Image optimization method based on water conservancy monitoring environment

Abstract

The invention relates to the technical field of water conservancy monitoring, and discloses a water conservancy monitoring environment-based image optimization method, which comprises a water conservancy data monitoring module, a water conservancy data calculation module, an optimization processing module and a management module, the water conservancy data calculation module is divided into a water surface brightness adjustment unit, a hydro meteorological influence unit and a flood flow measurement anomaly analysis unit, and calculates a water surface color fluctuation index Uz, a hydro meteorological influence index Gz and a flood flow measurement anomaly score Ez; the optimization processing module adjusts parameters of the unmanned aerial vehicle according to a calculation result, optimizes an image processing function of the system and switches a monitoring point, and the management module issues an adjustment instruction to the water conservancy environment unit, the weather data unit and the unmanned aerial vehicle monitoring unit according to an adjustment result of the optimization processing module. Therefore, the device has normal arrangement and measurement capabilities in the high flood period.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy monitoring, and in particular to an image optimization method based on a water conservancy monitoring environment. Background Art

[0002] Water conservancy projects play an important role in flood control, waterlogging removal, irrigation, power generation, etc., and are important infrastructure for national economic and social development. However, with the changes in river basin environment and management methods, traditional water conservancy project information recording methods have been unable to meet the needs of modern hydrological monitoring, especially in complex hydrological environments, such as during high floods, traditional contact flow measurement methods cannot be arranged and measured normally, and have low data rates and poor adaptability. Summary of the invention

[0003] 1. Technical issues to be solved

[0004] In view of the shortcomings of the prior art, the present invention provides an image optimization method based on a water conservancy monitoring environment, which has the advantages of being able to arrange and measure normally during high floods, and having a high data rate and strong adaptability. It solves the problem that traditional contact flow measurement methods cannot be arranged and measured normally during high floods, and have a low data rate and poor adaptability.

[0005] (II) Technical solution

[0006] To achieve the above object, the present invention provides the following technical solution: an image optimization method based on a water conservancy monitoring environment, comprising the following steps:

[0007] Step 1: Establish a water conservancy data monitoring module, a water conservancy data calculation module, an optimization processing module and a management module. The water conservancy data monitoring module is divided into a water conservancy environment unit, a weather data unit and a drone monitoring unit;

[0008] Step 2: The water conservancy and environment unit monitors water conservancy and environment data through radar, collects water level data and flow rate data in real time, numbers them, and transmits them to the optimization processing module through the network;

[0009] Step 3: The weather data unit obtains real-time weather data through forecasts issued by the meteorological station and real-time radar monitoring, numbers them, and transmits them to the optimization processing module through the network;

[0010] Step 4: The drone monitoring unit uses the drone to capture water surface image data in real time to obtain water color data of light and dark color difference, and records the brightness and color information of different areas, numbers them, and transmits them to the optimization processing module through the network;

[0011] Step 5, the water conservancy data calculation module is divided into a water surface brightness adjustment unit, a hydrological and meteorological influence unit and a flood flow measurement anomaly analysis unit, the water surface brightness adjustment unit calculates the water surface color fluctuation index Uz according to the water conservancy environment data and the water surface image data, the hydrological and meteorological influence unit calculates the hydrological and meteorological influence index Gz according to the water conservancy environment data and the real-time weather data, the flood flow measurement anomaly analysis unit calculates the flood flow measurement anomaly score Ez according to the water conservancy environment data, the water surface image data and the real-time weather data, and the water conservancy data calculation module is connected to the water conservancy data management module through a network;

[0012] Step 6: The optimization processing module adjusts the UAV parameters according to the water surface color fluctuation index Uz;

[0013] Step 7: The optimization processing module optimizes the image processing function of the system according to the hydrological and meteorological impact index Gz;

[0014] Step 8: The optimization processing module switches the monitoring point according to the flood flow measurement abnormality score Ez, and the optimization processing module and the management module are connected through the network;

[0015] Step nine: The management module issues adjustment instructions to the water environment unit, weather data unit and drone monitoring unit according to the adjustment results of the optimization processing module.

