Three-dimensional visual engineering project management system

Abstract

The invention belongs to the field of engineering project management, and provides a three-dimensional visual engineering project management system, which comprises a three-dimensional model modeling module, and a monitoring module, a query module, a state judgment module, a display module and a decision and management module which are respectively connected with the three-dimensional model modeling module. According to the method, a Cesium forUnity plug-in is introduced through the three-dimensional model modeling module, so that the computational power requirement of the model is reduced, and meanwhile, the spatial analysis capability of the model is expanded; the GIS is introduced through the query module, so that extraction of engineering information is realized; through a state discrimination module and a decision and management module, reasonable recommendation of decisions and real-time management and control of projects are realized.

Description

Technical Field

[0001] The present invention relates to the field of engineering project management, and in particular to a three-dimensional visual engineering project management system. Background Art

[0002] There are two mainstream technical paths for digital city platforms. One is based on game engines such as Unreal Engine and Unity, which is mainly used for game development. Advantages: It has powerful graphics rendering capabilities and physical simulation, suitable for developing immersive experiences. Disadvantages: It may be too heavyweight for simple applications, consumes a lot of resources, and has no spatial computing and analysis capabilities. The second is based on the Cesium open source 3D earth and map engine, which provides plug-ins that integrate geospatial data into Unreal Engine or Unity, allowing developers to easily load and display global terrain and image data in these game engines, which can support the display of multi-source data, and can support various types of spatial computing and spatial analysis; it has low requirements for hardware devices.

[0003] At present, the relevant technologies for 3D visualization engineering project management are all based on software such as Unity, which results in high computing power requirements and is not suitable for small-scale project management. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a three-dimensional visualization engineering project management system, which introduces the Cesium for Unity plug-in through a three-dimensional model modeling module to reduce the model computing requirements while expanding the spatial analysis capabilities of the model; introduces GIS through a query module to realize the extraction of engineering information; and realizes reasonable decision recommendations and real-time control of the project through a state discrimination module and a decision and management module.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A three-dimensional visualization engineering project management system, comprising: a three-dimensional model building module and a monitoring module, a query module, a state determination module, a display module, a decision and management module respectively connected to the three-dimensional model building module;

[0007] The three-dimensional modeling module is used to use the Cesium for Unity plug-in to perform modeling to obtain a three-dimensional structural model of a target building; the monitoring module is used to use a preset number of groups of cameras to collect construction site images of a target project, extract structural information of the construction site images, and map the structural information to the three-dimensional structural model of the target building; the query module is used to use the GIS plug-in to extract the project progress information of the three-dimensional structural model of the target building when receiving a query command from a target user, and send the project progress information to the display module; the state discrimination module is used to determine the state color of each building in the three-dimensional structural model of the target building according to the project progress information, the structural information and a preset project progress plan; the state colors include: green, red, blue and yellow; the display module is used to display the project progress information or the three-dimensional structural model of the target building with the state color; the decision and management module is used to infer according to the state color, the project progress plan and the structural information to obtain a decision reference plan; the decision reference plan includes: a project progress plan control plan and a project priority adjustment plan.

[0008] Preferably, the three-dimensional model building module includes: a code input unit, a model generation unit, a project progress entry unit, a building grid splitting unit and a priority adjustment unit;

[0009] The code input unit is used to obtain the basic structural data of the three-dimensional structural model of the target building; the model generation unit is used to input the basic structural data into the Cesium for Unity plug-in for model generation; the project progress entry unit is used to obtain the project progress plan; the building grid splitting unit is used to divide the three-dimensional structural model of the target building according to the preset minimum project block to obtain a plurality of task grids; the priority adjustment unit is used to assign a weight value to each of the task grids according to the project progress plan, and arrange the task grids in sequence according to the weight value.

