Meteorological disaster risk assessment method and system based on multi-source data feedback

Abstract

The invention discloses a meteorological disaster risk assessment method and system based on multi-source data feedback, and relates to the technical field of meteorological disaster risk assessment, and the method comprises the steps: obtaining the information of a to-be-assessed region containing a special terrain, dividing the to-be-assessed region based on the special terrain and the information of the to-be-assessed region, and obtaining a to-be-assessed region; obtaining the agricultural exposure degree, population exposure degree and economic exposure degree of a secondary to-be-evaluated area in the secondary to-be-evaluated area library, dividing the secondary to-be-evaluated area, obtaining geological disaster susceptible condition factors of the secondary to-be-evaluated area in the secondary to-be-evaluated area library, extracting disaster-causing danger factors in real-time multi-source meteorological data, and obtaining the geological disaster susceptible condition factors of the secondary to-be-evaluated area in the secondary to-be-evaluated area library; and according to the disaster-causing danger factor and the geological disaster susceptible condition factor, acquiring a disaster risk index of the secondary to-be-assessed region in the secondary to-be-assessed region library, so that the assessment system can still predict and obtain a target variable with relatively high precision under the condition of lacking a part of characteristic variables, and the efficiency and applicability of meteorological disaster risk assessment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological disaster risk assessment, and in particular to a meteorological disaster risk assessment method and system based on multi-source data feedback. Background Art

[0002] The meteorological disaster risk assessment method based on multi-source data feedback uses comprehensive information from multiple sources such as meteorological data, remote sensing information, and geographic data to assess and predict meteorological disaster risks in real time. This method can more accurately capture and analyze the dynamic changes of disasters, provide timely warnings, and improve response capabilities. Heavy rain disasters are one of the most frequent natural disasters in the world. There have been many heavy rain disasters and their associated disasters in my country. Therefore, it is of great significance to more accurately capture and analyze the dynamic changes of heavy rain disasters, provide timely warnings, and improve response capabilities.

[0003] The existing meteorological disaster risk assessment method based on multi-source data feedback uses rainfall data to perform disaster assessment zoning results. However, when conducting meteorological disaster risk assessment in areas with special terrain, it will recalculate and set weights due to the lack of some influencing factors, which consumes a lot of time and resources. It is difficult to carry out risk assessment and zoning of rainstorm disaster events in historical time series. At the same time, due to the influence of the special terrain in the region, it is difficult to ensure that the assessment system performs accurate risk assessment of the region. Therefore, it is necessary to provide a meteorological disaster risk assessment method and system based on multi-source data feedback to solve the above-mentioned problems. Summary of the invention

[0004] In order to solve the above technical problems, a meteorological disaster risk assessment method and system based on multi-source data feedback are provided. This technical solution solves the problem that the existing meteorological disaster risk assessment method based on multi-source data feedback proposed in the above background technology uses rainfall data to perform disaster assessment zoning results. Although the results are relatively accurate, when conducting meteorological disaster risk assessment in areas with special terrain, it will recalculate and set weights due to the lack of some influencing factors, which consumes a lot of time and resources and makes it difficult to carry out risk assessment and zoning of rainstorm disaster events in historical time series. At the same time, due to the influence of the special terrain in the region, it is difficult to ensure that the assessment system performs accurate risk assessment of the region.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A meteorological disaster risk assessment method based on multi-source data feedback, including:

[0007] Obtaining information on the area to be assessed that contains special terrain, including mountains, hills, plains, basins and wetlands;

[0008] Divide the area to be assessed based on special terrain and information of the area to be assessed, and obtain a secondary database of areas to be assessed;

[0009] Obtain the agricultural exposure, population exposure and economic exposure of the secondary areas to be assessed in the secondary areas to be assessed database, divide the secondary areas to be assessed, and establish a secondary area to be assessed database;

[0010] Obtain geological disaster proneness condition factors of the secondary to-be-assessed areas in the secondary to-be-assessed areas database, obtain real-time multi-source meteorological data, and extract disaster risk factors from the real-time multi-source meteorological data;

[0011] According to the disaster risk factors and geological disaster proneness factors, the disaster risk index of the secondary to-be-assessed area in the secondary to-be-assessed area database is obtained;

[0012] Use the disaster risk index to conduct risk assessment on the secondary areas to be assessed in the secondary area to be assessed database, obtain the disaster risk level, and mark the secondary areas to be assessed;

[0013] Based on the marked secondary areas to be assessed, the areas to be assessed are assessed to obtain a comprehensive disaster risk index for the areas to be assessed;

[0014] The comprehensive disaster risk index of the area to be assessed is used to assess the area to be assessed, and a meteorological disaster prevention plan is formulated based on the assessment results.

[0015] Preferably, the step of dividing the area to be evaluated based on the special terrain and the information of the area to be evaluated to obtain a secondary area library to be evaluated specifically includes:

[0016] Based on the information of the area to be evaluated, the location information of the area to be evaluated is obtained, and the maximum area value of the area to be evaluated is determined;

[0017] According to the location information of the area to be evaluated, the regional topographic map is consulted to obtain the topographic feature information of the area to be evaluated;

[0018] A secondary division window is set based on the maximum area value of the area to be evaluated, and the area to be evaluated is intercepted by sliding the secondary division window to establish a secondary division area library to be evaluated;

[0019] Based on the terrain feature information and the special terrain, obtain the special terrain category index of the secondary division area to be evaluated in the secondary division area to be evaluated library, and determine the special terrain category threshold;

[0020] Based on the special terrain type threshold and the special terrain type index, the secondary division area to be evaluated in the secondary division area to be evaluated library is divided to establish a secondary division area to be evaluated library;

[0021] Obtaining comprehensive special terrain information of the secondary divided area to be evaluated in the secondary divided area to be evaluated database;

[0022] According to the comprehensive information of special terrain and the location information of the area to be assessed, the terrain division impact index of the area to be assessed in the secondary division is obtained;

[0023] The secondary division areas to be assessed in the secondary division area library are divided according to the terrain division influence index of the secondary division areas to be assessed, and a secondary division area library is established.

