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

Abstract

The invention discloses an intelligent big data processing method and system based on a digital sand table. The method comprises the following steps: obtaining an engineering drawing and a real-time engineering progress record; taking the engineering drawing as a framework, and supplementing framework content according to the engineering progress record to obtain a digital sand table model; and correcting the digital sand table model according to a high-precision image collected in real time to obtain a digital sand table reflecting a real condition. By combining engineering drawings, real-time construction progress and high-precision image data, the digital sand table model can be dynamically corrected, and real-time visualization of the construction progress is realized. Compared with a traditional method, the technology provides higher precision and timeliness, and the problems of information lag and error accumulation are effectively avoided. Through accurate progress evaluation and real-time monitoring, managers can better master project progress, optimize resource allocation, identify construction deviation in advance, ensure that the project is completed on time and according to quality, and improve construction efficiency and project quality.

Description

Technical Field

[0001] The present invention relates to the technical field of digital sandboxes, and in particular to an intelligent big data processing method and system based on a digital sandbox. Background Art

[0002] In construction projects, tracking and monitoring construction progress has always been a management challenge. Traditional project progress management relies mainly on manual records and updates, which lack real-time and accuracy. This can easily lead to delayed feedback on construction deviations, affecting the overall progress and quality control of the project. In addition, existing technologies have difficulty responding to real-time dynamic changes at the construction site during progress monitoring, and there is a disconnect between the processing of construction data and the updating of engineering drawings. This management method not only increases labor costs, but also makes it difficult to accurately assess the actual status of the project. Although existing digital modeling technologies can provide a certain degree of three-dimensional visualization, most systems are unable to effectively integrate engineering drawings, construction progress, and real-time image data, resulting in a lack of accuracy in dynamic corrections 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 the present invention is that the existing data processing method of the digital sandbox lacks dynamic correction and monitoring accuracy.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent big data processing method based on a digital sandbox, comprising:

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

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

[0008] The digital sand table model is modified according to the high-precision images collected in real time to obtain a digital sand table reflecting the actual situation.

[0009] As a preferred solution of the intelligent big data processing method based on digital sandbox described in the present invention, wherein: the engineering drawings are design drawings of the entire project, including but not limited to plan views, elevation views, and cross-section views;

[0010] The project progress record includes records manually recorded by the construction party or the supervisor, which contain 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 solution of the digital sandbox-based intelligent big data processing method of the present invention, wherein: using the engineering drawing as a framework includes inputting the engineering drawing into a three-dimensional modeling tool to obtain a three-dimensional 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 rebuilt according to the project progress record to obtain a digital sand table reflecting the real-time project progress record.

[0013] As a preferred solution of the digital sandbox-based intelligent big data processing method of the present invention, wherein: supplementing the framework content according to the project progress record includes meshing the three-dimensional model;

[0014] Locating the construction location according to the coordinates in the real-time project progress record and determining the grid area corresponding to the construction location in the three-dimensional model;

[0015] The grid area includes, after the project is disassembled, each structural part that can be independently accounted for;

[0016] Generating a rendering direction for the grid according to the construction content, and rendering the grids in the grid area according to the completion progress based on the rendering direction; reconstructing the rendered part of the three-dimensional model 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 of the project in the construction content, and rendering in accordance with the construction steps and the construction direction of the project in sequence.

[0018] As a preferred solution of the digital sandbox-based intelligent big data processing method of the present 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 construction 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 scale factor, the value of which can be preset in the sandbox model.

[0021] 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;

[0022] By adjusting the k value in the digital sandbox, detailed and macroscopic evaluations of the project progress can be achieved; as the k value decreases, the evaluation results of the project progress become more accurate.

[0023] As a preferred solution of the intelligent big data processing method based on digital sandbox described in the present invention, the high-precision images collected in real time include combining images taken by a high-precision camera and image processing algorithms to construct a three-dimensional model of the appearance of each grid area in the construction site.

