Submarine geological parameter fused photovoltaic foundation construction resource allocation method

Abstract

The present application relates to the technical field of marine geology engineering, in particular to a photovoltaic foundation construction resource allocation method based on seabed geological parameter fusion, comprising: collecting seabed topographic data, geotechnical physical parameters and stratum bearing capacity parameters through a shipborne multi-beam sounding system, a seabed drilling sampling device and a static sounding device; mapping all kinds of parameters to a three-dimensional geographic coordinate system to construct a standardized geological data cube; extracting stratum shear wave velocity, standard penetration number and water content, and fusing to calculate a comprehensive geological stiffness index; dividing three types of construction units, i.e. hard rock area, transition area and soft soil area, according to the index threshold value, and matching a hydraulic impact hammer, a rotary drilling machine and a vibrating pile driver; when a geological variation exceeding a set range is monitored, automatically adjusting the equipment configuration of the adjacent area to generate a dynamic resource allocation scheme. The present application can realize equipment precise matching and intelligent scheduling under the geological driving, and improve the efficiency and safety of seabed photovoltaic foundation construction.

Description

Technical Field

[0001] This invention relates to the field of marine geological engineering technology, and in particular to a method for allocating resources for photovoltaic foundation construction based on the integration of seabed geological parameters. Background Technology

[0002] Offshore photovoltaics, as a new energy utilization method that combines renewable energy with potential for spatial expansion, is becoming a key focus of energy infrastructure construction in coastal and near-shore areas. During the construction of submarine photovoltaic foundations, the selection of construction equipment and resource allocation are highly dependent on the geological conditions of the target sea area. To ensure the stability of the pile foundation installation and the safety of the project, it is often necessary to obtain key information such as seabed topographic data, geotechnical parameters, and stratum bearing capacity parameters, and to divide the area and configure construction equipment accordingly.

[0003] Currently, photovoltaic foundation construction relies heavily on static survey data and manual experience for equipment configuration, which presents the following problems: First, the lack of unified data standards and spatial models makes it difficult to integrate various geological parameters and form a continuous and stable geological index map; second, the division of construction areas is often simplified using single parameters or contour lines, which cannot reflect the comprehensive impact of multiple indicators and easily leads to unbalanced equipment configuration; third, most existing resource allocation strategies are one-time static configurations, lacking the ability to respond in real time to dynamic changes in geological conditions during construction, and unable to adjust and optimize unit scheduling when geological changes occur, which can easily lead to decreased construction efficiency or even project risks. Summary of the Invention

[0004] This invention provides a method for allocating resources for photovoltaic foundation construction by fusing seabed geological parameters. It is an intelligent resource allocation method that integrates multi-source geological information, has quantitative assessment capabilities for rigidity, and can dynamically respond to geological changes.

[0005] A method for allocating resources for photovoltaic foundation construction based on the integration of seabed geological parameters includes the following steps:

[0006] S1: Simultaneously acquire seabed topographic data, geophysical parameters, and stratum bearing capacity parameters of the construction area through a shipborne multibeam echo sounder, seabed drilling and sampling device, and static cone penetrometer.

[0007] S2: Map all parameters in S1 to a three-dimensional geographic coordinate system to generate a standardized geological data cube with timestamps;

[0008] S3: Extract formation shear wave velocity, SPT blow count and water content from the standardized geological data cube, and generate the comprehensive geological stiffness index of each grid cell through a weighted fusion algorithm;

[0009] S4: Based on the comprehensive geological stiffness index threshold, the construction sea area is divided into three geological construction units: hard rock zone, transition zone and soft soil zone.

[0010] S5: Allocate hydraulic impact hammer units to hard rock areas, vibratory pile driving units to soft soil areas, and rotary drilling rig units to transition areas to form an initial equipment allocation plan;

[0011] S6: When the change in the comprehensive geological stiffness index of a certain unit exceeds ±15% in real time, the equipment scheduling instruction of the adjacent area is triggered to update the dynamic resource allocation plan.

[0012] Optionally, S1 includes:

[0013] S11: The shipborne multibeam echo sounder system performs a full-coverage scan along the planned survey line to collect raw seabed topographic point cloud data. After coordinate calculation and tide level correction, the seabed topographic data is output.

[0014] S12: Obtain rock core samples at preset grid nodes through a seabed drilling sampling device, perform density testing, particle analysis and mineral composition detection on the rock core samples, and generate geophysical parameters including porosity, saturation and permeability coefficient.

[0015] S13: Penetration tests were conducted at locations adjacent to the seabed drilling and sampling device using a static cone penetration tester. The cone tip resistance curve and sidewall friction curve were recorded in real time. After depth normalization, the formation bearing capacity parameters were extracted.

[0016] S14: Synchronously record the timestamps and spatial coordinates of seabed topographic data, geophysical parameters, and stratum bearing capacity parameters to form a spatiotemporally aligned original parameter set.

[0017] Optionally, S2 includes:

[0018] S21: Transform the seabed topography data, geotechnical parameters and stratum bearing capacity parameters from the spatiotemporally aligned original parameter set into the WGS-84 three-dimensional geographic coordinate system to generate seabed topography grid data, geotechnical parameter grid data and stratum bearing capacity parameter grid data with coordinate labels.