[0024] Preferably, the step of obtaining the agricultural exposure, population exposure and economic exposure of the secondary areas to be assessed in the secondary areas to be assessed database, dividing the secondary areas to be assessed and establishing a secondary area to be assessed database specifically includes:

[0025] Respectively obtain the area value of the secondary to-be-assessed area, the area value of the population activity area and the maximum economic value of the region of the secondary to-be-assessed area in the secondary to-be-assessed area database;

[0026] Obtain the agricultural area value, the total area of ​​intensive population activity area and the maximum regional income value in the secondary to-be-assessed area in the secondary to-be-assessed area database respectively;

[0027] The ratio of the agricultural area value in the secondary area to be assessed to the area value of the secondary area to be assessed is taken as the agricultural exposure of the secondary area to be assessed;

[0028] The ratio of the total area of ​​densely populated areas to the area of ​​densely populated areas in the secondary area to be assessed is used as the population exposure of the secondary area to be assessed.

[0029] The ratio of the maximum regional income value to the maximum regional economic value in the secondary area to be assessed is used as the economic exposure of the secondary area to be assessed;

[0030] Determine the economic exposure threshold, divide the secondary areas to be assessed according to their economic exposure, obtain high economic exposure areas, medium economic exposure areas and low economic exposure areas, and establish secondary areas to be assessed;

[0031] The high economic exposure areas, medium economic exposure areas and low economic exposure areas are respectively divided into different columns in the secondary area database to be assessed;

[0032] A four-quadrant model is constructed to divide the high economic exposure areas, medium economic exposure areas and low economic exposure areas into different columns in the secondary area library for evaluation.

[0033] Preferably, the step of obtaining geological disaster proneness condition factors of the secondary area to be assessed, obtaining real-time multi-source meteorological data, and extracting disaster risk factors from the real-time multi-source meteorological data specifically includes:

[0034] Based on the terrain feature information of the area to be evaluated, obtaining the secondary area terrain feature information of the secondary area to be evaluated;

[0035] According to the regional topographic map, obtain the location information of the secondary area to be assessed, and obtain the terrain structure information, soil condition information, groundwater level information and vegetation cover information of the secondary area to be assessed;

[0036] Based on the secondary regional terrain feature information, terrain structure information, soil condition information, groundwater level information and vegetation coverage information of the secondary area to be assessed, obtain the geological disaster proneness condition factors;

[0037] Based on the real-time multi-source meteorological data obtained from the Meteorological Bureau, the maximum hourly precipitation forecast, maximum daily precipitation forecast, process cumulative rainfall forecast and rainstorm forecast duration days in the real-time multi-source meteorological data are extracted to obtain the disaster risk factors.

[0038] Preferably, obtaining the disaster risk index of the secondary area to be assessed based on the disaster risk factors and the geological disaster prone condition factors specifically includes:

[0039] Construct k weak regression models and obtain historical multi-source meteorological data, historical terrain structure information, historical soil condition information, historical groundwater level information and historical vegetation cover information of the secondary assessment area;

[0040] Based on the historical topographic structure information, historical soil condition information, historical groundwater level information and historical vegetation coverage information of the secondary area to be assessed as a disaster risk sample set, the historical geological disaster proneness condition factors are obtained;

[0041] Extract historical disaster risk factors from historical multi-source meteorological data, and add the historical disaster risk factors to the disaster risk sample set;

[0042] The disaster risk sample set is divided into a training set and a validation set in a ratio of 7:3;

[0043] The historical disaster risk factors and historical geological disaster prone condition factors in the training set are used as disaster risk characteristic variables, i data points are set, and each data point has a disaster risk characteristic vector converted from the disaster risk characteristic variables;

[0044] Combine k weak regression models into a strong regression model, and train and verify the strong regression model based on the disaster risk sample set to obtain the disaster risk index;

[0045] Based on the disaster risk index, the historical agricultural exposure, historical population exposure and historical economic exposure of the secondary assessment area are obtained;

[0046] The historical agricultural exposure, historical population exposure and historical economic exposure of the secondary assessed area are used as the disaster risk sample set of the disaster-prone body;

[0047] Integrate the data with the disaster hazard sample set to obtain an integrated sample set and obtain an integrated feature vector;

[0048] The strong regression model is trained and verified based on the integrated sample set to obtain the disaster risk index of the disaster-prone body;

[0049] Obtain disaster risk index based on disaster hazard index and disaster risk index of disaster-bearing body;

[0050] Among them, the calculation formula for obtaining the disaster hazard index and the disaster risk index of the disaster-bearing body based on the strong regression model is:

[0051]

[0052] In the formula, is the model prediction value, k represents the number of weak regression models, F is the collection space of weak regression models, X i represents the feature vector of the i-th data point, Represents the predicted value and the target true value y i The training error between , Ω is the sum of the complexity of the penalty model, γ and λ are the model penalty coefficients, and Ω(f) are both loss functions;

[0053] If X i is the disaster risk characteristic vector, then the output of the strong regression model is is the disaster risk index, if X i To integrate the feature vector, the output of the strong regression model is It is the disaster risk index of the disaster-prone body.

[0054] Preferably, the method of using the disaster risk index to conduct risk assessment on the secondary area to be assessed, obtaining the disaster risk level, and marking the secondary area to be assessed specifically includes:

[0055] The second-level assessment areas with the highest 20% disaster risk index are classified as high-risk areas and marked in red;

[0056] The 60% of the secondary assessment areas with disaster risk index in the middle are classified as medium-risk areas and marked in yellow;

[0057] The secondary assessment areas with the lowest disaster risk index in the 20% are classified as low-risk areas and marked in green.

