Geomorphologic channelization using autonomous robotic dredgers

Abstract

A computer-implemented method for dynamic geomorphic channelization using an autonomous robotic dredger. The method derives one or more landform metrics, wherein the one or more landform metrics include a soil metric, an environmental condition, and a vegetation water consumption metric. The method further generates a digital twin of suggested trench and ridge metrics based on the derived one or more geomorphic metrics, wherein the suggested trench and ridge metrics include a depth of the trench, a distance between the one or more trenches, a flow size, a slope of the trench, and a shape of the trench. The method also determines a trench pattern based on the generated digital twinning of the suggested trench and ridge metrics, and cooperates with the autonomous robotic dredger to generate the trench pattern.

Description

Background Art

[0001] The present disclosure relates generally to the fields of cognitive computing, the Internet of Things (IoT), and more particularly to data processing and dynamic landscape channeling.

[0002] Trenching or channeling of landforms (e.g., barren, sandy, mountainous, contour, etc.) is primarily used for water conservation, groundwater recharge, and moisture recharge in various soils (e.g., clay or sand). Troughs are small parallel channels used to carry / retain / recharge water to irrigate landforms and / or vegetation.

[0003] Field trenches increase precipitation concentration by breaking up the slope of the ground, thereby reducing water velocity. Field trenches also improve water infiltration and reduce soil erosion by reducing water runoff. Existing studies and use cases have found that with optimal trenching, barren landscapes can be transformed into lush oases.

[0004] However, channelizing trenches can present pre-existing problems depending on the landforms involved. Summary of the invention

[0005] Embodiments of the present invention disclose a method, a computer program product, and a system.

[0006] According to an embodiment of the present invention, a method in a data processing system including a processor and a memory. The method includes deriving one or more geomorphic metrics, wherein the one or more geomorphic metrics include soil metrics, environmental conditions, and vegetation water consumption metrics. The method further includes generating a digital twin of suggested groove and ridge metrics based on the derived one or more geomorphic metrics, wherein the suggested groove and ridge metrics include the depth of the groove, the distance between one or more grooves, the flow size, the slope of the groove, and the shape of the groove. The method also includes determining a groove pattern based on the generated digital twin of the suggested groove and ridge metrics, and collaborating with an autonomous robotic dredge to generate the groove pattern.

[0007] A computer program product according to an embodiment of the present invention includes a non-transitory tangible storage device having program code embodied therewith. The program code is executable by a processor of a computer to perform a method. The method includes deriving one or more geomorphic metrics, wherein the one or more geomorphic metrics include soil metrics, environmental conditions, and vegetation water consumption metrics. The method further includes generating a digital twin of suggested groove and ridge metrics based on the derived one or more geomorphic metrics, wherein the suggested groove and ridge metrics include the depth of the groove, the distance between one or more grooves, the flow size, the slope of the groove, and the shape of the groove. The method also includes determining a groove pattern based on the generated digital twin of the suggested groove and ridge metrics, and collaborating with an autonomous robotic dredge to generate the groove pattern.

[0008] A computer system according to an embodiment of the present invention includes one or more computer devices, each having one or more processors and one or more tangible storage devices; and a program contained on at least one of the one or more storage devices, the program having a plurality of program instructions executed by the one or more processors. The program instructions implement a method. The method includes deriving one or more geomorphic metrics, wherein the one or more geomorphic metrics include soil metrics, environmental conditions, and vegetation water consumption metrics. The method further includes generating a digital twin of suggested groove and ridge metrics based on the derived one or more geomorphic metrics, wherein the suggested groove and ridge metrics include the depth of the groove, the distance between one or more grooves, the flow size, the slope of the groove, and the shape of the groove. The method also includes determining a groove pattern based on the generated digital twin of the suggested groove and ridge metrics, and collaborating with an autonomous robotic dredge to generate the groove pattern. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A landform channelization computing environment according to an embodiment of the present disclosure is shown.

[0010] Figure 2 The present invention is a diagram showing an embodiment of the present invention. Figure 1 Flowchart of the operation of the landform channelization procedure 120.

[0011] Figure 3 is a diagram showing an embodiment of the present disclosure Figure 1 Diagram of the hardware components of a geomorphic channelized computing environment.

[0012] Figure 4 A cloud computing environment according to an embodiment of the present disclosure is depicted.

[0013] Figure 5 Describes an embodiment of the present disclosure Figure 4 Abstract model layers of an illustrative cloud computing environment. DETAILED DESCRIPTION

[0014] The present invention discloses a method for dynamic landform channelization using an autonomous robotic soildredger.

[0015] Channeling or furrowing can be considered a more general form of ploughing. Furrowing improves water infiltration and reduces soil erosion by reducing water runoff.

[0016] Trenching comes with various pre-existing problems that the present disclosure takes into account. For example, the terrain may need to be optimally leveled to allow excess water to flow, as the water flow uses gravity to transport water runoff downhill. Leveling the terrain allows water to flow evenly across the field.

[0017] Other pre-existing problems include barren landscapes or wind erosion that require frequent and continuous trenching behind winds to keep the trenches flooded with eroded soil. In addition, longer, steeper slopes or trenches can lead to potential soil erosion.

[0018] Currently, when there is no information about the amount of water absorbed (e.g. soil moisture metrics), water flow / storage / replenishment is controlled manually, resulting in surges and runoff of water. The soil is not optimally irrigated according to soil type, vegetation, etc.

