Power information construction is developing towards intensive, platform, distributed direction with the expansion of power grid and improvement of information technology. In order to meet the trend, power WebGIS was designed and developed. In this paper, we first discuss the architecture and functionality of power WebGIS, and then we study caching technology in detail, which contains dynamic display cache model, caching structure based on mobile agent, and cache data model. We have designed experiments of different data capacity to contrast performance between WebGIS with the proposed caching model and traditional WebGIS. The experimental results showed that, with the same hardware environment, the response time of WebGIS with and without caching model increased as data capacity growing, while the larger the data was, the higher the performance of WebGIS with proposed caching model improved.
As the state grid corporation “three sets of five” (intensive management in human resources; financial resources; and material resources; large-scale movements in programming, construction, operation, overhaul, and production) system construction plan is put forward, the power information construction develops toward intensive, platform, distributed shared direction. As the foundation platform, GIS should be placed in the first place during information construction. And in order to realize spatial information sharing and interoperability, the demand of integrating WebGIS in power information system is increasingly urgent [
Power WebGIS platform could integrate all types of equipment belonging to power enterprise, which contains power equipment, substation, transmission and substation network, power users and power load, production and management, and other core business, and form composite management system to meet the requirements (safe, reliable, high-quality, efficient, and economic operation) of power enterprise and its customers. Plan and manage the power grid by using modern technology and management means; enhance power grid equipment asset management, operation management, and regulatory capacity; improve the power supply reliability and power quality; and provide high quality, efficient, and safe service for electricity customers timely [
The caching technology is a common mean to solve the WebGIS space data access efficiency. In WebGIS platform, the main impact of spatial data access efficiency consists of two parts: first, database access efficiency and second, the transmission efficiency of the network [
To solve spatial data access, efficiency problem existed in the process of electric power construction of WebGIS;
This paper is organized as follows: first it provides a brief description of independent research and development of electric power WebGIS platform architecture, and then it presents dynamic display cache model and agent-based dynamic display cache method, finally it verifies the proposed method; finally, conclusion and further research direction are given.
Power WebGIS platform is designed based on SOA architecture and is aimed at achieving power enterprise labor and material resources intensive management and business centralized operation, using spatial data sharing and business systems integration as the starting point, to construct a service-oriented, comprehensive, and real time power system comprehensive information integration platform.
Besides the general function and the performance of WebGIS platform, Power WebGIS platform also needs to meet the requirement of electric power industry. Such as integrating electric power business data, providing power characteristic service and providing secondary development function to support related business systems integration at the same time; power WebGIS platform architecture is shown in Figure
Power WebGIS architecture.
The data service layer is at the bottom of power WebGIS platform; it contains all kinds of data mentioned in the spatial data model such as spatial index for spatial data and database-side vector data cache and the background map tile server.
The application services layer is the core of the power WebGIS platform. It contains most application logic of WebGIS and various types of data cache Web server for client service agent server spatial information server (SGA server), Various types of functions are provided as services for the client calls. Unlike general WebGIS, we establish three types of caching service at the layer of the application logic, consisting metadata cache, the permissions cache, and data cache (background map cache and vector graphics data cache). The cache uses a unique design and can greatly improve the speed of response of the WebGIS. The cached data can also be shared between agent server, coordinated distributed deployment servers, and clients.
Platform sharing layer interacts directly with the user, in order to enhance the human-computer interaction capacity of WebGIS and improve client’s performance. On the basis of general browser, based on Flex RIA technologies, one can realize the map control and caching mechanism. At the same time in order to reduce the network load and customer service network latency and improve WebGIS strength and fault tolerance, designed multiagent structure in Flex RIA achieves synergies in the client and speed up map response.
By using Flex technology and agent technology, power WebGIS platform consummate power business integration application system, while improve the user experience at the same time. In addition, Power WebGIS platform establishs two types of cache: cache separately for user data (vector data cache and metadata cache) and background map tile cache at display interface layer, which completes the maps collaborative function and cache data sharing between clients by moving agent. User interface control layer functions involve map display, map edit, resources display, resource query, resource location, spatial analysis, thematic map display, and so on.
Dynamic display cache model is a Caching model set up basing on power GIS spatial data model, making database, GIS server, the client as the research object and caching sharing data in the way of memory and file. Its goal is to improve the response speed of the power WebGIS sharing platform and speed up the map rendering efficiency, rich client space operation level.
