Geological disaster risk area division method, system and product based on slope units

Abstract

The invention provides a geological disaster risk area division method, system and product based on slope units, and the method comprises the steps: carrying out the slope unit division of initial geographic model data, and generating a plurality of slope units; performing terrain analysis on the initial geographic model data to generate a terrain evaluation factor group of each slope unit; performing multi-item geological disaster thematic evaluation on each slope unit by utilizing the comprehensive evaluation factor group of each slope unit, and generating risk grade data of each slope unit; wherein the comprehensive evaluation factor group at least comprises a terrain evaluation factor group; based on the risk level data of the slope unit and the pre-input administrative partition boundary map layer, the geological disaster risk partition map is generated, a standardized and normalized division process for improving the geological disaster risk area is provided, and the working efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular to a method for dividing geological disaster risk areas based on slope units. Background Art

[0002] The division of geological hazard risk zones is based on the results of geological hazard risk investigation and evaluation, and the target area is divided according to the geological hazard risk level (extremely high risk, high risk, medium risk, and low risk). In the division results, the geological hazard risk level in each independent area is the same, and the geological hazard risk level in adjacent areas is different. The results of the geological hazard risk zone division can serve as an effective reference for the government and relevant decision-making departments to carry out geological hazard risk prevention and control and risk management. However, there is currently no complete method flow specifically for the division of geological hazard risk zones, which needs to be done manually step by step, and the division standards are not unified, which is inefficient. Summary of the invention

[0003] The embodiments of the present application provide a method, system and product for dividing geological hazard risk areas based on slope units, which provide a normalized, standardized and unified process for dividing geological hazard risk areas, thereby improving division efficiency and prediction accuracy.

[0004] In a first aspect, an embodiment of the present application provides a method for dividing geological disaster risk areas based on slope units, the method comprising:

[0005] Dividing the initial geographic model data into slope units to generate a plurality of slope units;

[0006] Performing terrain analysis on the initial geographic model data to generate a terrain evaluation factor group for each slope unit; wherein the terrain evaluation factor group indicates the terrain characteristics of the slope unit;

[0007] Using the comprehensive evaluation factor group of each slope unit, multiple geological disaster special evaluations are conducted on each slope unit to generate risk level data for each slope unit; wherein the comprehensive evaluation factor group at least includes a terrain evaluation factor group;

[0008] Based on the risk level data of the slope unit and the pre-input administrative division boundary layer, a geological hazard risk zoning map is generated; the geological hazard risk zoning map indicates the risk zoning data of each administrative division.

[0009] In some embodiments, terrain analysis is performed on the initial geographic model data to generate a set of terrain evaluation factors for each slope unit, including:

[0010] The terrain parameters of the initial geographic model data are extracted to generate multiple terrain parameter groups, which include aspect values, slope values, slope height values ​​and curvature values; the aspect values, slope values, slope height values ​​and curvature values ​​contained in each slope unit are statistically processed respectively, and a terrain evaluation factor group is formed according to the results of the statistical processing; wherein the statistical processing includes one or more of the following processing: mode processing, median processing, average processing, maximum value processing and minimum value processing.

[0011] In some embodiments, after generating a set of terrain evaluation factors for each slope unit, the method further comprises:

[0012] Obtain pre-input geological hazards and hidden danger data: geological hazards and hidden danger data are used to indicate the spatial location of geological hazards that have occurred and the spatial location of geological hazards; take geological hazards and hidden danger data as dependent variables, and conduct correlation analysis on each evaluation factor in the comprehensive evaluation factor group. If the correlation between the evaluation factor and the dependent variable does not pass the significance test, the evaluation factor will be deleted from the comprehensive evaluation factor group; perform weight analysis on the comprehensive evaluation factor group according to the geological hazards and hidden danger data to generate the weight value of each evaluation factor; wherein, the correlation analysis adopts one or more of the following algorithms: Pearson coefficient method, Kendall coefficient method and Spearman coefficient method; the weight analysis adopts hierarchical analysis method.

[0013] In some embodiments, the comprehensive evaluation factor group further includes one or more of the following: an evaluation factor for the thickness of the overburden layer, an evaluation factor for the origin of the soil mass, an evaluation factor for the structure of the rock mass, an evaluation factor for the structure of the slope, an evaluation factor for the height of the cut slope, and an evaluation factor for the distance from the geological structure;

[0014] The comprehensive evaluation factor group of each slope unit is used to conduct multiple geological disaster special evaluations on each slope unit to generate risk level data for each slope unit, including:

[0015] Based on the geological disaster and hidden danger data and the comprehensive evaluation factor group, the geological disaster susceptibility of each slope unit is evaluated to generate the susceptibility index data of each slope unit; based on the susceptibility index data of each slope unit, the rainfall data that induces geological disasters, and the stability coefficient of the slope unit, the geological disaster hazard of each slope unit is evaluated to generate the hazard index data of each slope unit; based on the pre-input personnel data, building data and road data, the vulnerability of the disaster-bearing body of each slope unit is evaluated to generate the personnel vulnerability index data, building vulnerability index data and road vulnerability index data of each slope unit; based on the hazard index data, personnel vulnerability index data, building vulnerability index data and road vulnerability index data, the risk level data of each slope unit is calculated.

[0016] In some embodiments, a geological hazard susceptibility evaluation is performed on each slope unit based on the geological hazard and hidden danger data and the comprehensive evaluation factor group to generate susceptibility index data for each slope unit, including:

[0017] Based on a plurality of preset susceptibility evaluation algorithms, the geological disaster susceptibility is evaluated for the geological disaster and hidden danger data and the comprehensive evaluation factor group of each slope unit, and a plurality of initial susceptibility data are generated; based on a preset test algorithm, the pre-input geological disaster and hidden danger data and each type of initial susceptibility data are tested, and a test report is output; the test report is provided to the user; the test report is provided to the user and the user's selection instruction is received, and one of the initial susceptibility data is used as susceptibility index data based on the selection instruction; wherein the preset test algorithm includes one or more of the following: disaster point proportion method test, ROC curve test, slope unit field survey consistency proportion method test.

[0018] In some embodiments, the comprehensive evaluation factor group is embodied in a vector layer and / or a raster layer; for the comprehensive evaluation factor group embodied in a vector layer, the preset susceptibility evaluation algorithm includes one or more of the following: a comprehensive index method model, a principal component analysis method model, a random forest algorithm model, a support vector machine algorithm model, a logistic regression algorithm model, and a neural network model; for the comprehensive evaluation factor group embodied in a raster layer, the preset susceptibility evaluation algorithm includes one or more of the following algorithms: an information method, a fuzzy comprehensive evaluation method, and an evidence weight method; the algorithms used for geological hazard risk assessment include one or more of the following: calculating the hazard index based on a 24-hour extreme rainfall hypothesis algorithm, calculating the hazard index based on the effective rainfall before the geological hazard risk group, and calculating the hazard index based on the slope stability of the slope unit; the formula for calculating the hazard index based on the effective rainfall before the geological hazard risk group is as follows:

[0019] Among them, L is the value of 24-hour rainfall condition, L 有效 is the effective precipitation before the occurrence of geological disasters in history, Y i is the susceptibility index data of the slope unit, Y max is the maximum value of the susceptibility index data, X i is the effective precipitation on the i-th day before the geological disaster occurs; n is the number of effective rainfall days before the geological disaster occurs; K i is the precipitation attenuation coefficient on the ith day before the geological disaster occurs, H i It is the hazard index data of the slope unit.