[0016] Preferably, the water conservancy environment unit numbers the water level data and flow rate data collected in real time at a certain time point as H and H respectively according to the characteristics of the water conservancy environment data. i 、V i .

[0017] Preferably, the real-time weather data in the weather data unit includes temperature, humidity, air pressure, wind speed, wind direction, precipitation and cloud cover data near the water area, and the temperature, humidity, wind speed, wind direction, precipitation and cloud cover data near the water area are numbered T, X, J, S, L, P.

[0018] Preferably, the drone monitoring unit numbers the water surface color data of different areas as q1, q2, q3, ...q according to the characteristics of the water surface image data. n .

[0019] Preferably, the water surface brightness adjustment unit calculates the water surface color fluctuation index Uz according to the water environment data and the water surface image data, and the calculation formula is:

[0020]

[0021] In the formula, Uz represents the water surface color fluctuation index, V i represents the flow rate data collected in real time at a certain point in time, k i Indicates the influence coefficient of the underwater undercurrent, q1, q2, q3, ...q nRepresents the water surface color data of different regions, q i Indicates the color contrast of the water surface in a certain area, a i Indicates the adjustment value between the contrast and brightness of the water surface color in a certain area.

[0022] Preferably, the hydrological and meteorological impact unit calculates the hydrological and meteorological impact index Gz according to the water environment data and the real-time weather data, and the calculation formula is:

[0023]

[0024] In the formula, Gz represents the hydrological and meteorological impact index, H i represents the water level data collected in real time at a certain point in time, f i It represents the influence coefficient of floating objects on the water surface. T, X, S, L, and P represent the temperature, humidity, wind speed, precipitation, and cloud cover data near the water area, respectively. α represents the influence constant of saturated steam on temperature. B represents the influence constant of air pressure on humidity. S1 represents the wind speed at 1 meter above the water surface. P1 represents the cloud cover data outside the water area. The variable coefficient representing the effect of weather on water surface height.

[0025] Preferably, the flood flow measurement anomaly analysis unit calculates the flood flow measurement anomaly score Ez based on the water environment data, the water surface image data and the real-time weather data, and the calculation formula is:

[0026] Ez=V m *c1+L m *c2+q m *c3

[0027] In the formula, Ez represents the flood flow measurement anomaly score, V i It represents the abnormal flow rate collected in real time at a certain point in time, q m Indicates the abnormal water surface color contrast in a certain area, L m Indicates the abnormal precipitation within a certain period of time, V m *c1, L m *c2,q m *c3 represents the abnormal flow velocity score, abnormal water surface color contrast score and abnormal precipitation score respectively, c1, c2 and c3 represent the weights of the abnormal flow velocity score, abnormal water surface color contrast score and abnormal precipitation score in the flood flow measurement anomaly score respectively.

[0028] Preferably, the optimization processing module sets a standard value range of the water surface color fluctuation index based on historical water surface monitoring data and historical flood frequency. When the water surface color fluctuation index Uz exceeds or falls below the standard value range of the water surface color fluctuation index, the optimization processing module adjusts the drone monitoring route and monitoring angle.

[0029] Preferably, the optimization processing module sets the standard value range of the hydrometeorological impact index based on the historical weather data and historical flood data around the monitored water surface. When the hydrometeorological impact index Gz exceeds or is lower than the standard value range of the hydrometeorological impact index, the optimization processing module adjusts the contrast and brightness of the image to reduce the impact of rain on the image quality. In foggy conditions, a defogging algorithm can be used to improve the visibility data of the image to optimize the image processing function of the system.

[0030] Preferably, the optimization processing module evaluates the abnormal flow measurement of the system during the flood according to the flood flow measurement anomaly score Ez, and when the water flow measurement anomaly score Ez is lower than 100 points, the optimization processing module adds an acoustic Doppler flow profiler monitoring point.

