A river ecology detection method and system based on digital twin

Abstract

The present invention discloses a river ecology detection method and system based on digital twins, including: through the fusion of multi-source data, the catchment area of ​​each pixel can be calculated more accurately, and the rivers and tributaries can be determined according to the flow accumulation threshold, so as to obtain the accurate watershed watershed, which greatly improves the accuracy of watershed division and overcomes the shortcomings of traditional methods in detecting subtle terrain changes; in addition, by creating a digital twin model of the river basin, the dynamic simulation and prediction of the river ecosystem are realized; especially when multiple types of sensors fail, the present invention can intelligently choose whether to continue to use satellite remote sensing or drone for data replacement and update the digital twin model by judging the failure results; this mechanism ensures the continuity and reliability of river ecological monitoring and avoids the data interruption problem caused by sensor failure in traditional methods.

Description

Technical Field

[0001] The present invention relates to the technical field of watershed planning, detection and digital twin model construction, and in particular to a river ecology detection method and system based on digital twins. Background Art

[0002] With the continuous impact of global climate change and human activities on the ecological environment, the monitoring and management of river ecosystems has become increasingly important. Traditional river monitoring methods mainly rely on limited hydrological station data and manual field surveys. This method is not only costly and inefficient, but also unable to provide high-frequency and full-coverage information on the dynamic changes of the river basin.

[0003] In recent years, the development of remote sensing technology, Internet of Things technology and digital twin technology has brought new opportunities for river ecological monitoring. Large-scale, high-resolution data obtained through satellite remote sensing makes real-time and comprehensive river monitoring possible. In addition, the development of Internet of Things technology enables various sensors to collect multiple parameters such as water quality, water level, flow, etc. in real time, providing a rich source of data for river ecological monitoring. Digital twin technology builds virtual models and interacts and updates synchronously with the real world to achieve dynamic simulation and prediction of river ecosystems. This technology can integrate multi-source data, perform highly complex system simulations, and provide a full range of monitoring and management methods. However, although the development of these technologies has brought new possibilities for river ecological monitoring, there are still unresolved problems: First, traditional remote sensing data processing methods have limitations in detecting subtle terrain changes, and are prone to ignoring subtle depressions in the basin and slight changes in water flow paths, resulting in large errors in catchment area calculation and watershed identification. Second, although digital twin technology provides a powerful tool, in practical applications, how to organically combine multiple types of sensor data with remote sensing data and update the model in real time is a complex technical challenge. In addition, the existing digital twin model lacks an effective mechanism for dealing with emergencies. For example, when a sensor fails, the data source cannot be backed up in time, which affects the continuity and reliability of river ecological monitoring. If the deployed equipment is not locally but in another place, people must be sent immediately to repair the equipment, especially for companies that do not have off-site personnel. This will not only cause the possibility of data loss but also waste manpower. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a river ecology detection method based on digital twins to solve the problems raised in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a river ecology detection method based on digital twins, comprising:

[0008] Obtaining a river basin range, setting multiple types of sensors within the river basin range, synchronizing data from the multiple types of sensors through satellite remote sensing, eliminating slight depressions between the data, and obtaining a water flow path;

[0009] Generate a flow direction grid using the water flow path, calculate the catchment area of ​​each pixel, set a flow accumulation threshold, determine rivers and tributaries by judging the flow accumulation threshold, and obtain a watershed watershed based on the flow direction grid and the determined rivers and tributaries;

[0010] Fusing watershed divides with multiple types of sensors to form a comprehensive watershed dataset, and creating a digital twin model of the river basin from the comprehensive watershed dataset;

[0011] By determining whether multiple types of sensors fail, the digital twin model of the river basin is updated according to the failure results.

[0012] As a preferred solution of the river ecology detection method based on digital twins described in the present invention, wherein: the river basin range is obtained, and multiple types of sensors are set within the river basin range, including:

[0013] The river basin range divisions are defined as sedimentation-rheology section, aquatic-water quality section, flood-ecology section, climate-rheology section and water quality-sedimentation section;

[0014] The multiple types of sensors are water quality sensors, aquatic plant sensors, sediment sensors, flow sensors, water level sensors, flow sensors, ecological sensors, meteorological sensors and infiltration sensors;

[0015] Among them, the definition of river basin range division is a combination of two types of sensors.