[0020] In some embodiments, the risk level data of each slope unit is calculated based on the hazard index data, the personnel vulnerability index data, the building vulnerability index data and the road vulnerability index data, including:

[0021] Obtain pre-input personnel quantity data, building value data, and road value data; generate the casualty risk level and economic loss risk level for each slope unit based on the hazard index data, personnel quantity data, personnel vulnerability index data, building value data, building vulnerability index data, road value data, and road vulnerability data; for each slope unit, use the higher level of casualty risk level and economic loss risk level as the risk level data of the slope unit.

[0022] In some embodiments, the risk level data of the slope unit and the pre-input administrative division boundary layer are used to generate a geological disaster risk zoning map, including:

[0023] The adjacent slope units with the same risk level data are merged to generate geological hazard risk zone level data; the geological hazard risk zone level data and the administrative division boundary layer are divided and superimposed to generate a geological hazard risk zoning map; among them, one of the principles followed in the division of slope units is: for slope units adjacent to administrative boundaries, the boundary line of the slope unit coincides with the adjacent administrative boundary line.

[0024] In a second aspect, an embodiment of the present application provides a geological disaster risk zone division system based on slope units, the division system comprising:

[0025] The preprocessing module is configured to divide the initial geographic model data into slope units to generate multiple slope units; and to perform terrain analysis on the initial geographic model data to generate a terrain evaluation factor group for each slope unit; wherein the terrain evaluation factor group indicates the terrain characteristics of the slope unit; the geological disaster special evaluation module is configured to use the comprehensive evaluation factor group of each slope unit to perform multiple geological disaster special evaluations on each slope unit to generate risk level data for each slope unit; wherein the comprehensive evaluation factor group at least includes the terrain evaluation factor group; the risk area division module is configured to generate a geological disaster risk zoning map based on the risk level data of the slope unit and the pre-input administrative division boundary layer; the geological disaster risk zoning map indicates the risk zoning data of each administrative division.

[0026] In a third aspect, an embodiment of the present application provides a computer program product, including a computer program, wherein the computer program includes a mapping system as in the first aspect, and when the computer program is executed by a processor, implements the steps of the method described in the second aspect.

[0027] The embodiments of the present application provide a method, system and program product for dividing geological hazard risk areas based on slope units, and propose a complete and standardized method for dividing geological hazard risk areas, which significantly improves work efficiency. The division method uses slope units as basic units for evaluation, covers a variety of prediction model algorithms, and also provides a verification algorithm to facilitate user selection and make the results more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of the process of the division method provided by the present application is shown;

[0029] Figure 2 A schematic diagram of the structure of the partitioning system provided by the present application is shown;

[0030] Figure 3 A schematic diagram showing the working process of a slope unit division module provided by the present application is shown;

[0031] Figure 4 A schematic diagram showing the working process of an evaluation factor extraction module provided by the present application is shown;

[0032] Figure 5 A schematic diagram showing the working process of an evaluation factor correlation analysis module provided by the present application is shown;

[0033] Figure 6 A schematic diagram showing the working process of an evaluation factor weight calculation module provided by the present application is shown;

[0034] Figure 7 A schematic diagram of the working process of a geological disaster susceptibility assessment module provided by the present application is shown;

[0035] Figure 8 A schematic diagram showing the working process of a geological disaster susceptibility assessment result verification module provided by the present application is shown;

[0036] Fig. 9 A schematic diagram of the working process of a geological hazard risk assessment module provided by the present application is shown;

[0037] Fig.10 A schematic diagram of the working process of a geological hazard-bearing body vulnerability assessment module provided by the present application is shown;

[0038] Fig.11 A schematic diagram showing the working process of a geological disaster risk assessment module provided by the present application is shown;

[0039] Fig.12 A schematic diagram of the working process of a slope unit division module provided in the present application is shown. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. It is understood that the specific embodiments described herein are only used to explain the related applications, rather than to limit the present application. It should also be noted that, for the convenience of description, only the parts related to the related applications are shown in the drawings.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0042] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0043] At present, the division of geological disaster risk areas has the following shortcomings:

[0044] (1) The method system for dividing geological hazard risk areas is incomplete. Most of the current methods or studies are aimed at studying or evaluating a certain aspect of geological hazards, and a standardized and complete method system for dividing geological hazard risk areas has not been formed.

[0045] (2) Single evaluation method. The evaluation methods for geological disasters are relatively single. For example, most studies on the susceptibility of geological disasters only use the comprehensive index method for evaluation, and rarely use multiple models for evaluation and comparison to select evaluation results with higher accuracy.

[0046] (3) The accuracy of grid-based evaluation is low. Most existing methods are based on grid units for thematic evaluation of geological hazards. Especially in the study of large-scale geological hazards at a small scale, the comprehensive index method or information method based on grid units is widely used. This method is limited by the selection of the grid unit neighborhood window size, which has a great impact on the extracted indicators, resulting in a decrease in the accuracy of thematic evaluation results.

[0047] In order to solve the above problems, the embodiment of the present application provides a full-process method for the division of geological disaster risk areas based on slope units, providing an effective and reliable technical method for carrying out work in this area.

[0048] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0049] In one embodiment, see Figure 1 , which provides a flow chart of a method for dividing geological hazard risk areas based on slope units. Figure 1 As shown, the division method includes:

[0050] S11: Divide the initial geographic model data into slope units to generate a plurality of slope units.

[0051] It should be noted that the initial geographic model data can be digital elevation model data (DEM), and the specific division method is the hydrological analysis method. The specific principles and steps can be found in the system description. In this way, the division of slope units has taken hydrological factors into consideration, and the internal geological background of each basic evaluation unit (i.e., a slope unit) is relatively consistent, and the evaluation results are more accurate.

[0052] In this way, through step S11, all slope units in the initial geographic model data can be automatically divided, and each slope unit will be processed separately later.

[0053] S12: Perform terrain analysis on the initial geographic model data to generate a set of terrain evaluation factors for each slope unit.

[0054] Here, the terrain evaluation factor group indicates the terrain characteristics of the slope unit, such as slope aspect characteristics, slope gradient characteristics, slope height characteristics, curvature characteristics, etc.

[0055] In a specific embodiment, step S12 specifically includes:

[0056] S121: extracting terrain parameters from the initial geographic model data to generate multiple terrain parameter groups; each terrain parameter group includes a slope aspect value, a slope value, a slope height value, and a curvature value;

[0057] Specifically, (1) for the raster layer formed by the initial geographic model data, the aspect value, slope value, height value and curvature value of the raster are derived from each pixel of the raster surface using a moving 3×3 pixel window (the size of the window can be adjusted according to actual needs), and the aspect raster layer (i.e., the layer corresponding to the aspect value) and the slope raster layer (i.e., the layer corresponding to the slope value) are generated accordingly; (2) the height value of the central pixel of each window is obtained by calculating the difference between the maximum and minimum values ​​in the neighborhood window, and the height raster layer (i.e., the layer corresponding to the height value) is generated; (3) by calculating the second-order derivative of the raster surface, the central pixel of the neighborhood window is fitted with eight adjacent pixels, and the output curvature is divided into the profile curvature value along the maximum slope direction and the plane curvature value perpendicular to the maximum slope direction, and the plane curvature and profile curvature raster layers (i.e., the layers corresponding to the curvature values) are generated accordingly.

