Dynamic evaluation method and system of earthquake disaster losses based on multi-source disaster data

Abstract

The present invention provides a method and system for dynamic assessment of earthquake disaster losses based on multi-source disaster data, and relates to the technical field of data processing. The method comprises acquiring multi-source original data sets; cleaning information data sets, removing redundant information and extracting time tags, and unifying all time tags to coordinated time; screening traffic data sets, removing abnormal flow values, and extracting effective traffic flow information; performing spatial coordinate conversion on image data sets, and aligning image coordinates with the geographical coordinate system of the disaster area; calculating the traffic access index of each area, calculating the building damage rate and road access rate in the area, analyzing the access conditions and resource arrival difficulty of each area, and combining the personnel distribution and important facility location in the information data set; marking different loss levels for each area in the disaster area, and generating a dynamic assessment report. The present invention has a good dynamic assessment effect.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for dynamically evaluating earthquake disaster losses based on multi-source disaster data. Background Art

[0002] Current earthquake disaster loss assessment methods usually rely on a single data source for loss estimation, such as remote sensing data, historical earthquake loss data, or post-disaster field survey data. Traditional assessment methods predict earthquake-affected areas and losses by building mathematical models based on these data sources. However, this method is powerless when dealing with dynamically changing disaster information after an earthquake. After an earthquake disaster occurs, the environment and loss situation in the disaster area will continue to change over time. Assessments that rely on a single data source are difficult to reflect the full picture of the disaster in a timely manner, resulting in delayed assessment results, which is not conducive to supporting real-time rescue decisions.

[0003] In the existing technology of dynamic loss assessment, the way of gradually updating data mainly relies on continuously inputting newly acquired post-disaster information, such as real-time monitoring data and feedback data from the disaster area, into the model to update the assessment results. However, this process may have technical defects when processing multi-source heterogeneous data, that is, it is difficult to achieve real-time integration and consistency of data. For example, in the early post-disaster period, data sources such as information platforms, traffic monitoring, and satellite images are different, and the data formats and resolutions are also different, requiring complex preprocessing to unify the data format and coordinates. This preprocessing step not only increases the complexity of the assessment, but may also cause distortion or delay in the data during transmission, affecting the accuracy and timeliness of the disaster assessment, and is not conducive to the precise allocation of rescue resources. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for dynamic assessment of earthquake disaster losses based on multi-source disaster data, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a method for dynamic assessment of earthquake disaster losses based on multi-source disaster data comprises:

[0007] Obtain multi-source original data sets in the early post-disaster period, and format and convert the multi-source original data sets according to the data source type to generate information data sets, traffic data sets, and image data sets;

[0008] The information data set is cleaned, redundant information is removed and time tags are extracted, all time tags are unified to the coordinated time, and a standardized information data set is obtained; the traffic data set is screened, abnormal traffic values ​​are removed, valid traffic flow information is extracted, and a standardized traffic flow data set is generated; the image data set is converted into spatial coordinates, the image coordinates are aligned with the disaster area geographic coordinate system, and an aligned image data set is generated;

[0009] The traffic flow information in the standardized traffic flow dataset is divided into regions, and the traffic accessibility index of each region is calculated to obtain the traffic accessibility dataset;

[0010] Based on the traffic accessibility data set, the traffic status of the main roads in the disaster area is identified and the traffic road data of the disaster area is generated;

[0011] Based on the aligned image dataset, identify and analyze the buildings and roads in the image, compare the buildings and roads with the coordinates of the disaster area map, calculate the building damage rate and road traffic rate in the area, and generate a preliminary damage dataset;

[0012] Based on the preliminary damage data set and the road data in the disaster area, the traffic conditions and the difficulty of reaching resources in each area were analyzed to form regional loss assessment data;

[0013] Based on the regional loss assessment data, combined with the personnel distribution and important facility locations in the information dataset, a disaster area loss analysis dataset is generated, including building damage rate, facility damage information, material traffic rate, and material list;

[0014] According to the disaster area loss analysis data set, the loss distribution and resource accessibility of each region are extracted, and the loss index and impact index of the disaster area are calculated to obtain the disaster area impact assessment data set;

[0015] Dynamically compare and update the disaster area impact assessment data set with the continuously updated traffic monitoring data and information platform data to generate dynamic loss assessment data for the disaster area;

[0016] Based on the dynamic loss assessment data of the disaster area, different loss levels are marked for each area in the disaster area and a dynamic assessment report is generated.

[0017] Preferably, the traffic flow information in the standardized traffic flow data set is divided into regions, and the traffic accessibility index of each region is calculated to obtain a traffic accessibility data set, including:

[0018] Based on the geographical location of the disaster area, the standardized traffic flow dataset is spatially divided and divided according to the administrative or natural boundaries within the disaster area to generate multiple regional traffic datasets;

[0019] According to the regional traffic data set, the time series change characteristics of the traffic flow in the region are analyzed, and the traffic density at each time point is calculated to generate the traffic index. The traffic index is calculated by the following formula:

[0020] ,in, For Region Traffic access index, For Region The number of roads, For the The traffic index of each road, , For the The traffic volume of a road, For the The length of the road, is the road traffic attenuation factor, For the The congestion time of each road, For the The weight coefficient of each road, For the The obstacle factor of the road, For Region The total road area, For Region The demand for vehicles, For the The vehicle load on the road, , For Region The area of , For the The number of obstacles on the road, For the The amount of materials needed to repair a road, For the The maximum flow rate of a road;

[0021] The traffic index is divided according to the preset index threshold, and marked as unblocked area, blocked area and blocked area respectively, to obtain the regional traffic data set;

[0022] According to the regional access dataset and regional traffic dataset, the traffic status of roads in the region is identified to form a traffic accessibility dataset.

