Slope stability calculation method

Abstract

The invention discloses a slope stability calculation method, and relates to the technical field of stability analysis, and the method comprises the steps: obtaining historical engineering data, and generating a regional geological data map; acquiring historical meteorological data and meteorological prediction data, and generating a meteorological data map; based on historical engineering data, obtaining various sliding surface data, and constructing an intelligent computing framework according to the various sliding surface data; obtaining an engineering construction area, and obtaining target geological data and target meteorological data; obtaining engineering construction parameters, and obtaining target sliding surface data; substituting the target sliding surface data, the target geological data and the target meteorological data into an intelligent calculation framework to obtain a slope stability value; and deploying a data monitoring device at the slope, collecting current state data of the slope, and updating and predicting the slope stability value in real time according to the current state data to obtain a predicted stability value. The slope stability calculation method has the effect of improving the comprehensiveness and foresight of slope stability calculation.

Description

Technical Field

[0001] This application relates to the technical field of stability analysis, and in particular to a method for calculating slope stability. Background Technology

[0002] Embankment slope stability is one of the key factors in ensuring the safety of engineering structures such as roads and embankments. Embankment slope stability refers to the ability of an embankment slope to maintain its geometric shape and stable state under various external loads such as vehicles, its own weight, wind force, and water force.

[0003] In the construction of roads, especially highways in the south, the calculation of slope stability is particularly important. The terrain and geology of the south are highly variable; a highway typically traverses different geological regions. Furthermore, the south experiences abundant rainfall, and slope stability varies under different weather conditions. Moreover, slope stability is not static after road construction, and current technologies lack suitable monitoring methods to predict future changes in slope stability. Summary of the Invention

[0004] The purpose of this invention is to provide a method for calculating slope stability to solve the problems mentioned in the background art.

[0005] This application provides a method for calculating slope stability, the method comprising: Acquire historical engineering data, construct a regional geological database based on the historical engineering data, and generate a regional geological data map based on the regional geological database; Acquire historical meteorological data and meteorological forecast data, construct a regional meteorological database based on the historical meteorological data and the meteorological forecast data, and generate a meteorological data map based on the regional meteorological database; Based on the historical engineering data, slope calculation data is extracted from the historical engineering data, and various slip surface data are obtained based on the slope calculation data. An intelligent computing framework is then constructed based on the various slip surface data. The engineering construction area is obtained, and the target geological data and target meteorological data are obtained by filtering the geological data map and the meteorological data map based on the engineering construction area. Obtain engineering construction parameters, obtain slope shape data based on the engineering construction parameters, and obtain target slip surface data based on the slope shape data; Substituting the target slip surface data, the target geological data, and the target meteorological data into the intelligent computing framework, slope stability values ​​are obtained; A data monitoring device is deployed on the slope to collect the current state data of the slope. Based on the current state data, the slope stability value is updated and predicted in real time to obtain the predicted stability value.

[0006] Preferably, the step of constructing a regional meteorological database based on the historical meteorological data and the meteorological forecast data, and generating a meteorological data map based on the regional meteorological database, specifically includes: Construct a timeline, arrange the historical meteorological data on the timeline to obtain a historical meteorological data sequence, and obtain historical meteorological change data based on the historical meteorological data sequence; Based on the historical meteorological change data, the meteorological change pattern is obtained, and the meteorological forecast data is adjusted according to the meteorological change pattern to obtain the target forecast meteorological data; Based on the predicted meteorological data, the meteorological data is classified and sorted according to time order to construct a regional meteorological database; Extract the coverage area of ​​the target predicted meteorological data, extract map data based on the coverage area, and combine the target predicted meteorological data and the map data to generate a meteorological data map.