[0031] Compared with the prior art, the present invention provides an image optimization method based on water conservancy monitoring environment, which has the following beneficial effects:

[0032] 1. The present invention calculates the water surface color fluctuation index Uz. The formula integrates flow rate data, underwater undercurrent influence coefficient and water surface color data of different areas, and can more comprehensively reflect the real-time status of the water area. The calculated index is compared with the preset value, and the water surface color contrast and brightness of each area are reset or adjusted, which is helpful to accurately capture abnormal changes in water flow in local areas. When the calculated water surface color fluctuation index exceeds or falls below the preset range, the optimization processing module can automatically adjust the monitoring route and angle of the drone, which can reduce manual intervention while improving monitoring efficiency. The calculation formula can also be used as part of the early warning system to monitor the fluctuation of water surface color, timely predict possible floods, and provide support for flood control measures.

[0033] 2. The present invention calculates the flood flow measurement anomaly score Ez. The formula simultaneously considers data from three dimensions: abnormal flow velocity, water surface color contrast, and abnormal precipitation, so that the evaluation result is more comprehensive. This multi-dimensional method can capture multi-angle risk signals, thereby improving the accuracy of early warning. The optimization processing module evaluates the system flow measurement anomaly during the flood according to the flood flow measurement anomaly score Ez. When the water flow measurement anomaly score Ez is lower than 100 points, the optimization processing module will automatically add acoustic Doppler flow profiler monitoring points to enhance the monitoring capability of key areas. This adaptive monitoring strategy can effectively utilize resources, thereby improving the monitoring efficiency of the system, enabling the system to be normally arranged and measured during high floods, and has the advantages of high data rate and strong adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0035] 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.

[0036] See also Figure 1 , an image optimization method based on water conservancy monitoring environment, comprising the following steps:

[0037] Step 1: Establish a water conservancy data monitoring module, a water conservancy data calculation module, an optimization processing module and a management module. The water conservancy data monitoring module is divided into a water conservancy environment unit, a weather data unit and a drone monitoring unit;

[0038] Step 2: The water conservancy and environment unit monitors water conservancy and environment data through radar, collects water level data and flow rate data in real time, numbers them, and transmits them to the optimization processing module through the network;

[0039] Step 3: The weather data unit obtains real-time weather data through forecasts issued by the meteorological station and real-time radar monitoring, numbers them, and transmits them to the optimization processing module through the network;

[0040] Step 4: The drone monitoring unit uses the drone to capture water surface image data in real time to obtain water color data of light and dark color difference, and records the brightness and color information of different areas, numbers them, and transmits them to the optimization processing module through the network;

[0041] Step 5, the water conservancy data calculation module is divided into a water surface brightness adjustment unit, a hydrological and meteorological influence unit and a flood flow measurement anomaly analysis unit, the water surface brightness adjustment unit calculates the water surface color fluctuation index Uz according to the water conservancy environment data and the water surface image data, the hydrological and meteorological influence unit calculates the hydrological and meteorological influence index Gz according to the water conservancy environment data and the real-time weather data, the flood flow measurement anomaly analysis unit calculates the flood flow measurement anomaly score Ez according to the water conservancy environment data, the water surface image data and the real-time weather data, and the water conservancy data calculation module is connected to the water conservancy data management module through a network;

[0042] Step 6: The optimization processing module adjusts the UAV parameters according to the water surface color fluctuation index Uz;

[0043] Step 7: The optimization processing module optimizes the image processing function of the system according to the hydrological and meteorological impact index Gz;

[0044] Step 8: The optimization processing module switches the monitoring point according to the flood flow measurement abnormality score Ez, and the optimization processing module and the management module are connected through the network;

[0045] Step nine: The management module issues adjustment instructions to the water environment unit, weather data unit and drone monitoring unit according to the adjustment results of the optimization processing module.

[0046] The water conservancy and environment unit numbers the water level data and flow rate data collected in real time at a certain point in time as H and i 、V i .

[0047] The real-time weather data in the weather data unit include temperature, humidity, air pressure, wind speed, wind direction, precipitation and cloud cover data near the water area. The temperature, humidity, wind speed, wind direction, precipitation and cloud cover data near the water area are numbered T, X, J, S, L, and P.

[0048] The drone monitoring unit numbers the water surface color data of different areas as q1, q2, q3, ...q according to the characteristics of the water surface image data. n .

