Intelligent big data processing method and system based on digital sand table

Abstract

This invention discloses an intelligent big data processing method and system based on a digital sand table, comprising: acquiring engineering drawings and real-time project progress records; using the engineering drawings as a framework, supplementing the framework content according to the project progress records to obtain a digital sand table model; and correcting the digital sand table model based on real-time acquired high-precision images to obtain a digital sand table reflecting the actual situation. By combining engineering drawings, real-time construction progress, and high-precision image data, the digital sand table model can be dynamically corrected, achieving real-time visualization of construction progress. Compared with traditional methods, this technology provides higher accuracy and timeliness, effectively avoiding information lag and error accumulation problems. Through accurate progress assessment and real-time monitoring, managers can better grasp project progress, optimize resource allocation, identify construction deviations in advance, ensure timely and high-quality project completion, and improve construction efficiency and project quality.

Description

Technical Field

[0001] This invention relates to the field of digital sand table technology, specifically to an intelligent big data processing method and system based on digital sand tables. Background Technology

[0002] In construction projects, tracking and monitoring construction progress has always been a challenge in management. Traditional project progress management relies mainly on manual recording and updates, lacking real-time accuracy and precision. This can easily lead to delayed feedback on construction deviations, affecting the overall project schedule and quality control. Furthermore, existing technologies struggle to respond promptly to real-time dynamic changes at the construction site during progress monitoring, resulting in a disconnect between construction data processing and updated engineering drawings. This management approach not only increases labor costs but also makes it difficult to accurately assess the actual state of the project. While existing digital modeling technologies can provide some 3D visualization, most systems cannot effectively integrate engineering drawings, construction progress data, and real-time video data, leading to a lack of precision in dynamic correction and monitoring of the construction site. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by this invention is that existing data processing methods for digital sand tables lack dynamic correction and monitoring accuracy.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: an intelligent big data processing method based on a digital sandbox, comprising:

[0006] Obtain engineering drawings and real-time project progress records;

[0007] Using the engineering drawings as a framework, the content of the framework is supplemented according to the engineering progress records to obtain a digital sand table model.

[0008] The digital sand table model is corrected based on real-time high-precision images to obtain a digital sand table that reflects the actual situation.

[0009] As a preferred embodiment of the intelligent big data processing method based on digital sand table described in this invention, the engineering drawings are design drawings of the entire project, including but not limited to plan views, elevation views, and sectional views;

[0010] The project progress records include records manually recorded by the construction party or supervisor, showing the specific progress of the project construction; including but not limited to the construction coordinates, construction content, and completion progress recorded during the construction process.

[0011] As a preferred embodiment of the intelligent big data processing method based on digital sand table described in this invention, the engineering drawings are included as a framework, and the engineering drawings are input into a 3D modeling tool to obtain a 3D model of the project.

[0012] The three-dimensional model is not displayed in the sand table, but only constrains the three-dimensional structure of the construction process; during the construction process, the three-dimensional model of the digital sand table is reconstructed through the project progress record to obtain a digital sand table reflecting the real-time project progress record.

[0013] As a preferred embodiment of the intelligent big data processing method based on digital sand table described in this invention, the method of supplementing the framework content according to the project progress record includes dividing the three-dimensional model into grids.

[0014] Based on the coordinates in the real-time project progress record, the construction location is located, and the corresponding grid area in the three-dimensional model is determined.

[0015] The grid area includes each independently calculable structural component after the project is broken down;

[0016] The rendering direction of the grid is generated according to the construction content. Based on the rendering direction, the grid in the grid area is rendered according to the completion progress. The rendered part of the three-dimensional model is reconstructed in the digital sand table to obtain a real-time digital sand table.

[0017] The rendering direction includes obtaining the construction steps and the construction direction during the construction of the project from the construction content, and rendering them sequentially according to the construction steps and the construction direction during the construction of the project.

