Building construction engineering construction optimization system integrating surveying and mapping and dynamic modeling

Abstract

The invention provides a house building engineering construction optimization system integrating surveying and mapping and dynamic modeling, and the system comprises a dynamic surveying and mapping module which obtains point cloud data and position data, and collects the environment data of each scanning region; the structure analysis module is used for determining the geometric deviation of a completed structure, determining a concrete early setting risk area in combination with environmental data, and further determining the maintenance priority and a structure acceptance report of each scanning area; the instruction generation module is used for determining an equipment GPS track, further predicting the time when a concrete tank car arrives at each scanning area, generating a watering cart route and determining a vehicle scheduling instruction set; the cloud picture construction module is used for generating a three-dimensional progress deviation cloud picture based on the structure acceptance report and a preset BIM model; and the signal generation module is used for generating an early warning signal based on the vehicle scheduling instruction set, the three-dimensional progress deviation cloud picture and a preset early warning strategy. The construction quality and the accuracy of progress and resource scheduling are effectively improved, and the construction management process is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of building engineering construction optimization, and in particular to a housing construction optimization system integrating surveying and mapping with dynamic modeling. Background Art

[0002] Current construction projects face numerous challenges related to progress, quality control, and equipment scheduling, particularly regarding structural deviations, construction maintenance, and vehicle scheduling accuracy. Traditional construction project management methods often rely on manual measurement and scheduling, which are inefficient and prone to errors. Furthermore, as the scale and complexity of construction projects continue to increase, traditional methods of construction monitoring and management are becoming increasingly difficult.

[0003] Existing technical solutions primarily focus on monitoring and optimizing the construction process through computer modeling and IoT technologies. However, most of these solutions lack real-time data updates and rapid response to changes in the construction environment. This makes them unable to effectively address the complex and ever-changing realities of the construction process, such as equipment failures, construction schedule delays, and environmental fluctuations. Therefore, existing technical solutions still have significant room for improvement in construction optimization, structural monitoring, and equipment scheduling.

[0004] Therefore, the present invention provides a housing construction engineering optimization system that integrates surveying and mapping with dynamic modeling. Summary of the Invention

[0005] This invention provides a housing construction optimization system that integrates surveying and dynamic modeling. By integrating dynamic surveying and modeling technologies, this system enables real-time monitoring and optimization of construction sites. The dynamic surveying module collects point cloud data, equipment location, and environmental information; the structural analysis module analyzes structural deviations and environmental risks; the instruction generation module rationally dispatches construction equipment and vehicles; the cloud map construction module visualizes construction progress; and the signal generation module promptly issues early warning signals. This system effectively improves construction quality, progress, and the accuracy of resource scheduling, optimizing the construction management process.

[0006] According to the present invention, a housing construction engineering optimization system integrating surveying and mapping with dynamic modeling is provided, comprising:

[0007] Dynamic mapping module: The construction area is divided into scanning areas according to the construction stage, and point cloud data is generated in real time based on the lidar array. At the same time, the location data of the construction equipment is tracked based on the engineering vehicle measurement equipment, and the environmental data of each scanning area is collected based on the preset collection equipment;

[0008] Structural Analysis Module: Determines geometric deviations of completed structures based on point cloud data, environmental data, and a standard CAD contour library of pre-set steel formwork. Combined with environmental data, it identifies areas at risk of premature setting of concrete, and subsequently determines maintenance priorities and structural acceptance reports for each scanned area.

[0009] Instruction generation module: This module determines the equipment's GPS trajectory based on the equipment's location data, predicts the arrival time of concrete trucks at each scanned area, generates water truck routes based on the maintenance priority of each scanned area, and determines the vehicle dispatch instruction set.

[0010] Cloud map construction module: Generates a 3D progress deviation cloud map based on the structural acceptance report and the preset BIM model;

[0011] Signal generation module: Generates warning signals based on the vehicle scheduling instruction set, the three-dimensional progress deviation cloud map, and the preset warning strategy.

[0012] Preferably, the dynamic mapping module includes:

[0013] Coordinate construction unit: construct a spatiotemporal four-dimensional coordinate system in the preset BIM model;

[0014] Regional division unit: Based on the space-time coordinate system, the construction area is decomposed into two types of topological domains: permanent structure units and temporary construction units;

[0015] Area determination unit: Based on the two types of topological domains, permanent structure units and temporary construction units, the system divides them into preset levels and determines several scanning areas;

[0016] Data acquisition unit: deploys a lidar array in each scanning area to generate point cloud data in real time;

[0017] Data determination unit: tracking location data of construction equipment based on engineering vehicle measurement equipment;

[0018] Environmental collection unit: collects environmental data of each scanning area based on preset collection equipment.

[0019] Preferably, the laser radar array comprises:

[0020] High-precision ground-mounted scanning base stations are deployed at core control points on construction floors;

[0021] The mobile scanning terminal mounted on the tower crane boom adjusts the scanning angle synchronously with the increase of construction height;

[0022] The auxiliary scanning unit attached to the concrete pump truck is used to fill the blind spots of the equipment.

[0023] Preferably, the structural analysis module includes:

[0024] Deviation determination unit: Determines the geometric deviation of the completed structure based on point cloud data, preset allowable deviation thresholds, and a standard CAD contour library of preset steel structure templates;

[0025] Level determination unit: determines the risk level of each scanned area based on geometric deviations of the completed structure and environmental data;

[0026] Area determination unit: determines several concrete early setting risk areas based on the risk level of each scanned area and the preset risk level;

[0027] Maintenance determination unit: determines the corresponding maintenance level based on the risk level of each concrete early setting risk area;

[0028] Report Generation Unit: Generates a structural acceptance report based on the deviations of the completed structure and the risk level of all scanned areas.

