CN-121977511-A - River hydrologic measurement system based on big data

CN121977511ACN 121977511 ACN121977511 ACN 121977511ACN-121977511-A

Abstract

The invention relates to the technical field of river hydrologic measurement, in particular to a river hydrologic measurement system based on big data. The system comprises a data perception integration module, a drainage basin characteristic analysis module, a self-adaptive parameter optimization module and a hydrological measurement early warning module, wherein the drainage basin characteristic analysis module is used for receiving drainage basin characteristic data and carrying out drainage basin characteristic analysis on the drainage basin characteristic data to obtain drainage basin characteristic information, and finally, the parameter optimization analysis is carried out on the drainage basin characteristic information to obtain an early warning parameter adjustment strategy, so that the problem of early warning deviation caused by general parameters of a traditional system is avoided, the pertinence and the accuracy of hydrological early warning are improved, the drainage basin characteristic information and the drainage basin stability capability value are analyzed and output to finally execute frequency, and the finally execute frequency is updated to the current acquisition execute frequency, so that the acquisition frequency is reduced when the drainage basin state is stable and fluctuation is small, the frequency is increased when the state is unstable, the system operation cost is reduced, and the monitoring efficiency is ensured.

Inventors

ZHANG LUQI
WU YUNXIN
CHEN HUARONG
Dai Xuanping
SHI YANHUA
WANG FENG
TANG YANAN

Assignees

浙江水文新技术开发经营有限公司

Dates

Publication Date: 20260505
Application Date: 20260113

Claims (10)