[0007] Preferably, the steps of extracting slope calculation data from the historical engineering data, obtaining multiple slip surface data based on the slope calculation data, and constructing an intelligent computing framework based on the multiple slip surface data are as follows: Extract slope calculation data from the historical engineering data, and extract calculation influence parameters, calculation modes, and various slip surface data from the slope calculation data; Based on the calculated impact parameters, the calculated impact range and the calculated impact depth are obtained, and the impact compensation parameters are generated based on the calculated impact range and the calculated impact depth. Based on the aforementioned calculation mode, multiple data calculation processes are obtained, and based on these multiple data calculation processes, a process selection library is generated. Substitute the various slip surface data into the process selection library to generate a slope calculation framework. Combine the slope calculation framework with the impact compensation parameters to generate an intelligent calculation framework.

[0008] Preferably, the step of obtaining the engineering construction area and filtering it in the geological data map and the meteorological data map to obtain target geological data and target meteorological data is as follows: Obtain the construction area, extract the geographic coordinate data of the construction area, and determine the scope of the construction area based on the geographic coordinate data; Generate a range mask on the geological data map and the meteorological data map for the construction area; Based on the range mask, the geological data map and the meteorological data map are filtered by region to determine the target geological range and the target meteorological range; Based on the target geological range and the target meteorological range, data are collected from the geological data map and the meteorological data map to obtain the target geological data and the target meteorological data, respectively.

[0009] Preferably, the steps of obtaining engineering construction parameters, obtaining slope shape data based on the engineering construction parameters, and obtaining target slip surface data based on the slope shape data are as follows: Obtain engineering construction parameters, and based on these parameters, determine the road construction width and road elevation. Based on the target geological data, target terrain data is obtained, and a three-dimensional geographic model is constructed based on the target terrain data; Based on the road construction width and the road elevation, the three-dimensional geographic model is used to simulate construction excavation to obtain slope data along the road. Based on the slope data, the slope shape is described to obtain slope shape data, and the initial slip surface data is obtained based on the slope shape data. Based on the target meteorological data and the target geological data, hydrological data and stratigraphic lithology data are obtained. Based on the hydrological data and the stratigraphic lithology data, hydrological influencing factors and stratigraphic lithology influencing factors are generated. The initial slip surface data are dynamically corrected using the hydrological influencing factors and the stratigraphic lithology influencing factors to obtain the target slip surface data.

[0010] Preferably, the step of substituting the target slip surface data, the target geological data, and the target meteorological data into the intelligent computing framework to obtain the slope stability value specifically includes: The target slip surface data is substituted into the intelligent computing framework, and the intelligent computing framework extracts the slope, geological structure, and slope height of the target slip surface data; The intelligent computing framework generates the initial stability value of the slope based on the slope gradient, the geological structure, and the slope height. The intelligent computing framework obtains the geological composition data of the slope based on the target geological data, and generates a first stability influence value based on the geological composition data; The intelligent computing framework generates a second stable influence value for the slope based on the wind and rainfall influencing factors. Substitute the first influence value and the second influence value into the initial stability value to correct the initial stability value and obtain the slope stability value.

[0011] Preferably, the step of the intelligent computing framework generating the initial stability value of the slope based on the slope gradient, the geological structure, and the slope height specifically includes: Based on the historical engineering data, extract multiple slope stability calculation processes from the historical engineering data; Multiple slope stability calculation processes are combined to generate a slope stability calculation set; Extract the pre-input parameters of the slope stability calculation process, generate selection labels based on the pre-input parameters, and add the selection labels to each corresponding slope stability calculation process in the slope stability calculation set; The intelligent computing framework generates parameter labels based on the slope, geological structure, and slope height; The parameter labels are compared with the selected labels in the slope stability calculation set to determine the target selected label; Extract the target slope stability calculation process corresponding to the target selection label, and extract the matching value between the target slope stability calculation process and the parameter label; The target slope stability calculation process is adaptively modified based on the fit value and the parameter label to obtain the target calculation process; Substituting the slope slope, geological structure, and slope height into the target calculation process, the initial stability value of the slope is obtained.

