CN105654414A - Urban multi-disaster risk loss evaluation system based on open source system framework and building spatial database and method thereof - Google Patents

Abstract

The invention provides an urban multi-disaster risk loss evaluation system based on an open source system framework and a building spatial database. The urban multi-disaster risk loss evaluation system comprises an 1a) MAEviz open source framework development platform based on an Eclipse RCP frame, wherein the MAEviz open source framework development platform is used for extending a flood, wind damage and tsunami disaster loss evaluation module so as to realize multi-disaster urban risk loss evaluation; a 1b) disaster information module which is used for analysis of the risk and the dangerousness of flood, typhoon, earthquake and other natural disasters and evaluation of regional disaster-causing possibility; a 1c) spatial geographic information module which provides a common GIS function so as to realize construction of a localized urban disaster-affected body geographic information system property database; an 1d) urban disaster-affected body type classification and vulnerability analysis module which is used for establishing a building vulnerability module base of typical building structures under different disasters; and a 1e) disaster loss evaluation module which is used for damage prediction of disaster-affected body structures and damage loss evaluation of the disaster-affected bodies.

Description

Based on the many calamity source loss evaluating systems in city and the method for increase income architectural framework and building space database

Technical field

The present invention relates to the many calamity source loss evaluating systems in a kind of city based on EclipseRCP framework technology and method. A series of decision-makings such as restoration and reconstruction after the rapid evaluation lost for disaster in the supporting body strengthening reconstruction under venture analysis before calamity, calamity and optimize allocation rescue material and facility, calamity are provided important help by this system.

Background technology

In recent years, the risk loss evaluating system of various purposes and the development building-up work of building space database has been carried out successively. Developing into from simple functions such as early stage figure generations only at present can in the decision system of the auxiliary analysis and assessment of various specific application area. But mostly the risk loss appraisal system of great majority development is to close origin system, majority all specifically can only be analyzed on a topical application field. The risk model, the disaster data that comprise database design during owing to being limited to system development are all integrated in the software structure system that is closed source exploitation, this just determines system and is difficult to expand original function along with demand application change, is also difficult to launch disaster loss evaluation and test application in analogously district.

In addition, the existing risk loss evaluating system carried out, lays particular emphasis on seismic hazard analysis more, and less for disaster loss appraisal module research contents such as flood, disaster caused by a windstorm, tsunamis, various geologic hazard combines in the analysis of system in also not to be had. In addition popular some increase income Framework Architecture as, EclipseRCP framework, SpringOSGi framework and HibernateORM framework etc. are all for earthquake disaster, lack many calamity source loss evaluating system.

Summary of the invention

For the deficiencies in the prior art, technical problem to be solved by this invention is to provide the disaster-ridden evil loss evaluating system in a kind of city based on EclipseRCP framework technology and method. By this framework technology of increasing income, the city calamity risk loss evaluation and test realizing different areas that can be very fast and economic. By multiple disaster module venture analyses such as earthquake, flood, typhoons, it is achieved the city risk loss evaluation and test of a kind of disaster-ridden evil.

In order to solve the problems of the technologies described above, the present invention by the following technical solutions:

Based on the many calamity source loss evaluating systems in city of increase income architectural framework and building space database, comprising:

1a) increase income framework development platform based on the MAEviz of EclipseRCP framework: for expand flood, disaster caused by a windstorm,

Tsunami disaster loss evaluation and test module, it is achieved disaster-ridden harmful city risk loss evaluation and test;

1b) disaster information module: for flood, typhoon, earthquake natural hybridized orbit, risk analysis,

Evaluation and test region causes calamity possibility;

1c) spatial geographic information module: adopt GeoTools open source software and loss evaluation and test to increase income framework synergy, common provides common GIS function to realize the structure of the city hazard-affected body geographical information system(GIS) attribute database of localization;

1d) city hazard-affected body classification of type and vulnerability analyze module: for hazard-affected body architecture information by structure type and purposes classification, the building vulnerability model storehouse of typical building structure under the different disaster of foundation;

1e) disaster loss evaluation and test module: for the failure prediction of hazard-affected body structure with for loss of life and personal injury, directly or indirectly financial loss and the evaluation and test of hazard-affected body failure loss.

Described disaster information module, comprising:

2a) risk analysis module under typhoon disaster: for determining the wind-field model of specific region, estimates with the Maximum wind speed at the nearly center of probability and typhoon zone determining varying strength wind;

2b) flood disaster risk analyzes module: for determining flood and the city waterlogging water accumulating volume of different areas, set up flood disaster risk quantitative model;

2c) risk analysis module under earthquake disaster: for setting up place, the place model of different areas, comprises the tectonic structure distribution of different areas, tomography, clay distribution and rock stratum mechanism, and sets up seismic ground motion attenuation model;

Described hazard-affected body geographical information system(GIS) attribute database comprises:

3a) urban architecture attributive character geographical information system(GIS) attribute database: gather the longitude and latitude residing for building in each administrative region, structure type, building importance, construction applications, construction age, build the number of plies, floor area of building, layer are high, build plane and vertical planning drawing;

3b) road traffic attributive character geographical information system(GIS) attribute database: gather the structure type of bridge or road, build age, particular location coordinate and material information; Provinces and cities' main line in the 1:50000 numeral line data that simultaneously contain 1:250000 numeral line data, cover disaster key area, digital raster map data, place name annotation data, disaster emphasis monitoring area, rural area highway, important population center data and the disaster area ground mulching type extracted by remote sensing image data and elevation, grade information breath;

3c) emergency resources attributive character geographical information system(GIS) attribute database: gather the particular location of emergency management and rescue force distribution and rescue facility, build age, importance and vulnerability information; Rescue goods and materials deposit storehouse, rescue medical strength and distribution, emergency shelter space distribution information.

Described city hazard-affected body classification of type and vulnerability are analyzed module and are comprised:

4a) flood building vulnerability analyzes module: set up urban flooding building Damage rate curve model with buildings value, the depth of water and buildings lost value 3 key elements;

4b) typhoon disaster building vulnerability analyzes module: for calculating building structure vulnerability predictor, sets up the single, double typhoon disaster fragility curves model across Lightweight Steel Construction industry mill building frame structure of typical case;

4c) earthquake disaster building vulnerability analyzes module: the fragility curves choosing typical reinforced concrete frame structure, the distribution probability of the various collapse states that prediction reinforced concrete frame structure building occurs in earthquake.

