Urban earthquake disaster situation risk degree and loss big data assessment platform

Abstract

According to the urban earthquake disaster situation risk degree and loss big data evaluation platform, modeling is carried out from three aspects of earthquake disaster-inducing factor dangerousness, disaster-bearing body vulnerability and urban earthquake disaster situation response capability, and a big data analysis method based on multi-index decision is adopted to evaluate the earthquake risk degree of an earthquake-prone city. A GIS technology is integrated and combined with an earthquake damage loss matrix evaluation method based on historical big data, an earthquake disaster situation loss evaluation system is established, and the earthquake disaster situation loss condition in a region is evaluated and calculated. Before an earthquake occurs, the risk degree or probability that the region may suffer from the earthquake is analyzed and predicted, the government is helped to scientifically and effectively carry out urban development planning and disaster prevention and reduction decision making, the regional disaster prevention capability construction is improved, and the earthquake disaster situation loss is reduced; after an earthquake occurs, the emergency rescue and response capability is improved, the distribution and scheduling of disaster relief materials are facilitated, and the method has practical significance and effect on regional earthquake risk degree and loss assessment.

Description

Technical Field

[0001] The present application relates to an earthquake risk and loss assessment platform, and in particular to an urban earthquake disaster risk and loss big data assessment platform, belonging to the field of geographic disaster big data assessment technology. Background Art

[0002] Earthquakes are highly destructive, posing a serious threat to life and property. Analyzing and predicting the extent of damage to a city and the probability or likelihood of loss risk before an earthquake strikes can help governments effectively implement urban development planning and disaster prevention and mitigation decisions, enhance regional disaster preparedness, and thus effectively reduce earthquake losses. After an earthquake strikes, rapid and accurate earthquake loss assessments serve as a crucial basis for government decision-making in disaster relief efforts. They can help disaster relief authorities efficiently and effectively dispatch and distribute relief supplies, enhance the emergency response capabilities of rescue personnel, and serve as a crucial factor in mobilizing social forces for disaster relief. Therefore, conducting urban earthquake risk assessments and earthquake loss assessments is of great practical significance.

[0003] The problems that need to be solved in the existing earthquake disaster risk and loss assessment technologies and the key technical difficulties of this application include:

[0004] (1) After a huge destructive earthquake occurs, the government needs to understand the scale, size and damage of the earthquake in a short period of time so that it can quickly and effectively implement earthquake relief and emergency rescue work. The scale of the earthquake disaster and the losses in the affected area are important foundations and scientific bases for the government to formulate disaster relief and rescue strategies. However, the existing technology cannot quickly and accurately conduct scientific loss assessments of the affected area after the earthquake occurs, which is very unfavorable for the government to implement rescue plans, accept international aid and restore and rebuild the disaster area in the future. The existing technology obtains detailed disaster investigation results and then conducts accurate loss assessments, which not only requires a lot of manpower and material resources, but also wastes a lot of precious earthquake relief time. It cannot meet the needs of the government to formulate emergency measures and implement earthquake relief and disaster relief decisions. The existing technology cannot conduct rapid and accurate loss assessments of earthquake-stricken areas. The accuracy and timeliness of urban earthquake disaster risk and loss big data assessments are relatively poor. It is impossible to formulate appropriate earthquake relief and disaster relief strategies based on the loss assessment results in the shortest time after the earthquake occurs.

[0005] (2) The existing technology lacks a reliable and accurate urban earthquake risk assessment method, lacks historical information on near-source earthquakes in the city, cannot select a reasonable earthquake intensity attenuation model, does not calculate the equivalent total energy based on the simulation model of magnitude and energy and the distance between the earthquake source and the city, and does not use the equivalent total energy to construct the earthquake hazard factor risk index; lacks the use of regional urban population density, urban built-up area per unit area, and urban gross domestic product (GDP) per unit area to construct the urban earthquake disaster vulnerability index; lacks the use of relevant factors such as local fiscal budget revenue, number of hospital beds, per capita road area, and per capita disposable income to construct the disaster response capability index. There is a lack of comprehensive consideration of the three indicators of earthquake hazard factor risk index, vulnerability index of hazard-bearing body, and urban disaster response capability index to establish an urban earthquake risk assessment system. The existing technology lacks an accurate earthquake loss assessment method and cannot accurately assess earthquake losses.

[0006] (3) The existing technology lacks an earthquake risk assessment platform and lacks the comprehensive use of geographic information systems and network platform construction related technologies to establish an earthquake risk assessment platform in combination with the earthquake damage matrix loss assessment method based on historical earthquake damage data. The existing technology system does not use the earthquake damage matrix loss assessment method based on historical earthquake damage data for assessment work, resulting in a cumbersome and complicated method, requiring a high level of detail in the data of the disaster-prone body, and the assessment results are relatively rough. It is not possible to select the corresponding house earthquake damage matrix based on the loss ratio of different types of buildings to conduct a rapid assessment of earthquake losses. It is impossible to analyze and predict the degree of earthquake damage to the city and the probability or possibility of loss risk before the earthquake occurs. It cannot help the government to scientifically and effectively carry out urban development planning and disaster prevention and mitigation decisions, cannot improve regional disaster prevention capacity building, and cannot reduce earthquake disaster losses. It is impossible to quickly and accurately assess the earthquake disaster losses after the earthquake occurs, and it is impossible to provide a decision-making basis for government departments to carry out disaster relief and rescue. It is impossible to help disaster relief management departments to efficiently and reasonably dispatch and distribute disaster relief materials, and it is impossible to improve the emergency rescue and response capabilities of rescue personnel to earthquake disasters. It is not conducive to the government mobilizing social forces for disaster relief and cannot effectively reduce earthquake disaster casualties and losses. Summary of the Invention

[0007] This application models earthquake risk in earthquake-prone cities using a big data analysis method based on multi-indicator decision-making. By integrating GIS technology with a historical big data-based earthquake loss matrix assessment method, an earthquake loss assessment system is established to assess and calculate regional earthquake losses. A reliable and accurate urban earthquake risk and loss assessment method is established, along with a stable and efficient WebGIS-based earthquake loss assessment platform. This platform analyzes and predicts the risk or probability of a regional earthquake before an earthquake occurs, helping governments effectively implement urban development planning and disaster prevention and mitigation decisions, improving regional disaster prevention capacity and reducing earthquake losses. After an earthquake, rapid and accurate earthquake loss assessment results serve as a crucial basis for government decision-making in disaster relief efforts, enhancing the emergency response capabilities of rescue personnel and facilitating the distribution and dispatch of disaster relief supplies. This platform has significant practical significance for regional earthquake risk assessment and loss assessment.

[0008] To achieve the above technical effects, the technical solutions adopted in this application are as follows:

[0009] The urban earthquake disaster risk and loss big data assessment platform includes:

[0010] A) Urban Earthquake Disaster Risk Assessment: Using data from regional historical earthquake damage information, an earthquake intensity attenuation model is established. Based on simulation models of magnitude and energy, as well as the distance from the epicenter to the city, the equivalent total energy is calculated. This equivalent total energy is then used to construct an earthquake hazard risk index. The regional population density, urban construction area per unit area, and GDP per capita are used to construct an urban earthquake disaster vulnerability index. The local fiscal budget revenue, number of hospital beds, per capita road area in the city, and per capita disposable income are used to construct an urban disaster response capability index. Finally, the impact of the three factors—the hazard risk index, the vulnerability index, and the urban disaster response capability index—is comprehensively calculated to assess the urban earthquake risk.

[0011] B) Earthquake damage loss matrix assessment based on historical big data: B1) Calculate regional earthquake parameters; B2) Based on this information, establish a seismic motion attenuation relationship model to derive regional earthquake intensity zoning; B3) Based on the earthquake intensity zoning results and combined with regional socioeconomic data, determine exposure data for each intensity zone, including affected area, population, and number of houses of different structures; B4) Develop a corresponding housing damage matrix based on the proportion and number of different housing types in the region to conduct a rapid assessment of earthquake damage losses;

[0012] C) Constructing a WebGIS-based earthquake loss assessment platform: This system combines the urban earthquake damage matrix loss assessment method based on decision-making big data to establish an earthquake risk assessment system, using the SpringMVC framework and the Bootstrap adaptive style for the front-end interface design; C1) Calculating the scope of the earthquake disaster and obtaining regional geological and topographic data; C2) Calculating the loss results based on the regional earthquake parameters using the earthquake damage matrix loss assessment method; C3) Calculating and summarizing the loss results.