[0049] The advantage is that by collecting water surface image data in real time, it can provide a data basis for the immediate water surface color fluctuation index Uz, so that the water surface image can quickly adapt to environmental changes.

[0050] The water surface brightness adjustment unit calculates the water surface color fluctuation index Uz according to the water environment data and the water surface image data. The calculation formula is:

[0051]

[0052] In the formula, Uz represents the water surface color fluctuation index, V i represents the flow rate data collected in real time at a certain point in time, k i Indicates the influence coefficient of the underwater undercurrent, q1, q2, q3, ...q n Represents the water surface color data of different regions, q i Indicates the color contrast of the water surface in a certain area, a i It indicates the adjustment value between the contrast and brightness of the water surface color in a certain area. It can be adjusted positively or negatively.

[0053] The advantages are: by calculating the water surface color fluctuation index Uz, the formula integrates flow rate data, underwater undercurrent influence coefficient and water surface color data in different areas, which can more comprehensively reflect the real-time status of the water area, and compare the calculated index with the preset value, reset or adjust the water surface color contrast and brightness of each area, which helps to accurately capture abnormal changes in water flow in local areas. When the calculated water surface color fluctuation index exceeds or falls below the preset range, the optimization processing module can automatically adjust the monitoring route and angle of the drone, which can reduce manual intervention while improving monitoring efficiency. The calculation formula can also be used as part of the early warning system to monitor the fluctuation of water surface color and timely predict possible floods to provide support for flood control measures.

[0054] The hydrological and meteorological impact unit calculates the hydrological and meteorological impact index Gz based on the water environment data and real-time weather data. The calculation formula is:

[0055]

[0056] In the formula, Gz represents the hydrological and meteorological impact index, H i represents the water level data collected in real time at a certain point in time, f i represents the influence coefficient of floating objects on the water surface. T, X, S, L, and P respectively represent the temperature near the water area (representing temperature. Changes in temperature can affect the density and fluidity of water, and thus affect the reflective characteristics of the water surface), humidity (representing humidity. Changes in humidity can affect the content of water vapor in the air, which may affect the clarity of the image), wind speed (representing wind speed, which can affect the size of waves on the water surface, thereby affecting the clarity of the water surface image), precipitation (representing precipitation, which directly affects the reflectivity of the water surface and the dynamics of water flow) and cloud cover data (cloud cover data can affect the ground's reception of solar radiation, and thus affect the amount of saturated steam). α represents the influence constant of saturated steam on temperature. It represents the influence constant of air pressure on humidity, S1 represents the wind speed at 1 meter above the water surface, and P1 represents the cloud cover data outside the water area. The variable coefficient representing the effect of weather on water surface height.

[0057] The advantages are: by calculating the hydrological and meteorological impact index Gz, it helps emergency management personnel to quickly identify the impact of potential hydrological and meteorological risks on water areas and take preventive measures in time. When the value of the monitored hydrological and meteorological impact index Gz is not within the preset range, the optimization processing module will issue a flood warning in time to remind emergency management personnel to take preventive measures in advance and reduce potential losses caused by floods.

[0058] The flood flow measurement anomaly analysis unit calculates the flood flow measurement anomaly score Ez based on the water conservancy environment data, water surface image data and real-time weather data. The calculation formula is:

[0059] Ez=V m *c1+L m *c2+q m *c3

[0060] In the formula, Ez represents the flood flow measurement anomaly score, V i It represents the abnormal flow rate collected in real time at a certain point in time, q m Indicates the abnormal water surface color contrast in a certain area, L m Indicates the abnormal precipitation within a certain period of time, V m *c1, L m *c2,q m *c3 represents the abnormal flow velocity score, abnormal water surface color contrast score and abnormal precipitation score respectively, c1, c2 and c3 represent the weights of the abnormal flow velocity score, abnormal water surface color contrast score and abnormal precipitation score in the flood flow measurement anomaly score respectively.