[0018] As a preferred embodiment of the intelligent big data processing method based on digital sand table described in this invention, the grid division includes dividing the grid size according to the historical average engineering time per square meter of each grid area;

[0019] The historical average project time per square meter is linearly related to the grid size.

[0020] It is expressed as: y = k × x; where y represents the grid size, x represents the historical average construction time per square meter, and k represents the scaling factor, the value of which can be preset in the sand table model;

[0021] The ratio of the number of rendered meshes to the total number of meshes is used as a criterion for judging project progress.

[0022] By adjusting the value of k in the digital sand table, both detailed and macro-level assessments of project progress can be achieved; the smaller the value of k, the more accurate the assessment results of project progress.

[0023] As a preferred embodiment of the intelligent big data processing method based on digital sand table described in this invention, the high-precision images acquired in real time include the construction of a three-dimensional model of the appearance of each grid area in the construction site by combining images captured by a high-precision camera and image processing algorithms.

[0024] As a preferred embodiment of the intelligent big data processing method based on digital sand table described in this invention, the modification of the digital sand table model includes comparing the three-dimensional model of appearance with each corresponding grid area in the real-time digital sand table.

[0025] Let the 3D model of the appearance be Model 1, and the corresponding grid area in the real-time digital sandbox be Model 2;

[0026] The space within Model 1 is constrained. If Model 2 does not have any part that exceeds the constraints of Model 1, then the shape is directly corrected. If Model 2 has any part that exceeds the constraints of Model 1, then the high-precision image is resampled. If the resampling result still shows that Model 2 has any part that exceeds the constraints of Model 1, then the excess part is marked and a warning is issued. If the resampling result does not show that Model 2 has any part that exceeds the constraints of Model 1, then the shape is corrected.

[0027] The shape correction includes not replacing the second model, but only attaching the first model to the real-time sand table model to obtain the final sand table model.

[0028] A digital sandbox-based intelligent big data processing system employing any of the methods described in this invention, characterized in that:

[0029] The data acquisition unit obtains engineering drawings and real-time project progress records;

[0030] The rendering unit uses the engineering drawings as a framework and supplements the framework content according to the engineering progress records to obtain a digital sand table model.

[0031] The correction unit corrects the digital sand table model based on the high-precision images acquired in real time, resulting in a digital sand table that reflects the actual situation.

[0032] A computer device includes: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, it implements the steps of the method described in any one of the present invention.

[0033] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of the present invention.

[0034] The beneficial effects of this invention are as follows: The intelligent big data processing method based on a digital sand table provided by this invention combines engineering drawings, real-time construction progress, and high-precision image data to dynamically correct the digital sand table model, achieving real-time visualization of construction progress. Compared with traditional methods, this technology offers higher accuracy and timeliness, effectively avoiding information lag and error accumulation problems. Through precise progress assessment and real-time monitoring, managers can better grasp project progress, optimize resource allocation, identify construction deviations in advance, ensure timely and high-quality project completion, and improve construction efficiency and project quality. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 The overall flowchart of the intelligent big data processing method based on digital sand table provided in the first embodiment of the present invention is shown. Detailed Implementation

[0037] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0038] Example 1, referring to Figure 1 As one embodiment of the present invention, an intelligent big data processing method based on a digital sandbox is provided, comprising:

[0039] S1: Obtain engineering drawings and real-time project progress records.

[0040] The engineering drawings refer to the design drawings for the entire project, including but not limited to floor plans, elevations, and sections. The project progress records include records manually kept by the construction party or supervisor, showing the specific progress of the project construction; including but not limited to construction coordinates, construction content, and the progress recorded during the construction process.

[0041] Engineering drawings provide a static design reference for the digital sand table, ensuring that the model conforms to the overall planning and design requirements of the project. Real-time project progress records provide dynamic construction progress information, enabling the digital sand table to be updated in a timely manner, reflecting the actual construction progress, construction location, and completed sections.

[0042] S2: Using the engineering drawings as a framework, supplement the framework content according to the engineering progress record to obtain a digital sand table model.

