An intelligent observation system for exploring runoff mechanism of a river basin

Abstract

The application is suitable for the technical field of image processing, and particularly relates to an intelligent observation system for exploring runoff mechanism of a basin, which comprises: a database construction module, which is used for constructing a runoff mechanism database; a first screening module, which is used for acquiring video monitoring data of a to-be-observed area and topographic and geomorphic, soil and vegetation, geological, meteorological and hydrological condition data of the to-be-observed area, screening runoff mechanism based on the topographic and geomorphic, soil and vegetation, geological, meteorological and hydrological condition data of the to-be-observed area, and obtaining a first alternative runoff mechanism range; and a second screening module, which is used for determining whether to acquire observation data, narrowing the first alternative runoff mechanism range, and obtaining a second alternative runoff mechanism range. The application collects video data, and identifies runoff mechanism in combination with local hydrological observation data and other runoff-related condition data, completes an automatic identification process, and provides an intelligent operation scheme for identification and judgment of runoff mechanism.

Description

Technical Field

[0001] This invention belongs to the field of image processing technology, and in particular relates to an intelligent observation system for exploring the runoff generation mechanism of watersheds. Background Technology

[0002] Runoff occurs when rainfall intensity exceeds the soil's infiltration capacity, with the excess forming surface runoff. When soil moisture reaches field capacity, free water in the soil steadily enters the groundwater reservoir, forming groundwater runoff. During rainfall, the area in a watershed that generates runoff is called the runoff-producing zone, and its area is called the runoff-producing area. The runoff-producing area of ​​a watershed changes with the rainfall process, which is an important characteristic of watershed runoff. Watershed runoff can be quantitatively observed through a network of established hydrological and meteorological stations.

[0003] Current technologies using hydrological and meteorological stations for quantitative observation can only determine the runoff generation in the current area, but cannot determine the runoff generation mechanism of the watershed based directly on the observation data from the observation points. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent observation system for exploring the runoff generation mechanism of a watershed. This system aims to solve the problem that existing technologies, which use hydrological and meteorological stations for quantitative observation, can only determine the runoff generation of a watershed within the current area, but cannot determine the runoff generation mechanism of the watershed based directly on the observation data from the observation points.

[0005] This invention is implemented as follows: an intelligent observation method for exploring watershed runoff generation mechanisms, the method comprising:

[0006] Construct a production outflow mechanism database, wherein the production outflow mechanism database includes at least production outflow mechanism determination conditions;

[0007] Acquire video surveillance data of the area to be observed, as well as data on topography, soil vegetation, geology, meteorology and hydrology of the area to be observed. Based on the data on topography, soil vegetation, geology and meteorology and hydrology of the area to be observed, screen the runoff generation mechanism to obtain the range of the first candidate runoff generation mechanism.

[0008] Whether to acquire observation data is determined based on video surveillance data of the area to be observed. The range of the first alternative flow generation mechanism is narrowed down based on the observation data to obtain the range of the second alternative flow generation mechanism.

[0009] Runoff is identified based on the video monitoring data to be observed, the range of the second alternative runoff generation mechanism is verified, and hydrological observation results are generated.

[0010] Preferably, the step of acquiring video surveillance data and geological condition data of the area to be observed, and screening runoff generation mechanisms based on the geological condition data to obtain a first candidate runoff generation mechanism range, specifically includes:

[0011] Based on the location data of the area to be observed, video surveillance data of the area to be observed, as well as topographic and geomorphological parameters, soil and vegetation parameters, geological parameters, and meteorological and hydrological conditions data of the area to be observed are obtained.

[0012] Query the prolapse mechanism database to determine the prolapse mechanism judgment conditions for each prolapse mechanism and generate judgment condition keywords.

[0013] Keyword extraction is performed on the topographic parameters, soil and vegetation parameters, geological parameters, and meteorological and hydrological conditions data of the observation area to obtain topographic parameters, soil and vegetation parameters, geological parameters, and meteorological and hydrological keywords. The range of the first candidate runoff generation mechanism is generated by comparing the topographic parameters, soil and vegetation parameters, geological parameters, meteorological and hydrological keywords with the judgment condition keywords.

