Risk area optimization method

Abstract

The invention discloses a risk area optimization method, and belongs to the technical field of geological disasters. The method comprises the steps of 1, preparing basic data of each city; step 2, acquiring a whole province slope unit database based on the basic data; inputting the basic data into a whole province DEM, and adjusting the basic data by adopting a slope unit algorithm program to obtain a whole province slope unit result; step 3, acquiring a framework database based on a whole province slope unit; 4, acquiring a risk area database based on the framework database; the method comprises the following steps: marking a house-containing Thiessen polygon based on Thiessen polygon data and prepared house data to obtain a house-containing Thiessen polygon, and then carrying out risk division on the house-containing Thiessen polygon to obtain a house-containing risk area database. And the scale and the framework can be unified in the whole province, and seamless replacement with the subsequently completed 1: 10000 risk area achievements can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disasters, and specifically provides a method for optimizing risk areas. Background Technique

[0002] The risk assessment of geological disasters mainly evaluates the risks of population casualties and economic losses that geological disasters may cause, and is usually divided into four levels: extremely high risk, high risk, medium risk, and low risk. This classification standard aims to help relevant departments and the public better understand the potential threats of geological disasters and take corresponding prevention and control measures.

[0003] To further promote the transformation of geological disaster prevention and control from single prevention and control of "hidden danger points" to a dual-control mode of "hidden danger points + risk areas", combined with the current frequent extreme rainfall and other weather conditions and the complex and severe flood control situation, the Natural Resources Department of Shaanxi Province has actively explored a dual-control management mechanism for geological disasters of "hidden danger points + risk areas". On this basis, the optimization work of the 1:50,000 risk areas in the whole province has been carried out to ensure the implementation of responsibilities and facilitate management and control.

[0004] The existing methods for determining risk areas mostly adopt qualitative or quantitative methods. Specifically, first, geological hazard risk analysis, by analyzing the occurrence mechanism, inducing factors, and historical disaster events of geological disasters, to evaluate the degree of geological hazard risk in a certain area. This includes considering natural factors such as geological structure, geomorphic features, rock and soil materials, and climate change, as well as human factors such as unreasonable development and utilization, over-mining, and damage to the ecological environment. Second, geological hazard sensitivity analysis, analyzing the sensitivity degree of the geological conditions, geomorphic features, climate conditions, etc. in a certain area to the occurrence of geological disasters, so as to judge the possibility of geological disasters occurring in this area. Third, geological hazard loss assessment, after a geological disaster occurs, evaluating the losses in the affected area, including casualties and property losses. This helps to understand the actual impact of geological disasters and provides a basis for subsequent prevention and control work. Fourth, comprehensive assessment and zoning, comprehensively considering the results of geological hazard risk, sensitivity, and loss assessment, evaluating the geological hazard risk of a certain area or engineering project, and dividing different risk-level areas.

[0005] The resulting risk area achievement data obtained by the above methods has problems such as inconsistent scales of the achievements of each county, no unified standard and framework for the whole province, some achievements being fragmented, and some achievements having a large area. Summary of the Invention

[0006] To solve the problems of the existing technology, the present invention provides a method for optimizing risk areas, including the following steps:

[0007] Step 1, prepare the basic data of each city;

[0008] Step 2, obtain the provincial slope unit database based on the basic data;

[0009] Input the DEM data of the whole province, adopt the slope unit algorithm program to obtain the initial slope unit results, and use the administrative boundaries of cities, counties, townships, and villages in the basic data to crop and adjust the initial slope unit results to obtain the slope unit results divided by city in the whole province;

[0010] Step 3: Obtain the control framework database based on the slope unit results divided by city in the whole province;

[0011] Create Thiessen polygons, overlay the Thiessen polygons with the slope unit results divided by city in the whole province to obtain the control framework database required for risk area optimization;

[0012] Step 4: Obtain the risk area database based on the control framework database;

[0013] Mark the Thiessen polygons containing houses based on the data of Thiessen polygons and the prepared house data to obtain the Thiessen polygons containing houses, and then conduct risk division on the Thiessen polygons containing houses to obtain the risk area database containing houses.

[0014] Further, in Step 1, the basic data includes original slope units, houses, FX5W, and village boundaries, and the FX5W is the risk zoning result of 1:50,000 for each city.

[0015] Further, in Step 2, perform an intersection process on the original slope unit data and the administrative village boundary data, and perform a multi-part to single-part process to obtain the slope unit results that do not cross the administrative village boundary.

