Construction method and system for optimizing pile driving position of photovoltaic project and terminal

Abstract

The invention relates to the technical field of engineering intelligent construction and management, and particularly discloses a construction method, system and terminal for optimizing a pile driving position of a photovoltaic project, which combines digital elevation model data and computer aided design data, comprehensively analyzes and adjusts and optimizes installation parameters of the pile position from a string level and an array level, and improves the construction efficiency. According to the method, intelligent construction control of a group cascade level and an array level is achieved, refined digital optimization before construction and a real-time feedback mechanism in construction form a closed loop, construction technicians can obtain real-time and accurate construction positioning information, specific installation parameters of all pile positions are clear, and then the construction efficiency and the operation precision are improved.

Description

Technical Field

[0001] This application relates to the field of intelligent construction and management technology, and more specifically, to a construction method, system and terminal for optimizing the piling positions of photovoltaic projects. Background Technology

[0002] In traditional photovoltaic project construction, controlling the connection between pile tops is usually necessary to ensure construction accuracy. However, in actual construction sites, variations in terrain slope often mean that the equipment installation locations marked on the design drawings cannot meet the actual site requirements.

[0003] The current common solution is to manually pull a line at the top of the pile and measure a fixed distance to determine the pile driving position, thereby controlling the construction results. However, this method has some problems: it is highly dependent on manual operation, consumes a lot of manpower, and the measurement accuracy is easily affected by human factors, making it difficult to guarantee accuracy.

[0004] Therefore, an optimized technical solution is expected. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide a construction method, system and terminal for optimizing the piling positions of photovoltaic projects. It solves the problems of high dependence on manual operation, high manpower consumption and difficulty in guaranteeing measurement accuracy in the prior art.

[0006] The technical problem to be solved in this application is achieved by the following technical solution:

[0007] Firstly, this application provides a construction method for optimizing piling positions in photovoltaic projects, comprising:

[0008] Acquire digital elevation model data and computer-aided design data for the photovoltaic project area;

[0009] Data verification and data fusion are performed on digital elevation model data and computer-aided design data to form a construction foundation database;

[0010] For each set of photovoltaic strings in the photovoltaic project area, the elevation difference between adjacent pile positions within the photovoltaic string is calculated based on the construction foundation database. If all calculated elevation differences within the same set of photovoltaic strings do not exceed the first preset threshold, the soil clearance height of the pile position is adjusted. If any calculated elevation difference within the same set of photovoltaic strings exceeds the first preset threshold, an optimization algorithm is used to determine the installation angle of the photovoltaic string.

[0011] Calculate the statistical dispersion of the installation angles of all photovoltaic strings within the same array; if the statistical dispersion exceeds the second preset threshold, readjust the installation angles of the photovoltaic strings to ensure the consistency of angles within the same array.

[0012] Calculate the spatial coordinates of each pile location based on the installation angle and the height of the pile exposed.

[0013] The pile position data is transmitted to the construction terminal so that the construction terminal can calculate the deviation between the current pile position and the target pile position in real time after receiving the pile position data, and generate a direction prompt signal based on the deviation. The pile position data includes the spatial coordinates of the pile position and the installation angle of the pile position.

[0014] Furthermore, data verification and fusion are performed on the digital elevation model data and computer-aided design data to form a construction foundation database, including:

[0015] Outlier identification is performed on the digital elevation model data, and the outliers are corrected to obtain the corrected digital elevation model data;

[0016] Calculate the data integrity coefficient of the computer-aided design data to assess the completeness of the data; if the data integrity coefficient is lower than the preset integrity threshold, mark the missing data in the computer-aided design data and generate a data completion prompt signal to wait for the user to complete the computer-aided design data until the data integrity coefficient of the computer-aided design data is not lower than the preset integrity threshold.

[0017] The corrected digital elevation model data and computer-aided design data are combined into a coordinate system to form a construction foundation database.

[0018] Furthermore, outlier identification is performed on the digital elevation model data, and the outliers are corrected to obtain corrected digital elevation model data, including: using the Z-score method to identify outliers in the digital elevation model data.

[0019] Furthermore, outlier identification is performed on the digital elevation model data, and the outliers are corrected to obtain corrected digital elevation model data, including:

[0020] The following formula is used to correct outliers using neighborhood interpolation.