[0058] In this example, the slope unit itself includes many pixels, and the size of the slope unit is much larger than the pixel window used for extraction, so each slope unit will generate many slope aspect values, many slope values, many slope height values ​​and many curvature values.

[0059] S122: Perform statistical processing on the aspect value, slope value, slope height value and curvature value contained in each slope unit, and form a terrain evaluation factor group of the slope unit according to the result of the statistical processing; wherein the statistical processing includes one or more of the following processing: mode processing, median processing, average processing, maximum value processing and minimum value processing.

[0060] In one embodiment, the result of the statistical processing may be directly used as the corresponding terrain evaluation factor, or the result of the statistical processing may be cross-calculated and extracted according to a preset evaluation model to form a terrain evaluation factor.

[0061] Thus, through step S122, the raster data is converted into vector data, that is, the terrain evaluation factor group of the slope unit exists in the form of vector data.

[0062] After step S122, the method further includes:

[0063] S123: Obtain pre-input geological disaster and hidden danger data; take the geological disaster and hidden danger data as the dependent variable, and perform correlation analysis on each evaluation factor in the comprehensive evaluation factor group. If the correlation between the evaluation factor and the dependent variable fails the significance test, the evaluation factor is deleted from the comprehensive evaluation factor group.

[0064] It should be noted that the geological disaster and hidden danger data are used to indicate the spatial location of the geological disasters that have occurred and the spatial location of the geological disaster hidden dangers, that is, the spatial distribution data layer of the geological disaster and hidden danger points.

[0065] Here, the correlation analysis uses one or more of the following algorithms: Pearson coefficient method, Kendall coefficient method, and Spearman coefficient method.

[0066] In one example, the pass condition of the significance test can be preset. If the result of the significance test does not meet the pass condition, the corresponding terrain evaluation factor is automatically deleted. In another example, all the results of the significance test can also be provided to the user, and the user can choose to delete the corresponding terrain evaluation factor. Thus, the terrain evaluation factor is more reasonable.

[0067] In this way, the terrain evaluation factors are screened through the inspection algorithm, so that irrelevant and low-correlation terrain evaluation factors are eliminated, so that the subsequent prediction results are more accurate.

[0068] S124: Perform weight analysis on the comprehensive evaluation factor group according to the geological disaster and hidden danger data to generate a weight value for each evaluation factor.

[0069] For example, weight analysis can adopt the hierarchical analysis method.

[0070] In this way, different terrain evaluation factors can be weighted so that relatively important indicators have a greater impact on the results, making the final division results of geological disaster risk areas more reasonable.

[0071] S13: Conduct multiple geological hazard thematic evaluations on each slope unit using the comprehensive evaluation factor group of each slope unit to generate risk level data for each slope unit.

[0072] Here, the comprehensive evaluation factor group includes at least the aforementioned terrain evaluation factor group, and may also include other evaluation factors that have a greater impact on the occurrence of geological disasters, such as the cover layer thickness evaluation factor, the soil genesis evaluation factor, the rock structure evaluation factor, the slope structure evaluation factor, the cut slope height evaluation factor, and the distance evaluation factor from the geological structure.

[0073] The risk level data is the graded data of the probability of geological disasters occurring in slope units, for example, high-risk area, medium-risk area, and low-risk area.

[0074] Here, the comprehensive evaluation factor group is embodied as a raster layer and / or a vector layer, so that more methods can be used for subsequent processing.

[0075] In a specific embodiment, the aforementioned step S13 specifically includes:

[0076] S131: Based on the geological disaster and hidden danger data and the comprehensive evaluation factor group, the geological disaster susceptibility of each slope unit is evaluated to generate the susceptibility index data of each slope unit.

[0077] Here, the susceptibility index data indicates the probability value of the slope unit occurring in a disaster.

[0078] It should be noted that: (1) for the comprehensive evaluation factor group represented by the vector layer, the preset susceptibility evaluation algorithm includes one or more of the following: comprehensive index method model, principal component analysis model, random forest algorithm model, support vector machine algorithm model, logistic regression algorithm model and neural network model; (2) for the comprehensive evaluation factor group represented by the raster layer, the preset susceptibility evaluation algorithm includes one or more of the following algorithms: information quantity method, fuzzy comprehensive evaluation method and weight of evidence method.

[0079] That is, there are many preset susceptibility evaluation algorithms, which can work in the following ways.

[0080] In one example, a user may select one of the specific preset susceptibility evaluation algorithms for calculation;

[0081] In another example, calculations are performed according to each preset susceptibility evaluation algorithm, and a test report is issued for each calculation result, and then the result is reported to the user, so that the user can select the most reasonable prediction result corresponding to the preset evaluation algorithm according to the test report. In this case, step S131 specifically includes:

[0082] S1311: Based on a plurality of preset susceptibility evaluation algorithms, geological disaster susceptibility evaluation is performed on the geological disaster and hidden danger data and the comprehensive evaluation factor group of each slope unit to generate a plurality of initial susceptibility data.

[0083] S1312: Based on the preset inspection algorithm, the geological disaster and hidden danger data and each type of susceptibility initial data are inspected and an inspection report is output; the inspection report is provided to the user.

[0084] It should be noted that the susceptibility initial data is essentially index data. When conducting an inspection, the susceptibility initial data is first graded to generate susceptibility graded data, and then the susceptibility graded data and geological disaster and hidden danger data are used for correlation inspection to generate an inspection report. Among them, the susceptibility graded data is the graded data corresponding to the probability of disasters occurring in the slope unit, and the specific form and classification can be defined according to the actual application scenario. Exemplarily, the susceptibility graded data can be reflected in four levels: high susceptibility area, medium susceptibility area, low susceptibility area and non-susceptibility area.

[0085] In this way, the susceptibility classification data can also be output to users, thereby providing a reference for disaster prevention and control work.

[0086] Here, the preset test algorithms include one or more of the following: disaster point proportion test, ROC curve test, slope unit field survey consistency proportion test.

[0087] S1313: providing the inspection report to the user and receiving the user's selection instruction, and taking one of the susceptibility initial data as susceptibility index data based on the selection instruction.

[0088] In this way, the embodiments of the present application provide a variety of evaluation algorithms for the susceptibility of geological disasters, which can be adapted to different application scenarios; at the same time, the embodiments of the present application also provide relevant verification algorithms, so that staff can also select the best evaluation algorithm for evaluation based on the verification results of each evaluation algorithm.

[0089] In addition, in some embodiments, susceptibility index data is graded to generate susceptibility graded data, which can be output to users to provide a reference for disaster prevention and control work.