[0024] As a preferred embodiment of the digital sandbox-based intelligent big data processing method of the present invention, 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;

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

[0026] Constrain the space within Model 1. If Model 2 does not have any part that exceeds the constraints of Model 1, then perform shape correction directly. If Model 2 has any part that exceeds the constraints of Model 1, then perform high-precision image resampling. If the resampling result still shows that Model 2 has any part that exceeds the constraints of Model 1, then mark the excess part and issue a warning. If the resampling result does not show that Model 2 has any part that exceeds the constraints of Model 1, then perform shape correction.

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

[0028] An intelligent big data processing system based on a digital sandbox using any method of the present invention, characterized in that:

[0029] Collection unit, to obtain engineering drawings and real-time engineering progress records;

[0030] A rendering unit, which uses the engineering drawing as a framework and supplements the framework content according to the engineering progress record to obtain a digital sand table model;

[0031] The correction unit corrects the digital sand table model according to the high-precision images collected in real time to obtain a digital sand table reflecting the actual situation.

[0032] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, the steps of any one of the methods of the present invention are implemented.

[0033] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0034] The beneficial effects of this invention are as follows: The intelligent big data processing method based on a digital sandbox, provided by this invention, combines engineering drawings, real-time construction progress, and high-precision image data to dynamically modify the digital sandbox model, achieving real-time visualization of construction progress. Compared to traditional methods, this technology provides higher accuracy and timeliness, effectively avoiding information lag and error accumulation. Through precise progress assessment and real-time monitoring, managers can better understand project progress, optimize resource allocation, and proactively identify construction deviations, ensuring on-time and high-quality project completion, thereby improving construction efficiency and project quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 This is an overall flow chart of the intelligent big data processing method based on a digital sandbox provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0037] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0038] Example 1, with reference to Figure 1 , as one embodiment of the present invention, provides an intelligent big data processing method based on a digital sandbox, comprising:

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

[0040] 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 manual records made by the construction party or supervisor, which record the specific progress of the project construction, including but not limited to construction coordinates, construction content, and completion progress recorded during the construction process.

[0041] Engineering drawings provide a static design reference for the digital sandbox, ensuring the model complies with the overall project planning and design requirements. Real-time project progress records provide dynamic construction progress information, enabling the digital sandbox to be updated promptly to reflect actual construction progress, construction locations, and completed sections.

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

[0043] The engineering drawings are used as a framework and input into a 3D modeling tool to generate a 3D model of the project. This 3D model is not displayed in the sandbox, but only serves as a 3D structural constraint during the construction process. During construction, the 3D model in the digital sandbox is rebuilt using the project progress records, resulting in a digital sandbox that reflects the real-time project progress.

[0044] It's important to note that converting engineering drawings into 3D models not only provides spatial and structural visualization but also provides a clear structural reference for subsequent construction. This 3D structural constraint ensures that all construction activities must be carried out within this 3D framework during actual construction, ensuring that the construction process is carried out in accordance with design requirements. During the construction process, as progress records are updated, the 3D model in the digital sandbox is rebuilt according to the real-time construction progress. This process combines dynamic construction information with the static 3D model, allowing the digital sandbox to reflect the latest construction status in real time, ensuring that the actual progress on the construction site is synchronized with the 3D design model.

[0045] Furthermore, supplementing the framework content according to the project progress record includes meshing the three-dimensional model. According to the coordinates in the real-time project progress record, the construction location is located, and the grid area corresponding to the construction location in the three-dimensional model is determined. The grid area includes each independently accountable structural part (such as a column, a wall or a load-bearing structure) after the project is disassembled. A rendering direction for the grid is generated according to the construction content, and the grid in the grid area is rendered according to the completion progress based on the rendering direction; the three-dimensional model of the rendered part is rebuilt in the digital sandbox to obtain a real-time digital sandbox.

[0046] The rendering direction includes obtaining the construction steps and the construction direction of the project in the construction content, and rendering in accordance with the construction steps and the construction direction of the project in sequence (for example, building from inside to outside, and the construction direction is from top to bottom).

[0047] Breaking down the 3D model into independently accountable components (such as columns, walls, or load-bearing structures) allows for individual monitoring and progress assessment of each structural element. This breakdown transforms the complex overall structure into easily manageable and trackable units. Using coordinates from real-time project progress records, the specific locations of construction work can be accurately determined and mapped to grid areas in the 3D model. This ensures that every small step in the construction progress matches the 3D structure in the digital sandbox, avoiding management difficulties caused by deviations.