[0021]

[0022] in, Z represents the corrected outlier. j Let d be the elevation of the j-th normal point among n normal points surrounding the anomaly. ij Let be the distance between the outlier and the j-th normal point.

[0023] Furthermore, the data integrity coefficient of the computer-aided design data is calculated to assess the completeness of the data, including:

[0024] The computer-aided design data is grouped according to data categories, and the data integrity coefficient is calculated for the preset key data categories. The data integrity coefficient is calculated using the following formula.

[0025]

[0026] Where, N complete N represents the number of data entries that have been fully entered in the preset key data categories. total C represents the total number of data entries that should be included in the preset key data category, and C is the data integrity coefficient.

[0027] Furthermore, if all calculated elevation differences within the same group of photovoltaic strings do not exceed a first preset threshold, the elevation height of the pile position is adjusted, including:

[0028] The soil clearance height of the piles is adjusted based on the horizontal placement strategy. The soil clearance height of the piles is calculated using the following formula so that the line connecting the top of the photovoltaic strings is parallel to the horizontal plane.

[0029]

[0030] in, Z represents the average design elevation of the top of the photovoltaic string. i Let h be the elevation of the i-th pile position. r To fix the embedment depth, h i Let be the height of the soil at the i-th pile location.

[0031] Furthermore, if any calculated elevation difference within the same photovoltaic string exceeds a first preset threshold, an optimization algorithm is used to determine the installation angle of the photovoltaic string, including:

[0032] The following objective function is constructed in the optimization algorithm to execute the optimal tilt angle search strategy;

[0033]

[0034] Where P(α) is the daily power generation when the installation angle is α, α1 is the initial design installation angle, maxf(α) means to maximize the above expression about the installation angle α, and a1 and a2 are the preset first weight and preset second weight, respectively.

[0035] Solve the objective function to determine the installation angle.

[0036] Further, the statistical dispersion of the installation angles of all photovoltaic strings within the same array is calculated; if the statistical dispersion exceeds a second preset threshold, the installation angles of the photovoltaic strings are readjusted to ensure angle consistency within the array, including:

[0037] The standard deviation of the installation angles of all photovoltaic strings within the same array is calculated as the statistical dispersion.

[0038] If the statistical dispersion exceeds the second preset threshold, the installation angle of each photovoltaic string will be adjusted using the following distance-weighted correction formula;

[0039]

[0040] in, Let α be the installation angle after adjustment for the i-th pile position. i Let be the installation angle of the i-th pile position before adjustment. σ is the average installation angle of all photovoltaic strings within the same array. α a3 represents the standard deviation of the installation angles of all photovoltaic strings within the same array, and a3 is the preset third weight.

[0041] Secondly, this application provides a construction system for optimizing the piling positions of photovoltaic projects, used to execute the method provided by the first aspect of this application or any possible implementation of the first aspect of this application.

[0042] Thirdly, this application provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method provided by the first aspect of this application or any possible implementation of the first aspect of this application.

[0043] In summary, this application includes at least one of the following beneficial technical effects:

[0044] Based on digital elevation model data and computer-aided design data, the installation parameters of the pile positions are comprehensively analyzed and adjusted and optimized at the string level and array level to achieve intelligent construction control at the string level and array level, reduce manual intervention, ensure visual coordination and unity of the pile positions, improve the overall planning ability of the pile position layout, and thus improve the overall aesthetics and engineering quality of the photovoltaic project.

[0045] The pile location data is transmitted to the technical personnel at the construction site through the construction terminal, enabling the technical personnel to obtain real-time and accurate construction positioning information and to clarify the specific installation parameters of each pile location, thereby improving construction efficiency and operation accuracy.

[0046] Add a real-time feedback and dynamic adjustment mechanism to solve problems such as pile position deviation caused by terrain undulation, improve the ability to cope with complex terrain, avoid resource waste and construction delay caused by repeated adjustments on the construction site, and reduce the risk of rework in the later stage.

