CN115270509B - Complex special environment simulated terrain planning method and system - Google Patents
Complex special environment simulated terrain planning method and system Download PDFInfo
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- CN115270509B CN115270509B CN202210990411.4A CN202210990411A CN115270509B CN 115270509 B CN115270509 B CN 115270509B CN 202210990411 A CN202210990411 A CN 202210990411A CN 115270509 B CN115270509 B CN 115270509B
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Abstract
The embodiment of the specification provides a method for planning simulated terrains in a complex special environment, which comprises the following steps: acquiring terrain planning input information of a target garden; and performing terrain simulation, building simulation, water body simulation and water system planning on the target garden based on the input information of the terrain planning, determining and outputting a simulation result of the target garden, wherein the terrain simulation, the building simulation and the water body simulation are realized based on a three-dimensional simulation technology.
Description
Technical Field
The specification relates to the technical field of simulation, in particular to a method and a system for planning simulated terrains in a complex special environment.
Background
With the development of computer technology, the way of representing scenes has evolved from two-dimensional graphics to three-dimensional graphics based on the enhancement of computer graphics processing technology and the diversity of screen display systems. The simulation technology is favorably applied to various fields, has superiority in terrain simulation of a complex special environment due to the characteristics of the simulation technology such as situational property, interactivity, multi-perceptibility and the like, can provide sensory simulation of vision, auditory sense, touch sense and the like for a user, and enables the user to be immersed in the environment. Although the current simulation technology is mature, the simulation technology needs to be improved in details such as terrain simulation, water system simulation, and building simulation.
Therefore, it is desirable to provide a method and a system for planning a complex special environment simulation terrain, which are used for simulation of gardens and water systems thereof, reduce scheme modification cost, improve scheme optimization efficiency, obtain a better garden simulation effect, meet user requirements and improve user experience.
Disclosure of Invention
One of the embodiments of the present specification provides a method for planning a simulated terrain in a complex special environment, including: acquiring terrain planning input information of a target garden; and performing terrain simulation, building simulation, water body simulation and water system planning on the target garden based on the input information of the terrain planning, determining and outputting a simulation result of the target garden, wherein the terrain simulation, the building simulation and the water body simulation are realized based on a three-dimensional simulation technology.
One of the embodiments of the present specification provides a complex special environment simulated terrain planning system, which includes: the acquisition module is used for acquiring terrain planning input information of the target garden; the first determination module is used for performing terrain simulation, building simulation, water body simulation and water system planning on the target garden based on the input information of the terrain planning, determining and outputting a simulation result of the target garden, wherein the terrain simulation, the building simulation and the water body simulation are realized based on a three-dimensional simulation technology.
One of the embodiments of the present disclosure provides a complex special environment simulated terrain planning apparatus, which includes a processor, and the processor is configured to execute the complex special environment simulated terrain planning method according to any one of the above embodiments.
One of the embodiments of the present specification further provides a computer-readable storage medium, where the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the method for planning a simulated terrain in a complex special environment according to any one of the above embodiments.
Drawings
The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals refer to like structures, wherein:
FIG. 1 is a schematic diagram of an application scenario of a complex special environment simulated terrain planning system according to some embodiments of the present disclosure;
FIG. 2 is an exemplary block diagram of a complex special environment simulated terrain planning system, according to some embodiments of the present disclosure;
FIG. 3 is an exemplary flow diagram of yet another complex special environment simulated terrain planning method, according to some embodiments of the present description;
FIG. 4 is an exemplary flow chart illustrating the determination of water flow information for a water system in a target garden in accordance with some embodiments of the present description;
FIG. 5 is a schematic view of a water system simulation model according to some embodiments herein;
FIG. 6 is an exemplary flow chart illustrating the determination of the amount of water remaining in various sub-areas of a target garden according to some embodiments of the present description;
FIG. 7 is a schematic diagram of a water remaining prediction model in accordance with certain embodiments described herein.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not to be taken in a singular sense, but rather are to be construed to include a plural sense unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to or removed from these processes.
Fig. 1 is a schematic diagram of an application scenario of a complex special environment simulated terrain planning system according to some embodiments of the present disclosure.
In some embodiments, the complex special environment may include a garden. The application scenario 100 of the complex special environment simulated terrain planning system may include simulation results 110 of the target landscape architecture, a storage device 120, a processor 130, and a user 140.