[0058] Preferably, the step of evaluating the area to be evaluated based on the marked secondary area to be evaluated to obtain a comprehensive disaster risk index of the area to be evaluated specifically includes:

[0059] Based on the location information of the secondary area to be evaluated and the location information of the area to be evaluated, associating the marked secondary area to be evaluated with the area to be evaluated;

[0060] The disaster risk indexes of the secondary areas to be assessed that are related to the area to be assessed in the secondary area to be assessed database are added together to obtain a comprehensive disaster risk index for the area to be assessed.

[0061] Furthermore, a meteorological disaster risk assessment system based on multi-source data feedback is proposed, which is used to implement any of the above-mentioned assessment methods, including:

[0062] A main control module, which is used to receive data transmitted by the acquisition module, the division module, the evaluation module and the display module through the transmission module, and transmit the processed data to the acquisition module, the division module, the evaluation module and the display module through the transmission module, and is also used to build a regional library;

[0063] A division module, the division module is used to divide the area to be evaluated based on special terrain and information of the area to be evaluated, divide the secondary area to be evaluated, use the secondary division window to slide and intercept the area to be evaluated, divide the secondary division area to be evaluated in the secondary division area to be evaluated library, and divide the secondary division area to be evaluated in the secondary division area to be evaluated library;

[0064] An evaluation module, wherein the evaluation module is used to evaluate the data information and the divided areas;

[0065] A collection module, the collection module is used to collect information of the area to be evaluated, agricultural exposure, population exposure and economic exposure of the secondary area to be evaluated, and collect real-time multi-source meteorological data and historical multi-source meteorological data;

[0066] A transmission module, which is used for data transmission between the main control module, the division module, the evaluation module and the display module;

[0067] Display module, the display module is used to display the data and information processing process and results of the main control module.

[0068] Preferably, the main control module includes:

[0069] A receiving unit, the receiving unit is used to obtain information about the area to be evaluated containing special terrain, obtain data and information collected by the acquisition module, obtain information about the area divided by the division module, and obtain evaluation results of the evaluation module;

[0070] A processing unit, the processing unit is used to obtain geological disaster proneness condition factors of the secondary to-be-assessed area in the secondary to-be-assessed area library, extract disaster risk factors in real-time multi-source meteorological data, and obtain disaster risk indexes of the secondary to-be-assessed area in the secondary to-be-assessed area library according to the disaster risk factors and the geological disaster proneness condition factors;

[0071] A construction unit is used to construct a secondary to-be-evaluated region library, a secondary to-be-evaluated region library, a secondary divided to-be-evaluated region library and a secondary divided to-be-evaluated region library.

[0072] Preferably, the evaluation module comprises:

[0073] A data evaluation unit, the data evaluation unit is used to obtain a special terrain type index of a secondary-divided area to be evaluated in a secondary-divided area to be evaluated library, determine a special terrain type threshold, obtain a terrain division impact index of a secondary-divided area to be evaluated based on special terrain comprehensive information and location information of the area to be evaluated, and determine an economic exposure threshold;

[0074] The regional assessment unit is used to perform risk assessment on the secondary to-be-assessed areas in the secondary to-be-assessed area library, assess the to-be-assessed areas based on the marked secondary to-be-assessed areas, and assess the to-be-assessed areas using a comprehensive disaster risk index of the to-be-assessed areas.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] This scheme proposes a meteorological disaster risk assessment method and system based on multi-source data feedback. The secondary division area to be assessed is divided into regions by terrain division impact index, which is convenient for subsequent risk assessment of the area to be assessed. The terrain division impact index is obtained through the comprehensive information of special terrain and the location information of the area to be assessed. It comprehensively considers the impact of the special terrain in the area on the occurrence of meteorological disasters in the area when it is subjected to severe environments such as heavy rain. At the same time, the weight is determined by historical data, and the weight setting will not be recalculated due to the lack of some influencing factors, saving time and resources.

[0077] This scheme proposes a meteorological disaster risk assessment method and system based on multi-source data feedback. The disaster hazard index and the disaster risk index of the disaster-prone body are obtained through the geological disaster prone condition factors in the region and the disaster risk factors in the real-time multi-source meteorological data. Then, the disaster risk index is obtained based on the disaster hazard index and the disaster risk index of the disaster-prone body, so that the assessment system can still predict the target variables with high accuracy in the absence of some characteristic variables, thereby improving the efficiency and applicability of meteorological disaster risk assessment. At the same time, the geological disaster prone condition factors are obtained through terrain feature information, terrain structure information, soil condition information, groundwater level information and vegetation cover information, thereby avoiding the influence of the special terrain in the region on the risk assessment of the assessment system. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A flowchart of a meteorological disaster risk assessment method based on multi-source data feedback proposed by the present invention;

[0079] Figure 2 A flowchart for constructing a secondary region library to be evaluated in the present invention;

[0080] Figure 3 It is a flow chart for constructing a secondary area library to be evaluated in the present invention;

[0081] Figure 4 This is a system framework diagram of a meteorological disaster risk assessment system based on multi-source data feedback proposed by the present invention. DETAILED DESCRIPTION

[0082] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0083] Reference Figure 1 - Figure 4 As shown, a meteorological disaster risk assessment method based on multi-source data feedback includes:

[0084] Obtain information on the area to be assessed that contains special terrain, including mountains, hills, plains, basins and wetlands;

[0085] Divide the area to be assessed based on special terrain and information of the area to be assessed, and obtain a secondary database of areas to be assessed;

[0086] Obtain the agricultural exposure, population exposure and economic exposure of the secondary areas to be assessed in the secondary areas to be assessed database, divide the secondary areas to be assessed, and establish a secondary area to be assessed database;

[0087] Obtain geological disaster proneness condition factors of the secondary to-be-assessed areas in the secondary to-be-assessed areas database, obtain real-time multi-source meteorological data, and extract disaster risk factors from the real-time multi-source meteorological data;

[0088] According to the disaster risk factors and geological disaster proneness factors, the disaster risk index of the secondary to-be-assessed area in the secondary to-be-assessed area database is obtained;

[0089] Use the disaster risk index to conduct risk assessment on the secondary areas to be assessed in the secondary area to be assessed database, obtain the disaster risk level, and mark the secondary areas to be assessed;

[0090] Based on the marked secondary areas to be assessed, the areas to be assessed are assessed to obtain a comprehensive disaster risk index for the areas to be assessed;

[0091] The comprehensive disaster risk index of the area to be assessed is used to assess the area to be assessed, and a meteorological disaster prevention plan is formulated based on the assessment results.