[0061] The advantages are: by calculating the flood flow measurement anomaly score Ez, the formula simultaneously takes into account the data of three dimensions: abnormal flow velocity, water surface color contrast and abnormal precipitation, making the evaluation result more comprehensive. This multi-dimensional method can capture multi-faceted risk signals, thereby improving the accuracy of early warning. The optimization processing module evaluates the system flow measurement anomaly during the flood according to the flood flow measurement anomaly score Ez. When the water flow measurement anomaly score Ez is lower than 100 points, the optimization processing module will automatically add acoustic Doppler flow profiler monitoring points to enhance the monitoring capability of key areas. This adaptive monitoring strategy can effectively utilize resources, thereby improving the monitoring efficiency of the system and improving it.

[0062] The optimization processing module sets the standard value range of the water surface color fluctuation index according to the historical water surface monitoring data and the historical flood frequency. When the water surface color fluctuation index Uz exceeds or falls below the standard value range of the water surface color fluctuation index, the optimization processing module adjusts the drone monitoring route and monitoring angle.

[0063] The optimization processing module sets the standard value range of the hydrometeorological impact index based on the historical weather data and historical flood data around the monitored water surface. When the hydrometeorological impact index Gz exceeds or is lower than the standard value range of the hydrometeorological impact index, the optimization processing module adjusts the contrast and brightness of the image to reduce the impact of rain on the image quality. In foggy conditions, the defogging algorithm can be used to improve the visibility data of the image to optimize the image processing function of the system.

[0064] The optimization processing module evaluates the abnormal flow measurement of the system during floods according to the flood flow measurement anomaly score Ez. When the water flow measurement anomaly score Ez is lower than 100 points, the optimization processing module will add an acoustic Doppler flow profiler monitoring point.

[0065] The advantages are: the optimization processing module adjusts the drone parameters and optimizes the system's image processing function according to the comparison of the calculated result value with the preset value, and switches the monitoring points in time when the environment is unpredictable. This method can improve the accuracy and efficiency of water conservancy monitoring, and help ensure normal layout and measurement during high floods under complex meteorological conditions, with high data rate and strong adaptability.

[0066] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An image optimization method based on water conservancy monitoring environment, characterized in that: The following steps are involved: Step 1: Establish a water conservancy data monitoring module, a water conservancy data calculation module, an optimization processing module, and a management module, and divide the water conservancy data monitoring module into a water conservancy environment unit, a weather data unit, and a drone monitoring unit; Step 2: The water conservancy and environment unit monitors water conservancy and environment data through radar, collects water level data and flow rate data in real time, numbers them, and transmits them to the optimization processing module through the network; Step 3: The weather data unit obtains real-time weather data through forecasts issued by the meteorological station and real-time radar monitoring, numbers them, and transmits them to the optimization processing module through the network; Step 4: The drone monitoring unit uses the drone to capture water surface image data in real time to obtain water color data of light and dark color difference, and records the brightness and color information of different areas, numbers them, and transmits them to the optimization processing module through the network; Step 5, the water conservancy data calculation module is divided into a water surface brightness adjustment unit, a hydrological and meteorological influence unit and a flood flow measurement anomaly analysis unit, the water surface brightness adjustment unit calculates the water surface color fluctuation index Uz according to the water conservancy environment data and the water surface image data, the hydrological and meteorological influence unit calculates the hydrological and meteorological influence index Gz according to the water conservancy environment data and the real-time weather data, the flood flow measurement anomaly analysis unit calculates the flood flow measurement anomaly score Ez according to the water conservancy environment data, the water surface image data and the real-time weather data, and the water conservancy data calculation module is connected to the water conservancy data management module through a network; Step 6: The optimization processing module adjusts the UAV parameters according to the water surface color fluctuation index Uz; Step 7: The optimization processing module optimizes the image processing function of the system according to the hydrological and meteorological impact index Gz; Step 8: The optimization processing module switches the monitoring point according to the flood flow measurement abnormality score Ez, and the optimization processing module and the management module are connected through the network; Step nine: The management module issues adjustment instructions to the water environment unit, weather data unit and drone monitoring unit according to the adjustment results of the optimization processing module.

2. The image optimization method based on water conservancy monitoring environment according to claim 1 is characterized in that: The water conservancy environment unit numbers the water level data and flow rate data collected in real time at a certain time point as H according to the characteristics of the water conservancy environment data. i 、V i .