[0010] Preferably, the monitoring module comprises: a video acquisition unit, a key frame extraction unit, a three-dimensional point cloud mapping unit and a building matching unit;

[0011] The video acquisition unit is used to use the camera to record the on-site construction video of the target project; the key frame extraction unit is used to use a similarity formula to select the image with the highest similarity in the on-site construction video within a preset period to obtain a key frame image; the three-dimensional point cloud mapping unit is used to map the key frame image to a three-dimensional space through a pre-trained neural network to obtain point cloud data, and obtain the structural information after wavelet filtering the point cloud data; the building matching unit is used to match the structural information to the three-dimensional structural model of the target building using a pre-trained structure recognition model.

[0012] Preferably, the query module includes: an instruction parsing unit, an information extraction unit and a matching output unit;

[0013] The instruction parsing unit is used to determine the query category of the query command; the information extraction unit is used to use the GIS plug-in to extract the status information of each task grid, and integrate all the status information to obtain the project progress information; the matching output unit is used to transmit the status information corresponding to the query category to the display module.

[0014] Preferably, the state determination module includes: a grid progress statistics unit, an overall completion calculation unit, and a state matching unit;

[0015] The grid progress statistics unit is used to compare the status information with the project progress plan to obtain grid progress data; the overall completion calculation unit is used to calculate the product value of the weight value and the grid progress data to obtain completion data; the status matching unit is used to match the grid progress data and the completion data according to a preset grid threshold and an overall threshold to obtain the status color.

[0016] Preferably, the decision and management module includes: a data integration unit and a decision analysis unit;

[0017] The data integration unit is used to vectorize the state color, the engineering schedule and the structural information to obtain an input vector;

[0018] The decision analysis unit is used to perform multi-objective optimization, cluster analysis and text generation on the input vector to obtain the decision reference plan.

[0019] Preferably, the decision analysis unit includes: an NSGA-II subunit, a Knn subunit, an importance ranking subunit and an NLP subunit;

[0020] The NSGA-II subunit is used to perform non-dominated sorting, crowding distance calculation, fitness evaluation and genetic iteration on the task grid according to the status color, the project schedule and the structural information to obtain optimized structural data; the Knn subunit is used to perform cluster analysis on the optimized structural data using pre-collected project history data to obtain cluster data; the importance sorting subunit is used to eliminate the cluster data using a preset minimum correlation standard to obtain core optimized data; the NLP subunit is used to convert the core optimization data into a text description using a preset solution template to obtain the decision reference solution.

[0021] Preferably, the basic matching formula of the structure recognition model is:

[0022]

[0023] Among them, p i ′=T(p i ); P = {p 1 ,p 2 ,...,p n}; p i ′ is the i-th point cloud structure; q j is the jth target building structure; α j is the hyperparameter corresponding to the jth target building structure; n is the number of point cloud structures; m is the number of target building structures; T is the transformation matrix; p i is the i-th point cloud coordinate; P is the point cloud data.

[0024] Preferably, the monitoring module further comprises: a positioning unit and a sensor unit;

[0025] The positioning unit is used for positioning and point measurement by accessing the Beidou satellite system; the sensor unit is used for collecting on-site construction environment data of the target project through smoke sensors, vibration sensors, and light sensors.

[0026] The present invention discloses the following technical effects:

[0027] The present invention provides a three-dimensional visualization engineering project management system. By introducing the Cesium for Unity plug-in through a three-dimensional model modeling module, the problem of high computing power requirement when directly using Unity for engineering management in the traditional method is solved, and model analysis under low computing power is realized; by introducing GIS through a query module, the problem of traditional engineering management relying on manual analysis is solved, and the extraction of engineering information is realized; by using a state discrimination module and a decision-making and management module, the problem of unreliable and inaccurate traditional engineering management is solved, and reasonable recommendation of decision-making and real-time control of engineering are realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 creative labor.