The formal description of the Dynamic Display Cache Model is as follows. Cache Directory (CD): The metadata cache set Metadata ( Boundary ( Coordinate Reference System (CS): map reference coordinate system, including all standard coordinate system defined in EPSG, and supports custom extensions; Symbol Library (SL): the platform vector symbol library, for punctate facilities rendering; Feature Model (FM): facility model Relation Set (RS) is a set of possible relationships between the facilities, Version ( Facilities feature cache set Feature ( Topological relations cache set Topology ( Cache Level (CL): Cache Mode (CM): CM = {cm1 and cm2, cubic CM}, where cm1 stand for memory cache whose cache efficiency is high and is difficult to share and maintain; cm2 stand for file cache which is difficult to share synchronized copy; cm3 stand for memory file caching, has advantages of the two cache mentioned above, to maintain cache shared and cache consistency.
DDC model are defined as follows:
The creation and updating of cache is closely related to the version number stored in the metadata, and low-level cache relies on high level cache, take level 3 caches for example, to demonstrate the process of cache updating if (CD(cl3) is null), then create ( else if ( else dropCache ( create (
where CD(cl3) represent the client cache directory, create (
Cache model creation and maintenance should ensure the integrity and consistency of cache. In order to realize the cache efficiency at the same time, some rules need to be followed.
Caching replacement algorithm is established with space limitation and time limitation as theoretical basis. Space limitation behaved as if the most remote distance eliminated first, and time limitation behaved as if the longest time unvisited eliminated first. Comparing with the traditional FIFO, LRU, and LFU, we chose 2Q (two queues algorithm) to improve the efficiency of caching replacement.
2Q algorithm does not eliminate the page least visited from main cache but achieves through swapping with the page most visited. Similar with LRU/2 algorithm, 2Q distinguishes the pages with the time visited the second time, that is to say, 2Q puts the page first visited into a special cache called
In 2Q algorithm,
Formula (
The established condition of (
The practical significance of (
Because
Power GIS model has the following characteristics: complex, large amount of data, high real-time requirements, history play back, and real-time tracking operations. So multilevel cache on spatial data can effectively reduce the server pressure and network load, achieve efficient and real-time access to spatial data. It is also critical to improve the efficiency of the system and reduce the map response time [
In order to quickly and effectively deal with huge amounts of spatial data stored in the space database, we implement three-level cache in Power WebGIS platform including the database-side, server-side, and client. The platform dynamic cache model is shown in Figure
Power WebGIS dynamic display cache.
Server-side dynamic display high-speed cache, and background map tiles cache can satisfy power facilities data real-time change and meet vector graphics rendering requirements. The system can quickly generate maps without query background database so it still has high efficiency while managing huge amounts of data at the same time. The flexible cache can be placed anywhere in the network, considering the system performance, storage space, and network traffic. To meet the requirement of real-time updates, we use DELTA mechanism, update dynamic information through incremental form. Through the combination of DDC and DELTA, it can better support huge amounts of data, accelerate the access speed and ensure data correctness.
The client cache is based on the following fact: in a period of time, user’s inspection of the map and the retrieval of power facilities are concentrated in certain layer. According to the access frequency of the layer, the data is cached in the different levels of cache, when a user retrievals data start from the fastest cache. If does not exist, then retrievals in the next cache. Client local caching content includes all or part of the DDC, the background map tiles visited, all kinds of versions of space facilities data model, user permissions metadata, and others. When facilities updated, it realizes real-time requirements by the server-side DELTA mechanism.
By applying multiagent technology, the power WebGIS platform client provides basic maps show function, browse function, space analysis function, resources query localization function, local map cache data, and user data management function. In addition, each client registers a client agent, as the link between the clients completes the function of map synergy and cached data sharing between the clients by mobile agent.
Dynamic cache mechanism based on intelligent agent creates cache spaces on the server-side and the client-side, respectively. Server-side cache is maintained by multiple applications terminal jointing, while the client cache is maintained by each application terminal separately. The caching model adopts version-based caching strategy according to the layer version, the version of the spatial domain feature, and the version of cached data sheets. Due to the quad-split, relationship exists between the spatial domains trellises coded, so when retrieving in the index tree, we can skip certain levels of spatial domain node and query directly to a leaf node. Cache model is shown in Figure
Cache data model.