[0092] Furthermore, the area to be evaluated is divided based on the special terrain and the information of the area to be evaluated, and a secondary area library to be evaluated is obtained, which specifically includes:

[0093] Based on the information of the area to be evaluated, the location information of the area to be evaluated is obtained, and the maximum area value of the area to be evaluated is determined;

[0094] According to the location information of the area to be evaluated, the regional topographic map is consulted to obtain the topographic feature information of the area to be evaluated;

[0095] A secondary division window is set based on the maximum area value of the area to be evaluated, and the area to be evaluated is intercepted by sliding the secondary division window to establish a secondary division area library to be evaluated;

[0096] Based on the terrain feature information and the special terrain, obtain the special terrain category index of the secondary division area to be evaluated in the secondary division area to be evaluated library, and determine the special terrain category threshold;

[0097] Based on the special terrain type threshold and the special terrain type index, the secondary division area to be evaluated in the secondary division area to be evaluated library is divided to establish a secondary division area to be evaluated library;

[0098] Obtaining comprehensive special terrain information of the secondary divided area to be evaluated in the secondary divided area to be evaluated database;

[0099] According to the comprehensive information of special terrain and the location information of the area to be assessed, the terrain division impact index of the area to be assessed in the secondary division is obtained;

[0100] The secondary division areas to be assessed in the secondary division area library are divided according to the terrain division influence index of the secondary division areas to be assessed, and a secondary division area library is established.

[0101] Specifically, based on the information of the area to be evaluated, the location information of the area to be evaluated is obtained, and the maximum area value of the area to be evaluated is determined. The information of the area to be evaluated includes information such as the geographical location of the area to be evaluated. The geographical location information of the area to be evaluated can be used to dispatch surveying and mapping personnel or to determine the maximum area value of the area to be evaluated from the initial topographic map. If it is chosen to dispatch surveying and mapping personnel to survey the area to be evaluated, the area to be evaluated will be reduced according to a fixed ratio of the map, and the surveyed topographic map will be compared with the initial topographic map, and the areas in the surveyed topographic map of the area to be evaluated that are different from the initial topographic map will be added to the initial topographic map. If the maximum area value of the area to be evaluated is determined from the initial topographic map, the center point of the area to be evaluated in the initial topographic map is first determined, and the edge line of the area to be evaluated in the initial topographic map is connected from the center point to obtain several line segments, and a circle is drawn with the longest line segment as the radius and the center point as the center to obtain a circle with a maximum area, and the area of ​​the circle is the maximum area value of the area to be evaluated.

[0102] Furthermore, according to the location information of the area to be evaluated, the regional topographic map is consulted to obtain the topographic feature information of the area to be evaluated, wherein the regional topographic map is the above-mentioned initial topographic map or the initial topographic map after supplementation, and the topographic feature information includes the altitude, slope, water body distribution and other information of the area to be evaluated. A secondary division window is set based on the maximum area value of the area to be evaluated, and the area to be evaluated is intercepted by sliding the secondary division window, and a secondary division area library to be evaluated is established. A square is drawn with the diameter of the maximum area circle as the side length to obtain the maximum area square, and the center point of the maximum area square and the center of the maximum area circle are the same coordinates, and one quarter of one side of the maximum area square is the length value of the secondary division window, and the secondary division window is a square.

[0103] Wherein, based on the terrain feature information and the special terrain, the special terrain type index of the secondary division area to be evaluated in the secondary division area to be evaluated library is obtained, and the special terrain type threshold is determined. The special terrain type index is the total number of types of special terrain contained in the secondary division area to be evaluated. The special terrain type index of the secondary division area to be evaluated is obtained by comparing the terrain feature information with the special terrain, and the special terrain type threshold is determined. The special terrain type threshold is a variable threshold with values ​​of 1, 2 and 3. The secondary division area to be evaluated with a special terrain type index greater than or equal to 3 is divided into the same column in the secondary division area to be evaluated library, the secondary division area to be evaluated with a special terrain type index equal to 2 is divided into the same column in the secondary division area to be evaluated library, and the secondary division area to be evaluated with a special terrain type index equal to 1 is divided into the same column. Finally, the secondary division areas to be evaluated in these three columns are respectively divided into the secondary division area to be evaluated library, and are arranged in descending order according to the special terrain type index of the secondary division area to be evaluated. Wherein, the area information in the secondary division area to be evaluated library is named as the secondary division area to be evaluated.