[0014] Preferably, the step of determining whether to acquire observation data based on video surveillance data of the area to be observed, and narrowing down the range of the first candidate flow generation mechanism based on the observation data to obtain the range of the second candidate flow generation mechanism, specifically includes:

[0015] Determine whether a runoff phenomenon occurs in the monitored area based on video surveillance data of the area to be observed.

[0016] When determining the presence of a runoff phenomenon, the observation data of the corresponding area is obtained based on the location of the monitored area;

[0017] Based on the observation data, determine whether the current runoff data meets the runoff mechanism determination conditions corresponding to the second alternative runoff mechanism range, screen out the runoff mechanisms that meet the conditions, and generate the second alternative runoff mechanism range.

[0018] Preferably, the steps of identifying runoff based on the video monitoring data to be observed, verifying the range of the second alternative runoff mechanism, and generating hydrological observation results specifically include:

[0019] Image extraction is performed on the video surveillance data to be observed to obtain the watershed image to be identified;

[0020] By performing image recognition on the watershed image to be identified, the flow rate of the area to be observed can be identified.

[0021] The runoff generation mechanism is matched with the runoff generation mechanism determination conditions corresponding to the range of the second alternative runoff generation mechanism based on the runoff generation flow rate, and it is determined whether there is a corresponding runoff generation mechanism, and hydrological observation results are generated.

[0022] Another objective of this invention is to provide an intelligent observation system for exploring watershed runoff generation mechanisms, the system comprising:

[0023] A database construction module is used to construct a production flow mechanism database, which includes at least production flow mechanism determination conditions.

[0024] The first screening module is used to acquire video surveillance data of the area to be observed, as well as topography, soil vegetation, geology, meteorology and hydrology data of the area to be observed. Based on the topography, soil vegetation, geology and meteorology and hydrology data of the area to be observed, the runoff generation mechanism is screened to obtain the first candidate runoff generation mechanism range.

[0025] The second filtering module is used to determine whether to obtain observation data based on video surveillance data of the area to be observed, and to narrow down the range of the first candidate flow generation mechanism based on the observation data to obtain the range of the second candidate flow generation mechanism.

[0026] The observation and judgment module is used to identify runoff based on the video monitoring data to be observed, verify the range of the second alternative runoff mechanism, and generate hydrological observation results.

[0027] Preferably, the first screening module includes:

[0028] The data acquisition unit is used to acquire positioning data based on the area to be observed, and to acquire video surveillance data of the area to be observed, as well as topography, soil vegetation, geology, meteorology and hydrology data of the area to be observed, based on the positioning data.

[0029] The data acquisition unit is used to acquire positioning data based on the area to be observed, and to acquire video surveillance data of the area to be observed, as well as topography, soil vegetation, geology, meteorology and hydrology data of the area to be observed, based on the positioning data.

[0030] The keyword extraction unit is used to query the production and runoff mechanism database, determine the production and runoff mechanism judgment conditions corresponding to each production and runoff mechanism, and generate judgment condition keywords.

[0031] The keyword comparison unit is used to extract keywords from the topographic, soil and vegetation, geological, meteorological and hydrological conditions data of the observation area, and obtain topographic, soil and vegetation, geological and meteorological and hydrological keywords. By comparing the topographic, soil and vegetation, geological and meteorological and hydrological keywords with the judgment condition keywords, the range of the first candidate runoff generation mechanism is generated.

[0032] Preferably, the second filtering module includes:

[0033] The abortion identification unit is used to determine whether abortion occurs in the monitored area based on video surveillance data of the area to be observed.

[0034] The observation data acquisition unit is used to acquire observation data of the corresponding area based on the location of the monitored area when it is determined that a runoff phenomenon exists.

[0035] The runoff mechanism screening unit is used to determine whether the current runoff data meets the runoff mechanism judgment conditions corresponding to the second candidate runoff mechanism range based on the observation data, screen out the runoff mechanisms that meet the conditions, and generate the second candidate runoff mechanism range.

[0036] Preferably, the observation and determination module includes:

[0037] The image extraction unit is used to extract images from the video surveillance data to be observed, and obtain the image of the watershed to be identified.

[0038] The flow rate identification unit is used to identify the flow rate of the area to be observed by performing image recognition on the watershed image to be identified.