[0090] S132: Based on the susceptibility index data of each slope unit, the rainfall data that induces geological disasters, and the stability coefficient of the slope unit, the geological disaster risk assessment of each slope unit is performed to generate the risk index data of each slope unit.

[0091] Here, the rainfall data that induces geological disasters are rainfall-related data, such as 24-hour rainfall conditions, historical 24-hour maximum rainfall, etc.

[0092] It should be noted that the algorithms for geological disaster risk assessment include one or more of the following: calculating the risk index based on the 24-hour extreme rainfall hypothesis algorithm, calculating the risk index based on the effective rainfall before the outbreak of geological disasters, and calculating the risk index based on the slope stability of slope units.

[0093] They are introduced below respectively.

[0094] (1) The hazard index is calculated based on the 24-hour extreme rainfall hypothesis algorithm. The specific formula is as follows:

[0095]

[0096] Among them, L is the value of 24-hour rainfall condition, (L max / day ) Historical maximum rainfall in 24 hours, Y i is the susceptibility index data, Y max is the maximum value of the susceptibility index, H i In addition, in the formula, the 24-hour rainfall condition (L) is divided into light rain, moderate rain, heavy rain, rainstorm, and heavy rainstorm, and the value standard is referred to Table 1.

[0097] Table 1

[0098]

[0099] (2) Calculate the hazard index based on the effective rainfall before the occurrence of a cluster of geological disasters;

[0100] The formula for calculating the hazard index based on the effective rainfall before the occurrence of a cluster of geological disasters is as follows:

[0101]

[0102] Among them, L is the value of 24-hour rainfall condition, L 有效 is the effective precipitation before the occurrence of geological disasters in history, Y i is the susceptibility index data of the slope unit, Y max is the maximum value of the susceptibility index data, X iis the effective precipitation on the i-th day before the geological disaster occurs; n is the number of effective rainfall days before the geological disaster occurs (the value range is 3 to 7 days); K i is the precipitation attenuation coefficient on the ith day before the geological disaster occurs (usually 0.7 to 0.84), H i It is the hazard index data of the slope unit.

[0103] The embodiment of the present application proposes for the first time a risk index model for calculating geological disasters based on effective rainfall before a disaster. This model can better reflect the impact of rainfall-induced factors on geological disasters.

[0104] (3) Calculate the hazard index based on the slope stability of the slope unit.

[0105] The input data of the algorithm is the slope unit instability probability data P i , Susceptibility Index Data Y i , the maximum value of the susceptibility index Y max The slope unit instability probability data P i The stability factor (F s ) is estimated, and the specific comparison is shown in Table 2.

[0106] Table 2

[0107]

[0108] In this way, several algorithms are used to evaluate the hazard index data of each slope unit to cope with different working scenarios, and this can also be reasonable and improve the accuracy of the evaluation.

[0109] S133: Based on the pre-input personnel data, building data and road data, the vulnerability of each slope unit is evaluated respectively, and the personnel vulnerability index data, building vulnerability index data and road vulnerability index data of each slope unit are generated.

[0110] It should be noted that the personnel vulnerability index data is used to indicate the probability of personnel casualties, the building vulnerability index data is used to indicate the probability of building damage, and the road vulnerability index data is used to indicate the probability of road damage.

[0111] Here, personnel data can be determined by assignment and calculation based on population density, personnel age structure, etc.; building data can be determined by assignment and calculation based on the structural type, building type, and number of floors of the building; road data can be determined by assignment based on the classification of the road (for example, first-class railways, expressways, national roads, provincial roads, etc.), and specific examples can be found in the subsequent embodiments.

[0112] In addition, in some embodiments, the hazard index data is graded to generate hazard graded data, which can be output to the user to provide a reference for disaster prevention and control work.

[0113] S134: Calculate the risk level data of each slope unit based on the hazard index data, the personnel vulnerability index data, the building vulnerability index data and the road vulnerability index data.

[0114] In one example, step S134 specifically includes:

[0115] S131: Acquire pre-input personnel quantity data, building value data, and pre-input road value data;

[0116] S132: generating a casualty risk level and an economic loss risk level for each slope unit based on the hazard index data, the number of personnel data, the personnel vulnerability vector data, the building value data, the building vulnerability index data, the road value data, and the road vulnerability data;

[0117] In this way, based on the hazard index data, personnel quantity data, and personnel vulnerability vector data, the risk level of casualties can be calculated; based on the hazard index data, building value data, building vulnerability index data, road value data, and road vulnerability data, the risk level of economic losses can be calculated.

[0118] S133: For each slope unit, the higher level of risk of casualties and economic losses is used as the risk level data of the slope unit.

[0119] For example, both the casualty risk level and the economic loss risk level are "the higher the level, the greater the risk". At this time, for the specific slope unit, the risk level data adopts the "higher principle", that is, the higher of the casualty risk level and the economic loss risk level is adopted.

[0120] S14: Based on the risk level data of the slope unit and the pre-input administrative division boundary layer, a geological hazard risk zoning map is generated; the geological hazard risk zoning map indicates the risk zoning data of each administrative division.

[0121] Here, the same administrative division is allowed to include multiple risk divisions, each risk division has its own risk level data (high risk area / medium risk area / low risk area), collectively referred to as the risk division data of the administrative division.

[0122] In one example, step S14 specifically includes:

[0123] S141: merging adjacent slope units with the same risk level data to generate geological disaster risk area level data;

[0124] Exemplarily, if the risk level data of two adjacent slope units are both "low risk", the two slope units are merged.

[0125] S142: Overlay the geological hazard risk zone level data with the administrative division boundary layer to generate a geological hazard risk zoning map.

[0126] In other words, the geological disaster risk zone level data is segmented using the administrative division boundary layer, thereby displaying the risk zoning data of each administrative division, so that the government can carry out corresponding risk management and disaster prevention in its jurisdiction according to its respective administrative management powers.

[0127] Here, administrative divisions can be divided according to the township area scale, or according to the county and city area size, depending on the actual application scenario.

[0128] It should be noted that in the aforementioned step S11, one of the principles followed in the division of the slope units is that the administrative boundary lines all coincide with the boundary lines of the slope units. That is, the administrative boundary lines will not be located within the scope of a certain slope unit, but will serve as a separation of different slope units, avoiding confusion after the superposition process in step S142.

[0129] In this way, the generated geological disaster risk zoning map can indicate the risk level zones corresponding to different administrative divisions, making it convenient for the government to conduct risk management and disaster prevention.