Based on the many calamity source loss evaluating methods in city of increase income architectural framework and building space database, comprise the following steps:

5a) expansion is increased income framework development platform based on the MAEviz of EclipseRCP framework, expansion flood, disaster caused by a windstorm, tsunami disaster loss evaluation and test module, it is achieved the loss evaluation and test of disaster-ridden harmful city risk;

5b) to flood, typhoon, earthquake natural hybridized orbit, risk analysis, evaluation and test region causes calamity possibility;

5c) adopt GeoTools open source software and loss evaluation and test to increase income framework synergy, common provide common GIS function to realize the structure of the city hazard-affected body geographical information system(GIS) attribute database of localization;

5d) to hazard-affected body architecture information by structure type and purposes classification, the building vulnerability model storehouse of typical building structure under the different disaster of foundation;

5e) to the failure prediction of hazard-affected body structure, for loss of life and personal injury, directly or indirectly financial loss and the evaluation and test of hazard-affected body failure loss.

Wherein step 5b) further comprising the steps:

6a) under typhoon disaster, risk analysis mainly comprises: a1) determine the wind-field model of specific region, with the Maximum wind speed estimation at the nearly center of probability and typhoon zone of determining varying strength wind; A2) not enough and have impact on the region of wind field modeling for history typhoon observed data, the method dropping on the THE MAXIMUM WIND SPEED OF TYPHOON data in each region by statistics is carried out analyzed area and is caused calamity possibility;

6b) flood disaster risk analysis mainly comprises: b1) determine flood and the city waterlogging water accumulating volume of different areas;

B2) for lacking flood and waterlogging water accumulating volume observational data data area, by region annual peak flood, flood season rainfall, water surface area ratio, cities and towns area ratio, terrain slope are carried out calculating heavy rain, underlying produce the conditional parameter that confluxes; B3) according to above-mentioned data analysis, flood disaster risk quantitative model is set up.

6c) under earthquake disaster, risk analysis mainly comprises: c1) set up the place model of different areas, comprise the tectonic structure distribution of different areas, tomography, clay distribution and rock stratum mechanism; C2) for the area lacking seismologic record, by peak value ground motion parameter, response spectrum etc. being studied, the ground motion parameter in this region is synthesized; C3) by carrying out existing earthquake data analyzing research, seismic ground motion attenuation model is set up.

Wherein step 6b) further comprising the steps: select heavy rain flood risk index, elevation, topographic relief amplitude, river density, vegetation coverage, geologic hazard risk level, the density of population, agriculture value, road direction density, all GDP, agricultural land proportion, fiscal revenue, farmers' per capita's income, the medical treatment insured number of industrial injury, hospital's sick bed position, medical aid personnel, agriculture, forestry, water conservancy financial input and health care financial input totally 19 influence factors, set up flood risk assessment storm intensity model:

N_rain=P_i*F_i(1)

In formula: N_rainFor typhoon heavy rain hazard index; P_iFor the outcross probability of more than daily maximum rainfall amount i (mm) occurs in study area; F_iFor the generation frequency more than average annual daily maximum rainfall amount i (mm), above strength model is used to draw the typhoon heavy rain hazard index of more than daily maximum rainfall amount 50mm, the typhoon heavy rain hazard index of each meteorological site is loaded into ANUSPLIN model simultaneously, and introducing the three thin dish smoothing splines in variablees local using elevation as concomitant variable, to carry out spatial interpolation discrete, changed by index, obtain the dangerous layer of corresponding Flood inducing factors, finally utilize ARCGIS nature breakpoint staging to be 5 grades by the risk regionalization of each Flood inducing factors.

Wherein step 5c) further comprising the steps: adopt GoogleEarth to carry out collection and the calibration of remote sensing image; ARCmap is used to be calibrated by the remote sensing image of the picture form obtained; Through registration gained remote sensing image due to do not comprise concrete geography information and building relevant data, by ArcGISArcCatalo software carry out architectural vector obtain building data information carry out attributes edit, add ID, structure type, number of plies attribute, last in ArcMap, load attribute information that the vector that the above-mentioned remote sensing image carrying out registration carries out remote sensing image edits each building, to corresponding building input ID, structure type, number of plies attribute information, complete image vector to obtain facet vector data.

Compared with prior art invent useful effect as follows:

(1) based on the framework of increasing income of MAEviz loss appraisal platform, localization urban architecture object space database is built.

System integration buildings distribution density, building structural materials characteristic data are integrated, open towards the public, support the Development control area sharing type database, for city based on the vacancy offer contribution of the building attributive character geography information attribute database in disaster loss appraisal field and is supported.

(2) GoogleEarth and ArcGIS research and development are realized utilizing to gather the method for building data information.

Baidu's map panorama technology and ArcGIS research and development are utilized to gather the method for building data information. Compare remote sensing map clearly by intercepting, described, transform and the methods such as check of sampling on the spot, obtain fairly perfect urban architecture engineering information data, thus greatly reduce based on the expense needed for the means such as on the-spot investigation and remote sensing image.

(3) realize the Java algorithm based on the building vulnerability computation model under typhoon, losses due to flood and waterlogging analysis to realize.

The corresponding analysis module provided by typhoon, losses due to flood and waterlogging analysis is illustrated and algorithm logic, with the algorithm programming model needed for Java language expansion and definition Realization analysis. Owing to domestic disaster loss risk assesses the deficiency accumulated experience, find disaster hazardness quantitative model and the building vulnerability function of applicable this area all very difficult, it is necessary to be solved through probability-statistics calculating with building vulnerability function by the hazard model inquired about with arrange external similar region or close regional distribution.

(4) original framework of increasing income is expanded, it is achieved the disaster-ridden harmful loss appraisal module in city.

Framework is analyzed in module dynamic integrity based on framework of increasing income, flood, typhoon etc. can be expanded disaster module dynamic integrity in the architectural framework of original disaster loss appraisal system, by providing corresponding analysis module to illustrate and algorithm logic, it is achieved self-defined theoretical analysis module integration is incorporated into system and increases income in framework.

Accompanying drawing explanation

Fig. 1 overall system system structure framework and data stream figure;

The danger distribution of Fig. 2 typhoon heavy rain flood Flood inducing factors;

Fig. 3 house and inner property Damage rate curve;

Fig. 4 BP neural network structure;

The low layer business/industrial building structure fragility curves of Fig. 5;

Fig. 6 structure fragility curves;

The disaster-ridden harmful loss appraisal system dynamics function expansion system assumption diagram in Fig. 7 city;

Fig. 8 is based on the loss appraisal analysis process figure of CRM;

Fig. 9 expanding of system function point dynamic publishing schema;

Figure 10 OSGI system assumption diagram;

Figure 11 client platform framework EclipseRCP system assumption diagram.