[0104] It can be understood that the special terrain comprehensive information of the secondary division area to be evaluated includes the altitude, height difference, slope, terrain complexity, etc. of the mountains, as well as the slope change, height characteristics, soil type, fertility, etc. of the hills, the terrain depression degree, water body distribution, etc. of the plains, and the water level change and wetness of the wetlands. Based on the special terrain comprehensive information and the location information of the area to be evaluated, the terrain division impact index of the secondary division area to be evaluated can be obtained by Obtain the terrain division impact index of the secondary division area to be evaluated, where T impact is the terrain division influence index, α m is the weight of the mth characteristic index of the special terrain comprehensive information of the secondary division area to be evaluated, C m is the mth characteristic index of the special terrain comprehensive information of the secondary division area to be evaluated, n is the total number of characteristic indexes of the special terrain comprehensive information of the secondary division area to be evaluated, and the characteristic index is the information that the special terrain comprehensive information of the secondary division area to be evaluated is easily affected by severe weather such as heavy rain. For example, during heavy rain, the mountains will cause landslides and mud-rock flows due to their altitude, height difference, slope, etc., while the hills will cause soil erosion and small-scale landslides due to their slope changes, height characteristics, soil types, etc. The plain terrain will cause water accumulation due to its terrain depression and water distribution, resulting in floods and urban waterlogging and other disasters. At this time, it is necessary to quantify the special terrain comprehensive information and substitute it into C m For example, if the altitude of the mountain is 800, then C m The value of is 800, and α m It can be judged based on historical data, α mThe value of α is between 0 and 1. For example, during heavy rain, the higher the altitude of the mountain, the higher the probability of landslides and mud-rock flows. m The higher the value of .

[0105] Furthermore, the agricultural exposure, population exposure and economic exposure of the secondary areas to be assessed in the secondary areas to be assessed database are obtained, and the secondary areas to be assessed are divided to establish a secondary area database to be assessed, which specifically includes:

[0106] Respectively obtain the area value of the secondary to-be-assessed area, the area value of the population activity area and the maximum economic value of the region of the secondary to-be-assessed area in the secondary to-be-assessed area database;

[0107] Obtain the agricultural area value, the total area of ​​intensive population activity area and the maximum regional income value in the secondary to-be-assessed area in the secondary to-be-assessed area database respectively;

[0108] The ratio of the agricultural area value in the secondary area to be assessed to the area value of the secondary area to be assessed is taken as the agricultural exposure of the secondary area to be assessed;

[0109] The ratio of the total area of ​​densely populated areas to the area of ​​densely populated areas in the secondary area to be assessed is used as the population exposure of the secondary area to be assessed.

[0110] The ratio of the maximum regional income value to the maximum regional economic value in the secondary area to be assessed is used as the economic exposure of the secondary area to be assessed;

[0111] Determine the economic exposure threshold, divide the secondary areas to be assessed according to their economic exposure, obtain high economic exposure areas, medium economic exposure areas and low economic exposure areas, and establish secondary areas to be assessed;

[0112] The high economic exposure areas, medium economic exposure areas and low economic exposure areas are respectively divided into different columns in the secondary area database to be assessed;

[0113] A four-quadrant model is constructed to divide the high economic exposure areas, medium economic exposure areas and low economic exposure areas into different columns in the secondary area library for evaluation.

[0114] Specifically, the area value of the population activity area is the area value of a specific area in the secondary area to be assessed when there is no disaster, such as squares, shopping malls and other areas with high population density. The maximum economic value of the region is the total regional economic value of the secondary area to be assessed in the past. The maximum income value of the region is the maximum economic value that can be created by a specific area in the secondary area to be assessed, such as shopping malls, tourist attractions, etc.

[0115] It is understandable that the four-quadrant model is constructed to divide the region into four quadrants by subdividing the high economic exposure area, the medium economic exposure area and the low economic exposure area respectively, and each quadrant represents a different exposure combination: high agricultural exposure, high population exposure; high agricultural exposure, low population exposure; low agricultural exposure, high population exposure; low agricultural exposure, low population exposure.

[0116] Furthermore, the geological disaster proneness condition factors of the secondary area to be assessed are obtained, and real-time multi-source meteorological data are obtained, and the disaster risk factors in the real-time multi-source meteorological data are extracted, including:

[0117] Based on the terrain feature information of the area to be evaluated, obtaining the secondary area terrain feature information of the secondary area to be evaluated;

[0118] According to the regional topographic map, obtain the location information of the secondary area to be assessed, and obtain the terrain structure information, soil condition information, groundwater level information and vegetation cover information of the secondary area to be assessed;

[0119] Based on the secondary regional terrain feature information, terrain structure information, soil condition information, groundwater level information and vegetation coverage information of the secondary area to be assessed, obtain the geological disaster proneness condition factors;

[0120] Based on the real-time multi-source meteorological data obtained from the Meteorological Bureau, the maximum hourly precipitation forecast, maximum daily precipitation forecast, process cumulative rainfall forecast and rainstorm forecast duration days in the real-time multi-source meteorological data are extracted to obtain the disaster risk factors.

[0121] Furthermore, based on the disaster risk factors and geological disaster proneness factors, the disaster risk index of the secondary area to be assessed is obtained, including:

[0122] Construct k weak regression models and obtain historical multi-source meteorological data, historical terrain structure information, historical soil condition information, historical groundwater level information and historical vegetation cover information of the secondary assessment area;

[0123] Based on the historical topographic structure information, historical soil condition information, historical groundwater level information and historical vegetation coverage information of the secondary area to be assessed as a disaster risk sample set, the historical geological disaster proneness condition factors are obtained;

[0124] Extract historical disaster risk factors from historical multi-source meteorological data, and add the historical disaster risk factors to the disaster risk sample set;

[0125] The disaster risk sample set is divided into a training set and a validation set in a ratio of 7:3;

[0126] The historical disaster risk factors and historical geological disaster prone condition factors in the training set are used as disaster risk characteristic variables, i data points are set, and each data point has a disaster risk characteristic vector converted from the disaster risk characteristic variables;

[0127] Combine k weak regression models into a strong regression model, and train and verify the strong regression model based on the disaster risk sample set to obtain the disaster risk index;

[0128] Based on the disaster risk index, the historical agricultural exposure, historical population exposure and historical economic exposure of the secondary assessment area are obtained;

[0129] The historical agricultural exposure, historical population exposure and historical economic exposure of the secondary assessed area are used as the disaster risk sample set of the disaster-prone body;

[0130] Integrate the data with the disaster hazard sample set to obtain an integrated sample set and obtain an integrated feature vector;