[0039] The observation result generation unit is used to match the runoff generation mechanism judgment conditions corresponding to the range of the second alternative runoff generation mechanism based on the runoff volume, determine whether there is a corresponding runoff generation mechanism, and generate hydrological observation results.

[0040] Preferably, the conditions for determining the runoff mechanism include at least runoff parameters, topographic parameters, soil and vegetation parameters, geological parameters, and meteorological and hydrological data parameters.

[0041] Preferably, the meteorological and hydrological data parameters include cumulative rainfall and instantaneous rainfall.

[0042] Preferably, the runoff generation mechanism includes at least the permeable surface runoff generation mechanism, the interflow runoff generation mechanism, and the groundwater runoff generation mechanism.

[0043] Preferably, the hydrological observation results are encrypted and stored, and the data is merged according to a preset period.

[0044] Preferably, the video surveillance data of the area to be observed is obtained by a vortex device installed in the area to be observed.

[0045] This invention provides an intelligent observation system for exploring runoff generation mechanisms in watersheds. By setting up eddy current measurement equipment in the area to be observed, the system actively collects video data when runoff occurs. Combined with local observation data, topographic parameters, soil and vegetation parameters, geological parameters, and meteorological and hydrological data, the system identifies the runoff generation mechanism, completing the automated identification process and providing an intelligent operation solution for runoff generation mechanism identification. Attached Figure Description

[0046] Figure 1 A flowchart illustrating an intelligent observation method for investigating watershed runoff generation mechanisms, provided as an embodiment of the present invention;

[0047] Figure 2The flowchart illustrates the steps of obtaining video surveillance data and geological condition data of the area to be observed, and screening runoff generation mechanisms based on the topography, soil vegetation, geology, meteorology and hydrology data of the area to be observed to obtain the first candidate runoff generation mechanism range.

[0048] Figure 3 The flowchart of the steps provided in this embodiment of the invention is as follows: determining whether to acquire observation data based on video surveillance data of the area to be observed, narrowing down the range of the first alternative flow generation mechanism based on the observation data, and obtaining the range of the second alternative flow generation mechanism.

[0049] Figure 4 A flowchart of the steps for identifying runoff based on video surveillance data to be observed, verifying the range of a second alternative runoff mechanism, and generating hydrological observation results, provided in an embodiment of the present invention.

[0050] Figure 5 An architecture diagram of an intelligent observation system for exploring watershed runoff generation mechanisms is provided in an embodiment of the present invention;

[0051] Figure 6 An architecture diagram of a first screening module provided in an embodiment of the present invention;

[0052] Figure 7 An architecture diagram of a second screening module provided in an embodiment of the present invention;

[0053] Figure 8 This is an architecture diagram of an observation and determination module provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0055] It is understood that the terms "first," "second," etc., used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of this application, a first script may be referred to as a second script, and similarly, a second script may be referred to as a first script.

[0056] In this invention, the vorticity device is a vortex tower, which is equipped with video acquisition equipment and multiple sensors for collecting observation data. The vortex tower is an eddy observation system, installed in layers on a 10-meter triangular tower. A TE525MM (rain gauge) is installed at the top 10 meters, an HMP155A (air temperature and humidity sensor) at 5 meters, an EC150 (carbon dioxide and water vapor analyzer) and a CSAT3A (three-dimensional ultrasonic anemometer) at 3.5 meters, facing southwest. A CNR4 (four-component net radiation sensor) and a CMP3 (total radiation sensor) are also installed. The PQS1 (Photosynthetically Active Radiation) sensor is installed on the south-facing horizontal arm of the tower at a height of 3 meters. The CR6 (data acquisition unit), AM16 / 32B (expansion board), and CS100 (atmospheric pressure sensor) are installed in a chassis at a height of 1.7 meters, facing north. The TDR_315L (soil moisture sensor) and HFP01 (soil heat flux plate) are installed in the soil south of the tower. The TDR_315L (soil moisture sensor) is installed at depths of 5cm, 15cm, 25cm, 40cm, and 60cm, respectively, while the HFP01 is installed at depths of 5cm and 50cm.

[0057] like Figure 1 The diagram shows a flowchart of an intelligent observation method for investigating watershed runoff generation mechanisms provided by an embodiment of the present invention. The method includes:

[0058] S100, construct a production flow mechanism database, wherein the production flow mechanism database includes at least production flow mechanism determination conditions.