[0019] Another pre-existing problem is unsealed channels or breaches in channel barriers which result in water runoff into the channel where it is not needed, resulting in surges in one part of the irrigation and shortfalls in another part of the irrigation.

[0020] Currently, manual judgment is used to estimate soil absorption rates, as well as manual operation of the dam during the discharge of water into the channel. For example, water is generally absorbed at a higher rate in sandy soils as opposed to clay soils. A tangential problem with varying soil absorption rates is surge flow, which is the discharge of excess water into the channel.

[0021] The present disclosure details a method and system for cognitive, dynamic trenching (i.e., channeling) to achieve water conservation, optimal soil water absorption, groundwater recharge based on the terrain profile (e.g., barren, contoured, mountainous, etc.), and thus based on the context of water demand, an autonomous robotic dredge is able to generate dynamic trenches, channeling metrics, and channeling patterns based on the terrain profile, water availability and consumption, soil profile, etc.

[0022] The present disclosure uses IoT and smart agriculture to implement its novel methods and systems, including three different layers: devices, networks, and applications. The device layer consists of physical objects that are connected to the Internet and can be automatically identified, sensed, or actuated. The network layer uses communication protocols to transmit data to the Internet (i.e., the cloud). The application layer generally stores and facilitates end-user access to processed / analyzed information.

[0023] The proposed system will be able to derive geomorphic metrics like land size, land slope, soil properties (e.g. soil type, soil absorption rate, etc.), and weather and climate conditions.

[0024] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0025] The present disclosure is not limited to the following exemplary embodiments, but can be implemented through various modifications within the scope of the present disclosure.In addition, the drawings used herein are for illustrative purposes and may not show actual sizes.

[0026] Figure 1 A landform channeling computing environment 100 is shown in accordance with an embodiment of the present disclosure. The landform channeling computing environment 100 includes a host server 110, a digital twin trench 130, and an autonomous robotic dredge 140, all connected via a network 102. Figure 1 The arrangements in represent example embodiment configurations of the present disclosure, and one is not limited to the depicted arrangements to obtain the benefits of the present disclosure.

[0027] In an exemplary embodiment, the host server 110 includes a geomorphic channelization program 120. In various embodiments, the host server 110 may be a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smart phone, a server, or any programmable electronic device capable of communicating with the digital twin trench 130 and the autonomous robotic dredge 140 via the network 102. The host server 110 may include internal and external hardware components, as depicted and described below with reference to Figure 3 In other embodiments, the host server 110 may be implemented in a cloud computing environment, such as described herein with respect to Figure 4 and Figure 5 The host server 110 may also have wireless connectivity, allowing the host server 110 to communicate with the digital twin trench 130 , the autonomous robotic dredge 140 , and other computers or servers via the network 102 .

[0028] Continue to refer Figure 1 , the digital twin trench 130 includes an Internet of Things (IoT) sensor 132 and a sensor database 134 .

[0029] In an exemplary embodiment, IoT sensor 132 may include a device, hardware component, module, or subsystem that is capable of recording, capturing, and detecting events (e.g., environment, weather, temperature changes, moisture, etc.) or changes in a user's environment or proximity, and sending the detected data to other electronic devices (e.g., host server 110), components (e.g., sensor database 134), or programs (e.g., landscape channelization program 120) within a system such as landscape channelization computing environment 100. In various embodiments, the detected data collected by IoT sensor 132 facilitates the creation of a knowledge corpus for a particular landscape.

[0030] In an exemplary embodiment, the IoT sensor 132 is located within the digital twin trench 130 and may be a global positioning system (GPS), a software application, a proximity sensor, a camera, a microphone, a light sensor, an infrared sensor, a weight sensor, a temperature sensor, a tactile sensor, a motion detector, an optical character recognition (OCR) sensor, an occupancy sensor, a thermal sensor, an analog sensor (e.g., a potentiometer, a force sensing resistor), a radar, a radio frequency sensor, a quick response (QR) code, a video camera, a digital camera, an Internet of Things (IoT) sensor, a laser, a gyroscope, an accelerometer, an actuator, a structured light system, a weather-based smart irrigation controller, an evapotranspiration (ET) controller, and any other device for measuring, detecting, and recording soil metrics, environmental conditions, vegetation growth, and water consumption.

[0031] In an exemplary embodiment, the ET controller uses local weather data to adjust the irrigation schedule. Evapotranspiration is a combination of evaporation from the soil surface and transpiration of plant material. The ET controller collects local weather information and makes runtime water adjustments so that the landscape receives the appropriate amount of water.

[0032] ET weather data uses four weather parameters: temperature, wind, solar radiation, and humidity. These weather-based ET controllers come in three basic forms.

[0033] First, there are signal-based ET controllers. Signal-based ET controllers use meteorological data from publicly available sources and calculate ET values ​​for the grass surface at the landscape site. The ET data is then sent to the controller via a wireless connection.

[0034] Second, there are historical ET controllers. Historical ET controllers use pre-programmed water usage curves based on historical water usage in different areas. The curves can be adjusted for temperature and solar radiation.

[0035] Third, there is an on-site weather measurement controller. The on-site weather measurement controller uses weather data collected on-site to calculate continuous ET measurements and water accordingly.

