CN102662729B - Method for dispatching growth models for simulation of vast forests - Google Patents
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Abstract
A method for dispatching growth models for simulation of vast forests. The method includes the following steps of firstly, acquiring simulation data of a field of the vast forests according to simulation target, initializing the structure of the simulation data, storing field information data excluding tree information in a quad-tree manner; storing tree information data by the memory pool technology; secondly, utilizing field blocks as units and traversing all the field blocks during computing tree growth models on the field of the vast forests, and computing tree growth in each field block; and thirdly, visualizing the field of the vast forests and storing computing results of the tree growth models in the field of the vast forest according to the computing results of the tree growth models in the field of the vast forest. The method for dispatching growth model for simulation of vast forests is excellent in instantaneity, high in speed of computing process and smooth in field visualization.
Description
Technical field
The present invention relates to computer virtual simulation technology, especially a kind of dispatching method of growth model of extensive forest emulation.
Background technology
Computer virtual simulation technology changes traditional forestry operating management pattern, enables people predict arboreal growth situation by computing machine.According to the change of specific condition dynamic similation scale Forest Scene, Management offorestry decision maker can be enable to make more scientific operation control decision-making.But, for the analog simulation of extensive scale Forest Scene, its contextual data amount very huge (comprising tree information data, terrain data etc.).Such as growth simulation is carried out to the trees in 5km*5km region, tree information required for plant-growth model calculates is probably in the data volume of 1.6-2G, scene drawing often uses LOD technology, the place nearer for distance viewpoint adopts meticulous three-dimensional model to draw, remotely adopt simplified model or utilize Billboard technology to use texture mapping, required amount of ram is roughly the data volume of about 2G.Carry out growth model calculating and emulation drafting if directly these data called in computing machine, the high-speed simulation of growing process cannot be realized with current hardware performance.Therefore, needed to anticipate original contextual data before carrying out scale Forest Scene analog simulation, in simulation process, adopt rational scheduling strategy to calculate growth model dispatch and optimize, thus make the emulation of extensive scale Forest Scene can be quicker.
Summary of the invention
In order to the real-time overcoming existing scale Forest Scene analog simulation is poor, computation process is longer, the more slack deficiency of environment Visualization, the invention provides that a kind of real-time is good, the dispatching method of growth model that time of reducing computation process, environment Visualization are more smooth emulates towards extensive forest.
The technical solution adopted for the present invention to solve the technical problems is:
Towards a dispatching method for the growth model that extensive forest emulates, described dispatching method comprises the following steps:
1) obtain the emulated data of extensive scale Forest Scene according to simulation objectives, carry out initialization to the data structure of emulated data, the scene information data acquisition not comprising tree information stores by the mode of quaternary tree; Tree information data acquisition memory pool technique stores;
2) carry out extensive scale Forest Scene carry out Trees growing models calculate time, in units of world subdivision, travel through all world subdivisions, and in each world subdivision arboreal growth calculate, arboreal growth calculate concrete steps as follows:
A. determine currently will calculate trees in scene, and according to the current influence circle scope calculating the property calculation trees of trees, then, according to influence circle scope, calculate relevant simulation parameter, and calculate the relevant trees of trees by the whole scene trees searching of traversal to current;
B. after obtaining relevant trees again, needing the data of attribute information of these trees to be all dispatched in internal memory, adopting the scheduling strategy of Forest Growth emulation to dispatch growing deposit data inside and outside in computation process;
C. growth model solves: after the data required for current calculating are all called in internal memory, and solving growth equation, calculates last result;
3) according to Trees growing models result of calculation in extensive scale Forest Scene, carry out the visual of extensive scale Forest Scene, and preserve the result of calculation of Trees growing models in extensive scale Forest Scene.
