Data Processing Methods Based on Geological Mapping of Power Transmission and Transformation Projects

Abstract

This invention discloses a data processing method based on geological maps of power transmission and transformation projects, belonging to the field of data processing technology. The method includes the following steps: fusing multi-source geological data of the power transmission and transformation project area with planned route information; dividing the project area into different types of geological structural units based on the fusion result; predicting engineering geological parameters of the project area using spatial prediction algorithms corresponding to the geological structural units; conducting an engineering risk assessment based on the predicted engineering geological parameters and power transmission and transformation project load information; and generating a geological map of the power transmission and transformation project based on the engineering risk assessment results. This invention can improve the accuracy and reliability of geological parameter prediction, achieve efficient fusion of multi-source data, and provide a scientific decision-making basis for line optimization, tower foundation selection, and construction safety of power transmission and transformation projects.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a data processing method based on geological maps of power transmission and transformation projects. Background Technology

[0002] Currently, in the field of power transmission and transformation engineering survey and design, the creation of geological maps is crucial for route optimization, tower foundation selection, and stability evaluation. The mainstream mapping method has shifted from traditional manual drawing to a Geographic Information System (GIS)-based approach, using general-purpose GIS software (such as ArcGIS and QGIS) for assisted mapping. While this approach achieves digital management and spatial visualization of geological data (such as remote sensing imagery, digital elevation models, and survey point locations), its core processing workflow still heavily relies on manual intervention.

[0003] To further improve efficiency, secondary development is carried out on a general GIS platform to integrate some professional functions (such as tower base coordinate management and line buffer analysis), or a preliminary professional geological database is constructed. This solution optimizes the workflow to some extent, but it still fails to fundamentally solve the core problems. First, its ability to deeply integrate and intelligently understand multi-source heterogeneous data (especially unstructured data such as survey reports and geotechnical parameter tables) is insufficient, still requiring a large amount of manual data extraction and entry. Second, its geological analysis functions are mostly based on preset, fixed rule bases, lacking adaptive and learning capabilities, and cannot cope with the intelligent identification and evaluation needs under complex geological conditions, making it difficult to meet the refined and dynamic assessment requirements for geological safety in power transmission and transformation projects. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a data processing method based on the drawing of geological maps for power transmission and transformation projects. By integrating multi-source geological data and planned route information, the method divides geological structural units, predicts engineering geological parameters, assesses engineering risks, and generates a visualized geological map. This addresses the problems of insufficient multi-source data fusion capabilities, limited accuracy in geological parameter prediction, and difficulty in intuitively expressing engineering risk assessment results in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The data processing method based on the geological map of power transmission and transformation projects includes the following steps: integrating the multi-source geological data of the acquired power transmission and transformation project area with the planned line information, and dividing the project area into different types of geological structural units based on the fusion result; for each geological structural unit, using a spatial prediction algorithm corresponding to the geological structural unit to predict the engineering geological parameters of the project area; conducting an engineering risk assessment based on the predicted engineering geological parameters and the power transmission and transformation project load information; and generating a geological map of the power transmission and transformation project based on the engineering risk assessment results.

[0007] In a preferred embodiment, dividing the engineering area into different types of geological structural units specifically involves: processing multi-source geological data through numerical simulation to generate a tectonic stress field for the engineering area; coupling the vector data of the planned route with the tectonic stress field to calculate the mechanical interaction index of each calculation point of the route; based on the mechanical interaction index of all calculation points, using a clustering algorithm to divide the route into segments, defining each segment type obtained by clustering as a geological structural unit, and outputting a spatial distribution map of the geological structural units.

[0008] In a preferred embodiment, the mechanical interaction index specifically involves: spatially registering the vector data of the planned route with the structural stress field; for each calculation point on the route, reading the direction and amplitude of the maximum horizontal principal stress at that point; calculating the angle between the route direction and the direction of the maximum horizontal principal stress at that point based on the stress tensor data at that location, thus obtaining a directional coupling parameter; obtaining a strength coupling parameter based on the design load and the amplitude of the maximum horizontal principal stress at that point; and combining the directional coupling parameter and the strength coupling parameter to form a mechanical interaction index.