[0013] Preferably, earthquake disaster multi-index decision-making big data analysis: divide the index factors affecting earthquake risk assessment into multiple levels, use multi-index decision-making big data analysis, and adopt multi-index and multi-level fuzzy evaluation methods to conduct risk assessment;

[0014] 1) Establish decision-making factors

[0015] The disaster, characteristics and the value of the characteristics in the earthquake disaster multi-objective decision-making scheme are represented by an ordered triple R mn To express:

[0016]

[0017] Where: M represents the disaster, C represents the characteristic or evaluation index, and x is the value given by the evaluation index;

[0018] 2) Calculate the membership function

[0019] For the decision of the larger the better type of indicators in the disaster indicators:

[0020]

[0021] Regarding the decision of the smaller the better type of indicators in the disaster indicators:

[0022]

[0023] Among them: U ji Indicates the degree of membership of each indicator, maxx ji and minx ji They represent the maximum and minimum values ​​of the corresponding values ​​of each indicator in the multi-indicator decision-making scheme respectively;

[0024] 3) Transformation from membership degree to correlation number

[0025] The conversion of the membership of each indicator to the correlation coefficient is the correlation transformation. The correlation coefficient and the membership function of the indicator are equivalent. The correlation coefficient ε ji By membership degree U ji OK, that is:

[0026] ε ji =Uji (j=1,2,…,n;i=1,2,…,m) Equation 4

[0027] 4) Establish an analysis model

[0028] Through the transformation of formula 4, the membership function is used to replace the value of the correlation coefficient, and the composite big data R of the correlation coefficient is established accordingly. ε ,Right now:

[0029]

[0030] 5) Determine weights and obtain multi-criteria decision-making solutions

[0031] Using W i Represents the weight of the i-th indicator of each decision-making plan, R W The composite factor representing the weight of each evaluation indicator is obtained:

[0032]

[0033] Finally, the weighted average is used to centrally process the calculated correlation between the decision-making schemes to achieve the final decision R of the earthquake disaster multi-index evaluation scheme. k ,Right now:

[0034] R k =R W ×R ε Formula 8

[0035] Obtain a multi-index decision-making plan for earthquake disasters.

[0036] Preferably, earthquake hazard assessment: when analyzing earthquake hazard, use regional historical earthquake damage data to establish an earthquake intensity attenuation model, calculate the equivalent total energy based on the simulation model of magnitude and energy, and finally use the equivalent total energy to construct an earthquake hazard factor hazard index to describe the earthquake hazard in the region, comprehensively considering the possibility of earthquakes of various magnitudes in the region. The specific steps are as follows:

[0037] Step 1: Calculate historical earthquake damage data in the region, including earthquake occurrence time, magnitude, and source latitude and longitude attributes. Perform a hazard analysis based on the region as a whole, and introduce an equivalent magnitude factor calculated from the magnitudes of all historical earthquakes in the region.

[0038] Step 2: After calculating and organizing historical earthquake damage data by administrative region, calculate the distance of each earthquake from the city center. Based on the impact of the distance between the earthquake source and the city on the city, establish the earthquake intensity attenuation relationship 9:

[0039] I=c1+c2M-c3ln(r+u) Formula 9

[0040] Where: c1, c2, c3 and u are semi-empirical constants, c1 = 4.524, c2 = 1.443, c3 = 1.844, u = 16, r is the distance from the earthquake source to the city center;

[0041] According to the distance between the epicenter and the city center, the attenuated earthquake intensity is calculated according to formula 9, and then the attenuated magnitude is calculated by taking r as zero according to the formula.

[0042] Step 3: Calculate the number of earthquakes in each region according to the magnitude of the earthquake. The number of earthquakes is expressed as N. i (i=4.0,4.1,4.2,…) indicates;

[0043] Step 4: Simulation model based on magnitude and energy:

[0044] E=10 1.5M+4.8 Formula 10

[0045] Calculate the equivalent total energy of all historical earthquake disasters in the area:

[0046]

[0047] Step 5: Calculate the equivalent magnitude factor of the region according to formula 10:

[0048]

[0049] Among them It is the equivalent magnitude factor. The equivalent magnitude factor is used to evaluate the urban earthquake risk and obtain the corresponding calculation chart. The ArcGIS thematic map is used to produce the urban earthquake risk assessment map.

[0050] Preferably, the vulnerability assessment of the hazard-prone body is carried out from three aspects: the physical exposure degree of the hazard-prone body, the sensitivity of the population, and the regional economic index;

[0051] 1) Physical exposure: The physical exposure of the hazard-bearing body is analyzed using the urban built-up area within the region;

[0052] 2) Population sensitivity: Population sensitivity analysis is performed using population density per unit area;

[0053] 3) Regional economic index: The GDP index per unit area within the region is used to analyze the regional economic index;

[0054] The larger the urban built-up area per unit area in a region, and the higher the population density per unit area, the greater the probability of damage and loss risk in the face of earthquake disasters. The higher the per capita GDP in a region, the stronger the earthquake resistance of the buildings in the region, and the lower the vulnerability of its disaster-bearing bodies.

[0055] The potential vulnerability of the city with the largest urban construction area per unit area in the region is defined as 1, and the potential vulnerability of the city with the smallest urban construction area per unit area is 0.1. The potential vulnerability of the urban construction area of ​​the city between the two is given by Equation 13;

[0056] The potential vulnerability of urban construction area per unit area is:

[0057]

[0058] Among them, A i represents the urban construction area per unit area of ​​the i-th region, V Ai represents the vulnerability index of the urban construction area per unit area in the i-th region;

[0059] The same method is used to calculate the potential vulnerability of each city's population density. The potential vulnerability of population density is:

[0060]

[0061] Among them, B i represents the population density of the ith region, V Bi represents the population density vulnerability index of the i-th region;

[0062] The calculation of the potential vulnerability index of GDP per unit area is obtained from formula 15:

[0063]

[0064] Where: C represents the GDP per capita of the ith region, Vc represents the GDP vulnerability index per capita of the ith region;

[0065] Get the potential vulnerability index V of the urban construction area per unit area A , Population density potential vulnerability index V B and the potential vulnerability index V of GDP per capita C Finally, based on the multi-index decision-making big data analysis method, the three vulnerability indices are comprehensively considered to obtain the final vulnerability index of the disaster-bearing body.

[0066] Preferably, the city's disaster response capacity is assessed by using the local government fiscal revenue, the number of hospital beds in the region, the number of urban roads per capita, and the per capita disposable income in the region to assess the city's disaster response capacity;

[0067] The disaster response capability index of a single indicator of the city with the highest local fiscal revenue in the region is defined as 1, and the disaster response capability index of a single indicator of the city with the lowest local fiscal revenue in the region is defined as 0.1. The disaster response capability of cities between the two indicators is given by Formula 16: The disaster response capability index of local fiscal revenue is:

[0068]

[0069] Among them, A i represents the local fiscal revenue of the ith region, R Ai represents the disaster response capacity index of local fiscal revenue in the i-th region;

[0070] The same method is used to obtain the disaster response capability index corresponding to the number of hospital beds in the region:

[0071]

[0072] Among them, B i represents the number of hospital beds in the i-th region, R Bi represents the disaster response capability index corresponding to the number of hospital beds in the i-th region;

[0073] Disaster response capability index corresponding to urban roads per capita in the region:

[0074]

[0075] Among them, C i represents the per capita urban roads in the ith region, R Ci represents the disaster response capability index corresponding to the per capita urban roads in the i-th region;

[0076] Disaster response capability index corresponding to per capita disposable income in the region:

[0077]

[0078] Where: D i represents the per capita disposable income of the ith region, R Di represents the disaster response capability index corresponding to the per capita disposable income of the i-th region;

[0079] Get the local fiscal revenue disaster response capability index R A , Hospital bed number disaster response capability index R B , Disaster Response Capacity Index R corresponding to per capita urban roads C And the disaster response capability index R corresponding to per capita disposable income D Afterwards, based on the multi-index decision-making big data analysis method, the four disaster response capability indices were comprehensively considered to obtain the final urban disaster response capability index.