Further, in described step B, the scheduling strategy of described Forest Growth emulation comprises draws thread and scheduling thread;
When user's viewpoint changes, draw thread and calculate current visibility region according to current view point, contextual data in internal memory is judged simultaneously, confirm that scene repaints required data whether in internal memory, if it is directly carry out the drafting of scene and send viewpoint updating message to data prefetching thread; If need the scene of drawing not in internal memory current, drawing thread then needs according to view information such as the position of current view point, the directions of current view point calculating in visibility region; After calculating completes, draw thread suspension and send data request information to data dispatch, proceeding to draw after calling in contextual data by data dispatch thread from external memory;
Data dispatch thread is responsible for data dispatching from external memory, by analyzing the message of drawing thread and passing over, judge the content specifically needing data dispatching, as current scene drawing data, scene prefetch data or scene calculate data etc., deletion contextual data from internal memory simultaneously;
When data dispatch thread receive draw contextual data request message that thread sends time, directly dispatch, now scheduling thread according to message directly from external memory scheduling draw the data required for thread, data dispatch priority is now the highest;
When data dispatch thread receive draw new viewpoint coordinate information that thread sends time, illustrates that contextual data that current scene draws is all at internal memory, drafting thread has entered drafting and has operated; At this moment, the groundwork of data dispatch thread is then judge to draw the data that may use according to the data of new viewpoint next time, and by these data dispatchs in internal memory; After receiving the information of drawing the viewpoint that thread sends, data dispatch thread is according to the view information more new viewpoint ken, and the region that calculating needs are looked ahead also judges the LOD level corresponding to prefetching areas; The numbering of prefetching areas scenario block directly calculates according to the numbering of visibility region; After the scenario block calculating all prefetching areas, because the data pre-fetching time must be less than the scene drawing time, so need to classify to the dispatching priority of these scenario block, the working direction according to mainly eve viewpoint of judgement;
Prefetching areas is divided into three priority by the corner dimension according to scenario block and viewpoint direction of motion, and scenario block i priority Ki is such as formula (1):
Wherein, K
irepresent the priority of scenario block i; θ
irepresent the angle of scenario block and viewpoint direction of motion; R represents the set of prefetching areas scenario block;
When being dispatched in internal memory by prefetch data, needing the contextual data of deleting corresponding size from internal memory, selecting LRU strategy to select the scenario block of deleting.
Further again, described step 1) in, with scene chained list for index, tree information is stored in MemoryBlock, each scenario block in scene chained list may have the tree information that multiple MemoryData comes in storage block, trees in MemoryData are sorted, with the search of trees relevant after accelerating simultaneously.
Beneficial effect of the present invention is mainly manifested in: the information feature that the full standing forest that (1) is developed by parse forest overall dynamics standing forest that is grade simulated, plant interphase interaction single wood that is grade simulated and individual plant trees dynamic growth situation is grade simulated, design organization is carried out to scene external memory data structure, and landform, atural object (non-trees atural object), data texturing are split, set up three and to be mutually related index and external memory storage organization, when making simulation calculation, data reading speed is faster;
(2) step relatively consuming time in the solution procedure according to coarseness scene dynamics evolution model, middle granularity plant interphase interaction model, fine granularity individual trees dynamic growth model, design organization is carried out, to reduce the time of scale Forest Scene model computation process to the storage of contextual data in internal memory;
(3) solve for extensive scale Forest Scene growth model and growth result is visual time cannot disposable problem of contextual data being called in internal memory, to solve and in visualization process, inside and outside deposit data dispatching method is optimized at growth model, comprise the optimizing scheduling when calculating of coarseness, middle granularity and fine granularity scene growth model and emulation, make last environment Visualization more smooth.
Accompanying drawing explanation
Fig. 1 is contextual data memory organization structural representation.
Fig. 2 is the storage organization schematic diagram of contextual data in internal memory.
Fig. 3 is the schematic diagram of scene multithread scheduling.
Fig. 4 is the schematic diagram that LOD hierarchical model is selected.
Fig. 5 is the schematic diagram of scene drawing data pre-fetching.
Embodiment
Below in conjunction with accompanying drawing, the invention will be further described.
With reference to Fig. 1 ~ Fig. 5, a kind of dispatching method of the growth model emulated towards extensive forest, the emulated data of extensive scale Forest Scene is obtained according to simulation objectives, and be stored in calculator memory, then respectively initialization is carried out to data structure (storage organization in interior external memory) according to extensive scale Forest Scene data in the difference of the scheduling mode of simulation process, as shown in Figure 1.
In calculator memory, for scene information data (not comprising tree information), we adopt the mode of quaternary tree to store according to the Quadtree Partition of landform.
For tree information data, consider needs to compare the data in internal memory to delete frequently and store in computation process, adopts memory pool technique to store, as shown in Figure 2.
In internal memory, with scene chained list for index, tree information is stored in MemoryBlock, and each scenario block in scene chained list may have the tree information that multiple MemoryData comes in storage block.Trees in MemoryData are sorted, with the search of trees relevant after accelerating simultaneously.
2. pair extensive scale Forest Scene carries out the calculating of Trees growing models, and every the trees obtained in scene carry out the result calculated that interacts.
Carry out extensive scale Forest Scene carry out Trees growing models calculate time, in units of world subdivision, travel through all world subdivisions, and in each world subdivision arboreal growth calculate.The concrete steps that arboreal growth calculates are as follows:
A. determine currently will calculate trees in scene, and according to the current influence circle scope calculating the property calculation trees of trees, then, according to influence circle scope, calculate relevant simulation parameter, and calculate the relevant trees of trees by the whole scene trees searching of traversal to current.