[0016] Further, in Step 1, obtaining the slope unit results that do not cross the administrative village boundary includes the following steps:

[0017] After performing an intersection process on the original slope unit data and the administrative village boundary data, there are many extremely small debris polygons, and process them to eliminate the debris polygons to obtain the final slope unit layer, that is, the slope unit database of the whole province.

[0018] Further, in Step 3, perform sub-division of faces and element to point on the original slope unit data obtained based on the DEM of the whole province, create Thiessen polygons, and perform an intersection and debris elimination process on the obtained final slope unit layer and the Thiessen polygons to finally obtain the control framework database required for risk area optimization.

[0019] Further, in Step 4, the process of obtaining the Thiessen polygons containing houses includes: using FX5W to assign risks to the houses and link them to the Thiessen polygons to obtain the Thiessen polygons containing houses with risk areas.

[0020] Further, in step 4, the risk levels include low-risk areas, medium-risk areas, high-risk areas, and extremely high-risk areas.

[0021] Further, in step 4, after spatially connecting the Thiessen polygons containing houses and the village boundaries, the house risk levels and village name fields of the Thiessen polygons containing houses are fused to obtain the Thiessen polygons containing houses after being fused by risk area and village name.

[0022] Further, in step 4, the houses crossing risk areas are screened to ensure that each house has only one risk area. The houses crossing risks are screened by using the contour intersection relationship, and a new field, the merged field, is added. The merged field is the houses crossing risks to be processed.

[0023] Assign risks to the houses crossing risks to be processed. Spatially connect the houses to be processed and the Thiessen polygons of the houses after fusion. The spatial relationship is that the center is within the element range, and assign values to the risk level fields of the houses to be processed. The value is the risk level of the Thiessen polygons of the houses after fusion, including the following steps:

[0024] Combine the houses crossing risk areas with risk levels and the Thiessen polygons of the houses after being fused by risk village names, and screen out the data with the merged field being "yes" in the combined results. Calculate the field "final risk" as the risk level, assign values to the data village name fields, then inverse-select the results to assign risks to the final risk, and finally check whether there are empty fields in the final risk and the village name. By manually filling the results, the final risk area database containing houses is obtained, and at the same time, there are no houses crossing risks.

[0025] Advantages of the present invention:

[0026] After optimizing the original data in this application, the whole province can have a unified scale and framework, which can be seamlessly replaced with the 1:10,000 risk area results completed later. At the same time, the risk areas such as the areas, quantities, and disaster-bearing bodies of the areas without threats are streamlined and optimized to conform to the actual situation. In addition, it is divided by village, based on slopes, and does not cross mountain ridges, thus facilitating management. When it is distributed from the provincial level to the village level layer by layer and then investigated on the spot, one-key update can be realized. Moreover, a series of unreasonable problems such as crossing villages, houses, mountain ridges, fragments, and topological errors in the original risk areas are corrected, and more optimal thresholds are set by technical means, thereby improving the practicality of the optimization results of the whole province. Description of the Drawings

[0027] Figure 1 It is a schematic diagram of the process framework provided by the present invention. Detailed Embodiments

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] Please refer to Figure 1 , the present invention provides a risk area optimization method, including the following steps:

[0030] Step 1: Prepare the basic data of each city;

[0031] Step 2: Obtain the provincial slope unit database based on the basic data;

[0032] Input the provincial DEM data, use the slope unit algorithm program to obtain the initial slope unit result, and use the administrative boundaries of cities, counties, towns, and villages in the basic data to clip and adjust the initial slope unit result to obtain the slope unit result divided by city in the province;

[0033] Step 3: Obtain the control framework database based on the slope unit result divided by city in the province;

[0034] Create Thiessen polygons, overlay the Thiessen polygons with the slope unit result divided by city in the province to obtain the control framework database required for risk area optimization;

[0035] Step 4: Obtain the risk area database based on the control framework database;

[0036] Based on the data of the Thiessen polygons and the prepared housing data, mark the Thiessen polygons containing houses to obtain the Thiessen polygons containing houses, and then perform risk division on the Thiessen polygons containing houses to obtain the risk area database containing houses.

[0037] In some embodiments, in step 1, the basic data includes original slope units, houses, FX5W, and village boundaries, and the FX5W is the 1:50,000 risk zoning result of each city.

[0038] In some embodiments, in step 2, the original slope unit data and the administrative village boundary data are intersected and processed from multi-parts to single-parts to obtain the slope unit result that does not cross the administrative village boundary.