[0131] The strong regression model is trained and verified based on the integrated sample set to obtain the disaster risk index of the disaster-prone body;

[0132] Obtain disaster risk index based on disaster hazard index and disaster risk index of disaster-bearing body;

[0133] Among them, the calculation formula for obtaining the disaster hazard index and the disaster risk index of the disaster-bearing body based on the strong regression model is:

[0134]

[0135] In the formula, is the model prediction value, k represents the number of weak regression models, F is the collection space of weak regression models, X i represents the feature vector of the i-th data point, Represents the predicted value and the target true value y i The training error between , Ω is the sum of the complexity of the penalty model, γ and λ are the model penalty coefficients, and Ω(f) are both loss functions;

[0136] If X i is the disaster risk characteristic vector, then the output of the strong regression model is is the disaster risk index, if X i To integrate the feature vector, the output of the strong regression model is It is the disaster risk index of the disaster-prone body.

[0137] Furthermore, the disaster risk index is used to conduct risk assessment on the secondary areas to be assessed, obtain the disaster risk level, and mark the secondary areas to be assessed, including:

[0138] The second-level assessment areas with the highest 20% disaster risk index are classified as high-risk areas and marked in red;

[0139] The 60% of the secondary assessment areas with disaster risk index in the middle are classified as medium-risk areas and marked in yellow;

[0140] The secondary assessment areas with the lowest disaster risk index in the 20% are classified as low-risk areas and marked in green.

[0141] Furthermore, the area to be assessed is assessed based on the marked secondary area to be assessed, and a comprehensive disaster risk index of the area to be assessed is obtained, including:

[0142] Based on the location information of the secondary area to be evaluated and the location information of the area to be evaluated, associating the marked secondary area to be evaluated with the area to be evaluated;

[0143] The disaster risk indexes of the secondary areas to be assessed that are related to the area to be assessed in the secondary area to be assessed database are added together to obtain a comprehensive disaster risk index for the area to be assessed.

[0144] Furthermore, a meteorological disaster risk assessment system based on multi-source data feedback is proposed, which is used to implement any of the above-mentioned assessment methods, including:

[0145] The main control module is used to receive data transmitted by the acquisition module, the division module, the evaluation module and the display module through the transmission module, and transmit the processed data to the acquisition module, the division module, the evaluation module and the display module through the transmission module, and is also used to build a regional library;

[0146] A division module is used to divide the area to be evaluated based on special terrain and information of the area to be evaluated, divide the secondary area to be evaluated, use the secondary division window to slide and intercept the area to be evaluated, divide the secondary division area to be evaluated in the secondary division area to be evaluated library, and divide the secondary division area to be evaluated in the secondary division area to be evaluated library;

[0147] An evaluation module, which is used to evaluate data information and divided areas;

[0148] The collection module is used to collect information about the area to be assessed, the agricultural exposure, population exposure and economic exposure of the secondary area to be assessed, and collect real-time multi-source meteorological data and historical multi-source meteorological data;

[0149] A transmission module, which is used for data transmission between the main control module, the partitioning module, the evaluation module and the display module;

[0150] Display module, the display module is used to display the data and information processing process and results of the main control module.

[0151] Furthermore, the main control module includes:

[0152] A receiving unit, the receiving unit is used to obtain information about the area to be evaluated containing special terrain, obtain data and information collected by the acquisition module, obtain information about the area divided by the division module, and obtain evaluation results of the evaluation module;

[0153] A processing unit, the processing unit is used to obtain geological disaster proneness condition factors of the secondary to-be-assessed area in the secondary to-be-assessed area library, extract disaster risk factors in real-time multi-source meteorological data, and obtain disaster risk indexes of the secondary to-be-assessed area in the secondary to-be-assessed area library according to the disaster risk factors and the geological disaster proneness condition factors;

[0154] A construction unit is used to construct a secondary area library to be evaluated, a secondary area library to be evaluated, a secondary divided area library to be evaluated, and a secondary divided area library to be evaluated.

[0155] Furthermore, the assessment modules include:

[0156] The data evaluation unit is used to obtain the special terrain type index of the secondary-divided area to be evaluated in the secondary-divided area to be evaluated database, and determine the special terrain type threshold, obtain the terrain division impact index of the secondary-divided area to be evaluated based on the special terrain comprehensive information and the location information of the area to be evaluated, and determine the economic exposure threshold;

[0157] The regional assessment unit is used to conduct risk assessment on the secondary areas to be assessed in the secondary areas to be assessed library, to assess the areas to be assessed based on the marked secondary areas to be assessed, and to assess the areas to be assessed using the comprehensive disaster risk index of the areas to be assessed.

[0158] In summary, the advantages of the present invention are: the secondary divided area to be evaluated is divided into regions by the terrain division influence index, which is convenient for subsequent risk assessment of the area to be evaluated, and the terrain division influence index is obtained by the special terrain comprehensive information and the location information of the area to be evaluated, as well as a fixed weighting formula, which comprehensively considers the special terrain in the area when it is subjected to severe environments such as heavy rain, and what impact will it have on meteorological disasters in the area. At the same time, the weight is determined by historical data, and the weight setting will not be recalculated due to the lack of some influencing factors, saving time and resources;

[0159] Through the geological disaster proneness factors in the region and the disaster risk factors in the real-time multi-source meteorological data, the disaster hazard index and the disaster risk index of the disaster-prone body are obtained. Then, based on the disaster hazard index and the disaster risk index of the disaster-prone body, the disaster risk index is obtained, so that the assessment system can still predict the target variables with high accuracy in the absence of some characteristic variables, thereby improving the efficiency and applicability of meteorological disaster risk assessment. At the same time, the geological disaster proneness factors are obtained through terrain feature information, terrain structure information, soil condition information, groundwater level information and vegetation cover information, so as to avoid the influence of the special terrain in the region on the risk assessment of the assessment system.