[0023] Preferably, according to the traffic accessibility data set, the traffic status of the main roads in the disaster area is identified, and the traffic road data of the disaster area is generated, including:

[0024] According to the traffic accessibility index in the traffic accessibility dataset, the traffic status of the main roads in the disaster area is identified;

[0025] According to the traffic status of the main roads, the main roads are divided into emergency rescue channels, general rescue channels and auxiliary channels, and the traffic status of each channel is marked to obtain the classification data of the main roads;

[0026] According to the classification data of the main roads, the traffic data of the disaster area is generated, and the traffic data of the disaster area includes the real-time traffic information of each main road.

[0027] Preferably, according to the aligned image dataset, buildings and roads in the image are identified and analyzed, the buildings and roads are compared with the coordinates of the disaster area map, the building damage rate and road traffic rate in the area are calculated, and a preliminary damage dataset is generated, including:

[0028] According to the aligned image data set, the spatial position of the structure in the image is identified and processed to obtain position identification data, wherein the structure includes buildings and roads;

[0029] Cross-check the location identification data with the coordinate system of the disaster area map and adjust the location identification data to ensure that the building and road information are marked on the disaster area map to obtain accurate location data;

[0030] According to the precise location data, the damage rate of each building and road is analyzed in combination with the pre-disaster image data to generate a preliminary damage data set, which includes the building damage rate, road damage rate and location marks.

[0031] Preferably, based on the preliminary damage data set and the road data in the disaster area, the traffic conditions and the difficulty of reaching resources in each area are analyzed to form regional loss assessment data, including:

[0032] The preliminary damage dataset was cross-correlated with the road data in the disaster area, and the damage severity and road accessibility of each area were analyzed to obtain the road accessibility dataset;

[0033] Regional loss assessment data is generated based on the road accessibility score dataset and the disaster area accessible road data.

[0034] Preferably, based on the regional loss assessment data, combined with the personnel distribution and important facility locations in the information data set, a disaster area loss analysis data set is generated, including building damage rate, facility damage information, material traffic rate and material list, including:

[0035] Based on the regional loss assessment data, the building damage rate and road traffic rate are comprehensively analyzed to preliminarily calculate the amount of accessible materials in each area to form a data set of accessible materials;

[0036] According to the personnel distribution information and facility damage information provided in the information data set, the resource demand information is analyzed to obtain a material list;

[0037] According to the material list and the data set of available material quantity, the material access rate is analyzed to form the material access condition data; the material access rate is calculated by the following formula:

[0038] ,in, For Region The material flow rate, For Region The number of roads, For the The amount of materials transported on each road, For the The traffic index of each road, For the The obstacle factor of the road, , For Region population, For Region The per capita material demand For the The length of the road, For the Vehicle load on each road;

[0039] Based on the data on material access conditions, combined with regional loss assessment data and information data sets, a disaster area loss analysis data set is generated, including building damage rate, facility damage information, material access rate and material list.

[0040] Preferably, based on the disaster area loss analysis data set, the loss distribution and resource accessibility of each region are extracted, and the loss index and impact index of the disaster area are calculated to obtain the disaster area impact assessment data set, including:

[0041] Based on the disaster area loss analysis data set, the resource demand and damage distribution of each area were extracted to obtain the building damage rate, facility damage information, material access rate and material list;

[0042] Calculate the loss index of each area based on the building damage rate and facility damage information;

[0043] ,in, For Region The loss index, is the building damage index, is the facility damage index, For Region The demand for materials, To estimate the rescue time, and is the weight coefficient; ,in, is the number of buildings, For Architecture The total area, For Architecture The damaged area, ,in, To repair speed, is the average vehicle speed;

[0044] According to the loss index, material flow rate and material list, the impact index is calculated to obtain the disaster area impact assessment data set;

[0045] ,in, For Region The impact index, For Region The amount of material transported, , and is the weight coefficient.

[0046] In the second aspect, a dynamic earthquake disaster loss assessment system based on multi-source disaster data includes:

[0047] The data acquisition module is used to obtain the multi-source original data set in the early stage after the disaster, and format and convert the multi-source original data set according to the data source type to generate information data set, traffic data set and image data set. The multi-source original data set includes information platform data, traffic monitoring data and satellite image data;

[0048] The data preprocessing module is used to clean the information data set, remove redundant information and extract time tags, unify all time tags to the coordinated time, and obtain a standardized information data set; perform data screening on the traffic data set, remove abnormal traffic values, extract valid traffic flow information, and generate a standardized traffic flow data set; perform spatial coordinate conversion on the image data set, align the image coordinates with the disaster area's geographic coordinate system, and generate an aligned image data set;

[0049] Traffic accessibility generation module, used to divide the traffic flow information in the standardized traffic flow data set into regions, and calculate the traffic accessibility index of each region to obtain the traffic accessibility data set;

[0050] The disaster area access road generation module is used to identify the accessibility status of the main roads in the disaster area based on the traffic accessibility data set and generate the disaster area access road data;

[0051] A preliminary damage data generation module is used to identify and analyze buildings and roads in the image based on the aligned image data set, compare the buildings and roads with the coordinates of the disaster area map, calculate the building damage rate and road traffic rate in the area, and generate a preliminary damage data set;

[0052] The regional loss assessment module is used to analyze the traffic conditions and resource access difficulties in each area based on the preliminary damage data set and the road data of the disaster area, and form regional loss assessment data;

[0053] The disaster area loss analysis module is used to generate a disaster area loss analysis data set based on the regional loss assessment data and the personnel distribution and important facility locations in the information data set, including building damage rate, facility damage information, road access rate and rescue access conditions;

[0054] The disaster area impact assessment module is used to extract the loss distribution and resource accessibility of each region based on the disaster area loss analysis data set, and calculate the loss index and impact index of the disaster area to obtain the disaster area impact assessment data set;

[0055] The disaster area dynamic loss assessment module is used to dynamically compare and update the disaster area impact assessment data set with the continuously updated traffic monitoring data and information platform data to generate dynamic loss assessment data for the disaster area;

[0056] The dynamic assessment report generation module is used to mark different loss levels for each area in the disaster area based on the dynamic loss assessment data of the disaster area and generate a dynamic assessment report.