[0047] By using digital means to record and manage the entire construction process, traceability and monitoring of the entire construction process can be achieved, providing strong data support for project quality control and subsequent operation and maintenance. Attached Figure Description

[0048] Figure 1 A flowchart illustrating the construction method for optimizing piling positions in photovoltaic projects;

[0049] Figure 2 This is a schematic diagram illustrating the adjustments in the embodiments of this application. Detailed Implementation

[0050] To facilitate a clear understanding of the technical means, creative features, objectives, and effects of this application, the following description, in conjunction with specific illustrations, further elaborates on this application.

[0051] Example 1:

[0052] This application discloses a construction method for optimizing the piling positions of photovoltaic projects. It combines digital elevation model data and computer-aided design data to comprehensively analyze and adjust the installation parameters of the piling positions at the string level and array level, thereby realizing intelligent construction control at the string level and array level. This optimizes the process of determining the piling positions of photovoltaic projects, reduces manual intervention to solve the problems of low accuracy and high manpower consumption in traditional manual string measurement methods, ensures visual coordination and uniformity of the piling positions, improves the overall planning ability of the piling positions, and thus improves the overall aesthetics and engineering quality of the photovoltaic project.

[0053] Secondly, the refined digital optimization before construction and the real-time feedback mechanism during construction form a closed loop, which constitutes the core of the technical solution of this application. That is, the pile location data is transmitted to the technical personnel at the construction site through the construction terminal, so that the construction technical personnel can obtain real-time and accurate construction positioning information and clarify the specific installation parameters of each pile location, thereby improving construction efficiency and operation accuracy.

[0054] like Figure 1 As shown, this application provides a construction method for optimizing the piling positions in photovoltaic projects, which specifically includes the following steps:

[0055] S1. Obtain digital elevation model data and computer-aided design data for the photovoltaic project area.

[0056] The Digital Elevation Model (DEM) data provides topographic information about the photovoltaic project area, accurately reflecting the terrain's undulations. It is acquired through airborne LiDAR, UAV photogrammetry, traditional surveying (total station or real-time dynamic differential KD), or publicly available satellite data. Computer-Aided Design (CAD) data contains the equipment locations and preset layouts from the design drawings, acquired through user import.

[0057] The acquisition of these two types of data provides reliable geospatial information and design basis requirements for subsequent calculations, ensuring that the subsequent construction optimization process is based on the actual terrain and design intent, and avoiding additional construction deviations.

[0058] S2. Perform data verification and data fusion on digital elevation model data and computer-aided design data to form a construction foundation database.

[0059] Specifically, data verification and fusion are performed on digital elevation model data and computer-aided design data to form a construction foundation database, including:

[0060] First, outlier identification is performed on the digital elevation model (DEM) data, and then outliers are corrected to obtain corrected DEM data. This step detects errors and anomalies in the DEM data, ensuring data quality and improving the accuracy of subsequent calculations.

[0061] In one example, the standard score (Z-score) method is used to identify outliers in the digital elevation model data, and then the outliers are corrected by neighborhood interpolation using the following formula;

[0062]

[0063] in, Z represents the corrected outlier. j Let d be the elevation of the j-th normal point among n normal points surrounding the anomaly. ij Let be the distance between the outlier and the j-th normal point.

[0064] It is understandable that the Z-score method can identify abnormal elevations that may be present in the original data. By calculating the deviation of each elevation point from the average elevation of the entire region, the Z-score method can objectively and quantitatively identify those values ​​that are statistically highly unlikely to occur. Then, neighborhood interpolation is used to correct these outliers to preserve the true shape of the terrain and spatial autocorrelation to the greatest extent possible, avoiding distortion in subsequent calculations.

[0065] Next, the data integrity coefficient of the computer-aided design data is calculated to assess the completeness of the data. If the data integrity coefficient is lower than the preset integrity threshold, the missing data in the computer-aided design data is marked, and a data completion prompt signal is generated to wait for the user to complete the computer-aided design data until the data integrity coefficient of the computer-aided design data is not lower than the preset integrity threshold.

[0066] In one example, calculating the data integrity coefficient of computer-aided design data to assess the completeness of the data includes:

[0067] The computer-aided design data is grouped according to data categories, and the data integrity coefficient is calculated for the preset key data categories. The data integrity coefficient is calculated using the following formula.

[0068]

[0069] Where, N complete N represents the number of data entries that have been fully entered in the preset key data categories. total C represents the total number of data entries that should be included in the preset key data category, and C is the data integrity coefficient.