[0019] S22: Perform dimensionless processing on the seabed topography grid data, geotechnical physical parameter grid data, and stratum bearing capacity parameter grid data respectively, and output standardized seabed topography data, standardized geotechnical physical parameters, and standardized stratum bearing capacity parameters;

[0020] S23: Standardize seabed topographic data, standardized geophysical parameters, and standardized stratigraphic bearing capacity parameters are spatiotemporally matched according to the same spatial grid unit, and the timestamps recorded in S14 are embedded to construct a standardized geological data cube that includes spatial coordinates, timestamps, and multidimensional geological attributes.

[0021] Optionally, the dimensionless processing of the seabed topography grid data, geotechnical physical parameter grid data, and stratum bearing capacity parameter grid data includes:

[0022] The seabed topography grid data is normalized using the elevation normalization method to output standardized seabed topography data;

[0023] The geotechnical physical parameter grid data is standardized using the range standardization method to output standardized geotechnical physical parameters;

[0024] The formation bearing capacity parameter grid data is standardized using the Z-score method to output standardized formation bearing capacity parameters.

[0025] Optionally, S3 includes:

[0026] S31: Extract three geological parameters from the standardized geological data cube according to spatial grid units: standardized formation shear wave velocity, standardized standard penetration test blow number, and standardized water content.

[0027] S32: Preset weighting coefficients are assigned to the standardized formation shear wave velocity, standardized SPT blow count, and standardized water content, respectively, to construct the computational framework of the weighted fusion algorithm, specifically as follows:

[0028] Standardized formation shear wave velocity weighting factor (0.45≤ ≤0.55);

[0029] Standardized Standard Penetration Test (SPT) Blow-up Factor (0.25≤ ≤0.35);

[0030] Standardized moisture content weighting factor (0.15≤ ≤0.25);

[0031] The constraints are satisfied: ;

[0032] S33: Calculate the comprehensive geological stiffness index using a weighted fusion algorithm to generate a comprehensive geological stiffness index numerical matrix for each grid cell.

[0033] Optionally, the comprehensive geological stiffness index is calculated as follows:

[0034] ;

[0035] in, For the first The comprehensive geological stiffness index of the grid cells, Here, N represents the standardized formation shear wave velocity, N represents the standardized standard penetration test (SPT) blow number, and w represents the standardized water content.

[0036] Optionally, S4 includes:

[0037] S41: Set the threshold range for the comprehensive geological stiffness index, specifically as follows:

[0038] Hard rock boundary threshold: Comprehensive geological stiffness index ≥ upper threshold limit;

[0039] Soft soil boundary threshold: Comprehensive geological stiffness index ≤ lower limit of threshold;

[0040] Transition boundary threshold: lower threshold < comprehensive geological stiffness index < upper threshold;

[0041] The upper threshold is set at 0.8 times the maximum value of the comprehensive geological stiffness index numerical matrix, and the lower threshold is set at 1.2 times the minimum value of the comprehensive geological stiffness index numerical matrix.

[0042] S42: Traverse each grid cell of the comprehensive geological stiffness index numerical matrix and perform geological type determination:

[0043] When the unit value is greater than or equal to the upper threshold, it is marked as a hard rock zone;

[0044] When the unit value is less than or equal to the lower threshold, it is marked as a soft soil area;

[0045] When the lower threshold is less than the unit value and the upper threshold is less than the upper threshold, it is marked as a transition zone;

[0046] Output a matrix of geological construction unit type identifiers with spatial coordinates.

[0047] S43: Perform spatial clustering on the geological construction unit type identifier matrix, including merging adjacent grid units of the same type and removing isolated units with an area of ​​less than 50m², to generate spatial distribution maps of three types of geological construction units: hard rock zone, transition zone, and soft soil zone.

[0048] Optionally, S5 includes:

[0049] S51: Calculate the total area of ​​the hard rock area based on the spatial distribution map of the hard rock area, and configure the number of units according to the construction efficiency of the hydraulic impact hammer unit per unit area.

[0050] S52: Calculate the total area of ​​the soft soil area based on the spatial distribution map of the soft soil area, and configure the number of units according to the construction efficiency of the vibratory pile driving unit per unit area.

[0051] S53: Calculate the total area of ​​the transition zone based on the spatial distribution map of the transition zone, and configure the number of units according to the construction efficiency per unit area of ​​the rotary drilling rig;

[0052] S54: Generate the initial device allocation scheme, including:

[0053] Deployment plan for hard rock areas: hydraulic impact hammer unit numbering and corresponding construction grid coordinate set;

[0054] Deployment plan for soft soil areas: Vibratory pile driving unit number and corresponding construction grid coordinate set;

[0055] Transition zone deployment plan: Rotary drilling rig unit number and corresponding construction grid coordinate set;

[0056] Equipment scheduling list: total number of hydraulic impact hammer units, total number of vibratory pile driving units, and total number of rotary drilling rig units.

[0057] Optionally, S6 includes:

[0058] S61: Real-time monitoring of the updated data of the standardized geological data cube. When the change of the comprehensive geological stiffness index of a certain grid cell relative to the initial value exceeds ±15%, the cell is marked as a geological variation cell, and its spatial coordinates and the variation of the comprehensive geological stiffness index are extracted.