[0012] Preferably, the steps of deploying a data monitoring device on the slope to collect the current state data of the slope, and updating and predicting the slope stability value in real time based on the current state data to obtain the predicted stability value are as follows: Data monitoring devices are deployed on the slope to collect the current state of the slope. Based on the current state, the changes in the geological viscosity and the changes in the slope displacement are obtained. Based on the changes in geological viscosity, a viscosity change curve is constructed; based on the changes in slope displacement, a slope position change curve is constructed. By combining the viscosity change curve and the slope position change curve, a slope stability change curve is obtained, and based on the slope stability change curve, a stability change law is obtained; Based on the aforementioned stable change pattern, the stability state of the slope is predicted to obtain the predicted stable state. The slope stability values ​​are updated and predicted in real time based on the current state and the predicted stable state to obtain the predicted stable values.

[0013] In summary, this application includes at least one of the following beneficial technical effects: By acquiring historical engineering data, historical meteorological data, and meteorological forecast data, the historical engineering data is processed to construct a regional geological database and generate a geological data map. Based on historical meteorological data and meteorological forecast data, a regional meteorological database is generated, and a meteorological data map is produced. Then, slope calculation data is extracted from the historical engineering data, and various image data are obtained from this data. An intelligent computing framework is constructed based on these image data. Next, the construction area is acquired, and target geological and meteorological data are filtered on the geological and meteorological data maps to obtain target geological and meteorological data. Then, based on the construction parameters, the designed slope shape data is obtained, and the target slip surface data is derived from the slope shape data. The target slip surface data, target geological data, and target meteorological data are then substituted into the intelligent computing framework to obtain the slope stability value. Monitoring devices are deployed on the slope to collect current slope status data. Based on the current status data, the slope stability is updated and predicted in real time to obtain the predicted stability value. This improves the comprehensiveness and foresight of slope stability calculations during road construction. Attached Figure Description

[0014] Figure 1 This is a step-by-step flowchart of a slope stability calculation method provided in the embodiments of this application. Detailed Implementation

[0015] The following combination Figure 1 This application will be described in further detail, but the embodiments of the present invention are not limited thereto. This application discloses a method for calculating slope stability.

[0016] In this embodiment, a slope stability calculation method is provided, the method comprising: S100: Acquire historical engineering data, construct a regional geological database based on the historical engineering data, and generate a regional geological data map based on the regional geological database; S200: Acquire historical meteorological data and meteorological forecast data, construct a regional meteorological database based on historical meteorological data and meteorological forecast data, and generate a meteorological data map based on the regional meteorological database; S300: Based on historical engineering data, extract slope calculation data from historical engineering data, obtain various slip surface data based on slope calculation data, and construct an intelligent computing framework based on various slip surface data; S400: Obtain the engineering construction area, and filter the geological data map and meteorological data map according to the engineering construction area to obtain the target geological data and target meteorological data; S500: Obtain engineering construction parameters, obtain slope shape data based on engineering construction parameters, and obtain target slip surface data based on slope shape data; S600: Substitute the target slip surface data, target geological data, and target meteorological data into the intelligent computing framework to obtain slope stability values; S700: Deploy data monitoring devices on the slope to collect the current state data of the slope, and update and predict the slope stability value in real time based on the current state data to obtain the predicted stability value.

[0017] It should be noted that the above process is only the basic steps of this embodiment. In the specific implementation process, some steps may be added, reduced or modified appropriately without affecting the overall implementation effect.

[0018] The steps for constructing a regional meteorological database based on historical meteorological data and meteorological forecast data, and generating a meteorological data map based on the regional meteorological database, are as follows: Construct a timeline, arrange historical meteorological data on the timeline to obtain a historical meteorological data sequence, and obtain historical meteorological change data based on the historical meteorological data sequence; Based on historical meteorological change data, meteorological change patterns are obtained. Based on these patterns, meteorological forecast data is adjusted to obtain target meteorological forecast data. Based on the target forecast meteorological data, the meteorological data is classified and sorted according to time order to construct a regional meteorological database; Extract the coverage area of ​​the target predicted meteorological data, extract map data based on the coverage area, and combine the target predicted meteorological data and map data to generate a meteorological data map.