[0080] Preferably, comprehensive urban earthquake risk assessment: obtain earthquake hazard factor risk index After the vulnerability index V of the disaster-bearing body and the urban disaster response capacity index R are calculated, the comprehensive risk level of the region is assessed by combining the three indicators.

[0081] Based on the first-level indicators in the risk assessment system, each city to be assessed is taken as a plan and a decision factor is established, which is recorded as R m,n , where C i is the i-th evaluation index, M j is the jth city;

[0082] Earthquake hazard and vulnerability of disaster-bearing bodies are indicators of the type that the larger the better, while the city's disaster emergency response capability is an indicator of the type that the smaller the better. The membership degree of each evaluation indicator is calculated and, after correlation transformation, the composite big data R is obtained. ε , get the indicator weight factor R W , and then the final comprehensive earthquake risk assessment result is obtained by the correlation formula.

[0083] Preferably, earthquake damage loss matrix assessment based on historical big data:

[0084] Step 1: Calculate regional earthquake parameters, including epicenter location, magnitude, and spatial distribution characteristics of fault ruptures at the epicenter;

[0085] Step 2: Select an appropriate earthquake intensity attenuation model based on the parameters of the earthquake motion to obtain the earthquake intensity zoning;

[0086] Step 3: Calculate the county population data of the region and the per capita housing area of ​​the county-level geographical units within the region, and calculate the total residential area data of the residents in the county-level geographical units;

[0087] Step 4: Based on the sampling survey data of houses of different structures in the region, obtain the proportion of houses of different structural types in urban and rural areas;

[0088] Step 5: Based on the earthquake intensity zoning of the region, calculate the population distribution and residential housing area of ​​each county-level geographical unit in different intensity areas. Based on the loss ratio of different types of buildings, select the corresponding house damage matrix for rapid assessment of earthquake losses.

[0089] Preferably, a WebGIS-based earthquake loss assessment platform is constructed: an earthquake disaster loss assessment platform is established based on the ArcGIS software interface and various development software and database management software, and earthquake loss results under multiple scenarios are calculated for the user-selected area based on the simulated earthquake data, and relevant reports are generated based on the calculated loss results; the overall structure of the system includes:

[0090] (1) System module composition: The earthquake disaster risk assessment system adopts the SpringMVC framework and builds an integrated WebGIS platform based on the B / S architecture. The system includes earthquake basic data management module, calculation result management module, epicenter automatic generation module, multi-scenario risk assessment module and assessment result summary module;

[0091] (2) System architecture: A multi-layer architecture model is adopted. The overall system framework consists of four layers, including data layer, platform layer, service layer and application layer. The data layer includes business data layer, spatial data layer and file data layer. The data types include spatial data and non-spatial data. Spatial data reflects the spatial location or range of the earthquake disaster at a certain point in time. Non-spatial data reflects the level of the natural disaster and descriptive information related to the loss situation. The data content includes vector data of regional risk areas, loss assessment result data, loss assessment result calculation diagram and other related data.

[0092] The platform layer includes GIS middleware and application middleware. GIS middleware is ArcGIS-related services that provide a framework for creating and configuring GIS applications and services. Application middleware includes J2EE application servers and database middleware.

[0093] The service layer encapsulates different functional implementations and generates corresponding services. Users access applications and combine different services to implement various system functions.

[0094] The application layer provides services to system users, who assess earthquake losses based on the services and functions provided by the system.

[0095] (3) Technical Architecture: The entire system is based on SOA architecture, which features componentization, interoperability, modularization, and scalability, meeting the system scalability requirements. The basic data in the data layer is managed based on Oracle 11g, and the platform layer is developed based on ArcGIS 10.1, JDK 1.8, and Tomcat 8.0. The service layer uses Web Service and Map Service to provide services. The application layer system adopts B / S mode, and users access the system through a browser.

[0096] Preferably, the detailed scheme of the earthquake loss assessment platform module based on WebGIS is divided into five parts: earthquake basic data management module, calculation result management module, epicenter automatic generation module, multi-scenario risk assessment module and assessment result summary module. The detailed scheme of each module is as follows:

[0097] (1) The earthquake basic data management module stores, manages, and browses risk area vector data, providing data support for earthquake loss assessment operations and is the basis for earthquake loss assessment;

[0098] (2) The calculation result management module manages the result data, including querying, viewing, and exporting the result data table. In the entire risk assessment business process, it generates various intermediate result data and the final assessment result summary data table, and manages the result data, including querying, viewing, and exporting the result data table.

[0099] (3) The automatic epicenter generation module uses a random algorithm to automatically generate epicenters based on the risk zone we selected and the setting conditions of earthquake-related parameters, and visualizes the generated results. After the data is stored in the database, the user selects the time and the corresponding risk zone, manually sets the number or density of epicenters, magnitude range and step size. Based on these input parameters, the system automatically generates epicenters according to the random algorithm and automatically generates event codes according to the coding method of "date-time-hazard unit number-random code", and then displays the generated results on the map;

[0100] (4) The multi-scenario risk assessment module uses a damage matrix assessment method based on historical big data to evaluate and calculate earthquake loss results, and implements related operations for querying the assessment results according to conditions. After the epicenter is generated, the system uses the damage matrix assessment model to evaluate and calculate the earthquake loss results based on the results of the selected event, and displays the calculation results under various scenarios in a table.

[0101] Preferably, earthquake disaster loss assessment:

[0102] 1) Basic data management: Before calculating regional loss assessment, you first need to import the regional vector data map into the system database. Click the "Import Data" button on the system interface, select the year and type of data to be imported, and the file to be uploaded to store the data. The imported data is a compressed rar or zip file, and its content is a shp file containing all risk areas.

[0103] 2) Automatic generation of epicenters: After importing regional data, enter the automatic generation interface of epicenters, select the risk area for earthquake loss assessment, set the number of epicenters and magnitude parameters in the area, click the "Calculate" button to calculate the epicenter, set the number of epicenters to 2, the magnitude range to 4-5, and the step parameter to 0.5, that is, there are two epicenters that affect each other in the area, and each epicenter has three earthquake modes of magnitude 4, 4.5, and 5.

[0104] 3) Multi-scenario risk assessment: After the epicenter results are generated, the resulting losses of different magnitudes of each epicenter are assessed. Select the epicenter result event obtained in the previous step and click the "Calculate" button on the system interface to obtain the assessment results of the regional losses under different magnitudes of damage at different epicenters. The results can be exported as an Excel spreadsheet and saved locally.

[0105] 4) Summary of assessment results: After the earthquake loss assessment is carried out in the region, the loss results of the same administrative unit in the region are calculated and summarized, and the corresponding calculation charts are generated. The system divides the calculation of the loss results into two parts: the overall result summary and the same magnitude result summary. Among them, the overall result summary calculates the damage of all magnitudes of earthquakes occurring at the epicenter in the region, and the same magnitude result summary calculates the loss results of different magnitudes of the epicenter in the region separately.

[0106] Compared with the existing technology, the innovation and advantages of this application are:

[0107] (1) This application models the earthquake risk factor, the vulnerability of the disaster-bearing body, and the urban earthquake disaster response capability from three aspects. It uses a big data analysis method based on multi-index decision-making to evaluate the earthquake risk of earthquake-prone cities. It integrates GIS technology with the earthquake damage loss matrix evaluation method based on historical big data to establish an earthquake disaster loss evaluation system to evaluate and calculate the earthquake disaster losses in the region. A set of reliable and accurate urban earthquake risk evaluation methods and earthquake loss evaluation methods have been established, and a stable and efficient earthquake loss evaluation platform based on WebGIS has been constructed. Before an earthquake occurs, the risk level or probability of an earthquake in a region is analyzed and predicted, helping the government to scientifically and effectively carry out urban development planning and disaster prevention and mitigation decisions, improve regional disaster prevention capacity building, and thus reduce earthquake disaster losses. After an earthquake occurs, the rapid and accurate earthquake disaster loss evaluation results are an important decision-making basis for government departments to carry out disaster relief and rescue, improve the emergency rescue and response capabilities of rescue personnel to earthquake disasters, and facilitate the distribution and dispatch of disaster relief supplies. It has important practical significance and role in regional earthquake risk assessment and earthquake loss assessment work.