[0059] S62: Define the neighborhood zone as a 3×3 grid extending outwards from the geological variation unit, and generate equipment scheduling instructions for the neighborhood zone:

[0060] When the increase in the comprehensive geological stiffness index after the mutation is ≥15%, a conversion command is issued for the rotary drilling rig unit in the adjacent area to the hydraulic impact hammer unit.

[0061] When the decrease in the comprehensive geological stiffness index after the mutation is ≥15%, a conversion command is issued for the hydraulic impact hammer unit in the adjacent area to the vibratory pile driving unit.

[0062] When the variation is within ±15%, maintain the current equipment configuration;

[0063] S63: Update the equipment deployment plan according to the equipment scheduling instructions of the neighboring area, including:

[0064] Release the original unit's construction grid binding in the variation unit and adjacent areas;

[0065] Reassign unit types according to S62 instruction type;

[0066] Revise the distribution of unit numbers in the equipment scheduling list;

[0067] Generate a dynamic resource allocation scheme that includes the new construction grid coordinate set.

[0068] The beneficial effects of this invention are:

[0069] This invention utilizes a combination of equipment, including a shipborne multibeam echo sounder, seabed drilling and sampling devices, and a static cone penetrometer, to jointly collect seabed topographic data, geotechnical physical parameters, and stratum bearing capacity parameters. A standardized geological data cube is then constructed using a WGS-84 three-dimensional geographic coordinate system. By employing various dimensionless processing methods such as elevation normalization, range normalization, and Z-score normalization, the comparability of different physical quantities is ensured, and spatial alignment and temporal synchronization are achieved. This significantly improves the comprehensive expressive power of geological data and the accuracy of downstream calculations, effectively supporting intelligent decision-making in subsequent regional division and equipment allocation.

[0070] This invention integrates three key geological indicators—formation shear wave velocity, SPT blow count, and water content—and employs a weighted fusion algorithm to calculate a comprehensive geological stiffness index, accurately reflecting the differences in rigidity and construction difficulty among various grid units. Based on this, the system dynamically sets classification thresholds for hard rock, transition, and soft soil zones according to the index values, achieving intelligent zoning of the construction area and matching different types of construction equipment (hydraulic impact hammer units, rotary drilling rig units, and vibratory pile driving units). This mechanism is more scientific and objective than traditional manual experience-based classification methods, effectively reducing energy waste and construction errors caused by improper equipment selection.

[0071] This invention introduces a dynamic resource allocation mechanism that monitors changes in geological parameters in real time. When the comprehensive geological stiffness index fluctuates by more than ±15% relative to its initial state, it automatically identifies geological variation units and defines adjacent zones centered on these units, intelligently issuing instructions to switch construction equipment types. Through dynamic switching of equipment types, updating of construction grid binding relationships, and reconstruction of the scheduling list, a closed-loop optimization response for equipment configuration is achieved. This mechanism has the advantages of strong adaptability, high resource utilization, and low on-site intervention requirements, and can continuously ensure the effectiveness and stability of construction plans in complex seabed geological environments. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0074] Figure 2 This is a schematic diagram of the S1 process in an embodiment of the present invention. Detailed Implementation

[0075] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0076] like Figures 1-2 As shown, the resource allocation method for photovoltaic foundation construction based on the integration of seabed geological parameters includes the following steps:

[0077] S1: Simultaneously acquire seabed topographic data, geotechnical physical parameters, and stratum bearing capacity parameters of the construction area using a shipborne multibeam echo sounder, seabed drilling and sampling equipment, and a static cone penetrometer. Specifically:

[0078] S11, Seabed Topographic Data Acquisition and Processing: Using a shipborne multibeam echo sounder deployed on the bottom of the survey vessel, a full-coverage scan of the construction area is conducted according to a pre-set survey line scheme. The survey line spacing is set to 10–30 meters based on the required resolution.

[0079] During the scanning process, water depth data and the ship's heading, roll, and pitch angles output by the attitude sensor are recorded simultaneously, and the three-dimensional spatial coordinates of each measurement point are obtained by combining the RTK-GPS positioning system.

[0080] The acquired raw seabed topographic point cloud data is processed through the following workflow to generate standardized seabed topographic data:

[0081] Coordinate calculation: The geographic coordinates of the measurement points are uniformly converted to the three-dimensional geographic coordinate system (WGS84-ECEF).

[0082] Tide level correction: Tide level correction is performed using measured tide level data from nearby tide gauge stations to eliminate depth deviations affected by tidal changes.

[0083] Gridded interpolation: Construct a regular gridded seabed topographic surface model using Kriging or bilinear interpolation algorithms.

[0084] S12, Geophysical Parameter Acquisition: Based on the seabed topographic data output in S11, the sampling grid nodes of the seabed drilling sampling device are laid out according to the equidistant rule, with a recommended grid spacing of 100 meters. Core samples are drilled vertically at each preset grid node, with the drilling depth generally controlled between 10 and 20 meters.

[0085] The obtained core samples were subjected to the following tests in the laboratory:

[0086] Density test: Natural density and dry density were measured using the Archimedes method.