Embodiment

System of the present invention comprises increase income framework development platform, disaster information module, spatial geographic information module, city hazard-affected body classification of type and vulnerability of the MAEviz based on EclipseRCP framework and analyzes module, disaster loss appraisal module.

Increase income framework development platform based on the MAEviz of EclipseRCP framework: for expanding flood, disaster caused by a windstorm, tsunami disaster loss evaluation and test module, it is achieved the loss evaluation and test of disaster-ridden harmful city risk; MAEviz is the earthquake earthquake risk assessment platform of a framework of increasing income of Sino-U.S.'s earthquake research centre and country's supercomputing application center cooperative development.

Disaster information module: for flood, typhoon, earthquake natural hybridized orbit, risk analysis, evaluation and test region causes calamity possibility; Mainly it is unfolded as follows from flood, typhoon, earthquake natural hybridized orbit module:

1, under typhoon disaster, risk analysis module mainly comprises: the wind-field model 1) determining specific region, estimates with the Maximum wind speed at the nearly center of probability and typhoon zone determining varying strength wind;2) not enough and have impact on the region of wind field modeling for history typhoon observed data, the method dropping on the THE MAXIMUM WIND SPEED OF TYPHOON data in each region by statistics is carried out analyzed area and is caused calamity possibility;

2, flood disaster risk analysis module mainly comprises: flood and the city waterlogging water accumulating volume 1) determining different areas; 2) for lacking flood and waterlogging water accumulating volume observational data data area, by region annual peak flood, flood season rainfall, water surface area ratio, cities and towns area ratio, terrain slope etc. carry out calculate heavy rain, underlying product confluxes conditional parameter; 3) according to above-mentioned data analysis, flood disaster risk quantitative model is set up.

3, under earthquake disaster, risk analysis module mainly comprises: 1) the place model of different areas, comprises the tectonic structure distribution of different areas, tomography, clay distribution and rock stratum mechanism etc.; 2) for the area lacking seismologic record, by peak value ground motion parameter, response spectrum etc. being studied, the ground motion parameter in this region is synthesized; 3) by carrying out existing earthquake data analyzing research, seismic ground motion attenuation model is set up.

Spatial geographic information module adopts GeoTools open source software and loss evaluation and test to increase income framework synergy, common provides common GIS function to realize the structure of the city hazard-affected body geographical information system(GIS) attribute database of localization;

This attribute database function comprises such as display city landforms digital elevation model, administrative area under one's jurisdiction geographical information system(GIS) attribute database that builds each, realizes vector element object by gathering city, different zones geographical position hazard-affected body attributive character information, point region based on the query analysis of geographical space and search function.

Realize the city hazard-affected body data information of different-format:

1, urban architecture attributive character geographical information system(GIS) attribute database

Gather the longitude and latitude residing for building in each administrative region, structure type, building importance, construction applications, construction age, build the number of plies, floor area of building, layer are high, the building attribute data construct such as plane and vertical planning drawing building geographical information system(GIS) attribute database.

2, road traffic attributive character geographical information system(GIS) attribute database

Traffic lifeline attributes feature comprises the structure type of bridge or road etc., builds the information such as age, particular location coordinate and importance; 1:50000 numeral line data, digital raster map data, the place name annotation data simultaneously contain 1:250000 numeral line data (comprising water system, boundary, traffic, landform etc.), covering disaster key area; Provinces and cities' main line in network of highways data comprise disaster emphasis monitoring area, rural area highway; Important population center data and the disaster area ground mulching type extracted by remote sensing image data and elevation, grade information etc.

3, emergency resources attributive character geographical information system(GIS) attribute database

This attributive character database comprises the particular location of emergency management and rescue force distribution and rescue facility, builds the information such as age, importance and vulnerability; The information such as spatial distribution of rescue goods and materials deposit storehouse, rescue medical strength and distribution thereof, emergency shelter.

City hazard-affected body classification of type and vulnerability analyze module: for hazard-affected body architecture information by structure type and purposes classification, the building vulnerability model storehouse of typical building structure under the different disaster of foundation; Comprise: flood building vulnerability analyzes module: set up urban flooding building Damage rate curve model with buildings value, the depth of water and buildings lost value 3 key elements;

Typhoon disaster building vulnerability analyzes module: for calculating building structure vulnerability predictor, sets up the single, double typhoon disaster fragility curves model across Lightweight Steel Construction industry mill building frame structure of typical case;

Earthquake disaster building vulnerability analyzes module: the fragility curves choosing typical reinforced concrete frame structure, the distribution probability of the various collapse states that prediction reinforced concrete frame structure building occurs in earthquake.

The vulnerability of building is defined as building structure under the disaster influence such as given typhoon Maximum wind speed, given intensity earthquake action, given flood grade size and flood regional geography absolute altitude, reaches the conditional probability of predetermined inefficacy state. Vulnerability analyzes the building vulnerability model storehouse of module by typical building structure under the different disaster of foundation, provides calculating and decision support for predicting the distribution probabilities of the various collapse states that these buildings occur in various disaster.

Disaster loss appraisal module: support the disaster loss appraisal under flood, typhoon, the disaster-ridden harmful scene of earthquake: comprise the disasters danger data according to input, hazard-affected body vulnerability data and attributive character spatial distribution data and carry out the failure prediction evaluation and test of hazard-affected body structure and evaluate and test for loss of life and personal injury, directly or indirectly financial loss and hazard-affected body failure loss.

Further describe the concrete system architecture of the present invention and method below:

General architecture framework and data stream figure, as shown in Figure 1. The overall analytical work flow process of this diagrammatic representation general architecture framework and loss appraisal and interior data input and output flow to. Achieve and the module dynamic integrity analysis framework of framework of increasing income based on EclipseRCP (EclipseRichClientPlatform) expands structure. RCP is a developing plug platform framework, this kind of extensibility mechanism ensure that the expansion disaster module such as flood, typhoon dynamic integrity can be incorporated in the framework of increasing income of original disaster evaluating system and go, and the system overhead that RCP application runs can not be increased, provide the custom option of a lot of user interface simultaneously, support various disaster expansion self-defined to respective system interface presentation layer of module, it is also possible to add all kinds of algorithm and analytical procedure according to the physical logic layer that this characteristic is each expansion module.