[0019] In practice, taking a highway construction project as an example, located in a rainy region in southern China, the process begins by acquiring historical meteorological data for the past 10 years, including daily rainfall, wind speed, and temperature. This data is arranged chronologically to form a historical meteorological data sequence. For instance, data from January 2013 to December 2023 shows a significant increase in rainfall from June to September each year, averaging 200 mm per month, while winter wind speeds are higher, averaging 5 m / s. Based on this pattern, the meteorological trend analysis reveals an annual growth rate of 3% in summer rainfall. Subsequently, combined with meteorological forecast data (such as rainfall forecasts for the next 5 years), the predicted values ​​are adjusted: the predicted summer rainfall for 2024 is revised from 180 mm to 190 mm to match the historical growth trend. Next, the revised target meteorological forecast data is categorized (e.g., rainfall, wind speed, and temperature) and sorted chronologically to construct a regional meteorological database. For example, rainfall data for June 2024 is labeled as "heavy rainfall," and wind speed data is labeled as "moderate wind speed." Finally, the coverage area of ​​the target predicted meteorological data was extracted (e.g., the latitude and longitude range of the project area is 110°-112°E, 28°-30°N), and combined with map data to generate a meteorological data map. This map uses different colors to indicate rainfall intensity (red indicates >150 mm / month, yellow indicates 100-150 mm / month) and wind speed distribution (arrow size indicates wind speed), forming an intuitive and visual meteorological data map. The entire process took approximately two weeks, the database contained over 100,000 meteorological records, and the map covered an area of ​​500 square kilometers.

[0020] The steps for extracting slope calculation data from historical engineering data, obtaining various slip surface data based on the slope calculation data, and constructing an intelligent computing framework based on these various slip surface data are as follows: Extract slope calculation data from historical engineering data, and extract calculation influence parameters, calculation modes, and various slip surface data from the slope calculation data; Based on the calculated impact parameters, the calculated impact range and depth are obtained, and the impact compensation parameters are generated based on the calculated impact range and depth. Based on the calculation mode, multiple data calculation processes are obtained, and based on the multiple data calculation processes, a process selection library is generated; By inputting various slip surface data into the process selection library, a slope calculation framework is generated. Combining the slope calculation framework with the impact compensation parameters, an intelligent calculation framework is generated.

[0021] In practice, taking the same highway project as an example, slope calculation data from 10 similar projects were extracted from a historical engineering database. The extracted data included calculation influence parameters (such as soil viscosity and groundwater depth), calculation models (such as finite element analysis and limit equilibrium method), and various slip surface data (such as circular slip surface and broken line slip surface). For example, in a project with a soil viscosity of 0.5 kPa·s and a groundwater depth of 2 meters, the limit equilibrium method was used to calculate slip surface stability. Based on the calculated influence parameters, the range of influence on the slope (e.g., soil viscosity influence depth is 1.5 times the slope height) and the depth of influence (e.g., groundwater influence within a 3-meter range at the slope bottom) were analyzed, generating influence compensation parameters (e.g., viscosity compensation coefficient 1.2, groundwater compensation coefficient 0.8). Then, multiple data calculation workflows were generated based on the calculation models: Workflow A (finite element method) is suitable for complex geology, and Workflow B (limit equilibrium method) is suitable for homogeneous soil. These workflows were stored in a workflow selection library and tagged (e.g., the tag "complex geology" corresponds to workflow A). Subsequently, various slip surface data were substituted into the process selection library: circular slip surface data matched the tag "homogeneous soil" and automatically selected process B; broken line slip surface data matched the tag "complex geology" and selected process A. Finally, an intelligent calculation framework was generated by combining the slope calculation framework and impact compensation parameters. For example, when the soil viscosity was input as 0.6 kPa·s, the framework automatically called the viscosity compensation coefficient of 1.2 and selected process B for stability calculation.