The simulation result 110 of the target garden may refer to a garden simulation scene that meets the user's requirements and is finally obtained through the three-dimensional simulation technology. In some embodiments, the simulation result 110 of the target garden may include factors related to a garden simulation scenario, such as a simulated building 111, a simulated water body 112, a simulated water system 114, a simulated plant 114, and a simulated terrain (not shown). In some embodiments, the simulation result 110 of the target garden may be processed (e.g., 3 degree, directX, openGL, etc.) by the processor 130 using a three-dimensional simulation technique based on the terrain planning input information for garden simulation modeling acquisition. In some embodiments, information (e.g., terrain elevation data, building height data, etc.) relating to the simulation results 110 of the target landscape may be stored in the memory device 120.
In some embodiments, the storage device 120 may be part of the processor 130. Storage device 120 may include one or more storage components, each of which may be a separate device or part of another device. In some embodiments, storage device 120 may include Random Access Memory (RAM), read Only Memory (ROM), mass storage, removable storage, volatile read-write memory, and the like, or any combination thereof. Illustratively, mass storage may include magnetic disks, optical disks, solid state disks, and the like. In some embodiments, the storage device 120 may be implemented on a cloud platform. Illustratively, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
The processor 130 may be used to process data and/or information from at least one component of the application scenario 100 of the complex special environment simulated terrain planning system or an external data source (e.g., a cloud data center). Processor 130 may be connected to storage device 120 via a network (not shown) or directly to access and/or receive data and information. For example, the processor 130 may be directly connected to the storage device 120 and receive information related to the simulation result 110 of the target garden.
In some embodiments, the processor 130 may include one or more sub-processing devices (e.g., single core processing devices or multi-core processing devices). Illustratively, the processor 130 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a programmable logic circuit (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, and the like or any combination thereof.
The user 140 may refer to a person or a group who makes a demand and feedback opinion on the simulation result 110 of the target garden. For example, a group that needs simulation of gardens for design teaching, and for example, a group that needs simulation of gardens for game development. In some embodiments, the user 140 may select or autonomously input the terrain planning input information of the target garden according to the demand. In some embodiments, historical, autonomously entered terrain planning information for the user 140 may be stored in the storage device 120. In some embodiments, the requirements and feedback opinions made by the user 140 about the simulated gardens may be processed by the processor 130 to model and/or adjust the simulation results 110 of the target gardens.
In some embodiments, the application scenario 100 of the complex special environment simulation terrain planning system may further include a visualization device (not shown in the figures). A visualization device may refer to one or more terminal devices used by a user to implement visualization. In some embodiments, the visualization device may be one or any combination of a mobile device, a tablet computer, a laptop computer, a desktop computer, 3D glasses, or other viewing-enabled device.
It should be noted that the application scenarios are provided for illustrative purposes only and are not intended to limit the scope of the present specification. It will be apparent to those skilled in the art that various modifications and variations can be made in light of the description of the present specification. For example, the application scenario may also include a database. As another example, an application scenario may implement similar or different functionality on other devices. However, variations and modifications may be made without departing from the scope of the present description.
Fig. 2 is an exemplary block diagram of a complex special environment simulated terrain planning system according to some embodiments of the present description.
As shown in fig. 2, the complex special environment simulated terrain planning system 200 may include an acquisition module 210 and a first determination module 220.
The obtaining module 210 may be used to obtain the terrain planning input information of the target garden. For more on the target garden, terrain planning input information and acquisition mode, see fig. 3 and its associated description.
The first determining module 220 may be configured to perform terrain simulation, building simulation, water body simulation and water system planning on the target garden based on the input information of the terrain planning, and determine and output a simulation result of the target garden, where the terrain simulation, the building simulation and the water body simulation are implemented based on a three-dimensional simulation technology. For more on the terrain simulation, the building simulation, the water body simulation, the water system planning, and the simulation result of determining the target garden and outputting, refer to fig. 3 and the related description thereof.
In some embodiments, the first determination module 220 is further configured to determine first attribute information and first water volume information for the water scene based on the terrain planning input information; determining second attribute information and second water quantity information of the flow channel based on the terrain planning input information; determining the water flow information of the water system in the target forest based on the first attribute information, the first water amount information, the second attribute information, and the second water amount information. For more on the first attribute information, the first water amount information, the second attribute information, the second water amount information, the water flow information of the water system, and the determination manner, refer to fig. 4 and its related description.