[0043] The engineering drawings are used as a framework, and then input into a 3D modeling tool to obtain a 3D model of the project. This 3D model is not displayed in the digital sand table; it only provides 3D structural constraints for the construction process. During construction, the 3D model of the digital sand table is reconstructed using the project progress records, resulting in a digital sand table reflecting the real-time project progress.

[0044] It's important to understand that converting engineering drawings into 3D models not only provides visualization of space and structure but also offers a clear structural reference for subsequent construction. This 3D structural constraint ensures that all construction activities must be conducted within this 3D framework, guaranteeing that the construction process follows design requirements. During construction, as progress records are updated, the 3D model in the digital sand table is reconstructed based on the real-time construction progress. This process combines dynamic construction information with the static 3D model, enabling the digital sand table to reflect the latest construction status in real time and ensuring synchronization between the actual progress on site and the 3D design model.

[0045] Furthermore, supplementing the framework content based on the project progress record includes meshing the 3D model. The construction location is determined based on the coordinates in the real-time project progress record, identifying the corresponding mesh area in the 3D model. The mesh area includes each independently calculable structural component (e.g., a column, a wall, or a load-bearing structure) after the project is broken down. A rendering direction for the mesh is generated based on the construction content. Based on this rendering direction, the meshes within the mesh area are rendered according to the completion progress. The rendered portion of the 3D model is then reconstructed in a digital sandbox, resulting in a real-time digital sandbox.

[0046] The rendering direction includes, in the construction content, obtaining the construction steps and the construction direction during the construction, and rendering in sequence according to the construction steps and the construction direction during the construction (for example, construction from the inside out, and construction direction from top to bottom).

[0047] It's worth noting that breaking down a 3D model into independent, accountable components (such as columns, walls, or load-bearing structures) allows for individual monitoring and progress assessment of each structural part. This breakdown transforms the complex overall structure into easily manageable and trackable units. By using coordinates from real-time project progress records, the specific location of construction can be accurately determined and mapped onto a grid area in the 3D model. This ensures that every small step of the construction progress matches the 3D structure in the digital sandbox, avoiding management difficulties caused by deviations.

[0048] The rendering order is determined by the construction steps and the direction of construction. Each construction step has a clear execution direction and time schedule. Rendering step by step ensures that the progress of each stage in the digital sand table is clearly visible, facilitating real-time monitoring by construction managers. Based on the grid area, rendering is performed according to the construction progress, ensuring that the completion level of each structural component is accurately represented in the 3D model. The rendering effect not only reflects the actual progress of the current construction but also visually displays completed and uncompleted parts, helping construction personnel and managers better identify problems. As each grid is rendered, real-time progress information is reconstructed through the digital sand table, forming a digital model that matches the actual progress on the construction site. In this way, managers can understand the latest status of the entire construction process at any time through the digital sand table, and promptly identify and resolve problems.

[0049] The grid division includes dividing the grid size according to the historical average construction time per square meter for each grid region. The historical average construction time per square meter is linearly related to the grid size, expressed as: y = k × x; where y represents the grid size, x represents the historical average construction time per square meter, and k represents a scaling factor, the value of which can be preset in the sand table model.

[0050] The ratio of the number of rendered meshes to the total number of meshes is used as a criterion for judging the progress of the project.

[0051] By adjusting the value of k in the digital sandbox, both detailed and macro-level assessments of project progress can be achieved. As the value of k decreases, the assessment results of project progress become more accurate (but the amount of calculation will increase, so it is necessary to adjust k to generate assessment results that meet the requirements).

[0052] The design, which linearly correlates historical average project time per square meter with grid size, aims to rationally predict and divide the size of each grid area through the accumulation of historical data. Historical average project time can be used to adjust the grid size based on the characteristics of different construction areas and processes, ensuring the rationality and accuracy of construction progress assessments. This design closely links the size of each grid to the actual construction time requirements, avoiding errors that may arise from simply dividing the grid equally. The ratio of rendered grids to the total number of grids serves as a criterion for judging project progress, intuitively reflecting the completion status. As construction progresses and the number of rendered grids increases, the completion ratio also changes, helping managers monitor project progress in real time.