[0022] The steps for obtaining the project construction area, and then filtering it from geological and meteorological data maps to obtain target geological and meteorological data, are as follows: Obtain the construction area, extract the geographic coordinate data of the construction area, and determine the scope of the construction area based on the geographic coordinate data; Generate a range mask for the construction area on the geological data map and meteorological data map; Based on the range mask, the geological data map and meteorological data map are filtered by region to determine the target geological range and the target meteorological range. Based on the target geological and meteorological ranges, data is collected from geological and meteorological data maps to obtain target geological and meteorological data, respectively.

[0023] In practice, for this highway project, the geographical coordinates of the construction area (111.5°E, 29.2°N) were obtained. Based on these coordinates, the construction area was determined to be a strip-shaped region 20 km long and 1 km wide. A rectangular mask was generated over this area on the geological data map (including rock strata type and soil hardness) and the meteorological data map (including rainfall and wind speed distribution). The map was then filtered using the mask: in the geological data map, the target geological area was defined as 60% sandstone and 40% clay; in the meteorological data map, the target meteorological area was defined as an average annual rainfall of 1200 mm and a maximum wind speed of level 8. Subsequently, data was collected from the filtered data maps: geological data included rock strata thickness (average sandstone layer thickness 5 meters) and soil parameters (clay viscosity 0.4 kPa·s); meteorological data included the predicted rainfall for the next three rainy seasons (June 2024 rainfall 190 mm). Finally, the target geological data (rock strata structure, soil type) and target meteorological data (rainfall and wind speed) were obtained for subsequent calculations.

[0024] The steps for obtaining engineering construction parameters, obtaining slope shape data based on engineering construction parameters, and obtaining target slip surface data based on slope shape data are as follows: Obtain engineering construction parameters, and based on these parameters, determine the road construction width and road elevation. Based on the target geological data, obtain the target topographic data, and construct a three-dimensional geographic model based on the target topographic data; Based on the road construction width and road elevation, the three-dimensional geographic model is used to simulate construction excavation to obtain slope data along the road. Based on the slope data, the slope shape is described to obtain slope shape data, and the initial slip surface data is obtained based on the slope shape data. Based on the target meteorological and geological data, hydrological and stratigraphic lithological data are obtained. Based on the hydrological and stratigraphic lithological data, hydrological influencing factors and stratigraphic lithological influencing factors are generated. The initial slip surface data are dynamically corrected by the hydrological and stratigraphic lithological influencing factors to obtain the target slip surface data.

[0025] In application, based on engineering construction parameters, the road construction width was determined to be 30 meters and the road elevation to be 200 meters. Combining topographic data from the target geological data (e.g., slope 15°, elevation change 50 meters), a 3D geographic model was constructed. Simulated excavation: Virtual excavation was performed on the model at a width of 30 meters and a depth of 5 meters to generate roadside slope data (slope height 10 meters, slope angle 45°). Based on the slope data describing the shape (e.g., the slope surface is straight), slope shape data was obtained. Based on this, initial slip surface data (slip surface dip angle 40°) was generated. Then, combining target meteorological and geological data, and rainfall data (monthly rainfall 190 mm), hydrological influencing factors (permeability coefficient 0.5) and stratigraphic lithology influencing factors (rock stability coefficient 0.7) were generated. The initial slip surface data were dynamically corrected by these two factors. The hydrological influence reduced the slip surface dip angle to 38° and the stability of the strata rock to 0.35 kPa·s, finally obtaining the target slip surface data (corrected slip surface dip angle 38°, strata stability 0.35 kPa·s).

[0026] The steps for obtaining slope stability values ​​by substituting target slip surface data, target geological data, and target meteorological data into the intelligent computing framework are as follows: The target slip surface data is fed into the intelligent computing framework, which then extracts the slope, geological structure, and slope height of the target slip surface data. The intelligent computing framework generates initial stability values ​​for the slope based on the slope gradient, geological structure, and slope height. The intelligent computing framework obtains the geological composition data of the slope based on the target geological data, and generates the first stability influence value based on the geological composition data; The intelligent computing framework generates a second stability impact value for the slope based on wind and rainfall factors. Substitute the first and second influence values ​​into the initial stability values ​​to correct the initial stability values ​​and obtain the slope stability values.