In some embodiments, the first determination module 220 is further configured to determine the water flow information of the water system in the target garden through a water system simulation model based on the first attribute information, the first water amount information, the second attribute information, and the second water amount information, wherein the water system simulation model is a machine learning model. See FIG. 5 and its associated description for more on the water system model.
In some embodiments, the complex special environment simulated terrain planning system 200 further comprises a second determination module 230 and a third determination module 240. The second determination module 230 is used for determining the topographic information and water system information of the target garden based on the topographic planning input information. The third determination module 240 is configured to determine the remaining water amount of each sub-area in the target garden based on the climate simulation information, the terrain information, and the water system information. For more on the terrain information, water system information, climate simulation information and determining the amount of water remaining in the respective sub-areas in the target garden, reference is made to fig. 6 and its associated description.
It should be understood that the system and its modules shown in FIG. 2 may be implemented in a variety of ways. For example, in some embodiments the system and its modules may be implemented in hardware, software, or a combination of software and hardware.
It should be noted that the above description of the system and its modules is for convenience only and should not limit the present disclosure to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. In some embodiments, the acquisition module 210, the first determination module 220, the second determination module 230, and the third determination module 240 disclosed in fig. 2 may be different modules in a system, or may be a module that implements the functions of two or more of the above-described modules. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.
Fig. 3 is an exemplary flow diagram of yet another complex special environment simulated terrain planning method, according to some embodiments of the present description. In some embodiments, the process 300 may be performed by a processor. As shown in fig. 3, the process 300 includes the following steps:
in step 310, the input information of the terrain planning of the target garden is obtained.
The target gardens may refer to gardens that the user needs to perform the simulation. For example, the target garden may be a garden in a plan with a construction plan.
The terrain planning input information may refer to various design data and/or planning data of the target landscape architecture. For example, the terrain planning input information may include, but is not limited to, terrain data, building data, water data, plant data, etc. for the target landscape. The terrain data can comprise the height of the earth surface of the target garden, the change situation of the terrain and the like; the building data may include the floor space and distribution of all buildings and/or structures in the target garden, etc.; the water body data can comprise water flow in a target garden, floor area and distribution conditions of a pond and the like; plant data may refer to data such as the type, size, footprint, and water demand of all plants in a target garden, which may include, but is not limited to, trees, shrubs, and/or herbs.
In some embodiments, terrain planning input information may be obtained using a variety of possible methods. For example, the corresponding construction drawings of the target garden may be analyzed, and the terrain planning input information extracted therefrom may be input to the processor. For another example, based on the image data related to the target garden, the terrain data, building and/or structure data, water body data, plant data, etc. in the image may be acquired, and the terrain planning input information may be determined and input to the processor.
And 320, performing terrain simulation, building simulation, water body simulation and water system planning on the target garden based on the input information of the terrain planning, and determining and outputting a simulation result of the target garden, wherein the terrain simulation, the building simulation and the water body simulation are realized based on a three-dimensional simulation technology.
In some embodiments, the processor may perform simulation analysis and output a simulation result of the target garden according to the terrain planning input information. The simulation result may refer to a simulation modeling result of the target garden, and the simulation result may be three-dimensional model data. For example, the simulation result may include three-dimensional modeling models of terrain simulation, building simulation, water body simulation, plant simulation, and the like of the target garden.
In some embodiments, the aforementioned terrain simulation, building simulation, and water body simulation may be implemented based on three-dimensional simulation techniques. The three-dimensional simulation technique may refer to a related technique for simulation modeling. For example, a three-dimensional simulation technology can be utilized to perform simulation modeling on the target garden based on the input information of the terrain planning, and data of the terrain simulation, the building simulation, the water body simulation, the water system planning and the like of the target garden are obtained. The three-dimensional simulation technology can be realized based on at least one of 3D development engines such as 3DUnity, directX, openGL and the like.
Terrain simulation may refer to simulation modeling related to terrain data in the terrain planning input information. For example, the terrain simulation may include simulation modeling based on data such as terrain relief, surface height variation, and terrain variation of the target garden, and the like, to obtain simulation information that is the same as or similar to the terrain data of the target garden. Building simulation may refer to simulation modeling related to building data in terrain planning input information. Wherein, the building simulation may include performing simulation modeling based on the building and/or structure information of the target garden, and acquiring simulation information that is the same as or similar to the building data of the target garden. Water body simulation may refer to simulation modeling related to water body data in terrain planning input information. For example, the water body simulation may be simulation information determined by simulation modeling based on the current in the target gardens and the position, orientation, and footprint of the pond.