[0087] Particle size distribution was determined using a laser particle size analyzer to differentiate the proportions of sand, silt, and clay.

[0088] Mineral composition analysis: The proportions of major mineral components are analyzed using X-ray diffraction.

[0089] The above tests are used to generate the geophysical parameters of each grid point core, including porosity saturation and permeability coefficient.

[0090] S13, Extraction of formation bearing capacity parameters: Static cone penetrometers are arranged within a range of about 2 to 3 meters adjacent to each seabed drilling sampling device deployed in S12 to carry out in-situ penetration tests. The penetration depth is 10 meters and the penetration speed is controlled at 20 mm / s.

[0091] Record the following curve data in real time: cone tip resistance curve and sidewall friction curve. Normalize the curve data according to the penetration depth and output standardized profile data.

[0092] Calculate the formation bearing capacity parameters, including unconsolidated shear strength and bearing ratio (specific penetration resistance).

[0093] The unconsolidated shear strength is calculated as follows:

[0094] ;in, For cone tip resistance, For the total vertical stress, This is the drag coefficient, typically ranging from 15 to 20 (18 is recommended for clayey sediments);

[0095] Bearing capacity ratio (specific penetration resistance) Conversion: Then, the ultimate bearing capacity is estimated using the Terzaqhi or Meyerhof formula, or used for the classification of foundation bearing capacity levels.

[0096] S14, Construction of the original parameter set: Associate the seabed topography data, geotechnical parameters and stratum bearing capacity parameters output in the above sub-steps with their sampling time and three-dimensional spatial coordinates to construct a unified parameter structure.

[0097] The structure of each data entry is as follows: ;

[0098] in, In three-dimensional space coordinates, For sampling timestamps, Because of the water depth, For rock density, Porosity For saturation, For cone tip resistance, For side friction resistance, This represents the unconsolidated shear strength.

[0099] Ultimately, a set of spatiotemporally aligned original parameters is formed, providing basic data support for the subsequent steps of 3D standardized modeling and comprehensive geological stiffness index calculation.

[0100] S2: Map all parameters in S1 to a three-dimensional geographic coordinate system to generate a standardized geological data cube with timestamps, specifically:

[0101] S21, Geographic Coordinate System Transformation and Spatial Gridding: This involves uniformly projecting and gridding various types of data from the spatiotemporally aligned original parameter set. The specific process is as follows:

[0102] 1. Geographic coordinate transformation:

[0103] The WGS-84 three-dimensional geographic coordinate system is adopted as a unified benchmark;

[0104] For each point cloud data coordinate in the seabed topography data Perform geocentric coordinate transformation;

[0105] The coordinates of spatial sampling points for geotechnical physical parameters and stratum bearing capacity parameters are simultaneously converted to ensure a consistent coordinate system.

[0106] 2. Spatial grid construction:

[0107] Construct a regular 3D spatial mesh, with the mesh cell side length set to... .

[0108] The original sampled data is projected and mapped to the corresponding spatial grid cells to generate three types of initial grid data: seabed topography grid data, geotechnical physical parameter grid data, and stratum bearing capacity parameter grid data.

[0109] S22, Dimensionless Processing of Grid Data: To eliminate the influence of different parameter dimensions and improve the subsequent multi-source index fusion capability, dimensionless processing is performed on three types of grid data, including:

[0110] 1. Seabed topographic grid data: Elevation normalization method, represented as:

[0111] ;

[0112] in, This represents the original water depth value (negative values ​​represent seabed depth). The system outputs standardized seabed topography data, which represents the shallowest and deepest water depths within the entire region.

[0113] Geotechnical physical parameter grid data: The range normalization method is used to perform the following transformations on each parameter (such as porosity, saturation, and permeability coefficient):

[0114] ;

[0115] The normalized geophysical parameters of each type are reorganized into a standardized geophysical parameter tensor.

[0116] Formation bearing capacity parameter grid data: Z-score normalization method, expressed as:

[0117] ;

[0118] Where X represents the original bearing capacity parameter value, such as cone tip resistance and unconsolidated shear strength. and These are the sample mean and standard deviation, respectively, and the output is the standardized formation bearing capacity parameter.

[0119] S23, Fusion and Cube Construction of Standardized Data: The three types of standardized grid data are fused in spatial and temporal dimensions to construct the final standardized geological data cube. The specific process is as follows:

[0120] 1. Spatial integration: according to unified grid numbering The standardized seabed topography values, soil and rock physical properties, and stratum bearing capacity parameters in each cell are paired to ensure that each grid cell contains three sets of standardized values.

[0121] 2. Time Matching: Retrieves the timestamp recorded in S14. Bind to each grid cell to ensure the spatiotemporal consistency of the data. It is recommended to control the timestamp accuracy to the hour level to reflect the timeliness of data collection.

[0122] 3. Cube Structure Output: Each standardized mesh cell is represented as a structure, as follows:

[0123] The first four items are spatial and temporal information, followed by standardized attribute data, which ultimately form a standardized geological data cube that includes spatial coordinates, timestamps, and multidimensional geological attributes.

[0124] S3: Extract formation shear wave velocity, SPT blow count, and water content from the standardized geological data cube, and generate a comprehensive geological stiffness index for each grid cell using a weighted fusion algorithm.

