of the water quality sensor, oi is expressed as a twin component of the flowmeter, pi is expressed as an actual component of the flowmeter, li is expressed as a twin component of the temperature sensor, jiDenoted as actual components of the temperature sensor, lambda being the influencing factor;
according to the related parameters of the modified rain gauge, the water quality sensor, the flowmeter and the temperature sensor, the operation state coverage rate calculation formula of the intelligent water conservancy model platform is obtained by evaluation, wherein the operation state coverage rate calculation formula is as follows:
wherein F is represented as the coverage rate of the running state, q1, q2, q3 and q4 are respectively represented as weight factors corresponding to a rain gauge, a water quality sensor, a flowmeter and a temperature sensor, and w i Twinning assembly, W, denoted rain gauge i Represented as actual components of the rain gauge, k i Twin assembly, K, denoted water quality sensor i Represented as the actual assembly of the water quality sensor, d i Twin assembly, D, denoted flowmeter i Represented as the actual assembly of the flowmeter, r i Twin assembly, R, denoted temperature sensor i Denoted as the actual component of the temperature sensor, lambda is denoted as the influencing factor.
In a preferred embodiment, the real-time efficiency and prediction accuracy of the intelligent water conservancy monitoring model platform are specifically calculated as follows:
according to water level data, water quality data, water flow velocity data and water temperature data acquired by the rain gauge, the water quality sensor, the flowmeter and the temperature sensor, the real-time efficiency calculation formula of the intelligent water conservancy model platform is estimated and obtained:
where T is denoted as real-time efficiency, b i Expressed as data block length, l i Expressed as transmission distance, v i hair Expressed as transmission rate, v i turn Expressed as a signal retransmission rate, lambda is expressed as an influencing factor;
according to the rainfall gauge, the water quality sensor, the flowmeter, the temperature sensor parameters and the running state of the intelligent water conservancy model platform, and the predicted running parameters and the actual running parameters of the intelligent water conservancy model platform after t hours are predicted, the prediction accuracy calculation formula of the intelligent water conservancy model platform is obtained by evaluation:
wherein Y is expressed as prediction accuracy, Q i real Expressed as actual operating parameter, Q i pre-preparation Denoted as predicted operating parameters, lambda is denoted as an influencing factor.
In a preferred embodiment, the effective credibility calculation formula of the intelligent water conservancy model platform is as follows:
where G is denoted as effective confidence, Z is denoted as twinning component coverage, F is denoted as running state coverage, T is denoted as real-time efficiency, Y is denoted as prediction accuracy, and λ is denoted as an impact factor.
The invention has the technical effects and advantages that:
the invention provides a water conservancy monitoring method based on digital twin, which comprises the steps of collecting hydrological water conservancy data through hardware equipment, constructing a digital twin body, namely an intelligent water conservancy model platform, and predicting operation parameters of a future time period; the cloud platform of the water conservancy monitoring system monitors the target area in real time through a data production data report or graph acquired by hardware equipment of the target area; calculating the effective credibility of the intelligent water conservancy model platform according to the running state coverage rate, the twin component coverage rate, the real-time efficiency and the prediction accuracy rate of the intelligent water conservancy model platform; and according to the effective credibility G of the intelligent water conservancy model platform and a predefined effective credibility threshold G Threshold value Performing comparison and judgment, and performing early warning reminding according with expectations; therefore, the cloud platform of the water conservancy monitoring system responds to the dispatching staff to conduct field investigation on the early warning and reminding area, precautionary measures are taken in advance, actual precautionary efficiency is greatly improved, economic loss and casualties probability are reduced, and national economy development and social stability are facilitated.
Drawings
Fig. 1 is a schematic diagram of the overall structure of the present invention.
FIG. 2 is a flow chart of the method steps of the present invention.