[0016] As a preferred solution of the river ecology detection method based on digital twins described in the present invention, wherein: synchronizing data from multiple types of sensors through satellite remote sensing, eliminating subtle depressions between data, and obtaining a water flow path, including:

[0017] The data from multiple types of sensors are converted into pixel elevations, and the area position of the current pixel elevation is marked as a concave area, and the concave area is filled to the same height as the surrounding pixel elevation area position in an iterative calculation manner;

[0018] Among them, pixel elevation refers to the altitude of the surface point represented by the grid unit.

[0019] As a preferred solution of the river ecology detection method based on digital twins described in the present invention, wherein: using the water flow path to generate a flow direction grid, calculating the catchment area of ​​each pixel, including:

[0020] According to the elevation difference between each pixel and its neighboring pixels, the water flow direction is determined, and a corresponding flow direction code value is assigned to each pixel;

[0021] Initialize the water collection area of ​​all pixels to 1, where 1 represents the area of ​​each pixel itself;

[0022] For each pixel (i, j), traverse the eight neighboring pixels around the pixel and determine which pixels’ water flows to the pixel based on the flow direction code value;

[0023] For all neighboring pixels that converge to this pixel, their water collection areas are accumulated until the water collection areas of all pixels no longer change.

[0024] As a preferred solution of the river ecology detection method based on digital twins described in the present invention, wherein: setting a flow accumulation threshold value by judging the flow accumulation threshold value, determining rivers and tributaries, and obtaining a watershed watershed according to the flow direction grid and the determined rivers and tributaries, including:

[0025] Set the minimum catchment area of ​​all pixels as the flow accumulation threshold. For each pixel (i, j), determine whether its flow accumulation value exceeds the flow accumulation threshold. If it exceeds, it is a river pixel. If it is equal to, it is a tributary pixel. If it is less than, determine whether the flow accumulation value of the next pixel exceeds the flow accumulation threshold.

[0026] For each river and tributary pixel, mark all upstream pixels that flow to the pixel. For unmarked pixels, mark their watershed numbers according to their flow directions.

[0027] Draw the boundary lines of different watersheds according to the watershed numbers.

[0028] As a preferred solution of the river ecology detection method based on digital twins described in the present invention, the digital twin model of the river basin is created, including:

[0029] The digital twin model is expressed as:

[0030]

[0031] Where M(x,y) is the digital twin model of the pixel position (x,y), Z is the digital twin model period, A is the area integration area of ​​the entire river basin, N(x,y) is the set N of neighboring pixels at the pixel position (x,y), River(i,j) represents the flow accumulation threshold judgment function, θ is the attenuation coefficient that controls the influence of the water flow path, |H(i n ,j m )-H(i n-1 ,j m-1 )| represents the absolute value of the elevation difference between the current pixel elevation and the next pixel elevation; S k represents the transmission data in sensor k; N is the integration area represented by the transmission data in the sensor; T k represents the fault data in sensor k; C is the integration area of ​​sensor fault detection, and G(i,j) is the catchment area G of pixel (i,j).

[0032] As a preferred solution of the river ecology detection method based on digital twins described in the present invention, wherein: by determining whether multiple types of sensors have failed, and updating the digital twin model of the river basin according to the failure results, the method includes:

[0033] A fault detection function is established. If the absolute value of the data transmitted by the sensor in a certain time period and the historical data transmitted in the same time period in the past is higher than the sensor fault threshold, a drone is used to obtain data instead of the sensor, and the digital twin model of the river basin is updated at the same time. The sensor fault threshold is given by ∫ C T k dC is obtained.

[0034] In a second aspect, the present invention provides a river ecology detection system based on digital twins, which includes:

[0035] A river basin data processing module is configured to obtain a river basin range, set multiple types of sensors within the river basin range, synchronize data from the multiple types of sensors through satellite remote sensing, eliminate slight depressions between the data, and obtain a water flow path;

[0036] A watershed division module is configured to generate a flow direction grid using the water flow path, calculate the catchment area of ​​each pixel, set a flow accumulation threshold, determine rivers and tributaries by judging the flow accumulation threshold, and obtain a watershed watershed according to the flow direction grid and the determined rivers and tributaries;

[0037] a digital twin model building module configured to fuse the watershed of the river basin with multiple types of sensors to form a comprehensive watershed dataset, and to create a digital twin model of the river basin from the comprehensive watershed dataset;

[0038] The digital twin model update module is configured to determine whether multiple types of sensors fail and update the digital twin model of the river basin according to the failure results.

