Lake salinity layering dynamic monitoring method and system

Abstract

The invention provides a lake salinity layering dynamic monitoring method and system, and relates to the technical field of lake ecology, and the method comprises the following steps: obtaining geographic attribute data, hydrological characteristic data and temperature data of different positions of a to-be-monitored lake; according to the hydrological characteristic data and the temperature data, carrying out layered division on the lake to be monitored in the vertical direction to obtain a corresponding division result; constructing a corresponding water body salinity calculation model according to the division result, and obtaining a corresponding water body initial salinity value according to the water body salinity calculation model; judging whether the initial salinity value of the water body needs to be corrected or not, and if yes, correcting the initial salinity value of the water body to obtain a final lake water body salinity estimation result; according to the invention, effective monitoring and management of lake salinity are realized, and powerful support is provided for lake protection and sustainable development.

Description

Technical Field

[0001] The present invention relates to the field of lake ecological technology, and more specifically to a lake salinity stratification dynamic monitoring method and system. Background Art

[0002] At present, water salinity is one of the important parameters of lakes. It is a basic parameter that describes the physical and chemical properties of lake water and plays an important role in the water ecology, hydrology and water environment of lakes. Lake salinity is one of the important indicators for measuring lake water quality and is affected by many factors. By scientifically measuring and analyzing lake salinity, we can understand the water quality of lakes and their changing trends, and provide a scientific basis for lake protection and rational use.

[0003] However, the monitoring process for lake water salinity in the existing technology is rather cumbersome. It is impossible to conduct on-site measurement surveys in many lakes and accurately assess the salinity of lake water. In addition, the relevant hydrological and physical characteristics of lake water at different water levels vary greatly and cannot be directly calculated, resulting in low calculation accuracy.

[0004] Therefore, how to provide a method for dynamic monitoring of lake salinity stratification that can solve the above problems is an issue that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of this, the present invention provides a method and system for dynamic monitoring of lake salinity stratification, which realizes effective monitoring and management of lake salinity through precise stratified monitoring, dynamic salinity estimation, correction mechanism, environmental change trend prediction and intelligent water replenishment decision-making, and provides strong support for lake protection and sustainable development.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A lake salinity stratification dynamic monitoring method comprises the following steps: Obtaining geographic attribute data, hydrological characteristic data, and temperature data at different locations of the lake to be monitored, and dividing the lake to be monitored into layers in the vertical direction according to the hydrological characteristic data and the temperature data to obtain corresponding division results; A corresponding water body salinity calculation model is constructed according to the division results, and a corresponding water body preliminary salinity value is obtained according to the water body salinity calculation model; It is determined whether the preliminary salinity value of the water body needs to be corrected. If necessary, the preliminary salinity value of the water body is corrected to obtain a final lake water salinity estimation result.

[0007] Preferably, the specific process of correcting the preliminary salinity value of the water body to obtain the final lake water body salinity estimation result includes: Obtain climate characteristic data and environmental emergency data of the location of the lake to be monitored through big data, and analyze the climate characteristic data and environmental emergency data to obtain corresponding climate analysis results and environmental emergency analysis results; Constructing an analysis model, inputting the climate analysis results and the environmental burst analysis results into the analysis model for processing to obtain corresponding analysis results, wherein the analysis model is a fusion model of a decision tree and a convolutional neural network; Determine whether the salinity of the monitored lake is affected based on the analysis results. If not, no correction will be made.

[0008] Preferably, the specific process of correcting the preliminary salinity value of the water body to obtain the final lake water body salinity estimation result also includes: If there is an impact, a salinity prediction model is constructed, and the climate analysis result, the environmental sudden analysis result and the preliminary salinity value of the water body are input into the salinity prediction model for processing to obtain a corresponding salinity prediction value; The salinity prediction value and the preliminary salinity value of the water body are weighted and fused to obtain the final lake water salinity estimation result.

[0009] Preferably, it also includes: The lake water salinity estimation result is used for inversion to obtain the environmental change trend of the lake to be monitored.