[0029] Preferably, the deviation determination unit includes:

[0030] Point cloud determination subunit: Determine the point cloud data set based on the point cloud data: P = {p i ∣p i =(x i ,y i ,z i ),i=1,2,...,m}, where the point cloud data set contains m discrete points;

[0031] Theoretical determination subunit: Match the point cloud data with the preset standard CAD contour library to determine the CAD theoretical contour point set: Among them, the CAD theoretical contour points are the theoretical contour points of the corresponding positions in the standard steel structure formwork CAD model, including n characteristic points;

[0032] Deviation determination subunit: determines the tolerance threshold matrix based on the preset allowable deviation threshold:

[0033] δ=[δ 梁 ,δ 柱 ,δ 楼板 ]

[0034] Among them, δ 梁 ,δ 柱 ,δ 楼板 Indicates the allowable geometric deviation thresholds for different component types;

[0035] Surface fitting subunit: Use the moving least squares method to perform local surface fitting on the point cloud data P to generate a continuous surface S(P);

[0036] Pairing subunit: Align the CAD contour point set C to the fitting surface S(P) through rigid transformation, and pair corresponding points based on local curvature and normal features

[0037] Distance calculation subunit: For each pair of matching points (p i ,c j ), calculate point p i To the corresponding theoretical point c j Distance deviation along the normal direction:

[0038] d ij =(p i -c j )·n i

[0039] Among them, d ij For point p i To the corresponding theoretical point c j Distance deviation along the normal direction, p i is the point cloud coordinate of the completed structure surface, c j is the coordinate of the theoretical contour point of the CAD model, represents the vector dot product, and the result is the projection length along the normal vector direction, n i Point p is the point after local surface fitting of point cloud data P i The surface normal vector at ;

[0040] Overall determination of subunits: based on point p i To the corresponding theoretical point c j The distance deviation along the normal direction, along with preset weights for each build type, determines the geometric deviation index:

[0041]

[0042] Where D is the geometric deviation index, k represents the component type, N k is the number of matching points of the k-th component, max(0,|d ij |-δ k ) represents the geometric deviation of the part that exceeds the specification tolerance. Only the part that exceeds the tolerance is evaluated. k is the allowable deviation threshold of the k-th type component, w k is the preset weight coefficient of the kth type component.

[0043] Preferably, the instruction generation module includes:

[0044] Path identification unit: identifies the permanent path of the construction equipment based on the GPS trajectory data of the construction equipment;

[0045] Time determination unit: predicts the time for construction equipment to arrive at each scanning area based on the GPS trajectory data of the construction equipment and the maintenance level of each concrete early setting risk area;

[0046] Matrix generation unit: This unit constructs and solves a planning model based on the maintenance level of each concrete early setting risk area, the time it takes for construction equipment to arrive at each scanning area, and the capacity constraints of the construction equipment, and outputs a vehicle-task matching matrix.

[0047] Path determination unit: determines the navigation path set based on the vehicle-task matching matrix and the preset digital map of the construction site;

[0048] Matrix construction unit: Instruction generation unit: Determines the maintenance task for each concrete early setting risk zone based on the maintenance level of each concrete early setting risk zone and a preset level-task database, and then constructs a maintenance task matrix;

[0049] Instruction generation unit: Based on the navigation path set and the maintenance task matching matrix, the instructions are packaged according to the ID of the construction equipment to generate a vehicle scheduling instruction set.

[0050] Preferably, the cloud map construction module includes:

[0051] Schedule Deviation Determination Unit: Compares the actual construction completion time in the structural acceptance report with the planned time preset in the BIM model, determines the number of days overdue and ahead of schedule in each scanned area, and determines this as schedule deviation;

[0052] Spatial deviation determination unit: performs grid matching between the actual structural surface fitted by point cloud data and the theoretical surface of the BIM model to determine the normal distance deviation of the feature points;

[0053] Comprehensive deviation quantification unit: combines the construction period deviation and the normal distance deviation of the feature point to generate the comprehensive deviation value of each scanning area according to the preset weight;

[0054] Cloud map generation unit: Based on the three-dimensional spatial framework of the BIM model, with the scanning area as the basic unit, the comprehensive deviation value is mapped through color gradient and transparency to generate a three-dimensional progress deviation cloud map.

[0055] Preferably, the signal generating module includes:

[0056] Strategy parsing unit: parses the preset warning strategy library to determine the warning type and the corresponding triggering conditions;

[0057] Risk matching unit: performs risk matching based on the vehicle dispatch instruction set, the three-dimensional progress deviation cloud map, the warning type, and the corresponding trigger conditions to determine whether a warning is triggered and the warning level;

[0058] Signal generation unit: Generates specific warning signals based on risk matching results.

[0059] Compared with the prior art, the present invention has the following advantages:

[0060] By integrating dynamic mapping and modeling technologies, real-time monitoring and optimization of construction sites are achieved. The dynamic mapping module collects point cloud data, equipment location, and environmental information. The structural analysis module analyzes structural deviations and environmental risks. The instruction generation module rationally dispatches construction equipment and vehicles. The cloud map construction module visualizes construction progress. The signal generation module promptly issues early warning signals. This effectively improves construction quality, progress, and the accuracy of resource scheduling, optimizing the construction management process. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0062] Figure 1 It is a structural schematic diagram of a housing construction engineering construction optimization system that integrates surveying and mapping with dynamic modeling, provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some 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 technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0064] Example 1:

[0065] The embodiment of the present invention provides a building construction optimization system integrating surveying and mapping with dynamic modeling, such as Figure 1 As shown, including:

[0066] Dynamic mapping module: The construction area is divided into scanning areas according to the construction stage, and point cloud data is generated in real time based on the lidar array. At the same time, the location data of the construction equipment is tracked based on the engineering vehicle measurement equipment, and the environmental data of each scanning area is collected based on the preset collection equipment;

[0067] Structural Analysis Module: Determines geometric deviations of completed structures based on point cloud data, environmental data, and a standard CAD contour library of pre-set steel formwork. Combined with environmental data, it identifies areas at risk of premature setting of concrete, and subsequently determines maintenance priorities and structural acceptance reports for each scanned area.