[0070] This step, through the quantitative indicator of data integrity coefficient, can automatically and objectively assess whether computer-aided design data contains all necessary information (such as pile coordinates, array boundaries, etc.), rather than relying on subjective human judgment. This proactively prevents subsequent calculation errors or interruptions caused by missing data.

[0071] Subsequently, the corrected digital elevation model data and computer-aided design data are coupled into a coordinate system to form a construction foundation database.

[0072] In one example, first, the projection information file is extracted from the digital elevation model (DEM) data, or the coordinate system recorded in the metadata is directly searched to identify the coordinate system currently used by the DEM data, while simultaneously determining the coordinate system currently used by the computer-aided design (CAD) data. Next, a unified target coordinate system is selected, such as the CGCS2000 national geodetic coordinate system, and the data model is used to transform the data from the original coordinate system to the target coordinate system. For example, for DEM data, GIS software is used to automatically convert parameters to generate values ​​in the new coordinate system. For CAD data, if the coordinate system definition is correct, a reprojection operation similar to that of a DEM can be performed directly in GIS or CAD software. Finally, after the coordinate system transformation is complete, the CAD data is overlaid on the DEM data to form a construction foundation database. At this point, each pile design point in the construction foundation database has accurate plane coordinates and elevation information.

[0073] This step establishes spatial correspondence, ensuring the integration of design intent with terrain information and providing a reliable data source for subsequent calculations.

[0074] S3. For each group of photovoltaic strings in the photovoltaic project area, calculate the elevation difference between adjacent pile positions within the photovoltaic string based on the construction foundation database; if all calculated elevation differences within the same group of photovoltaic strings do not exceed the first preset threshold, adjust the soil clearance height of the pile position; if any calculated elevation difference within the same group of photovoltaic strings exceeds the first preset threshold, use an optimization algorithm to determine the installation angle of the photovoltaic string.

[0075] In one example, if the calculated elevation difference of all photovoltaic strings in the same group does not exceed the first preset threshold, the soil height of the pile position is adjusted based on the horizontal placement strategy.

[0076] The following formula is used to calculate the height of the pile from the ground, so that the line connecting the top of the photovoltaic string is parallel to the horizontal plane;

[0077]

[0078] in, Z represents the average design elevation of the top of the photovoltaic string. i Let h be the elevation of the i-th pile position. r To fix the embedment depth, h i Let be the height of the soil at the i-th pile location.

[0079] Furthermore, if any calculated elevation difference within the same photovoltaic string exceeds the first preset threshold, the specific implementation steps for determining the installation angle of the photovoltaic string using the optimization algorithm include: First, constructing the following objective function in the optimization algorithm to execute the optimal tilt angle search strategy;

[0080]

[0081] Where P(α) is the daily power generation when the installation angle is α, α1 is the initial design installation angle, maxf(α) means maximizing the above expression for the installation angle α, and a1 and a2 are the preset first weight and preset second weight, respectively; then the objective function is solved to determine the installation angle.

[0082] In step S3, for each photovoltaic string, the elevation difference between adjacent pile positions is checked to quickly identify changes in terrain slope. If the elevation difference is small, it indicates that the terrain is flat, and only the soil clearance height needs to be adjusted to keep the string level; if the elevation difference is large, it indicates that the terrain is undulating, and the installation angle needs to be optimized to adapt to the terrain to ensure the installation effect and power generation efficiency of the string.

[0083] S4. Calculate the statistical dispersion of the installation angles of all photovoltaic strings in the same array; if the statistical dispersion exceeds the second preset threshold, readjust the installation angles of the photovoltaic strings to ensure the consistency of the angles within the same array.

[0084] In one example, the standard deviation of the installation angles of all photovoltaic strings within the same array is calculated as the statistical dispersion. If the statistical dispersion exceeds a second preset threshold, the installation angle of each photovoltaic string group is adjusted using the following distance-weighted correction formula.

[0085]

[0086] in, Let α be the installation angle after adjustment for the i-th pile position. i Let be the installation angle of the i-th pile position before adjustment. σ is the average installation angle of all photovoltaic strings within the same array. α a3 represents the standard deviation of the installation angles of all photovoltaic strings within the same array, and a3 is the preset third weight.