Illustratively, based on the terrain planning input information, the location, size, and type of the building are determined, and the building of the size of the corresponding type is modeled at the corresponding location in the virtual scene. When the ratio of the size of the virtual space to the actual planned size of the target garden is 1, the size and position of the building in the terrain planning input information can be directly determined as the corresponding size and position of the building in the virtual space.
Some embodiments of the present description may clearly and intuitively check the three-dimensional simulation effect of the target garden through simulation, and determine whether the input information of the terrain planning meets the requirements.
In some embodiments, the simulation results may further include a water system plan for the target garden determined based on the terrain planning input information. The water system planning may utilize information regarding the water system of the target garden determined by the simulation modeling technique, which may include, but is not limited to, the flow direction, the flow volume, etc. of the water system. The aforementioned water systems may include water spots in the target landscape as well as water channels. The water system plan includes water flow information for the water system in the target garden, which may be included in the simulation results. The water flow information may include the flow rate and the water flow direction of the water system. In some embodiments, the processor may process the terrain planning input information to determine a water system plan in the target property. The processor may process initial water flow information for an initial water system in the terrain planning input information to determine a water system plan in the target garden when operating based on the initial water flow information. The initial water flow information may refer to related information of water flow preset or default by a user in the terrain planning input information, and may include first water amount information of the scenic spot and second water amount information of the water flow channel. For more on the first water amount information and the second water amount information, refer to fig. 4 and its related description.
In some embodiments, the processor may analyze the terrain planning input information of the target garden, determine and output water system planning data in the simulation result, wherein the analysis may include establishing a machine learning model, regression analysis, and/or establishing a mathematical function, etc. The outputting may be outputting the simulation result data by the processor on a screen of the user terminal or inputting the simulation result by the processor into a storage device.
In some embodiments, the processor may also determine first attribute information and first water volume information for the water scene based on the terrain planning input information; determining second attribute information and second water quantity information of the flow channel based on the terrain planning input information; and determining water flow information of the water system in the target garden based on the first attribute information, the first water amount information, the second attribute information and the second water amount information. See fig. 4 and its associated description for more on the foregoing embodiment.
In some embodiments, the processor may further determine whether the water system plan of the target garden meets a preset condition, and when the water system plan does not meet the preset condition, the processor may adjust the water flow information of the target garden until the preset condition is met, and determine the water system plan of the target garden.
In some embodiments of the present description, a target garden is modeled and analyzed based on a three-dimensional simulation technology, so that a user can intuitively obtain a simulation effect of the target garden determined based on terrain planning input information, and can accurately analyze whether relevant parameters of the target garden can meet the cost requirements, the use requirements and the aesthetic requirements of the user. And the most appropriate simulation result is selected as the planning scheme of the target garden, so that the planning cost is reduced to a certain extent.
FIG. 4 is an exemplary flow chart illustrating the determination of water flow information for a water system in a target garden according to some embodiments of the present description. In some embodiments, flow 400 may be performed by processor 130. As shown in fig. 4, the process 400 includes the following steps:
A water spot may refer to simulated data about a water flow path location determined based on simulation of a target landscape. For example, waterscape sites may include, but are not limited to, lakes, rivers, pools, impoundments, and the like. In some embodiments, some of the water spots may have the function of storing water and adjusting the flow of water. Water spots may absorb a portion of the amount of water, wherein absorption may include, but is not limited to, plant absorption, soil infiltration and/or evaporation, and the like.
The first attribute information may refer to self information of the water scenery spot. For example, the first attribute information may include, but is not limited to, the size, shape, footprint, depth, location, number of water inlets, number of water outlets, and the like of the water spot.
The first water volume information may refer to a parameter of the waterscape point related to the water volume. The first water amount information may include, but is not limited to, an initial water inflow, an initial water outflow, an initial water storage amount, and the like of the water attraction.
In some embodiments, the processor may determine the first attribute information and the first water volume information for the water scene based on information about the water system in the terrain planning input information.
And step 420, determining second attribute information and second water quantity information of the flow channel based on the terrain planning input information.
The water flow channel may refer to simulation data regarding a water flow path determined based on simulation of the target landscape architecture. For example, a water channel may include, but is not limited to, a gutter, a trough, a pipe, and the like. In some embodiments, the maximum allowable water flow may be different for different flow channels.