[0108] (2) This application establishes an urban earthquake risk assessment system, uses regional historical earthquake damage information calculation data to establish an earthquake intensity attenuation model, calculates the equivalent total energy based on the simulation model of magnitude and energy and the distance between the earthquake source and the city, and then uses the equivalent total energy to construct an earthquake disaster factor hazard index; uses the regional population density, urban construction area per unit area and GDP per capita to construct the urban earthquake disaster vulnerability index; uses the local fiscal budget revenue, number of hospital beds, per capita road area in the city and per capita disposable income in the region to construct the urban disaster response capability index; finally, comprehensively calculates the influence of three factors: the hazard factor hazard index, the hazard vulnerability index and the urban disaster response capability index to assess the urban earthquake risk; establishes an earthquake damage loss matrix assessment based on historical big data, determines the exposure data of different intensity zones based on the earthquake intensity zoning results and combines the regional socio-economic calculation data, and establishes a corresponding house earthquake damage matrix based on the proportion and number of different types of housing in the region, which can quickly and accurately assess the earthquake disaster losses.

[0109] (3) This application establishes an earthquake loss assessment platform based on WebGIS, and combines the urban earthquake damage matrix loss assessment method based on decision-making big data to establish an earthquake risk assessment system. It is based on B / S architecture and adopts SpringMVC framework. The front-end interface is designed using Bootstrap's adaptive style, and the back-end algorithm is implemented in Java programming. By calculating the scope of the earthquake disaster, the geological and topographic data of the region are obtained. Based on the regional earthquake parameters, the loss results are calculated according to the earthquake damage matrix loss assessment method, and the loss results are calculated, summarized and analyzed. By establishing an earthquake disaster loss assessment system, the earthquake disaster loss situation in the region is evaluated and calculated, and the probability or possibility of the earthquake damage level and loss risk of the city are analyzed and predicted, which helps the government to scientifically and effectively carry out urban development planning and disaster prevention and mitigation decision-making, improve regional disaster prevention capacity building, and thus effectively reduce earthquake disaster losses. It helps disaster relief management departments to efficiently and reasonably dispatch and distribute disaster relief materials, and improves the emergency rescue and response capabilities of rescue personnel to earthquake disasters, which plays a significant role in responding to earthquake disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1 It is a technical framework diagram for urban risk assessment.

[0111] Figure 2 It is a flowchart of earthquake damage loss matrix assessment based on historical big data.

[0112] Figure 3 It is a schematic diagram of the average loss ratio of residential buildings with different degrees of damage.

[0113] Figure 4This is a schematic diagram of the earthquake risk assessment module.

[0114] Figure 5 It is an architecture diagram for building an earthquake loss assessment platform based on WebGIS.

[0115] Figure 6 This is the technical architecture diagram of the earthquake loss assessment platform system.

[0116] Figure 7 It is an automatically generated epicenter map for earthquake disaster loss assessment. DETAILED DESCRIPTION

[0117] Below, in conjunction with the accompanying drawings, the technical solution of the urban earthquake disaster risk and loss big data assessment platform provided by this application is further described so that technical personnel in this field can better understand this application and implement it.

[0118] This application models the three aspects of earthquake hazard risk, vulnerability of disaster-bearing structures, and urban earthquake disaster response capabilities. It uses a big data analysis method based on multi-indicator decision-making to assess the earthquake risk of earthquake-prone cities. It integrates GIS technology with a seismic damage loss matrix assessment method based on historical big data to establish an earthquake disaster loss assessment system to evaluate and calculate earthquake disaster losses in the region. The work of this application is as follows:

[0119] (1) Establish an urban earthquake risk assessment system to assess the risk of earthquake-prone cities. Using the disaster data of all earthquakes with a magnitude of 4 or greater in the region, as well as data related to the population density, urban construction, economic conditions, and other infrastructure construction of each city, a comprehensive urban risk assessment model is established based on a big data analysis method with multiple indicators for decision-making to assess the earthquake risk of the city.

[0120] (2) Build an earthquake loss assessment platform based on WebGIS, and combine it with the urban earthquake damage matrix loss assessment method based on decision-making big data to establish an earthquake risk assessment system. Based on the B / S architecture, the SpringMVC framework is adopted. The front-end interface is designed using the adaptive style of Bootstrap, and the back-end algorithm is implemented in Java programming.

[0121] 1. Urban Earthquake Disaster Risk Assessment

[0122] Using data from regional historical earthquake damage information, an earthquake intensity attenuation model was established. The equivalent total energy was calculated based on the simulation model of magnitude and energy and the distance between the epicenter and the city. The equivalent total energy was then used to construct the earthquake hazard factor risk index. The regional population density, urban construction area per unit area, and GDP per capita were used to construct the urban earthquake disaster vulnerability index. The local fiscal budget revenue, number of hospital beds, per capita road area in the city, and per capita disposable income were used to construct the urban disaster response capability index. Finally, the impact of the three factors of hazard factor risk index, hazard vulnerability index, and urban disaster response capability index were comprehensively calculated to evaluate the urban earthquake risk. Figure 1 This is the technical framework for urban risk assessment for this application.

[0123] (1) Big data analysis of earthquake disaster multi-indicator decision-making

[0124] The index factors affecting earthquake risk assessment are divided into multiple levels, and risk assessment is carried out using multi-index decision-making big data analysis and a multi-index and multi-level fuzzy evaluation method.

[0125] 1) Establish decision-making factors

[0126] The disaster, characteristics and the value of the characteristics in the earthquake disaster multi-objective decision-making scheme are represented by an ordered triple R mn To express:

[0127]

[0128] Where: M represents disaster, C represents characteristic or evaluation index, and x is the value given by the evaluation index.

[0129] 2) Calculate the membership function

[0130] For the decision of the larger the better indicator among the disaster indicators:

[0131]

[0132] Regarding the decision of the smaller the better type of indicators in the disaster indicators:

[0133]

[0134] Among them: U ji Indicates the degree of membership of each indicator, maxx ji and minx ji They respectively represent the maximum and minimum values ​​corresponding to each indicator in the multi-indicator decision-making scheme.

[0135] 3) Transformation from membership degree to association number

[0136] The conversion of the membership of each indicator to the correlation coefficient is the correlation transformation. The correlation coefficient and the membership function of the indicator are equivalent. The correlation coefficient ε ji By membership degree U ji OK, that is:

[0137] ε ji =U ji (j=1,2,…,n;i=1,2,…,m) Equation 4

[0138] 4) Establish an analysis model

[0139] Through the transformation of formula 4, the membership function is used to replace the value of the correlation coefficient, and the composite big data R of the correlation coefficient is established accordingly. ε ,Right now:

[0140]

[0141] 5) Determine weights and obtain multi-criteria decision-making solutions

[0142] Using W i Represents the weight of the i-th indicator of each decision-making plan, R W The composite factor representing the weight of each evaluation indicator is obtained:

[0143]

[0144]

[0145] Finally, the weighted average is used to centrally process the calculated correlation between the decision-making schemes to achieve the final decision R of the earthquake disaster multi-index evaluation scheme. k ,Right now:

[0146] R k =R W ×R ε Formula 8

[0147] Obtain a multi-index decision-making plan for earthquake disasters.

[0148] 2. Urban risk assessment based on decision-making big data

[0149] (1) Earthquake hazard assessment

[0150] When analyzing earthquake hazard, this application uses regional historical earthquake damage data to establish an earthquake intensity attenuation model. The equivalent total energy is calculated based on a simulation model of magnitude and energy. Finally, the equivalent total energy is used to construct an earthquake hazard factor hazard index to describe the earthquake hazard in the region, comprehensively considering the probability of earthquakes of various magnitudes within the region. The specific steps are as follows:

[0151] Step 1: Calculate historical earthquake damage data in the region, including earthquake time, magnitude, and source latitude and longitude attribute information, conduct a hazard analysis based on the entire region, and introduce an equivalent magnitude factor calculated from the magnitude of all historical earthquakes in the region, which is more scientific than single magnitude data.