After hazard-affected body architecture information is classified by structure type and purposes, select relevant vulnerability model to the destruction assessment of scenario of building structure under given disaster effect, and calculate directly and indirect economic loss based on this structure deteriorate result. Therefore, the classification of devastated, city architecture information and structural information perfect are the calamity source loss appraisal tasks important with prediction. The structural information gathering every building in detailed collection region must be gathered before building attribute database, this is a huge engineering, as obtain building geographical position, highly, area, the construction age, three dimensional mass distribution and the two dimension parameter such as floor plan. Data gathering mainly through selecting GoogleEarth and GIS technology to describe, transform and the method such as on the-spot investigation obtains that the longitude and latitude residing for the building in survey region, structure type, building importance, construction applications, construction age, the building number of plies, floor area of building, layer are high, the building attribute data such as plane and vertical planning drawing.

Key step comprises: adopt GoogleEarth to carry out collection and the calibration of remote sensing image; ARCmap is used to be calibrated by the remote sensing image of the picture form obtained; Through registration gained remote sensing image due to do not comprise concrete geography information and building relevant data, by ArcGISArcCatalo software carry out architectural vector obtain building data information carry out attributes edit, add the attributes such as ID, structure type, the number of plies, last in ArcMap, load attribute information that the vector that the above-mentioned remote sensing image carrying out registration carries out remote sensing image edits each building, to corresponding building input ID, structure type, etc. attribute information, complete image vector to obtain facet vector data.

Building data are the most important parts of Evaluation of Earthquake, detailed data data (such as construction applications, structure type, height etc.) can make assessment result more accurately rationally, the building data file of system needs to adopt shapefile form, and building data comprise data title, data have a talk about bright and data type. According to analyzing the levels of precision required, building data parameters is divided into three levels: extremely important, important, generally important. Building data data is more complete, and corresponding building vulnerability is more accurate, and assessment result is more accurate. As table 1 gives detailed building data parameters classification.

Data parameters classification built by table 1

Native system selects NCSAGIS card module storehouse to complete the store management obtaining attribute database. NCSAGIS card module storehouse has good data storing characteristic: by creating an abstract layer for the storage of data and the mutual of general extension point key, calling of current local data source can well be processed, it is possible to for future all kinds of on line data source connection and call. Therefore the data that native system selects this card module storehouse to build attribute information geographic information database as hazard-affected body store, management platform. NCSAGIS plug-in unit class storehouse is based upon to provide data management as main functional objective on RCP core inserter storehouse, and data management function module provides data classification, assembles, access and data are traced to the source support.

Adopt the pattern that visualized data stream drives, it is provided that a user friendly input and output assay surface. Visualized data streaming system is supported in NCSAGIS plug-in unit class storehouse based on 2.3 versions, allows user to check self-defined analytical results, process status information with patterned way. Status information provides user to check all performed analysis collection that is optional and that prepare to perform, is currently performing to analyze collection etc. In addition, NCSAGIS plug-in unit class storehouse NCSAGIS has also possessed exploitation geospatial information function OpenGeospatialConsortium [8], and this function can be achieved support by one group of open space service agreement technological standard. This open space information processing class storehouse is Geotools.Geotools Spatial information processing class storehouse and provides many GIS vector format function collection and contain ESRIShape file and other GML, WFS, PostGIS, OracleSpatial, ArcSDE, MySQL, GeoMedia, Tiger, VPF, andMIF formatted file. more powerful is that it provides the powerful support to several grid format file to comprise ESRIArcInfo, ASCIIGrid form, GRASSASCIIGrid form too, geo-referencedimage form and WMS form.

Disaster information module:

1) risk analysis under flood

Heavy rain refers to that the water yield dropping to ground in air reaches and more than the rainfall of 50mm every day. Heavy rain comes soon, the force of rain violent, and persistence heavy rain and concentrated extra torrential rain, very easily form flood especially on a large scale, and heavy rain flood is the complex system of a various factors coupling. Typhoon heavy rain flood risk has diversity and the feature of fuzzy property, it is by many multifactor impacts such as Flood inducing factors, pregnant calamity environment, hazard-affected body, and multiplicity, complicacy, uncertainty, inaccuracy are inherently existed for multifactor geography information, sensor information and Statistical information, interaction present stage more difficult realization really between the origin cause of formation of quantitative analysis typhoon heavy rain flood mechanism and each influence factor, builds typhoon heavy rain flood quantitative model by disaster Systems Theory and still needs further investigation.According to current research, the technology of calamity source zoning and model roughly can be classified as the statistics of geological disaster situation model, probability Distribution Model and simple factor Additive Model. For this reason Miao Qi dragon wait from multi-angle, high precision carry out flood risk assessment and modeling in conjunction with model of fuzzy synthetic evaluation for region, Hangzhou based on GIS, ArcGIS spatial analysis technology; TYPHOON PRECIPITATION data according to this locality, select the Risk Evaluation Factors bigger with typhoon heavy rain flood relational degree, multi-source, magnanimity grid data analysis is carried out based on GIS technology, consider from the outcross probability of Flood inducing factors intensity and the generation frequency two aspect and build model of fuzzy synthetic evaluation, solve research in the past and cannot embody the problem of the frequency feature of typhoon heavy rain.

Consider the present Research of above model, select heavy rain flood risk index, elevation, topographic relief amplitude, river density, vegetation coverage, geologic hazard risk level, the density of population, agriculture value, road direction density, ground is GDP all, agricultural land proportion, fiscal revenue, farmers' per capita takes in, the medical treatment insured number of industrial injury, hospital's sick bed position, medical aid personnel, agriculture, forestry, water conservancy financial input and health care financial input totally 19 influence factors, the more difficult collection of factor of influence data related to, realize its analytical algorithm program and dynamic integrity in the Analysis of Disaster Loss model bank in disaster loss appraisal system framework. form theory according to model, it be model called after flood risk assessment storm intensity model, consider the frequency two aspect with occurring from outcross probability:

N_rain=P_i*F_i(1)

In formula: N_rainFor typhoon heavy rain hazard index; P_iFor the outcross probability of more than daily maximum rainfall amount i (mm) occurs in study area; F_iFor the generation frequency more than average annual daily maximum rainfall amount i (mm).