[0087] Here, standard deviation is used to assess the consistency of photovoltaic string angles within the same array. That is, if the standard deviation is too large (exceeding the second preset threshold), it indicates that the angle differences between photovoltaic strings within the same array are too significant, which may affect the overall power generation performance and aesthetics of the array. The installation angle of each photovoltaic string is recalculated and optimized using a distance-weighted correction formula to ensure overall consistency within the array, thereby optimizing light capture efficiency and reducing power generation losses due to angle deviations.

[0088] S5. Calculate the spatial coordinates of each pile location based on the installation angle and the height of the pile exposed.

[0089] In one example, the spatial coordinates of the pile location are calculated based on the following formula;

[0090]

[0091] Among them, (X) Z,i ,Y Z,i Z Z,i Let (X) be the spatial coordinates of the i-th pile location. i ,Y i Z i Let be the design spatial coordinates of the i-th pile location. h represents the adjusted installation angle for the i-th pile position. i Let A be the height of the soil at the i-th pile location, A be the topographic slope, and D be the topographic slope. i This refers to the spacing between pile positions.

[0092] In other words, based on the installation angle and the height of the pile, the three-dimensional coordinates of each pile location are accurately calculated, and the installation parameters are converted into specific construction points, providing accurate positioning data for construction.

[0093] S6. Transmit the pile position data to the construction terminal so that the construction terminal can calculate the deviation between the current pile position and the target pile position in real time after receiving the pile position data, and generate a direction prompt signal based on the deviation. The pile position data includes the spatial coordinates of the pile position and the installation angle of the pile position.

[0094] In one example, the spatial coordinates (X) of the pile location Z,i ,Y Z,i Z Z,i and the installation angle of the pile position The data is packaged into pile location data and pushed to the construction terminal via the MQTT protocol. The construction terminal has a built-in tilt sensor that calculates and displays the deviation between the current pile location and the target pile location in real time. , , This system generates directional signals to guide and support on-site technicians, further improving construction accuracy and efficiency.

[0095] In summary, based on digital elevation model data and computer-aided design data, the installation parameters of the pile positions are comprehensively analyzed and optimized at both the string and array levels. This enables intelligent construction control at both the string and array levels, reducing manual intervention, ensuring visual harmony and uniformity of the pile positions, and improving the overall planning capability of the pile position layout. Consequently, this enhances the overall aesthetics and engineering quality of the photovoltaic project. Secondly, the pile position data is transmitted to on-site technical personnel through construction terminals, enabling them to obtain real-time and accurate construction positioning information and understand the specific installation parameters of each pile position, thereby improving construction efficiency and operational accuracy.

[0096] Example 2:

[0097] Step 1: Obtain the data.

[0098] Topographic data: Acquire regional digital elevation models (DEMs) with a point cloud density ≥ 200 points / m². 2 Elevation accuracy ±5mm; Ground control points (GCPs) are simultaneously collected via RTK-GPS (Real-Time Kinematics and GPS) every 500m. 2 At least one control point is required for DEM calibration.

[0099] Import the photovoltaic array parameters from the CAD design drawings, including:

[0100] Dual-axis tracking bracket: single pile load (F=3.5kN), bracket span (L=8m), tracking angle range (azimuth ±45°, tilt 0°-60°).

[0101] Single-axis tracking bracket: fixed tilt angle (α0=25°), string spacing (D=5m);

[0102] Pile parameters: pile diameter (d=300mm), pile length, designed penetration depth (h=1.8m), adjustable penetration depth range, etc.

[0103] Step two: Determine the validity of the data.

[0104] Design Data Verification: Based on the intelligent verification module, a point integrity check is performed on the Excel design spreadsheet. This module uses multi-threaded parallel processing technology, which can complete the scanning of tens of thousands of data points within 30 seconds, focusing on screening for missing data in 12 key areas, including string pile coordinates, component tilt angle parameters, and inverter connection point numbers. The data integrity coefficient C is calculated using the following formula to scientifically assess the completeness of the design point data:

[0105]

[0106] Where, N complete N represents the number of key data entries that have been fully entered. total This refers to the total number of key data entries that should be included in the design table. When C < 95%, the system will automatically generate a red alert, highlight missing data cells, and push a detailed report with data completion suggestions to the designer's workbench. The validation process supports custom threshold settings to meet the differentiated data integrity management needs of different projects.