LM represents the layer data block metadata; version is the number of the version for the cache layer elements sets; index Tree is the quad index tree for finding spatial elements, the type of each node of the index tree is domain metadata. A one-to-one relationship exists between each node and a quad-split map (spatial domain). The maximum depth of the search tree equals to the zoom level supported by the platform.
DM represents spatial index tree node, version is the version number of cache space domain element sets; visit time represents the last access time of the spatial domain used in the LRU algorithm; extent represents the scope of the space domain used in priority replacement algorithm based on farthest distance; children represents the pointer pointing to the subspace domain; and features is used to save entry address of feature set in the region.
The feature collections are saved in the cache data sheet Buffer table.
BT represents data sheet, featureColl is cached data collection, the features cached in the data table can be shared by all spatial domain.
FI represents cached feature items model, feature represents the cached elements; version represents the version of the cached feature items; and referenceCount stand for the times a feature is referenced. When the counter is 0, the occupied storage space can be recycled.
Based intelligent agent dynamic caching mechanism, where the power WebGIS platform is able to meet the efficient data access and real-time requirements, significantly improve the hit rate of the data and effectively accelerate the speed of data access and graphics rendering efficiency.
According to the dynamic cache model proposed in this paper, we design the test for vector data cache. That is to say, the elements in the vector layer cache are to be tested. Database cache refers to the unique index created on the feature id field of feature table. Create cluster index so as to index features by spatial domain (tiles) id field. Server-side and client cached data means to establish the mapping between layer and space domain set in layer; the mapping between spatial domain and features set entry address buffered in spatial domain to improve features search efficiency.
Test object: low-voltage power lines of a provincial power company. Hardware environment: shown in Table Test data size: 80000 features on sever-side and 20000 features on client-side. Test mode: 10 client access WebGIS server concurrently. Test goal: comparing the vector data loading time when using dynamic display cache model proposed in this paper with the vector data loading time without caching pattern.
Hardware environment.
Item | Server | Client |
---|---|---|
OS | Windows server 2003 | Windows 7 |
CPU | Intel(R) i3 | Intel(R) i3 |
Memory size | 4 GB | 2 GB |
CPU Clock Speed | 2.4 GHz | 2.4 GHz |
Table
Performance comparison.
Vector data number | Nonindexed cache mode load time (ms) | Indexed cache mode loading time (ms) | Load faster ratio |
---|---|---|---|
1,541 | 942 | 320 | 2.94 |
2,075 | 1,245 | 805 | 1.54 |
4,111 | 2,421 | 1,167 | 2.10 |
9,215 | 5,381 | 1,675 | 3.21 |
17,598 | 9,472 | 2,950 | 3.21 |
40,393 | 21,364 | 5,089 | 4.19 |
78,980 | 45,898 | 9,374 | 4.89 |
141,481 | 98,507 | 21,569 | 4.58 |
Test object and hardware environment was the same as test case 1. Test data size: 4000 features. Test mode: Test goal: comparing the vector data loading time with dynamic display cache model proposed in this paper with the vector data loading time without caching pattern in different concurrent users.
Figure The response time of the two ways was low with the smaller concurrent users, but the response time of noncaching mode was 2 times that of caching mode. The response time of the two ways both increased as the concurrent users added, but the response time of noncaching mode increased faster, and the response time of noncaching mode was 4.2 times than that of caching mode when the concurrent users reached 100. The response time could be lower obviously when the dynamic display caching model was used, while the advantage with caching mode was greater and greater as the concurrent users. Dynamic display caching could shorten the system response time by 2–4.2 times.
Average response time using different number of concurrency request.
Aiming at the key problem affecting the power performance of WebGIS-caching mechanism, this paper presented a dynamic display cache model, which makes a research on dynamic display cache technology based on intelligent agent, designs the power WebGIS dynamic cache structure, and cached data model. The application of the intelligent caching technology can greatly improve the WebGIS graphics loading speed and the response efficiency when dealing huge amounts of data, improve user concurrent traffic, and balance the network load. But research is still insufficient; the future research direction is described below. Smart cache can improve the loading speed of vector graphics and meet the needs of two-dimensional GIS, but for Digital Elevation Model (DEM), aerial data, there are no efficient load acceleration solutions. Mobile agent can improve the client cache sharing and improve the response speed of the map, but further study on cache data security and the mobile agent secure communications is needed.