[0059] In this step, a runoff generation mechanism database is constructed. For different topographic features, soil vegetation, geological, meteorological and hydrological conditions, there are multiple types of watershed runoff generation, that is, multiple runoff generation mechanisms exist, such as surface runoff generation mechanism with excessive infiltration, interflow runoff mechanism, and groundwater runoff generation mechanism. Different runoff generation mechanisms require special conditions. By analyzing the known runoff generation mechanisms, a runoff generation mechanism database is constructed. The runoff generation mechanism database records the conditions for determining runoff generation mechanisms and the runoff generation performance under different runoff generation mechanisms.

[0060] S200: Acquire video surveillance data of the area to be observed, as well as data on the topography, soil vegetation, geology, meteorology and hydrology of the area to be observed. Based on the data on the topography, soil vegetation, geology and meteorology and hydrology of the area to be observed, screen the runoff generation mechanism to obtain the range of the first candidate runoff generation mechanism.

[0061] In this step, video surveillance data of the area to be observed, as well as data on topography, soil vegetation, geology, and meteorological and hydrological conditions, are acquired. To determine the runoff generation mechanism in the current area, eddy current monitoring equipment is installed in the area to be observed, and video acquisition begins when rainfall occurs to obtain video surveillance data of the area. At the same time, the topography, soil vegetation, geology, and meteorological and hydrological data of each area have already been acquired, meaning that the topography, soil vegetation, geology, and meteorological and hydrological conditions data of the area to be observed can be obtained through querying. To reduce the amount of data processing, the topography, soil vegetation, geology, and meteorological and hydrological conditions data of the area to be observed are analyzed to determine the type of runoff generation mechanism that may occur in the current area. However, the topography, soil vegetation, geology, and meteorological and hydrological conditions data of the area to be observed do not contain absolutely detailed topography, soil vegetation, geology, and meteorological and hydrological conditions, so they are insufficient to directly determine the type of runoff generation mechanism. Only the known conditions contained therein can be used to filter out the possible runoff generation mechanisms in the current area to obtain the first candidate runoff generation mechanism range.

[0062] S300 determines whether to acquire observation data based on video surveillance data of the area to be observed, and narrows down the range of the first alternative flow generation mechanism based on the observation data to obtain the range of the second alternative flow generation mechanism.

[0063] In this step, the decision to acquire observation data is made based on video surveillance data of the area to be observed. To determine the type of runoff generation mechanism, it is first necessary to confirm that runoff has occurred in the current area and that runoff has been formed. Therefore, the video surveillance data of the area to be observed is analyzed to determine whether runoff has occurred. If it has occurred, local observation data is acquired. By analyzing the observation data, the rainfall trend in the current area can be determined. Based on the current rainfall situation, it is determined whether the occurrence conditions corresponding to each runoff generation mechanism are met, so as to achieve the purpose of screening and obtain the range of the second candidate runoff generation mechanism.

[0064] S400 identifies runoff generation based on the video monitoring data to be observed, verifies the range of the second alternative runoff generation mechanism, and generates hydrological observation results.

[0065] In this step, flow generation is identified based on the video surveillance data to be observed. Image recognition technology is used to identify the images extracted from the video surveillance data to further verify the flow generation mechanism. Specifically, the flow generation in the area is identified. Different flow generation mechanisms correspond to different flow generation rates, and therefore the images generated are also different. After verification, hydrological observation results are generated, which record the specific type of flow generation mechanism in the current area to be observed after the judgment.

[0066] like Figure 2As shown, in a preferred embodiment of the present invention, the step of acquiring video surveillance data of the area to be observed, as well as topographic, soil and vegetation, geological, meteorological and hydrological condition data of the area to be observed, and screening runoff generation mechanisms based on the topographic, soil and vegetation, geological, meteorological and hydrological condition data of the area to be observed to obtain a first candidate runoff generation mechanism range, specifically includes:

[0067] S201, based on the area to be observed, obtain positioning data, and based on the positioning data, obtain video surveillance data of the area to be observed, as well as topography, soil vegetation, geology, meteorological and hydrological conditions data of the area to be observed.