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. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, the hydraulic monitoring system based on digital twin specifically comprises a region dividing module, a hydraulic data acquisition module, a hydraulic data preprocessing module, a hydraulic data drawing module, a digital twin construction module, a digital twin parameter calculation module, an effective credibility comparison judgment module and an early warning processing monitoring module. The regional division module is connected with the hydrologic water conservancy data acquisition module, the hydrologic water conservancy data acquisition module is connected with the hydrologic water conservancy data preprocessing module, the hydrologic water conservancy data preprocessing module is connected with the hydrologic water conservancy data drawing module, the hydrologic water conservancy data drawing module is connected with the early warning processing monitoring module, the hydrologic water conservancy data acquisition module is connected with the digital twin body construction module, the digital twin body construction module is connected with the digital twin body parameter calculation module, the digital twin body parameter calculation module is connected with the effective credibility calculation module, the effective credibility calculation module is connected with the effective credibility comparison judging module, and the effective credibility comparison judging module is connected with the early warning processing monitoring module.
Example 2
The invention provides a digital twinning-based water conservancy monitoring method as shown in fig. 2, which comprises the following steps:
step S1, marking an area where a water conservancy monitoring system is located as a target monitoring area, dividing the target monitoring area into monitoring subareas according to an equal area dividing mode, and numbering the monitoring subareas in the target monitoring area as 1,2, n;
the embodiment needs to specifically explain that the target monitoring area is divided into each monitoring subarea according to an equal area dividing mode, the monitoring subareas need to be more than three times, and hardware equipment which is arranged in each monitoring subarea of each target monitoring area comprises a rain gauge, a water quality sensor, a flowmeter, a temperature sensor and a monitoring camera;
step S2, corresponding water level data, water quality data, water flow speed data, water temperature data and actual geographical hardware equipment condition data are collected in real time for 24 hours according to the rain gauge, the water quality sensor, the flowmeter, the temperature sensor and the monitoring camera arranged in each monitoring subarea of each target monitoring area, and the data are transmitted to a cloud platform of a water conservancy monitoring system through a wireless network, wherein the transmission frequency is accurate to one second;
the embodiment needs to specifically explain that the specific collection mode of the 24-hour real-time collection is as follows: according to the rain gauge, the water quality sensor, the flowmeter, the temperature sensor and the monitoring camera of each monitoring subarea of the target monitoring area, respectively acquiring water level data, water quality data, water flow speed data, water temperature data and actual geographical hardware equipment conditions through a wireless network, and respectively recording the water level data, the water quality data, the water flow speed data, the water temperature data and the actual geographical hardware equipment conditions as A i ，B i ，C i ，D i ，E i Where i=1, 2,..n, i denotes the i-th sub-region number;
step S3, calculating the average value, the standard deviation, the variation coefficient and the deviation coefficient of the obtained water level data, water quality data, water flow velocity data and water temperature data through a water conservancy monitoring system cloud platform;
the embodiment should specifically explain that the calculation formula for calculating the average value of the obtained water level data, water quality data, water flow velocity data and water temperature data is as follows:
wherein X represents an average value of the values,xi comprises water level data, water quality data, water flow speed data and water temperature data, and lambda is expressed as an influence factor;
the embodiment should specifically explain that the calculation formula for performing standard deviation calculation on the obtained water level data, water quality data, water flow velocity data and water temperature data is as follows:
wherein S represents standard deviation, xi comprises water level data, water quality data, water flow speed data and water temperature data, and lambda represents an influence factor;
the embodiment should specifically explain that the calculation formula for calculating the variation coefficient of the obtained water level data, water quality data, water flow velocity data and water temperature data is as follows:
wherein CV represents a coefficient of variation, S represents a standard deviation, X represents an average value, and λ represents an influence factor;
the embodiment needs to specifically explain that the specific processing procedure of performing the bias coefficient calculation on the obtained water level data, water quality data, water flow velocity data and water temperature data is as follows:
according to the average value and standard deviation of the water level data of each monitoring subarea, the calculation formula of the bias coefficient is obtained as follows:
where SK is expressed as a bias coefficient, S is expressed as standard deviation, < >>The weight factors are represented, xi comprises water level data, water quality data, water flow speed data and water temperature data, and lambda is represented as an influence factor;