[0068] Instruction generation module: This module determines the equipment's GPS trajectory based on the equipment's location data, predicts the arrival time of concrete trucks at each scanned area, generates water truck routes based on the maintenance priority of each scanned area, and determines the vehicle dispatch instruction set.

[0069] Cloud map construction module: Generates a 3D progress deviation cloud map based on the structural acceptance report and the preset BIM model;

[0070] Signal generation module: Generates warning signals based on the vehicle scheduling instruction set, the three-dimensional progress deviation cloud map, and the preset warning strategy.

[0071] In this embodiment, the construction phase refers to the various specific implementation phases in the construction project, which are divided according to the different work contents and time schedules of the project. For example, the project can be divided into the foundation construction phase, the main construction phase, the roof construction phase, the decoration phase, etc. Each phase usually has clear goals, tasks and work content. For example: in a house construction project, the construction phase can be divided in the following order: Foundation construction phase: including foundation excavation, foundation reinforcement, etc. Main construction phase: including structural reinforced concrete frame construction, wall masonry, etc. Decoration phase: including interior wall painting, floor paving, installation of electrical appliances, etc.;

[0072] In this embodiment, point cloud data achieves dynamic fusion of multi-source scanning data through a time synchronization protocol;

[0073] In this embodiment, the environmental data of each scanning area is collected including: a temperature and humidity sensor network arranged on the working surface, an anemometer installed on the tower crane to monitor the concrete curing environmental parameters, and wind load data of the high-altitude working area is collected to output: an environmental parameter matrix of each construction zone + a dangerous weather warning signal.

[0074] In this embodiment, the completed structure refers to the portion of the building structure that has been completed according to the design requirements under the current construction schedule. For example, the completed frame structure of a building's first floor includes poured concrete columns and constructed steel beams.

[0075] In this embodiment, the structural acceptance report generally includes sections such as basic project information (project name, construction unit, structure type, etc.), acceptance basis (relevant building codes, design drawings, etc.), and acceptance content (various parameters of the completed structure). The acceptance content includes: Geometric Deviation: Detailed record of the geometric deviation statistics of the completed structure, including the deviation values of key components, deviation ranges, the number and location of components exceeding the allowable range, etc. For example, the report states that among the 100 columns inspected, the verticality deviations of 5 columns exceeded the allowable range, specifically the columns located in the X-axis 5-8 and Y-axis 3-5 areas. Concrete Early Setting Risk Assessment: Records the division of concrete early setting risk zones, including the area, location, and corresponding maintenance priority of each risk zone. For example, the report indicates that there is a 50-square-meter high-risk area in the east wing of the building, requiring urgent maintenance; and a 100-square-meter medium-risk area in the west wing, requiring prompt maintenance. Comprehensive Acceptance Conclusion: Based on the geometric deviations and early setting risk assessment, a comprehensive conclusion is given as to whether the structure has passed the acceptance. If the geometric deviation is within the allowable range and the risk of premature setting is controllable, the conclusion is that the acceptance is passed; if there are many deviations beyond the allowable range or the area of the high-risk area is too large, the conclusion is that the acceptance is not passed, and rectification suggestions are put forward, such as reworking the components that exceed the deviation and strengthening maintenance measures in the high-risk area.

[0076] In this embodiment, the location data of construction equipment (e.g., concrete tankers, cranes, excavators, etc.) is tracked and recorded in real time using positioning technology (e.g., GPS). The GPS trajectory of a piece of equipment refers to the path or motion trajectory mapped out based on the equipment's location data, reflecting the equipment's movement trajectory and range of activity at the construction site. For example, the location data of construction equipment, such as the specific location of a concrete tanker at a construction site, can be obtained in real time using a GPS positioning system. The GPS trajectory can reveal the concrete tanker's route, speed, and estimated arrival time from construction area A to construction area B. This helps predict the equipment's work progress and rationally schedule subsequent construction tasks.

[0077] The beneficial effects of this technical solution include: by integrating dynamic mapping and modeling technologies, real-time monitoring and optimization of construction sites are achieved. The dynamic mapping module collects point cloud data, equipment location, and environmental information; the structural analysis module analyzes structural deviations and environmental risks; the instruction generation module rationally dispatches construction equipment and vehicles; the cloud map construction module visualizes construction progress; and the signal generation module issues timely warning signals. This effectively improves construction quality, progress, and the accuracy of resource scheduling, optimizing the construction management process.

[0078] Example 2:

[0079] The embodiment of the present invention provides a housing construction project optimization system integrating surveying and mapping with dynamic modeling, and the dynamic surveying and mapping module includes:

[0080] Coordinate construction unit: construct a spatiotemporal four-dimensional coordinate system in the preset BIM model;

[0081] Regional division unit: Based on the space-time coordinate system, the construction area is decomposed into two types of topological domains: permanent structure units and temporary construction units;

[0082] Area determination unit: Based on the two types of topological domains, permanent structure units and temporary construction units, the system divides them into preset levels and determines several scanning areas;

[0083] Data acquisition unit: deploys a lidar array in each scanning area to generate point cloud data in real time;

[0084] Data determination unit: tracking location data of construction equipment based on engineering vehicle measurement equipment;

[0085] Environmental collection unit: collects environmental data of each scanning area based on preset collection equipment.