[0048] The order of rendering is determined by the construction steps and the direction of construction. Each construction step has a clear execution direction and timeline. Rendering by step ensures that the progress of each stage in the digital sandbox is clearly visible, facilitating real-time inspection by construction managers. Based on the grid area, rendering is performed according to the progress of construction completion, ensuring that the degree of completion of each structural component is accurately reflected in the 3D model. The rendering effect not only reflects the actual progress of the current construction, but also intuitively displays completed and unfinished sections, helping construction workers and managers better identify problems. As each grid is rendered, real-time progress information is reconstructed through the digital sandbox, forming a digital model that matches the actual progress of the construction site. This allows managers to stay updated on the latest status of the entire construction process through the digital sandbox, allowing them to identify and resolve problems promptly.

[0049] The grid division includes dividing the grid size based on the historical average construction time per square meter of each grid area. The historical average construction time per square meter is linearly related to the grid size. It can be 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 sandbox 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 k value in the digital sandbox, detailed and macroscopic evaluations of the project progress can be achieved; as the k value decreases, the evaluation results of the project progress become more accurate (at the same time, the amount of calculation will increase, so it can be adjusted, and it is very necessary to adjust k to generate evaluation results that meet the needs).

[0052] It's important to note that the linear relationship between the historical average construction time per square meter and grid size is designed to rationally predict and divide the size of each grid area based on the accumulation of historical data. This historical average construction time allows for grid size adjustments based on the characteristics of different construction areas and processes, ensuring reasonable and accurate construction progress assessments. This design closely aligns the size of each grid with the actual construction time requirements, avoiding the potential errors introduced by simply dividing the grid equally. The ratio of rendered grids to the total number of grids serves as a criterion for project progress and provides a visual reflection of the completion status of the construction process. As construction progresses, the number of rendered grids increases, and the completion ratio also changes, helping managers to monitor project progress in real time.

[0053] By adjusting the k value, an optimal balance can be found between detailed and macro-evaluations. Smaller k values provide more accurate progress assessments, but require more computation; larger k values are suitable for more macro-level assessments, reducing computational complexity. By flexibly adjusting the k value, the digital sandbox can find the optimal balance between accuracy and computational efficiency based on project needs, meeting management requirements at different stages. This design allows the digital sandbox to flexibly adjust the accuracy of progress assessments according to the needs of different construction stages and content. Managers can adjust the k value according to actual needs, thereby implementing a sophisticated 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 the varying needs of the project, providing more decision-making support for project management.

[0054] S3: Modify the digital sand table model according to the high-precision images collected in real time to obtain a digital sand table reflecting the actual situation.

[0055] The high-precision images collected in real time include images taken by a high-precision camera and an image processing algorithm to construct a three-dimensional model of the appearance of each grid area in the construction site.

[0056] Compare the three-dimensional model of the appearance with each corresponding grid area in the real-time digital sandbox. Assume that the three-dimensional model of the appearance is Model 1, and the corresponding grid area in the real-time digital sandbox is Model 2. Constrain the space within Model 1. If Model 2 does not contain any portion that exceeds the constraints of Model 1, then perform shape correction directly. If Model 2 does contain any portion that exceeds the constraints of Model 1, then resample the image with high precision. If the resampling result still shows that Model 2 contains any portion that exceeds the constraints of Model 1, then mark the excess portion and issue a warning. If the resampling result does not show that Model 2 contains any portion that exceeds the constraints of Model 1, then perform shape correction.

[0057] It should be noted that Model 2 is a digital sandbox that represents a standardized three-dimensional model generated based on engineering drawings, design specifications, and construction plans. It is an ideal, standardized spatial model that represents the goals and expected state of construction and is usually created based on design drawings (such as floor plans, elevations, etc.). The various parts of Model 2 (including columns, walls, load-bearing structures, etc.) have clear spatial dimensions, positions, and structural requirements, which are set according to design drawings and specifications. Therefore, it is a strictly restricted "framework." If the actual structure does not conform to this framework, it means that it has not been carried out according to the drawings, which can easily lead to hidden dangers.