[0036] In an exemplary embodiment, IoT sensors 132 are capable of continuously monitoring, collecting, and saving collected data on a local storage device, such as sensor database 134, or sending collected data to landform channelization program 120. In alternative embodiments, IoT sensors 132 may be capable of detecting, communicating, pairing, or syncing with Internet of Things (IoT) devices, thereby creating opportunities for more direct integration of the physical world into computer-based systems and generating increased efficiency, accuracy, and economic benefits in addition to reduced human intervention.

[0037] In an exemplary embodiment, the sensor database 134 may be a local data storage device on the digital twin trench 130 that contains one or more sets of learning data. The learning data may include a data set that includes data collected from the IoT sensors 132, such as geomorphic metrics (e.g., dimensions of similar land, land slope, etc.), soil metrics (e.g., soil type, soil absorption rate, etc.), groove and ridge metrics (e.g., depth of grooves, distance between grooves, stream size, slope of grooves, shape of grooves, etc.), environmental conditions (e.g., historical rainfall and water availability, etc.), and vegetation water consumption (e.g., vegetation and plant types).

[0038] Although the sensor database 134 is depicted as being stored on the digital twin trench 130, in other embodiments, the sensor database 134 may be stored as a separate database on the host server 110, the landform channelization program 120, or any other device or database connected via the network 102. In alternative embodiments, the sensor database 136 may include a cluster or multiple computing devices working together or individually.

[0039] Continue to refer Figure 1 , the autonomous robotic dredge 140 may include a device, hardware component, module, or subsystem that is capable of recording, capturing, and detecting events (e.g., environment, weather, temperature changes, moisture, etc.) or changes in the user's environment or proximity, and sending the detected data to other electronic devices (e.g., host server 110), components (e.g., sensor database 134), or programs (e.g., geomorphic channelization program 120) within a system such as the geomorphic channelization computing environment 100. In various embodiments, the autonomous robotic dredge 140 is a device component of the proposed system (i.e., geomorphic channelization program 120) that constructs desired ridges and grooves based on derived geomorphic metrics, determined groove patterns, generated soil water retention and absorption patterns, and determined soil volume to be excavated.

[0040] In alternative embodiments, the autonomous robotic dredge 140 may include the same or similar components as the digital twin trench 130, such as IoT sensors 132 and sensor database 134. In an exemplary embodiment, the autonomous robotic dredge 140 is capable of communicating with the digital twin trench 130 and the host server 110 via the network 102, wireless fidelity (WiFi) and radio frequency identification (RFID), or by any other means known to those of ordinary skill in the art.

[0041] In an exemplary embodiment, the autonomous robotic dredge 140 may be a remotely controlled, fully automated and / or manned dredge that provides irrigation channel maintenance according to exemplary embodiments herein.

[0042] Continue to refer Figure 1 In an exemplary embodiment, the geomorphic channelization program 120 may be a computer application on a host server 110 that includes a set of instructions executable by a processor. The set of instructions may be described using a set of functional modules. In an exemplary embodiment, the geomorphic channelization program 120 may receive input from the digital twin trench 130 and the autonomous robotic dredge 140 via the network 102. In alternative embodiments, the geomorphic channelization program 120 may be a computer application included within the digital twin trench 130 or a stand-alone program on a separate electronic device.

[0043] Continue to refer Figure 1 The functional modules of the landform channelization program 120 include an export module 122 , a generation module 124 , a determination module 126 and a collaboration module 128 .

[0044] Figure 2 The present invention is a diagram showing an embodiment of the present invention. Figure 1 Flowchart of the operation of the landform channelization procedure 120.

[0045] refer to Figure 1 and Figure 2 The export module 122 includes a set of programming instructions in the landform channelization program 120 to export one or more landform metrics, wherein the one or more landform metrics include soil metrics, environmental conditions, and vegetation water consumption metrics (step 202). The set of programming instructions is executable by a processor.

[0046] In an exemplary embodiment, soil metrics may include, but are not limited to, soil type and soil absorption rate. The proposed system determines soil metrics via IoT sensors 132.

[0047] Referring to an illustrative example, Farmer Joe is having difficulty growing his crops due to insufficient irrigation on his land in upstate New York. Farmer Joe's land is subject to heavy easterly winds that continually flood the soil in his irrigation trenches. Using the geomorphic channelization program 120, Farmer Joe's geomorphic metrics are exported via IoT sensors 132. The export module 122 provides metrics depicting soil metrics <sand quality; absorption 0.05 liters / second>, environmental conditions <30" rainfall per year>, vegetation water consumption <potatoes; 2" / week><tomatoes; 1" / week><carrots; 1" / wk>.

[0048] Continue to refer Figure 1 and Figure 2 , the generation module 124 includes a set of programming instructions in the landform channelization program 120 to generate a digital twin of suggested groove and ridge metrics based on the derived one or more landform metrics, wherein the suggested groove and ridge metrics include the depth of the groove, the distance between one or more grooves, the flow size, the slope of the groove, and the shape of the groove (step 204). The set of programming instructions is executable by a processor.

[0049] A digital twin is a virtual model designed to accurately reflect a physical object. The object being studied (e.g., a trench) is equipped with various sensors (e.g., IoT sensors 132) related to soil metrics, environmental conditions, and vegetation water consumption. This data is then relayed to a processing system and applied to the digital replica.

[0050] Once informed by this data, the virtual digital twin can be used to run simulations, study performance issues, and generate possible improvements, all with the goal of generating valuable insights that can then be applied back to the original physical object.