[0130] In summary, the embodiments of the present application provide a method for dividing geological hazard risk areas, and its progress is reflected in the following aspects: (1) For the first time, a complete standardized process for dividing geological hazard risk areas is established, and a complete set of full-process geological hazard risk area division methods is proposed, starting from the basic unit division of geological hazard risk assessment to the final geological hazard risk area division, which completely includes all processes of geological hazard risk area division. (2) The results are highly accurate; this method uses the slope unit divided based on basic geological conditions such as hydrology as the basic thematic evaluation unit. The advantage of this method is that it comprehensively considers the regional hydrogeological conditions, and the geological background within the same evaluation unit is relatively consistent, so its evaluation results are more accurate. (3) The results are highly practical and can be used as a direct data reference for the government and relevant decision-making departments to deploy geological hazard risk prevention and control work. There are many evaluation methods for geological disasters at present, but most of these method models are evaluations or studies on a certain aspect of geological disasters, for example, susceptibility evaluation of regional geological disasters, or hazard evaluation. These methods are all local analyses or studies of geological disasters from a certain aspect, and their results can be used to illustrate the characteristics of a certain aspect of geological disasters, but cannot reflect the comprehensive characteristics and zoning characteristics of regional geological disaster risks. This application analyzes or studies from multiple aspects, comprehensively divides geological disaster risk areas, has high application value, and accurate prediction results. (4) Diversification of calculation models. Some modules include multiple calculation models to meet the evaluation needs of different regional characteristics and geological disaster occurrence characteristics. For example, the geological disaster susceptibility includes a variety of evaluation method models such as comprehensive index method, principal component analysis model, random forest algorithm model, etc., which can meet the susceptibility evaluation of different regional characteristics; the geological disaster risk assessment module proposes three evaluation methods such as extreme rainfall, effective rainfall, and slope stability. According to the geological disaster development characteristics of different regions, appropriate method models can be selected for more accurate evaluation; a complete and detailed geological disaster risk assessment method is proposed. The vulnerability assessment of the risk body comprehensively considers multiple factors such as slope unit population density, slope unit age structure, building structure, number of building floors, roads, etc.

[0131] In another embodiment of the present application, see Figure 2 , a system 20 for dividing geological disaster risk areas is provided, the system comprising:

[0132] The preprocessing module 21 is configured to divide the initial geographic model data into slope units to generate a plurality of slope units; and perform terrain analysis on the initial geographic model data to generate a terrain evaluation factor group for each slope unit; wherein the terrain evaluation factor group indicates the terrain characteristics of the slope unit;

[0133] The geological disaster special evaluation module 22 is configured to use the comprehensive evaluation factor group of each slope unit to perform multiple geological disaster special evaluations on each slope unit to generate risk level data for each slope unit; wherein the comprehensive evaluation factor group at least includes a terrain evaluation factor group;

[0134] The risk zone division module 23 is configured to generate a geological disaster risk zoning map based on the risk level data of the slope unit and the pre-input administrative division boundary layer; the geological disaster risk zoning map indicates the risk zoning data of each administrative division.

[0135] The above-mentioned division system 20 is applied to computing devices, such as desktop computers, laptops, large computing devices, handheld computing devices, etc., and specifically exists in the form of software. At the same time, the pre-processing module 21, the geological disaster special evaluation module 22, and the risk zone division module 23 are integrated into a whole. In other words, the processing module 21, the geological disaster special evaluation module 22, and the risk zone division module 23 are distinguished based on functions, rather than physically existing independently. These five parts may be integrated with each other in the software concept.

[0136] The following is a detailed description of each module.

[0137] Preprocessing module 21

[0138] It should be noted that data preprocessing is to prepare data for the division of geological disaster risk areas and establish a special evaluation factor system as data input for subsequent special evaluation. The preprocessing module 21 includes four modules: slope unit division module 211, evaluation factor extraction module 212, evaluation factor correlation analysis module 213 and evaluation factor weight calculation module 214.

[0139] a. Slope unit division module 211

[0140] See also Figure 3 The slope unit division module 211 is mainly used to realize the automatic division of the basic slope units for risk investigation and evaluation. The input data source of the slope unit division module 211 is the digital elevation model data (DEM), which is divided by the hydrological analysis method. The main division process includes:

[0141] ①Fill the original digital elevation model data (DEM) to generate a depression-free elevation model. The input data is DEM data, and the output data is a depression-free digital elevation model data.

[0142] ② Extract flow direction data based on step ①, the input data is the digital elevation model data without depressions, and the output data is the flow direction raster data;

[0143] ③ Extract flow data based on step ②, the input data is flow direction grid data, and the output data is flow accumulation grid data;

[0144] ④ Extract river network data based on step ③, input data is flow accumulation grid data, and river network grid data is obtained by setting flow threshold;

[0145] ⑤Based on step ④, link the river network, the input data is the river network raster data and the flow direction raster data, and the output data is the river link raster data;

[0146] ⑥ Extract the watershed based on step ⑤, the input data is the river network link raster data and the flow direction raster data, and the output data is the watershed raster data;

[0147] ⑦ Vectorize the watershed data in step ⑥, the input data is the watershed raster data, and the output data is the watershed vector data;

[0148] Calculate the watershed vector data of the inverse elevation model. The input data is the inverse digital elevation model data. Repeat steps ①-⑦. The output data is the watershed vector data of the inverse elevation model.

[0149] Generate slope unit data. The input data is the catchment vector data and the inverse elevation model catchment vector data. The surface geometric elements in the two vector layer data are topologically merged to obtain the divided slope unit vector data, which corresponds to the "multiple slope units" described in the method side.

[0150] b. Evaluation factor extraction module 212

[0151] See also Figure 4 The evaluation factor extraction module 212 is used to extract some terrain factors in the geological disaster special evaluation, which is realized by digital terrain surface analysis and neighborhood analysis. The input data of this module is the digital elevation model (DEM), and the grid surface analysis method and the grid neighborhood analysis method are used to extract specific terrain evaluation factors:

[0152] ① Slope aspect extraction: the aspect value of the grid is derived from each pixel of the grid surface by using a moving 3×3 pixel window (the size of the window can be adjusted according to actual needs). The output data is the aspect raster layer, which corresponds to the aspect value on the method side.

[0153] ② Slope extraction: by using a moving 3×3 pixel window (the window size can be adjusted according to actual needs) to calculate the slope value of each pixel from the grid, the output data is a slope raster layer, corresponding to the slope value on the method side;

[0154] ③ Slope height extraction: the slope height value of the central pixel of each window is obtained by calculating the difference between the maximum and minimum values ​​in the neighborhood window. The output data is a slope height raster layer, corresponding to the slope height value on the method side;

[0155] ④Curvature extraction: by calculating the second-order derivative of the grid surface, it is obtained by fitting the central pixel of the neighborhood window with eight adjacent pixels. The output curvature is divided into the profile curvature along the direction of the maximum slope and the plane curvature perpendicular to the direction of the maximum slope. The output data are plane curvature and profile curvature raster layers, corresponding to the curvature value on the method side.

[0156] ⑤ Evaluation factor vectorization: by using the partition statistics method, calculate the mode value of all grids within each slope unit in the factor layer obtained in the evaluation factor extraction steps ①-④, or take the median, average, maximum, minimum, etc. as the value of the corresponding evaluation factor slope unit. Through this step, all terrain factors are converted from raster data format to vector data format. In the figure, TIFF (TaggedImageFileFormat) is a flexible bitmap image format, and shp (esrishapefile) is a vector graphics format.