[0039] In some embodiments, in step 1, obtaining the slope unit result that does not cross the administrative village boundary includes the following steps:

[0040] After intersecting the original slope unit data and the administrative village boundary data, there are many very small debris polygons. Process them to eliminate the debris polygons and obtain the final slope unit layer, that is, the provincial slope unit database.

[0041] In some embodiments, in step 3, the original slope unit data obtained based on the provincial DEM is subdivided into faces and elements are converted into points, Thiessen polygons are created, and the obtained final slope unit layer and the Thiessen polygons are intersected and debris is eliminated, and finally the control framework database required for risk area optimization is obtained.

[0042] In some embodiments, in step 4, the process of obtaining the Thiessen polygons containing houses includes: using FX5W to assign risks to the houses and linking them to the Thiessen polygons to obtain the Thiessen polygons containing houses with risk areas.

[0043] In some embodiments, in step 4, the risk levels include low-risk areas, medium-risk areas, high-risk areas, and extremely high-risk areas.

[0044] In some embodiments, in step 4, after spatially joining the Thiessen polygons containing houses and the village boundaries, the house risk levels and village name fields of the Thiessen polygons containing houses are fused to obtain the Thiessen polygons containing houses after fusion by risk area and village name.

[0045] In some embodiments, in step 4, the houses crossing risk areas are screened to ensure that each house has only one risk area. The houses crossing risks are screened out by using the contour intersection relationship, and a new field is added to merge the fields. The merged field is the house crossing risks to be processed;

[0046] Assign risks to the houses to be processed crossing risks. Spatially join the houses to be processed and the Thiessen polygons of the fused houses. The spatial relationship is that the center is within the element range, and the risk level field of the houses to be processed is assigned a value, and the value is the risk level of the Thiessen polygons of the fused houses, including the following steps:

[0047] Combine the houses crossing risk areas with risk levels and the Thiessen polygons of the houses fused by risk village names, and screen out the data with the merged field being "yes" in the combined results. Calculate the field "final risk" as the risk level, assign a value to the data village name field, then inverse-select the results and assign the risk to the final risk. Finally, check whether there are empty fields in the final risk and the village name, and fill the results manually, and the final risk area database containing houses is obtained, while ensuring that there are no houses crossing risks.

[0048] It should be noted that the basic data of each city (original slope units, houses, FX5W, village boundaries) are prepared to make obstacle lines, where FX5W is the 1:50,000 risk zoning results of each city.

[0049] ① Intersect the original slope unit data with the administrative village boundary data and perform the multi-part to single-part process to obtain the slope unit results that do not cross the administrative village boundaries.

[0050] ② Since the slope unit data that does not cross the administrative village boundaries obtained in step ① contains many extremely small debris polygons after the intersection process, it is necessary to process them to eliminate the debris polygons. In the example, polygons with an area less than 20,000 m 2 are processed, and finally the slope unit layer, that is, the slope superposed with the village boundary to eliminate the area data, which is the slope unit database of the whole province.

[0051] ③ To prepare for the optimization and subdivision of the 1:50,000 risk area, generate Thiessen polygons using the slope unit data and eliminate debris. In the software, first perform the subdivision of faces, feature to point, and create Thiessen polygons based on the original slope unit data, and then intersect and eliminate debris between the final result obtained in step ② and the Thiessen polygons (after each intersection process in the software, there will be extremely small debris polygons, which need to be processed to eliminate debris polygons, and will not be elaborated in the subsequent steps). Finally, obtain the management framework database required for the risk area optimization.

[0052] ④ Use the Thiessen polygon data generated in step ③ and the house data prepared at the beginning to mark the Thiessen polygons containing houses, and extract the Thiessen polygons containing houses.

[0053] ⑤ Use FX5W (1:50,000 risk zoning) to assign risks to the houses and link them to the small slope units (the Thiessen polygons containing houses obtained in step ④). During the risk assignment process, follow the principle of taking the higher value to obtain the Thiessen polygons containing houses with risk areas. Among them, the risk levels include low-risk areas, medium-risk areas, high-risk areas, and extremely high-risk areas

[0054] ⑥ After spatially joining the Thiessen polygons containing houses with the village boundary, use the fusion tool to fuse the house risk levels and village name fields of the Thiessen polygons containing houses to obtain the Thiessen polygons containing houses after fusion by risk area and village name (ensure that the Thiessen polygons containing houses do not cross the administrative village boundaries and risk areas).

[0055] ⑦ Screen out the houses that cross the risk areas (ensure that each house has only one risk area), use the Select by Location tool to screen out the houses that cross the risks using the contour intersection relationship, and add a new field to merge the fields. The houses with the merged field being "yes" are the houses that cross the risks to be processed.