[0053] By adjusting the k-value, an optimal balance can be found between detailed and macro-level assessments. A smaller k-value provides more accurate progress assessments but increases computational complexity; a larger k-value is suitable for more macro-level assessments, reducing computational complexity. Through flexible k-value adjustments, the digital sand table can find the most suitable balance between accuracy and computational efficiency according to project needs, meeting management requirements at different stages. This design allows the digital sand table to flexibly adjust the accuracy of progress assessments based on the needs of different construction stages and contents. Managers can adjust the k-value according to actual needs, thereby achieving a refined progress monitoring and early warning mechanism. This dynamic assessment system not only makes construction progress monitoring more accurate but also allows for real-time adjustments based on different project requirements, providing greater decision support for project management.

[0054] S3: The digital sand table model is corrected based on the high-precision images collected in real time to obtain a digital sand table that reflects the actual situation.

[0055] The real-time high-precision images include images captured by a high-precision camera and image processing algorithms, which are used to construct a three-dimensional model of the appearance of each grid area in the construction site.

[0056] The 3D model of the appearance is compared with each corresponding grid region in the real-time digital sandbox. Let the 3D model of the appearance be Model 1, and the corresponding grid region in the real-time digital sandbox be Model 2. Constraints are applied to the space within Model 1. If Model 2 does not have any part exceeding the constraints of Model 1, then shape correction is performed directly. If Model 2 has any part exceeding the constraints of Model 1, then high-precision image resampling is performed. If the resampling result still shows that Model 2 has any part exceeding the constraints of Model 1, then the exceeding part is marked and a warning is issued. If the resampling result does not show that Model 2 has any part exceeding the constraints of Model 1, then shape correction is performed.

[0057] It's important to understand that Model 2 is a digital sand table, representing a standardized 3D model generated based on engineering drawings, design specifications, and construction plans. It's an ideal, standardized spatial model representing the construction goals and expected state, typically created based on design drawings (such as floor plans and elevations). Each part of Model 2 (including columns, walls, and load-bearing structures) has clearly defined dimensions, locations, and structural requirements in space, all set according to design drawings and standards. Therefore, it's a strictly constrained "framework." If the actual structure doesn't conform to this framework, it indicates a deviation from the drawings, potentially leading to potential problems.

[0058] Model 1 is a 3D model generated using high-precision imaging and image processing technology, representing the actual appearance and state of the construction site. It reflects the real progress of construction, including completed and under-construction sections. Model 1 may differ from Model 2 in shape and size because unforeseen circumstances may arise during construction, causing the actual result to deviate from the original design. The core constraint is that Model 2 (the standard design framework) must be contained within Model 1 (the actual construction appearance). This model represents the "desired result," but during construction, the actual situation may deviate from these standards, and Model 2 should always be contained within Model 1 during actual construction (similar to outward expansion and decoration of a building). In other words, Model 1 should not extend inward beyond the boundary of Model 2, and Model 2 should not extend outward beyond the boundary of Model 1.

[0059] By combining images captured by high-precision cameras with image processing algorithms, a 3D model of the appearance of each grid area at the construction site is constructed. This process ensures that the digital sand table is highly consistent with the spatial and appearance features of the actual construction site, reflecting changes in construction progress and site conditions in real time. Comparing the acquired 3D model of the appearance with the corresponding grid area in the real-time digital sand table effectively detects differences between the model and reality, allowing for corrections to the digital sand table to ensure it always accurately reflects the actual site conditions.

[0060] By comparing and analyzing data, if inconsistencies exist between the real-time digital sand table model and the actual 3D model (e.g., construction quality issues or schedule deviations), resampling and comparison are used to determine if the deviation persists. If the deviation continues, the affected portion is marked and an alert is issued. This mechanism allows managers to quickly identify problems during construction and take timely corrective measures to prevent the accumulation of deviations and reduce potential risks. When no significant deviations are found in the comparison results, the digital sand table can be directly modified in appearance. In this way, the digital sand table can be updated in real time to reflect the actual progress and changes at the construction site, improving management efficiency and accuracy.