[0010] Preferably, the specific processing process after obtaining the environmental change trend of the lake to be monitored includes: Determining a salinity threshold value according to the environmental change trend, and comparing the salinity estimation result of the lake water body with the salinity threshold value; When the estimated result of the salinity of the lake water body is greater than or equal to the salinity threshold, the monitored lake is replenished with water.

[0011] Preferably, the specific processing process of comparing the lake water salinity estimation results also includes: When the estimated result of the salinity of the lake water body is less than the salinity threshold, it is determined whether the lake to be monitored needs water replenishment in combination with the hydrological characteristic data of the lake to be monitored and the environmental change trend.

[0012] Preferably, the specific process of constructing the water body salinity calculation model includes: Determine water quality parameters related to salinity of water bodies; A water body salinity calculation model is constructed, and the water quality parameters are input into the water body salinity calculation model for processing to obtain a corresponding preliminary salinity value of the water body.

[0013] Preferably, the specific process of constructing the water body salinity calculation model also includes: A water body salinity calculation model is constructed, wherein the water body salinity calculation model includes a Transformer-XGBoost hybrid model and a stacked generalization layer.

[0014] The present invention also provides a lake salinity stratification dynamic monitoring system, comprising: The data acquisition and division module is used to obtain the geographical attribute data, hydrological characteristic data and temperature data of different locations of the lake to be monitored, and divide the lake to be monitored into layers in the vertical direction according to the hydrological characteristic data and temperature data to obtain the corresponding division results; A data processing module is used to construct a corresponding water body salinity calculation model according to the division result, and obtain a corresponding preliminary water body salinity value according to the water body salinity calculation model; The data judgment module is used to judge whether the preliminary salinity value of the water body needs to be corrected. If necessary, the preliminary salinity value of the water body is corrected to obtain the final lake water salinity estimation result. It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for dynamic monitoring of lake salinity stratification, which has the following beneficial effects: 1. The present invention obtains the geographical attribute data, hydrological characteristic data and temperature data of different locations of the lake, and performs stratification in the vertical direction, so that the monitoring is more accurate to the specific lake water body layer, which helps to understand the vertical distribution of salinity inside the lake, and is of great significance to the lake ecology and water quality management.

[0015] 2. The present invention constructs a water body salinity calculation model based on the stratification results and calculates the preliminary salinity value, thereby realizing the dynamic estimation of lake salinity, reflecting the real-time changes of lake salinity, and providing timely data support for lake management.

[0016] 3. The present invention obtains climate characteristic data and environmental emergency data through big data, and analyzes them to determine whether these factors have an impact on the salinity of the lake. If so, the salinity prediction model is used to correct the preliminary salinity value to obtain a more accurate salinity estimation result. This correction mechanism improves the accuracy and reliability of salinity estimation.

[0017] 4. The present invention uses the estimated results of lake water salinity to perform inversion, and can obtain the environmental change trend of the lake, which helps to predict the future ecological status of the lake and provide a scientific basis for lake protection and management.

[0018] 5. The present invention determines the salinity threshold value based on the salinity estimation results of the lake water body and the environmental change trend, and judges whether the lake needs to be replenished with water based on this. This intelligent water replenishment decision-making mechanism helps to maintain the water quality balance and ecological balance of the lake. When deciding whether to replenish water, not only the salinity threshold value is considered, but also a comprehensive analysis is conducted in combination with the hydrological characteristic data of the lake and the environmental change trend. This comprehensive consideration method makes the water replenishment decision more scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0020] Figure 1 An overall flow chart of a lake salinity stratification dynamic monitoring method provided by the present invention; Figure 2 A structural principle block diagram of a lake salinity stratification dynamic monitoring system provided by the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 As shown, the embodiment of the present invention discloses a method for dynamic monitoring of lake salinity stratification, comprising the following steps: Obtaining geographic attribute data, hydrological characteristic data, and temperature data at different locations of the lake to be monitored, and dividing the lake to be monitored into layers in the vertical direction according to the hydrological characteristic data and the temperature data to obtain corresponding division results, wherein the geographic attribute data may be the location data of the lake to be monitored, and the hydrological characteristic data may include the net flow of the lake, water quality data, and spectral data of the water body; A corresponding water body salinity calculation model is constructed according to the division results, and a corresponding water body preliminary salinity value is obtained according to the water body salinity calculation model; Determine whether the preliminary salinity value of the water body needs to be corrected. If necessary, correct the preliminary salinity value of the water body to obtain the final lake water salinity estimation result.