[0057] The above solution of the present invention includes at least the following beneficial effects:

[0058] The method of the present invention integrates and standardizes the multi-source original data sets in the early post-disaster period, effectively overcoming the problem in the prior art that the evaluation results are incomplete and untimely due to the single data source. The processing efficiency and quality of multi-source data are significantly improved by formatting and converting the information platform data, traffic monitoring data and satellite image data to generate information data sets, traffic data sets and image data sets. In particular, the inconsistency problem caused by the time difference between different data sources is solved by cleaning the information data set, extracting time tags and unifying operations; the abnormal traffic in the traffic data set is eliminated to ensure the authenticity and reliability of the traffic data; the spatial coordinate conversion of the image data achieves high-precision alignment of the image and the disaster area map, laying a solid data foundation for subsequent disaster analysis.

[0059] By dividing the standardized traffic flow data set into regions, comprehensively analyzing the spatiotemporal variation characteristics of the traffic flow data and calculating the traffic accessibility index, this method generates a traffic accessibility data set that fully reflects the traffic accessibility conditions in the post-disaster area. Compared with the traditional method of evaluating only based on flow or road length, the traffic accessibility index significantly improves the accuracy of traffic evaluation. On this basis, the traffic status of the main roads in the disaster area is further identified, and the traffic road data of the disaster area is dynamically generated, providing real-time optimized road guidance for the transportation of rescue vehicles and resources.

[0060] By analyzing the aligned image dataset, this method identified and analyzed the damage to buildings and roads, and calculated the building damage rate and road traffic rate by spatially comparing the image with the disaster area map, generating a preliminary damage dataset. Compared with the existing methods that rely on field surveys or single remote sensing images, this method significantly shortens the data acquisition and analysis time, and achieves a comprehensive quantitative description of the damage in a large area of ​​the disaster area. Combining the preliminary damage dataset and the road data in the disaster area, the traffic conditions and resource access difficulties of each area were further analyzed to form regional loss assessment data, which provides a scientific basis for the subsequent allocation and priority sorting of rescue resources.

[0061] In addition, the present invention generates a disaster area loss analysis data set by combining the regional loss assessment data with the personnel distribution and important facility locations in the information data set, including building damage rate, facility damage information, material access rate and material list. The loss analysis data set integrates multidimensional data and can comprehensively reflect the loss situation and resource needs of the disaster area, providing key support for the accurate allocation of rescue resources. Based on the loss distribution and resource accessibility of each region extracted from the loss analysis data set, this method calculates the loss index and impact index of the disaster area and generates a disaster area impact assessment data set. Through the impact assessment data set, the specific impact of the disaster on different regions can be intuitively quantified to ensure that rescue work can quickly focus on priority areas.

[0062] To ensure the dynamic adaptability of disaster assessment, this method uses the continuous update of traffic monitoring data and information platform data to dynamically compare and update the disaster area impact assessment data set to generate dynamic loss assessment data for the disaster area. Dynamic loss assessment data can reflect the latest changes in the disaster situation in real time, providing scientific support for the rescue decision-making of the command center. Finally, a dynamic assessment report was generated by marking different loss levels in each area of ​​the disaster area. This report shows the loss distribution and rescue priority of the disaster area in an intuitive form, providing comprehensive support for post-disaster resource allocation and subsequent recovery work.

[0063] Compared with the existing technology, the innovation of the present invention lies in that it greatly improves the comprehensiveness and real-time performance of disaster assessment through the real-time integration and dynamic update mechanism of multi-source data, and solves the assessment bias problem caused by inconsistent data and delayed updates in traditional methods. Through the full process automation processing steps, the scientificity and efficiency of rescue resource allocation are significantly improved, and the speed and effect of post-disaster response are fully optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a flowchart of a method for dynamic earthquake disaster loss assessment based on multi-source disaster data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0066] like Figure 1 As shown, an embodiment of the present invention proposes a method for dynamic assessment of earthquake disaster losses based on multi-source disaster data, the method comprising:

[0067] S100, obtaining a multi-source original data set in the early stage after the disaster, and formatting and converting the multi-source original data set according to the data source type to generate an information data set, a traffic data set, and an image data set, wherein the multi-source original data set includes information platform data, traffic monitoring data, and satellite image data;

[0068] S200, cleaning the information data set, removing redundant information and extracting time tags, unifying all time tags to the coordinated time, and obtaining a standardized information data set; filtering the traffic data set, removing abnormal traffic values, extracting effective traffic flow information, and generating a standardized traffic flow data set; performing spatial coordinate conversion on the image data set, aligning the image coordinates with the disaster area geographic coordinate system, and generating an aligned image data set;

[0069] S300, dividing the traffic flow information in the standardized traffic flow data set into regions, and calculating the traffic accessibility index of each region to obtain a traffic accessibility data set;

[0070] S400, identifying the traffic status of the main roads in the disaster area according to the traffic accessibility data set, and generating traffic road data in the disaster area;

[0071] S500, identifying and analyzing buildings and roads in the image according to the aligned image data set, comparing the buildings and roads with the coordinates of the disaster area map, calculating the building damage rate and road traffic rate in the area, and generating a preliminary damage data set;

[0072] S600, analyzing the traffic conditions and resource access difficulties of each area based on the preliminary damage data set and the road data of the disaster area, and forming regional loss assessment data;

[0073] S700, generating a disaster area loss analysis data set based on the regional loss assessment data and combining the personnel distribution and important facility locations in the information data set, including building damage rate, facility damage information, material traffic rate, and material list;

[0074] S800, extracting the loss distribution and resource accessibility of each region according to the disaster area loss analysis data set, and calculating the loss index and impact index of the disaster area to obtain the disaster area impact assessment data set;

[0075] S900, dynamically compare and update the disaster area impact assessment data set with the continuously updated traffic monitoring data and information platform data to generate dynamic loss assessment data for the disaster area;

[0076] S1000. Based on the dynamic loss assessment data of the disaster area, different loss levels are marked for each area in the disaster area, and a dynamic assessment report is generated.