The second attribute information may refer to self information of the pipeline. For example, the second attribute information may include a position, a length, a width, a depth, a terrain height, and the number of connected waterscape points, etc. In some embodiments, the different terrain heights of different water flow channels may affect the water inflow. It will be appreciated that under the same conditions, the water flow will tend to flow preferentially to the lower lying channels.
The second water amount information may refer to a parameter of the water flow channel related to the amount of water. The second water amount information may include, but is not limited to, a water flow rate, a water flow direction, etc. of the water flow channel.
In some embodiments, the processor may determine second attribute information and second water volume information for the flow channel based on information about water systems in the terrain planning input information.
And 430, determining water flow information of the water system in the target garden based on the first attribute information, the first water amount information, the second attribute information and the second water amount information.
The water flow information may refer to water amount information related to a plurality of water flow channels and waterscape points of the target landscape architecture while operating. The water flow information may include, but is not limited to, an operational water intake, an operational water output, an operational water storage, and an operational water flow of the flowing water channel, etc.
In some embodiments, the processor may model or analyze the first attribute information, the first water amount information, the second attribute information, and the second water amount information using various data analysis algorithms, such as regression analysis, discriminant analysis, and the like, to obtain water flow information of the water system in the target garden.
In some embodiments, the processor may determine water flow information for a water system in the target garden via a water system simulation model based on the first attribute information, the first water amount information, the second attribute information, and the second water amount information, wherein the water system simulation model is a machine learning model. For more explanation regarding the determination of water flow information based on a water system model, see FIG. 5 and its associated description.
In some embodiments of the present disclosure, an actual water system plan of the target garden during operation may be determined based on the input information of the terrain plan, so that a simulation result of the target garden that is more suitable for actual conditions may be determined.
FIG. 5 is a schematic view of a water system simulation model, according to some embodiments herein.
In some embodiments, the water system simulation model may be GNN (Graph Neural Networks) or the like. GNN is a neural network model directly acting on a graph, and can enable each node in the graph to exchange attribute information through edges based on an information propagation mechanism, so that the node information of the node is continuously updated until a stop condition is met. And after the output data of the node to be predicted, namely the graph neural network model, stops updating, updating the information of the node to be predicted.
As shown in fig. 5, the processor may construct the graph structure data 550 based on the first attribute information 510, the first water amount information 520, the second attribute information 530, and the second water amount information 540, input the graph structure data 550 into the water system simulation model 560, and output water flow information 570 of the water system.
In some embodiments, a trained water system simulation model may be obtained by training alone. The water system simulation model may be obtained by obtaining a plurality of training samples and performing training based on the plurality of training samples and their corresponding labels. The training sample can comprise sample graph structure data constructed based on sample first attribute information, sample first water quantity information, sample second attribute information and sample second water quantity information of the sample garden, and the label comprises water flow information of a water system in the sample garden. In some embodiments, training samples and labels may be obtained based on historical data, for example, samples and labels may be obtained based on historical water system planning data. In some embodiments, the processor may also be based on processing the simulated analog data to obtain the sample and the label. By utilizing the processor to adjust and modify the simulation data, a large number of samples and labels required by the training water system simulation model can be efficiently obtained, the model training efficiency can be improved, and the training cost can be reduced. Inputting a training sample into an initial water system simulation model, constructing a loss function based on the output and the label of the initial water system simulation, updating the parameters of the initial water system simulation model through the loss function until the trained initial water system simulation model meets a preset condition, and obtaining the trained water system simulation model, wherein the preset condition can be that the loss function is smaller than a threshold value, convergence is realized, or a training period reaches the threshold value and the like.
In some embodiments, the trained water system simulation model can be obtained by performing combined training on the water system simulation model and the residual water amount model. For more on the joint training, see fig. 6 and its related description.
In some embodiments of the present description, the trained water system simulation model is used to process simulation data of the target garden, so that different water flow information under different simulation conditions can be obtained more accurately, the water system planning efficiency is effectively improved, and the planning cost is reduced.
Fig. 6 is an exemplary flowchart illustrating the determination of the amount of water remaining in each of the sub-areas in the target garden according to some embodiments of the present description. In some embodiments, flow 600 may be performed by a processor. As shown in fig. 6, the process 600 includes the following steps:
and step 610, determining the terrain information and the water system information of the target garden based on the terrain planning input information.
Topographical information may refer to information about the topography and relief features of the ground in a target garden. For example, the terrain information may include, but is not limited to, the elevation of the terrain in each area of the target property, the slope and direction of the relief of the terrain, and the like.