[0107] DEM data verification: Detect outliers in the DEM (such as elevation jumps caused by tree obstruction), using the Z-score method to identify outliers.

[0108]

[0109] in, Let σ be the average elevation of the DEM within a certain area (1m×1m), and σ be the standard deviation; when | When | > 3, it is identified as an outlier and corrected using neighborhood interpolation.

[0110]

[0111] In the formula, Z represents the corrected outlier. j Let d be the elevation of the j-th normal point among n normal points surrounding the anomaly. ij Let be the distance between the outlier and the j-th normal point.

[0112] Data fusion: The verified design data and DEM data are unified to the CGCS2000 coordinate system through coordinate transformation (Gauss-Kruger projection) to form a construction foundation database.

[0113] Step 3: Process by grouping by string and Y, calculate the initial angle and spacing, and determine if the elevation difference is within the threshold. If it is within the threshold, place the decoration points as horizontally as possible. If it exceeds the threshold, search for the optimal tilt angle.

[0114] Slope and aspect calculations:

[0115] Based on DEM data, the topographic slope (S) and aspect (A) at each design pile location are calculated using the third-order inverse distance weighted method, as shown in the following formula:

[0116]

[0117] In the formula, f x f y These are the elevation gradients of the DEM in the x and y directions, respectively (calculated through 3×3 window convolution), with S in ° and A in ° (0° is due north, increasing clockwise).

[0118] Bracket installation constraint modeling:

[0119] Based on the different characteristics of dual-axis / single-axis brackets, establish installation constraints:

[0120] Dual-axis support: The tracking angle must avoid terrain obstruction. The constraints are: no obstacles higher than 10m within the azimuth angle range, and the distance between the lowest point of the support and the ground ≥ 0.5m when the tilt angle changes.

[0121] Single-axis bracket: The fixed tilt angle α0 must match the terrain slope S, satisfying α0+S≤60° (to avoid the component being blocked).

[0122] Group-level optimization (grouped by Y-axis):

[0123] Initial parameter calculation: The photovoltaic array is grouped along the Y-axis (each group contains 16 pile positions), and the initial installation angle (α1) and pile spacing (D1) of each group are calculated:

[0124] Initial angle: For dual-axis supports, the median design tilt angle (30°) is used; for single-axis supports, α0 (25°) is used.

[0125] Initial spacing: Based on the design drawing value (5m), calculate the elevation difference ΔZ between adjacent pile positions within the string using the following formula:

[0126] ΔZ=|Z i+1 -Z i |,(i=1,2,...,15)

[0127] In the formula, Z i Let be the DEM elevation of the i-th pile location.

[0128] Elevation difference threshold judgment: Set elevation difference threshold ΔZ0 (ΔZ0=0.8m for dual-axis support, ΔZ0=1.2m for single-axis support):

[0129] If all ΔZ < ΔZ0: the pile positions are optimized for "horizontal placement", i.e., the pile height above the ground h is adjusted. i To make the line connecting the tops of the strings parallel to the horizontal plane, use the following formula:

[0130]

[0131] In the formula, The average elevation of the top of the string is designed as h. r To fix the burial depth (1.8m);

[0132] If ΔZ > ΔZ0 exists: initiate the optimal tilt angle search, with the objective function being to maximize string power generation and minimize angle deviation.

[0133]

[0134] In the formula, P(α) is the daily power generation corresponding to the tilt angle α (calculated based on local solar irradiance data), α∈[α min ,α max ](α min =5°, α max =55°); a genetic algorithm was used to solve the problem, with a population size of 50, 30 iterations, and a convergence accuracy of 10. -4 .

[0135] Step 4: Calculate the new location and elevation. If it is a double row of stakes, determine whether all points in the other group are valid. Record the adjusted data, draw the profile, and save the results to Excel.

[0136] Step 5: Check the strings in the same array. Determine if they meet the threshold.

[0137] 5.1 Array-level optimization

[0138] Angle consistency check: Calculate the average angle (αᵢ) of all strings within the same array after optimization. With standard deviation σ α :

[0139]

[0140] In the formula, N is the number of strings in the array (usually N=8); the standard deviation threshold σ0 is set to 0.5°. If σα≤σ0, the optimization is qualified; otherwise, the adjustment process is initiated.