[0009] In a preferred embodiment, the method for defining the geological structural unit specifically involves: for the clustered route segments, calculating the statistical characteristics of the directional coupling degree parameters and the intensity coupling degree parameters of all calculation points within them, wherein the statistical characteristics include the average value of the directional coupling degree parameters and the average value of the intensity coupling degree parameters; based on the comparison of the statistical characteristics with a preset threshold, classifying each route segment into different geological structural unit types; wherein the geological structural unit types include at least stress-coordinated stable segments and stress-conflict high-risk segments.

[0010] In a preferred embodiment, the step of predicting engineering geological parameters of the engineering area using a spatial prediction algorithm corresponding to the geological structural unit specifically involves: matching a corresponding spatial interpolation algorithm based on the type of geological structural unit; using known exploration point data within the target geological structural unit and adjacent units as boundary conditions to perform interpolation calculations to obtain an engineering geological parameter prediction map and a prediction variance map; identifying high uncertainty areas where the prediction variance is higher than a preset variance threshold; automatically generating virtual exploration point data and re-interpolating for high uncertainty areas until the prediction variance of all areas is lower than the preset variance threshold, and outputting the engineering geological parameter map.

[0011] In a preferred embodiment, the method for automatically generating virtual exploration point data specifically comprises: calculating the spatial gradient field of the uncertain region based on the prediction variance map, and determining the direction of the fastest growth of the prediction variance; taking the existing exploration points as the starting point, performing a path search in the high uncertainty region along the prediction variance gradient direction to generate the optimal path that penetrates the high variance region; automatically deploying virtual exploration points along the optimal path at preset intervals, and assigning engineering geological parameter values ​​to the virtual exploration points.

[0012] In a preferred embodiment, the engineering risk assessment includes: calculating a time-varying safety factor sequence for each tower foundation point within a geological structural unit based on predicted engineering geological parameters and power transmission and transformation engineering load information; determining the baseline risk level of each tower foundation point based on the minimum value in the time-varying safety factor sequence; and generating a static risk influence envelope by using each tower foundation point as a diffusion source and employing an anisotropic risk attenuation model related to the principal direction of the local tectonic stress field.

[0013] In a preferred embodiment, the engineering risk assessment further includes: spatially superimposing the risk envelopes of different tower base points to identify the overlapping areas of the risk envelopes as critical areas of chain risks; calculating the sum of the superimposed benchmark risk levels within each critical area of ​​chain risks to obtain the superimposed risk intensity; and generating a comprehensive engineering risk assessment map based on the risk level of a single tower base point, the distribution of critical areas of chain risks, and the superimposed risk intensity.

[0014] In a preferred embodiment, generating a power transmission and transformation engineering geological map based on the engineering risk assessment results specifically involves: spatially registering and overlaying the spatial distribution map of geological structural units, the engineering geological parameter map, and the engineering risk assessment map to generate a comprehensive digital base map; based on the comprehensive digital base map, using the starting and ending points of the line planning as constraints and minimizing the objective function composed of superimposed risk intensity and economic cost as the optimization objective, using a path search algorithm to generate a recommended path; and obtaining the power transmission and transformation engineering geological map based on the comprehensive digital base map and the recommended path.

[0015] In a preferred embodiment, the integrated digital base map includes: responding to a user's simulated reinforcement operation on any tower base point on the integrated digital base map; updating the baseline risk level and its risk impact envelope of the tower base point in real time based on the simulated reinforcement operation; and recalculating and visualizing the range and superimposed risk intensity of the cascading risk critical area related to the updated risk impact envelope.

[0016] This invention integrates multi-source geological data with planned route information and selects appropriate spatial prediction algorithms based on geological structural unit types, enabling accurate prediction of key engineering geological parameters under complex geological conditions. Compared to traditional methods relying on a single data source or interpolation method, this invention fully utilizes the complementarity between different geological information, improving the spatial resolution and reliability of the prediction results, thereby effectively reducing the uncertainty caused by geological parameter estimation errors in engineering design and construction. By further combining the predicted engineering geological parameters with load information from the power transmission and transformation project for engineering risk assessment, and visually mapping the assessment results onto a geological map, this invention achieves an organic unity of data acquisition, parameter prediction, risk quantification, and graphical representation. This method not only accurately identifies high-risk areas and potential engineering hazards but also provides intuitive decision-making basis for site selection optimization, design adjustments, and construction management of power transmission and transformation projects, enhancing the scientific rigor and safety of the overall project construction. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the data processing method based on geological maps of power transmission and transformation projects according to the present invention.