[0058] Model 1 is a 3D model generated using high-precision imaging and image processing technology, representing the actual appearance and status of the construction site. It reflects the actual progress of the construction process, including completed sections and those under construction. Model 1 may differ from Model 2 in terms of form and size, as unforeseen circumstances may arise during construction, causing the actual results to deviate from the original design. The core constraint is that Model 2 (the standard design framework) must be contained by Model 1 (the actual construction appearance). This model represents the "desired result," but during construction, actual conditions may deviate from these standards, and Model 2 must always be contained by Model 1 during actual construction (similar to the case of an exterior extension or decorative work). This means that Model 1 should not extend inward beyond the boundaries of Model 2, and Model 2 should not extend outward beyond the boundaries of Model 1.

[0059] By combining high-precision camera images with image processing algorithms, a 3D model of the exterior appearance of each grid area on the construction site is constructed. This process ensures that the digital sand table closely matches the spatial and exterior characteristics of the actual construction site, reflecting changes in construction progress and site conditions in real time. Comparing the acquired 3D exterior model with the corresponding grid area in the real-time digital sand table effectively detects discrepancies between the model and reality, allowing corrections to the digital sand table to ensure it always accurately reflects the actual site conditions.

[0060] Through comparative analysis, if any discrepancies between the real-time sand table model and the 3D exterior model occur (e.g., due to construction quality issues or progress deviations), resampling and comparison are used to determine whether the discrepancy persists. If so, the exceeding portion is marked and an alert is issued. This mechanism enables managers to quickly identify problems during construction and take timely corrective measures, preventing the accumulation of deviations and reducing potential risks. If the comparison results reveal no significant deviations, the digital sand table's exterior can be directly corrected. 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] This shape correction involves appending Model 1 to the live sandbox model without replacing Model 2, resulting in the final sandbox model. This shape correction design avoids direct modification of Model 2, which is crucial for long-term project management and oversight. Simply appending Model 1 to Model 2 and displaying the differences visually reflects engineering deviations, allowing for dynamic correction and comparison, thus avoiding any design drawing changes and maintaining design consistency and integrity.

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

[0063] Collection unit, obtains engineering drawings and real-time engineering progress records.

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

[0065] The correction unit corrects the digital sand table model according to the high-precision images collected in real time to obtain a digital sand table reflecting the actual situation.

[0066] If the above functions are implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0067] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the 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 (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0068] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0069] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

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

[0071] In order to verify the effectiveness and advantages of this method, a construction project was selected as the experimental object for specific implementation.

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

[0073] Implementation process:

[0074] Engineering drawings and 3D modeling tool input:

[0075] Obtain complete engineering drawings for the building project, including floor plans, elevations, and sections. Use a 3D modeling tool to import these drawings and generate a basic 3D model for the digital sandbox (Model 2).

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

[0077] Real-time progress recording and positioning:

[0078] Mobile devices are used at the construction site to record real-time construction progress, including the coordinates, content, and completion progress of each grid area. Each grid's construction location is precisely located using GPS, and a 3D model is reconstructed based on the actual progress, creating a digital sand table (Model 1) of the construction site.

[0079] High-precision image acquisition and correction:

[0080] A high-precision camera is used to photograph the construction site, and combined with image processing algorithms, a 3D model of the appearance of each grid area is generated (Model 1). Using an image matching algorithm, the high-precision image of the construction site is compared with a digital sand table (Model 2).

[0081] During the comparison process, if it is found that part of Model 2 exceeds the range of Model 1, the system will mark the exceeding part and issue an early warning; if Model 2 is completely wrapped by Model 1, the corrected part of Model 1 will be attached to Model 2.

[0082] Model appearance correction:

[0083] During the revision process, Model 2 is not replaced, but is revised based on the actual construction appearance of Model 1 and appended to Model 2. This ensures that actual deviations in construction are effectively corrected without changing the standard framework in the design drawings.

[0084] In this way, the final digital sandbox model can fully reflect the construction progress and on-site status while maintaining consistency with the design drawings.