[0051] By having better and constantly updated data relevant to a wide range of domains, combined with the increased computing power that accompanies virtual environments, digital twins can study more problems from more vantage points than standard simulations, with greater ultimate potential to improve products and processes.

[0052] In an exemplary embodiment, the digital twin model identifies each sensor feed via IoT sensors 132 of the landscape and creates a digital twin thereof (ie, digital twin trench 130 ).

[0053] In an exemplary embodiment, the digital twin trench 130 of the proposed generation of trench and ridge metrics can dynamically generate soil water retention and absorption patterns based on the relative positioning of one or more vegetation roots, one or more adjacent trenches, soil metrics, and water retention schedules and runoff volumes.

[0054] Based on the generated digital twin trench 130, the proposed system will be able to generate a crop profile. The generated crop profile includes the type of crop, the location of the crop, the amount of sunlight required, and the amount of moisture or water required, nutritional requirements, etc.

[0055] In an exemplary embodiment, the generation module 124 may dynamically generate a wetting pattern for the crop based on the relative positioning of the crop root system and one or more adjacent furrow parameters, soil parameters, water flow schedules, and water runoff volumes.

[0056] Based on the soil, weather, and landform profiles, the generation module 124 may generate crop recommendations and / or alternatively determine suitability of crops in the soil profile.

[0057] In an exemplary embodiment, the digital twin trench 130 of the generation of suggested trench and ridge metrics can dynamically generate soil water retention and absorption patterns based on the relative positioning of one or more vegetation roots, one or more adjacent trench soil metrics, and water retention schedules and runoff volumes.

[0058] In various exemplary embodiments, the shape of the groove may be U-shaped, drum-shaped, or the like.

[0059] In an alternative embodiment, the generation module 124 may perform a digital twin simulation to identify optimal trench and ridge metrics based on plant root growth patterns, water consumption patterns, humidity, weather conditions, and plant types to improve water infiltration and reduce soil erosion. In doing so, the digital twin trench 130 simulation system will take into account IoT sensor 132 feeds, visual data, and will simulate optimal channelization.

[0060] In an alternative embodiment, the generation module 124 may generate a three-dimensional (3D) visualization of aspects of the agricultural landscape (eg, water flow rate, soil wetting, soil wetting depth, depth of furrows, crop root conditions, etc.).

[0061] In an exemplary embodiment, the digital twin trench 130 may use IoT data and its digital twin to generate relative seed positioning with respect to trenches and ridges based on crop and seed profiling, water demand and supply, weather conditions, and shading of the seeds.

[0062] Continuing with reference to the illustrative example above, the geomorphic channelization program 120 assists farmer Joe by generating a digital twin of suggested groove and ridge metrics for Joe's field based on the derived geomorphic metrics. The digital twin simulates farmer Joe's field, the amount of water and soil absorption required for optimal vegetation growth. Based on the geomorphic metrics, the generation module 124 proposes square grooves with a depth of 1', a distance of 6" between one or more grooves, and a slope of 25 degrees so that water will reach the downstream end without excessive infiltration losses.

[0063] Continue to refer Figure 1 and Figure 2 The determination module 126 includes a set of programming instructions in the geomorphic channelization program 120 to determine a channel pattern based on the generated digital twin of the proposed channel and ridge metrics (step 206). The set of programming instructions is executable by a processor.

[0064] In various exemplary embodiments, the groove patterns may include, but are not limited to, zigzag, square, circular, outline, rectangular, T-shaped, and / or any other pattern that allows for optimal irrigation of the landscape.

[0065] In an exemplary embodiment, the slope of the groove pattern may include, but is not limited to, uniform grooves, flat basin grooves, wide slope grooves, medium slope grooves, gentle slope grooves, narrow slope grooves, and / or any other slope that allows for optimal irrigation of the terrain.

[0066] Uniform, flat basins or gently sloping trenches are preferred for the channel and should not exceed a 0.5% grade. Typically, gently sloping trenches provide a slope of up to 0.5% grade to assist in drainage after a runoff or high intensity excess rainfall.

[0067] In an exemplary embodiment, the spacing between the trenches is determined by the natural environment (ie, slope, soil type, and available flow size). However, other factors may affect the design of the flood channel system (eg, depth of the trenches, farming practices, and field length).

[0068] In alternative embodiments, the maximum recommended trench row slope is 0.5% grade, although the trenches may be longer when the land slopes steeper, to avoid soil erosion.

[0069] The trenches can also be horizontal, and thus closely resemble long, narrow pots. However, a minimum grade of 0.05% is recommended so that effective drainage can occur after a flood or excessive rainfall. If the land slope is steeper than 0.5%, the trenches can be set at an angle to the main slope, or even along a contour to keep the trench slope within the recommended range.

[0070] Water infiltration rates vary depending on the type of soil. In sandy soils, water infiltrates quickly, therefore, the trench should be short so that the water will reach the downstream end without excessive infiltration losses. In clay soils, water infiltration rates are much lower than in sandy soils, therefore, the trench can be much longer than in sandy soils.

[0071] In an exemplary embodiment, the water flow varies depending on the flow size. Typically, flow sizes up to 0.5 liters per second (l / sec) provide adequate water flow if the channel is not too long. When larger water flows are available, water moves quickly down the flood channel, and therefore, the channel can be longer. The maximum flow size that does not cause soil erosion depends on the slope of the flood channel. In an exemplary embodiment, it is recommended not to use flow sizes greater than 3.0 l / sec.