[0029] Figure 1 A schematic diagram of the structure of a three-dimensional visual engineering project management system provided by an embodiment of the present invention;

[0030] Figure 2 A schematic diagram of a three-dimensional model building module provided in an embodiment of the present invention;

[0031] Figure 3 A schematic diagram of a query module provided by an embodiment of the present invention;

[0032] Description of reference numerals:

[0033] 1-3D model building module, 11-code input unit, 12-model generation unit, 13-project progress entry unit, 14-building grid splitting unit, 15-priority adjustment unit, 2-monitoring module, 21-video acquisition unit, 22-key frame extraction unit, 23-3D point cloud mapping unit, 24-building matching unit, 3-query module, 4-state judgment module, 5-display module, 6-decision-making and management module. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The purpose of the present invention is to provide a three-dimensional visualization engineering project management system, which introduces the Cesium for Unity plug-in through a three-dimensional model modeling module to reduce the model computing power requirements while expanding the model's spatial analysis capabilities; introduces GIS through a query module to achieve the extraction of engineering information; and achieves reasonable decision recommendations and real-time control of engineering through a state discrimination module and a decision and management module.

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] Figure 1A schematic diagram of the structure of a three-dimensional visualization engineering project management system provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides a three-dimensional visualization engineering project management system, including: a three-dimensional model building module 1 and a monitoring module 2, a query module 3, a state determination module 4, a display module 5, and a decision and management module 6 respectively connected to the three-dimensional model building module 1;

[0038] The three-dimensional modeling module 1 is used to use the Cesium for Unity plug-in to perform modeling to obtain a three-dimensional structural model of a target building; the monitoring module 2 is used to use a preset number of groups of cameras to collect construction site images of a target project, extract structural information of the construction site images, and map the structural information to the three-dimensional structural model of the target building; the query module 3 is used to use the GIS plug-in to extract the project progress information of the three-dimensional structural model of the target building when receiving a query command from a target user, and send the project progress information to the display module 5; the state discrimination module 4 is used to determine the state color of each building in the three-dimensional structural model of the target building according to the project progress information, the structural information and a preset project progress plan; the state colors include: green, red, blue and yellow; the display module 5 is used to display the project progress information or the three-dimensional structural model of the target building with the state color; the decision and management module 6 is used to infer based on the state color, the project progress plan and the structural information to obtain a decision reference plan; the decision reference plan includes: a project progress plan control plan and a project priority adjustment plan.

[0039] Further, the three-dimensional model building module 1 includes: a code input unit 11, a model generation unit 12, a project progress entry unit 13, a building grid splitting unit 14 and a priority adjustment unit 15;

[0040] The code input unit 11 is used to obtain the basic structural data of the three-dimensional structural model of the target building; the model generation unit 12 is used to input the basic structural data into the Cesium for Unity plug-in for model generation; the project progress entry unit 13 is used to obtain the project progress plan; the building grid splitting unit 14 is used to divide the three-dimensional structural model of the target building according to the preset minimum project block to obtain a plurality of task grids; the priority adjustment unit 15 is used to assign a weight value to each of the task grids according to the project progress plan, and arrange the task grids in sequence according to the weight value.

[0041] Specifically, the monitoring module 2 includes: a video acquisition unit 21, a key frame extraction unit 22, a three-dimensional point cloud mapping unit 23 and a building matching unit 24;

[0042] The video acquisition unit 21 is used to use the camera to record the on-site construction video of the target project; the key frame extraction unit 22 is used to use a similarity formula to select the image with the highest similarity in the on-site construction video within a preset period to obtain a key frame image; the three-dimensional point cloud mapping unit 23 is used to map the key frame image to a three-dimensional space through a pre-trained neural network to obtain point cloud data, and obtain the structural information after wavelet filtering the point cloud data; the building matching unit 24 is used to match the structural information to the three-dimensional structural model of the target building using a pre-trained structure recognition model.

[0043] Furthermore, the query module 3 includes: an instruction parsing unit, an information extraction unit and a matching output unit;

[0044] The instruction parsing unit is used to determine the query category of the query command; the information extraction unit is used to use the GIS plug-in to extract the status information of each task grid, and integrate all the status information to obtain the project progress information; the matching output unit is used to transmit the status information corresponding to the query category to the display module 5.

[0045] Specifically, the state determination module 4 includes: a grid progress statistics unit, an overall completion calculation unit and a state matching unit;

[0046] The grid progress statistics unit is used to compare the status information with the project progress plan to obtain grid progress data; the overall completion calculation unit is used to calculate the product value of the weight value and the grid progress data to obtain completion data; the status matching unit is used to match the grid progress data and the completion data according to a preset grid threshold and an overall threshold to obtain the status color.