[0077] In the embodiment of the present invention, by acquiring the multi-source original data set in the early stage after the disaster and classifying it into information data set, traffic data set and image data set according to the data source type, the multi-source data of the information platform, traffic monitoring system and satellite remote sensing image are fully integrated. In the process of data standardization, redundant information is removed by cleaning, and the time difference of data sources is eliminated by unifying the time tags; the abnormal flow of traffic data is eliminated to ensure the reliability of the data; the spatial coordinate conversion of the image data accurately aligns the disaster area map. This step significantly improves the processing efficiency of multi-source data and lays a high-quality data foundation for subsequent disaster assessment.

[0078] Based on standardized data, the traffic conditions in each area are evaluated by calculating the traffic access index. The access index combines parameters such as flow, length, obstacle factor, and congestion time to truly reflect the complex traffic dynamics after the disaster. At the same time, the damage to buildings and roads is analyzed based on image data to generate a preliminary damage data set, which provides intuitive damage indicators for regional loss assessment. After combining regional loss assessment and resource allocation analysis, a disaster area loss analysis data set was generated, including multi-dimensional indicators such as building damage rate, facility damage information, and material access rate. Through the dynamic update of the above data, a dynamic assessment report of the disaster area was formed, realizing the comprehensive quantification of the post-disaster loss situation.

[0079] In the existing technology, the integration and analysis of post-disaster data has the problems of low efficiency and insufficient precision. This method significantly improves the reliability of data and the comprehensiveness of evaluation through a full-process multi-source data processing and evaluation method. For example, in a certain earthquake, this method completed the process from data acquisition to the generation of a dynamic assessment report of the disaster area within 20 minutes after the disaster, providing strong support for the timely deployment of rescue resources, while traditional manual analysis would take hours or even longer.

[0080] In a preferred embodiment of the present invention, the traffic flow information in the standardized traffic flow data set is divided into regions, and the traffic accessibility index of each region is calculated to obtain a traffic accessibility data set, including:

[0081] Based on the geographical location of the disaster area, the standardized traffic flow dataset is spatially divided and divided according to the administrative or natural boundaries within the disaster area to generate multiple regional traffic datasets;

[0082] According to the regional traffic data set, the time series change characteristics of the traffic flow in the region are analyzed, and the traffic density at each time point is calculated to generate the traffic index. The traffic index is calculated by the following formula:

[0083] ,in, For Region Traffic access index, For Region The number of roads, For the The traffic index of each road, , For the The traffic volume of a road, For the The length of the road, is the road traffic attenuation factor, For the The congestion time of each road, For the The weight coefficient of each road, For the The obstacle factor of the road, For Region The total road area, For Region The demand for vehicles, For the The vehicle load on the road, , For Region The area of , For the The number of obstacles on the road, For the The amount of materials needed to repair a road, For the The maximum flow rate of a road;

[0084] The traffic access index is screened according to the preset index threshold, and marked as a smooth area, a blocked area, and a blocked area, respectively, to obtain a regional traffic data set;

[0085] According to the regional access dataset and regional traffic dataset, the traffic status of roads in the region is identified to form a traffic accessibility dataset.

[0086] In the embodiment of the present invention, by dividing the standardized traffic flow data set into regions, based on the geographical location of the disaster area and combined with administrative boundaries or natural boundaries, multiple logically complete regional traffic data sets are formed. This partitioning processing method significantly improves the manageability of the data and provides basic support for the refined analysis of regional traffic conditions. Compared with the traditional overall analysis method, regional division effectively solves the problem of insufficient analysis granularity caused by excessive data coverage, making the evaluation results more targeted and practical.

[0087] On this basis, the traffic access index of each region is dynamically calculated by analyzing the time series change characteristics of the regional traffic data set. The access index comprehensively considers multiple factors such as traffic flow density, congestion time and road availability, and can fully reflect the traffic capacity of the roads within the region. Through the dynamic division of the traffic access index, the region is further divided into unblocked areas, blocked areas and blocked areas, and a regional access data set is generated. While intuitively displaying the regional traffic conditions, the regional access data set can also quickly identify key sections with restricted access, providing an accurate basis for the subsequent optimization of rescue roads and resource allocation.

[0088] The steps to generate the traffic accessibility dataset further integrate the traffic status of road nodes based on the regional traffic dataset. This dataset not only provides high-quality input data for subsequent trunk road analysis by comprehensively quantifying and annotating the traffic capacity within the region, but also intuitively reflects the overall traffic network operation status of the disaster area, providing effective support for rescue vehicle road planning.

[0089] In a preferred embodiment of the present invention, the traffic status of the main roads in the disaster area is identified based on the traffic accessibility data set, and the traffic road data of the disaster area is generated, including:

[0090] According to the traffic accessibility index in the traffic accessibility dataset, the traffic status of the main roads in the disaster area is identified;

[0091] According to the traffic status of the main roads, the main roads are divided into emergency rescue channels, general rescue channels and auxiliary channels, and the traffic status of each channel is marked to obtain the classification data of the main roads;

[0092] According to the classification data of the main roads, the traffic data of the disaster area is generated, and the traffic data of the disaster area includes the real-time traffic information of each main road.

[0093] In the embodiment of the present invention, based on the traffic accessibility data set, the traffic status of the main roads is further refined and analyzed, and the traffic accessibility index and road classification are combined to dynamically evaluate the smoothness, partial obstruction and complete interruption of the main roads. By refining the traffic capacity of the main roads, this method can clarify the functional role of the main roads in the post-disaster transportation network and provide a scientific basis for the priority allocation of rescue resources to key roads.