[0157] c. Evaluation factor correlation analysis module 213

[0158] See also Figure 5 This module is used to analyze the correlation of evaluation factors. Through this step, factors whose correlation with the dependent variable fails to pass the significance test can be eliminated from the terrain evaluation factors, thus simplifying the evaluation factor system. Figure 5 The data input is the geological disaster and hidden danger data layer (the format is vector data, which is the dependent variable for correlation analysis) and the terrain evaluation factors and other evaluation factors extracted in the evaluation factor extraction module 212 (which are the independent variables for correlation analysis). All independent variable factor layers are vector format layer data based on slope units. The correlation analysis methods include Pearson coefficient method, Kendall coefficient method and Spearman coefficient method. The output is the correlation coefficient of the factor and the significance test result.

[0159] d. Evaluation factor weight calculation module 214

[0160] This module is used to calculate the weight value of the evaluation factor. Figure 6 The data input of this module is the comprehensive evaluation factor group (including the terrain evaluation factor group and possible other evaluation factors) established based on the analysis results of the evaluation factor correlation analysis module 213 and the pre-input geological disaster and hidden danger data layer. The method is the hierarchical analysis method, and the data output is the weight value of each evaluation factor in the comprehensive evaluation factor group.

[0161] Geological Hazards Special Evaluation Module 22

[0162] The geological disaster special evaluation module 22 includes five modules, namely, a geological disaster susceptibility evaluation module 221, a geological disaster susceptibility evaluation result verification module 222, a geological disaster risk evaluation module 223, a geological disaster hazard-bearing body vulnerability evaluation module 224 and a geological disaster risk evaluation module 225.

[0163] a. Geological Hazard Susceptibility Assessment Module 221

[0164] See also Figure 7 The geological disaster susceptibility evaluation module 221 is the first step of the special evaluation of geological disaster risk zone division, and the data input is the comprehensive evaluation factor group established in the preprocessing module 21. There are two evaluation methods, one is to use the vector layer of the comprehensive evaluation factor group based on the slope unit as the input factor, and the other is to use the raster layer of the comprehensive evaluation factor group as the input factor, compare the two evaluation results, and select the result with high accuracy as the special evaluation result of susceptibility.

[0165] See also Figure 7 The data input for vector layer evaluation is the vector layer of the comprehensive evaluation factor group and the vector data layer of geological hazards and hidden dangers (pre-input). The evaluation method can select the comprehensive index method model, the principal component analysis method model, the random forest algorithm model, the support vector machine algorithm model, the logistic regression algorithm model and the neural network model. The output result is the vector layer of the geological hazard susceptibility index (corresponding to the susceptibility index data on the method side). According to the relevant standards for the classification of geological hazard susceptibility, the calculated geological hazard susceptibility index is graded and assigned ( Figure 7 An assignment rule is shown as an example), and the susceptibility of geological disasters is divided into four levels: high-susceptibility area, medium-susceptibility area, low-susceptibility area and non-susceptibility area, to obtain susceptibility classification data (in the format of vector data).

[0166] The data input for the evaluation based on raster layers is the raster layer of the comprehensive evaluation factor group and the vector data layer of geological hazards and hidden dangers. The evaluation methods can select the information method, fuzzy comprehensive evaluation method and evidence weight method. The output result is the raster layer of the geological hazard susceptibility index. For the calculated susceptibility index raster evaluation results, numerical analysis is first performed. According to the data characteristics of the index, the natural break point classification method, equal interval method, custom interval method, quantile method, geometric interval method and standard deviation method can be selected to classify the geological hazard susceptibility index; secondly, based on the slope unit, the susceptibility index classification results are subjected to zoning statistical calculations. The zoning statistical method can select the mean value, mode value, maximum value, median value, minimum value, difference value, standard deviation, total value, etc. Since the output result of zoning statistics is vector data, the final output result of this method is also susceptibility classification data (in the format of vector data). The result is in the same format as the evaluation result corresponding to the vector layer, and the results can be directly compared.

[0167] b. Geological hazard susceptibility assessment result verification module 222

[0168] This module is the basis for subsequent geological hazard hazard assessment, geological hazard vulnerability assessment, and geological hazard risk assessment. It verifies the results of the geological hazard susceptibility assessment module 221. Figure 8 ,This method provides three methods for verifying the results of geological hazard susceptibility assessment.

[0169] The first test method is the disaster point proportion method test. The input data is the output result of the geological disaster susceptibility evaluation module 221, that is, the susceptibility classification data layer. At the same time, the geological disaster vector data layer (geological disaster and hidden danger data on the corresponding method side) needs to be input, which includes geological disasters and hidden danger points, and the stability of slope unit field surveys. The specific method is to perform an overlay analysis on the two input layers, that is, to perform spatial connection of the vector layers, match the connection elements (geological disaster vector data layer) to the target elements (susceptibility classification data layer) according to the relative spatial position of the elements, and count the number and proportion of the connection elements in the target elements. The output result is the proportion of geological disasters in the four geological disaster prone areas (high prone area, medium prone area, low prone area, and non-prone area).

[0170] The second test method is the ROC curve test. The data input is the susceptibility classification data layer and the geological disaster vector data layer. The result output is the ROC index, which indicates the accuracy of the geological disaster susceptibility zoning.

[0171] The third method is the slope unit field survey consistency ratio test, the input data is the susceptibility classification data layer and the slope unit stability vector layer of the field survey. The specific method is to perform identification analysis on the two input layers, that is, to calculate the geometric intersection of the input elements (susceptibility classification data layer) and the identification elements (slope unit stability vector layer of the field survey), and the input elements overlapping with the identification elements can obtain the attributes of the identification elements. The result of the calculation is a geological disaster susceptibility zoning layer with the field survey slope unit stability attribute field added. The slope unit stability is divided into stable, basically stable, unstable and unstable, and the corresponding rules are established with the high, medium, low and non-prone levels of geological disaster susceptibility in turn. The field statistical analysis is performed in the calculation result layer to obtain the consistency ratio of high-prone slope units, the consistency ratio of medium-prone slope units, the consistency ratio of low-prone slope units, and the consistency ratio of non-prone slope units.

[0172] Finally, the inspection reports obtained using these three inspection methods are provided to the user, who selects the susceptibility index data generated by the most accurate susceptibility evaluation method as the basis for subsequent calculations.

[0173] c. Geological Hazard Risk Assessment Module 223

[0174] This module calculates the hazard index data based on the high accuracy susceptibility index data selected by the geological disaster susceptibility evaluation result verification module 222. It is necessary to first calculate the slope unit instability probability P i , and then further calculate the risk index data H i . See Fig. 9 This module includes three evaluation models: (1) Calculating the hazard index based on the 24-hour extreme rainfall assumption; (2) Calculating the hazard index based on the effective rainfall before the occurrence of a cluster of geological disasters; (3) Calculating the hazard index based on the slope stability of the slope unit.

[0175] d. Geological hazard vulnerability assessment module 224

[0176] This module includes the vulnerability assessment of geological disaster personnel-bearing bodies, the vulnerability assessment of building-bearing bodies, and the vulnerability assessment of roads.

[0177] ① Evaluation of the vulnerability of personnel to geological hazards. The input data of the evaluation model are the slope unit population density vector data layer and the slope unit age structure vector data layer. The specific method is: first, the input data layer is segmented and assigned values. This step can be achieved by reclassifying the vector layer; secondly, the weighted summation map algebra operation is performed on the reclassified vector layer. The specific classification assignment standards and vector layer weight values ​​are shown in the following table:

[0178] Table 3

[0179]

[0180] The output of this module is the human vulnerability index vector data layer, that is, the human vulnerability index data.