B. after obtaining relevant trees again, need the data of attribute information of these trees to be all dispatched in internal memory, can use fast when carrying out growth calculating to current trees so that follow-up.But, because calculator memory size is limited, may cause disposable all trees data being all dispatched in internal memory, therefore may occur the situation needing to carry out inside and outside deposit data scheduling in computation process.In order to computer resource can be utilized fully, the scheduling strategy of Forest Growth emulation is adopted to dispatch growing deposit data inside and outside in computation process.
According to different functions, thread is divided into drafting thread and data dispatch thread two class.When viewpoint changes, playing up of scene is called in can work simultaneously with data, to improve the drafting efficiency of scene.In scene drawing process, the calculating of CPU primary responsibility visibility region, scene biomass calculate and the judgement of model of place LOD level, and the drawing of scene is then performed by graphic process unit.So when CPU gives the contextual data processed after graphic process unit draws, CPU at this moment will be in idle condition, can utilize this section free time scheduling scenario data.Fig. 3 is multithreading working model.
(1) thread is drawn
Drawing thread major function is calculate the visibility region of current scene according to view information and whether the drawing data judging visibility region sends prefetch message to data dispatch thread in internal memory.
When user's viewpoint changes, draw thread and calculate current visibility region according to current view point, contextual data in internal memory is judged simultaneously, confirm that scene repaints required data whether in internal memory, if it is directly carry out the drafting of scene and send viewpoint updating message to data prefetching thread.If need the scene of drawing not in internal memory current, drawing thread then needs to calculate in visibility region according to view information such as the position of current view point, the directions of current view point.After calculating completes, draw thread suspension and send data request information to data dispatch, proceeding to draw after calling in contextual data by data dispatch thread from external memory.As shown in Figure 4, in figure, LODO is meticulous plant three-dimensional model, LOD1 is plant three-dimensional model after simplifying, LOD2 then adopts Billboard technology directly to use texture mapping.
(2) scheduling thread
The work of data dispatch thread is mainly responsible for data dispatching from external memory, by analyzing the message of drawing thread and passing over, judge the content specifically needing data dispatching, as current scene drawing data, scene prefetch data or scene calculate data etc., deletion contextual data from internal memory simultaneously.
When data dispatch thread receive draw thread send contextual data request message time, illustrates need draw contextual data and incomplete in internal memory, needs directly dispatch.At this moment scheduling thread is according to the data of message directly from external memory required for scheduling drafting thread, and data dispatch priority is now the highest.
When data dispatch thread receive draw new viewpoint coordinate information that thread sends time, illustrates that contextual data that current scene draws is all at internal memory, drafting thread has entered drafting and has operated.At this moment, the groundwork of data dispatch thread is then judge to draw the data that may use according to the data of new viewpoint next time, and by these data dispatchs in internal memory.As shown in Figure 5, scenario block A is current view point position, and white portion is current visibility region, i.e. the current region needing to draw, and region B, C, D are then prefetching areas.After receiving the information of drawing the viewpoint that thread sends (coordinate and direction), data dispatch thread is according to the view information more new viewpoint ken, and the region that calculating needs are looked ahead also judges the LOD level corresponding to prefetching areas.The numbering of prefetching areas scenario block directly can calculate according to the numbering of visibility region.After the scenario block calculating all prefetching areas, due to the restriction (the data pre-fetching time must be less than the scene drawing time) of each data pre-fetching life period, so need to classify to the dispatching priority of these scenario block, the working direction according to mainly eve viewpoint of judgement.
Prefetching areas is divided into three priority by the corner dimension according to scenario block and viewpoint direction of motion.As shown in Figure 4, arrow represents the direction of motion of viewpoint previous moment, prefetching areas B is labeled as the high priority data priority of region current scene block (its priority still), and prefetching areas C is labeled as medium priority, and prefetching areas D is then labeled as low priority.Scenario block i priority Ki is such as formula (1):
Wherein, K
irepresent the priority of scenario block i; θ
irepresent the angle of scenario block and viewpoint direction of motion; R represents the set of prefetching areas scenario block.
When being dispatched in internal memory by prefetch data, needing the contextual data of deleting corresponding size from internal memory, selecting LRU (least recently used) strategy to select the scenario block of deleting herein.
C. growth model solves.After data required for calculating before single are all called in internal memory, solving growth equation, calculates last result.
3. according to Trees growing models result of calculation in extensive scale Forest Scene, carry out the visual of extensive scale Forest Scene, and preserve the result of calculation of Trees growing models in extensive scale Forest Scene.