[0068] In this step, location data is obtained based on the area to be observed. The location of each area to be observed can be determined, and the location data can be obtained. Based on the location data, the corresponding topography, soil vegetation, geology, meteorological and hydrological conditions data of the area to be observed can be queried online. Furthermore, the eddy current device set up in the area can be identified to retrieve the video monitoring data of the area to be observed.

[0069] S202, query the spurious flow mechanism database, determine the spurious flow mechanism judgment conditions corresponding to each spurious flow mechanism, and generate judgment condition keywords.

[0070] In this step, the dysentery mechanism database is queried. Since the dysentery mechanism database has recorded the judgment conditions corresponding to different types of dysentery mechanisms, keywords are generated based on the corresponding judgment conditions to obtain judgment condition keywords.

[0071] S203, extract keywords from the topography, soil vegetation, geology, meteorology and hydrology data of the area to be observed, and obtain topography, soil vegetation, geology, meteorology and hydrology keywords. By comparing the topography, soil vegetation, geology, meteorology and hydrology keywords with the judgment condition keywords, the range of the first candidate runoff generation mechanism is generated.

[0072] In this step, keywords are extracted from the topography, soil vegetation, geology, and meteorological and hydrological conditions data of the area to be observed. By extracting keywords, topography, soil vegetation, geology, and meteorological and hydrological keywords are obtained. If the condition keywords can match the topography, soil vegetation, geology, and meteorological and hydrological keywords, it means that the current area has matched the corresponding runoff generation mechanism, and the range of the first candidate runoff generation mechanism is determined accordingly.

[0073] like Figure 3 As shown, in a preferred embodiment of the present invention, the step of determining whether to acquire observation data based on video surveillance data of the area to be observed, and narrowing down the range of the first candidate flow generation mechanism based on the observation data to obtain the range of the second candidate flow generation mechanism, specifically includes:

[0074] S301, determine whether a runoff phenomenon has occurred in the monitored area based on video surveillance data of the area to be observed.

[0075] In this step, it is determined whether a runoff phenomenon occurs in the monitored area based on the video surveillance data of the area to be observed. Specifically, multiple real-time images are obtained by extracting the images, and image recognition technology is used to determine whether a runoff phenomenon exists in the current area.

[0076] S302, when it is determined that there is a runoff phenomenon, the observation data of the corresponding area is obtained according to the location of the monitored area.

[0077] In this step, when it is determined that a runoff phenomenon exists, the corresponding observation data of the monitored area is obtained based on the location of the monitored area. Conversely, if there is no runoff phenomenon, no observation data is obtained. This is because the runoff mechanism cannot be analyzed when no runoff phenomenon occurs.

[0078] S303: Based on the observation data, determine whether the current runoff data meets the runoff mechanism determination conditions corresponding to the second alternative runoff mechanism range, screen out the runoff mechanisms that meet the conditions, and generate the second alternative runoff mechanism range.

[0079] In this step, the current runoff data is determined based on the observation data to see if it meets the criteria for the runoff mechanism corresponding to the second candidate runoff mechanism range. The observation data records information such as short-term rainfall, cumulative rainfall, and rainfall intensity. Based on the above criteria, it is determined whether various runoff mechanisms are met, and the runoff mechanisms that can be met are selected to generate the second candidate runoff mechanism range.

[0080] like Figure 4 As shown, in a preferred embodiment of the present invention, the steps of identifying runoff based on the video monitoring data to be observed, verifying the range of the second alternative runoff mechanism, and generating hydrological observation results specifically include:

[0081] S401, extract images from the video monitoring data to be observed to obtain the watershed image to be identified.

[0082] In this step, image extraction is performed on the video surveillance data to be observed. Specifically, this can be done by frame extraction, where 10% of the images out of every 60 images are selected as the watershed to be identified. The extracted images are extracted at fixed intervals.

[0083] S402 identifies the flow rate of the area to be observed by performing image recognition on the watershed image.

[0084] S403: Match the runoff generation mechanism determination conditions corresponding to the range of the second alternative runoff generation mechanism based on the runoff generation flow rate, determine whether there is a corresponding runoff generation mechanism, and generate hydrological observation results.