[0018] Figure 2 This is a schematic flowchart of the method for predicting engineering geological parameters according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1, Figure 1 The present invention provides a data processing method based on geological maps of power transmission and transformation projects, including:

[0021] S1 integrates the multi-source geological data of the power transmission and transformation project area with the planned line information, and divides the project area into different types of geological structural units based on the fusion results;

[0022] In this embodiment, dividing the engineering area into different types of geological structural units specifically refers to:

[0023] The process of generating the tectonic stress field of the engineering area through numerical simulation processing of multi-source geological data specifically involves:

[0024] Multi-source geological data is acquired, including topographic and geomorphological data, stratigraphic and lithological data, measured geostress data, and rock mass physical and mechanical parameter data. The topographic and geomorphological data is obtained through satellite remote sensing or aerial photogrammetry to clearly define the distribution of regional geomorphic units (such as watersheds, valleys, and slopes). The stratigraphic and lithological data is obtained through field geological mapping, borehole core logging, and digitization of regional geological maps. The measured geostress data is obtained through in-situ geostress testing using hydraulic fracturing or stress relief methods. The rock mass physical and mechanical parameter data is obtained by obtaining core samples through engineering geological drilling and conducting rock mechanics tests in the laboratory. Before numerical simulation, the multi-source geological data needs to be spatially registered and standardized to ensure that all data have a unified geographic coordinate system and data format. Commonly used numerical simulation methods include the finite element method (FEM), the finite difference method (FD), and the discrete element method (DEM). This invention prioritizes the FEM or FD for simulation. First, based on topographic, stratigraphic, and geological structure data from multi-source geological data, a three-dimensional geomechanical model of the engineering area is established using professional numerical simulation software (such as MIDAS GTS and FLAC3D). This model should accurately depict the main topographic reliefs, the spatial distribution of different lithological strata, and the geometric morphology of major faults and other structures. According to the lithological classification results, corresponding mechanical parameters are assigned to different rock mass units in the model. These parameters are determined by indoor test results. A suitable constitutive model is selected for this model, preferably the Mohr-Coulomb elastoplastic model, to simulate the plastic deformation behavior of the rock mass after reaching its yield strength. Setting the model boundary conditions is crucial for generating a realistic tectonic stress field. The bottom of the model is set as a fixed constraint, and normal displacement constraints are applied to the sides of the model. The initial stress field is formed by the superposition of the self-weight stress field and the tectonic stress field. First, by applying gravitational acceleration, the stress distribution of the model under its own weight is calculated. Then, the influence of regional geological tectonic movement is simulated by applying equivalent tectonic forces (e.g., applying horizontal displacement or stress boundaries on the sides of the model) to the model boundaries. The stress states at key points obtained from the simulation calculations (especially the direction and magnitude of the maximum horizontal principal stress) are repeatedly compared with measured geostress data. By adjusting the magnitude and direction of the boundary tectonic forces, iterative calculations are performed until the simulation results and measured data achieve the best fit within a preset error tolerance (e.g., direction error less than 15°, magnitude error less than 20%). After the model has been inverted and validated and has reached equilibrium, the required tectonic stress field, including the magnitude and three-dimensional spatial vector direction of the maximum principal stress, intermediate principal stress, and minimum principal stress, can be extracted from the model.

[0025] The process involves coupling the vector data of the planned route with the tectonic stress field to calculate the mechanical interaction indices at each calculation point of the route. Specifically:

[0026] The planned route vector data undergoes a topology check to ensure it is a continuous linear feature. Subsequently, it is spatially registered with the tectonic stress field with high precision. To accurately reflect the interaction between the route and the stress field, an adaptive strategy is adopted for the layout of route calculation points, rather than simple equal-spaced placement. Basic calculation points are placed along the route centerline at a certain initial interval (e.g., 50 to 100 meters). For route curves or arcs, the spacing needs to be increased according to the radius of curvature. Preferably, when the radius of curvature R < 500m, the spacing should be shortened to 1 / 2 or even 1 / 5 of the initial interval to ensure sufficient sampling points in areas with drastic directional changes. Near known fault zones, weak interlayers, and other geologically complex areas, the spacing also needs to be increased, with intervals shortened to 10-25 meters, ultimately forming a set of route calculation points. .