[0125] S31, Geological parameter extraction: Extract three geological parameters from the standardized geological data cube according to spatial grid units: standardized formation shear wave velocity, standardized standard penetration test blow count, and standardized water content.

[0126] S32, Weighting Coefficient Configuration and Fusion Model Construction: To reflect the relative importance of each parameter's contribution to stiffness, the following preset weighting coefficient ranges are set, and a fusion constraint model is established:

[0127] Standardized formation shear wave velocity weighting factor The value range is 0.45≤ ≤0.55;

[0128] Standardized Standard Penetration Test (SPT) Blow-up Factor The value range is 0.25≤ ≤0.35;

[0129] Standardized moisture content weighting factor The value range is 0.15≤ ≤0.25;

[0130] All three must satisfy the normalization constraint: ;

[0131] In this invention It is 0.5. It is 0.3. It is 0.2;

[0132] S33, Weighted Fusion Calculation and Index Matrix Generation: For each spatial grid cell, a weighted fusion algorithm is applied to calculate its comprehensive geological stiffness index. 。 is represented as:

[0133] ;

[0134] in, For the first The comprehensive geological stiffness index of the grid cells, For normalized formation shear wave velocity, N is the normalized standard penetration test blow number, and w is the normalized water content.

[0135] Finally, the stiffness indices of all grid cells are aggregated to generate a comprehensive geological stiffness index numerical matrix:

[0136] ;

[0137] in This matrix represents the length, width, and height dimensions of the spatial grid. It serves as the input for the subsequent classification of geological construction units in S4, providing quantitative closed-loop support from raw geological parameters to construction decisions.

[0138] S4: Based on the comprehensive geological stiffness index threshold, the construction sea area is divided into three geological construction units: hard rock zone, transition zone, and soft soil zone.

[0139] S41, Setting the threshold range for the comprehensive geological stiffness index: To achieve the hierarchical classification of stiffness levels, a relative threshold strategy is adopted to set the classification boundaries for the comprehensive geological stiffness index matrix, including:

[0140] 1. Extracting extrema of the index: From the comprehensive geological stiffness index numerical matrix In the middle, extract the global maximum value respectively. and minimum value .

[0141] 2. Calculate the region classification threshold:

[0142] Hard rock boundary threshold : ;

[0143] Soft soil boundary threshold : ;

[0144] Transition zone: meets the requirements .

[0145] This setting is relatively adaptable and can float horizontally as the geological stiffness distribution changes.

[0146] S42, Geological Type Determination and Identification Matrix Generation: Traversing all spatial grid cells of the comprehensive geological stiffness index numerical matrix. Execute the following type determination logic:

[0147] ;

[0148] 1. Output structure encapsulation: The output structure of each unit is as follows: The Type value can be hard rock zone, soft soil zone or transition zone.

[0149] 2. Generate the identifier matrix: Construct an identifier matrix with the same dimensions as G. , used for spatial cluster analysis.

[0150] S43, Spatial Clustering Processing of Geological Construction Units: To eliminate discrete misclassifications and boundary blemishes, and improve the engineering practicality of regional division, the following clustering and correction operations are performed:

[0151] 1. Spatial Connectivity Identification: To eliminate discrete misclassifications and boundary spikes, and improve the engineering practicality of region partitioning, the following clustering and correction operations are performed:

[0152] Perform a three-dimensional eight-neighbor connected component search on each of the three types in the identifier matrix M;

[0153] Each contiguous area is marked using either floodfill or disjoint-set data structure algorithms.

[0154] Calculate the area of ​​each connected component. : ;

[0155] Where n is the number of connected units. (Same as grid size)

[0156] 2. Removal of isolated small units: For areas Areas that are considered to lack independent construction significance are subject to a correction strategy, whereby the type of the current isolated block is replaced with the type of the adjacent largest connected block.

[0157] 3. Output spatial distribution map: Encode the clustering results into a spatial type layer and output it in GeoTIFF or Shapefile format. Each pixel / vector unit contains attributes: spatial coordinates, type identifier, area and boundary contour.

[0158] S5: Hydraulic impact hammer units are allocated to hard rock areas, vibratory pile driving units to soft soil areas, and rotary drilling rigs to transition areas, forming the initial equipment allocation plan, specifically:

[0159] S51, Configuration of Hydraulic Impact Hammer Units: First, extract all grid cells marked as hard rock areas from the spatial distribution map of the hard rock area, count their total number, and multiply by the cell area to obtain the total area of ​​the hard rock area. The cell area can be set according to the grid resolution. For example, if the standard grid resolution is 10 meters by 10 meters, then the area of ​​each cell is 100 square meters.

[0160] Next, based on the unit area construction efficiency of the hydraulic impact hammer unit, the total area of ​​the hard rock area is divided by the daily processing capacity of a single unit to determine the theoretical number of units required. To ensure complete construction coverage, any area less than the capacity of one unit is rounded up to the nearest whole unit.

[0161] Subsequently, based on the spatial distribution of each grid in the hard rock area, all hard rock grids were divided into blocky connected regions, with priority given to assigning hydraulic impact hammer units with higher numbers to areas with larger areas. Each device was bound to the set of grid coordinates it was responsible for, and the device number and working area were marked in the spatial layer.