[0152] Step 2: After calculating and organizing historical earthquake damage data by administrative region, calculate the distance of each earthquake from the city center. Based on the impact of the distance between the earthquake source and the city on the city, establish the earthquake intensity attenuation relationship 9:

[0153] I=c1+c2M-c3ln(r+u) Formula 9

[0154] Where: c1, c2, c3 and u are semi-empirical constants, c1 = 4.524, c2 = 1.443, c3 = 1.844, u = 16, r is the distance from the earthquake source to the city center;

[0155] According to the distance between the epicenter and the city center, the attenuated earthquake intensity is calculated according to formula 9, and then the attenuated magnitude is calculated by taking r as zero according to the formula.

[0156] Step 3: Calculate the number of earthquakes in each region according to the magnitude of the earthquake. The number of earthquakes is expressed as N. i (i=4.0,4.1,4.2,…) indicates;

[0157] Step 4: Simulation model based on magnitude and energy:

[0158] E=10 1.5M+4.8 Formula 10

[0159] Calculate the equivalent total energy of all historical earthquake disasters in the area:

[0160]

[0161] Step 5: Calculate the equivalent magnitude factor of the region according to formula 10:

[0162]

[0163] Among them It is the equivalent magnitude factor. The equivalent magnitude factor is used to evaluate the urban earthquake risk and obtain the corresponding calculation chart. The ArcGIS thematic map is used to produce the urban earthquake risk assessment map.

[0164] (2) Vulnerability assessment of hazard-bearing bodies

[0165] The vulnerability of the hazard-prone body is assessed from three aspects: the physical exposure of the hazard-prone body, the sensitivity of the population, and the regional economic index.

[0166] 1) Physical exposure: refers to the number and value of buildings, farmland, and other facilities within the disaster-stricken area, reflecting the total amount of disaster-prone objects that may be damaged when facing a disaster factor of a certain intensity. The greater the physical exposure, the greater the corresponding loss risk in the face of an earthquake disaster. The area of ​​urban built-up areas reflects the physical exposure of disaster-prone objects to a certain extent. This application uses the area of ​​urban built-up areas within the region to analyze the physical exposure of disaster-prone objects.

[0167] 2) Population sensitivity: It refers to the susceptibility of the population characteristics of the disaster-prone body to damage when facing earthquake disasters. It reflects the population's ability to resist the impact of disaster-causing factors. The primary task of reducing earthquake losses is to minimize casualties. Population sensitivity is related to the population density in the area. The population density per unit area is used to conduct population sensitivity analysis.

[0168] (3) Regional economic index: reflects the losses in a region under the influence of a disaster factor of a certain intensity, and also reflects the level of disaster prevention and response capacity building in the region. The GDP index per unit area in the region is used to analyze the regional economic index.

[0169] The larger the urban built-up area per unit area in a region, and the higher the population density per unit area, the greater the probability of damage and loss risk in the face of earthquake disasters. The higher the per capita GDP in a region, the stronger the earthquake resistance of the buildings in the region, and the lower the vulnerability of its disaster-bearing bodies.

[0170] The potential vulnerability of the city with the largest urban construction area per unit area in the region is defined as 1, and the potential vulnerability of the city with the smallest urban construction area per unit area is 0.1. The potential vulnerability of the urban construction area of ​​the city between the two is given by Equation 13;

[0171] The potential vulnerability of urban construction area per unit area is:

[0172]

[0173] Among them, A i represents the urban construction area per unit area of ​​the i-th region, V Ai represents the vulnerability index of the urban construction area per unit area in the i-th region;

[0174] The same method is used to calculate the potential vulnerability of each city's population density. The potential vulnerability of population density is:

[0175]

[0176] Among them, Bi represents the population density of the ith region, V Bi represents the population density vulnerability index of the i-th region;

[0177] The calculation of the potential vulnerability index of GDP per unit area is obtained from formula 15:

[0178]

[0179] Where: C represents the per capita GDP value of the i-th region, Vc represents the per capita GDP vulnerability index of the i-th region.

[0180] Get the potential vulnerability index V of the urban construction area per unit area A , Population density potential vulnerability index V B and the potential vulnerability index V of GDP per capita C Finally, based on the multi-index decision-making big data analysis method, the three vulnerability indices are comprehensively considered to obtain the final vulnerability index of the disaster-bearing body.

[0181] (3) Assessment of urban disaster response capabilities

[0182] The city's disaster response capacity is assessed using regional local government revenue, number of hospital beds, per capita urban roads, and per capita disposable income.

[0183] The disaster response capability index of a single indicator of the city with the highest local fiscal revenue in the region is defined as 1, and the disaster response capability index of a single indicator of the city with the lowest local fiscal revenue in the region is defined as 0.1. The disaster response capability of cities between the two indicators is given by Formula 16: The disaster response capability index of local fiscal revenue is:

[0184]

[0185] Among them, A i represents the local fiscal revenue of the i-th region, R Ai represents the disaster response capacity index of local fiscal revenue in the i-th region;

[0186] The same method is used to obtain the disaster response capability index corresponding to the number of hospital beds in the region:

[0187]

[0188] Among them, B i represents the number of hospital beds in the i-th region, R Bi represents the disaster response capability index corresponding to the number of hospital beds in the i-th region;

[0189] Disaster response capability index corresponding to urban roads per capita in the region:

[0190]

[0191] Among them, C i represents the per capita urban roads in the ith region, R Ci represents the disaster response capability index corresponding to the per capita urban roads in the i-th region;

[0192] Disaster response capability index corresponding to per capita disposable income in the region:

[0193]

[0194] Where: D i represents the per capita disposable income of the ith region, R Di represents the disaster response capability index corresponding to the per capita disposable income of the i-th region;

[0195] Get the local fiscal revenue disaster response capability index R A , Hospital bed number disaster response capability index R B , Disaster Response Capacity Index R corresponding to per capita urban roads C And the disaster response capability index R corresponding to per capita disposable income D Afterwards, based on the multi-index decision-making big data analysis method, the four disaster response capability indices were comprehensively considered to obtain the final urban disaster response capability index.

[0196] (4) Comprehensive urban earthquake risk assessment

[0197] Get the earthquake hazard factor risk index After the vulnerability index V of the disaster-bearing body and the urban disaster response capacity index R are calculated, the comprehensive risk level of the region is assessed by combining the three indicators.

[0198] Based on the first-level indicators in the risk assessment system, each city to be assessed is taken as a plan and a decision factor is established, which is recorded as R m,n , where C i is the i-th evaluation index, M j is the jth city;

[0199] Earthquake hazard and vulnerability of disaster-bearing bodies are indicators of the type that the larger the better, while the city's disaster emergency response capability is an indicator of the type that the smaller the better. The membership degree of each evaluation indicator is calculated and, after correlation transformation, the composite big data R is obtained. ε , get the indicator weight factor R W , and then the final comprehensive earthquake risk assessment result is obtained by the correlation formula.

[0200] 2. Earthquake Damage Loss Matrix Assessment Based on Historical Big Data

[0201] Based on the vulnerability analysis of regional buildings, the existing earthquake damage matrix was applied to evaluate the losses of various types of buildings in the region under different levels of earthquake damage. The final disaster loss results were obtained. This loss assessment method was combined with GIS technology to establish an earthquake disaster loss assessment platform based on WebGIS. The specific process includes:

[0202] Step 1: Calculate regional earthquake parameters, including epicenter location, magnitude, and spatial distribution characteristics of fault ruptures at the epicenter;

[0203] Step 2: Select an appropriate earthquake intensity attenuation model based on the parameters of the earthquake motion to obtain the earthquake intensity zoning;

[0204] Step 3: Calculate the county population data of the region and the per capita housing area of ​​the county-level geographical units within the region, and calculate the total residential area data of the residents in the county-level geographical units;

[0205] Step 4: Based on the sampling survey data of houses of different structures in the region, obtain the proportion of houses of different structural types in urban and rural areas;

[0206] Step 5: Based on the earthquake intensity zoning of the region, calculate the population distribution and residential housing area of ​​each county-level geographical unit in different intensity areas, and select the corresponding house damage matrix according to the loss ratio of different types of buildings to conduct a rapid assessment of earthquake losses. The specific assessment technical process is as follows: Figure 2 .