[0027] The constructed stress field is essentially the stress tensor data for each grid node, and the stress tensor is symmetric. A matrix is ​​usually represented as:

[0028] For each line calculation point The coordinates of the point are used to calculate the stress tensor at that point from the stress tensor data of its corresponding mesh element using bilinear interpolation. Eigenvalue decomposition is then performed on this stress tensor to obtain the direction of the maximum horizontal principal stress. and maximum horizontal principal stress amplitude Calculation points based on the line Centered on a point, take three points before and three points before it, for a total of seven points. Use the least squares method to fit a straight line, and the direction of this line is the calculation point for the route. Route at the location .

[0029] Calculate the route to this point With respect to the direction of maximum horizontal principal stress The angle between them yields the directional coupling parameter. Specifically:

[0030]

[0031]

[0032] in, This represents the absolute angle difference. Directional coupling parameter. The smaller the value, the more coordinated the route direction is with the direction of ground stress.

[0033] Calculate the ratio of the design load amplitude to the maximum horizontal principal stress amplitude at this point to obtain the strength coupling parameter, specifically:

[0034] Obtain line calculation points Design load of tower foundation Based on the basic design dimensions, such as the expanded foundation area Convert it to equivalent design stress :

[0035]

[0036] Strength Coupling Parameter The calculation formula is:

[0037]

[0038] Combining directional coupling parameters and strength coupling parameters, a calculation point for each line is formed. parameter group As an indicator of mechanical interaction.

[0039] Using the mechanical interaction indices of all calculated points along the railway line as classification features, the K-means clustering algorithm is employed to divide the line into segments. Each segment type obtained from clustering is defined as a geological structural unit. Each geological structural unit includes at least stress-coordinated stable segments and stress-conflict high-risk segments. Specifically, this is achieved by comparing the statistical characteristics of the directional coupling degree parameters and intensity coupling degree parameters of all calculated points within the geological structural unit with preset thresholds. The statistical characteristics include the average values ​​of the directional coupling degree parameters and the intensity coupling degree parameters. The preset thresholds include a first threshold, a second threshold, a third threshold, a fourth threshold, and a fifth threshold. A railway segment whose average directional coupling degree parameter is less than the first threshold and whose average intensity coupling degree parameter is between the second and third thresholds is considered a stress-coordinated stable segment. A railway segment whose average directional coupling degree parameter is greater than the fourth threshold and whose average intensity coupling degree parameter is greater than the fifth threshold is considered a stress-conflict high-risk segment. The first threshold... Second threshold The third threshold The fourth threshold The fifth threshold The threshold setting is based on rock mechanics theory, underground engineering practice experience, and rock mass failure criteria.

[0040] Finally, a spatial distribution map of geological structural units is output.

[0041] S2, For the geological structural unit, a spatial prediction algorithm corresponding to the geological structural unit is used to predict the engineering geological parameters of the engineering area;

[0042] In this embodiment, for the geological structural unit, a spatial prediction algorithm corresponding to the geological structural unit is used to predict the engineering geological parameters of the engineering area, specifically:

[0043] Based on the type of geological structural unit, a corresponding spatial interpolation algorithm is matched. For high-risk stress conflict sections, an anisotropic kriging interpolation algorithm is matched, with a spherical model selected for the variogram function model. The principal direction of the algorithm's variogram function is aligned with the principal stress direction of the local tectonic stress field, and the initial value of the anisotropy ratio (i.e., the ratio of the range in the principal direction to the range perpendicular to the principal direction) is preferably set to 2.0. For stress-coordinated stable sections or other sections, a conventional kriging interpolation algorithm (isotropic) is configured, with a spherical model also used for its variogram function.