[0027] In application, the target slip surface data (slip surface inclination angle 38°, slip surface length 15 meters, slope height 10 meters) is input into the intelligent calculation framework. The framework extracts the slope gradient (38°), geological structure (straight type), and slope height (10 meters) to generate an initial stability value (safety factor 1.5). Subsequently, based on the geological composition in the target geological data (sandstone layer thickness 5 meters, clay layer thickness 3 meters), a first stability influence value (sandstone strength coefficient 1.2, clay softening coefficient 0.9) is generated. Then, combined with the wind influence factor (wind pressure coefficient 0.3) and rainfall influence factor (permeability coefficient 0.5) in the target meteorological data, a second stability influence value (wind reduction coefficient 0.95, rainfall reduction coefficient 0.85) is generated. Substituting these two influence values ​​into the initial stability value: 1.5 × 0.95 (wind influence) × 0.85 (rainfall influence) × 1.2 (sandstone strength) × 0.9 (clay softening) = final slope stability value 1.32.

[0028] The intelligent computing framework generates initial stability values ​​for a slope based on slope gradient, geological structure, and slope height, specifically through the following steps: Based on historical engineering data, extract multiple slope stability calculation processes from the historical engineering data; Multiple slope stability calculation processes are combined to generate a slope stability calculation set. Extract the pre-input parameters of the slope stability calculation process, generate selection labels based on the pre-input parameters, and add the selection labels to each corresponding slope stability calculation process in the slope stability calculation set. The intelligent computing framework generates parameter labels based on slope gradient, geological structure, and slope height; Compare the parameter labels with the selected labels in the slope stability calculation set to determine the target selection label; Extract the target slope stability calculation process corresponding to the target selection label, and extract the matching value between the target slope stability calculation process and the parameter label; The target slope stability calculation process is adapted based on the fit value and parameter labels to obtain the target calculation process; By substituting the slope slope, geological structure, and slope height into the target calculation process, the initial stability values ​​of the slope are obtained.

[0029] In application, based on historical engineering data, three slope stability calculation processes were extracted: Process X (based on slope height and slope), Process Y (based on slip surface length), and Process Z (based on comprehensive geological parameters). A slope stability calculation set was generated, and selection tags were added to each process: Process X was tagged "simple slope type," Process Y was tagged "long slip surface," and Process Z was tagged "complex geology." The intelligent calculation framework generated parameter tags ("medium slope," "short slip surface") based on the slope gradient (38°), geological structure (straight type), and slope height (10 meters) of the target slip surface data. The parameter tags were compared with the tags in the calculation set: Process X (tagged "simple slope type") had the highest matching value (90%). Process X was adaptively modified by adding a slope height correction coefficient (1.1 for 10 meters). Substituting the slope gradient of 38° and the slope height of 10 meters into the modified Process X, the initial stability value was calculated to be 1.5.

[0030] The steps involved in deploying data monitoring devices on the slope to collect current slope status data, and then updating and predicting the slope stability values ​​in real time based on this data to obtain the predicted stability values ​​are as follows: Data monitoring devices are deployed on the slope to collect the current state of the slope. Based on the current state, the changes in the geological viscosity and slope displacement of the slope are obtained. Based on the changes in geological viscosity, a viscosity change curve is constructed; based on the changes in slope displacement, a slope position change curve is constructed. By combining the viscosity change curve and the slope position change curve, the slope stability change curve is obtained, and the stability change law is obtained based on the slope stability change curve; Based on the laws of stable change, the stability state of the slope is predicted, and the predicted stability state is obtained. The slope stability values ​​are updated and predicted in real time based on the current state and the predicted stable state, thus obtaining the predicted stable values.