[0085] In this step, image recognition is performed on the watershed image to be identified. The flow rate in the current observation area is identified by image recognition technology. The flow rate is an estimated value. The current topography, soil vegetation, geology, meteorological and hydrological conditions and observation data are combined to determine whether there is a flow generation mechanism that matches the current estimated value. The most matching flow generation mechanism is selected and hydrological observation results are generated.

[0086] like Figure 5 As shown in the figure, an intelligent observation system for exploring watershed runoff generation mechanisms is provided by an embodiment of the present invention. The system includes:

[0087] The database construction module 100 is used to construct a production flow mechanism database, which includes at least production flow mechanism determination conditions.

[0088] In this system, the database construction module 100 constructs a runoff generation mechanism database. For different topographic features, soil vegetation, geological, meteorological and hydrological conditions, there are multiple types of watershed runoff generation, that is, multiple runoff generation mechanisms exist, such as surface runoff generation mechanism with excessive infiltration, interflow runoff mechanism and groundwater runoff mechanism. Different runoff generation mechanisms require special conditions. By analyzing the known runoff generation mechanisms, a runoff generation mechanism database is constructed. The runoff generation mechanism database records the conditions for determining runoff generation mechanisms and the runoff performance under different runoff generation mechanisms.

[0089] The first screening module 200 is used to acquire video surveillance data of the area to be observed, as well as topographic, soil and vegetation, geological, meteorological and hydrological conditions data of the area to be observed. Based on the topographic, soil and vegetation, geological and meteorological and hydrological conditions data of the area to be observed, the runoff generation mechanism is screened to obtain the first candidate runoff generation mechanism range.

[0090] In this system, the first filtering module 200 acquires video surveillance data of the area to be observed, as well as data on the topography, soil vegetation, geology, and meteorological and hydrological conditions of the area. To determine the runoff generation mechanism in the current area, video acquisition is initiated when rainfall occurs by setting up eddy current devices in the area to be observed, thereby obtaining video surveillance data of the area to be observed. At the same time, the topography, soil vegetation, geology, and meteorological and hydrological data of each area have already been acquired, meaning that the topography, soil vegetation, geology, and meteorological and hydrological conditions data of the area to be observed can be obtained through querying. To reduce the amount of data processing, the topography, soil vegetation, geology, and meteorological and hydrological conditions data of the area to be observed are analyzed to determine the type of runoff generation mechanism that may occur in the current area. However, the topography, soil vegetation, geology, and meteorological and hydrological conditions data of the area to be observed do not contain absolutely detailed topography, soil vegetation, geology, and meteorological and hydrological conditions, so they are insufficient to directly determine the type of runoff generation mechanism. Only the known conditions contained therein can be used to filter out the possible runoff generation mechanisms in the current area, thus obtaining the first candidate runoff generation mechanism range.

[0091] The second screening module 300 is used to determine whether to acquire observation data based on the video surveillance data of the area to be observed, and to narrow down the range of the first candidate flow generation mechanism based on the observation data to obtain the range of the second candidate flow generation mechanism.

[0092] In this system, the second screening module 300 determines whether to acquire observation data based on the video surveillance data of the area to be observed. In order to determine the type of runoff generation mechanism, it is first necessary to determine that runoff generation has occurred in the current area and that runoff has been formed. Therefore, by analyzing the video surveillance data of the area to be observed, it is determined whether runoff generation has occurred. If it has occurred, the local observation data is acquired. By analyzing the observation data, the rainfall trend of the current area can be determined. Based on the current rainfall situation, it is determined whether the occurrence conditions corresponding to each runoff generation mechanism are met, so as to achieve the purpose of screening and obtain the range of the second candidate runoff generation mechanism.

[0093] The observation and judgment module 400 is used to identify the flow generation based on the video monitoring data to be observed, verify the range of the second alternative flow generation mechanism, and generate hydrological observation results.

[0094] In this system, the observation and judgment module 400 identifies runoff based on the video surveillance data to be observed. It uses image recognition technology to identify the images extracted from the video surveillance data to further verify the runoff mechanism. Specifically, it identifies the runoff in the area. Different runoff mechanisms correspond to different runoffs, and therefore the images generated are also different. After verification, it generates hydrological observation results, which record the specific type of runoff mechanism in the current area to be observed after judgment.