[0047] Furthermore, the decision and management module 6 includes: a data integration unit and a decision analysis unit;

[0048] The data integration unit is used to vectorize the state color, the engineering schedule and the structural information to obtain an input vector;

[0049] The decision analysis unit is used to perform multi-objective optimization, cluster analysis and text generation on the input vector to obtain the decision reference plan.

[0050] Specifically, the decision analysis unit includes: an NSGA-II subunit, a Knn subunit, an importance ranking subunit, and an NLP subunit;

[0051] The NSGA-II subunit is used to perform non-dominated sorting, crowding distance calculation, fitness evaluation and genetic iteration on the task grid according to the status color, the project schedule and the structural information to obtain optimized structural data; the Knn subunit is used to perform cluster analysis on the optimized structural data using pre-collected project history data to obtain cluster data; the importance sorting subunit is used to eliminate the cluster data using a preset minimum correlation standard to obtain core optimized data; the NLP subunit is used to convert the core optimization data into a text description using a preset solution template to obtain the decision reference solution.

[0052] Alternatively, the core idea of ​​NSGA-II (Non-dominated Sorting Genetic Algorithm II) is non-dominated sorting, which can effectively divide solutions into different levels to find the Pareto frontier of multi-objective optimization problems. Through this sorting method, diversity can be better preserved. It introduces the crowding distance measurement to evaluate the diversity of solutions. This indicator helps the algorithm maintain the diversity of the population during the selection phase and avoid premature convergence. It has good adaptability when dealing with multi-objective optimization problems of different types and scales, and can effectively handle objective functions of different numbers and complexities. Compared with other multi-objective optimization algorithms, NSGA-II has higher computational efficiency, especially for large-scale problems, and can find high-quality solutions within an acceptable time. Compared with some other multi-objective optimization methods, the parameter setting of NSGA-II is relatively simple, and usually only a small number of parameters such as population size, crossover rate, and mutation rate need to be set, which is easy to use. NSGA-II is suitable for parallel computing and can use modern multi-core and distributed computing architectures to accelerate the computing process. By combining non-dominated sorting and crowding distance, NSGA-II can usually find more evenly distributed solutions in the multi-objective space, which helps to explore the solution space and maintain the diversity and distribution of solutions. It can be combined with other optimization algorithms (such as local search, particle swarm optimization, etc.) to form a hybrid strategy to improve the optimization effect. NSGA-II can be applied to complex problems in different fields, such as engineering design, financial investment, data mining, path planning, etc., demonstrating its wide applicability and flexibility.

[0053] Preferably, the basic matching formula of the structure recognition model is:

[0054]

[0055] Among them, p i ′=T(p i ); P = {p 1 ,p 2 ,...,p n}; p i′ is the i-th point cloud structure; q j is the jth target building structure; α j is the hyperparameter corresponding to the jth target building structure; n is the number of point cloud structures; m is the number of target building structures; T is the transformation matrix; p i is the i-th point cloud coordinate; P is the point cloud data.

[0056] Specifically, the structure recognition model adopts a minimum gap matching mechanism, in which the hyperparameters have a greater impact on the matching performance. The parameters can be optimized using the minimum gradient descent method during model training.

[0057] Furthermore, the monitoring module 2 further comprises: a positioning unit and a sensor unit;

[0058] The positioning unit is used for positioning and point measurement by accessing the Beidou satellite system; the sensor unit is used for collecting on-site construction environment data of the target project through smoke sensors, vibration sensors, and light sensors.