[0072] In an exemplary embodiment, the trench depth affects water infiltration. Applying a greater trench depth generally means that the trench can be longer because more time is available for water to flow down the flood channel and infiltrate the soil.

[0073] In various exemplary embodiments, the trench shape is based on the soil type. For example, in sandy soil, water moves faster vertically than laterally (e.g., transversely). Narrow, deep, V-shaped flood channels are desirable to reduce the area of ​​soil through which water infiltrates. However, sandy soil is less stable and tends to collapse, which can reduce water flow efficiency.

[0074] There is more lateral movement of water in clay soils than in sand soils, and the permeability is much lower than in sand soils. Therefore, wide shallow flood channels are required to obtain a large wetted area to promote infiltration.

[0075] Referring back to the illustrative example above, the landform channelization program 120 determines that, given the landform metrics and results of the simulation run on the digital twin 130 , the optimal channel pattern for the irrigation system on Farmer Joe's field is a rectangular channel pattern.

[0076] Continue to refer Figure 1 and Figure 2 , the collaboration module 128 collaborates with the autonomous robotic dredge 140 to generate a trench pattern (step 208).

[0077] In an exemplary embodiment, the autonomous robotic dredge 140 may determine the amount of soil to be excavated to construct the trench based on the proposed trench and ridge measurements.

[0078] In further exemplary embodiments, the geomorphic channelization program 120 may create and dissolve one or more micro-dams based on proposed groove and ridge metrics, soil metrics, water supply, and the amount of water flow desired.

[0079] In an exemplary embodiment, the autonomous robotic dredge 140 may dynamically determine the need to block or release sustained water flow through the trench and narrow or widen the trench over a period of time based on soil metrics, desired wetting amount, flow size, water supply, and fertilizer drift off.

[0080] The autonomous robotic dredge 140 may detect one or more anomalies, wherein the one or more anomalies include water runoff, flooding, water blockage, soil erosion, and water overflowing from one trench to another.

[0081] In an exemplary embodiment, the autonomous robotic dredge 140 may perform ridge building or blockage release to mitigate one or more detected anomalies.

[0082] In an alternative embodiment, the autonomous robotic dredge 140 may synchronize water volume, water flow rate, and water runoff duration with a water flow controller based on environmental conditions, trench patterns, soil metrics, and wetting patterns.

[0083] In a further exemplary embodiment, the autonomous robotic dredge 140 may dynamically control the initiation, termination, and pause of water flow into the trench.

[0084] In an exemplary embodiment, the geomorphic channelization program 120 may send alerts based on any changes to one or more characteristics of the trench and automatically initiate maintenance and re-trenching before or after drainage to maintain the shape, size, pattern, and slope of the trenches and ridges.

[0085] For example, in barren or desert terrain with frequent wind shifts, large amounts of sand and soil are deposited into the trenches, making the trenches unusable. In addition, the deposition of sand and soil into the trenches may cause blockages (e.g., water blockages), thereby causing undesirable soil erosion. To avoid this, the autonomous robotic dredge 140 can dynamically determine any changes in the terrain and re-dig the trenches when necessary.

[0086] Referring to the illustrative example above, the landscape channelization program 120 is connected to the autonomous robotic dredge 140 via the network 102 to generate a determined trench pattern. The autonomous robotic dredge 140 digs dredge trenches in the landscape according to specific parameters (e.g., depth of the trench, distance between trenches, flow size, slope of the trench, and shape of the trench) based on the digital twin trench 130. Farmer Joe now has an optimal irrigation system that dynamically generates soil water retention and absorption patterns for the soil.

[0087] In an exemplary embodiment, the network 102 is a communication channel capable of transmitting data between connected devices, and may be a telecommunications network for facilitating telephone calls between two or more parties, including a landline network, a wireless network, a closed network, a satellite network, or any combination thereof. In another embodiment, the network 102 may be the Internet, representing a worldwide collection of networks and gateways that support communications between devices connected to the Internet. In this other embodiment, the network 102 may include, for example, a wired, wireless, or fiber optic connection, which may be implemented as an intranet network, a local area network (LAN), a wide area network (WAN), or any combination thereof. In yet another embodiment, the network 102 may be a A network, a WiFi network, a vehicle-to-vehicle (V2V) network, a vehicle-to-infrastructure (V2I) network, a peer-to-peer (P2P) communication network, a mesh network, or a combination thereof. In general, the network 102 can be any combination of connections and protocols that will support communication between the host server 110, the digital twin trench 130, and the autonomous robotic dredge 140.

[0088] Figure 3 is a diagram illustrating a computing device (such as a host server 110, such as Figure 1 It should be understood that Figure 3 This merely provides an illustration of one embodiment and does not imply any limitations with regard to the environments in which different embodiments may be implemented.Many modifications to the depicted environments may be made.

[0089] Figure 3 The computing device of the system may include one or more processors 902, one or more computer-readable RAMs 904, one or more computer-readable ROMs 906, one or more computer-readable storage media 908, device drivers 912, read / write drivers or interfaces 914, and network adapters or interfaces 916, all interconnected by a communications fabric 918. Communications fabric 918 may be implemented with any architecture designed to pass data and / or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within the system.

[0090] One or more operating systems 910 and one or more application programs 911, such as the geomorphic channelization program 120, may be stored on one or more computer-readable storage media 908 for execution by one or more processors 902 via one or more corresponding RAMs 904 (which typically include cache memory). In the illustrated embodiment, each of the computer-readable storage media 908 may be a disk storage device of an internal hard drive, a CD-ROM, a DVD, a memory stick, a tape, a disk, an optical disk, a semiconductor storage device (such as RAM, ROM, EPROM, flash memory), or any other computer-readable tangible storage device that can store computer programs and digital information.