[0039] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: the processor implements any step of the above method when executing the computer program.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program implements any step of the above method when executed by a processor.

[0041] Compared with the prior art, the invention has the following beneficial effects: the invention can more accurately calculate the catchment area of ​​each pixel through the fusion of multi-source data, and determine rivers and tributaries according to the flow accumulation threshold, so as to obtain accurate watershed watersheds, which greatly improves the accuracy of watershed division and overcomes the shortcomings of traditional methods in detecting subtle terrain changes; in addition, by creating a digital twin model of the river basin, dynamic simulation and prediction of the river ecosystem are realized; especially when multiple types of sensors fail, the invention can intelligently choose whether to continue using satellite remote sensing or drones for data replacement and update the digital twin model by judging the failure results; this mechanism ensures the continuity and reliability of river ecological monitoring and avoids the data interruption problem caused by sensor failure in traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0043] Figure 1 This is an overall flow chart of a river ecology detection method based on digital twins according to an embodiment of the present invention;

[0044] Figure 2 A comparison diagram of the impact of water flow coverage on terrain in a river basin according to a river ecology detection method based on digital twins described in an embodiment of the present invention;

[0045] Figure 3 This is a digital twin model data processing process under fault and non-fault conditions of a river ecology detection method based on digital twins described in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0049] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0050] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0051] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0052] Example 1

[0053] Reference Figure 1, which is the first embodiment of the present invention, and provides a river ecology detection method based on digital twins, including:

[0054] S1. Obtain the scope of the river basin, set up multiple types of sensors within the river basin, synchronize the data from the multiple types of sensors through satellite remote sensing, eliminate the slight depressions between the data, and obtain the water flow path;

[0055] Furthermore, the river basin range divisions are defined as the sedimentation-rheology section, the aquatic-water quality section, the flood-ecology section, the climate-rheology section, and the water quality-sedimentation section;

[0056] Specifically, the multiple types of sensors are divided into water quality sensors, aquatic plant sensors, sediment sensors, flow sensors, water level sensors, ecological sensors, meteorological sensors, and infiltration sensors;

[0057] Among them, the definition of river basin range division is the combination of two types of sensors among multiple types of sensors;

[0058] Specifically, the sedimentation-rheology section is defined by sediment sensors and flow sensors. Sediment sensors measure the concentration of suspended matter and sediment particle size in water, while flow sensors measure water flow velocity, monitoring the sedimentation and rheology effects on water flow and ecology.

[0059] Specifically, the aquatic-water quality section is defined by the aquatic plant sensor and the water quality sensor. The water quality sensor measures the pH value, dissolved oxygen, and turbidity in the water, and the aquatic plant sensor measures the biomass and aquatic plant coverage in the water, and monitors the growth status of aquatic plants under different water quality environments.

[0060] Specifically, the flood-ecological section is defined by water level sensors and ecological sensors. The water level sensors measure the water level changes, and the ecological sensors measure the looseness of the soil adjacent to the water, monitoring the water level changes and soil collapse in the flood-prone area;

[0061] Specifically, the climate-rheological section is defined by meteorological sensors and water level sensors. The meteorological sensors measure the temperature and humidity in the air and monitor the impact of climate change on the water level;

[0062] Specifically, the water quality-sedimentation section is defined by water quality sensors and infiltration sensors, which measure changes in groundwater and monitor the exchange between groundwater and river water bodies;

[0063] It should be noted that by defining the range of the river basin, each section of the basin has the same segment, and the definition of each segment is related to the water flow parameters collected by the sensor, which further improves the data interaction ability between sensors;

[0064] Furthermore, to eliminate the depressions between data, it is necessary to convert the data from multiple types of sensors into pixel elevations, mark the area position of the current pixel elevation as the depression area, and iterate the calculation until the depression area is filled to the same height as the surrounding pixel elevation area position;