[0162] For example, if the total area of ​​the hard rock zone is 16,000 square meters, the system will automatically configure eleven hydraulic impact hammer units and generate equipment numbers such as "HY-001" to "HY-011", which correspond to the grid list of the divided construction sub-areas.

[0163] S52, Vibratory pile driving unit quantity configuration: The treatment process for soft soil areas is the same as in S51. The system reads the spatial distribution map of the soft soil area, accumulates the number of all grid cells marked as soft soil areas, and converts them into the total construction area.

[0164] The required number of equipment is calculated based on the daily coverage area of ​​the vibratory pile driving unit, and the actual number of units is configured using the rounding-up method.

[0165] Spatial aggregation is performed on the grid in the soft soil area, and the numbering of the vibratory pile driving unit is reasonably assigned according to the size and shape of the area, for example, using the prefix "ZD-", such as "ZD-001", "ZD-002", etc.

[0166] Simultaneously, a set of construction grid coordinates for each vibratory pile driving unit is generated, and the deployment location and service area of ​​the equipment are marked in the visualization layer.

[0167] S53, Rotary Drilling Rig Quantity Configuration: For the treatment of the transition zone, first count all transition zone grid cells in the spatial distribution map of the transition zone and calculate the total area.

[0168] Because the geological characteristics of the transition zone are characterized by alternating soft and hard surfaces or gradual changes, the construction efficiency of rotary drilling rigs is relatively low. Therefore, when configuring the number of rigs, a smaller construction capacity per unit area is used as the benchmark.

[0169] The number of rotary drilling rigs is divided according to the area, and the deployment area of ​​each rig is divided according to spatial connectivity. Each rotary drilling rig is assigned a unique number, such as "ZX-001", "ZX-002", etc., and is bound to the coordinate set of the grid it belongs to.

[0170] To avoid wasting resources, priority should be given to ensuring good geographical continuity in the areas served by each unit, thereby reducing the frequency of unit movement.

[0171] S54, Initial Equipment Allocation Plan Generation: Finally, the system summarizes the deployment information of the three types of construction equipment to form a complete initial equipment allocation plan, which includes:

[0172] Deployment plan for hard rock areas: includes information such as the total number of hydraulic impact hammer units and the unit number, the corresponding construction grid coordinate set, and the service area boundary of the equipment.

[0173] Deployment plan for soft soil areas: includes information such as the total number of vibratory pile driving units and the number of each unit, the corresponding construction grid coordinate set, and the location of the equipment distribution center point.

[0174] Transition zone deployment plan: includes the total number of rotary drilling rigs and the number of each rig, the set of spatial grids it is responsible for, and the description of the area shape.

[0175] Equipment scheduling list: Includes the total number of hydraulic impact hammer units, vibratory pile driving units and rotary drilling rig units. List and summarize fields such as the number, service area number and planned start time of each type of equipment to form a standard input template for the construction plan and operation and maintenance scheduling interface;

[0176] S6: When the real-time monitoring shows that the change in the comprehensive geological stiffness index of a certain unit exceeds ±15%, a scheduling instruction for equipment in the adjacent area is triggered, and the dynamic resource allocation plan is updated, specifically as follows:

[0177] S61, Geological Variation Unit Detection and Labeling: Continuously monitor the real-time update status of standardized geological data cubes, paying particular attention to the comprehensive geological stiffness index field.

[0178] Within each update cycle (set to daily or every 4 hours), all spatial grid cells are traversed from the latest cube data to extract the current comprehensive geological stiffness index and compare it with the initial value recorded in the initial equipment allocation scheme.

[0179] When the variation of the comprehensive geological stiffness index of any grid cell relative to the initial value exceeds ±15%, the system marks the grid cell as a geological variation cell.

[0180] For each geological variation unit, the system extracts its three-dimensional spatial coordinates and the comprehensive geological stiffness index after variation, writes them into the geological variation log, and generates a visual highlighting layer for scheduling system calls.

[0181] For example, in a certain construction area, the original comprehensive geological stiffness index of the grid cell numbered G_132_57 was 0.62, and the updated value was 0.72. The change was determined to be 16.1%, which met the variation condition. Therefore, the grid cell was marked as a geological variation cell.

[0182] S62, Neighborhood Area Scheduling Instruction Generation: Perform neighborhood area expansion analysis on each geological variation unit, and expand a grid unit in the directions of up, down, left, right and diagonal of the unit as the center, forming a three-by-three nine-grid neighborhood area.

[0183] Subsequently, based on the exponential change direction of the geological variation units, the following scheduling rules are executed:

[0184] If the comprehensive geological stiffness index increases by ≥15% after the mutation, the system determines that the regional geology has hardened, and the rotary drilling rig originally deployed in the adjacent area may no longer be suitable. It needs to be switched to a hydraulic impact hammer unit. The system issues an equipment conversion command and records it in the scheduling log.

[0185] If the comprehensive geological stiffness index decreases by ≥15% after the mutation, the system determines that the regional geology has softened and the originally deployed hydraulic impact hammer unit may be over-supplied, requiring a switch to a vibratory pile driving unit. The system will automatically issue the corresponding equipment switching command.