[0056] ⑧ Assign risks to the houses to be processed across risks, connect the Thiessen polygon spaces of the houses to be processed in ⑦ and the merged houses in ⑥, with the spatial relationship that the center is within the element range, and assign a value to the risk level field of the houses to be processed, and the value is the risk level of the Thiessen polygon of the merged houses.

[0057] ⑨ Combine the houses (houses to be processed) in the cross-risk area with risk levels in the result of ⑧ and the result in ⑥ merged by risk village names, and filter out the data with the merged field being "yes" in the combined result. Calculate the field "final risk" as the risk level, assign a value to the data village name field, and then reverse-select the result to assign the risk to the final risk.

[0058] ⑩ Finally, check whether there are empty fields in the final risk and the village name, and manually fill in the results, that is, the final risk area database containing houses is obtained.

[0059] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A risk area optimization method, characterized in that: The following steps are involved: Step 1: Prepare basic data for each city; Step 2: Obtain the slope unit database of the whole province based on the basic data; Input the DEM data of the whole province, use the slope unit algorithm program to obtain the initial slope unit results, use the basic data of the administrative boundaries of each city, county, township, and village to trim and adjust the initial slope unit results, and obtain the slope unit results divided by city in the whole province; Step 3: Obtain the control framework database based on the slope unit results divided by city in the province; Create Thiessen polygons and fit them with the slope unit results divided by cities across the province to obtain the control framework database required for risk area optimization; Step 4: Obtain the risk area database based on the management and control framework database; Based on the Thiessen polygon data and the prepared house data, the Thiessen polygons containing the houses are marked to obtain the Thiessen polygons containing the houses, and then the Thiessen polygons containing the houses are divided into risks to obtain a risk area database containing the houses.

2. The risk area optimization method according to claim 1, characterized in that: In step 1, the basic data includes original slope units, houses, FX5W and village boundaries, and the FX5W is the 1:50000 risk zoning result of each city.

3. The risk area optimization method according to claim 2, characterized in that: In step 2, the original slope unit data and the administrative village boundary data are intersected, and multiple components are converted into a single component to obtain slope unit results that do not cross administrative village boundaries.

4. The risk area optimization method according to claim 3, characterized in that: In step 1, obtaining the slope unit results that do not cross administrative village boundaries includes the following steps: After the original slope unit data and administrative village boundary data are intersected, there are many debris polygons with very small areas. These are processed to eliminate the debris polygons and obtain the final slope unit layer, that is, the provincial slope unit database.

5. The risk area optimization method according to claim 4, characterized in that: In step 3, the original slope unit data obtained based on the provincial DEM is subdivided into surfaces and features, and Thiessen polygons are created. The final slope unit layer and Thiessen polygons are intersected and de-debrised to finally obtain the management and control framework database required for risk area optimization.

6. The risk area optimization method according to claim 5, characterized in that: In step 4, the process of obtaining the Thiessen polygons containing the house includes: using FX5W to add risks to the house and link it to the Thiessen polygons to obtain the Thiessen polygons containing the house with risk areas.

7. The risk area optimization method according to claim 6, characterized in that: In step 4, the risk levels include low risk area, medium risk area, high risk area and extremely high risk area.

8. The risk area optimization method according to claim 6, characterized in that: In step 4, after spatially connecting the Thiessen polygons containing the houses and the village boundaries, the house risk level and village name fields of the Thiessen polygons containing the houses are fused to obtain the Thiessen polygons containing the houses after fusion of the risk area and the village name.

9. The risk area optimization method according to claim 8, characterized in that: In step 4, houses that cross risk zones are screened out to ensure that a house has only one risk zone. Houses that cross risks are screened out by using the profile cross relationship, and new fields are added to merge fields. The merged fields are cross-risk houses to be processed. Assign risks to the houses to be processed that cross risks, connect the Thiessen polygons of the houses to be processed and the fused houses in space, with the spatial relationship being that the center is within the feature range, and assign a value to the risk level field of the houses to be processed, which is the risk level of the Thiessen polygons of the fused houses, including the following steps: Union the Thiessen polygons of the houses across risk zones with risk levels and the houses merged by risk village names, and filter out the data with the merged field as yes in the union result. Calculate the final risk of the field as the risk level, assign a value to the data village name field, and then invert the result to assign the final risk to the risk. Finally, check whether there are empty fields between the final risk and the village name. By manually filling in the results, we can obtain the final risk zone database containing houses, while ensuring that there are no houses across risks.

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