[0094] Based on the traffic status of the main roads, they are further classified into emergency rescue channels, general rescue channels and auxiliary channels according to their functions and traffic status, and the real-time traffic status of each channel is marked. Through the classification data of the main roads, the efficiency and scientificity of rescue road planning have been significantly improved, making resource allocation more accurate. Especially in emergency rescue scenarios, the marking of emergency rescue channels can quickly guide medical supplies and rescue teams to pass first, avoiding delays caused by traffic obstructions.

[0095] The resulting disaster area access road data includes real-time traffic information and optimization suggestions for main roads, providing dynamic support for rescue vehicles’ road selection. Compared with traditional methods that rely on fixed road planning, this method improves the flexibility and reliability of rescue operations under complex traffic conditions by dynamically adjusting road selection and access strategies.

[0096] In a preferred embodiment of the present invention, based on the aligned image data set, buildings and roads in the image are identified and analyzed, the buildings and roads are compared with the coordinates of the disaster area map, the building damage rate and road traffic rate in the area are calculated, and a preliminary damage data set is generated, including:

[0097] According to the aligned image data set, the spatial position of the structure in the image is identified and processed to obtain position identification data, wherein the structure includes buildings and roads;

[0098] Cross-check the location identification data with the coordinate system of the disaster area map and adjust the location identification data to ensure that the building and road information are marked on the disaster area map to obtain accurate location data;

[0099] According to the precise location data, the damage rate of each building and road is analyzed in combination with the pre-disaster image data to generate a preliminary damage data set, which includes the building damage rate, road damage rate and location marks.

[0100] In the embodiment of the present invention, the damage of buildings and roads is analyzed through image data, the building damage rate and road traffic rate are calculated, and a preliminary damage data set is generated. This data set not only covers the degree of building damage and road damage, but also provides detailed damage characteristics for subsequent disaster assessment.

[0101] In the existing technology, damage analysis of buildings and roads mostly relies on manual assessment, which is time-consuming and easily affected by subjective factors. This method completes a large-scale post-disaster damage assessment in a short time through automated analysis of remote sensing images. For example, in a certain earthquake disaster, this method generated damage assessment data for the entire urban area within 1 hour after the disaster, providing a scientific basis for the command center's rescue decision-making.

[0102] Specifically, according to the aligned image data set, the spatial position of the structures in the image is identified and processed to obtain position identification data. The structures include buildings and roads. Buildings and roads in post-disaster images can be identified through remote sensing image processing technology, and edge detection algorithms such as Sobel operator or Canny edge detection are used to extract the contour information of buildings and roads. The contours are segmented and labeled through the region growing method or clustering algorithm to generate spatial position data. This method can accurately locate the key facilities and their distribution in the disaster area, providing support for subsequent analysis.

[0103] Specifically, the location identification data is cross-checked with the coordinate system of the disaster area map, and the location identification data is adjusted to ensure that the building and road information are marked in the disaster area map to obtain accurate location data. The coordinate system of the remote sensing image can be converted into a unified geographic coordinate system, such as WGS84, through geographic registration technology. By introducing control point matching technology, such as SIFT feature point detection, the alignment accuracy of the identification data and the actual map is ensured, so as to accurately mark the location of buildings and roads.

[0104] Specifically, based on the precise location data and combined with the pre-disaster image data, the damage rate of each building and road is analyzed to generate a preliminary damage data set. By comparing the pre-disaster images with the post-disaster images, the damaged area of ​​​​buildings and roads can be identified, and the damage rate can be obtained by calculating the ratio with the intact area.

[0105] In a preferred embodiment of the present invention, based on the preliminary damage data set and the road data of the disaster area, the traffic conditions and the difficulty of resource arrival in each area are analyzed to form regional loss assessment data, including:

[0106] The preliminary damage dataset was cross-correlated with the road data in the disaster area, and the damage severity and road accessibility of each area were analyzed to obtain the road accessibility dataset;

[0107] Regional loss assessment data is generated based on the road accessibility score dataset and the disaster area accessible road data.

[0108] In the embodiment of the present invention, the preliminary damage data set and the road data of the disaster area are combined to comprehensively analyze the traffic conditions and resource access difficulties of each area, and generate regional loss assessment data. This data provides detailed support for the priority allocation of rescue resources and road optimization.

[0109] Unlike existing methods that are only based on the degree of damage, this method comprehensively reflects the feasibility of resource arrival through cross-analysis of traffic conditions and damage data. For example, in a certain earthquake rescue, a high-impact area was repaired first due to road blockage, and the difficulty of resource arrival in the area was rapidly reduced, thus ensuring the efficient distribution of relief supplies.

[0110] Specifically, the preliminary damage dataset was cross-correlated with the road data in the disaster area, and the damage severity and road accessibility of each area were analyzed to obtain the road accessibility dataset, including:

[0111] Extract damage information of all buildings and roads in the area from the preliminary damage data set. The damage information of buildings includes the damaged area and total area of ​​each building. The building damage index is calculated and expressed as a percentage.

[0112] The damage information of all roads in the region, including the damaged area and total area, is extracted from the preliminary damage dataset and combined with the traffic access index to calculate the road accessibility. The road accessibility result is expressed as a percentage with a detailed description, for example, "the road accessibility in area A is 75%, the main roads are in good condition, but the secondary roads are partially blocked, causing delays in logistics."

[0113] Through this method, the damage severity and road traffic rate will reflect the disaster impact and traffic conditions in the region respectively, providing support for resource allocation and rescue decision-making. After the two are combined, regional loss assessment data can be further formed.

[0114] Specifically, based on the road accessibility score dataset and the disaster area access road data, regional loss assessment data is generated, including:

[0115] Analyze the traffic rate of each road, mark the roads with traffic rates below a certain threshold as "blocked areas", and record the reasons for the blockage, such as bridge damage or road obstacles, to obtain the blocked areas;

[0116] The road accessibility score dataset is associated with the road access data in the disaster area, and the blocked areas are identified through the road optimization algorithm, and the road planning data is generated;

[0117] Based on the blocked areas and road planning data, the average road access rate of each area is calculated to form regional loss assessment data.