s4, automatically updating data in a second unit to record hydrological meteorological data by producing a data report or a graph through water level data, water quality data, water flow speed data, average value, standard deviation, variation coefficient and deviation coefficient calculated by a water conservancy monitoring system cloud platform;
s5, constructing a data twin body according to the target monitoring area range, the hardware facilities, the acquired water level data, the acquired water quality data, the acquired water flow velocity data and the acquired water temperature data, and recording the data twin body as an intelligent water conservancy monitoring model platform, wherein the intelligent water conservancy monitoring model platform is a subsystem of a water conservancy monitoring system cloud platform;
the embodiment needs to specifically explain that the process of constructing the data twin belongs to the technical field of digital twin and belongs to the prior art means, so the embodiment does not make specific explanation;
s6, according to the intelligent water conservancy monitoring model platform, simulation test and verification are carried out on relevant parameters of the rain gauge, the water quality sensor, the flowmeter and the temperature sensor through modification, and the running state coverage rate, the twin component coverage rate, the real-time efficiency and the prediction accuracy rate of the intelligent water conservancy monitoring model platform are calculated;
the embodiment needs to specifically explain that the specific running state coverage rate and the twin component coverage rate of the intelligent water conservancy monitoring model platform are calculated as follows:
according to the relevant parameters of the modified rain gauge, the water quality sensor, the flowmeter and the temperature sensor, the twin component coverage rate calculation formula of the intelligent water conservancy model platform is obtained by evaluation:
wherein Z is expressed as a twin component coverage rate, k1, k2, k3, k4 are respectively expressed as a weight factor corresponding to a rain gauge, a water quality sensor, a flowmeter and a temperature sensor, hi is expressed as a twin component of the rain gauge, mi is expressed as an actual rain gauge component, ui is expressed as a twin component of the water quality sensor, vi is expressed as an actual water quality sensor component, oi is expressed as a twin component of the flowmeter, pi is expressed as an actual flowmeter component, li is expressed as a temperature sensorIs denoted as the actual component of the temperature sensor, lambda is denoted as the influencing factor;
according to the related parameters of the modified rain gauge, the water quality sensor, the flowmeter and the temperature sensor, the operation state coverage rate calculation formula of the intelligent water conservancy model platform is obtained by evaluation, wherein the operation state coverage rate calculation formula is as follows:
wherein F is represented as the coverage rate of the running state, q1, q2, q3 and q4 are respectively represented as weight factors corresponding to a rain gauge, a water quality sensor, a flowmeter and a temperature sensor, and w i Twinning assembly, W, denoted rain gauge i Represented as actual components of the rain gauge, k i Twin assembly, K, denoted water quality sensor i Represented as the actual assembly of the water quality sensor, d i Twin assembly, D, denoted flowmeter i Represented as the actual assembly of the flowmeter, r i Twin assembly, R, denoted temperature sensor i Denoted as actual components of the temperature sensor, lambda being the influencing factor;
the embodiment needs to specifically explain that the real-time efficiency and the prediction accuracy of the intelligent water conservancy monitoring model platform are specifically calculated as follows:
according to water level data, water quality data, water flow velocity data and water temperature data acquired by the rain gauge, the water quality sensor, the flowmeter and the temperature sensor, the real-time efficiency calculation formula of the intelligent water conservancy model platform is estimated and obtained:
where T is denoted as real-time efficiency, b i Expressed as data block length, l i Expressed as transmission distance, v i hair Expressed as transmission rate, v i turn Expressed as a signal retransmission rate, lambda is expressed as an influencing factor;
according to the rainfall gauge, the water quality sensor, the flowmeter, the temperature sensor parameters and the running state of the intelligent water conservancy model platform, and the predicted running parameters and the actual running parameters of the intelligent water conservancy model platform after t hours are predicted, the prediction accuracy calculation formula of the intelligent water conservancy model platform is obtained by evaluation:
wherein Y is expressed as prediction accuracy, Q i real Expressed as actual operating parameter, Q i pre-preparation Denoted as predicted operating parameters, lambda is denoted as an influencing factor;
s7, calculating the effective credibility of the intelligent water conservancy model platform according to the running state coverage rate, the twin component coverage rate, the real-time efficiency and the prediction accuracy rate of the intelligent water conservancy model platform, and predefining an effective credibility threshold G Threshold value Performing comparison and judgment; wherein G is Threshold value Is a constant;
the embodiment needs to specifically explain that the effective credibility calculation formula of the intelligent water conservancy model platform is as follows:
wherein G is represented as effective credibility, Z is represented as twin component coverage, F is represented as running state coverage, T is represented as real-time efficiency, Y is represented as prediction accuracy, and lambda is represented as an influence factor;