[0181] ② Evaluation of the vulnerability of geological disaster-bearing buildings. The input data of the evaluation model are the vector layer data of the slope unit building structure type, the vector layer data of the slope unit building building type, and the vector layer data of the number of floors of the slope unit building. The calculation method of this module is consistent with the method of the vulnerability evaluation of the personnel disaster-bearing body. The output of the model is the vector layer data of the building vulnerability index, that is, the building vulnerability index data. The specific grading and assignment standards and vector layer weight values ​​are shown in the table below.

[0182] Table 4

[0183]

[0184] ③ Evaluation of the vulnerability of geological disaster road hazard-bearing bodies. The input data of the evaluation model is the road grade vector data layer. The calculation method of the module is consistent with the method of evaluating the vulnerability of human hazard-bearing bodies. The output of the model is the road vulnerability index vector layer, that is, the road vulnerability index data. The specific classification and value assignment standards and vector layer weight values ​​are shown in the table below.

[0185] Table 5

[0186]

[0187] e. Geological Hazard Risk Assessment Module 225

[0188] The geological disaster risk assessment is based on the hazard index data output by the geological disaster hazard assessment module 223 and the vulnerability index data (including personnel vulnerability index data, road vulnerability index data, and traffic vulnerability index data) output by the geological disaster hazard assessment module 224. The input data of this module are the slope unit hazard index vector data layer (i.e. hazard index data), the slope unit personnel quantity vector data layer (i.e. personnel quantity data), the slope unit building value vector data layer (i.e. building value data), the slope unit road value vector data layer (i.e. road value data), the slope unit personnel vulnerability vector data layer (i.e. personnel vulnerability index data), the slope unit building vulnerability vector data layer (i.e. building vulnerability index data), and the slope unit road vulnerability vector data layer (i.e. road vulnerability index data). Please refer to Fig.11 The specific evaluation process includes three steps:

[0189] Step one is to calculate the risk index of casualties caused by geological disasters and the risk index of economic losses caused by geological disasters;

[0190] Step 2: classify the calculated results of step 1 into risk levels;

[0191] Step three, calculate the comprehensive risk level of geological hazards based on the results of step two.

[0192] The calculation model of step one is as follows:

[0193]

[0194] In the formula, R i H is the risk index of casualties or economic losses of the i-th slope unit; i is the hazard index of the i-th slope unit; E ij is the value of the jth disaster-bearing body of the i-th slope unit (number of people, per person; economic value, ten thousand yuan), where the number of people is based on the field survey data, and the number of registered household population, permanent population or frequent residents in each independent building (structure) in the slope unit is counted; the economic value is based on the field survey data, and the economic value of each independent economic disaster-bearing body in the slope unit is counted. ij is the vulnerability of the jth hazard-bearing body in the i-th slope unit; n is the number of hazard-bearing bodies in the i-th slope unit. The model calculation is realized through spatial map algebra of vector layers, and the output result is the geological disaster casualty risk index (R i人员 ) vector layer data and geological disaster economic loss risk index (R i经济 ) vector layer data.

[0195] The second step is to classify the risk level of the calculation results of step one, which is achieved by reclassifying the vector data layer, that is, assigning grades according to different risk index value ranges. The specific classification standards for geological disaster casualties and economic losses are shown in the following table:

[0196] Table 6

[0197]

[0198] Step three is to implement spatial layer logical operations on the output result layer of step two. The logical operation rule is "the higher the level, the higher the risk", that is, the casualty risk level and the economic loss risk level of the same slope unit are compared, and the attribute with a high risk level is assigned the risk level data of the slope unit in the output result.

[0199] The output result of the geological disaster risk assessment module 225 is a comprehensive risk level vector layer, that is, providing the risk level.

[0200] Risk Area Division Module 23

[0201] The risk zone division module 23 divides the geological disaster risk zones based on the geological disaster special evaluation module 22. The input data are: risk level data layer, administrative division boundary layer. Fig.12 , the specific calculation method includes two steps:

[0202] ① Attribute fusion is performed on the risk level data layer, that is, elements are aggregated based on the specified attributes. Elements with the same value combination will be aggregated into a single element. The attribute field of the fused layer is specified as the risk level data. The output of this operation is a geological hazard risk area level vector layer. The main difference from before the merger is that the slope unit boundaries in the layer are eliminated, that is, slope units with the same risk level data are merged.

[0203] ② Segment the geological hazard risk zone level vector layer output by ① according to administrative divisions. For example, the administrative division can be specifically a village scale, in which case the administrative division boundary layer is specifically an administrative village division boundary layer, the input layer of the segmentation operation is the geological hazard risk zone level vector layer, the segmentation layer is the administrative division boundary layer, the segmentation field is specified as the administrative village name or administrative division code, the total number of output layer feature classes is equal to the number of unique segmentation field values, and its range is the overlapping part of the input feature and the segmentation feature, and the output layer is a geological hazard risk zone level vector layer with administrative division boundaries, that is, a geological hazard risk zoning map.

[0204] In this way, the division system 20 can realize the division of geological disaster risk areas in a standardized and complete manner, and includes a variety of calculation models for users to choose from, which is suitable for different work scenarios and geographical areas, reducing the workload and time used for the division of geological disaster risk areas.

[0205] Based on the aforementioned embodiments, an embodiment of the present application provides a computer-readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the aforementioned partitioning method.

[0206] Based on the aforementioned embodiments, an embodiment of the present application provides a computer program product, which includes the aforementioned method for dividing geological hazard risk areas, and the computer program implements the steps of the aforementioned method for dividing when executed by a processor.

[0207] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of hardware embodiments, software embodiments, or embodiments in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0208] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable posture evaluation device to generate a machine, so that the instructions executed by the processor of the computer or other programmable posture evaluation device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0209] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable posture assessment device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0210] These computer program instructions may also be loaded onto a computer or other programmable posture evaluation device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0211] Details not disclosed in the embodiments of the present application may be understood by referring to the description of the aforementioned embodiments.

[0212] The above are only preferred embodiments of the present application and are not intended to limit the protection scope of the present application.

[0213] It should be noted that, in this application, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0214] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0215] The methods disclosed in several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0216] The features disclosed in several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0217] The features disclosed in several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0218] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for dividing geological disaster risk areas based on slope units, characterized in that: The method comprises: Dividing the initial geographic model data into slope units to generate a plurality of slope units; Performing terrain analysis on the initial geographic model data to generate a terrain evaluation factor group for each slope unit; wherein the terrain evaluation factor group indicates the terrain characteristics of the slope unit; Using the comprehensive evaluation factor group of each slope unit, multiple geological disaster special evaluations are performed on each slope unit to generate risk level data for each slope unit; wherein the comprehensive evaluation factor group at least includes the terrain evaluation factor group; Based on the risk level data of the slope unit and the pre-input administrative division boundary layer, a geological disaster risk zoning map is generated; the geological disaster risk zoning map indicates the risk zoning data of each administrative division.