[0118] Among them, the blocked area description is generated, and the scoring results of each road can be classified:

[0119] The traffic rate is >80%, marked as "unobstructed".

[0120] Traffic flow rate 30%-80%, marked as "partially blocked".

[0121] A traffic rate of <30% is marked as "completely blocked".

[0122] For blocked or partially blocked areas, record detailed reasons, such as the number of obstacles and the extent of damage.

[0123] In a preferred embodiment of the present invention, based on the regional loss assessment data, combined with the personnel distribution and important facility locations in the information data set, a disaster area loss analysis data set is generated, including building damage rate, facility damage information, material traffic rate and material list, including:

[0124] Based on the regional loss assessment data, the building damage rate and road traffic rate are comprehensively analyzed to preliminarily calculate the amount of accessible materials in each area to form a data set of accessible materials;

[0125] According to the personnel distribution information and facility damage information provided in the information data set, the resource demand information is analyzed to obtain a material list;

[0126] According to the material list and the data set of available material quantity, the material access rate is analyzed to form the material access condition data; the material access rate is calculated by the following formula:

[0127] ,in, For Region The material flow rate, For Region The number of roads, For the The amount of materials transported on each road, For the The traffic index of each road, For the The obstacle factor of the road, , For Region population, For Region The per capita material demand For the The length of the road, For the Vehicle load on each road;

[0128] Based on the data on material access conditions, combined with regional loss assessment data and information data sets, a disaster area loss analysis data set is generated, including building damage rate, facility damage information, material access rate and material list.

[0129] In the embodiment of the present invention, a disaster area loss analysis data set is generated by combining regional loss assessment data with personnel distribution information and the location of important facilities. This data set integrates building damage rate, facility damage information, material traffic rate and material list, providing support for the scientific dispatch of rescue resources.

[0130] In the prior art, the assessment of material demand usually relies on static population data, which is difficult to reflect dynamic changes in real time. This method quickly adjusts the material list by dynamically updating the material flow rate and damage data. For example, after an earthquake, as the population migrates, this method updates the material distribution plan in high-demand areas to ensure the accurate supply of materials.

[0131] Specifically, based on the regional loss assessment data, the building damage rate and road traffic rate are comprehensively analyzed, and the amount of passable materials in each area is preliminarily calculated to form a data set of passable material volume. Based on the logistics road optimization algorithm, the buildings and roads with high damage rates in the region can be marked as "restricted areas", and the capacity upper limit of material transportation can be calculated in combination with the traffic capacity of material vehicles and the road traffic rate.

[0132] Specifically, based on the personnel distribution information and facility damage information provided in the information data set, the resource demand information is analyzed to obtain a list of materials. The population density data and facility damage rate can be used to calculate the material demand in the area and match the corresponding material types.

[0133] Specifically, based on the material traffic conditions data, combined with the regional loss assessment data and information data set, a disaster area loss analysis data set is generated, including:

[0134] Comprehensively analyze the building damage rate and road traffic rate in the regional loss assessment data, give priority to marking the areas with high damage and low traffic rate, and screen out key traffic nodes by matching them with the traffic capacity in the material traffic condition data;

[0135] Compare the screened key nodes with the personnel distribution and facility locations in the information data set to generate a list of priority material demand areas.

[0136] By matching material lists, such as medical supplies and food, with regional demand, a disaster area loss analysis data set is formed, which includes building damage rate, facility damage information, material access rate and material lists.

[0137] In a preferred embodiment of the present invention, based on the disaster area loss analysis data set, the loss distribution and resource accessibility of each region are extracted, and the loss index and impact index of the disaster area are calculated to obtain the disaster area impact assessment data set, including:

[0138] Based on the disaster area loss analysis data set, the resource demand and damage distribution of each area were extracted to obtain the building damage rate, facility damage information, material traffic rate and material list;

[0139] Calculate the loss index of each area based on the building damage rate and facility damage information;

[0140] ,in, For Region The loss index, is the building damage index, is the facility damage index, For Region The demand for materials, To estimate the rescue time, and is the weight coefficient; ,in, is the number of buildings, For Architecture The total area, For Architecture The damaged area, ,in, To repair speed, is the average vehicle speed;

[0141] According to the loss index, material flow rate and material list, the impact index is calculated to obtain the disaster area impact assessment data set;

[0142] ,in, For Region The impact index, For Region The amount of material transported, , and is the weight coefficient.

[0143] In the embodiment of the present invention, the loss index and impact index of each area are calculated through the disaster area loss analysis data set, and the disaster area impact assessment data set is generated. The loss index is based on a comprehensive analysis of the building damage index, facility damage index and material traffic rate, while the impact index further combines the resource delivery efficiency, providing a scientific basis for the division of priority rescue areas.

[0144] Compared with the existing technology that only uses a single indicator for qualitative analysis, this method comprehensively reflects the extent of the disaster in the disaster area through quantitative calculation. For example, in a certain earthquake, a certain area was assessed as a first-level priority rescue area due to its high damage rate and low traffic rate, and received priority medical resources, which significantly improved the rescue effect.

[0145] Specifically, the facility damage index is calculated using the same calculation logic as the building damage index. ,in, is the number of roads, For the road The total area, For the road of damaged area.

