Author: Jiang Er
Table of contents
2. Detailed explanation of GP model
4. Possible problems and error reports
1. Introduction to model data
SuperMap iDesktopX supports many imported model file formats. Common model formats are not limited to SketchUp 3D model files (*.skp), OBJ model files (*.obj), DAE model files (*.dae), and IFC model files (*.ifc ), FBX model files (*.fbx), etc. In business projects, when a large amount of model data is summarized and needs to be published uniformly for front-end browsing, common functions need to add data multiple times and cache the data in batches, which makes it difficult to avoid manpower losses. At this point, the benefits of processing automated GP models become apparent. After building the automated model, it can be generated with one click without the need for personnel to follow, saving time and effort.
This article mainly introduces the use of SuperMap iDesktopX to build a processing automation model and realize one-click generation of model cache S3MB.
2. Detailed explanation of GP model
Common basic functions realize the generation of S3MB cache of model data sets. You can multi-select multiple model data sets in the same data source [right-click on the data set] - [Generate three-dimensional tiles]. Batch model data set caching from multiple data sources includes but is not limited to Operations such as data set merging, splitting, filtering, merging root nodes, etc. usually require a large number of operations to be completed one by one. Labor costs are increased to a certain extent. For users who are not familiar with the software, a large number of scattered operations also increase the difficulty of the operator's work. Operators can be used to build automated models flexibly and freely, and to a certain extent, the operation of component codes can also be realized. The GP operator is a good product of low coding and realizes efficient and convenient data processing.
In the iDesktop X11.1.1 version, premade GP processing automation models are provided in [3D Data] - [Process Operation] - [Model Tiles]. The operators [iterate model data set], [data set merge], [data set split by SQL query], [model cache], [model cache merge root node], etc. are used.
In the process of implementing model caching, the construction of its operators is not rigid and cannot be adjusted. Usually batch model data caching can be implemented using simplified operators such as [Iterative Model Data Set], [Data Set Merge], [Model Generation Cache], [Model Cache Merge Root Node] and other operators. This automated model can integrate all data sets into one data set, generate a cache, and optimize the model by merging root nodes.
[Model Cache Merge Root Node] has the same function as "Model Cache Rebuild Top Level" and is suitable for fine model cache, and the cache file type only supports S3M and S3MB. It merges adjacent root nodes in a certain spatial range into one root node, that is Upward thinning generates a rougher LOD level. Each time it is merged, the number of model root nodes is reduced by approximately 1/4 of the original number. The details of the operator parameters are shown in the figure below:
If you do not want to change the classification of the original data set and do not have root node optimization requirements for cached results, you can even use [Iterative Model Data Set] and [Model Generation Tiles] to implement it more simply. This automated model generates caches for all data source datasets in the folder. [Model generation tile] operator has many parameters and is more suitable for business scenarios with higher optimization requirements for model caching. Its parameters are detailed as shown in the figure below:
What's more, you can directly use [Batch generation of model tiles with the same name] to cache all data source models in a folder with one click. Compared with the [Model Generation Tiles] operator, this method has simpler parameters and is more suitable for usage scenarios where data optimization requirements are not high and data processing is fast. This operator should be used with caution when there are too many data sources in the data set. Because this operator processes the data sets in each data source into a cache folder, if there are many folders. For example, as shown below:
3. GP model construction
This article takes [Iterative Model Data Set]-[Model Generation Tiles] as an example to briefly describe the model building process.
1. How to build GP
The connection method between operators is relatively simple. By dragging the result of the previous operator to the next operator, the connection information can be displayed. The connection methods of different operators are different. This article uses operators. When connecting, the connection information includes [source data] or [prerequisite].
The precondition means that after the execution of the previous operator is completed, when a certain condition is met, the execution of the next operator starts. Source data refers to the parameter [data set] required in the [Model Generation Tile] operator. This parameter is the result of the [Iterative Model Data Set] set. The result is the next parameter, so [source data] can be used as a connection. relation.
After the connection is completed, process the parameters by entering the required data. When the parameters are completely filled in, its operator is highlighted. It can be executed.
2. Check the result data
After the execution results are completed, check [Task Management] to check the progress. 100% means the execution is completed. Check the execution log. If the tool operator execution result has an output value, you can check the cached output in the folder. Caching can be added to the scene for browsing, saving and subsequent publishing processing.
4. Possible problems and error reports
1. How to publish the model operator to iServer for use after it is built?
Answer: There are two ways. The first way is to fill in the parameters in iDesktop X, save the processing automation model, and publish it directly to iserver for use; the second way is to export the processing automation model XML file in iDesktop Use the model through the [File] import tab.