[0091] Figure 3 The computing device may also include a R / W drive or interface 914 to read from and write to one or more portable computer-readable storage media 926 . Figure 3 The application programs 911 on the computing device may be stored on one or more of the portable computer-readable storage media 926 , read via the corresponding R / W drive or interface 914 and loaded into the corresponding computer-readable storage media 908 .

[0092] Figure 3 The computing device may also include a network adapter or interface 916, such as a TCP / IP adapter card or a wireless communication adapter (such as a 4G wireless communication adapter using OFDMA technology). Figure 3 The application program 911 on the computing device can be downloaded to the computing device from an external computer or external storage device via a network (e.g., the Internet, a local area network or other wide area network or wireless network) and a network adapter or interface 916. The program can be loaded onto the computer-readable storage medium 908 from the network adapter or interface 916. The network can include copper wires, optical fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers.

[0093] Figure 3 The computing device may also include a display screen 920, a keyboard or keypad 922, and a computer mouse or touchpad 924. The device driver 912 interfaces to the display screen 920 for imaging, interfaces to the keyboard or keypad 922, interfaces to the computer mouse or touchpad 924, and / or interfaces to the display screen 920 for alphanumeric character input and pressure sensing for user selection. The device driver 912, the R / W driver or interface 914, and the network adapter or interface 916 may include hardware and software (stored on the computer-readable storage medium 908 and / or ROM 906).

[0094] The programs described herein are identified based on the applications in which they are implemented in specific embodiments of the invention. However, it should be understood that any specific program naming herein is used only for convenience, and therefore the present invention should not be limited to use in any specific application identified and / or implied by such naming.

[0095] It should be understood that although the present disclosure includes detailed descriptions about cloud computing, the implementation of the teachings recorded herein is not limited to cloud computing environments. Instead, embodiments of the present invention can be implemented in conjunction with any other type of computing environment now known or later developed.

[0096] Cloud computing is a service delivery model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be quickly provisioned and released with minimal management effort or interaction with a service provider. The cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0097] Features are as follows:

[0098] On-demand self-service: Cloud consumers can automatically and unilaterally provision computing capabilities, such as server time and network storage, as needed, without requiring human interaction with the service provider.

[0099] Broad network access: Capabilities are available over the network and accessed through standard mechanisms that facilitate the use of heterogeneous thin-client platforms or thick-client platforms (e.g., mobile phones, laptops, and PDAs).

[0100] Resource pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically allocated and reallocated based on demand. There is a sense of location independence, as consumers generally have no control or knowledge of the exact location of the resources provided, but may be able to specify a location at a higher level of abstraction (e.g., country, state, or data center).

[0101] Rapid elasticity: Capacity can be provisioned quickly and elastically, in some cases automatically, to scale up quickly and released quickly to scale out quickly. To the consumer, the capacity available for provisioning often appears to be unlimited and can be purchased in any quantity at any time.

[0102] Metered Services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both providers and consumers of the utilized services.

[0103] The service model is as follows:

[0104] Software as a Service (SaaS): The capability provided to consumers is to use the provider's applications running on a cloud infrastructure. Applications can be accessed from a variety of client devices through a thin client interface such as a web browser (e.g., web-based email). Consumers do not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

[0105] Platform as a Service (PaaS): The capability provided to consumers is to deploy applications created or acquired by consumers onto the cloud infrastructure. Applications created or acquired by consumers are created using programming languages ​​and tools supported by the provider. Consumers do not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but have control over deployed applications and possibly the configuration of the application hosting environment.

[0106] Infrastructure as a Service (IaaS): The capability provided to consumers is the provisioning of processing, storage, networking, and other basic computing resources, where consumers can deploy and run arbitrary software, which may include operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but have control over the operating system, storage, deployed applications, and may have limited control over select networking components (e.g., host firewalls).

[0107] The deployment model is as follows:

[0108] Private Cloud: Cloud infrastructure is operated for only one organization. It can be managed by the organization or a third party and can exist on-premises or off-premises.

[0109] Community cloud: The cloud infrastructure is shared by several organizations and supports a specific community with common concerns (e.g., mission, security requirements, policies, and compliance considerations). It can be managed by the organization or a third party and can exist on-premises or off-premises.

[0110] Public cloud: Cloud infrastructure is available to the general public or large industry groups and is owned by an organization that sells cloud services.

[0111] Hybrid cloud: A cloud infrastructure is a combination of two or more clouds (private, community, or public) that remain distinct entities but are bound together by standardized or proprietary technologies that enable data and application portability (e.g., cloud bursting for load balancing between clouds).

[0112] The cloud computing environment is service-oriented, with characteristics centered on statelessness, low coupling, modularity, and semantic interoperability. The core of cloud computing is the infrastructure consisting of a network of interconnected nodes.