[0031] In application, 10 data monitoring devices (such as displacement sensors and soil moisture meters) are deployed on the slope to collect real-time data on the current state. For example, the sensors show that the slope's geological viscosity decreases from 0.4 kPa·s to 0.35 kPa·s, and the slope displacement increases by 2 mm per day. A curve is constructed based on viscosity changes (viscosity decreases linearly over time), and another curve is constructed based on displacement changes (displacement increases exponentially). Combining these two curves generates a slope stability change curve, and the stability change pattern is analyzed: for every 0.1 kPa·s decrease in viscosity, the displacement rate increases by 1 mm / day. Based on this pattern, it is predicted that the viscosity will decrease to 0.3 kPa·s in the next 30 days, and the displacement rate will reach 5 mm / day. Combining the current state (viscosity 0.35 kPa·s) and the predicted state (viscosity 0.3 kPa·s), the slope stability value (originally 1.32) is updated in real time: the decrease in viscosity reduces the safety factor to 1.25, and the increase in displacement further reduces the factor to 1.18. The final output is a predicted stability value of 1.18, triggering an early warning mechanism.

[0032] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for calculating slope stability, characterized in that, include: Acquire historical engineering data, construct a regional geological database based on the historical engineering data, and generate a regional geological data map based on the regional geological database; Acquire historical meteorological data and meteorological forecast data, construct a regional meteorological database based on the historical meteorological data and the meteorological forecast data, and generate a meteorological data map based on the regional meteorological database; Based on the historical engineering data, slope calculation data is extracted from the historical engineering data, and various slip surface data are obtained based on the slope calculation data. An intelligent computing framework is then constructed based on the various slip surface data. The engineering construction area is obtained, and the target geological data and target meteorological data are obtained by filtering the geological data map and the meteorological data map based on the engineering construction area. Obtain engineering construction parameters, obtain slope shape data based on the engineering construction parameters, and obtain target slip surface data based on the slope shape data; Substituting the target slip surface data, the target geological data, and the target meteorological data into the intelligent computing framework, slope stability values ​​are obtained; A data monitoring device is deployed on the slope to collect the current state data of the slope. Based on the current state data, the slope stability value is updated and predicted in real time to obtain the predicted stability value.

2. The slope stability calculation method according to claim 1, characterized in that, The steps of constructing a regional meteorological database based on the historical meteorological data and the meteorological forecast data, and generating a meteorological data map based on the regional meteorological database, are as follows: Construct a timeline, arrange the historical meteorological data on the timeline to obtain a historical meteorological data sequence, and obtain historical meteorological change data based on the historical meteorological data sequence; Based on the historical meteorological change data, the meteorological change pattern is obtained, and the meteorological forecast data is adjusted according to the meteorological change pattern to obtain the target forecast meteorological data; Based on the predicted meteorological data, the meteorological data is classified and sorted according to time order to construct a regional meteorological database; Extract the coverage area of ​​the target predicted meteorological data, extract map data based on the coverage area, and combine the target predicted meteorological data and the map data to generate a meteorological data map.

3. The slope stability calculation method according to claim 2, characterized in that, The steps of extracting slope calculation data from the historical engineering data, obtaining various slip surface data based on the slope calculation data, and constructing an intelligent computing framework based on the various slip surface data are as follows: Extract slope calculation data from the historical engineering data, and extract calculation influence parameters, calculation modes, and various slip surface data from the slope calculation data; Based on the calculated impact parameters, the calculated impact range and the calculated impact depth are obtained, and the impact compensation parameters are generated based on the calculated impact range and the calculated impact depth. Based on the aforementioned calculation mode, multiple data calculation processes are obtained, and based on these multiple data calculation processes, a process selection library is generated. Substitute the various slip surface data into the process selection library to generate a slope calculation framework. Combine the slope calculation framework with the impact compensation parameters to generate an intelligent calculation framework.