[0086] In this embodiment, the spatiotemporal four-dimensional coordinate system is: XYZ coordinates + stage milestone timeline, where the three-dimensional geographic coordinate system is superimposed on the fourth-dimensional timeline (construction stage milestone nodes); the output is a topological network containing the following attributes: the spatial constraint envelope of the permanent structural unit (core tube, etc.), and the life cycle timestamp of the temporary construction unit (scaffolding erection / dismantling time).

[0087] In this embodiment, permanent structural units refer to parts that will eventually remain in the building and become part of the main structure of the building. These parts have long-term use functions and load-bearing roles, such as the foundation, columns, beams, load-bearing walls, floor panels, roof panels, etc. of the building. Taking a residential building as an example, its reinforced concrete foundation, concrete columns and beams supporting the floors, and load-bearing brick walls separating the spaces are all permanent structural units. They constitute the basic skeleton and stable structure of the building, bearing the building's own weight and various loads during use;

[0088] In this embodiment, temporary construction units are temporary structures or areas set up during the construction process to assist in construction. They are usually dismantled or no longer have any practical function after construction is completed. Examples include scaffolding, crane foundations, construction access roads, and material storage platforms erected during construction. Taking high-rise building construction as an example, external scaffolding used for worker operations and material transportation will be dismantled after the main structure is completed. There are also temporary crane foundations temporarily cast to house construction equipment, which are no longer functional after the crane is dismantled. These are all temporary construction units.

[0089] In this embodiment, the progressive division according to preset levels is based on the topological network and is progressively divided into the following levels: structural dependency layering: based on load transfer analysis, marking the rigid boundaries of key force transmission areas (such as the root of the main beam cantilever); process timing constraint layering: associating the construction schedule and calculating the time and space occupancy of each process (such as the inviolable area during the concrete curing period); resource flow dynamic layering: real-time collection of equipment GPS data and generation of equipment impact Thiessen polygons.

[0090] The beneficial effects of this technical solution include: By integrating surveying and mapping with dynamic modeling technologies, it enhances real-time monitoring and optimization capabilities for construction. By constructing a four-dimensional spatiotemporal coordinate system, construction areas are precisely demarcated, and LiDAR and environmental data are combined to monitor construction progress and structural deviations in real time. Dynamic collection and analysis of construction equipment and environmental data effectively optimizes equipment scheduling, maintenance priorities, and construction quality, addressing the information lag and inaccurate scheduling issues inherent in traditional construction management and improving construction efficiency and quality control.

[0091] Example 3:

[0092] An embodiment of the present invention provides a housing construction optimization system integrating surveying and mapping with dynamic modeling, including a laser radar array, comprising:

[0093] High-precision ground-mounted scanning base stations are deployed at core control points on construction floors;

[0094] The mobile scanning terminal mounted on the tower crane boom adjusts the scanning angle synchronously with the increase of construction height;

[0095] The auxiliary scanning unit attached to the concrete pump truck is used to fill the blind spots of the equipment.

[0096] In this embodiment, a high-precision ground-based fixed scanning base station: a laser radar device fixedly installed at the core control point of the construction floor can stably obtain point cloud data of the surrounding area. For example: during the construction of a high-rise residential building, a base station is set up near the elevator shaft of each floor to perform high-precision scanning of the columns, beams, walls and other structures on the floor, providing basic data for structural analysis.

[0097] In this embodiment, a mobile scanning terminal mounted on the tower crane arm is a device installed on the tower crane arm that can change the scanning angle and range as the tower crane moves and the construction progresses. For example, as construction continues to advance upward and the tower crane is lifting materials, the terminal synchronously adjusts the viewing angle to scan construction areas at different heights, thereby making up for the scanning height limitation of the ground base station.

[0098] In this embodiment, the auxiliary scanning unit attached to the concrete pump truck is a laser radar device installed on the concrete pump truck for scanning the blind spots of large equipment operations. For example, when the pump truck is pouring large-area floor slab concrete, scanning blind spots are easily formed around the body and some corners. The unit can scan these areas and improve the point cloud data collection of the construction area.

[0099] The beneficial effects of this technical solution include the integration of a fixed ground-based scanning base station, a mobile scanning terminal on a tower crane boom, and an auxiliary scanning unit on a concrete pump truck, creating a highly efficient LiDAR array that overcomes the viewing angle limitations of traditional surveying and mapping equipment. The ground-based base station provides stable core control, the tower crane terminal automatically adjusts the viewing angle based on the construction height, and the auxiliary scanning unit on the concrete pump truck effectively fills in equipment blind spots. This enables comprehensive and accurate construction site monitoring and data collection, improving the accuracy and efficiency of construction progress monitoring and quality control.

[0100] Example 4:

[0101] The embodiment of the present invention provides a housing construction project optimization system integrating surveying and mapping with dynamic modeling, and a structural analysis module, including:

[0102] Deviation determination unit: Determines the geometric deviation of the completed structure based on point cloud data, preset allowable deviation thresholds, and a standard CAD contour library of preset steel structure templates;

[0103] Level determination unit: determines the risk level of each scanned area based on geometric deviations of the completed structure and environmental data;

[0104] Area determination unit: determines several concrete early setting risk areas based on the risk level of each scanned area and the preset risk level;

[0105] Maintenance determination unit: determines the corresponding maintenance level based on the risk level of each concrete early setting risk area;

[0106] Report Generation Unit: Generates a structural acceptance report based on the deviations of the completed structure and the risk level of all scanned areas.