The water system information refers to information on a water network system composed of all water bodies, water systems, and the like in the target garden. For example, the water system information may include, but is not limited to, the volume of water in each area of the target garden, the area of water, the flow direction and flow rate of the water system, the evaporation amount of water per unit time, and the like.
In some embodiments, the processor may analyze the terrain planning input information to generate corresponding terrain information and water system information. In some embodiments, the processor may directly process the terrain planning input information to screen the terrain information and water system information of the target garden. In some embodiments, the processor may further perform terrain simulation, building simulation, water body simulation and water system planning on the target garden based on the terrain planning input information, determine a simulation result of the target garden, and determine terrain information and water system information of the target garden based on the simulation result of the target garden. See fig. 3 and its associated description for more on the simulation results for determining the target garden.
And step 620, determining the residual water amount of each subarea in the target garden based on the climate simulation information, the terrain information and the water system information.
The climate simulation information refers to information for simulating the climate in the target forest. For example, the climate simulation information may include, but is not limited to, lighting information, air temperature information, precipitation information, etc. of the target garden. In some embodiments, the climate simulation information may be determined in a variety of ways. For example, a user may autonomously input desired climate simulation information. For another example, the processor may collect and analyze a large amount of historical weather data for the area in which the target garden is located, thereby determining climate simulation information for the target garden. The climate simulation information of the target garden may be the most common weather and/or extreme weather (e.g., heavy rain, etc.) of the area in which the target garden is located.
The sub-area refers to a local area in the target garden. For example, the sub-regions may be divided by different sights, with sight a being one sub-region and sight B being another sub-region. For another example, the sub-areas may be divided into sub-areas having the same area by area division, one sub-area per 100 square meters. For another example, the sub-regions may be divided in combination with the sights and areas, with sight a being divided into sub-regions of 150 square meters and sight B being divided into sub-regions of 100 square meters.
The residual water amount may refer to the amount of water remaining in each sub-area of the target garden except for being absorbed by vegetation and soil within a certain time. The residual water volume can be used for judging whether each subregion of the target garden can drain water in time or not and whether the water volume of each subregion of the target garden is sufficient or not. The amount of residual water can be expressed in mm or other units of measure. In some embodiments, the remaining water amount may be represented by different levels, for example, a year of the remaining water amount in a certain sub-area of the target garden is set as level i when the year of the remaining water amount is more than 200mm, which indicates that the remaining water amount is too small, and a year of the remaining water amount in a certain sub-area of the target garden is set as level v when the year of the remaining water amount is more than 800mm, which indicates that the remaining water amount is too large.
In some embodiments, when the climate simulation information is common weather, the processor may determine whether the amount of water remaining for the target garden sub-area is sufficient for daily use (e.g., irrigation, viewing, etc.) based on the climate simulation information; when the climate simulation information is extreme weather, the processor may determine whether there is a danger (e.g., drought, flooding, etc.) in the remaining water volume of the target garden subregion based on the climate simulation information. When the residual water amount of each subregion of the target garden does not meet the preset conditions, the processor can adjust the water system plan so that the residual water amount of each subregion meets the preset conditions.
In some embodiments, the processor may model or analyze the climate simulation information, the terrain information, and the water system information in the target garden using various data analysis algorithms, such as regression analysis, discriminant analysis, etc., to determine the remaining water amount in each sub-area of the target garden.
In some embodiments, the processor may process the climate simulation information, the terrain information, and the water system information through the residual water amount prediction model to determine the residual water amount of each sub-area of the target garden.
In some embodiments, the processor may determine the amount of water remaining in each sub-area of the target garden through the water remaining amount prediction model based on the weather characteristics and the characteristics of the features of the sub-area of the target garden.
As shown in fig. 7, the residual water amount prediction model 740 may include a first feature extraction layer 741, a second feature extraction layer 742, and a residual water amount determination layer 743. The residual water amount prediction model 740 may process the topographic information 710, the water system information 720, and the climate simulation information 730 to determine the residual water amount 770 for each sub-area.
In some embodiments, the first feature extraction layer may process the topographic information and the water system information to obtain surface features of each sub-region. As shown in fig. 7, the input of the first feature extraction layer 741 may include terrain information 710 and water system information 720, and the output may include surface features 750 of each sub-area. The first feature extraction layer 741 may be a deep learning model.