1. A big data based river hydrological measurement system, comprising: The data perception integration module is used for acquiring data through the data acquisition unit and based on the energy-saving control strategy to obtain drainage basin characteristic data and hydrological measurement data, sending the drainage basin characteristic data to the drainage basin characteristic analysis module and sending the hydrological measurement data to the hydrological measurement early warning module; The drainage basin characteristic analysis module is used for carrying out drainage basin characteristic analysis on the drainage basin characteristic data to obtain drainage basin characteristic information, and sending the drainage basin characteristic information to the adaptive parameter optimization module; The adaptive parameter optimization module comprises a dynamic parameter optimization unit and an energy-saving control unit, wherein the river basin characteristic information is respectively input into the dynamic parameter optimization unit and the control unit, and then an early warning parameter adjustment strategy and an energy-saving control strategy are respectively output; And the hydrological measurement early warning module is used for carrying out early warning, monitoring and outputting early warning information according to the early warning parameter adjustment strategy and hydrological measurement data.
2. The river hydrological measurement system based on big data, which is disclosed in claim 1, is characterized in that the data acquisition unit comprises a hydrological parameter acquisition unit and a river basin characteristic acquisition unit, the river basin characteristic acquisition unit acquires the river basin characteristics of a target river basin based on a data acquisition strategy to obtain river basin characteristic data, the river basin characteristic data comprises vegetation coverage data, river channel structure data and intervention dynamic data, and the hydrological measurement data acquires the hydrological data of the target river basin based on the data acquisition strategy to obtain hydrological measurement data.
3. The river hydrological measurement system based on big data according to claim 2, wherein the river basin characteristic data is subjected to a river basin characteristic analysis to obtain river basin characteristic information, and the specific analysis content is as follows: identifying river basin characteristic data to obtain vegetation coverage data, river structure data and intervention dynamic data; The vegetation coverage data is analyzed to obtain a vegetation coverage value, river characteristics of a target river basin, namely river cross-section area, river slope, river siltation quantity and circulation intensity, are obtained according to the river structure data, all the river characteristics are marked as river characteristic vectors of the target river basin, the characteristic vectors are obtained to correspond to preset ideal reference values, the river characteristic vectors and the corresponding ideal reference values are input into a preset kernel function mapping formula to calculate and output the river structure capability value, interference dynamic data is analyzed to obtain an interference influence value, and the vegetation coverage value, the river structure capability value and the interference influence value of the target river basin are packed to obtain river basin characteristic information.
4. A river hydrological measurement system based on big data according to claim 3, wherein the vegetation coverage data is analyzed to obtain a vegetation coverage value, which specifically is: The method comprises the steps of obtaining vegetation coverage data and soil data according to the vegetation coverage data, identifying the vegetation data to obtain vegetation coverage, obtaining a preset vegetation coverage boundary value, comparing the vegetation coverage with the vegetation coverage boundary value to output a vegetation uniform signal value, and outputting the vegetation uniform signal value to be 1 when the vegetation coverage is larger than the vegetation coverage boundary value, otherwise, outputting the vegetation uniform signal value to be 0; Acquiring water and soil conservation capability corresponding to each vegetation type in a database, dividing the water and soil conservation capability according to a preset water and soil conservation capability threshold to obtain water and soil conservation capability types, namely a high water and soil conservation capability type and a low water and soil conservation capability type; Obtaining soil infiltration capacity of various soil types based on a database, and dividing the soil types into a low infiltration capacity type, a medium infiltration capacity type and a high infiltration capacity type based on a preset soil infiltration capacity threshold; the value of the soil permeability coefficient of each soil type is reduced along with the reduction of the water permeability, for example, the soil permeability coefficient corresponding to the high water permeability type is smaller than the soil permeability coefficient of the low water permeability type; Substituting the vegetation coverage, the vegetation uniform signal value, the ratio of the high water and soil conservation vegetation and the numerical value corresponding to the soil permeability coefficient into a preset multi-parameter fusion formula to calculate and sum so as to obtain the vegetation coverage capacity value.
5. The big data based river hydrological measurement system of claim 4, wherein the analysis of the intervention dynamic data results in an intervention impact value, which is specifically: acquiring a drainage basin intervention event according to the intervention dynamic data, acquiring the intervention duration of each drainage basin intervention event, acquiring a history drainage basin intervention event prestored in a database, acquiring the influence effect value of each event in unit time according to the history drainage basin intervention event, acquiring core dense intervals corresponding to the influence effect values corresponding to a plurality of similar history drainage basin intervention events through a data density estimation algorithm, and marking the influence effect value of the core dense intervals as the number of influence effect time sequence intervals; Extracting a pipe network drainage event in a river basin intervention event, obtaining pipe network drainage flow based on the pipe network drainage event, calculating the ratio of the pipe network drainage flow to the river course natural flow prestored in a database to obtain the pipe network drainage flow ratio, and calculating and outputting an intervention influence value by using a preset fractional Fourier transform formula.
6. The river hydrologic measurement system based on big data according to claim 5, wherein the dynamic parameter optimizing unit is used for performing parameter optimization analysis on the river basin characteristic information to obtain an early warning parameter adjustment strategy, and obtaining a river basin stability value generated by the parameter optimization analysis and sending the river basin stability value to the energy-saving control unit; The energy-saving control unit is used for carrying out energy-saving control analysis on the river basin characteristic information and the river basin stability capacity value to obtain an energy-saving control strategy.
7. The river hydrological measurement system based on big data according to claim 6, wherein the parameters of the river basin characteristic information are optimized to obtain an early warning parameter adjustment strategy, and the specific analysis content is as follows: Obtaining a vegetation coverage capability value, a river channel structure capability value and an intervention influence value based on the river basin characteristic information, carrying out normalization processing on the river basin characteristic information, normalizing the values to be within a [0,1] interval, substituting the river basin characteristic information into a preset Euclidean norm coupling formula, and calculating and outputting a river basin stability capability value; the method comprises the steps of obtaining a preset capacity defining interval, matching the capacity defining interval with a river basin stability capacity value to generate a river basin stability state, enabling the river basin stability state to be a medium stability state when the river basin stability capacity value is in the capacity defining interval, enabling the river basin stability capacity value to be a high stability state when the river basin stability capacity value is larger than the maximum value of the capacity defining interval, enabling the river basin stability capacity value to be a low stability state when the river basin stability capacity value is smaller than the minimum value of the capacity defining interval, and generating an early warning parameter adjustment strategy based on the river basin stability state, wherein the high stability state corresponds to a low hydrologic early warning threshold value, the medium stability state corresponds to a normal hydrologic early warning threshold value, and the low stability state corresponds to a high hydrologic early warning threshold value.
8. The river hydrological measurement system based on big data according to claim 7, wherein the energy-saving control strategy is obtained by performing energy-saving control analysis on the river basin characteristic information and the river basin stability capability value, and the specific implementation contents are as follows: Acquiring a preset time step, namely acquiring corresponding vegetation coverage capacity value, river channel structure capacity value and intervention influence value of preset number of analysis moments in the time step, inputting the corresponding river basin characteristic information of each analysis moment into a preset fractional order local Holder index calculation formula to calculate and output Holder indexes corresponding to the river basin characteristic information at each moment; Acquiring a preset Holder index maximum value corresponding to each river basin characteristic information, inputting the Holder index corresponding to each moment of the river basin characteristic information into a preset comprehensive fluctuation index calculation formula, and calculating and outputting a comprehensive fluctuation index; The method comprises the steps of obtaining a comprehensive fluctuation index and a watershed stability capacity value corresponding to a current analysis moment, inputting the comprehensive fluctuation index and the watershed stability capacity value into a preset frequency adjustment coefficient calculation formula to calculate an output frequency adjustment coefficient, obtaining a preset initial execution frequency corresponding to a data acquisition unit, calculating the frequency adjustment coefficient and the initial execution frequency through a preset nonlinear execution correction formula to output a final execution frequency, and marking the final execution frequency as an energy-saving control strategy.
9. The river hydrological measurement system based on big data according to claim 1, wherein the method is characterized in that the method performs early warning monitoring according to an early warning parameter adjustment strategy and hydrological measurement data to output early warning information, and specifically comprises the steps of obtaining a current corresponding early warning threshold value, adjusting the early warning threshold value according to the early warning parameter adjustment strategy to obtain an updated early warning threshold value, monitoring the hydrological measurement data based on the updated early warning threshold value, and outputting the early warning information.
10. The river hydrological measurement system based on big data of claim 1, wherein the acquisition strategy specifically comprises a preset current acquisition execution frequency, which corresponds to an initial execution frequency, the energy-saving adjustment strategy is acquired, a final execution frequency is acquired, and the final execution frequency is updated in the current acquisition execution frequency.