[0107] In this embodiment, determining the risk level of each scanned area based on the geometric deviations of the completed structure and environmental data includes the following steps: Step 1: Data Integration: Collecting geometric deviation information of the completed structure, including the distribution of out-of-specification points for each component and the overall geometric deviation index. Environmental data for each scanned area is obtained from the dynamic mapping module, such as real-time temperature and humidity collected by temperature and humidity sensors, ambient wind speed collected by an anemometer, and the concrete surface temperature distribution collected by an infrared camera. Step 2: Risk Level Assessment Model: Establishing a risk level assessment model that considers the impact of geometric deviations and environmental data on concrete structures. For example, when the ambient temperature T is greater than 30 degrees Celsius, the relative humidity RH is less than 40%, and the concrete pouring time t is greater than 2 hours, and the geometric deviation index D exceeds a certain threshold, the risk level is considered high. Based on different combinations of influencing factors, the risk level is divided into high, medium, and low levels. For example: High risk: High ambient temperature, low humidity, long pouring time, and severe geometric deviations. Medium risk: Partially unfavorable environmental conditions and some geometric deviations. Low risk: Good environmental conditions and minimal geometric deviations. Step 3: Risk Level Determination: The geometric deviation information and environmental data for each scanned area are substituted into the risk level assessment model to determine the risk level for that scanned area. The risk level assessment model establishment process includes the following: Factor Determination: The geometric deviation index D, ambient temperature T, relative humidity RH, and concrete pouring time t are identified as factors influencing the risk level. Level Classification: Based on different combinations of factors, risk levels are divided into high, medium, and low. High risk refers to high ambient temperature, low humidity, long pouring time, and severe geometric deviation; medium risk refers to partially unfavorable environmental conditions with some geometric deviation; and low risk refers to favorable environmental conditions with minimal geometric deviation. Rule Development: Based on the characteristics of each level, corresponding judgment rules are developed. For example, when T > 30°C, RH < 40%, t > 2 hours, and D exceeds the threshold, the risk is considered high.

[0108] In this embodiment, the preset risk level standard is read: the preset risk level classification standard is determined, such as the risk index range corresponding to the high risk level, the risk index range corresponding to the medium risk level, and the risk index range corresponding to the low risk level; the concrete premature setting risk area is screened: according to the risk level of each scanning area, the scanning areas whose risk levels reach or exceed the preset high risk level standard are screened out, and these areas are determined as concrete premature setting risk areas.

[0109] In this embodiment, determining the corresponding maintenance level based on the risk level corresponding to each concrete early setting risk zone includes: establishing a maintenance level correspondence relationship, and formulating maintenance level standards corresponding to different risk levels. For example: a high risk level corresponds to an emergency maintenance level, which requires maintenance to start within 1 hour. A medium risk level corresponds to a priority maintenance level, which requires maintenance to start within 4 hours, and a low risk level corresponds to a regular maintenance level, which requires maintenance to start within 8 hours; determining the maintenance level: according to the risk level corresponding to each concrete early setting risk zone, searching and determining the corresponding maintenance level from the maintenance level correspondence relationship.

[0110] The beneficial effects of this technical solution include: the structural analysis module accurately analyzes structural deviations and risks during construction. The deviation determination unit combines point cloud data with a standard CAD contour library to precisely detect geometric deviations in completed structures. The level determination unit assesses risk levels based on environmental data. The area determination unit identifies areas at risk of premature setting of concrete. The maintenance determination unit prioritizes maintenance based on risk levels and generates a structural acceptance report. This system effectively improves the accuracy of structural quality monitoring and maintenance management, ensuring construction progress and quality control.

[0111] Example 5:

[0112] An embodiment of the present invention provides a housing construction project optimization system integrating surveying and mapping with dynamic modeling, wherein the deviation determination unit includes:

[0113] Point cloud determination subunit: Determine the point cloud data set based on the point cloud data: P = {p i ∣p i =(x i ,y i ,z i ),i=1,2,...,m}, where the point cloud data set contains m discrete points;

[0114] Theoretical determination subunit: Match the point cloud data with the preset standard CAD contour library to determine the CAD theoretical contour point set: Among them, the CAD theoretical contour points are the theoretical contour points of the corresponding positions in the standard steel structure formwork CAD model, including n characteristic points;

[0115] Deviation determination subunit: determines the tolerance threshold matrix based on the preset allowable deviation threshold:

[0116] δ=[δ 梁 ,δ 柱 ,δ 楼板 ]

[0117] Among them, δ 梁 ,δ 柱 ,δ 楼板 Indicates the allowable geometric deviation thresholds for different component types;

[0118] Surface fitting subunit: Use the moving least squares method to perform local surface fitting on the point cloud data P to generate a continuous surface S(P);

[0119] Pairing subunit: Align the CAD contour point set C to the fitting surface S(P) through rigid transformation, and pair corresponding points based on local curvature and normal features

[0120] Distance calculation subunit: For each pair of matching points (p i ,c j ), calculate point p i To the corresponding theoretical point c j Distance deviation along the normal direction:

[0121] d ij =(p i -c j )·n i

[0122] Among them, d ij For point p i To the corresponding theoretical point c j Distance deviation along the normal direction, p i is the point cloud coordinate of the completed structure surface, c j is the coordinate of the theoretical contour point of the CAD model, represents the vector dot product, and the result is the projection length along the normal vector direction, n i Point p is the point after local surface fitting of point cloud data P i The surface normal vector at ;

[0123] Overall determination of subunits: based on point p i To the corresponding theoretical point c j The distance deviation along the normal direction, along with preset weights for each build type, determines the geometric deviation index:

[0124]

[0125] Where D is the geometric deviation index, k represents the component type, N k is the number of matching points of the k-th component, max(0,|d ij |-δ k ) represents the geometric deviation of the part that exceeds the specification tolerance. Only the part that exceeds the tolerance is evaluated. k is the allowable deviation threshold of the k-th type component, w k is the preset weight coefficient of the kth type component.