4. The slope stability calculation method according to claim 3, characterized in that, The steps for obtaining the engineering construction area and filtering it from the geological data map and meteorological data map to obtain target geological data and target meteorological data are as follows: Obtain the construction area, extract the geographic coordinate data of the construction area, and determine the scope of the construction area based on the geographic coordinate data; Generate a range mask on the geological data map and the meteorological data map for the construction area; Based on the range mask, the geological data map and the meteorological data map are filtered by region to determine the target geological range and the target meteorological range; Based on the target geological range and the target meteorological range, data are collected from the geological data map and the meteorological data map to obtain the target geological data and the target meteorological data, respectively.

5. The slope stability calculation method according to claim 4, characterized in that, The steps of obtaining engineering construction parameters, obtaining slope shape data based on the engineering construction parameters, and obtaining target slip surface data based on the slope shape data are as follows: Obtain engineering construction parameters, and based on these parameters, determine the road construction width and road elevation. Based on the target geological data, target terrain data is obtained, and a three-dimensional geographic model is constructed based on the target terrain data; Based on the road construction width and the road elevation, the three-dimensional geographic model is used to simulate construction excavation to obtain slope data along the road. Based on the slope data, the slope shape is described to obtain slope shape data, and the initial slip surface data is obtained based on the slope shape data. Based on the target meteorological data and the target geological data, hydrological data and stratigraphic lithology data are obtained. Based on the hydrological data and the stratigraphic lithology data, hydrological influencing factors and stratigraphic lithology influencing factors are generated. The initial slip surface data are dynamically corrected using the hydrological influencing factors and the stratigraphic lithology influencing factors to obtain the target slip surface data.

6. The slope stability calculation method according to claim 5, characterized in that, The steps for substituting the target slip surface data, the target geological data, and the target meteorological data into the intelligent computing framework to obtain slope stability values ​​are as follows: The target slip surface data is substituted into the intelligent computing framework, and the intelligent computing framework extracts the slope, geological structure, and slope height of the target slip surface data; The intelligent computing framework generates the initial stability value of the slope based on the slope gradient, the geological structure, and the slope height. The intelligent computing framework obtains the geological composition data of the slope based on the target geological data, and generates a first stability influence value based on the geological composition data; The intelligent computing framework generates a second stable influence value for the slope based on the target meteorological data; Substitute the first influence value and the second influence value into the initial stability value to correct the initial stability value and obtain the slope stability value.

7. The slope stability calculation method according to claim 6, characterized in that, The steps of the intelligent computing framework generating initial stability values ​​for the slope based on the slope gradient, geological structure, and slope height are as follows: Based on the historical engineering data, extract multiple slope stability calculation processes from the historical engineering data; Multiple slope stability calculation processes are combined to generate a slope stability calculation set; Extract the pre-input parameters of the slope stability calculation process, generate selection labels based on the pre-input parameters, and add the selection labels to each corresponding slope stability calculation process in the slope stability calculation set; The intelligent computing framework generates parameter labels based on the slope, geological structure, and slope height; The parameter labels are compared with the selected labels in the slope stability calculation set to determine the target selected label; Extract the target slope stability calculation process corresponding to the target selection label, and extract the matching value between the target slope stability calculation process and the parameter label; The target slope stability calculation process is adaptively modified based on the fit value and the parameter label to obtain the target calculation process; Substituting the slope slope, geological structure, and slope height into the target calculation process, the initial stability value of the slope is obtained.

8. The slope stability calculation method according to claim 6, characterized in that, The steps of deploying a data monitoring device on the slope to collect the current state data of the slope, and updating and predicting the slope stability value in real time based on the current state data to obtain the predicted stability value are as follows: Data monitoring devices are deployed on the slope to collect the current state of the slope. Based on the current state, the changes in the geological viscosity and the changes in the slope displacement are obtained. Based on the changes in geological viscosity, a viscosity change curve is constructed; based on the changes in slope displacement, a slope position change curve is constructed. By combining the viscosity change curve and the slope position change curve, a slope stability change curve is obtained, and based on the slope stability change curve, a stability change law is obtained; Based on the aforementioned stable change pattern, the stability state of the slope is predicted to obtain the predicted stable state. The slope stability values ​​are updated and predicted in real time based on the current state and the predicted stable state to obtain the predicted stable values.

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