[0146] The present invention also provides a dynamic earthquake disaster loss assessment system based on multi-source disaster data, the system comprising:

[0147] The data acquisition module is used to obtain the multi-source original data set in the early stage after the disaster, and format and convert the multi-source original data set according to the data source type to generate information data set, traffic data set and image data set. The multi-source original data set includes information platform data, traffic monitoring data and satellite image data;

[0148] The data preprocessing module is used to clean the information data set, remove redundant information and extract time tags, unify all time tags to the coordinated time, and obtain a standardized information data set; perform data screening on the traffic data set, remove abnormal traffic values, extract valid traffic flow information, and generate a standardized traffic flow data set; perform spatial coordinate conversion on the image data set, align the image coordinates with the disaster area's geographic coordinate system, and generate an aligned image data set;

[0149] Traffic accessibility generation module, used to divide the traffic flow information in the standardized traffic flow data set into regions, and calculate the traffic accessibility index of each region to obtain the traffic accessibility data set;

[0150] The disaster area access road generation module is used to identify the accessibility status of the main roads in the disaster area based on the traffic accessibility data set and generate the disaster area access road data;

[0151] A preliminary damage data generation module is used to identify and analyze buildings and roads in the image based on the aligned image data set, compare the buildings and roads with the coordinates of the disaster area map, calculate the building damage rate and road traffic rate in the area, and generate a preliminary damage data set;

[0152] The regional loss assessment module is used to analyze the traffic conditions and resource access difficulties in each area based on the preliminary damage data set and the road data of the disaster area, and form regional loss assessment data;

[0153] The disaster area loss analysis module is used to generate a disaster area loss analysis data set based on the regional loss assessment data and the personnel distribution and important facility locations in the information data set, including building damage rate, facility damage information, road access rate and rescue access conditions;

[0154] The disaster area impact assessment module is used to extract the loss distribution and resource accessibility of each region based on the disaster area loss analysis data set, and calculate the loss index and impact index of the disaster area to obtain the disaster area impact assessment data set;

[0155] The disaster area dynamic loss assessment module is used to dynamically compare and update the disaster area impact assessment data set with the continuously updated traffic monitoring data and information platform data to generate dynamic loss assessment data for the disaster area;

[0156] The dynamic assessment report generation module is used to mark different loss levels for each area in the disaster area based on the dynamic loss assessment data of the disaster area and generate a dynamic assessment report.

[0157] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0158] The embodiment of the present invention further provides a computing device, including: a processor, a memory storing a computer program, and when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0159] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the above method. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0160] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A dynamic assessment method for earthquake disaster losses based on multi-source disaster data, characterized in that: The method comprises: Obtain multi-source original data sets in the early post-disaster period, and format and convert the multi-source original data sets according to the data source type to generate information data sets, traffic data sets, and image data sets; The information data set is cleaned, redundant information is removed and time tags are extracted, all time tags are unified to the coordinated time, and a standardized information data set is obtained; the traffic data set is screened, abnormal traffic values ​​are removed, valid traffic flow information is extracted, and a standardized traffic flow data set is generated; the image data set is converted into spatial coordinates, the image coordinates are aligned with the disaster area geographic coordinate system, and an aligned image data set is generated; The traffic flow information in the standardized traffic flow dataset is divided into regions, and the traffic accessibility index of each region is calculated to obtain the traffic accessibility dataset; Based on the traffic accessibility data set, the traffic status of the main roads in the disaster area is identified and the traffic road data of the disaster area is generated; Based on the aligned image dataset, identify and analyze the buildings and roads in the image, compare the buildings and roads with the coordinates of the disaster area map, calculate the building damage rate and road traffic rate in the area, and generate a preliminary damage dataset; Based on the preliminary damage data set and the road data in the disaster area, the traffic conditions and the difficulty of reaching resources in each area were analyzed to form regional loss assessment data; Based on the regional loss assessment data, combined with the personnel distribution and important facility locations in the information dataset, a disaster area loss analysis dataset is generated, including building damage rate, facility damage information, material traffic rate, and material list; According to the disaster area loss analysis data set, the loss distribution and resource accessibility of each region are extracted, and the loss index and impact index of the disaster area are calculated to obtain the disaster area impact assessment data set; Dynamically compare and update the disaster area impact assessment data set with the continuously updated traffic monitoring data and information platform data to generate dynamic loss assessment data for the disaster area; Based on the dynamic loss assessment data of the disaster area, different loss levels are marked for each area in the disaster area and a dynamic assessment report is generated.

2. The earthquake disaster loss dynamic assessment method based on multi-source disaster data according to claim 1 is characterized in that: The traffic flow information in the standardized traffic flow dataset is divided into regions, and the traffic accessibility index of each region is calculated to obtain the traffic accessibility dataset, including: Based on the geographical location of the disaster area, the standardized traffic flow dataset is spatially divided and divided according to the administrative or natural boundaries within the disaster area to generate multiple regional traffic datasets; According to the regional traffic data set, the time series change characteristics of the traffic flow in the region are analyzed, and the traffic density at each time point is calculated to generate the traffic index. The traffic index is calculated by the following formula: ,in, For Region Traffic access index, For Region The number of roads, For the The traffic index of each road, , For the The traffic volume of a road, For the The length of the road, is the road traffic attenuation factor, For the The congestion time of each road, For the The weight coefficient of each road, For the The obstacle factor of the road, For Region The total road area, For Region The demand for vehicles, For the The vehicle load on the road, , For Region The area of , For the The number of obstacles on the road, For the The amount of materials needed to repair a road, For the The maximum flow rate of a road; The traffic index is divided according to the preset index threshold, and marked as unblocked area, blocked area and blocked area respectively, to obtain the regional traffic data set; According to the regional access dataset and regional traffic dataset, the traffic status of roads in the region is identified to form a traffic accessibility dataset.

3. The method for dynamic assessment of earthquake disaster losses based on multi-source disaster data according to claim 2 is characterized in that: Based on the traffic accessibility dataset, the traffic status of the main roads in the disaster area is identified, and the traffic road data of the disaster area is generated, including: According to the traffic accessibility index in the traffic accessibility dataset, the traffic status of the main roads in the disaster area is identified; According to the traffic status of the main roads, the main roads are divided into emergency rescue channels, general rescue channels and auxiliary channels, and the traffic status of each channel is marked to obtain the classification data of the main roads; According to the classification data of the main roads, the traffic data of the disaster area is generated, and the traffic data of the disaster area includes the real-time traffic information of each main road.