3. The image optimization method based on water conservancy monitoring environment according to claim 1 is characterized in that: The real-time weather data in the weather data unit includes temperature, humidity, air pressure, wind speed, wind direction, precipitation and cloud cover data near the water area, and the temperature, humidity, wind speed, wind direction, precipitation and cloud cover data near the water area are numbered T, X, J, S, L, and P.

4. The image optimization method based on water conservancy monitoring environment according to claim 1 is characterized in that: The drone monitoring unit numbers the water surface color data of different areas as q1, q2, q3, ...q according to the characteristics of the water surface image data. n .

5. The image optimization method based on water conservancy monitoring environment according to claim 4 is characterized in that: The water surface brightness adjustment unit calculates the water surface color fluctuation index Uz according to the water environment data and the water surface image data, and the calculation formula is: In the formula, Uz represents the water surface color fluctuation index, V i represents the flow rate data collected in real time at a certain point in time, k i Indicates the influence coefficient of the underwater undercurrent, q1, q2, q3, ...q n Represents the water surface color data in different regions, q i Indicates the color contrast of the water surface in a certain area, a i Indicates the adjustment value between the contrast and brightness of the water surface color in a certain area.

6. The image optimization method based on water conservancy monitoring environment according to claim 3 is characterized in that: The hydrological and meteorological impact unit calculates the hydrological and meteorological impact index Gz based on the water environment data and real-time weather data, and its calculation formula is: In the formula, Gz represents the hydrological and meteorological impact index, H i represents the water level data collected in real time at a certain point in time, f i represents the influence coefficient of floating objects on the water surface, T, X, S, L, P represent the temperature, humidity, wind speed, precipitation and cloud cover data near the water area respectively, α represents the influence constant of saturated steam on temperature, It represents the influence constant of air pressure on humidity, S1 represents the wind speed at 1 meter above the water surface, and P1 represents the cloud cover data outside the water area. The variable coefficient representing the effect of weather on water surface height.

7. The image optimization method based on water conservancy monitoring environment according to claim 4 is characterized in that: The flood flow measurement anomaly analysis unit calculates the flood flow measurement anomaly score Ez based on the water environment data, water surface image data and real-time weather data, and its calculation formula is: Ez=V m *c1+L m *c2+q m *c3 In the formula, Ez represents the flood flow measurement anomaly score, V i represents the abnormal flow rate collected in real time at a certain point in time, q m Indicates the abnormal water surface color contrast in a certain area, L m Indicates the abnormal precipitation within a certain period of time, V m *c1, L m *c2,q m *c3 represents the abnormal flow velocity score, abnormal water surface color contrast score and abnormal precipitation score respectively, c1, c2 and c3 represent the weights of the abnormal flow velocity score, abnormal water surface color contrast score and abnormal precipitation score in the flood flow measurement anomaly score respectively.

8. The image optimization method based on water conservancy monitoring environment according to claim 5 is characterized in that: The optimization processing module sets the standard value range of the water surface color fluctuation index according to the historical water surface monitoring data and the historical flood frequency. When the water surface color fluctuation index Uz exceeds or falls below the standard value range of the water surface color fluctuation index, the optimization processing module adjusts the drone monitoring route and monitoring angle.

9. The image optimization method based on water conservancy monitoring environment according to claim 6 is characterized in that: The optimization processing module sets the standard value range of the hydrometeorological impact index based on the historical weather data and historical flood data around the monitored water surface. When the hydrometeorological impact index Gz exceeds or is lower than the standard value range of the hydrometeorological impact index, the optimization processing module adjusts the contrast and brightness of the image to reduce the impact of rain on the image quality. In foggy conditions, a defogging algorithm can be used to improve the visibility data of the image to optimize the image processing function of the system.

10. The image optimization method based on water conservancy monitoring environment according to claim 7 is characterized in that: The optimization processing module evaluates the abnormal flow measurement of the system during the flood according to the flood flow measurement abnormality score Ez. When the water flow measurement abnormality score Ez is lower than 100 points, the optimization processing module adds an acoustic Doppler current profiler monitoring point.

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