Description

River hydrologic measurement system based on big data Technical Field The invention relates to the technical field of river hydrologic measurement, in particular to a river hydrologic measurement system based on big data. Background The river hydrologic measurement system is a core support for water resource management and river basin treatment, provides data basis for water resource optimal allocation, guarantees production and living water demands of agricultural irrigation, urban water supply and the like by monitoring indexes such as rainfall, runoff and water level, builds firm disaster prevention and reduction lines, tracks flow and flood peak changes in real time, issues flood and drought early warning in advance, reduces disaster loss, can protect river ecology, monitors water quality, water temperature, sand content and the like, and provides support for river basin ecological restoration and biodiversity protection. At present, general parameters such as a unified water level early warning threshold value are mostly adopted in river hydrologic measurement, geographical characteristic differences of different river basins (such as rapid mountain river flow speed, rapid flood storm break, slow plain river flow speed and long flood duration time) are not considered, and therefore, users are required to modify parameters based on the geographical characteristics of the river basin in the region, model parameters cannot be automatically and dynamically adjusted according to the change of the landform of the river basin, and early warning deviation is easy to increase after long-term use. Disclosure of Invention The invention provides a river hydrological measurement system based on big data, which is used for solving the technical problems. The invention provides a river hydrological measurement system based on big data, which comprises a data perception integration module, a river basin characteristic analysis module, a self-adaptive parameter optimization module and a hydrological measurement early warning module. The data perception integration module acquires data through the data acquisition unit and based on a data acquisition strategy to obtain the drainage basin characteristic data and the hydrological measurement data, sends the drainage basin characteristic data to the drainage basin characteristic analysis module, and sends the hydrological measurement data to the hydrological measurement early warning module. The river basin characteristic data comprises vegetation coverage data, river channel structure data and intervention dynamic data, and the hydrological measurement data is obtained by acquiring hydrological data of a target river basin based on a data acquisition strategy. The acquisition strategy specifically comprises a preset current acquisition execution frequency which corresponds to an initial execution frequency, an energy-saving adjustment strategy is obtained, a final execution frequency is obtained, and the final execution frequency is updated in the current acquisition execution frequency. The drainage basin characteristic analysis module is used for receiving the drainage basin characteristic data, carrying out drainage basin characteristic analysis on the drainage basin characteristic data to obtain drainage basin characteristic information, and sending the drainage basin characteristic information to the self-adaptive parameter optimization module. As a further improvement of the invention, the flow field characteristic data is subjected to flow field characteristic analysis to obtain flow field characteristic information, and the specific analysis content is as follows: identifying river basin characteristic data to obtain vegetation coverage data, river structure data and intervention dynamic data; The vegetation coverage data is analyzed to obtain a vegetation coverage value, river characteristics of a target river basin, namely river cross-section area, river slope, river siltation quantity and circulation intensity, are obtained according to the river structure data, all the river characteristics are marked as river characteristic vectors of the target river basin, the characteristic vectors are obtained to correspond to preset ideal reference values, the river characteristic vectors and the corresponding ideal reference values are input into a preset kernel function mapping formula to calculate and output the river structure capability value, interference dynamic data is analyzed to obtain an interference influence value, and the vegetation coverage value, the river structure capability value and the interference influence value of the target river basin are packed to obtain river basin characteristic information. Further, the vegetation coverage data is analyzed to obtain a vegetation coverage value, which is specifically: The method comprises the steps of obtaining vegetation coverage data and soil data according to the vegetation coverage data, identifying the v