s8, according to the effective credibility G of the intelligent water conservancy model platform and a predefined effective credibility threshold G Threshold value Performing comparison and judgment; wherein G > G Threshold value When the intelligent water conservancy model platform is in accordance with the expectation, the effective credibility G of the intelligent water conservancy model platform is shown, the predicted operation parameters of the intelligent water conservancy model platform can be trusted, and the intelligent water conservancy model platform can carry out corresponding early warning reminding according to the predicted operation parameters; g is less than or equal to G Threshold value When the intelligent water conservancy model platform is in a state of being in a state of not meeting the expectation, the effective credibility G of the intelligent water conservancy model platform is indicated, and the predicted operation parameters of the intelligent water conservancy model platform cannot be trusted;
the embodiment needs to specifically explain that the corresponding early warning reminding includes hardware equipment state, water level data, water quality data, water flow velocity data and water temperature data of the target monitoring area according to the predicted operation parameters;
and S9, according to the expected effective credibility of the intelligent water conservancy model platform, scheduling workers to conduct on-site investigation on the early warning and reminding area through the water conservancy monitoring system cloud platform to take precautionary measures in advance, and producing a data report or graph through data acquired by hardware equipment of the target area to monitor the target area in real time.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (10)

1. The water conservancy monitoring method based on digital twinning is characterized by comprising the following steps of:
step S1, marking an area where a water conservancy monitoring system is located as a target monitoring area, dividing the target monitoring area into monitoring subareas according to an equal area dividing mode, and numbering the monitoring subareas in the target monitoring area as 1,2, n;
step S2, corresponding water level data, water quality data, water flow speed data, water temperature data and actual geographical hardware equipment condition data are collected in real time for 24 hours according to the rain gauge, the water quality sensor, the flowmeter, the temperature sensor and the monitoring camera arranged in each monitoring subarea of each target monitoring area, and the data are transmitted to a cloud platform of a water conservancy monitoring system through a wireless network, wherein the transmission frequency is accurate to one second;
step S3, calculating the average value, the standard deviation, the variation coefficient and the deviation coefficient of the obtained water level data, water quality data, water flow velocity data and water temperature data through a water conservancy monitoring system cloud platform;
s4, automatically updating data in a second unit to record hydrological meteorological data by producing a data report or a graph through water level data, water quality data, water flow speed data, average value, standard deviation, variation coefficient and deviation coefficient calculated by a water conservancy monitoring system cloud platform;
s5, constructing a data twin body according to the target monitoring area range, the hardware facilities, the acquired water level data, the acquired water quality data, the acquired water flow velocity data and the acquired water temperature data, and recording the data twin body as an intelligent water conservancy monitoring model platform, wherein the intelligent water conservancy monitoring model platform is a subsystem of a water conservancy monitoring system cloud platform;
s6, according to the intelligent water conservancy monitoring model platform, simulation test and verification are carried out on relevant parameters of the rain gauge, the water quality sensor, the flowmeter and the temperature sensor through modification, and the running state coverage rate, the twin component coverage rate, the real-time efficiency and the prediction accuracy rate of the intelligent water conservancy monitoring model platform are calculated;
s7, calculating the effective credibility of the intelligent water conservancy model platform according to the running state coverage rate, the twin component coverage rate, the real-time efficiency and the prediction accuracy rate of the intelligent water conservancy model platform, and predefining an effective credibility threshold G Threshold value Performing comparison and judgment; wherein G is Threshold value Is a constant;
s8, according to the effective credibility G of the intelligent water conservancy model platform and a predefined effective credibility threshold G Threshold value Performing comparison and judgment; wherein G > G Threshold value When the intelligent water conservancy model platform is in accordance with the expectation, the effective credibility G of the intelligent water conservancy model platform is shown, the predicted operation parameters of the intelligent water conservancy model platform can be trusted, and the intelligent water conservancy model platform can carry out corresponding early warning reminding according to the predicted operation parameters; g is less than or equal to G Threshold value When the intelligent water conservancy model platform is in a state of being in a state of not meeting the expectation, the effective credibility G of the intelligent water conservancy model platform is indicated, and the predicted operation parameters of the intelligent water conservancy model platform cannot be trusted;
and S9, according to the expected effective credibility of the intelligent water conservancy model platform, scheduling workers to conduct on-site investigation on the early warning and reminding area through the water conservancy monitoring system cloud platform to take precautionary measures in advance, and producing a data report or graph through data acquired by hardware equipment of the target area to monitor the target area in real time.