[0113] Reference now Figure 4 , depicts an illustrative cloud computing environment 50. As shown, the cloud computing environment 50 includes one or more cloud computing nodes 10 with which a local computing device used by a cloud consumer can communicate, such as a personal digital assistant (PDA) or cellular phone 54A, a desktop computer 54B, a laptop computer 54C, and / or an automobile computer system 54N. The nodes 10 can communicate with each other. They can be physically or virtually grouped in one or more networks (not shown), such as a private cloud, community cloud, public cloud, or hybrid cloud as described above, or a combination thereof. This allows the cloud computing environment 50 to provide infrastructure, platform, and / or software as a service that cloud consumers do not need to maintain resources on local computing devices. It should be understood that Figure 4 The types of computing devices 54A-N shown in are intended to be illustrative only, and computing node 10 and cloud computing environment 50 may communicate with any type of computerized device over any type of network and / or network-addressable connection (eg, using a web browser).

[0114] Reference now Figure 5 , showing the cloud computing environment 50 ( Figure 4 ) provides a set of functional abstraction layers. It should be understood in advance that Figure 5 The components, layers and functions shown in are intended to be illustrative only, and embodiments of the present invention are not limited thereto. As shown, the following layers and corresponding functions are provided:

[0115] The hardware and software layer 60 includes hardware and software components. Examples of hardware components include: host 61; server 62 based on RISC (Reduced Instruction Set Computer) architecture; server 63; blade server 64; storage device 65; and network and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.

[0116] Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual servers 71 ; virtual storage 72 ; virtual networks 73 , including virtual private networks; virtual applications and operating systems 74 ; and virtual clients 75 .

[0117] In one example, the management layer 80 may provide the functionality described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources for performing tasks within a cloud computing environment. Metering and pricing 82 provides cost tracking when utilizing resources within a cloud computing environment, as well as billing or invoicing for the consumption of these resources. In one example, these resources may include application software licenses. Security provides authentication for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management so that the required service levels are met. Service level agreement (SLA) planning and fulfillment 85 provides pre-scheduling and procurement of cloud computing resources, anticipating future demand for cloud computing resources based on the SLA.

[0118] The workload layer 90 provides examples of functionality that can take advantage of a cloud computing environment. Examples of workloads and functionality that can be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and controlling access to data objects 96.

[0119] The present invention may be a system, method and / or computer program product at any possible level of technical detail integration. The computer program product may include a computer-readable storage medium (or medium) having computer-readable program instructions thereon for causing a processor to perform various aspects of the present invention.

[0120] A computer-readable storage medium may be a tangible device that can retain and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device (such as a punched card or a raised structure in a groove on which instructions are recorded), and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be interpreted as a temporary signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0121] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a corresponding computing / processing device, or downloaded to an external computer or external storage device via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network). The network can include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in a computer-readable storage medium in the corresponding computing / processing device.

[0122] The computer-readable program instructions for performing the operation of the present invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and procedural programming languages ​​such as "C" programming language or similar programming languages. The computer-readable program instructions can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using the Internet of an Internet service provider). In some embodiments, the electronic circuit including, for example, a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA) can execute the computer-readable program instructions to personalize the electronic circuit by utilizing the state information of the computer-readable program instructions, so as to perform various aspects of the present invention.

[0123] Various aspects of the present invention are described herein with reference to the flow charts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each frame of the flow chart diagram and / or block diagram and the combination of frames in the flow chart diagram and / or block diagram can be implemented by computer-readable program instructions.

[0124] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device create components for implementing the functions / actions specified in one or more boxes of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can instruct a computer, a programmable data processing device, and / or other equipment to function in a specific manner, so that the computer-readable storage medium having the instructions stored therein includes an article of manufacture, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0125] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0126] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present invention. In this regard, each frame in the flow chart or block diagram can represent a module, segment or part of an instruction, which includes one or more executable instructions for realizing a specified logical function. In some alternative embodiments, the function annotated in the frame may not occur in the order annotated in the figure. For example, the two frames shown in succession can actually be performed substantially simultaneously, or these frames can sometimes be performed in reverse order, depending on the functions involved. It will also be noted that each frame of the block diagram and / or flow chart illustration and the combination of frames in the block diagram and / or flow chart illustration can be implemented by a system based on dedicated hardware that performs a specified function or action or performs a combination of dedicated hardware and computer instructions.

[0127] Based on the foregoing, computer systems, methods and computer program products have been disclosed. However, many modifications and substitutions may be made without departing from the scope of the present invention. Therefore, the present invention has been disclosed by way of example and not limitation.

Claims

1. A computer-implemented method for dynamic landform channelization using an autonomous robotic dredge, comprising: deriving one or more geomorphic metrics, wherein the one or more geomorphic metrics include soil metrics, environmental conditions, and vegetation water consumption metrics; generating a digital twin of suggested groove and ridge metrics based on the derived one or more geomorphic metrics, wherein the suggested groove and ridge metrics include a depth of the groove, a distance between one or more grooves, a flow magnitude, a slope of the groove, and a shape of the groove; determining a groove pattern based on the generated digital twin of the proposed groove and ridge metrics; as well as and cooperating with the autonomous robotic dredge to generate the trench pattern.

2. A computer-implemented method according to claim 1, wherein the generated digital twin of the proposed groove and ridge metrics can dynamically generate soil water retention and absorption patterns based on the relative positioning of one or more vegetation roots, one or more adjacent grooves, soil metrics, and water retention schedules and runoff volumes.

3. The computer-implemented method of claim 2, further comprising: Determine the amount of soil to be excavated to construct the trench based on the proposed trench and ridge measurements; as well as One or more micro-dams are created and dissolved based on proposed groove and ridge measurements, soil measurements, water supply, and the amount of water flow required.