[0059] Preferably, Cesium for Unity allows developers to access high-resolution earth data, such as satellite images, terrain, and building models, which enables developers to create a more realistic virtual world. The tool supports real-time data updates, can access geospatial data through the network, and present it in real time in the Unity environment, which is suitable for applications that require dynamic data. Integrating Cesium's powerful spatial query capabilities, developers can easily perform geographic location queries and spatial analysis. It supports the import of multiple geospatial data formats, and developers can use formats such as GLTF and GeoJSON to flexibly integrate external data. Based on the dual engines of Cesium and Unity, based on Cesium, Unity re-renders the output video stream to enhance the visual effect, and integrates the Cesium for Unity plug-in, which allows users to load Cesium's 3D files, terrain, and image services directly in Unity. Allows GIS functions to be called directly in the game engine to retain spatial analysis capabilities. Using instanced rendering technology, for a large number of identical objects that appear repeatedly but in different positions in the scene, the same vertex data and material settings are called.

[0060] Optionally, the target 3D model is divided into multiple engineering components, which correspond to the actual engineering parts and can be used to input information such as component name, material, location, engineering quantity, and cost. The GIS 3D topographic map creation plug-in obtains on-site topographic data through drone aerial photography technology, and uses Smart3D software to perform aerial triangulation and encryption calculations to generate point cloud data, and uses point cloud data to create an actual topographic map of the engineering site. The true 3D engineering model creation module combines the above two to provide visual information for staff and managers, making it easier to obtain information.

[0061] Specifically, when the display module 5 is working normally, the three-dimensional structural model of the target building will be displayed in a virtual form, and only the target structural framework will be retained for reference in actual construction. The building matching unit 24 will map the actual building of the target project to the target framework structure in a physical form, so as to facilitate the staff to compare and analyze. In addition, the display module 5 will distinguish the construction conditions of different situations with different colors. The task grids that fully comply with the project schedule will be displayed in green, and less than half of the task grids will be displayed in blue when they do not comply with the project schedule. When more than half but less than 80% of the task grids do not comply with the project schedule, they will be displayed in yellow, and when 80% to 100% of the task grids do not comply with the project schedule, they will be displayed in red. When all the task grids do not comply with the project schedule, the corresponding building units will flash for prompts.

[0062] Furthermore, the NLP subunit realizes the conversion of structured data into unstructured descriptions, which saves time for summarizing the solution and improves the reliability of the solution. The core of the NLP subunit lies in text generation, which uses a predefined text template to generate text by filling in variables. Since text generation is based on data files, conventional dialogue models are not applicable. Therefore, this embodiment uses a conditional generation model based on the Transformer architecture for reinforcement learning to obtain the NLP subunit that meets the requirements of this embodiment. In addition, since text generation requires a large amount of data text for training, a pre-trained model is used to improve the response speed of the model, and retraining can be performed when the model conditions or text model are updated.

[0063] The beneficial effects of the present invention are as follows:

[0064] The present invention introduces the Cesium for Unity plug-in through a three-dimensional model modeling module, thereby reducing the model computing power requirements while expanding the model's spatial analysis capabilities; introduces GIS through a query module to achieve the extraction of engineering information; and realizes reasonable decision recommendations and real-time control of engineering through a state discrimination module and a decision and management module.

[0065] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0066] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A three-dimensional visual engineering project management system, characterized in that: include: A three-dimensional model building module and a monitoring module, a query module, a state determination module, a display module, and a decision and management module respectively connected to the three-dimensional model building module; The three-dimensional modeling module is used to use the Cesium for Unity plug-in to perform modeling to obtain a three-dimensional structural model of the target building; the monitoring module is used to use a preset number of groups of cameras to collect construction site images of the target project, extract structural information of the construction site images, and map the structural information to the three-dimensional structural model of the target building; the query module is used to use the GIS plug-in to extract the project progress information of the three-dimensional structural model of the target building when receiving a query command from the target user, and send the project progress information to the display module; The state identification module is used to determine the state color of each building in the three-dimensional structural model of the target building according to the project progress information, the structure information and a preset project progress plan; The status colors include: green, red, blue and yellow; the display module is used to display the project progress information or the three-dimensional structural model of the target building with the status color; the decision-making and management module is used to infer according to the status color, the project progress plan and the structural information to obtain a decision reference plan; the decision reference plan includes: a project progress plan control plan and a project priority adjustment plan.