[0186] If the variation is within ±15%, the system determines that the geological condition is relatively stable, and the original equipment can continue to work without any adjustments.

[0187] The system encapsulates each neighboring area device scheduling instruction into a structured data packet, which includes the geological variation unit number, the set of neighboring area grid coordinates, the variation type, the target device type, and the execution priority.

[0188] S63, Dynamic Resource Allocation Scheme Update: Based on the scheduling instructions generated in S62, the system activates the dynamic resource allocation module and makes the following corrections to the equipment deployment scheme and equipment scheduling list:

[0189] 1. Unbind the original device binding relationship: First, find the device information bound to the current neighboring grid in the initial device allocation scheme, unbind the device from its corresponding grid coordinate set, and mark the device as pending redeployment.

[0190] 2. Reassign Equipment Types: Based on the target equipment type required in the scheduling instructions, reconfigure appropriate unit types for the neighboring grid. Prioritize assigning similar equipment that is currently idle or waiting; if no equipment is available, supplement with new equipment from the standby resource pool.

[0191] 3. Update the equipment scheduling list: Adjust the quantity statistics of various types of equipment in the equipment scheduling list, record the equipment number, change time and scheduling source of newly added or released equipment, and synchronize them to the resource scheduling database.

[0192] 4. Generate a dynamic resource allocation plan: Merge the latest equipment binding results of the mutated unit and its neighboring areas into the main equipment deployment layer. The system outputs a text list and layer file of the dynamic resource allocation plan, including the latest equipment number, construction grid coordinate set, equipment type, and scheduling source.

[0193] For example, if the comprehensive geological stiffness index of grid G_75_120 drops by 17.8% during construction, and the system detects that the original deployment was a hydraulic impact hammer unit, then under the scheduling instruction, the construction equipment in this area and its adjacent areas is reassigned as a vibratory pile driving unit, and an equipment instance with the number ZD-021 is generated and deployed to the target area. At the same time, the scheduling list is updated to record the change event.

[0194] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0195] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for allocating resources for photovoltaic foundation construction based on the integration of seabed geological parameters, characterized in that, Includes the following steps: S1: Simultaneously acquire seabed topographic data, geophysical parameters, and stratum bearing capacity parameters of the construction area through a shipborne multibeam echo sounder, seabed drilling and sampling device, and static cone penetrometer. S2: Map all parameters in S1 to a three-dimensional geographic coordinate system to generate a standardized geological data cube with timestamps; S3: Extract formation shear wave velocity, SPT blow count and water content from the standardized geological data cube, and generate the comprehensive geological stiffness index of each grid cell through a weighted fusion algorithm; S4: Based on the comprehensive geological stiffness index threshold, the construction sea area is divided into three geological construction units: hard rock zone, transition zone and soft soil zone. S5: Allocate hydraulic impact hammer units to hard rock areas, vibratory pile driving units to soft soil areas, and rotary drilling rig units to transition areas to form an initial equipment allocation plan; S6: When the change in the comprehensive geological stiffness index of a certain unit exceeds ±15% in real time, the equipment scheduling instruction of the adjacent area is triggered, and the dynamic resource allocation plan is updated. S3 includes: S31: Extract three geological parameters from the standardized geological data cube according to spatial grid units: standardized formation shear wave velocity, standardized standard penetration test blow number, and standardized water content. S32: Preset weighting coefficients are assigned to the standardized formation shear wave velocity, standardized SPT blow count, and standardized water content, respectively, to construct the computational framework of the weighted fusion algorithm, specifically as follows: Standardized formation shear wave velocity weighting factor (0.45≤ ≤0.55); Standardized Standard Penetration Test (SPT) Blow-up Factor (0.25≤ ≤0.35); Standardized moisture content weighting factor (0.15≤ ≤0.25); The constraints are satisfied: ; S33: Calculate the comprehensive geological stiffness index using a weighted fusion algorithm to generate a comprehensive geological stiffness index numerical matrix for each grid cell; The comprehensive geological stiffness index is calculated as follows: ； in, For the first The comprehensive geological stiffness index of the grid cells, For normalized formation shear wave velocity, N is the normalized standard penetration test blow number, and w is the normalized water content. S4 includes: S41: Set the threshold range for the comprehensive geological stiffness index, specifically as follows: Hard rock boundary threshold: Comprehensive geological stiffness index ≥ upper threshold limit; Soft soil boundary threshold: Comprehensive geological stiffness index ≤ lower limit of threshold; Transition boundary threshold: lower threshold < comprehensive geological stiffness index < upper threshold; The upper threshold is set at 0.8 times the maximum value of the comprehensive geological stiffness index numerical matrix, and the lower threshold is set at 1.2 times the minimum value of the comprehensive geological stiffness index numerical matrix. S42: Traverse each grid cell of the comprehensive geological stiffness index numerical matrix and perform geological type determination: When the unit value is greater than or equal to the upper threshold, it is marked as a hard rock zone; When the unit value is less than or equal to the lower threshold, it is marked as a soft soil area; When the lower threshold is less than the unit value and the upper threshold is less than the upper threshold, it is marked as a transition zone; Output a geological construction unit type identifier matrix with spatial coordinates; S43: Perform spatial clustering on the geological construction unit type identifier matrix, including merging adjacent grid units of the same type and removing isolated units with an area of ​​less than 50m², to generate spatial distribution maps of three types of geological construction units: hard rock zone, transition zone, and soft soil zone.