[0160] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A meteorological disaster risk assessment method based on multi-source data feedback, characterized in that: include: Obtaining information on the area to be assessed that contains special terrain, including mountains, hills, plains, basins and wetlands; Divide the area to be assessed based on special terrain and information of the area to be assessed, and obtain a secondary database of areas to be assessed; Obtain the agricultural exposure, population exposure and economic exposure of the secondary areas to be assessed in the secondary areas to be assessed database, divide the secondary areas to be assessed, and establish a secondary area to be assessed database; Obtain geological disaster proneness condition factors of the secondary to-be-assessed areas in the secondary to-be-assessed areas database, obtain real-time multi-source meteorological data, and extract disaster risk factors from the real-time multi-source meteorological data; According to the disaster risk factors and geological disaster proneness factors, the disaster risk index of the secondary to-be-assessed area in the secondary to-be-assessed area database is obtained; Use the disaster risk index to conduct risk assessment on the secondary areas to be assessed in the secondary area to be assessed database, obtain the disaster risk level, and mark the secondary areas to be assessed; Based on the marked secondary areas to be assessed, the areas to be assessed are assessed to obtain a comprehensive disaster risk index for the areas to be assessed; The comprehensive disaster risk index of the area to be assessed is used to assess the area to be assessed, and a meteorological disaster prevention plan is formulated based on the assessment results.

2. A meteorological disaster risk assessment method based on multi-source data feedback according to claim 1, characterized in that: The method of dividing the area to be evaluated based on the special terrain and the information of the area to be evaluated and obtaining a secondary area library to be evaluated specifically includes: Based on the information of the area to be evaluated, the location information of the area to be evaluated is obtained, and the maximum area value of the area to be evaluated is determined; According to the location information of the area to be evaluated, the regional topographic map is consulted to obtain the topographic feature information of the area to be evaluated; A secondary division window is set based on the maximum area value of the area to be evaluated, and the area to be evaluated is intercepted by sliding the secondary division window to establish a secondary division area library to be evaluated; Based on the terrain feature information and the special terrain, obtain the special terrain category index of the secondary division area to be evaluated in the secondary division area to be evaluated library, and determine the special terrain category threshold; Based on the special terrain type threshold and the special terrain type index, the secondary division area to be evaluated in the secondary division area to be evaluated library is divided to establish a secondary division area to be evaluated library; Obtaining comprehensive special terrain information of the secondary divided area to be evaluated in the secondary divided area to be evaluated database; According to the comprehensive information of special terrain and the location information of the area to be assessed, the terrain division impact index of the area to be assessed in the secondary division is obtained; The secondary division areas to be assessed in the secondary division area library are divided according to the terrain division influence index of the secondary division areas to be assessed, and a secondary division area library is established.

3. A meteorological disaster risk assessment method based on multi-source data feedback according to claim 2, characterized in that: The obtaining of agricultural exposure, population exposure and economic exposure of the secondary areas to be assessed in the secondary areas to be assessed database, and dividing the secondary areas to be assessed to establish a secondary area to be assessed database, specifically includes: Respectively obtain the area value of the secondary to-be-assessed area, the area value of the population activity area and the maximum economic value of the region of the secondary to-be-assessed area in the secondary to-be-assessed area database; Obtain the agricultural area value, the total area of ​​intensive population activity area and the maximum regional income value in the secondary to-be-assessed area in the secondary to-be-assessed area database respectively; The ratio of the agricultural area value in the secondary area to be assessed to the area value of the secondary area to be assessed is taken as the agricultural exposure of the secondary area to be assessed; The ratio of the total area of ​​densely populated areas to the area of ​​densely populated areas in the secondary area to be assessed is used as the population exposure of the secondary area to be assessed. The ratio of the maximum regional income value to the maximum regional economic value in the secondary area to be assessed is used as the economic exposure of the secondary area to be assessed; Determine the economic exposure threshold, divide the secondary areas to be assessed according to their economic exposure, obtain high economic exposure areas, medium economic exposure areas and low economic exposure areas, and establish secondary areas to be assessed; The high economic exposure areas, medium economic exposure areas and low economic exposure areas are respectively divided into different columns in the secondary area database to be assessed; A four-quadrant model is constructed to divide the high economic exposure areas, medium economic exposure areas and low economic exposure areas into different columns in the secondary area library for evaluation.

4. A meteorological disaster risk assessment method based on multi-source data feedback according to claim 3, characterized in that: The step of obtaining the geological disaster proneness condition factors of the secondary area to be assessed, obtaining real-time multi-source meteorological data, and extracting disaster risk factors from the real-time multi-source meteorological data specifically includes: Based on the terrain feature information of the area to be evaluated, obtaining the secondary area terrain feature information of the secondary area to be evaluated; According to the regional topographic map, obtain the location information of the secondary area to be assessed, and obtain the terrain structure information, soil condition information, groundwater level information and vegetation cover information of the secondary area to be assessed; Based on the secondary regional terrain feature information, terrain structure information, soil condition information, groundwater level information and vegetation coverage information of the secondary area to be assessed, obtain the geological disaster proneness condition factors; Based on the real-time multi-source meteorological data obtained from the Meteorological Bureau, the maximum hourly precipitation forecast, maximum daily precipitation forecast, process cumulative rainfall forecast and rainstorm forecast duration days in the real-time multi-source meteorological data are extracted to obtain the disaster risk factors.