[0207] The assessment work includes:

[0208] (1) Classification of residential building types: Residential buildings are divided into reinforced concrete structures, brick-concrete structures, brick-wood structures and other types of structures;

[0209] (2) Socioeconomic data used: The population and urban-rural population ratio data of county-level geographical units are based on regional calculation yearbook data; the per capita housing area data of county-level geographical units are based on regional population census data; the ratio of different urban and rural housing structures is based on the results of the national 1% sample survey;

[0210] (3) Degree of housing damage and its loss ratio: The degree of earthquake damage to residential buildings is divided into five categories: basically intact, slightly damaged, generally damaged, severely damaged, and completely damaged. Different degrees of damage correspond to different loss ratios (degree of damage). Figure 3 It is the loss ratio given by the direct loss assessment of the disaster.

[0211] 3. Building an earthquake loss assessment platform based on WebGIS

[0212] Because earthquake disasters are difficult to accurately and timely forecast, quickly assessing earthquake losses is crucial for disaster relief efforts. Rapid and accurate earthquake loss assessments are not only crucial for government emergency response and decision-making, but also facilitate the efficient and rational allocation and dispatch of relief supplies by disaster relief management departments. They also serve as a crucial factor in the government's mobilization of social forces for disaster relief. Furthermore, the extent of losses is crucial for government efforts to rebuild social order in disaster-stricken areas. By combining WebGIS with an earthquake loss matrix assessment method based on historical big data, an earthquake loss assessment platform was established to assess earthquake losses.

[0213] (1) Overall structure and framework

[0214] Earthquake risk assessment is the core of earthquake risk management and control, and is a crucial foundation for preventing natural earthquake disasters and reducing and mitigating earthquake risks. The international community and countries around the world have reached a consensus on attaching importance to earthquake risk assessment and management, strengthening disaster risk warning, and particularly building early warning capabilities. This application's system establishes an earthquake loss assessment platform based on the ArcGIS software interface and various development and database management software. It calculates earthquake loss results under multiple scenarios for a user-selected area based on simulated earthquake data, and generates relevant reports based on these results.

[0215] The overall structure of the system includes:

[0216] (1) System module composition: The earthquake disaster risk assessment system adopts SpringMVC framework and builds an integrated WebGIS platform based on B / S architecture. The system includes earthquake basic data management module, calculation result management module, epicenter automatic generation module, multi-scenario risk assessment module and assessment result summary module. The specific composition structure is as follows: Figure 4 ;

[0217] (2) System architecture: adopting a multi-layer architecture model, the overall system framework consists of four layers, including: data layer, platform layer, service layer and application layer, such as Figure 5 As shown;

[0218] The data layer includes the business data layer, the spatial data layer, and the file data layer. The data types include spatial data and non-spatial data. Spatial data reflects the spatial location or range of the earthquake disaster at a certain point in time, while non-spatial data reflects the level of the natural disaster and descriptive information about the loss situation. The data content includes vector data of regional risk areas, loss assessment results data, loss assessment result calculation diagrams, and other related data.

[0219] The platform layer includes GIS middleware and application middleware. GIS middleware is ArcGIS-related services that provide a framework for creating and configuring GIS applications and services to meet various client needs. Application middleware includes J2EE application servers and database middleware, providing a good mechanism for building scalable, flexible, and maintainable business systems.

[0220] The service layer encapsulates different functional implementations and generates corresponding services. Users access applications and combine different services to implement various system functions.

[0221] The application layer provides services to system users, who assess earthquake disaster losses based on the services and functions provided by the system.

[0222] (3) Technical Architecture

[0223] The entire system is based on SOA architecture, which has the characteristics of componentization, interoperability, modularization and scalability, meeting the system scalability requirements. The system architecture diagram is as follows Figure 6 shown.

[0224] The basic data in the data layer is managed based on Oracle 11g, and the platform layer is developed based on ArcGIS10.1, JDK1.8, and Tomcat8.0. The service layer uses Web Service and Map Service to provide services. The application layer system adopts B / S mode, and users access the system through a browser.

[0225] (2) Module Detailed Plan

[0226] The system modules are divided into five parts: earthquake basic data management module, calculation result management module, epicenter automatic generation module, multi-scenario risk assessment module, and assessment result summary module. The detailed plans of each module are as follows:

[0227] (1) The earthquake basic data management module stores, manages, and browses risk area vector data, providing data support for earthquake loss assessment operations and is the basis for earthquake loss assessment;

[0228] (2) The calculation result management module manages the result data, including querying, viewing, and exporting the result data table. In the entire risk assessment business process, it generates various intermediate result data (such as epicenter generation results) and final assessment result summary data tables (such as overall result summary, same magnitude result summary, etc.). It manages the result data, including querying, viewing, and exporting the result data table.

[0229] (3) The automatic epicenter generation module uses a random algorithm to automatically generate epicenters based on the risk zone we selected and the setting conditions of earthquake-related parameters, and visualizes the generated results. After the data is stored in the database, the user selects the time and the corresponding risk zone, manually sets the number or density of epicenters, magnitude range and step size. Based on these input parameters, the system automatically generates epicenters according to the random algorithm, and automatically generates event codes according to the coding method of "date-time (accurate to minutes)-hazard unit number-random code", and then displays the generated results on the map;

[0230] (4) The multi-scenario risk assessment module uses the earthquake damage matrix assessment method based on historical big data to evaluate and calculate the earthquake loss results, and implements related operations for querying the assessment results according to conditions. After the epicenter is generated, the system uses the earthquake damage matrix assessment model to evaluate and calculate the earthquake loss results based on the results of the selected event, and displays the calculation results under various scenarios in a table;

[0231] (5) The assessment result summary module calculates and summarizes the results of the multi-scenario risk assessment, performs special calculation and analysis on the loss results, and generates corresponding calculation graphs. It is divided into two submodules: overall result summary and same-magnitude result summary. In the overall result summary submodule, after the user selects the event, if the "All indicators" checkbox is unchecked, the assessment result summary table under the non-full indicator conditions is obtained; if the "All indicators" checkbox is checked, the assessment result summary table under the full indicator conditions is obtained. In addition, the user selects a single indicator, performs special calculation and analysis on the indicator, and generates a corresponding bar graph. In the same-magnitude result summary module, after the user selects the magnitude condition, if the "All indicators" checkbox is unchecked, the same-magnitude comparison result summary under the non-full indicator conditions is obtained; if the "All indicators" checkbox is checked, the same-magnitude comparison result summary under the full indicator conditions is obtained. The assessment results of both submodules can be exported and saved locally.

[0232] (3) Earthquake Disaster Loss Assessment

[0233] 1) Basic data management: Before performing loss assessment calculations for a region, you first need to import the region's vector data map into the system database. Click the "Import Data" button on the system interface, select the year and type of data to be imported, and the file to be uploaded to store the data. The imported data is a compressed rar or zip file, and its content is a shp file containing all risk areas.

[0234] 2) Automatic generation of epicenters: After importing regional data, enter the automatic generation interface of epicenters, select the risk area for earthquake loss assessment, set the number of epicenters in the area and magnitude parameters, and click the "Calculate" button to calculate the epicenter point. Figure 7 As shown, the number of epicenters is set to 2, the magnitude range is 4-5, and the step parameter is 0.5, that is, there are two epicenters that affect each other in the area, and each epicenter has three earthquake modes of magnitude 4, 4.5, and 5.

[0235] 3) Multi-scenario risk assessment: After the epicenter results are generated, the resulting losses of different magnitudes of each epicenter are assessed. Select the epicenter result event obtained in the previous step and click the "Calculate" button on the system interface to obtain the assessment results of the regional losses under different magnitudes of damage at different epicenters. The results can be exported as an Excel spreadsheet and saved locally.

[0236] 4) Assessment result summary: After the earthquake loss assessment is completed for a region, the loss results of the same administrative unit in the region are calculated and summarized, and corresponding calculation charts are generated. The system's loss result calculation is divided into two parts: overall result summary and same-magnitude result summary. The overall result summary calculates the damage of all magnitudes of earthquakes occurring at the epicenter in the region, and the same-magnitude result summary calculates the loss results of different magnitudes of earthquakes at the epicenter in the region separately;

[0237] After summarizing the overall results of regional earthquake losses, four indicators, namely the number of collapsed rooms, the number of severely damaged rooms, the total collapsed losses, and the expected losses, are selected for single indicator calculation to obtain a bar chart of the indicator.