[0061] The shape correction involves not replacing Model 2, but simply attaching Model 1 to the real-time sand table model to obtain the final sand table model. This shape correction design avoids direct modification of Model 2, which is crucial for long-term project management and supervision. As long as Model 1 is attached to Model 2 and the differences are displayed, engineering deviations can be intuitively reflected, and comparisons can be made through dynamic correction, thereby avoiding any changes to the design drawings and maintaining the consistency and integrity of the design.

[0062] On the other hand, this embodiment also provides an intelligent big data processing system based on a digital sand table, which includes:

[0063] The data acquisition unit obtains engineering drawings and real-time project progress records.

[0064] The rendering unit uses the engineering drawings as a framework and supplements the framework content according to the engineering progress records to obtain a digital sand table model.

[0065] The correction unit corrects the digital sand table model based on the high-precision images acquired in real time, resulting in a digital sand table that reflects the actual situation.

[0066] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0067] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0068] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0069] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0070] Example 2 is an embodiment of the present invention, which provides an intelligent big data processing method based on a digital sand table. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0071] To verify the effectiveness and advantages of this method, a construction project was selected as the experimental subject for specific implementation.

[0072] Experimental Subject: A commercial complex building project was selected as the experimental subject. This project includes multiple floors, various structures and construction techniques, and the data collection and progress monitoring involved in the construction process require high-precision visualization tools.

[0073] Implementation process:

[0074] Input of engineering drawings and 3D modeling tools:

[0075] Obtain the complete engineering drawings for the building project, including floor plans, elevations, and sections. Input the engineering drawings using a 3D modeling tool to generate the basic 3D model of the digital sand table (Model Two).

[0076] During this process, the 3D modeling tool automatically models different structural parts (such as walls, columns, and floors) in the drawings and defines a standard grid area for each structural part.

[0077] Real-time progress recording and location:

[0078] The construction site uses mobile devices to record real-time construction progress, including the construction coordinates, construction content, and completion progress of each grid area. The construction location of each grid is precisely located using GPS, and the 3D model is reconstructed based on the actual progress to form a digital sand table of the construction site (Model 1).

[0079] High-precision image acquisition and correction:

[0080] High-precision cameras were used to photograph the construction site, and image processing algorithms were combined to generate a 3D model of the appearance of each grid area (Model 1). An image matching algorithm was then used to compare the high-precision images of the construction site with a digital sand table (Model 2).

[0081] During the comparison process, if it is found that part of Model 2 exceeds the scope of Model 1, the system will mark the excess part and issue a warning; if Model 2 is completely wrapped by Model 1, the correction part of Model 1 will be added to Model 2.

[0082] Model shape correction:

[0083] During the correction process, Model 2 is not replaced. Instead, it is modified based on the actual construction appearance of Model 1 and then added to Model 2. This ensures that actual deviations in construction are effectively corrected without altering the standard framework in the design drawings.

[0084] In this way, the final digital sand table model can fully reflect the construction progress and site conditions, while maintaining consistency with the design drawings.

[0085] Data acquisition and processing:

[0086] During construction, construction data for each grid area is collected, including factors such as construction time, completion progress, and construction environment. By adjusting the linear relationship between the historical average construction time per square meter and the grid size, the project progress is assessed in real time.

[0087] Table 1 Experimental Data Table

[0088]

[0089]

[0090] Based on the experimental data table above, the following analytical results can be drawn:

[0091] Comparing the "Completion Progress" and "Actual Construction Time" with the "Theoretical Construction Time" in the table reveals that some grid areas (such as grid area 4) have exceeded the construction time limit. The actual construction time for grid area 4 was 350 hours, exceeding the theoretical construction time by 330 hours, indicating that the construction progress is lagging behind schedule.