Above Flood inducing factors strength model is used to draw typhoon heavy rain hazard index (table 2) of more than daily maximum rainfall amount 50mm, the typhoon heavy rain hazard index of each meteorological site is loaded into ANUSPLIN model simultaneously, and introducing the three thin dish smoothing splines in variablees local using elevation as concomitant variable, to carry out spatial interpolation discrete, changed by index, obtain the dangerous layer (Fig. 2) of corresponding Flood inducing factors. ARCGIS nature breakpoint staging (NaturalBreaks.Jenks) is finally utilized to be 5 grades by the risk regionalization of each Flood inducing factors.

Table 2 typhoon heavy rain causes calamity Hazard rank

Based on the above-mentioned research about flood risk assessment storm intensity model, in conjunction with the analysis requirement of Framework Software of increasing income, it is possible to expand this model and entered in original architectural framework by plug-in unit point function i ntegration. Module is analyzed according to above-mentioned flood risk assessment storm intensity model of setting up. Analytic explanation is as shown in table 3, includes ID, title and analyzes description etc.

Table 3 flood storm intensity model analytic explanation file

2) risk analysis under earthquake disaster

Earthquake risk analysis is on basis survey region Seismic activity rule and geological tectonic conditions etc. fully realized, compare accurately predict this region may meet with earthquake in the future after earthquake situation. Region is compared accurately predict this region may meet with earthquake in the future after earthquake situation. Region is carried out earthquake risk analysis, it is necessary to carry out the work of three aspects:

1, determine place that in following for some time, earthquake occurs, intensity etc., namely determine the ground motion parameter such as focus and earthquake magnitude;

2, according to the basic data such as historical summary and STRONG MOTION DATA, the attenuation law of ground vibrations is understood;

3, region earthquake risk analysis is carried out according to place condition (soil layer effect, site effection).

When carrying out earthquake risk and analyze, it is necessary to shake, when prediction varying strength earthquake is spread from earthquake centre to surrounding, the rule constantly decayed with the increase of epicentral distance. Ground motion parameter comprises seismic intensity, basement rock peak accelerator, peak value speed and peak displacement etc. Current seismic attenuation model mainly contains the attenuation model of seismic intensity based on earthquake intensity and the seismic ground motion attenuation model based on ground motion parameter (peak accelerator, peak displacement and spectral displacement etc.).

Owing to domestic macroseism observational data is deficienter, research about seismic ground motion attenuation model is not also very ripe, existing prediction of earthquake calamity method is general only relevant with seismic intensity, and seismic intensity itself is a very fuzzy concept, it is possible to bring bigger error. Comparatively speaking, decay model has advance, it is possible to be applied to the Forecast and simulation of the strong earthquake motion field of China preferably. Theoretical formula is:

Ln (Y)=f_mag+f_dis+f_site(2)

Wherein: Y is earthquake motion peak acceleration, peak value speed or response spectrum; Mag is that earthquake magnitude affects item; Disf is that earthquake propagation distance affects item; Sitef is local site effects on ground motion item; Detailed regression coefficient gets the matching value under a large amount of strong seismologic record.

Based on the research of the seismic risk assessment seismic ground motion attenuation model after above-mentioned correction, in conjunction with the analysis requirement of Framework Software of increasing income, it is possible to expand this model and entered in original architectural framework by plug-in unit point function i ntegration. Module is analyzed according to above-mentioned flood risk assessment storm intensity model of setting up. Analytic explanation is as shown in table 4, includes ID, title and analyzes description etc.

Table 4 seismic ground motion attenuation model analysis supporting paper

3) risk analysis under typhoon disaster

This model theory is based on very big wind speed statistic data, and root sets very big wind speed maximum value as the 33.1m/s of 8807 typhoons, and minimum value is the 8.5m/s of 6423 typhoons. Then, by the very big wind speed of all previous typhoon gale first carries out the optimization process of information distribution, the recycling histogram side estimation technique estimates probability distribution. EASYFIT software is used to carry out data analysis, draw generalized extreme value model (GEV) this data distribution form comparatively identical, and have passed the A.D inspection of 95% confidence level, obtain 3 parameters of its generalized extreme value distribution, according to generalized extreme value distribution (GEV) definition, if the distribution function of stochastic variable x is:

$H(\mathrm{\χ},\mathrm{\μ},\mathrm{\σ},\mathrm{\ϵ})=\exp \{-{(1+\frac{\mathrm{\χ}-\mathrm{\μ}}{\mathrm{\σ}})}^{-1/\mathrm{\ϵ}}\}---\left(3\right)$

Wherein, 1+ �� (��-��)/�� > 0; ��, �� �� R; �� > 0

City hazard-affected body classification of type and vulnerability analyze module:

Different calamity kind has varying strength parameter, and thus vulnerability curve also has different expression form.

1) urban flooding disaster building vulnerability analysis

The most ripe in current study on flood is that depth of water calamity damages (rate) curve, and the calamity of water speed and flood flooding time is damaged (rate) curve and also expanded to some extent. Also some parameter as polluted, deposit, wash away, stormy waves, speed of rising etc. also loss (rate) can be had an impact, but also do not goed deep into systematic study. In addition, for different hazard-affected bodies, defining dissimilar vulnerability curve, land use type, building (comprising inner property) are two kinds of research objects of greatest concern. This project is studied urban architecture its vulnerability function as main research hazard-affected body and is realized algorithm model for it.

When disaster occurs, hazard-affected body might not lose completely, and its degree of damage utilizes vulnerability curve to describe. Vulnerability analysis calculates hazard-affected body fragility curves exactly and is again vulnerability function, or make calamity damage (rate) function or calamity damage (rate) curve, it is form or the curve of the relation represented between each calamity kind of varying strength and loss (rate).Loss is compared with rate of loss, and the disaster loss under varying strength is very big with regional change in time. Ground object structure and value characteristic by region affect. For disaster loss, disaster rate of loss weighs the ratio that penalty values accounts for total value, more can embody the affected degree in region and comparatively stable, possess the region of similar features or be the key of Disaster Economy costing bio disturbance and risk assessment by the foundation of disaster vulnerability curve. Domestic existing achievement in research, urban flooding building Damage rate curve model, model is using buildings value, the depth of water and buildings lost value as 3 key elements setting up urban flooding building fragility curves modeling, within 2008, southern big flood carries out on the basis of survey, investigation and analysis according to 92 sampled datas, set up the relation built with inner property Damage rate and different water depth, by putting the calamity damage curve painted sample and build, as shown in Figure 3. Knowing by figure, relative to building itself, the vulnerability of inner property is bigger.