[0023] Specifically, in the process of dividing the monitored lake into vertical directions, it is achieved through multiple preset temperature threshold ranges.

[0024] In the process of establishing a water body salinity calculation model, an embodiment of the present invention can pre-divide the results according to the vertical direction of the lake, first use a correlation analysis method to study the relationship between different water quality parameters and water body salinity, and screen out water quality parameters that have a significant correlation with salinity. The water quality parameters screened here are: conductivity, water body temperature, and runoff data; construct a water body salinity calculation model, wherein the water body salinity calculation model includes a Transformer-XGBoost hybrid model and a stacking generalization layer (stacking layer), and the Transformer model is used as a feature extractor to capture complex features in the data, and then these features are provided as input to the XGBoost model for processing, and the stacking generalization layer can use the prediction results of multiple models as input features to generate the final result, thereby improving the data processing capability of the model and improving the calculation accuracy.

[0025] In a specific embodiment, the specific process of correcting the preliminary salinity value of the water body to obtain the final lake water body salinity estimation result includes: Obtain climate characteristic data and environmental emergency data at the location of the lake to be monitored through big data, and analyze the climate characteristic data and environmental emergency data to obtain corresponding climate analysis results and environmental emergency analysis results. Climate characteristic data may include rainfall, evaporation and other climate data that may affect water bodies, and environmental emergency data may include pollution leakage data and other data that may affect water quality. Construct an analysis model, input the climate analysis results and environmental sudden analysis results into the analysis model for processing, and obtain the corresponding analysis results; Determine whether the salinity of the monitored lake is affected based on the analysis results. If not, no correction will be made.

[0026] Specifically, the analysis model can be a fusion model of a decision tree and a convolutional neural network, which can be used to comprehensively analyze multiple factors and their interactions in environmental burst data and improve analysis capabilities.

[0027] In a specific embodiment, the specific process of correcting the preliminary salinity value of the water body to obtain the final lake water body salinity estimation result also includes: If there is an impact, a salinity prediction model is constructed, and the climate analysis results, environmental sudden analysis results and preliminary salinity values ​​of the water body are input into the salinity prediction model for processing to obtain the corresponding salinity prediction value, wherein the salinity prediction model can be a convolutional neural network model; The salinity prediction value and the preliminary salinity value of the water body are weighted and fused to obtain the final lake water salinity estimation result.

[0028] In a specific embodiment, it also includes: The estimated results of lake water salinity are used for inversion to obtain the environmental change trend of the lake to be monitored.

[0029] In a specific embodiment, the specific processing process after obtaining the environmental change trend of the lake to be monitored includes: Determine salinity thresholds based on environmental change trends and compare lake water salinity estimates with salinity thresholds; When the estimated result of the lake water salinity is greater than or equal to the salinity threshold, the monitored lake will be replenished with water.

[0030] In a specific embodiment, the specific processing of comparing the lake water body salinity estimation results also includes: When the estimated result of the salinity of the lake water is less than the salinity threshold, the hydrological characteristic data of the lake to be monitored and the environmental change trend are combined to determine whether the lake to be monitored needs water replenishment.

[0031] See also Figure 2 As shown, an embodiment of the present invention further provides a system for dynamically monitoring the lake salinity stratification using any one of the above embodiments, comprising: The data acquisition and division module is used to obtain the geographical attribute data, hydrological characteristic data and temperature data of different locations of the lake to be monitored, and divide the lake to be monitored into layers in the vertical direction according to the hydrological characteristic data and temperature data to obtain the corresponding division results; A data processing module is used to construct a corresponding water body salinity calculation model according to the division result, and obtain a corresponding preliminary water body salinity value according to the water body salinity calculation model; The data judgment module is used to judge whether the preliminary salinity value of the water body needs to be corrected. If necessary, the preliminary salinity value of the water body is corrected to obtain the final lake water salinity estimation result. In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0032] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for dynamic monitoring of lake salinity stratification, characterized in that: The following steps are involved: Obtaining geographic attribute data, hydrological characteristic data, and temperature data at different locations of the lake to be monitored, and dividing the lake to be monitored into layers in the vertical direction according to the hydrological characteristic data and the temperature data to obtain corresponding division results; A corresponding water body salinity calculation model is constructed according to the division results, and a corresponding water body preliminary salinity value is obtained according to the water body salinity calculation model; It is determined whether the preliminary salinity value of the water body needs to be corrected. If necessary, the preliminary salinity value of the water body is corrected to obtain a final lake water salinity estimation result.