Claims (3)
1. a dispatching method for the growth model emulated towards extensive forest, is characterized in that: described dispatching method comprises the following steps:
1) obtain the emulated data of extensive scale Forest Scene according to simulation objectives, carry out initialization to the data structure of emulated data, the scene information data acquisition not comprising tree information stores by the mode of quaternary tree; Tree information data acquisition memory pool technique stores;
2) when carrying out Trees growing models calculating to extensive scale Forest Scene, in units of world subdivision, travel through all world subdivisions, and calculate the arboreal growth in each world subdivision, the concrete steps that arboreal growth calculates are as follows:
A. in scene, determine the current trees that will calculate, and according to the current influence circle scope calculating the property calculation trees of trees, then, according to influence circle scope, calculate relevant simulation parameter, and calculate the relevant trees of trees by the whole scene trees searching of traversal to current;
B. after obtaining relevant trees, needing the data of attribute information of these trees to be all dispatched in internal memory, adopting the scheduling strategy of Forest Growth emulation to dispatch growing deposit data inside and outside in computation process;
C. growth model solves: the data required for current calculating solve growth equation after all calling in internal memory, calculates last result;
3) according to Trees growing models result of calculation in extensive scale Forest Scene, carry out the visual of extensive scale Forest Scene, and preserve the result of calculation of Trees growing models in extensive scale Forest Scene.
2. the dispatching method of the growth model emulated towards extensive forest as claimed in claim 1, it is characterized in that: in described step B, the scheduling strategy of described Forest Growth emulation comprises utilization drafting thread and the internal outer deposit data of data dispatch thread is dispatched;
When user's viewpoint changes, draw thread and calculate current visibility region according to current view point, contextual data in internal memory is judged simultaneously, confirm that scene repaints required data whether in internal memory, if it is directly carry out the drafting of scene and send viewpoint updating message to data prefetching thread; If the scene that current needs are drawn is not in internal memory, drafting thread then needs the position according to current view point, the direction view information of current view point calculates in visibility region; After calculating completes, draw thread suspension and send data request information to data dispatch, proceeding to draw after calling in contextual data by data dispatch thread from external memory;
Data dispatch thread is responsible for data dispatching from external memory, by analyzing the message of drawing thread and passing over, judge the content specifically needing data dispatching, as current scene drawing data, scene prefetch data or scene calculate data, deletion contextual data from internal memory simultaneously;
When data dispatch thread receive draw contextual data request message that thread sends time, directly dispatch, now scheduling thread according to message directly from external memory scheduling draw the data required for thread, data dispatch priority is now the highest;
When data dispatch thread receive draw new viewpoint coordinate information that thread sends time, illustrates that contextual data that current scene draws is all at internal memory, drafting thread has entered drafting and has operated; At this moment, the groundwork of data dispatch thread is then judge to draw the data that may use according to the data of new viewpoint next time, and by these data dispatchs in internal memory; After receiving the information of drawing the viewpoint that thread sends, data dispatch thread is according to the view information more new viewpoint ken, and the region that calculating needs are looked ahead also judges the LOD level corresponding to prefetching areas; The numbering of prefetching areas scenario block directly calculates according to the numbering of visibility region; After the scenario block calculating all prefetching areas, because the data pre-fetching time must be less than the scene drawing time, so need to classify to the dispatching priority of these scenario block, the working direction according to mainly eve viewpoint of judgement;
Prefetching areas is divided into three priority by the corner dimension according to scenario block and viewpoint direction of motion, and scenario block i priority Ki is such as formula (1):
Wherein, K
irepresent the priority of scenario block i; θ
irepresent the angle of scenario block and viewpoint direction of motion; R represents the set of prefetching areas scenario block;
When being dispatched in internal memory by prefetch data, needing the contextual data of deleting corresponding size from internal memory, selecting LRU strategy to select the scenario block of deleting.
3. the dispatching method of the growth model emulated towards extensive forest as claimed in claim 1 or 2, it is characterized in that: described step 1) in, with scene chained list for index, tree information is stored in MemoryBlock, each scenario block in scene chained list may have the tree information that multiple MemoryData comes in storage block, trees in MemoryData are sorted, with the search of trees relevant after accelerating simultaneously.
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| CN104778744B (en) * | 2014-10-25 | 2017-08-25 | 中国科学院植物研究所 | Extensive three-dimensional forest Visual Scene method for building up based on Lidar data |
| FR3065098A1 (en) | 2017-04-05 | 2018-10-12 | Stmicroelectronics (Rousset) Sas | METHOD FOR REAL TIME DETECTION OF A SCENE BY AN APPARATUS, FOR EXAMPLE A WIRELESS COMMUNICATION APPARATUS, AND CORRESPONDING APPARATUS |
| CN111354081A (en) * | 2018-12-20 | 2020-06-30 | 中科星图股份有限公司 | Large-scale three-dimensional forest drawing method |
| CN112668948B (en) * | 2021-02-08 | 2022-03-11 | 浙江弄潮儿智慧科技有限公司 | Forestry ecological environment man-machine interaction system and method based on multi-source information fusion |
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