2. The division method according to claim 1, characterized in that: The performing of terrain analysis on the initial geographic model data to generate a terrain evaluation factor group for each slope unit includes: Extracting terrain parameters from the initial geographic model data to generate a plurality of terrain parameter groups, wherein the terrain parameter groups include aspect values, slope values, slope height values, and curvature values; Performing statistical processing on the aspect value, slope value, slope height value and curvature value contained in each of the slope units, and forming the terrain evaluation factor group according to the statistical processing results; The statistical processing includes one or more of the following processing: mode processing, median processing, average processing, maximum value processing, and minimum value processing.

3. The division method according to claim 1, characterized in that: After generating a terrain evaluation factor group for each of the slope units, the method further comprises: Obtaining pre-input geological disaster and hidden danger data: the geological disaster and hidden danger data is used to indicate the spatial location of the geological disaster that has occurred and the spatial location of the geological disaster hidden danger; Taking the geological disaster and hidden danger data as dependent variables, a correlation analysis is performed on each evaluation factor in the comprehensive evaluation factor group. If the correlation between the evaluation factor and the dependent variable fails to pass the significance test, the evaluation factor is deleted from the comprehensive evaluation factor group; Performing weight analysis on the comprehensive evaluation factor group according to the geological disaster and hidden danger data to generate a weight value for each evaluation factor; The correlation analysis adopts one or more of the following algorithms: Pearson coefficient method, Kendall coefficient method and Spearman coefficient method; the weight analysis adopts hierarchical analysis method.

4. The division method according to claim 3, characterized in that: The comprehensive evaluation factor group also includes one or more of the following: overburden thickness evaluation factor, soil genesis evaluation factor, rock mass structure evaluation factor, slope structure evaluation factor, cut slope height evaluation factor, and distance evaluation factor from geological structure; The method of using the comprehensive evaluation factor group of each slope unit to conduct multiple geological disaster special evaluations on each slope unit to generate risk level data of each slope unit includes: Based on the geological disaster and hidden danger data and the comprehensive evaluation factor group, a geological disaster susceptibility evaluation is performed on each of the slope units to generate susceptibility index data for each of the slope units; Based on the susceptibility index data of each slope unit, the rainfall data that induces geological disasters, and the stability coefficient of the slope unit, the geological disaster risk assessment is performed on each slope unit to generate the risk index data of each slope unit; Based on the pre-input personnel data, building data and road data, the vulnerability evaluation of the disaster-bearing body is performed on each of the slope units, and the personnel vulnerability index data, building vulnerability index data and road vulnerability index data of each of the slope units are generated; Based on the hazard index data, the personnel vulnerability index data, the building vulnerability index data and the road vulnerability index data, the risk level data of each slope unit is calculated.

5. The division method according to claim 4, characterized in that: The method of evaluating the susceptibility of geological disasters for each slope unit based on the geological disaster and hidden danger data and the comprehensive evaluation factor group to generate susceptibility index data for each slope unit includes: Based on a plurality of preset susceptibility evaluation algorithms, the geological disaster susceptibility evaluation is performed on the geological disaster and hidden danger data and the comprehensive evaluation factor group of each slope unit to generate a plurality of initial susceptibility data; Based on a preset inspection algorithm, the geological disaster and hidden danger data and each type of initial susceptibility data are inspected and an inspection report is output; the inspection report is provided to the user; Providing the inspection report to a user and receiving a selection instruction from the user, and using one of the initial susceptibility data as the susceptibility index data based on the selection instruction; Among them, the preset inspection algorithm includes one or more of the following: disaster point proportion method inspection, ROC curve inspection, slope unit field survey consistency proportion method inspection.

6. The division method according to claim 5, characterized in that: The comprehensive evaluation factor group is embodied in a vector layer and / or a raster layer; for the comprehensive evaluation factor group embodied in a vector layer, the preset susceptibility evaluation algorithm includes one or more of the following: a comprehensive index method model, a principal component analysis method model, a random forest algorithm model, a support vector machine algorithm model, a logistic regression algorithm model, and a neural network model; for the comprehensive evaluation factor group embodied in a raster layer, the preset susceptibility evaluation algorithm includes one or more of the following algorithms: an information quantity method, a fuzzy comprehensive evaluation method, and a weight of evidence method; The algorithm used for the geological disaster risk assessment includes one or more of the following: calculating the risk index based on the 24-hour extreme rainfall hypothesis algorithm, calculating the risk index based on the effective rainfall before the geological disaster risk group, and calculating the risk index based on the slope stability of the slope unit; The formula for calculating the hazard index based on the effective rainfall before the occurrence of a cluster of geological disasters is as follows: Among them, L is the value of 24-hour rainfall condition, L 有效 is the effective precipitation before the occurrence of geological disasters in history, Y i is the susceptibility index data of the slope unit, Y max is the maximum value of the susceptibility index data, X i is the effective precipitation on the i-th day before the geological disaster occurs; n is the number of effective rainfall days before the geological disaster occurs; K i is the precipitation attenuation coefficient on the ith day before the geological disaster occurs, H i is the hazard index data of the slope unit.

7. The division method according to claim 4, characterized in that: The step of calculating the risk level data of each slope unit based on the hazard index data, the personnel vulnerability index data, the building vulnerability index data and the road vulnerability index data comprises: Obtain pre-input personnel quantity data, building value data, and road value data; Based on the hazard index data, the personnel quantity data, the personnel vulnerability index data, the building value data, the building vulnerability index data, the road value data, and the road vulnerability data, a casualty risk level and an economic loss risk level of each slope unit are generated; For each of the slope units, the higher level of risk of casualties and economic losses is used as the risk level data of the slope unit.

8. The division method according to claim 1, characterized in that: The method of generating a geological disaster risk zoning map based on the risk level data of the slope unit and the pre-input administrative zoning boundary layer comprises: Merging the adjacent slope units with the same risk level data to generate geological disaster risk area level data; Segment and overlay the geological disaster risk zone level data with the administrative division boundary layer to generate the geological disaster risk zoning map; Among them, one of the principles followed in the process of dividing the slope units is: for the slope units adjacent to the administrative boundary line, the boundary line of the slope unit coincides with the adjacent administrative boundary line.

9. A geological disaster risk zone division system based on slope units, characterized in that: The partitioning system comprises: A preprocessing module is configured to divide the initial geographic model data into slope units to generate a plurality of slope units; and perform terrain analysis on the initial geographic model data to generate a terrain evaluation factor group for each of the slope units; wherein the terrain evaluation factor group indicates the terrain characteristics of the slope unit; A geological disaster special evaluation module is configured to use the comprehensive evaluation factor group of each slope unit to perform multiple geological disaster special evaluations on each slope unit to generate risk level data for each slope unit; wherein the comprehensive evaluation factor group at least includes the terrain evaluation factor group; The risk zone division module is configured to generate a geological disaster risk zoning map based on the risk level data of the slope unit and the pre-input administrative division boundary layer; the geological disaster risk zoning map indicates the risk zoning data of each administrative division.

10. A computer program product, characterized in that Comprising a computer program comprising the steps of the method according to any one of claims 1-8.

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