2. The method for allocating photovoltaic foundation construction resources based on the fusion of seabed geological parameters according to claim 1, characterized in that, S1 includes: S11: The shipborne multibeam echo sounder system performs a full-coverage scan along the planned survey line to collect raw seabed topographic point cloud data. After coordinate calculation and tide level correction, the seabed topographic data is output. S12: Obtain rock core samples at preset grid nodes through a seabed drilling sampling device, perform density testing, particle analysis and mineral composition detection on the rock core samples, and generate geophysical parameters including porosity, saturation and permeability coefficient. S13: Penetration tests were conducted at locations adjacent to the seabed drilling and sampling device using a static cone penetration tester. The cone tip resistance curve and sidewall friction curve were recorded in real time. After depth normalization, the formation bearing capacity parameters were extracted. S14: Synchronously record the timestamps and spatial coordinates of seabed topographic data, geophysical parameters, and stratum bearing capacity parameters to form a spatiotemporally aligned original parameter set.

3. The method for allocating photovoltaic foundation construction resources based on the fusion of seabed geological parameters according to claim 2, characterized in that, S2 includes: S21: Transform the seabed topography data, geotechnical parameters and stratum bearing capacity parameters from the spatiotemporally aligned original parameter set into the WGS-84 three-dimensional geographic coordinate system to generate seabed topography grid data, geotechnical parameter grid data and stratum bearing capacity parameter grid data with coordinate labels. S22: Perform dimensionless processing on the seabed topography grid data, geotechnical physical parameter grid data, and stratum bearing capacity parameter grid data respectively, and output standardized seabed topography data, standardized geotechnical physical parameters, and standardized stratum bearing capacity parameters; S23: Standardize seabed topographic data, standardized geophysical parameters, and standardized stratigraphic bearing capacity parameters are spatiotemporally matched according to the same spatial grid unit, and the timestamps recorded in S14 are embedded to construct a standardized geological data cube that includes spatial coordinates, timestamps, and multidimensional geological attributes.

4. The method for allocating photovoltaic foundation construction resources based on the fusion of seabed geological parameters according to claim 3, characterized in that, The dimensionless processing of the seabed topographic grid data, geotechnical physical parameter grid data, and stratum bearing capacity parameter grid data includes: The seabed topography grid data is normalized using the elevation normalization method to output standardized seabed topography data; The geotechnical physical parameter grid data is standardized using the range standardization method to output standardized geotechnical physical parameters; The formation bearing capacity parameter grid data is standardized using the Z-score method to output standardized formation bearing capacity parameters.

5. The method for allocating photovoltaic foundation construction resources based on the fusion of seabed geological parameters according to claim 1, characterized in that, S5 includes: S51: Calculate the total area of ​​the hard rock area based on the spatial distribution map of the hard rock area, and configure the number of units according to the construction efficiency of the hydraulic impact hammer unit per unit area. S52: Calculate the total area of ​​the soft soil area based on the spatial distribution map of the soft soil area, and configure the number of units according to the construction efficiency of the vibratory pile driving unit per unit area. S53: Calculate the total area of ​​the transition zone based on the spatial distribution map of the transition zone, and configure the number of units according to the construction efficiency per unit area of ​​the rotary drilling rig; S54: Generate the initial device allocation scheme, including: Deployment plan for hard rock areas: hydraulic impact hammer unit numbering and corresponding construction grid coordinate set; Deployment plan for soft soil areas: Vibratory pile driving unit number and corresponding construction grid coordinate set; Transition zone deployment plan: Rotary drilling rig unit number and corresponding construction grid coordinate set; Equipment scheduling list: total number of hydraulic impact hammer units, total number of vibratory pile driving units, and total number of rotary drilling rig units.

6. The method for allocating photovoltaic foundation construction resources based on the fusion of seabed geological parameters according to claim 5, characterized in that, S6 includes: S61: Real-time monitoring of the updated data of the standardized geological data cube. When the change of the comprehensive geological stiffness index of a certain grid cell relative to the initial value exceeds ±15%, the cell is marked as a geological variation cell, and its spatial coordinates and the variation of the comprehensive geological stiffness index are extracted. S62: Define the neighborhood zone as a 3×3 grid extending outwards from the geological variation unit, and generate equipment scheduling instructions for the neighborhood zone: When the increase in the comprehensive geological stiffness index after the mutation is ≥15%, a conversion command is issued for the rotary drilling rig unit in the adjacent area to the hydraulic impact hammer unit. When the decrease in the comprehensive geological stiffness index after the mutation is ≥15%, a conversion command is issued for the hydraulic impact hammer unit in the adjacent area to the vibratory pile driving unit. When the variation is within ±15%, maintain the current equipment configuration; S63: Update the equipment deployment plan according to the equipment scheduling instructions of the neighboring area, including: Release the original unit's construction grid binding in the variation unit and adjacent areas; Reassign unit types according to S62 instruction type; Revise the distribution of unit numbers in the equipment scheduling list; Generate a dynamic resource allocation scheme that includes the new construction grid coordinate set.

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