[0044] When performing interpolation calculations on any geological structural unit, not only are known exploration point data within it used, but also known exploration point data within adjacent geological structural units are used as boundary constraints to construct and solve the Kriging equations. After interpolation, an initial engineering geological parameter prediction map and its corresponding prediction variance (Kriging variance) map are output. An operable variance threshold is set to identify high uncertainty areas. A preferred embodiment of this threshold is to take the arithmetic mean of the prediction variances of all grid points in the entire engineering area plus one standard deviation. All continuous grid areas in the prediction variance map that satisfy the prediction variance greater than the variance threshold are marked as high uncertainty areas. For high uncertainty areas, virtual exploration point data is automatically generated and added to the original dataset. Interpolation calculations are re-executed to generate new prediction surfaces and prediction variance maps. This iterative process is repeated until the prediction variances of all areas are lower than the preset variance threshold, and the engineering geological parameter map is output.

[0045] The method for automatically generating virtual exploration point data is as follows:

[0046] For the identified high uncertainty areas, the spatial gradient field of the predicted variance map is calculated to determine the direction of the fastest increase in predicted variance. The existing exploration point closest to the edge of the high uncertainty area and whose predicted variance is below the variance threshold is taken as the starting point of the path. Starting from the starting point, Dijkstra's shortest path algorithm in graph theory is used to search within the high uncertainty area. During the search, the travel cost of each grid cell is defined as the reciprocal of the predicted variance of that cell. The algorithm will automatically find a path with the lowest total cost. This path tends to traverse the area with the highest predicted variance, thereby achieving the goal of "maximum penetration of high predicted variance areas". This path is determined as the optimal path for the virtual exploration point layout. Along the optimal path, a series of virtual exploration points are automatically generated at preset intervals, and each generated virtual exploration point is assigned engineering geological parameter values.

[0047] The engineering geological parameter values ​​of the virtual exploration point are determined in the following way:

[0048] The engineering geological parameter values ​​of virtual exploration points specifically refer to the characteristic values ​​of foundation bearing capacity.

[0049] The virtual exploration point is obtained by weighted averaging of the measured values ​​of known exploration points within a certain range around the virtual exploration point using a pre-trained Kriging interpolation algorithm.

[0050] S3, based on predicted engineering geological parameters and power transmission and transformation project load information, conducts engineering risk assessment;

[0051] In this embodiment, the engineering risk assessment based on predicted engineering geological parameters and power transmission and transformation project load information specifically includes:

[0052] The aforementioned engineering risk assessment includes:

[0053] For the transmission tower foundation points in various geological structural units Based on the predicted engineering geological parameters (characteristic values ​​of foundation bearing capacity) and the load information of the power transmission and transformation project at that location, a time-varying safety factor is calculated. The time-varying nature is achieved by considering different working conditions, and a set of working conditions is defined. ,For example, Under normal operating conditions, For heavy rain conditions, For seismic conditions, a time-varying safety factor sequence is obtained. Time-varying safety factor The calculation formula is:

[0054]

[0055] in, For the base point of the transmission tower The characteristic value of the foundation bearing capacity, This refers to the vertical design load.

[0056] For each tower base point, take the minimum value in the time-varying safety factor sequence. As a basis for risk assessment, risk level thresholds are set. , Determine the baseline risk level for this point. Specifically:

[0057]

[0058] With each tower base point As a source of diffusion, the baseline risk level of this tower base point is determined. Defined as the initial risk intensity of the source point An anisotropic risk attenuation model is established, the core of which is to describe the intensity of risk impact. How does the decay rate decrease with distance *d* from the diffusion source and direction *θ*? The anisotropy refers to the decay rate being direction-dependent. This model uses an elliptical decay model, where the major axis of the ellipse aligns with the direction of the maximum horizontal principal stress at the tower base. Consistency indicates that the risk's influence extends further (attenuates more slowly) along the principal direction of tectonic stress. The minor axis being perpendicular to the major axis indicates that the risk attenuates faster in that direction. The intensity of the risk's influence in any direction θ... The decay formula with distance d can be expressed as:

[0059]

[0060] Where m is the base index, specifying which base point the current calculation is for, and d is the distance between the calculation point and the base. The Euclidean distance between them, where θ is the calculation point relative to the base point of the tower. azimuth angle, This is a function of attenuation coefficients related to direction and tower base location.