[0095] like Figure 6 As shown, in a preferred embodiment of the present invention, the first screening module 200 includes:

[0096] The data acquisition unit 201 is used to acquire positioning data based on the area to be observed, and to acquire video surveillance data of the area to be observed, as well as topography, soil vegetation, geology, meteorology and hydrology data of the area to be observed based on the positioning data.

[0097] In this module, the data acquisition unit 201 acquires positioning data based on the area to be observed. The location of each area to be observed can be determined, and positioning data can be obtained. Based on the positioning data, the corresponding topography, soil vegetation, geology, meteorological and hydrological conditions data of the area to be observed can be queried online. Furthermore, the vortex equipment set up in the area can be identified to retrieve video monitoring data of the area to be observed.

[0098] Keyword extraction unit 202 is used to query the production and runoff mechanism database, determine the production and runoff mechanism judgment conditions corresponding to each production and runoff mechanism, and generate judgment condition keywords.

[0099] In this module, the keyword extraction unit 202 queries the production flow mechanism database. Since the production flow mechanism database has recorded the judgment conditions corresponding to different types of production flow mechanisms, keywords are generated based on the corresponding judgment conditions to obtain judgment condition keywords.

[0100] Keyword comparison unit 203 is used to extract keywords from the topography, soil vegetation, geology, meteorology and hydrology data of the area to be observed, and obtain topography, soil vegetation, geology, meteorology and hydrology keywords. By comparing the topography, soil vegetation, geology, meteorology and hydrology keywords with the judgment condition keywords, the range of the first candidate runoff generation mechanism is generated.

[0101] In this module, the keyword comparison unit 203 extracts keywords from the topography, soil vegetation, geology, and meteorological and hydrological conditions data of the area to be observed. By extracting keywords, topography, soil vegetation, geology, and meteorological and hydrological keywords are obtained. If the judgment condition keywords can match the topography, soil vegetation, geology, and meteorological and hydrological keywords, it means that the current area has matched the corresponding runoff generation mechanism, and the range of the first candidate runoff generation mechanism is determined accordingly.

[0102] like Figure 7 As shown, in a preferred embodiment of the present invention, the second screening module 300 includes:

[0103] The abortion identification unit 301 is used to determine whether an abortion phenomenon has occurred in the monitored area based on video surveillance data of the area to be observed.

[0104] In this module, the abortion identification unit 301 determines whether an abortion phenomenon has occurred in the monitored area based on the video monitoring data of the area to be observed. Specifically, multiple real-time video images are obtained by extracting images, and image recognition technology is used to determine whether an abortion phenomenon exists in the current area.

[0105] The observation data acquisition unit 302 is used to acquire observation data of the corresponding area based on the location of the monitored area when it is determined that a runoff phenomenon exists.

[0106] In this module, when the observation data acquisition unit 302 determines that a runoff phenomenon exists, it acquires the observation data of the corresponding area based on the location of the monitored area. Conversely, if there is no runoff phenomenon, it does not acquire the observation data. This is because the runoff mechanism cannot be analyzed when no runoff phenomenon occurs.

[0107] The runoff mechanism screening unit 303 is used to determine whether the current runoff data meets the runoff mechanism judgment conditions corresponding to the second candidate runoff mechanism range based on the observation data, screen out the runoff mechanisms that meet the conditions, and generate the second candidate runoff mechanism range.

[0108] In this module, the runoff mechanism screening unit 303 determines whether the current runoff data meets the runoff mechanism judgment conditions corresponding to the second candidate runoff mechanism range based on the observation data. The observation data records information such as short-term rainfall, cumulative rainfall, and rainfall intensity. Based on the above conditions, it determines whether various runoff mechanisms are met, and selects the runoff mechanisms that can be met to generate the second candidate runoff mechanism range.

[0109] like Figure 8 As shown, in a preferred embodiment of the present invention, the observation and determination module 400 includes:

[0110] The image extraction unit 401 is used to extract images from the video monitoring data to be observed, and obtain the image of the watershed to be identified.

[0111] In this module, the image extraction unit 401 extracts images from the video monitoring data to be observed. Specifically, it can extract 10% of the images from every 60 images as the watershed images to be identified by frame extraction. The extracted images are extracted at fixed intervals.