[0126] In this embodiment, the rigid transformation includes: translation and rotation;

[0127] In this embodiment, the preset weight of each construction type is determined based on the following steps: Step 1: clarify the component type and function classification, and divide the component type: according to the "Concrete Structure Engineering Construction Quality Acceptance Code" (GB50204) and the "Construction Quality Acceptance Unified Standard for Building Engineering" (GB50300), the main structure components are divided into three categories: Category A: key load-bearing components (columns, shear walls), Category B: secondary load-bearing components (beams, load-bearing floor slabs), Category C: non-load-bearing components (non-load-bearing floor slabs, enclosure structures), (Note: This system focuses on the main structure, so columns, beams, and floor slabs are the core objects); functional importance Safety classification: Category A: directly affects the overall bearing capacity and stability of the structure (safety level I), Category B: bears the role of load transfer, affects the stiffness and deformation of the structure (safety level II), Category C: affects the use function, and has a low impact on structural safety (safety level III); Step 2: Extract the deviation limits and acceptance requirements in the specifications to extract the deviation limits and acceptance requirements in the specifications to determine the tolerance threshold; obtain the corresponding: cross-sectional size allowable deviation (mm), verticality allowable deviation (mm), and then determine the degree of structural safety impact, and determine the preset weight coefficient based on the functional importance classification and the degree of impact on structural safety.

[0128] The beneficial effects of the above technical solution are as follows: The present invention uses a deviation determination unit to accurately analyze the geometric deviations of completed structures. By matching point cloud data with a standard CAD contour library, a moving least squares method is used to perform local surface fitting, ensuring precise alignment of the point cloud with theoretical contour points. Based on a preset allowable deviation threshold, the geometric deviation of each component is calculated and, combined with a weighting factor, a geometric deviation index is generated, effectively identifying out-of-tolerance portions of the structure. This method improves the accuracy and precision of structural deviation detection, ensuring that construction quality meets design requirements.

[0129] Example 6:

[0130] The embodiment of the present invention provides a housing construction engineering optimization system integrating surveying and mapping with dynamic modeling, and an instruction generation module, including:

[0131] Path identification unit: identifies the permanent path of the construction equipment based on the GPS trajectory data of the construction equipment;

[0132] Time determination unit: predicts the time for construction equipment to arrive at each scanning area based on the GPS trajectory data of the construction equipment and the maintenance level of each concrete early setting risk area;

[0133] Matrix generation unit: This unit constructs and solves a planning model based on the maintenance level of each concrete early setting risk area, the time it takes for construction equipment to arrive at each scanning area, and the capacity constraints of the construction equipment, and outputs a vehicle-task matching matrix.

[0134] Path determination unit: determines the navigation path set based on the vehicle-task matching matrix and the preset digital map of the construction site;

[0135] Matrix construction unit: Instruction generation unit: Determines the maintenance task for each concrete early setting risk zone based on the maintenance level of each concrete early setting risk zone and a preset level-task database, and then constructs a maintenance task matrix;

[0136] Instruction generation unit: Based on the navigation path set and the maintenance task matching matrix, the instructions are packaged according to the ID of the construction equipment to generate a vehicle scheduling instruction set.

[0137] In this embodiment, the permanent path of the construction equipment is identified based on the GPS trajectory data of the construction equipment, including: trajectory clustering analysis: clustering the pre-processed GPS trajectory according to similarity to identify the permanent path of the construction equipment. Algorithm: DBSCAN (density-based spatial clustering algorithm) is used to divide the trajectory into different clusters according to the distance between trajectory points (such as Euclidean distance) and the density of the points; path feature extraction: Function: Extract the features of the permanent path from the clustering results, such as the path starting point, end point, average speed, driving frequency, etc., to achieve: traverse each cluster and calculate the statistical features of all points in the cluster, such as the starting point is the coordinate of the earliest time point in the cluster, the end point is the coordinate of the latest time point, and the average speed is the total distance in the cluster divided by the total time.

[0138] In this embodiment, the time determination unit includes: Subunit 1: Speed Model Establishment, which builds a speed prediction model based on historical travel data of construction equipment on different road sections (determined by the path identification unit), taking into account factors such as road conditions and equipment type. Algorithm: Using a multivariate linear regression model, speed v is correlated with a road condition factor r (e.g., whether it is congested, taking values of 0 / 1), an equipment type factor t (e.g., tower crane, concrete tanker, using one-hot encoding), and other environmental factors e (e.g., weather conditions). The model formula is: v = β0 + β1r + β2t + β3e + ∈ (the coefficient βi is determined by training the model with historical data). Subunit 2: Distance Calculation, which calculates the distance from the current location of the construction equipment to the center of each scanning area based on a digital map of the construction site (GIS map data can be used). Algorithm: Using the A* algorithm, the shortest path is searched within the road network on the digital map. The distance calculation formula is the sum of the lengths of each road section in the road network. Subunit 3: Time Prediction, which combines the speed model and distance calculation results to predict the time it will take for the construction equipment to arrive at each scanning area. Implementation: Time T = vd, where d is the distance and v is the speed predicted by the speed model. Taking into account the uncertainty of road conditions, the time buffer coefficient k can be set to 1.2 (i.e., the predicted time is T′ = kT).

[0139] In this embodiment, the optimization objectives of the planning model include: Objective 1: maximizing the maintenance coverage rate of high-priority areas; Objective 2: minimizing vehicle idle mileage.