2. A three-dimensional visual engineering project management system according to claim 1, characterized in that: The three-dimensional model building module includes: a code input unit, a model generation unit, a project progress entry unit, a building grid splitting unit and a priority adjustment unit; The code input unit is used to obtain the basic structural data of the three-dimensional structural model of the target building; the model generation unit is used to input the basic structural data into the Cesium for Unity plug-in for model generation; the project progress entry unit is used to obtain the project progress plan; the building grid splitting unit is used to divide the three-dimensional structural model of the target building according to the preset minimum project block to obtain a plurality of task grids; the priority adjustment unit is used to assign a weight value to each of the task grids according to the project progress plan, and arrange the task grids in sequence according to the weight value.

3. A three-dimensional visual engineering project management system according to claim 1, characterized in that: The monitoring module includes: a video acquisition unit, a key frame extraction unit, a three-dimensional point cloud mapping unit and a building matching unit; The video acquisition unit is used to use the camera to record the on-site construction video of the target project; the key frame extraction unit is used to use a similarity formula to select the image with the highest similarity in the on-site construction video within a preset period to obtain a key frame image; the three-dimensional point cloud mapping unit is used to map the key frame image to a three-dimensional space through a pre-trained neural network to obtain point cloud data, and obtain the structural information after wavelet filtering the point cloud data; the building matching unit is used to match the structural information to the three-dimensional structural model of the target building using a pre-trained structure recognition model.

4. A three-dimensional visual engineering project management system according to claim 2, characterized in that: The query module includes: an instruction parsing unit, an information extraction unit and a matching output unit; The instruction parsing unit is used to determine the query category of the query command; the information extraction unit is used to use the GIS plug-in to extract the status information of each task grid, and integrate all the status information to obtain the project progress information; the matching output unit is used to transmit the status information corresponding to the query category to the display module.

5. A three-dimensional visual engineering project management system according to claim 4, characterized in that: The state identification module includes: a grid progress statistics unit, an overall completion calculation unit, and a state matching unit; The grid progress statistics unit is used to compare the status information with the project progress plan to obtain grid progress data; the overall completion calculation unit is used to calculate the product value of the weight value and the grid progress data to obtain completion data; the status matching unit is used to match the grid progress data and the completion data according to a preset grid threshold and an overall threshold to obtain the status color.

6. A three-dimensional visual engineering project management system according to claim 1, characterized in that: The decision-making and management module includes: a data integration unit and a decision analysis unit; The data integration unit is used to vectorize the state color, the engineering schedule and the structural information to obtain an input vector; The decision analysis unit is used to perform multi-objective optimization, cluster analysis and text generation on the input vector to obtain the decision reference plan.

7. A three-dimensional visual engineering project management system according to claim 2, characterized in that: The decision analysis unit includes: an NSGA-II subunit, a Knn subunit, an importance ranking subunit and an NLP subunit; The NSGA-II subunit is used to perform non-dominated sorting, crowding distance calculation, fitness evaluation and genetic iteration on the task grid according to the status color, the project schedule and the structural information to obtain optimized structural data; the Knn subunit is used to perform cluster analysis on the optimized structural data using pre-collected project history data to obtain cluster data; the importance sorting subunit is used to eliminate the cluster data using a preset minimum correlation standard to obtain core optimized data; the NLP subunit is used to convert the core optimization data into a text description using a preset solution template to obtain the decision reference solution.

8. A three-dimensional visual engineering project management system according to claim 3, characterized in that: The basic matching formula of the structure recognition model is: Among them, p i ′=T(p i ); P = {p1, p2, ..., p n }; p i ′ is the i-th point cloud structure; q j is the jth target building structure; α j is the hyperparameter corresponding to the jth target building structure; n is the number of point cloud structures; m is the number of target building structures; T is the transformation matrix; p i is the i-th point cloud coordinate; P is the point cloud data.

9. A three-dimensional visual engineering project management system according to claim 3, characterized in that: The monitoring module also includes: a positioning unit and a sensor unit; The positioning unit is used for positioning and point measurement by accessing the Beidou satellite system; the sensor unit is used for collecting on-site construction environment data of the target project through smoke sensors, vibration sensors, and light sensors.

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