2. A lake salinity stratification dynamic monitoring method according to claim 1, characterized in that: The specific process of correcting the preliminary salinity value of the water body to obtain the final lake water salinity estimation result includes: Obtain climate characteristic data and environmental emergency data of the location of the lake to be monitored through big data, and analyze the climate characteristic data and environmental emergency data to obtain corresponding climate analysis results and environmental emergency analysis results; Constructing an analysis model, inputting the climate analysis results and the environmental burst analysis results into the analysis model for processing to obtain corresponding analysis results, wherein the analysis model is a fusion model of a decision tree and a convolutional neural network; Determine whether the salinity of the monitored lake is affected based on the analysis results. If not, no correction will be made.

3. A lake salinity stratification dynamic monitoring method according to claim 2, characterized in that: The specific process of correcting the preliminary salinity value of the water body to obtain the final lake water body salinity estimation result also includes: If there is an impact, a salinity prediction model is constructed, and the climate analysis result, the environmental sudden analysis result and the preliminary salinity value of the water body are input into the salinity prediction model for processing to obtain a corresponding salinity prediction value; The salinity prediction value and the preliminary salinity value of the water body are weighted and fused to obtain the final lake water salinity estimation result.

4. A lake salinity stratification dynamic monitoring method according to claim 1, characterized in that: Also includes: The lake water salinity estimation result is used for inversion to obtain the environmental change trend of the lake to be monitored.

5. A lake salinity stratification dynamic monitoring method according to claim 4, characterized in that: The specific processing process after obtaining the environmental change trend of the lake to be monitored includes: Determining a salinity threshold value according to the environmental change trend, and comparing the salinity estimation result of the lake water body with the salinity threshold value; When the estimated result of the salinity of the lake water body is greater than or equal to the salinity threshold, the monitored lake is replenished with water.

6. A lake salinity stratification dynamic monitoring method according to claim 5, characterized in that: The specific processing process of comparing the estimated results of the lake water salinity also includes: When the estimated result of the salinity of the lake water body is less than the salinity threshold, it is determined whether the lake to be monitored needs water replenishment in combination with the hydrological characteristic data of the lake to be monitored and the environmental change trend.

7. A lake salinity stratification dynamic monitoring method according to claim 1, characterized in that: The specific process of constructing a water body salinity calculation model includes: Determine water quality parameters related to salinity of water bodies; A water body salinity calculation model is constructed, and the water quality parameters are input into the water body salinity calculation model for processing to obtain a corresponding preliminary salinity value of the water body.

8. A lake salinity stratification dynamic monitoring method according to claim 7, characterized in that: The specific process of constructing a water body salinity calculation model also includes: A water body salinity calculation model is constructed, wherein the water body salinity calculation model includes a Transformer-XGBoost hybrid model and a stacked generalization layer.

9. A system using the lake salinity stratification dynamic monitoring method according to any one of claims 1 to 8, characterized in that: include: The data acquisition and division module is used to obtain the geographical attribute data, hydrological characteristic data and temperature data of different locations of the lake to be monitored, and divide the lake to be monitored into layers in the vertical direction according to the hydrological characteristic data and temperature data to obtain the corresponding division results; A data processing module is used to construct a corresponding water body salinity calculation model according to the division result, and obtain a corresponding preliminary water body salinity value according to the water body salinity calculation model; The data judgment module is used to judge whether the preliminary salinity value of the water body needs to be corrected. If necessary, the preliminary salinity value of the water body is corrected to obtain the final lake water salinity estimation result.

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