[0065] It should be noted that the grid cells at this time do not contain water flow;

[0066] Among them, pixel elevation refers to the altitude of the surface point represented by the grid unit;

[0067] S2. Generate a flow direction grid using the water flow path, calculate the catchment area of ​​each pixel, set the flow accumulation threshold, determine the rivers and tributaries by judging the flow accumulation threshold, and obtain the watershed watershed based on the flow direction grid and the determined rivers and tributaries;

[0068] Furthermore, the water flow direction is determined according to the elevation difference between each pixel and its neighboring pixels, and a corresponding flow direction coding value is assigned to each pixel;

[0069] Specifically, the D8 flow direction model is used to determine the water flow direction, and a corresponding flow direction coding value is assigned to each pixel;

[0070] Specifically, each pixel is assigned a corresponding flow direction encoding value expressed as:

[0071] F(i,j)={1 if flow to(i,j+1); 2 if flow to(i+1,j+1); 3 if flow to(i+1,j); 4 if flow to(i+1,j-1); 5 if flow to(i,j-1); 6 if flow to(i-1,j-1); 7if flowto(i-1,j); 8 if flow to(i-1,j+1)}

[0072] Among them, 1 means: flowing eastward; 2: flowing northeastward; 3: flowing northward; 4: flowing northwestward; 5: flowing westward; 6: flowing southwestward; 7: flowing southward; 8: flowing southeastward; F(i,j) is the flow direction grid value of pixel (i,j);

[0073] It should be noted that when the flow direction coding is wrong, the flow direction raster value can be obtained by reverse analysis to obtain the original flow direction coding. On the one hand, it can reduce the catchment area error by eliminating the depression, and on the other hand, it can retain the original catchment area calculation result when the flow direction coding is wrong.

[0074] Furthermore, the catchment area of ​​all pixels is initialized to 1, where 1 represents the area of ​​each pixel itself, and the formula is expressed as: G(i,j)=1;

[0075] Where G(i,j) is the catchment area G of pixel (i,j);

[0076] Furthermore, for each pixel (i, j), the eight neighboring pixels around the pixel are traversed, and the water in which pixels flows to the pixel is determined according to the flow direction encoding value;

[0077] It should be noted that 8 neighboring pixels correspond to 8 water flow directions;

[0078] Furthermore, for all neighboring pixels that converge to this pixel, their catchment areas are accumulated until the catchment areas of all pixels no longer change;

[0079] Furthermore, the minimum catchment area of ​​all pixels is set as the flow accumulation threshold. For each pixel (i, j), it is determined whether its flow accumulation value exceeds the flow accumulation threshold. If it exceeds, it is a river pixel. If it is equal to, it is a tributary pixel. If it is less than, it is determined whether the flow accumulation value of the next pixel exceeds the flow accumulation threshold.

[0080] Specifically, the judgment formula is expressed as:

[0081]

[0082] Among them, 1 represents a river pixel, 0 represents a tributary pixel, and -1 skips the current pixel judgment;

[0083] Furthermore, for each river and tributary pixel, all upstream pixels flowing to the pixel are marked, and for unmarked pixels, their watershed numbers are marked according to their flow directions;

[0084] Specifically, the upstream pixel refers to the pixel where all water flows into the target pixel, as shown in the following example, taking a 5×5 flow direction grid:

[0085] [6,7,7,7,6]

[0086] [5,1,1,1,5]

[0087] [5,1,1,1,5]

[0088] [4,1,1,1,4]

[0089] [3,3,3,3,3]

[0090] Suppose we want to determine the upstream pixel of pixel (3,2), here are the search steps:

[0091] The upstream pixel of pixel (3,2) is (3,1) because the flow direction of (3,1) is coded as 1 (flowing eastward).

[0092] The upstream pixel of pixel (3,1) is (3,0) because the flow direction of (3,0) is coded as 1 (flowing eastward).

[0093] The upstream pixel of pixel (3,0) is (4,0) because the flow direction of (4,0) is coded as 3 (flowing north).

[0094] The upstream pixel of pixel (4,0) is (4,1) because the flow direction of (4,1) is coded as 3 (flowing north).