Propose to weigh with this new ideas index of structure military service reliability building structure vulnerability method under the flood of assessment based on BP neural network model. Numerous owing to affecting the factor of structural reliability, and containing a large amount of uncertainties and fuzzy message. Therefore, it is very difficult to founding mathematical models carries out reliability evaluation. At present, for the evaluation of existing structure reliability, researchist has proposed some assessment methods. But these methods are applied in engineering practice mostly also exists some unworkable problems. Yang Chunxia proposes, and BP neural network has stronger learning functionality, can simulate preferably. Therefore adopting BP neural network model expert reasoning, the research also in addition with the reliability evaluation that unceasing study makes the open function of the continuous refinement of knowledge carry out flood damage building is attempted.

BP network is three layers of feedforward network, i.e. input layer, hidden layer and output layer, as shown in Figure 4. If input layer L_AThere is m node, output layer L_CThere is n node, hidden layer L_BInterstitial content be u, W_irFor the connection weight between input layer to hidden layer neuron; V_rjFor the connection weight between hidden layer neuron to output layer neurone.

Node in hidden layer exports function: b_r=f (W^TX-��), r=1, ��, u (4)

The output function of output layer interior joint is: c_j=f (V^TB-��), j=1, ��, n (5)

Activation function adopts Sigmoid function: $f\left(x\right)=\frac{1}{1+e^{-x}}---\left(6\right)$

Basic BP algorithm is based on the fastest gradient descent method, and the correction of its network weight can be described as:

$\▿{\mathrm{\ω}}^{k+1}=-\mathrm{\η}\frac{\∂E_k}{\∂{\mathrm{\ω}}^{\left(k\right)}}---\left(7\right)$

$e_k=(1/2m)\underset{i=1}{\overset{m}{\Σ}}{\[d_i^{\left(k\right)}-y_i^{\left(k\right)}\]}^2---\left(8\right)$

Wherein, �� ��^(k+1)Representing the correction amount of weight vector when revising for k+1 time, �� is study rate, e_kFor error function during kth time, n is that neural network exports node number,WithI-th that is respectively neural network with reference to exporting and actual output.

$e_{MSE}=(1/N)p_g\underset{i=1}{\overset{N}{\Σ}}\underset{j=1}{\overset{c}{\Σ}}{(y_{ji}-{\hat{y}}_{ji})}^2---\left(9\right)$

Wherein, N makes the sample number of training set; y_jiIt is the idea output of jth the network output node of i-th sample,It it is the real output value of jth the network output node of i-th sample; C is network output neuron number. Build vulnerability analysis modeling under flood can attempt selecting on above-mentioned studies in China basis, carry out optimum selecting in conjunction with actual geographic region, Hangzhou city and buildings actual state or revise in conjunction with external correlative study data.

2) city typhoon disaster building vulnerability analysis

Building structure vulnerability predictor is calculated for typhoon disaster hazardness analytical results, select 1-3 layer business/industrial building structure fragility curves (such as Fig. 5) that Unanwa provides, again in conjunction with the characteristic distributions of China's architecture structure form, set up the single, double vulnerability curve model across Lightweight Steel Construction industry mill building frame structure of typical case, and vulnerability curve is implanted MAEviz, for the realization of other structure type vulnerability modules of risk assessment region lays the foundation.

The upper border of this damage wave band curve model can think that the buildings wind of minimum wind force proofing design damages function, and lower boundary can think that the buildings wind of maximum wind force proofing design damages function, disaster wave band higher limit is defined as respectively with lower value to be set up to a series of building and connects characteristic high and minimum relevant probability of failure, as shown in formula (10), as long as this model shows that there is at least one possibility setting up generation inefficacy will cause buildings to suffer the damage of certain degree.

$DD\left(1\right)=\underset{i=1}{\overset{n}{\Σ}}{P}_{fi}\left({\mathrm{CCF}}_i\right){\mathrm{\α}}_i---\left(10\right)$

Damage roughly and estimated amount of damage can be obtained fast according to the above-mentioned direct statistical method based on colony's building or scarce vulnerability model, meet the accuracy requirement that reference frame is provided as government's decision-making, it is possible to for arranging that corresponding preparation is carried out in disaster relief work.

3) earthquake disaster building vulnerability analyzes city

The seismic vulnerability analysis of structure is structure under given intensity earthquake action, reaches that the conditional probability vulnerability of predetermined inefficacy state is traditional to be defined as under a certain specific seismic intensity effect, and structure suffers the probability that specific state damages. Namely under peak accelerator, spectrum acceleration, spectral displacement etc. act on, the conditional probability of structural member or thrashing. Earthquake causes the forfeiture with using function of collapsing of buildings to be loss of life and personal injury, building destruction, direct and indirect economy social loss major cause, therefore different buildingss is carried out vulnerability analysis, can be used for shaking the prediction of front disaster on the one hand, scientifically instruct the design of building; Also can be used for shaking rear loss appraisal on the other hand, for earthquake disaster damage assessment provides theoretical foundation. Owing to reinforced concrete frame structure is the main body of urban architecture, predict the distribution probability of the various collapse states that these buildings occur in earthquake, it is possible to use the analytical results of IDA, according to ultimate limit state definition, obtain the seismic vulnerability analysis of structure.

The probability of failure of structure I O, LS and CP state is tried to achieve according to chosen place seismic wave peak accelerator record and the IDA data that obtain:

Use (IO) immediately

$P_{IO}=\mathrm{\φ}\left\{\frac{\ln \[0.0269{\left(PGA\right)}^{0.81171}/0.01\]}{0.6723}\right\}---\left(11\right)$

Life security (LS)

$P_{LS}=\mathrm{\φ}\left\{\frac{\ln \[0.0269{\left(PGA\right)}^{0.81171}/0.02\]}{0.6723}\right\}---\left(12\right)$

Prevent collapse (CP)

$P_{CP}=\mathrm{\φ}\left\{\frac{ln\[0.0269{\left(PGA\right)}^{0.81171}/0.04\]}{0.6723}\right\}---\left(13\right)$

And draw fragility curves as shown in Figure 6

Building vulnerability is a part very important in Evaluation of Earthquake, different vulnerability functions is adopted for different building structure, the loss situation of each class formation can be assessed scientifically and rationally, project chooses the fragility curves of typical reinforced concrete frame structure, the implementation method of building vulnerability module is analyzed, for the vulnerability module of other different types of structure, different building age structure realizes providing method.