The surface characteristics can refer to characteristics used for representing information such as topographic relief, water system flow direction, water system water amount, soil type and the like of each subarea of the target garden. For example, the surface features may be ranked according to information such as the topography and water system of the surface. In some embodiments, the surface characteristics may be determined based on terrain information and water system information for various sub-areas of the target landscape architecture. In some embodiments, the surface features may be characterized by vectors. The positions of the elements in the aforementioned vectors characterize the type of surface information, e.g., terrain relief height, water system flow, soil penetration. The values of the elements represent the concrete conditions corresponding to the various surface information, for example, the surface characteristics may be (10, 60, 20), and respectively represent that the terrain relief average altitude of the target garden subregion is 10m, the water amount of the water system is 60mm, and the soil infiltration amount of the water system is 20mm.
In some embodiments, the second feature extraction layer may process the weather simulation information to obtain weather features. As shown in fig. 7, the input of the second feature extraction layer 742 may include climate simulation information 730 and the output may include weather features 760. The second feature extraction layer 742 may be a deep learning model.
The weather characteristics may refer to characteristics for characterizing precipitation, temperature, sunshine, etc. of each sub-area of the target garden. For example, the weather features may be classified into different levels according to the depth of precipitation, and the more precipitation, the higher the grade of the precipitation features, and vice versa. In some embodiments, the weather characteristics may be determined based on climate simulation information for various sub-areas of the target garden. In some embodiments, the weather features may be characterized by a vector. The positions of the elements in the aforementioned vector characterize the type of weather information, e.g., precipitation, temperature, insolation. The values of the elements represent specific situations corresponding to the respective weather information, for example, the weather characteristics may be (35, 24, 15), which respectively represent that the daily precipitation of the target garden subregion is 35mm, the average temperature is 24 ℃, and the average sunshine duration is 15h.
In some embodiments, the residual water amount determining layer may process the surface characteristics and the weather characteristics of each sub-area to obtain the residual water amount of each sub-area. As shown in fig. 7, the input of the residual water amount determining layer 743 may include the surface characteristics 750 and the weather characteristics 760 of each sub-region, and the output may include the residual water amount 770 of each sub-region. The residual water amount determining layer 743 may be a deep learning model.
In some embodiments, the water remaining prediction model is obtained by separate training. The training sample may include sample topographic information, sample water system information, and sample climate information of the sample garden, and the label of the training sample may include sample residual water amounts of various sub-areas in the sample garden. The training samples can be input into an initial residual water amount prediction model, a loss function is constructed based on the output of the initial residual water amount prediction model and the label, parameters of the initial residual water amount prediction model are updated iteratively based on the loss function until preset conditions are met, the parameters of the residual water amount prediction model are determined, and the trained residual water amount prediction model is obtained. The preset conditions may include, but are not limited to, loss function convergence, training period reaching a threshold, etc.
In some embodiments, the residual water prediction model may also be obtained by training in combination with a water system simulation model. The training sample may include sample topographic information, sample water system information, sample climate simulation information, sample first attribute information, sample first water amount information, sample second attribute information, and sample second water amount information of the sample garden, and the label of the training sample may include water flow information of the water system in the sample garden. The method comprises the steps of inputting sample terrain information, sample water system information and sample climate simulation information in a training sample into an initial residual water amount prediction model, constructing sample graph structure data based on the output of the initial residual water amount prediction model and first sample attribute information, first sample water amount information, second sample attribute information and second sample water amount information in the training sample, inputting the sample graph structure data into an initial water system simulation model, constructing a loss function based on the output of the initial water system simulation model and a label, and iteratively updating parameters of the initial residual water amount prediction model and the initial water system simulation model based on the loss function until preset conditions are met, so as to obtain the trained residual water amount prediction model and the water system simulation model. The aforementioned preset conditions may include, but are not limited to, loss function convergence, training period reaching a threshold, and the like.
In some embodiments of the present disclosure, the residual water amount prediction model has a multilayer structure, and can input and output various parameters simultaneously, determine the residual water amount based on a large amount of extensive data, effectively improve the prediction efficiency and accuracy of the residual water amount, determine the residual water amount through the residual water amount prediction model, and effectively determine whether the water amount of each sub-area of the target garden can meet the daily requirements of the target garden (e.g., sufficient water amount is used for irrigation, appreciation, etc.) and whether there is a danger (e.g., drought, flood, drainage, etc.); when the residual water quantity does not meet the preset condition, corresponding adjustment is convenient to be made to the water system planning in time, normal operation of the target garden water system is guaranteed, and experience feeling and comfort of a user are improved.