4. The earthquake disaster loss dynamic assessment method based on multi-source disaster data according to claim 3 is characterized in that: Based on the aligned image dataset, identify and analyze the buildings and roads in the image, compare the buildings and roads with the coordinates of the disaster area map, calculate the building damage rate and road traffic rate in the area, and generate a preliminary damage dataset, including: According to the aligned image data set, the spatial position of the structure in the image is identified and processed to obtain position identification data, wherein the structure includes buildings and roads; Cross-check the location identification data with the coordinate system of the disaster area map and adjust the location identification data to ensure that the building and road information are marked on the disaster area map to obtain accurate location data; According to the precise location data, the damage rate of each building and road is analyzed in combination with the pre-disaster image data to generate a preliminary damage data set, which includes the building damage rate, road damage rate and location marks.

5. The method for dynamic assessment of earthquake disaster losses based on multi-source disaster data according to claim 4 is characterized in that: Based on the preliminary damage data set and the road data in the disaster area, the traffic conditions and the difficulty of reaching resources in each area were analyzed to form regional loss assessment data, including: The preliminary damage dataset was cross-correlated with the road data in the disaster area, and the damage severity and road accessibility of each area were analyzed to obtain the road accessibility dataset; Regional loss assessment data is generated based on the road accessibility score dataset and the disaster area accessible road data.

6. The method for dynamic assessment of earthquake disaster losses based on multi-source disaster data according to claim 5, characterized in that: Based on the regional loss assessment data, combined with the personnel distribution and important facility locations in the information dataset, a disaster area loss analysis dataset is generated, including building damage rate, facility damage information, material traffic rate and material list, including: Based on the regional loss assessment data, the building damage rate and road traffic rate are comprehensively analyzed to preliminarily calculate the amount of accessible materials in each area to form a data set of accessible materials; According to the personnel distribution information and facility damage information provided in the information data set, the resource demand information is analyzed to obtain a material list; According to the material list and the data set of available material quantity, the material access rate is analyzed to form the material access condition data; the material access rate is calculated by the following formula: ,in, For Region The material flow rate, For Region The number of roads, For the The amount of materials transported on each road, For the The traffic index of each road, For the The obstacle factor of the road, , For Region population, For Region The per capita material demand For the The length of the road, For the Vehicle load on each road; Based on the data on material access conditions, combined with regional loss assessment data and information data sets, a disaster area loss analysis data set is generated, including building damage rate, facility damage information, material access rate and material list.

7. The method for dynamic assessment of earthquake disaster losses based on multi-source disaster data according to claim 6, characterized in that: According to the disaster area loss analysis dataset, the loss distribution and resource accessibility of each region are extracted, and the loss index and impact index of the disaster area are calculated to obtain the disaster area impact assessment dataset, including: Based on the disaster area loss analysis data set, the resource demand and damage distribution of each area were extracted to obtain the building damage rate, facility damage information, material traffic rate and material list; According to the building damage rate and facility damage information, the loss index of each area is calculated; The loss index is calculated by the following formula: ,in, For Region The loss index, is the building damage index, is the facility damage index, For Region The demand for materials, To estimate the rescue time, and is the weight coefficient; ,in, is the number of buildings, For Architecture The total area, For Architecture The damaged area, ,in, To repair speed, is the average vehicle speed; According to the loss index, material flow rate and material list, the impact index is calculated to obtain the disaster area impact assessment data set; The impact index is calculated by the following formula: ,in, For Region The impact index, For Region The amount of material transported, , and is the weight coefficient.

8. A dynamic earthquake disaster loss assessment system based on multi-source disaster data, characterized in that: Applied to the method according to any one of claims 1 to 7, the system comprises: The data acquisition module is used to obtain the multi-source original data set in the early stage after the disaster, and format and convert the multi-source original data set according to the data source type to generate information data set, traffic data set and image data set. The multi-source original data set includes information platform data, traffic monitoring data and satellite image data; The data preprocessing module is used to clean the information data set, remove redundant information and extract time tags, unify all time tags to the coordinated time, and obtain a standardized information data set; perform data screening on the traffic data set, remove abnormal traffic values, extract valid traffic flow information, and generate a standardized traffic flow data set; perform spatial coordinate conversion on the image data set, align the image coordinates with the disaster area's geographic coordinate system, and generate an aligned image data set; Traffic accessibility generation module, used to divide the traffic flow information in the standardized traffic flow data set into regions, and calculate the traffic accessibility index of each region to obtain the traffic accessibility data set; The disaster area access road generation module is used to identify the accessibility status of the main roads in the disaster area based on the traffic accessibility data set and generate the disaster area access road data; A preliminary damage data generation module is used to identify and analyze buildings and roads in the image based on the aligned image data set, compare the buildings and roads with the coordinates of the disaster area map, calculate the building damage rate and road traffic rate in the area, and generate a preliminary damage data set; The regional loss assessment module is used to analyze the traffic conditions and resource access difficulties in each area based on the preliminary damage data set and the road data of the disaster area, and form regional loss assessment data; The disaster area loss analysis module is used to generate a disaster area loss analysis data set based on the regional loss assessment data and the personnel distribution and important facility locations in the information data set, including building damage rate, facility damage information, road access rate and rescue access conditions; The disaster area impact assessment module is used to extract the loss distribution and resource accessibility of each region based on the disaster area loss analysis data set, and calculate the loss index and impact index of the disaster area to obtain the disaster area impact assessment data set; The disaster area dynamic loss assessment module is used to dynamically compare and update the disaster area impact assessment data set with the continuously updated traffic monitoring data and information platform data to generate dynamic loss assessment data for the disaster area; The dynamic assessment report generation module is used to mark different loss levels for each area in the disaster area based on the dynamic loss assessment data of the disaster area and generate a dynamic assessment report.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

Images