2. The digital twinning-based water conservancy monitoring method as set forth in claim 1, wherein:
the target monitoring area is divided into monitoring subareas in an equal area dividing mode, the monitoring subareas need to be more than three parts, and hardware equipment comprising a rain gauge, a water quality sensor, a flowmeter, a temperature sensor and a monitoring camera is arranged in each monitoring subarea of each target monitoring area.
3. The digital twinning-based water conservancy monitoring method as set forth in claim 1, wherein:
the specific collection mode of the 24-hour real-time collection is as follows: according to the rain gauge, the water quality sensor, the flowmeter, the temperature sensor and the monitoring camera of each monitoring subarea of the target monitoring area, respectively acquiring water level data, water quality data, water flow speed data, water temperature data and actual geographical hardware equipment conditions through a wireless network, and respectively recording the water level data, the water quality data, the water flow speed data, the water temperature data and the actual geographical hardware equipment conditions as A i ，B i ，C i ，D i ，E i Where i=1, 2,..n, i denotes the i-th sub-region number.
4. The digital twinning-based water conservancy monitoring method as set forth in claim 1, wherein:
the calculation formula for calculating the average value of the obtained water level data, water quality data, water flow velocity data and water temperature data is as follows:
wherein X represents an average value, xi comprises water level data, water quality data, water flow rate data and water temperature data, and lambda is represented as an influence factor.
5. The digital twinning-based water conservancy monitoring method as set forth in claim 1, wherein:
the calculation formula for calculating the standard deviation of the acquired water level data, water quality data, water flow velocity data and water temperature data is as follows:
where S represents the standard deviation and Xi includes water level data, water quality data, water flow rate data, water temperature data, and λ represents an influence factor.
6. The digital twinning-based water conservancy monitoring method as set forth in claim 1, wherein:
the calculation formula for calculating the variation coefficient of the obtained water level data, water quality data, water flow velocity data and water temperature data is as follows:
where CV represents the coefficient of variation, S represents the standard deviation, X represents the mean, and λ represents the influence factor.
7. The digital twinning-based water conservancy monitoring method as set forth in claim 1, wherein:
the specific processing procedure for calculating the deviation coefficient of the acquired water level data, water quality data, water flow velocity data and water temperature data is as follows:
according to the average value and standard deviation of the water level data of each monitoring subarea, the calculation formula of the bias coefficient is obtained as follows:
where SK is expressed as a bias coefficient, S is expressed as standard deviation, < >>And (3) representing weight factors, wherein Xi comprises water level data, water quality data, water flow rate data and water temperature data, and lambda is represented as an influence factor.