The present specification also provides a complex special environment simulated terrain planning apparatus, the apparatus comprising at least one processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute at least a portion of the computer instructions to implement the method according to any one of the embodiments of the present specification.
The present specification also provides a computer readable storage medium storing computer instructions which, when executed by a processor, implement a method as described in any one of the embodiments of the specification.
It should be noted that the above description of the flow is for illustration and description only and does not limit the scope of the application of the present specification. Various modifications and alterations to the flow may occur to those skilled in the art, given the benefit of this description. However, such modifications and variations are still within the scope of the present specification.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, certain features, structures, or characteristics may be combined as suitable in one or more embodiments of the specification.
Additionally, the order in which elements and sequences are described in this specification, the use of numerical letters, or other designations are not intended to limit the order of the processes and methods described in this specification, unless explicitly stated in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Where numerals describing the number of components, attributes or the like are used in some embodiments, it is to be understood that such numerals used in the description of the embodiments are modified in some instances by the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range in some embodiments of the specification are approximations, in specific embodiments, such numerical values are set forth as precisely as possible within the practical range.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into the specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of the present specification shall control if they are inconsistent or inconsistent with the statements and/or uses of the present specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.
Claims (4)
1. A method for planning a simulated terrain in a complex special environment is characterized by comprising the following steps:
acquiring terrain planning input information of a target garden;
determining terrain information and water system information of the target garden based on the terrain planning input information;
determining the residual water amount of each subarea in the target garden based on the climate simulation information, the terrain information and the water system information; and
performing terrain simulation, building simulation, water body simulation and water system planning on the target garden based on the terrain planning input information, determining and outputting a simulation result of the target garden, wherein the terrain simulation, the building simulation and the water body simulation are realized based on a three-dimensional simulation technology;
the water system planning method comprises the following steps of obtaining water flow information of a water system in the target garden, wherein the water flow information is contained in the simulation result, the water system comprises water scenic spots in the target garden and a water flow channel, and the water system planning of the target garden comprises the following steps:
determining first attribute information and first water amount information of the water scenery spot based on the terrain planning input information;
determining second attribute information and second water quantity information of the flow channel based on the terrain planning input information;
determining the water flow information of the water system in the target garden through a water system simulation model based on the first attribute information, the first water amount information, the second attribute information and the second water amount information, wherein the water system simulation model is a machine learning model.
2. A system for planning a complex special environment simulated terrain, comprising:
the acquisition module is used for acquiring terrain planning input information of the target garden;
a second determination module for determining topographic information and water system information of the target garden based on the topographic planning input information;
the third determining module is used for determining the residual water amount of each subarea in the target garden based on the climate simulation information, the terrain information and the water system information;
the first determination module is used for performing terrain simulation, building simulation, water body simulation and water system planning on the target garden based on the terrain planning input information, determining and outputting a simulation result of the target garden, wherein the terrain simulation, the building simulation and the water body simulation are realized based on a three-dimensional simulation technology;
the water system plan includes water flow information of a water system in the target garden, the water flow information being included in the simulation result, wherein the water system includes the water scenery in the target garden and a water flow channel, the first determination module is further configured to:
determining first attribute information and first water amount information of the water scenery spots based on the terrain planning input information;
determining second attribute information and second water quantity information of the flow channel based on the terrain planning input information;
determining the water flow information of the water system in the target garden through a water system simulation model based on the first attribute information, the first water amount information, the second attribute information and the second water amount information, wherein the water system simulation model is a machine learning model.
3. A complex special environment simulated terrain planning device is characterized by comprising at least one processor and at least one memory;
the at least one memory is for storing computer instructions;
the at least one processor is configured to execute at least a portion of the computer instructions to implement the method as recited in claim 1.
4. A computer-readable storage medium, wherein the storage medium stores computer instructions that, when executed by a processor, implement the method of claim 1.
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| US11107162B1 (en) * | 2019-01-10 | 2021-08-31 | State Farm Mutual Automobile Insurance Company | Systems and methods for predictive modeling via simulation |
| CN110245445B (en) * | 2019-06-21 | 2020-04-07 | 浙江城建规划设计院有限公司 | Ecological garden landscape design method based on computer three-dimensional scene simulation |
| CN111105170B (en) * | 2019-12-31 | 2023-04-18 | 张旭 | Water resource simulation configuration calculation method and water resource configuration method |
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