[0140] In this embodiment, determining a navigation path set based on a vehicle-task matching matrix and a preset digital map of a construction site includes: Subunit 1: Map data loading, function: loading a high-precision digital map of the construction site, including information such as roads, obstacles, and construction areas; implementation: supporting common map formats such as GeoJSON and Shapefile, and reading map data through a map parsing library (such as GDAL); Subunit 2: Path planning algorithm selection: function: selecting a suitable path planning algorithm for each vehicle based on the vehicle-task matching matrix, and calculating the shortest achievable route; algorithm: for areas with simpler road conditions, the Dijkstra algorithm is used; for areas with complex road conditions and high real-time requirements, the A algorithm is used. The Dijkstra algorithm gradually expands to obtain the global shortest path by maintaining a set of shortest distances from the source point; the A algorithm introduces a heuristic function to prioritize the search for paths that are more likely to reach the target point; Subunit 3: Path Optimization and Adjustment: Function: Considering real-time road conditions (such as road closures caused by temporary construction), the planned path is dynamically optimized and adjusted; Implementation: By connecting with a real-time road condition monitoring system (such as a camera-based road condition recognition system), when obstacles or congestion are detected on the path, the path is replanned to ensure that the vehicle can reach the maintenance area in a timely and safe manner.

[0141] In this embodiment, the matrix encapsulates instructions according to the ID of the construction equipment: navigation instructions: a sequence of path coordinate points; operation instructions: coordinates of the maintenance area and watering amount (increase the watering amount by 20% in areas with high risk of premature setting); time window instructions: the time interval during which the operation is allowed (to avoid interfering with other processes);

[0142] In this embodiment, the instruction generation unit includes: Subunit 1: Instruction format definition:

[0143] Function: Define the format of construction equipment scheduling instructions, including instruction header (device ID, instruction type), instruction content (maintenance task, navigation path), instruction tail (check code), etc.; Implementation: The instruction header uses fixed-length encoding, such as the device ID is 8 digits, and the instruction type is 2 digits (such as 01 for maintenance instruction). The instruction content is encapsulated in JSON format to facilitate data parsing. The instruction tail uses a CRC check code to ensure the accuracy of instruction transmission; Subunit 2: Instruction encapsulation: Function: According to the instruction format, the maintenance task matrix and the real-time navigation path set are encapsulated into scheduling instructions according to the ID of the construction equipment. Implementation: Traverse the construction equipment list, and for each device, extract its corresponding maintenance task and navigation path, fill it into the instruction format, and generate a complete scheduling instruction. Instruction example: "device id″:″12345678″,″instruction t ype″:″01″,″task″:[″Spray curing for 30 minutes″,″Cover with moisturizing cloth″],″ro Sub-unit 3: Instruction set generation and transmission: Function: Summarize the scheduling instructions of all construction equipment into an instruction set, and transmit it to the control system of the construction equipment through a wireless communication module (such as a 4G / 5G module), to achieve: sort the encapsulated instructions by device ID to form an instruction set file.

[0144] The beneficial effects of this technical solution include optimizing construction equipment scheduling and maintenance task allocation through the instruction generation module. The path recognition unit accurately identifies the typical paths of construction equipment, while the time determination unit predicts the equipment's arrival time in the scanned area. The matrix generation unit combines the risk zone's maintenance level, equipment capacity, and arrival time to construct and solve a vehicle-task matching matrix, thereby determining the optimal navigation path. By combining the maintenance task matrix and task matching, the system generates an efficient vehicle scheduling instruction set, improving construction efficiency and concrete curing accuracy.

[0145] Example 7:

[0146] The embodiment of the present invention provides a housing construction project optimization system integrating surveying and mapping with dynamic modeling, and a cloud map construction module, including:

[0147] Schedule Deviation Determination Unit: Compares the actual construction completion time in the structural acceptance report with the planned time preset in the BIM model, determines the number of days overdue and ahead of schedule in each scanned area, and determines this as schedule deviation;

[0148] Spatial deviation determination unit: performs grid matching between the actual structural surface fitted by point cloud data and the theoretical surface of the BIM model to determine the normal distance deviation of the feature points;

[0149] Comprehensive deviation quantification unit: combines the construction period deviation and the normal distance deviation of the feature point to generate the comprehensive deviation value of each scanning area according to the preset weight;

[0150] Cloud map generation unit: Based on the three-dimensional spatial framework of the BIM model, with the scanning area as the basic unit, the comprehensive deviation value is mapped through color gradient and transparency to generate a three-dimensional progress deviation cloud map.

[0151] In this embodiment, the actual structural surface fitted by the point cloud data is meshed with the theoretical surface of the BIM model: based on the spatiotemporal four-dimensional coordinate system constructed by the dynamic surveying and mapping module, the theoretical coordinates of the BIM model and the surveying and mapping coordinates of the actual point cloud data are spatially unified through rigid transformation (translation, rotation, scaling); for BIM models of different construction stages (such as foundation, main body, decoration), indexes are established according to floors and component types, which correspond one-to-one to the scanning area division rules (permanent / temporary topological domains as described in claim 2).

[0152] In this embodiment, the preset weight is a numerical ratio set artificially to comprehensively consider the impact of different types of deviations on the overall construction status. When generating the comprehensive deviation value of each scanning area, the weight is used to weigh the relative importance of the construction period deviation and the spatial deviation. Taking the construction of a commercial complex as an example, the progress deviation determination unit concludes that the construction period of a certain scanning area is delayed by 5 days. According to a certain conversion rule, it is assumed that it is quantified as a progress deviation value of 80 (the numerical value is only for the convenience of example calculation). The spatial deviation determination unit calculates the average normal distance deviation of the feature points in the area to be 10mm through grid matching, which is also converted into a spatial deviation value of 90. If the preset construction period accounts for 40% of the weight and the spatial deviation accounts for 60% of the weight, then the comprehensive deviation value of the scanning area is calculated as: (80×40%)+(90×60%)=32+54=86. Calculating the comprehensive deviation value by preset weights can comprehensively reflect the construction progress and quality, and help managers judge the construction status of each area so that targeted optimization measures can be taken.