[0112] The flow rate identification unit 402 is used to identify the flow rate of the area to be observed by performing image recognition on the watershed image to be identified.

[0113] The observation result generation unit 403 is used to match the runoff generation mechanism judgment conditions corresponding to the range of the second alternative runoff generation mechanism based on the runoff volume, determine whether there is a corresponding runoff generation mechanism, and generate hydrological observation results.

[0114] In this module, the runoff identification unit 402 performs image recognition on the watershed image to be identified, and identifies the runoff in the current observation area through image recognition technology. The runoff is an estimated value. Combining the current topography, soil vegetation, geology, meteorological and hydrological conditions and observation data, it judges whether there is a runoff mechanism that matches the current estimated value, selects the most matching runoff mechanism, and generates hydrological observation results.

[0115] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0116] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0117] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0118] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0119] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent observation system for investigating watershed runoff generation mechanisms, characterized in that, The system includes: A database construction module is used to construct a production flow mechanism database, which includes at least production flow mechanism determination conditions. The first screening module is used to acquire video surveillance data of the area to be observed, as well as topography, soil vegetation, geology, meteorology and hydrology data of the area to be observed. Based on the topography, soil vegetation, geology and meteorology and hydrology data of the area to be observed, the runoff generation mechanism is screened to obtain the first candidate runoff generation mechanism range. The second filtering module is used to determine whether to obtain observation data based on video surveillance data of the area to be observed, and to narrow down the range of the first candidate flow generation mechanism based on the observation data to obtain the range of the second candidate flow generation mechanism. The observation and judgment module is used to identify runoff based on the video monitoring data to be observed, verify the range of the second alternative runoff generation mechanism, and generate hydrological observation results. The first filtering module includes: The data acquisition unit is used to acquire positioning data based on the area to be observed, and to acquire video surveillance data of the area to be observed, as well as topography, soil vegetation, geology, meteorology and hydrology data of the area to be observed, based on the positioning data. The keyword extraction unit is used to query the production and flow mechanism database, determine the production and flow mechanism judgment conditions corresponding to each production and flow mechanism, and generate judgment condition keywords. The keyword comparison unit is used to extract keywords from the topographic, soil and vegetation, geological, meteorological and hydrological conditions data of the observation area, and obtain topographic, soil and vegetation, geological and meteorological and hydrological keywords. By comparing the topographic, soil and vegetation, geological and meteorological and hydrological keywords with the judgment condition keywords, the range of the first candidate runoff generation mechanism is generated.

2. The intelligent observation system for exploring watershed runoff generation mechanisms according to claim 1, characterized in that, The observation and determination module includes: The image extraction unit is used to extract images from the video surveillance data to be observed, and obtain the image of the watershed to be identified. The runoff identification unit is used to identify runoff data in the area to be observed by performing image recognition on the watershed image to be identified. The observation result generation unit is used to match the runoff with the runoff mechanism determination conditions corresponding to the range of the second alternative runoff mechanism based on the runoff, determine whether there is a corresponding runoff mechanism, and generate hydrological observation results.

3. The intelligent observation system for exploring watershed runoff generation mechanisms according to claim 1, characterized in that, The criteria for determining the runoff generation mechanism include at least the runoff generation parameters, topographic parameters, soil and vegetation parameters, geological parameters, and meteorological and hydrological data parameters.

4. The intelligent observation system for exploring watershed runoff generation mechanisms according to claim 3, characterized in that, The meteorological and hydrological data parameters include cumulative rainfall and instantaneous rainfall; the topographic parameters include topographic slope; the soil and vegetation parameters include soil type, tree species canopy width and root system data; and the geological parameters include rock structure and fissure data.

5. The intelligent observation system for exploring watershed runoff generation mechanisms according to claim 1, characterized in that, The runoff generation mechanisms include at least the permeable surface runoff generation mechanism, the interflow runoff generation mechanism, and the groundwater runoff generation mechanism.

6. The intelligent observation system for exploring watershed runoff generation mechanisms according to claim 3, characterized in that, The hydrological observation results are encrypted and stored, and the data is merged according to a preset period.

7. The intelligent observation system for exploring watershed runoff generation mechanisms according to claim 1, characterized in that, The video surveillance data of the area to be observed is obtained through vortex measurement equipment set up in the area to be observed.

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