[0061] The calculation is related to elliptic geometry, specifically:

[0062]

[0063] in, Base point of the tower The direction of the maximum horizontal principal stress at that location, This represents the characteristic attenuation distance along the direction of maximum horizontal principal stress (major axis). The characteristic attenuation distance is perpendicular to the direction of the maximum horizontal principal stress (minor axis) and satisfies... .

[0064] Set a lower threshold for the intensity of risk impact. The static risk impact envelope is all conditions that satisfy the following: A closed figure formed by points on a plane.

[0065] Spatial superposition analysis of risk envelopes at different base points is performed to identify areas where risk envelopes overlap, which are defined as critical cascading risk zones. The sum of the baseline risk levels corresponding to the superimposed risk envelopes within each critical cascading risk zone is calculated to obtain the superimposed risk intensity of the zone. The larger the superimposed risk intensity value, the higher the potential risk of cascading failure in the zone.

[0066] Based on the risk level of a single tower base point, the distribution of critical risk zones in a chain reaction, and the intensity of superimposed risks, a comprehensive engineering risk assessment map is generated.

[0067] S4 generates a geological map of the power transmission and transformation project based on the project risk assessment results.

[0068] In this embodiment, generating a geological map of the power transmission and transformation project based on the engineering risk assessment results specifically involves:

[0069] The process of generating a geological map of the power transmission and transformation project based on the engineering risk assessment results is as follows:

[0070] Spatial registration and overlay of geological structural unit spatial distribution maps, engineering geological parameter maps, and engineering risk assessment maps are managed as different layers. For example, the geological structural unit map is used as the base layer, the engineering geological parameter contour lines are used as a semi-transparent overlay layer, and the risk assessment map (especially critical areas of linked risks) is overlaid with a highlighted color on the top layer. The resulting integrated digital map, containing multiple geological and risk information, is the comprehensive digital base map.

[0071] Based on a comprehensive digital base map, with the starting point and ending point of the route planning as constraints, and minimizing the objective function composed of the superimposed risk intensity and economic cost of the route crossing areas as the optimization objective, one or more recommended routes are generated using a path search algorithm (such as a genetic algorithm).

[0072] The integrated digital base map includes:

[0073] The user selects any base point on the integrated digital base map. The simulated reinforcement operation is carried out, and the baseline risk level and its risk impact envelope of the tower base point are updated in real time according to the simulated reinforcement operation. The range and superimposed risk intensity of the cascading risk critical area related to the updated risk impact envelope are recalculated and visualized.

[0074] The recommended path generated by intelligent optimization is combined with the comprehensive digital base map, and map elements such as legend and scale are added to output the final geological map of the power transmission and transformation project.

[0075] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0076] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0077] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0078] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0079] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0080] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data processing method based on geological maps of power transmission and transformation projects, characterized in that, Includes the following steps: The multi-source geological data of the power transmission and transformation project area is integrated with the information of the planned route. Based on the integration results, the project area is divided into different types of geological structural units. This includes: processing the multi-source geological data through numerical simulation to generate the tectonic stress field of the project area; performing coupled analysis on the vector data of the planned route and the tectonic stress field to calculate the mechanical interaction index of each calculation point of the route; and using a clustering algorithm to divide the project area into different types of geological structural units. The index includes a directional coupling degree parameter determined by the angle between the route alignment and the direction of the maximum horizontal principal stress, and a strength coupling degree parameter determined by the design load and the amplitude of the maximum horizontal principal stress. The geological structural units are divided into different types based on the clustering of the line segments. The average values ​​of their directional coupling parameters and intensity coupling parameters are extracted and compared with preset thresholds. For the geological structural unit, a spatial prediction algorithm corresponding to the geological structural unit is used to predict the engineering geological parameters of the engineering area; Based on the predicted engineering geological parameters and power transmission and transformation project load information, an engineering risk assessment is conducted. Based on the results of the engineering risk assessment, a geological map of the power transmission and transformation project is generated.