4. The computer-implemented method of claim 3, further comprising: Dynamically determine the need to block or release the continuous flow of water through the trench and narrow or widen the trench over a period of time based on soil metrics, required wetting, stream size, water supply and fertilizer runoff.

5. The computer-implemented method of claim 3, further comprising: detecting, by the autonomous robotic dredge, one or more anomalies, wherein the one or more anomalies include water runoff, flooding, water blockage, soil erosion, water overflow from one trench to another trench; and Ridge building or blockage release is performed by the autonomous robotic dredge to mitigate the detected anomaly or anomalies.

6. The computer-implemented method of claim 3, further comprising: synchronizing, by the autonomous robotic dredge, water volume, water flow rate, and water runoff duration with a water flow controller based on the environmental conditions, groove pattern, soil metrics, and wetting pattern; as well as The initiation, termination and suspension of water flow into the channel are dynamically controlled.

7. The computer-implemented method of claim 3, further comprising: sending an alert based on any change in one or more characteristics of the groove; as well as Maintenance and re-trenching are automatically initiated before or after drainage to maintain the shape, size, pattern and slope of the trenches and ridges.

8. The computer-implemented method of claim 1 , further comprising: Digital twin simulations are performed to identify optimal groove and ridge metrics based on plant root growth patterns, water consumption patterns, humidity, weather conditions, and plant type.

9. A computer program product comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method comprising: deriving one or more geomorphic metrics, wherein the one or more geomorphic metrics include soil metrics, environmental conditions, and vegetation water consumption metrics; generating a digital twin of suggested groove and ridge metrics based on the derived one or more geomorphic metrics, wherein the suggested groove and ridge metrics include a depth of the groove, a distance between one or more grooves, a flow magnitude, a slope of the groove, and a shape of the groove; determining a groove pattern based on the generated digital twin of the proposed groove and ridge metrics; as well as Collaboration with an autonomous robotic dredge to generate the trench pattern.

10. A computer program product according to claim 9, wherein the generated digital twin of the proposed groove and ridge metrics can dynamically generate soil water retention and absorption patterns based on the relative positioning of one or more vegetation roots, one or more adjacent grooves, soil metrics, and water retention schedules and runoff volumes.

11. The computer program product of claim 10, further comprising: Determine the amount of soil to be excavated to construct the trench based on the proposed trench and ridge measurements; as well as One or more micro-dams are created and dissolved based on proposed groove and ridge measurements, soil measurements, water supply, and the amount of water flow required.

12. The computer program product of claim 11, further comprising: Dynamically determine the need to block or release the continuous flow of water through the trench and narrow or widen the trench over a period of time based on soil metrics, required wetting, stream size, water supply and fertilizer runoff.

13. The computer program product of claim 11, further comprising: detecting, by the autonomous robotic dredge, one or more anomalies, wherein the one or more anomalies include water runoff, flooding, water blockage, soil erosion, water overflow from one trench to another trench; and Ridge building or blockage release is performed by the autonomous robotic dredge to mitigate the detected anomaly or anomalies.

14. The computer program product of claim 11, further comprising: synchronizing, by the autonomous robotic dredge, water volume, water flow rate, and water runoff duration with a water flow controller based on the environmental conditions, groove pattern, soil metrics, and wetting pattern; as well as The initiation, termination and suspension of water flow into the channel are dynamically controlled.

15. A computer system comprising: one or more computer devices, each computer device having one or more processors and one or more tangible storage devices; as well as A program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors, the program instructions including instructions for: deriving one or more geomorphic metrics, wherein the one or more geomorphic metrics include soil metrics, environmental conditions, and vegetation water consumption metrics; generating a digital twin of suggested groove and ridge metrics based on the derived one or more geomorphic metrics, wherein the suggested groove and ridge metrics include a depth of the groove, a distance between one or more grooves, a flow magnitude, a slope of the groove, and a shape of the groove; determining a groove pattern based on the generated digital twin of the proposed groove and ridge metrics; as well as Collaboration with an autonomous robotic dredge to generate the trench pattern.

16. A computer system according to claim 15, wherein the generated digital twin of the proposed groove and ridge metrics can dynamically generate soil water retention and absorption patterns based on the relative positioning of one or more vegetation roots, one or more adjacent grooves, soil metrics, and water retention schedules and runoff volumes.

17. The computer system of claim 16, further comprising: Determine the amount of soil to be excavated to construct the trench based on the proposed trench and ridge measurements; as well as One or more micro-dams are created and dissolved based on proposed groove and ridge measurements, soil measurements, water supply, and the amount of water flow required.

18. The computer system of claim 17, further comprising: Dynamically determine the need to block or release the continuous flow of water through the trench and narrow or widen the trench over a period of time based on soil metrics, required wetting, stream size, water supply and fertilizer runoff.

19. The computer system of claim 17, further comprising: detecting, by the autonomous robotic dredge, one or more anomalies, wherein the one or more anomalies include water runoff, flooding, water blockage, soil erosion, water overflow from one trench to another trench; and Ridge building or blockage release is performed by the autonomous robotic dredge to mitigate the detected anomaly or anomalies.

20. The computer system of claim 17, further comprising: synchronizing, by the autonomous robotic dredge, water volume, water flow rate, and water runoff duration with a water flow controller based on the environmental conditions, groove pattern, soil metrics, and wetting pattern; as well as The initiation, termination and suspension of water flow into the channel are dynamically controlled.

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