Claims

1. Urban earthquake disaster risk and loss big data assessment platform, characterized by: include: A) Urban Earthquake Disaster Risk Assessment: Using data from regional historical earthquake damage information, an earthquake intensity attenuation model is established. Based on simulation models of magnitude and energy, as well as the distance from the epicenter to the city, the equivalent total energy is calculated. This equivalent total energy is then used to construct an earthquake hazard risk index. The regional population density, urban construction area per unit area, and GDP per capita are used to construct an urban earthquake disaster vulnerability index. The local fiscal budget revenue, number of hospital beds, per capita road area in the city, and per capita disposable income are used to construct an urban disaster response capability index. Finally, the impact of the three factors—the hazard risk index, the vulnerability index, and the urban disaster response capability index—is comprehensively calculated to assess the urban earthquake risk. B) Earthquake damage loss matrix assessment based on historical big data: B1) Calculate the earthquake parameters of the region; B2) Based on earthquake parameter information, establish a seismic motion attenuation relationship model to obtain regional seismic intensity zoning; B3) Based on the seismic intensity zoning results and combined with regional socioeconomic data, determine exposure data for different intensity zones, including affected area, population, and number of houses of different structures. B4) Develop a corresponding housing damage matrix based on the proportion and number of different housing types in the region to conduct a rapid assessment of earthquake damage. C) Build a WebGIS-based earthquake loss assessment platform: Combine the urban earthquake damage matrix loss assessment method based on decision-making big data to establish an earthquake risk assessment system. Use the SpringMVC framework and the front-end interface with Bootstrap's adaptive style design. C1) Calculate the scope of the earthquake disaster and obtain regional geological and topographic data. C2) Calculate the loss results based on the regional earthquake parameters using the earthquake damage matrix loss assessment method; C3) Calculate, summarize and analyze the loss results.

2. The urban earthquake disaster risk and loss big data assessment platform according to claim 1 is characterized in that: Multi-index decision-making big data analysis for earthquake disasters: The index factors affecting earthquake risk assessment are divided into multiple levels, and risk assessment is performed using multi-index decision-making big data analysis and a multi-index and multi-level fuzzy evaluation method; 1) Establish decision-making factors The disaster, characteristics and the value of the characteristics in the earthquake disaster multi-objective decision-making scheme are represented by an ordered triple R mn To express: Where: M represents the disaster, C represents the characteristic or evaluation index, and x is the value given by the evaluation index; 2) Calculate the membership function For the decision of the larger the better type of indicators in the disaster indicators: Regarding the decision of the smaller the better type of indicators in the disaster indicators: Among them: U ji Indicates the degree of membership of each indicator, maxx ji and minx ji They represent the maximum and minimum values ​​of the corresponding values ​​of each indicator in the multi-indicator decision-making scheme respectively; 3) Transformation from membership degree to correlation number The conversion of the membership of each indicator to the correlation coefficient is the correlation transformation. The correlation coefficient and the membership function of the indicator are equivalent. The correlation coefficient ε ji By membership degree U ji OK, that is: ε ji = U ji (j = 1, 2, …, n; i = 1, 2, …, m) Equation 4 4) Establish an analysis model Through the transformation of formula 4, the membership function is used to replace the value of the correlation coefficient, and the composite big data R of the correlation coefficient is established accordingly. ε ,Right now: 5) Determine weights and obtain multi-criteria decision-making solutions Using W i Represents the weight of the i-th indicator of each decision-making plan, R W The composite factor representing the weight of each evaluation indicator is obtained: Finally, the weighted average is used to centrally process the calculated correlation between the decision-making schemes to achieve the final decision R of the earthquake disaster multi-index evaluation scheme. k ,Right now: R k =R W ×R ε Formula 8 Obtain a multi-index decision-making plan for earthquake disasters.

3. The urban earthquake disaster risk and loss big data assessment platform according to claim 1 is characterized in that: Earthquake hazard assessment: When analyzing earthquake hazard, we use regional historical earthquake damage data to establish an earthquake intensity attenuation model. We calculate the equivalent total energy based on a simulation model of magnitude and energy. Finally, we use this equivalent total energy to construct an earthquake hazard factor hazard index to describe the earthquake hazard in the region, comprehensively considering the probability of earthquakes of various magnitudes in the region. The specific steps are as follows: Step 1: Calculate historical earthquake damage data in the region, including earthquake occurrence time, magnitude, and source latitude and longitude attributes. Perform a hazard analysis based on the region as a whole, and introduce an equivalent magnitude factor calculated from the magnitudes of all historical earthquakes in the region. Step 2: After calculating and organizing historical earthquake damage data by administrative region, calculate the distance of each earthquake from the city center. Based on the impact of the distance between the earthquake source and the city on the city, establish the earthquake intensity attenuation relationship 9: I=c1+c2M-c3ln(r+u) Formula 9 Where: c1, c2, c3 and u are semi-empirical constants, c1 = 4.524, c2 = 1.443, c3 = 1.844, u = 16, r is the distance from the earthquake source to the city center; According to the distance between the epicenter and the city center, the attenuated earthquake intensity is calculated according to formula 9, and then the attenuated magnitude is calculated by taking r as zero according to the formula. Step 3: Calculate the number of earthquakes in each region according to the magnitude of the earthquake. The number of earthquakes is expressed as N. i (i=4.0,4.1,4.2,…) indicates; Step 4: Simulation model based on magnitude and energy: E=10 1.5M+4.8 Formula 10 Calculate the equivalent total energy of all historical earthquake disasters in the area: Step 5: Calculate the equivalent magnitude factor of the region according to formula 10: Among them It is the equivalent magnitude factor. The equivalent magnitude factor is used to evaluate the urban earthquake risk and obtain the corresponding calculation chart. The ArcGIS thematic map is used to produce the urban earthquake risk assessment map.

4. The urban earthquake disaster risk and loss big data assessment platform according to claim 1 is characterized in that: Hazard-prone body vulnerability assessment: The vulnerability of the hazard-prone body is assessed from three aspects: physical exposure, population sensitivity, and regional economic index; 1) Physical exposure: The physical exposure of the hazard-bearing body is analyzed using the urban built-up area within the region; 2) Population sensitivity: Population sensitivity analysis is performed using population density per unit area; 3) Regional economic index: The GDP index per unit area within the region is used to analyze the regional economic index; The larger the urban built-up area per unit area in a region, and the higher the population density per unit area, the greater the probability of damage and loss risk in the face of earthquake disasters. The higher the per capita GDP in a region, the stronger the earthquake resistance of the buildings in the region, and the lower the vulnerability of its disaster-bearing bodies. The potential vulnerability of the city with the largest urban construction area per unit area in the region is defined as 1, and the potential vulnerability of the city with the smallest urban construction area per unit area is 0.

1. The potential vulnerability of the urban construction area of ​​the city between the two is given by Equation 13; The potential vulnerability of urban construction area per unit area is: Among them, A i represents the urban construction area per unit area of ​​the i-th region, V Ai represents the vulnerability index of the urban construction area per unit area in the i-th region; The same method is used to calculate the potential vulnerability of each city's population density. The potential vulnerability of population density is: Among them, B i represents the population density of the ith region, V Bi represents the population density vulnerability index of the i-th region; The calculation of the potential vulnerability index of GDP per unit area is obtained from formula 15: Where: C represents the GDP per capita of the ith region, Vc represents the GDP vulnerability index per capita of the ith region; Get the potential vulnerability index V of the urban construction area per unit area A , Population density potential vulnerability index V B and the potential vulnerability index V of GDP per capita C Finally, based on the multi-index decision-making big data analysis method, the three vulnerability indices are comprehensively considered to obtain the final vulnerability index of the disaster-bearing body.

5. The urban earthquake disaster risk and loss big data assessment platform according to claim 1 is characterized in that: Assessment of urban disaster response capacity: The city's disaster response capacity is assessed using regional local government fiscal revenue, number of hospital beds, per capita urban roads, and per capita disposable income; The disaster response capability index of a single indicator of the city with the highest local fiscal revenue in the region is defined as 1, and the disaster response capability index of a single indicator of the city with the lowest local fiscal revenue in the region is defined as 0.