2. The data processing method based on geological map drawing of power transmission and transformation projects according to claim 1, characterized in that, The mechanical interaction index is specifically as follows: Spatial registration is performed between the vector data of the planned route and the structural stress field; For each calculation point on the line, read the direction and amplitude of the maximum horizontal principal stress at that point; Based on the stress tensor data at its location, the angle between the line direction at that point and the direction of the maximum horizontal principal stress is calculated to obtain the directional coupling parameter; Based on the design load and the maximum horizontal principal stress amplitude at this point, the strength coupling parameter is obtained; By combining directional coupling parameters and strength coupling parameters, an index characterizing mechanical interactions is formed.

3. The data processing method based on the geological map drawing of power transmission and transformation projects according to claim 2, characterized in that, The geological structural unit types include at least stress-coordinated stable sections and stress-conflict high-risk sections.

4. The data processing method based on the geological map drawing of power transmission and transformation projects according to claim 3, characterized in that, Specifically, for the geological structural unit, a spatial prediction algorithm corresponding to the geological structural unit is used to predict the engineering geological parameters of the engineering area, as follows: Match the appropriate spatial interpolation algorithm according to the type of geological structural unit; Using known exploration point data within and adjacent units of the target geological structural unit as boundary conditions, interpolation calculations are performed to obtain engineering geological parameter prediction maps and prediction variance maps. Identify regions of high uncertainty where the predicted variance exceeds a preset variance threshold; For areas with high uncertainty, virtual exploration point data is automatically generated and re-interpolated until the predicted variance of all areas is lower than the preset variance threshold, and then an engineering geological parameter map is output.

5. The data processing method based on geological map drawing of power transmission and transformation projects according to claim 4, characterized in that, The method for automatically generating virtual exploration point data is as follows: The spatial gradient field of the uncertain region is calculated based on the prediction variance map to determine the direction of the fastest growth of the prediction variance. Starting from existing exploration points, a path search is performed along the direction of the predicted variance gradient in the high uncertainty region to generate the optimal path that penetrates the high variance region. Virtual exploration points are automatically deployed along the optimal path at preset intervals, and engineering geological parameter values ​​are assigned to the virtual exploration points.

6. The data processing method based on the geological map drawing of power transmission and transformation projects according to claim 5, characterized in that, The aforementioned engineering risk assessment includes: For the tower foundation points within each geological structural unit, a time-varying safety factor sequence is calculated based on predicted engineering geological parameters and power transmission and transformation project load information; The baseline risk level of each tower base point is determined based on the minimum value in the time-varying safety factor sequence. Using each tower base point as the diffusion source, an anisotropic risk attenuation model related to the principal direction of the local tectonic stress field is used for spatial simulation to generate a static risk influence envelope.

7. The data processing method based on the geological map drawing of power transmission and transformation projects according to claim 6, characterized in that, The aforementioned engineering risk assessment also includes: By spatially superimposing the risk envelopes of different base points, the overlapping areas of the risk envelopes are identified as critical areas of cascading risks. Calculate the sum of the baseline risk levels superimposed within each critical risk zone of the chain to obtain the superimposed risk intensity; Based on the risk level of a single tower base point, the distribution of critical risk zones in a chain reaction, and the intensity of superimposed risks, a comprehensive engineering risk assessment map is generated.

8. The data processing method based on the geological map drawing of power transmission and transformation projects according to claim 7, characterized in that, The process of generating a geological map of the power transmission and transformation project based on the engineering risk assessment results is as follows: Spatial registration and overlay of spatial distribution maps of geological structural units, engineering geological parameter maps, and engineering risk assessment maps are performed to generate a comprehensive digital base map. Based on a comprehensive digital base map, with the starting and ending points of the route planning as constraints, and minimizing the objective function composed of superimposed risk intensity and economic cost as the optimization objective, a path search algorithm is used to generate recommended paths. Based on the comprehensive digital base map and recommended routes, a geological map of the power transmission and transformation project is obtained.

9. The data processing method based on the geological map drawing of power transmission and transformation projects according to claim 8, characterized in that, The integrated digital base map includes: Respond to the user's simulated reinforcement operation on any tower base point on the integrated digital base map; Based on the simulated reinforcement operation, the baseline risk level and its risk impact envelope of the tower base point are updated in real time. Recalculate and visualize the extent and intensity of the cascading risk critical zone associated with the updated risk impact envelope.

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