[0095] The upstream pixel of pixel (4,1) is (4,2) because the flow direction of (4,2) is coded as 3 (flowing north).

[0096] By iterating the above steps, all upstream pixels flowing to (3,2) can be found;

[0097] Furthermore, the boundaries of different watersheds are drawn according to the watershed numbers;

[0098] It should be noted that by determining the flow accumulation threshold and the obtained catchment area, the division of the watershed can be accurately achieved;

[0099] S3, fuse the watershed with multiple types of sensors to form a comprehensive watershed dataset, and create a digital twin model of the river basin from the comprehensive watershed dataset;

[0100] Furthermore, the digital twin model is expressed as:

[0101]

[0102] Where M(x,y) is the digital twin model of the pixel position (x,y), Z is the digital twin model period, A is the area integration area of ​​the entire river basin, N(x,y) is the set of neighboring pixels at the pixel position (x,y), θ is the attenuation coefficient that controls the influence of the water flow path, |H(i n ,j m )-H(i n-1 ,j m-1 )| represents the absolute value of the elevation difference between the current pixel elevation and the next pixel elevation; S k represents the transmission data in sensor k; B is the integration area represented by the transmission data in the sensor; T k represents the fault data in sensor k; C is the integral area of ​​sensor fault detection, River(i,j) represents the flow accumulation threshold judgment function;

[0103] Specifically, the comprehensive watershed dataset will be uploaded to satellite remote sensing;

[0104] S4, by determining whether multiple types of sensors have failed, and updating the digital twin model of the river basin according to the failure results;

[0105] Furthermore, a fault detection function is established. If the absolute value of the data transmitted by the sensor in a certain time period and the historical data transmitted in the same time period in the past is higher than the sensor fault threshold, a drone is used to obtain data instead of the sensor, and the digital twin model of the river basin is updated at the same time. The sensor fault threshold β k By ∫ C T k dC is obtained;

[0106] Specifically, the drone receives interruption data from sensors by connecting to satellite remote sensing and updates the current digital twin model;

[0107] Specifically, the fault detection function F k It is expressed as:

[0108]

[0109] Among them, S k (t) is the data transmitted by sensor k at time t; μ k is the historical average value of sensor k; β k is the fault threshold of sensor k;

[0110] It should be noted that by establishing a fault detection function, the current digital twin model is judged and the status of the sensor is monitored. When a sensor status fails, the data breakpoint is intervened in time through the drone, thereby realizing 24-hour uninterrupted river basin detection and management.

[0111] Furthermore, this embodiment also provides a river ecology detection system based on digital twins, including:

[0112] A river basin data processing module is configured to obtain a river basin range, set multiple types of sensors within the river basin range, synchronize data from the multiple types of sensors through satellite remote sensing, eliminate slight depressions between the data, and obtain a water flow path;

[0113] A watershed division module is configured to generate a flow direction grid using the water flow path, calculate the catchment area of ​​each pixel, set a flow accumulation threshold, determine rivers and tributaries by judging the flow accumulation threshold, and obtain a watershed watershed according to the flow direction grid and the determined rivers and tributaries;

[0114] a digital twin model building module configured to fuse the watershed of the river basin with multiple types of sensors to form a comprehensive watershed dataset, and to create a digital twin model of the river basin from the comprehensive watershed dataset;

[0115] The digital twin model update module is configured to determine whether multiple types of sensors fail and update the digital twin model of the river basin according to the failure results.

[0116] This embodiment also provides a computer device, which is applicable to the case of a river ecology detection method based on digital twins, including:

[0117] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the river ecology detection method based on digital twins as proposed in the above embodiment.

[0118] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0119] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the river ecology detection method based on digital twins proposed in the above embodiment is implemented.