Disaster-ridden harmful loss appraisal module

Native system is increased income based on MAEviz expansion on structure system structure, has very big handiness and elasticity, and expansion module is loaded by Dynamic Recognition in core classes Registry local cache storage storehouse by being registered to, as shown in Figure 7. NCSAGIS geography information layer provides conventional GIS function, also provide some conventional extension points, such as space database, data storage method, 2D and 3D view, Local or Remote analysis execution, situational variables type, unit and Conversion of measurement unit, basis geometricdrawing etc. simultaneously; Loss appraisal layer (MAEviz layer) layer plug is mainly used in the application of science algorithm, it provides the extension point of different types of data (building, bridge, lifeline, disaster, vulnerability etc.) and different analysis type (loss and displacement etc.). Existing analysis module can be added new data set by MAEviz, passes through import wizard, it is possible to use the analysis module of new data set and software is well closely linked by family.Based on EclipseRCP (EclipseRichClientPlatform) increase income framework module dynamic integrity analyze framework, flood, typhoon etc. can be expanded disaster module dynamic integrity in the architectural framework of original disaster loss appraisal system, by providing, corresponding analysis module illustrates and algorithm logic the expansion disaster module such as flood, typhoon, it is achieved self-defined theoretical analysis module integration is incorporated into system and increases income in framework.

System analyzes Frame Design realization based on the loss appraisal of CRM, by the circulation analysis and assessment process of the dynamic complex relation between the reason to main matter and disaster management, result and disaster mitigation, as shown in Figure 8. CRM is a framework mode being used for performing loss appraisal, mainly assesses and loss is reduced to an acceptable level as goal in research, and aid decision making person reasonably assesses disaster loss and formulates scheme of preventing and reducing natural disasters. CRM is by probing into following three problems, it is possible to decision maker is placed on comprehensive position and takes scientific and reasonable decision-making scheme, for reasonably assessing disaster loss and formulate scheme of preventing and reducing natural disasters. In figure, " result " refers to that earthquake or other natural disasteies (disaster caused by a windstorm, flood) are on engineering, economy and social caused various impacts; " disaster affects minimumization " refers to that consequence natural disaster being caused by various measure (reinforcing engineering structure, strengthen regulation on life-network and alleviate social influence) reaches minimumization.

System dynamics expansion runs kernel and realize, concrete function expansion process based in EclipseRCP based on OSGI [26] service-oriented, assembly dynamic publishing, such as Fig. 9, and localization expansion schema. OSGI in EclipseRCP (EclipseRichClientPlatform) runs kernel [15] based on service-oriented, assembly dynamic publishing technology, as shown in Figure 10. Be that the various application based on RCP provide a safety and stability, have the open source code platform that can expand integrated development environment by means of the interior kernel normal form of OSGI and plug-in architecture, and MAEviz be exactly on RCP integrated exploitation and become. Therefore, MAEviz also has Highly Scalable and increasing income property, this for expansion MAEviz increase income framework realize localization disaster-ridden harmful loss appraisal system provide basis.

RCP is the Development Framework platform towards JAVA application jointly researched and developed such as IBM, Motorola, Sybase, Cisco and US National Aeronautics and Space Administration (NASA) by many global IT companies. The thousands of expansion plugin comprised in RCP ensure that MAEviz integrated in this frame foundation framework applications of increasing income can obtain coming from the state-of-the-art technology support of all these software service providers, and this is that the disaster-ridden harmful loss appraisal system of exploitation localization has established extremely important technical foundation. RCP allows to build the various application with this platform and other various instrument Seamless integration-, and restarts without the need to system and just can run dynamically on various hardware platform with applying by new expansion service, as shown in figure 11. RCP platform is except the small-sized kernel in inside and worktable block, and all the other card module groups having various specific function by one group set up jointly. By means of the plug-in unit class load mechanism of RCP, each plug-in unit group not only can also can be expanded according to application demand change and be inherited by independent operating dynamically. This kind of plug-in unit mechanism also allows by definition expansion node makes definition for how inheriting this plug-in unit of expansion. Standard control tool kit (SWT) in RCP is one and developer can be helped to build complete routine user interface (UI)

The card module of framework, can allow program easily design by it, customize out menu, tool bar, skeleton view, editing machine, option arrange and the UI outward appearance interface such as Shiftable window.The machine standard control that SWT is directly connected with operating system by calling realizes unified UI interface user's application program of the real cross-platform operation that developer is completely transparent. Worktable Workbench is exactly one group of plug-in unit set realized by the design of SWT standard control tool kit. Kernel when being exactly the Eclipse operation based on open service gateway initiative framework (OpenServiceGatewayinitiative is called for short OSGI) under worktable, by means of the service of OSGI, it is possible to help RCP framework dynamic expansion card module of increasing income to provide support. OSGI supports that service when expending minimum operation adds carrier technology, is only just loaded in internal memory during service when plug-in unit needs operation function to provide, and farthest decreases application and expend.

Claims (8)

1., based on the many calamity source loss evaluating systems in city of increase income architectural framework and building space database, comprising:

1a) increase income framework development platform based on the MAEviz of EclipseRCP framework: for expanding flood, disaster caused by a windstorm, tsunami disaster loss evaluation and test module, it is achieved the loss evaluation and test of disaster-ridden harmful city risk;

1b) disaster information module: for flood, typhoon, earthquake natural hybridized orbit, risk analysis, evaluation and test region causes calamity possibility;

1c) spatial geographic information module: adopt GeoTools open source software and loss evaluation and test to increase income framework synergy, common provides common GIS function to realize the structure of the city hazard-affected body geographical information system(GIS) attribute database of localization;

1d) city hazard-affected body classification of type and vulnerability analyze module: for hazard-affected body architecture information by structure type and purposes classification, the building vulnerability model storehouse of typical building structure under the different disaster of foundation;

1e) disaster loss evaluation and test module: for the failure prediction of hazard-affected body structure with for loss of life and personal injury, directly or indirectly financial loss and the evaluation and test of hazard-affected body failure loss.

2. the many calamity source in a kind of city based on increase income architectural framework and building space database lose evaluating system as claimed in claim 1, it is characterised in that described disaster information module, comprising:

2a) risk analysis module under typhoon disaster: for determining the wind-field model of specific region, estimates with the Maximum wind speed at the nearly center of probability and typhoon zone determining varying strength wind;

2b) flood disaster risk analyzes module: for determining flood and the city waterlogging water accumulating volume of different areas, set up flood disaster risk quantitative model;

2c) risk analysis module under earthquake disaster: for setting up place, the place model of different areas, comprises the tectonic structure distribution of different areas, tomography, clay distribution and rock stratum mechanism, and sets up seismic ground motion attenuation model.