8. The digital twinning-based water conservancy monitoring method as set forth in claim 1, wherein:
the specific running state coverage rate and the twin component coverage rate of the intelligent water conservancy monitoring model platform are calculated as follows:
according to the relevant parameters of the modified rain gauge, the water quality sensor, the flowmeter and the temperature sensor, the twin component coverage rate calculation formula of the intelligent water conservancy model platform is obtained by evaluation:
wherein Z is expressed as a twin component coverage rate, k1, k2, k3 and k4 are respectively expressed as a rain gauge, a water quality sensor, a flowmeter and a weight factor corresponding to a temperature sensor, hi is expressed as a twin component of the rain gauge, mi is expressed as an actual component of the rain gauge, ui is expressed as a twin component of the water quality sensor, vi is expressed as an actual component of the water quality sensor, oi is expressed as a twin component of the flowmeter, pi is expressed as an actual component of the flowmeter, li is expressed as a twin component of the temperature sensor, ji is expressed as an actual component of the temperature sensor, and lambda is expressed as an influence factor;
according to the related parameters of the modified rain gauge, the water quality sensor, the flowmeter and the temperature sensor, the operation state coverage rate calculation formula of the intelligent water conservancy model platform is obtained by evaluation, wherein the operation state coverage rate calculation formula is as follows:
wherein F is represented as the coverage rate of the running state, q1, q2, q3 and q4 are respectively represented as weight factors corresponding to a rain gauge, a water quality sensor, a flowmeter and a temperature sensor, and w i Twinning assembly, W, denoted rain gauge i Represented as actual components of the rain gauge, k i Twin assembly, K, denoted water quality sensor i Represented as the actual assembly of the water quality sensor, d i Twin assembly, D, denoted flowmeter i Represented as the actual assembly of the flowmeter, r i Twin assembly, R, denoted temperature sensor i Denoted as the actual component of the temperature sensor, lambda is denoted as the influencing factor.
9. The digital twinning-based water conservancy monitoring method as set forth in claim 1, wherein:
the real-time efficiency and the prediction accuracy of the intelligent water conservancy monitoring model platform are specifically calculated as follows:
according to water level data, water quality data, water flow velocity data and water temperature data acquired by the rain gauge, the water quality sensor, the flowmeter and the temperature sensor, the real-time efficiency calculation formula of the intelligent water conservancy model platform is estimated and obtained:
where T is denoted as real-time efficiency, b i Expressed as data block length, l i Expressed as transmission distance, v i hair Expressed as transmission rate, v i turn Expressed as a signal retransmission rate, lambda is expressed as an influencing factor;
according to the rainfall gauge, the water quality sensor, the flowmeter, the temperature sensor parameters and the running state of the intelligent water conservancy model platform, and the predicted running parameters and the actual running parameters of the intelligent water conservancy model platform after t hours are predicted, the prediction accuracy calculation formula of the intelligent water conservancy model platform is obtained by evaluation:
wherein Y is expressed as prediction accuracy, Q i real Expressed as actual operating parameter, Q i pre-preparation Denoted as predicted operating parameters, lambda is denoted as an influencing factor.
10. The digital twinning-based water conservancy monitoring method as set forth in claim 1, wherein:
the effective credibility calculation formula of the intelligent water conservancy model platform is as follows:
where G is denoted as effective confidence, Z is denoted as twinning component coverage, F is denoted as running state coverage, T is denoted as real-time efficiency, Y is denoted as prediction accuracy, and λ is denoted as an impact factor.
CN202310576673.0A 2023-05-22 2023-05-22 Water conservancy monitoring method based on digital twinning Pending CN116625324A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117494418A (en) * 2023-11-01 2024-02-02 湖北美辰环保股份有限公司 Intelligent phosphogypsum washing system based on digital twin mechanism
CN117494418B (en) * 2023-11-01 2024-05-14 湖北美辰环保股份有限公司 Intelligent phosphogypsum washing system based on digital twin mechanism

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117494418A (en) * 2023-11-01 2024-02-02 湖北美辰环保股份有限公司 Intelligent phosphogypsum washing system based on digital twin mechanism
CN117494418B (en) * 2023-11-01 2024-05-14 湖北美辰环保股份有限公司 Intelligent phosphogypsum washing system based on digital twin mechanism

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