[0092] Through real-time correction of high-precision images, this abnormal part was promptly identified, and corresponding adjustments could be made. The design framework of Model 2 remained unchanged, avoiding design inconsistencies caused by construction deviations.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart big data processing method based on a digital sand table, characterized in that, include: Obtain engineering drawings and real-time project progress records; Using the engineering drawings as a framework, the content of the framework is supplemented according to the engineering progress records to obtain a digital sand table model. The digital sand table model is corrected based on real-time high-precision images to obtain a digital sand table that reflects the actual situation. The engineering drawings are input into a 3D modeling tool to obtain a 3D model of the project; The framework content is supplemented based on the project progress record, including meshing the three-dimensional model; Based on the coordinates in the real-time project progress record, the construction location is located, and the corresponding grid area in the three-dimensional model is determined. The grid area includes each independently calculable structural component after the project is broken down; The rendering direction of the grid is generated according to the construction content. Based on the rendering direction, the grid in the grid area is rendered according to the completion progress. The rendered part of the three-dimensional model is reconstructed in the digital sand table to obtain a real-time digital sand table. The rendering direction includes, in the construction content, obtaining the construction steps and the construction direction during the construction, and rendering in sequence according to the construction steps and the construction direction during the construction. The real-time high-precision images include three-dimensional models of the appearance of each grid area in the construction site, which are constructed by combining images captured by high-precision cameras and image processing algorithms. The correction of the digital sandbox model includes comparing the three-dimensional model of the appearance with each corresponding grid area in the real-time digital sandbox. Let the 3D model of the appearance be Model 1, and the corresponding grid area in the real-time digital sandbox be Model 2; The space within Model 1 is constrained. If Model 2 does not have any part that exceeds the constraints of Model 1, the shape is directly corrected. If Model 2 has any part that exceeds the constraints of Model 1, the high-precision image is resampled. If the resampling result still shows that Model 2 has any part that exceeds the constraints of Model 1, the excess part is marked and a warning is issued. If the resampling results do not show that Model 2 has a portion that exceeds the constraints of Model 1, then shape correction is performed; The shape correction includes not replacing the second model, but only attaching the first model to the real-time sand table model to obtain the final sand table model.

2. The intelligent big data processing method based on a digital sandbox as described in claim 1, characterized in that: The engineering drawings are the design drawings for the entire project, including but not limited to floor plans, elevations, and sections. The project progress records include records manually recorded by the construction party or supervisor, showing the specific progress of the project construction; including but not limited to the construction coordinates, construction content, and completion progress recorded during the construction process.

3. The intelligent big data processing method based on a digital sandbox as described in claim 2, characterized in that: The engineering drawings are used as a framework, and the three-dimensional model is not displayed in the sand table, but only constrains the three-dimensional structure of the construction process; during the construction process, the three-dimensional model of the digital sand table is reconstructed through the engineering progress record to obtain a digital sand table reflecting the real-time engineering progress record.

4. The intelligent big data processing method based on a digital sandbox as described in claim 3, characterized in that: The grid division includes dividing the grid size according to the historical average engineering time per square meter for each grid area; The historical average project time per square meter is linearly related to the grid size. It is expressed as: y=k×x; where y represents the grid size, x represents the historical average construction time per square meter, and k represents the scaling factor, the value of which can be preset in the sand table model; The ratio of the number of rendered meshes to the total number of meshes is used as a criterion for judging project progress. By adjusting the value of k in the digital sand table, both detailed and macro-level assessments of project progress can be achieved; the smaller the value of k, the more accurate the assessment results of project progress.

5. An intelligent big data processing system based on a digital sandbox, employing the method described in any one of claims 1-4, characterized in that: The data acquisition unit obtains engineering drawings and real-time project progress records; The rendering unit uses the engineering drawings as a framework and supplements the framework content according to the engineering progress records to obtain a digital sand table model. The correction unit corrects the digital sand table model based on the high-precision images acquired in real time, resulting in a digital sand table that reflects the actual situation.

6. A computer device, comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-4.

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