5. A meteorological disaster risk assessment method based on multi-source data feedback according to claim 4, characterized in that: The disaster risk index of the secondary area to be assessed is obtained based on the disaster risk factors and geological disaster prone condition factors, specifically including: Construct k weak regression models and obtain historical multi-source meteorological data, historical terrain structure information, historical soil condition information, historical groundwater level information and historical vegetation cover information of the secondary assessment area; Based on the historical topographic structure information, historical soil condition information, historical groundwater level information and historical vegetation coverage information of the secondary area to be assessed as a disaster risk sample set, the historical geological disaster proneness condition factors are obtained; Extract historical disaster risk factors from historical multi-source meteorological data, and add the historical disaster risk factors to the disaster risk sample set; The disaster risk sample set is divided into a training set and a validation set in a ratio of 7:3; The historical disaster risk factors and historical geological disaster prone condition factors in the training set are used as disaster risk characteristic variables, i data points are set, and each data point has a disaster risk characteristic vector converted from the disaster risk characteristic variables; Combine k weak regression models into a strong regression model, and train and verify the strong regression model based on the disaster risk sample set to obtain the disaster risk index; Based on the disaster risk index, the historical agricultural exposure, historical population exposure and historical economic exposure of the secondary assessment area are obtained; The historical agricultural exposure, historical population exposure and historical economic exposure of the secondary assessed area are used as the disaster risk sample set of the disaster-prone body; Integrate the data with the disaster hazard sample set to obtain an integrated sample set and obtain an integrated feature vector; The strong regression model is trained and verified based on the integrated sample set to obtain the disaster risk index of the disaster-prone body; Obtain disaster risk index based on disaster hazard index and disaster risk index of disaster-bearing body; Among them, the calculation formula for obtaining the disaster hazard index and the disaster risk index of the disaster-bearing body based on the strong regression model is: In the formula, is the model prediction value, k represents the number of weak regression models, F is the collection space of weak regression models, X i represents the feature vector of the i-th data point, Represents the predicted value and the target true value y i The training error between , Ω is the sum of the complexity of the penalty model, γ and λ are the model penalty coefficients, and Ω(f) are both loss functions; If X i is the disaster risk characteristic vector, then the output of the strong regression model is is the disaster risk index, if X i To integrate the feature vector, the output of the strong regression model is It is the disaster risk index of the disaster-prone body.

6. A meteorological disaster risk assessment method based on multi-source data feedback according to claim 5, characterized in that: The method of using the disaster risk index to conduct risk assessment on the secondary area to be assessed, obtaining the disaster risk level, and marking the secondary area to be assessed specifically includes: The second-level assessment areas with the highest 20% disaster risk index are classified as high-risk areas and marked in red; The 60% of the secondary assessment areas with disaster risk index in the middle are classified as medium-risk areas and marked in yellow; The secondary assessment areas with the lowest disaster risk index in the 20% are classified as low-risk areas and marked in green.

7. A meteorological disaster risk assessment method based on multi-source data feedback according to claim 6, characterized in that: The step of evaluating the area to be evaluated based on the marked secondary area to be evaluated to obtain a comprehensive disaster risk index of the area to be evaluated specifically includes: Based on the location information of the secondary area to be evaluated and the location information of the area to be evaluated, associating the marked secondary area to be evaluated with the area to be evaluated; The disaster risk indexes of the secondary areas to be assessed that are related to the area to be assessed in the secondary area to be assessed database are added together to obtain a comprehensive disaster risk index for the area to be assessed.

8. A meteorological disaster risk assessment system based on multi-source data feedback, used to implement the assessment method according to any one of claims 1 to 7, characterized in that: include: A main control module, which is used to receive data transmitted by the acquisition module, the division module, the evaluation module and the display module through the transmission module, and transmit the processed data to the acquisition module, the division module, the evaluation module and the display module through the transmission module, and is also used to build a regional library; A division module, the division module is used to divide the area to be evaluated based on special terrain and information of the area to be evaluated, divide the secondary area to be evaluated, use the secondary division window to slide and intercept the area to be evaluated, divide the secondary division area to be evaluated in the secondary division area to be evaluated library, and divide the secondary division area to be evaluated in the secondary division area to be evaluated library; An evaluation module, wherein the evaluation module is used to evaluate the data information and the divided areas; A collection module, the collection module is used to collect information of the area to be evaluated, agricultural exposure, population exposure and economic exposure of the secondary area to be evaluated, and collect real-time multi-source meteorological data and historical multi-source meteorological data; A transmission module, which is used for data transmission between the main control module, the division module, the evaluation module and the display module; Display module, the display module is used to display the data and information processing process and results of the main control module.

9. A meteorological disaster risk assessment system based on multi-source data feedback according to claim 8, characterized in that: The main control module comprises: A receiving unit, the receiving unit is used to obtain information about the area to be evaluated containing special terrain, obtain data and information collected by the acquisition module, obtain information about the area divided by the division module, and obtain evaluation results of the evaluation module; A processing unit, the processing unit is used to obtain geological disaster proneness condition factors of the secondary to-be-assessed area in the secondary to-be-assessed area library, extract disaster risk factors in real-time multi-source meteorological data, and obtain disaster risk indexes of the secondary to-be-assessed area in the secondary to-be-assessed area library according to the disaster risk factors and the geological disaster proneness condition factors; A construction unit is used to construct a secondary to-be-evaluated region library, a secondary to-be-evaluated region library, a secondary divided to-be-evaluated region library and a secondary divided to-be-evaluated region library.

10. A meteorological disaster risk assessment system based on multi-source data feedback according to claim 8, characterized in that: The evaluation module includes: A data evaluation unit, the data evaluation unit is used to obtain a special terrain type index of a secondary-divided area to be evaluated in a secondary-divided area to be evaluated library, determine a special terrain type threshold, obtain a terrain division impact index of a secondary-divided area to be evaluated based on special terrain comprehensive information and location information of the area to be evaluated, and determine an economic exposure threshold; The regional assessment unit is used to perform risk assessment on the secondary to-be-assessed areas in the secondary to-be-assessed area library, assess the to-be-assessed areas based on the marked secondary to-be-assessed areas, and assess the to-be-assessed areas using a comprehensive disaster risk index of the to-be-assessed areas.

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