[0141] Global angle adjustment: The angle of each string is adjusted using a "distance-weighted correction method".

[0142]

[0143] Ensure all adjustments are made Furthermore, the angular deviation between groups is ≤0.3°.

[0144] 5.2 Final Calculation of Pile Location

[0145] Based on the optimized angle and the height of the soil protrusion, the final spatial coordinates of each pile location are calculated using the following formula:

[0146]

[0147] Among them, (X) Z,i ,Y Z,i Z Z,i Let (X) be the spatial coordinates of the i-th pile location. i ,Y i Z i Let be the design spatial coordinates of the i-th pile location. h represents the adjusted installation angle for the i-th pile position. i Let A be the height of the soil at the i-th pile location, A be the topographic slope, and D be the topographic slope. i This refers to the spacing between pile positions.

[0148] Step Six: Data Transmission and Dynamic Feedback.

[0149] Data transmission and terminal interaction:

[0150] Transport layer: Adopting a 5G+edge computing architecture, the optimized pile location data is pushed to the construction terminal via the MQTT protocol, with a transmission latency of ≤200ms;

[0151] Construction terminal: Built-in Beidou positioning (accuracy ±2mm) and tilt sensor, which displays the deviation (ΔX, ΔY, ΔZ) between the current pile position and the target pile position in real time, and adjusts the direction through voice prompts.

[0152] Dynamic feedback and adjustment:

[0153] Error monitoring: After pile driving is completed, the terminal automatically collects the actual pile position parameters (X, Y, Z, αs) and calculates the error.

[0154]

[0155] Where, α s This refers to the actual installation angle.

[0156] Set the error threshold δ0 = 0.1m (position error) + 0.1° (angle error);

[0157] Compensation Adjustment: If δ > δ0, the system automatically calculates the compensation amount. The compensation command is pushed to the terminal in real time until the error is ≤ δ0.

[0158] Step seven, as Figure 2 As shown, the landing point of each point is calculated uniformly and transmitted to the pile driving personnel.

[0159] This application also discloses a construction system for optimizing the piling positions of photovoltaic projects, which is used to perform the above-described construction method for optimizing the piling positions of photovoltaic projects.

[0160] This application also discloses a terminal, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-described embodiments of the construction method for optimizing the piling positions of photovoltaic projects.

[0161] The terminal can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The terminal may include, but is not limited to, a processor and memory. Those skilled in the art will understand that a terminal device may include more or fewer components, or a combination of certain components, or different components; for example, a terminal device may also include input / output devices, network access devices, buses, etc.

[0162] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal, connecting all parts of the terminal through various interfaces and lines.

[0163] The memory can be used to store computer programs and / or modules. The processor implements various functions of the terminal by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0164] The foregoing has shown and described the basic principles, main features, and advantages of this application. Those skilled in the art should understand that this application is not limited to the above embodiments, and various changes and modifications can be made without departing from the spirit and scope of this application; all such changes and modifications fall within the scope of protection claimed in this application. The scope of protection of this application is defined by the appended claims and their equivalents.

Claims

1. A construction method for optimizing piling positions in photovoltaic projects, characterized in that, include: Acquire digital elevation model data and computer-aided design data for the photovoltaic project area; The digital elevation model data and the computer-aided design data are verified and fused to form a construction foundation database; For each group of photovoltaic strings in the photovoltaic project area, the elevation difference between adjacent pile positions within the photovoltaic string is calculated based on the construction foundation database. If all calculated elevation differences within the same group of photovoltaic strings do not exceed a first preset threshold, the soil clearance height of the pile position is adjusted. If any calculated elevation difference within the same group of photovoltaic strings exceeds the first preset threshold, an optimization algorithm is used to determine the installation angle of the photovoltaic string. Calculate the statistical dispersion of the installation angles of all photovoltaic strings within the same array; if the statistical dispersion exceeds a second preset threshold, readjust the installation angles of the photovoltaic strings to ensure angle consistency within the same array. Calculate the spatial coordinates of each pile location based on the installation angle and the height of the pile exposed. The pile position data is transmitted to the construction terminal so that the construction terminal can calculate the deviation between the current pile position and the target pile position in real time after receiving the pile position data, and generate a direction prompt signal based on the deviation. The pile position data includes the spatial coordinates of the pile position and the installation angle of the pile position.