1. The disaster response capability of cities between the two indicators is given by Formula 16: The disaster response capability index of local fiscal revenue is: Among them, A i represents the local fiscal revenue of the ith region, R Ai represents the disaster response capacity index of local fiscal revenue in the i-th region; The same method is used to obtain the disaster response capability index corresponding to the number of hospital beds in the region: Among them, B i represents the number of hospital beds in the i-th region, R Bi represents the disaster response capability index corresponding to the number of hospital beds in the i-th region; Disaster response capability index corresponding to urban roads per capita in the region: Among them, C i represents the per capita urban roads in the ith region, R Ci represents the disaster response capability index corresponding to the per capita urban roads in the i-th region; Disaster response capability index corresponding to per capita disposable income in the region: Where: D i represents the per capita disposable income of the ith region, R Di represents the disaster response capability index corresponding to the per capita disposable income of the i-th region; Get the local fiscal revenue disaster response capability index R A , Hospital bed number disaster response capability index R B , Disaster Response Capacity Index R corresponding to per capita urban roads C And the disaster response capability index R corresponding to per capita disposable income D Afterwards, based on the multi-index decision-making big data analysis method, the four disaster response capability indices were comprehensively considered to obtain the final urban disaster response capability index.

6. The urban earthquake disaster risk and loss big data assessment platform according to claim 1 is characterized in that: Comprehensive urban earthquake risk assessment: obtaining the earthquake hazard factor hazard index After the vulnerability index V of the disaster-bearing body and the urban disaster response capacity index R are calculated, the comprehensive risk level of the region is assessed by combining the three indicators. Based on the first-level indicators in the risk assessment system, each city to be assessed is taken as a plan and a decision factor is established, denoted as R m,n , where C i is the i-th evaluation index, M j is the jth city; Earthquake hazard and vulnerability of disaster-bearing bodies are indicators of the type that the larger the better, while the city's disaster emergency response capability is an indicator of the type that the smaller the better. The membership degree of each evaluation indicator is calculated and, after correlation transformation, the composite big data R is obtained. ε , get the indicator weight factor R W , and then the final comprehensive earthquake risk assessment result is obtained by the correlation formula.

7. The urban earthquake disaster risk and loss big data assessment platform according to claim 1 is characterized in that: Earthquake damage loss matrix assessment based on historical big data: Step 1: Calculate regional earthquake parameters, including epicenter location, magnitude, and spatial distribution characteristics of fault ruptures at the epicenter; Step 2: Select an appropriate earthquake intensity attenuation model based on the parameters of the earthquake motion to obtain the earthquake intensity zoning; Step 3: Calculate the county population data of the region and the per capita housing area of ​​the county-level geographical units within the region, and calculate the total residential area data of the residents in the county-level geographical units; Step 4: Based on the sampling survey data of houses of different structures in the region, obtain the proportion of houses of different structural types in urban and rural areas; Step 5: Based on the earthquake intensity zoning of the region, calculate the population distribution and residential housing area of ​​each county-level geographical unit in different intensity areas. Based on the loss ratio of different types of buildings, select the corresponding house damage matrix for rapid assessment of earthquake losses.

8. The urban earthquake disaster risk and loss big data assessment platform according to claim 1 is characterized by: Build an earthquake loss assessment platform based on WebGIS: Based on the ArcGIS software interface and various development software and database management software, establish an earthquake disaster loss assessment platform. Calculate earthquake loss results under multiple scenarios based on simulated earthquake data for the user-selected area, and generate relevant reports based on the loss results. The overall structure of the system includes: (1) System module composition: The earthquake disaster risk assessment system adopts the SpringMVC framework and builds an integrated WebGIS platform based on the B / S architecture. The system includes earthquake basic data management module, calculation result management module, epicenter automatic generation module, multi-scenario risk assessment module and assessment result summary module; (2) System architecture: A multi-layer architecture model is adopted. The overall system framework consists of four layers, including data layer, platform layer, service layer and application layer. The data layer includes business data layer, spatial data layer and file data layer. The data types include spatial data and non-spatial data. Spatial data reflects the spatial location or range of the earthquake disaster at a certain point in time. Non-spatial data reflects the level of the natural disaster and descriptive information related to the loss situation. The data content includes vector data of regional risk areas, loss assessment result data, loss assessment result calculation diagram and other related data. The platform layer includes GIS middleware and application middleware. GIS middleware is ArcGIS-related services that provide a framework for creating and configuring GIS applications and services. Application middleware includes J2EE application servers and database middleware. The service layer encapsulates different functional implementations and generates corresponding services. Users access applications and combine different services to implement various system functions. The application layer provides services to system users, who assess earthquake losses based on the services and functions provided by the system. (3) Technical Architecture: The entire system is based on SOA architecture, which features componentization, interoperability, modularization, and scalability, meeting the system scalability requirements. The basic data in the data layer is managed based on Oracle 11g, and the platform layer is developed based on ArcGIS 10.1, JDK 1.8, and Tomcat 8.

0. The service layer uses Web Service and Map Service to provide services. The application layer system adopts B / S mode, and users access the system through a browser.

9. The urban earthquake disaster risk and loss big data assessment platform according to claim 1 is characterized in that: The detailed scheme of the earthquake loss assessment platform module based on WebGIS is divided into five parts: earthquake basic data management module, calculation result management module, epicenter automatic generation module, multi-scenario risk assessment module and assessment result summary module. The detailed scheme of each module is as follows: (1) The earthquake basic data management module stores, manages, and browses risk area vector data, providing data support for earthquake loss assessment operations and is the basis for earthquake loss assessment; (2) The calculation result management module manages the result data, including querying, viewing, and exporting the result data table. In the entire risk assessment business process, it generates various intermediate result data and the final assessment result summary data table, and manages the result data, including querying, viewing, and exporting the result data table. (3) The automatic epicenter generation module uses a random algorithm to automatically generate epicenters based on the risk zone we selected and the setting conditions of earthquake-related parameters, and visualizes the generated results. After the data is stored in the database, the user selects the time and the corresponding risk zone, manually sets the number or density of epicenters, magnitude range and step size. Based on these input parameters, the system automatically generates epicenters according to the random algorithm and automatically generates event codes according to the coding method of "date-time-hazard unit number-random code", and then displays the generated results on the map; (4) The multi-scenario risk assessment module uses the earthquake damage loss matrix assessment method based on historical big data to evaluate and calculate the earthquake loss results, and implements related operations of querying the assessment results according to conditions. After the epicenter is generated, the system uses the earthquake damage matrix assessment model to evaluate and calculate the earthquake loss results based on the results of the selected event, and displays the calculation results under various scenarios in a list.

10. The urban earthquake disaster risk and loss big data assessment platform according to claim 1 is characterized in that: Earthquake disaster loss assessment: 1) Basic Data Management: Before performing a regional loss assessment, you must first import the regional vector data map into the system database. Click the "Import Data" button on the system interface, select the year and type of data to be imported, and select the file to be uploaded. The imported data is a compressed rar or zip file containing a shp file containing all risk areas. 2) Automatic epicenter generation: After importing the regional data, enter the automatic epicenter generation interface, select the risk area for earthquake loss assessment, set the number of epicenters in the area and the magnitude parameters, and click the "Calculate" button to calculate the epicenter. Set the number of epicenters to 2, the magnitude range to 4-5, and the step parameter to 0.

5. This means that there are two mutually influencing epicenters in the area, and each epicenter has three earthquake modes: magnitude 4, 4.5, and 5. 3) Multi-scenario risk assessment: After the epicenter results are generated, evaluate the resulting losses for each epicenter at different magnitudes. Select the epicenter result event obtained in the previous step and click the "Calculate" button on the system interface to obtain the loss assessment results for the region under different earthquake magnitudes at different epicenters. The results can be exported as an Excel spreadsheet and saved locally. 4) Summary of assessment results: After the earthquake loss assessment is carried out in the region, the loss results of the same administrative unit in the region are calculated and summarized, and the corresponding calculation charts are generated. The system divides the calculation of the loss results into two parts: the overall result summary and the same magnitude result summary. Among them, the overall result summary calculates the damage of all magnitudes of earthquakes occurring at the epicenter in the region, and the same magnitude result summary calculates the loss results of different magnitudes of the epicenter in the region separately.

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