[0120] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0121] Example 2

[0122] Reference Figure 2 and Figure 3, which is the second embodiment of the present invention, and this embodiment provides a river ecology detection method based on digital twins, including: in order to verify the effectiveness of the river ecology detection method and system based on digital twins of the present invention, a river basin is selected as the test object; the basin is divided into five main areas: sedimentation-rheology section, aquatic-water quality section, flooding-ecological section, climate-rheology section and water quality-sedimentation section; multiple types of sensors are deployed in each area, including YSI EXO2 water quality sensor, Hydrolab HL7 aquatic plant sensor, Greenspan PS700 sediment sensor, SonTek Argonaut flow sensor, Solinst Levelogger water level sensor, Campbell CR1000 ecological sensor, Davis Vantage Pro2 meteorological sensor and Decagon Devices ECH2O infiltration sensor;

[0123] Firstly, Landsat 8 satellite remote sensing technology is used to synchronously collect data from all sensors, convert them into pixel elevations, and mark the regional position of the current pixel elevation as the sunken area; these sunken areas are eliminated through iterative calculation until the elevations are consistent and an accurate water flow path is generated; secondly, based on the processed elevation data, the D8 flow direction model is used to generate a flow direction grid, and a corresponding flow direction code value is assigned to each pixel; then the catchment area of ​​each pixel is calculated, and the catchment area of ​​all pixels is initialized to 1 (indicating the area of ​​each pixel itself); the eight neighboring pixels of each pixel are traversed, and their catchment areas are accumulated according to the flow direction code value until the catchment area of ​​all pixels is no longer changes; then, set the flow accumulation threshold, and determine the river and tributary pixels by judging the flow accumulation value of each pixel; mark all upstream pixels that flow to the river and tributary pixels, mark their basin numbers according to their flow directions, and draw the boundaries of different basins to obtain accurate basin watersheds; finally, fuse the basin watershed with multi-type sensor data to form a comprehensive basin data set and create a digital twin model of the river basin; during the operation of the digital twin model, by judging whether multiple types of sensors have failed, if the absolute value difference between the data transmitted by the sensor and the historical data in a certain period of time exceeds the fault threshold, use the DJI Mavic 2Pro drone to replace the sensor for data acquisition and update the digital twin model; the experimental days are 30 days, and the comparison scheme uses the traditional single sensor data acquisition and iterative model update technology for comparison, and the data in Table 1 are obtained;

[0124] Table 1 Comparison of average parameters within 30 days

[0125]

[0126]

[0127] From Table 1, it can be seen that the scheme of the present invention performs particularly well in terms of watershed identification accuracy, among which the average identification accuracy of the watershed is as high as over 95%, which is 8% to 10% higher than the traditional method; and the average model update response time is shortened by 3 to 4 times compared with the traditional model update response time. This is because the scheme of the present invention can independently choose whether to continue to use satellite remote sensing or drone for data replacement by judging the fault result, thereby ensuring the continuity and reliability of river ecological monitoring, thereby achieving real-time update of the digital twin model; and through Figure 2 It can be seen that the influence of water cover on the terrain also affects the accuracy of watershed identification in Table 1;

[0128] In addition, reference Figure 3 The digital twin model can maintain high data reception and continuity in both fault and non-fault conditions, and does not cause data loss; it can be seen that the method of the present invention can ensure the continuity and reliability of river ecological monitoring.

[0129] Those skilled in the art will appreciate that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0130] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0133] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0134] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A river ecology detection method based on digital twins, characterized in that: include: Obtaining a river basin range, setting multiple types of sensors within the river basin range, synchronizing data from the multiple types of sensors through satellite remote sensing, eliminating slight depressions between the data, and obtaining a water flow path; Generate a flow direction grid using the water flow path, calculate the catchment area of ​​each pixel, set a flow accumulation threshold, determine rivers and tributaries by judging the flow accumulation threshold, and obtain a watershed watershed based on the flow direction grid and the determined rivers and tributaries; Fusing watershed divides with multiple types of sensors to form a comprehensive watershed dataset, and creating a digital twin model of the river basin from the comprehensive watershed dataset; The digital twin model of the river basin is created, including: The digital twin model is expressed as: Where M(x,y) is the digital twin model of the pixel position (x,y), Z is the digital twin model period, A is the area integration area of ​​the entire river basin, N(x,y) is the set N of neighboring pixels at the pixel position (x,y), River(i,j) represents the flow accumulation threshold judgment function, θ is the attenuation coefficient that controls the influence of the water flow path, |H(i n ,j m )-H(i n-1 ,j m-1 )| represents the absolute value of the elevation difference between the current pixel elevation and the next pixel elevation; S k represents the transmission data in sensor k; B is the integration area represented by the transmission data in the sensor; T k represents the fault data in sensor k; c is the integration area of ​​sensor fault detection, G(i,j) is the catchment area G of pixel (i,j); By determining whether multiple types of sensors fail, the digital twin model of the river basin is updated according to the failure results.