2. The construction method for optimizing piling positions in photovoltaic projects according to claim 1, characterized in that, The digital elevation model data and the computer-aided design data are validated and fused to form a construction foundation database, including: Outlier identification is performed on the digital elevation model data, and the outliers are corrected to obtain corrected digital elevation model data; Calculate the data integrity coefficient of the computer-aided design data to assess the completeness of the data; if the data integrity coefficient is lower than a preset integrity threshold, mark the missing data in the computer-aided design data, generate a data completion prompt signal to wait for the user to complete the computer-aided design data until the data integrity coefficient of the computer-aided design data is not lower than the preset integrity threshold; The corrected digital elevation model data and the computer-aided design data are combined into a coordinate system to form the construction foundation database.

3. The construction method for optimizing piling positions in photovoltaic projects according to claim 2, characterized in that, The process of identifying outliers in the digital elevation model data and correcting the outliers to obtain corrected digital elevation model data includes: using the Z-score method to identify outliers in the digital elevation model data.

4. The construction method for optimizing piling positions in photovoltaic projects according to claim 2, characterized in that, The digital elevation model data is subjected to outlier identification, and the outliers are corrected to obtain corrected digital elevation model data, including: The following formula is used to correct outliers using neighborhood interpolation. in, Z represents the corrected outlier. j Let d be the elevation of the j-th normal point among n normal points surrounding the anomaly. ij Let be the distance between the outlier and the j-th normal point.

5. The construction method for optimizing piling positions in photovoltaic projects according to claim 2, characterized in that, Calculating the data integrity coefficient of the computer-aided design data to assess the completeness of the data includes: The computer-aided design data is grouped according to data categories, and the data integrity coefficient is calculated for the preset key data categories. The data integrity coefficient is calculated using the following formula. Where, N complete N represents the number of data entries that have been fully entered in the preset key data categories. total C represents the total number of data entries that should be included in the preset key data category, and C is the data integrity coefficient.

6. The construction method for optimizing piling positions in photovoltaic projects according to claim 1, characterized in that, If the calculated elevation differences within the same group of photovoltaic strings do not exceed the first preset threshold, then the soil clearance height of the pile position will be adjusted, including: The soil clearance height of the piles is adjusted based on the horizontal placement strategy. The soil clearance height of the piles is calculated using the following formula so that the top line of the photovoltaic string is parallel to the horizontal plane. in, Z represents the average design elevation of the top of the photovoltaic string. i Let h be the elevation of the i-th pile location. r To fix the embedment depth, h i Let be the height of the soil at the i-th pile location.

7. The construction method for optimizing piling positions in photovoltaic projects according to claim 1, characterized in that, If any calculated elevation difference within the same photovoltaic string exceeds the first preset threshold, an optimization algorithm is used to determine the installation angle of the photovoltaic string, including: The optimization algorithm constructs the following objective function to execute the optimal tilt angle search strategy; Where P(α) is the daily power generation when the installation angle is α, α1 is the initial design installation angle, maxf(α) means to maximize the above expression about the installation angle α, and a1 and a2 are the preset first weight and preset second weight, respectively. Solve the objective function to determine the installation angle.

8. The construction method for optimizing piling positions in photovoltaic projects according to claim 1, characterized in that, Calculate the statistical dispersion of the installation angles of all photovoltaic strings within the same array; If the statistical dispersion exceeds a second preset threshold, the installation angle of the photovoltaic strings is readjusted to ensure angular consistency within the array, including: The standard deviation of the installation angles of all photovoltaic strings within the same array is calculated as the statistical dispersion. If the statistical dispersion exceeds the second preset threshold, the installation angle of each photovoltaic string is adjusted using the following distance-weighted correction formula; in, Let α be the installation angle after adjustment for the i-th pile position. i Let be the installation angle of the i-th pile position before adjustment. σ is the average installation angle of all photovoltaic strings within the same array. α a3 represents the standard deviation of the installation angles of all photovoltaic strings within the same array, and a3 is the preset third weight.

9. A construction system for optimizing piling positions in photovoltaic projects, characterized in that, Used to perform the method as described in any one of claims 1-8.

10. A terminal, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1-8.

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