3. the many calamity source in a kind of city based on increase income architectural framework and building space database lose evaluating system as claimed in claim 1, it is characterised in that described hazard-affected body geographical information system(GIS) attribute database comprises:

3a) urban architecture attributive character geographical information system(GIS) attribute database: gather the longitude and latitude residing for building in each administrative region, structure type, building importance, construction applications, construction age, build the number of plies, floor area of building, layer are high, build plane and vertical planning drawing;

3b) road traffic attributive character geographical information system(GIS) attribute database: gather the structure type of bridge or road, build age, particular location coordinate and material information; Provinces and cities' main line in the 1:50000 numeral line data that simultaneously contain 1:250000 numeral line data, cover disaster key area, digital raster map data, place name annotation data, disaster emphasis monitoring area, rural area highway, important population center data and the disaster area ground mulching type extracted by remote sensing image data and elevation, grade information breath;

3c) emergency resources attributive character geographical information system(GIS) attribute database: gather the particular location of emergency management and rescue force distribution and rescue facility, build age, importance and vulnerability information; Rescue goods and materials deposit storehouse, rescue medical strength and distribution, emergency shelter space distribution information.

4. the many calamity source in a kind of city based on increase income architectural framework and building space database lose evaluating system as claimed in claim 1, it is characterised in that described city hazard-affected body classification of type and vulnerability are analyzed module and comprised:

4a) flood building vulnerability analyzes module: set up urban flooding building Damage rate curve model with buildings value, the depth of water and buildings lost value 3 key elements;

4b) typhoon disaster building vulnerability analyzes module: for calculating building structure vulnerability predictor, sets up the single, double typhoon disaster fragility curves model across Lightweight Steel Construction industry mill building frame structure of typical case;

4c) earthquake disaster building vulnerability analyzes module: the fragility curves choosing typical reinforced concrete frame structure, the distribution probability of the various collapse states that prediction reinforced concrete frame structure building occurs in earthquake.

5., based on the many calamity source loss evaluating methods in city of increase income architectural framework and building space database, comprise the following steps:

5a) expansion is increased income framework development platform based on the MAEviz of EclipseRCP framework, expansion flood, disaster caused by a windstorm, tsunami disaster loss evaluation and test module, it is achieved the loss evaluation and test of disaster-ridden harmful city risk;

5b) to flood, typhoon, earthquake natural hybridized orbit, risk analysis, evaluation and test region causes calamity possibility;

5c) adopt GeoTools open source software and loss evaluation and test to increase income framework synergy, common provide common GIS function to realize the structure of the city hazard-affected body geographical information system(GIS) attribute database of localization;

5d) to hazard-affected body architecture information by structure type and purposes classification, the building vulnerability model storehouse of typical building structure under the different disaster of foundation;

5e) to the failure prediction of hazard-affected body structure, for loss of life and personal injury, directly or indirectly financial loss and the evaluation and test of hazard-affected body failure loss.

6. the many calamity source loss evaluating methods in the city based on increase income architectural framework and building space database, it is characterised in that wherein step 5b) further comprising the steps:

6a) under typhoon disaster, risk analysis mainly comprises: a1) determine the wind-field model of specific region, with the Maximum wind speed estimation at the nearly center of probability and typhoon zone of determining varying strength wind; A2) not enough and have impact on the region of wind field modeling for history typhoon observed data, the method dropping on the THE MAXIMUM WIND SPEED OF TYPHOON data in each region by statistics is carried out analyzed area and is caused calamity possibility;

6b) flood disaster risk analysis mainly comprises: b1) determine flood and the city waterlogging water accumulating volume of different areas; B2) for lacking flood and waterlogging water accumulating volume observational data data area, by region annual peak flood, flood season rainfall, water surface area ratio, cities and towns area ratio, terrain slope are carried out calculating heavy rain, underlying produce the conditional parameter that confluxes; B3) according to above-mentioned data analysis, flood disaster risk quantitative model is set up;

6c) under earthquake disaster, risk analysis mainly comprises: c1) set up the place model of different areas, comprise the tectonic structure distribution of different areas, tomography, clay distribution and rock stratum mechanism; C2) for the area lacking seismologic record, by peak value ground motion parameter, response spectrum etc. being studied, the ground motion parameter in this region is synthesized;C3) by carrying out existing earthquake data analyzing research, seismic ground motion attenuation model is set up.

7. the many calamity source loss evaluating methods in the city based on increase income architectural framework and building space database, it is characterized in that wherein step 6b) further comprising the steps: select heavy rain flood risk index, elevation, topographic relief amplitude, river density, vegetation coverage, geologic hazard risk level, the density of population, agriculture value, road direction density, ground is GDP all, agricultural land proportion, fiscal revenue, farmers' per capita takes in, the medical treatment insured number of industrial injury, hospital's sick bed position, medical aid personnel, agriculture, forestry, water conservancy financial input and health care financial input totally 19 influence factors, set up flood risk assessment storm intensity model:

N_rain=P_i*F_i(1)

In formula: N_rainFor typhoon heavy rain hazard index; P_iFor the outcross probability of more than daily maximum rainfall amount i (mm) occurs in study area; F_iFor the generation frequency more than average annual daily maximum rainfall amount i (mm), above strength model is used to draw the typhoon heavy rain hazard index of more than daily maximum rainfall amount 50mm, the typhoon heavy rain hazard index of each meteorological site is loaded into ANUSPLIN model simultaneously, and introducing the three thin dish smoothing splines in variablees local using elevation as concomitant variable, to carry out spatial interpolation discrete, changed by index, obtain the dangerous layer of corresponding Flood inducing factors, finally utilize ARCGIS nature breakpoint staging to be 5 grades by the risk regionalization of each Flood inducing factors.

8. the many calamity source loss evaluating methods in the city based on increase income architectural framework and building space database, it is characterised in that wherein step 5c) further comprising the steps: adopt GoogleEarth to carry out collection and the calibration of remote sensing image; ARCmap is used to be calibrated by the remote sensing image of the picture form obtained; Through registration gained remote sensing image due to do not comprise concrete geography information and building relevant data, by ArcGISArcCatalo software carry out architectural vector obtain building data information carry out attributes edit, add ID, structure type, number of plies attribute, last in ArcMap, load attribute information that the vector that the above-mentioned remote sensing image carrying out registration carries out remote sensing image edits each building, to corresponding building input ID, structure type, number of plies attribute information, complete image vector to obtain facet vector data.