2. The river ecology detection method based on digital twins according to claim 1 is characterized in that: The river basin range is obtained, and multiple types of sensors are set within the river basin range, including: The river basin range division is defined as the sedimentation-rheology section, the aquatic-water quality section, the flood-ecology section, the climate-rheology section and the water quality-sedimentation section; The multiple types of sensors are water quality sensors, aquatic plant sensors, sediment sensors, flow sensors, water level sensors, ecological sensors, meteorological sensors and infiltration sensors; Among them, the definition of river basin range division is a combination of two types of sensors.

3. The river ecology detection method based on digital twins according to claim 2 is characterized in that: Through satellite remote sensing, data from multiple types of sensors are synchronized to eliminate the slight depressions between the data and obtain the water flow path, including: The data from multiple types of sensors are converted into pixel elevations, and the area position of the current pixel elevation is marked as a concave area, and the concave area is filled to the same height as the surrounding pixel elevation area position in an iterative calculation manner; Among them, pixel elevation refers to the altitude of the surface point represented by the grid unit.

4. The river ecology detection method based on digital twins according to claim 3 is characterized in that: The flow path is used to generate a flow direction grid and the catchment area of ​​each pixel is calculated, including: According to the elevation difference between each pixel and its neighboring pixels, the water flow direction is determined, and a corresponding flow direction code value is assigned to each pixel; Initialize the water collection area of ​​all pixels to 1, where 1 represents the area of ​​each pixel itself; For each pixel (i, j), traverse the eight neighboring pixels around the pixel and determine which pixels’ water flows to the pixel based on the flow direction code value; For all neighboring pixels that converge to this pixel, their water collection areas are accumulated until the water collection areas of all pixels no longer change.

5. The river ecology detection method based on digital twins according to claim 4 is characterized in that: Setting the flow accumulation threshold By judging the flow accumulation threshold, rivers and tributaries are determined, and the watershed is obtained according to the flow direction grid and the determined rivers and tributaries, including: Set the minimum catchment area of ​​all pixels as the flow accumulation threshold. For each pixel (i, j), determine whether its flow accumulation value exceeds the flow accumulation threshold. If it exceeds, it is a river pixel. If it is equal to, it is a tributary pixel. If it is less than, determine whether the flow accumulation value of the next pixel exceeds the flow accumulation threshold. For each river and tributary pixel, mark all upstream pixels that flow to the pixel. For unmarked pixels, mark their watershed numbers according to their flow directions. Draw the boundary lines of different watersheds according to the watershed numbers.

6. The river ecology detection method based on digital twins according to claim 1 is characterized in that: By determining whether multiple types of sensors fail, and according to the failure results, updating the digital twin model of the river basin, including: A fault detection function is established. If the absolute value of the data transmitted by the sensor in a certain time period and the historical data transmitted in the same time period in the past is higher than the sensor fault threshold, a drone is used to obtain data instead of the sensor, and the digital twin model of the river basin is updated at the same time. The sensor fault threshold is given by ∫ c T k dC is obtained.

7. A river ecology detection system based on digital twins, based on the river ecology detection method based on digital twins according to any one of claims 1 to 6, characterized in that: include: A river basin data processing module is configured to obtain a river basin range, set multiple types of sensors within the river basin range, synchronize data from the multiple types of sensors through satellite remote sensing, eliminate slight depressions between the data, and obtain a water flow path; A watershed division module is configured to generate a flow direction grid using the water flow path, calculate the catchment area of ​​each pixel, set a flow accumulation threshold, determine rivers and tributaries by judging the flow accumulation threshold, and obtain a watershed watershed according to the flow direction grid and the determined rivers and tributaries; a digital twin model building module configured to fuse the watershed of the river basin with multiple types of sensors to form a comprehensive watershed dataset, and to create a digital twin model of the river basin from the comprehensive watershed dataset; The digital twin model update module is configured to determine whether multiple types of sensors fail and update the digital twin model of the river basin according to the failure results.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images