[0085] Data collection and processing:

[0086] During construction, we collect construction data for each grid area, 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, we can evaluate project progress in real time.

[0087] Table 1 Experimental data table

[0088]

[0089]

[0090] According to the above experimental data table, the following analysis results can be obtained:

[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, are experiencing construction overtime. The actual construction time for grid area 4 was 350 hours, exceeding the theoretical construction time by 330 hours, indicating that construction progress is lagging behind.

[0092] Through real-time correction using high-precision imagery, this anomaly was promptly identified, allowing for appropriate adjustments. 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 the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent big data processing method based on a digital sandbox, characterized in that: include: Obtain engineering drawings and real-time engineering progress records; Using the engineering drawings as a framework, the framework content is supplemented according to the engineering progress records to obtain a digital sand table model; The digital sand table model is modified according to the high-precision images collected in real time to obtain a digital sand table reflecting the actual situation.

2. The intelligent big data processing method based on a digital sandbox according to claim 1, characterized in that: The engineering drawings are design drawings of the entire project, including but not limited to plan views, elevations, and sections; The project progress record includes records manually recorded by the construction party or the supervisor, which contain 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 digital sandbox according to claim 2, characterized in that: The engineering drawing is included as a framework, and the engineering drawing is input into a three-dimensional modeling tool to obtain a three-dimensional model of the project; 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 rebuilt according to the project progress record to obtain a digital sand table reflecting the real-time project progress record.

4. The intelligent big data processing method based on a digital sandbox according to claim 3, characterized in that: Supplementing the framework content according to the engineering progress record includes meshing the three-dimensional model; Locating the construction location according to the coordinates in the real-time project progress record and determining the grid area corresponding to the construction location in the three-dimensional model; The grid area includes, after the project is disassembled, each structural part that can be independently accounted for; Generating a rendering direction for the grid according to the construction content, and rendering the grids in the grid area according to the completion progress based on the rendering direction; reconstructing the rendered part of the three-dimensional model in the digital sand table to obtain a real-time digital sand table; The rendering direction includes obtaining the construction steps and the construction direction of the project in the construction content, and rendering in accordance with the construction steps and the construction direction of the project in sequence.

5. The intelligent big data processing method based on digital sandbox according to claim 4, characterized in that: The grid division includes dividing the grid size according to the historical average engineering time per square meter of each grid area; The historical average construction 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 scale factor, the value of which can be preset in the sandbox model. 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; By adjusting the k value in the digital sandbox, detailed and macroscopic evaluations of the project progress can be achieved; as the k value decreases, the evaluation results of the project progress become more accurate.

6. The intelligent big data processing method based on digital sandbox according to claim 5, characterized in that: The high-precision images collected in real time include images taken by a high-precision camera and an image processing algorithm to construct a three-dimensional model of the appearance of each grid area in the construction site.

7. The intelligent big data processing method based on digital sandbox according to claim 6, characterized in that: Correcting the digital sand table model includes comparing the three-dimensional model of the appearance with each corresponding grid area in the real-time digital sand table; Let the three-dimensional model of the appearance be model 1, and the corresponding grid area in the real-time digital sandbox be model 2; Constrain the space within Model 1. If Model 2 does not have any part that exceeds the constraints of Model 1, the shape correction is performed directly. If Model 2 has any part that exceeds the constraints of Model 1, high-precision image resampling is performed. If the resampling result still shows that Model 2 has any part that exceeds the constraints of Model 1, the exceeding part is marked and an alert is issued. If the resampling results do not show that the model 2 has a part that exceeds the constraints of the model 1, then the shape correction is performed; The shape correction includes not replacing the second model, but only adding the first model to the real-time sand table model to obtain the final sand table model.

8. An intelligent big data processing system based on a digital sandbox using the method according to any one of claims 1 to 7, characterized in that: Collection unit, to obtain engineering drawings and real-time engineering progress records; A rendering unit, which uses the engineering drawing as a framework and supplements the framework content according to the engineering progress record to obtain a digital sand table model; The correction unit corrects the digital sand table model according to the high-precision images collected in real time to obtain a digital sand table reflecting the actual situation.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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