[0153] In this embodiment, based on the three-dimensional spatial framework of the BIM model, the scanning area is used as the basic unit, and the comprehensive deviation value L is mapped through color gradient and transparency: red area: the comprehensive deviation value is greater than 15% and there is a high risk level (refer to claim 4), indicating that the progress is seriously lagging behind and the structural deviation exceeds the standard; yellow area: 5% ≤ comprehensive deviation value ≤ 15% or medium risk level, indicating that the progress lag / deviation needs attention; green area: comprehensive deviation value is less than 5% and the risk level is low, indicating that the progress and quality are in line with expectations.

[0154] The beneficial effects of this technical solution include: A cloud map construction module enables visual monitoring of construction progress and structural deviations. The progress deviation determination unit compares actual construction progress with planned time to identify delays or delays. The spatial deviation determination unit detects structural deviations by matching point cloud data with the BIM model. The comprehensive deviation quantification unit combines construction progress and spatial deviations to generate comprehensive deviation values for each scanned area. This unit then visually displays construction status through a 3D cloud map, helping to optimize construction progress and quality control, thereby improving management efficiency.

[0155] Example 8:

[0156] The embodiment of the present invention provides a housing construction engineering optimization system integrating surveying and mapping with dynamic modeling. The signal generation module includes:

[0157] Strategy parsing unit: parses the preset warning strategy library to determine the warning type and the corresponding triggering conditions;

[0158] Risk matching unit: The vehicle dispatch instruction set and the three-dimensional progress deviation cloud map are matched with the warning type and the corresponding trigger conditions to determine whether the warning is triggered and the warning level;

[0159] Signal generation unit: Generates specific warning signals based on risk matching results.

[0160] In this embodiment, the warning type and the corresponding triggering conditions are: Strategy classification:

[0161] Progress warning: If the proportion of red areas in the three-dimensional progress deviation cloud map is greater than 5% or the construction period is delayed by more than 7 days, a "progress delay warning" will be triggered; quality warning: If the geometric deviation index D in the structure acceptance report is greater than 10mm and the number of high-risk areas is greater than 3, a "structural deviation warning" will be triggered; equipment warning: If the execution timeout of the construction equipment scheduling command is greater than 15 minutes or the vehicle trajectory deviates from the navigation path by more than 20m, an "equipment abnormality warning" will be triggered.

[0162] In this embodiment, risk matching is: extracting the comprehensive deviation value and risk level of each scanning area from the three-dimensional progress deviation cloud map, and comparing them with the progress / quality warning conditions; parsing the construction equipment scheduling instruction set, tracking the real-time position of the equipment (GPS trajectory from the dynamic mapping module) and the task completion status, and judging whether the equipment warning trigger conditions are met; using logic gate combinations (such as "and" and "or" relationships) to handle multi-condition triggers, for example, "progress lag > 7 days and the proportion of red areas > 5%" is judged as a high-priority warning.

[0163] In this embodiment, a specific warning signal is generated based on the risk matching results: Signal elements: Basic information: warning time, project name, scanning area number; Risk details: Deviation parameters (such as a 10-day delay in construction period, a 12mm spatial deviation), involved component types (such as columns / beams), equipment ID and abnormal status; Treatment suggestions: Maintenance level adjustment (such as upgrading to emergency maintenance), construction process suspension instructions, deviation rectification technical plan (reference preset BIM model correction parameters). Grading mechanism: Different colors / types of warning signals are generated according to risk level, for example: High risk: red sound and light alarm (on-site) + vibration pop-up window (mobile terminal) + voice broadcast of rectification requirements; Medium risk: yellow flashing prompt (on-site) + message push (mobile terminal) + highlighted deviation cloud map area; Low risk: blue status reminder (on-site signboard) + log record (management platform).

[0164] The beneficial effect of this technical solution is that the signal generation module provides real-time early warning of construction risks. The strategy analysis unit analyzes the preset early warning strategy and determines the triggering conditions. The risk matching unit combines the vehicle dispatch instruction set and the progress deviation cloud map with the triggering conditions to perform a risk matching, determining whether an early warning should be triggered and its level. The signal generation unit generates a specific early warning signal based on the risk matching results, prompting construction personnel to take timely measures, effectively preventing potential risks during the construction process and improving construction safety and efficiency.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A housing construction optimization system integrating surveying and mapping with dynamic modeling, characterized in that: include: Dynamic mapping module: The construction area is divided into scanning areas according to the construction stage, and point cloud data is generated in real time based on the lidar array. At the same time, the location data of the construction equipment is tracked based on the engineering vehicle measurement equipment, and the environmental data of each scanning area is collected based on the preset collection equipment; Structural Analysis Module: Determines geometric deviations of completed structures based on point cloud data, environmental data, and a standard CAD contour library of pre-set steel formwork. Combined with environmental data, it identifies areas at risk of premature setting of concrete, and subsequently determines maintenance priorities and structural acceptance reports for each scanned area. Instruction generation module: This module determines the equipment's GPS trajectory based on the equipment's location data, predicts the arrival time of concrete trucks at each scanned area, generates water truck routes based on the maintenance priority of each scanned area, and determines the vehicle dispatch instruction set. Cloud map construction module: Generates a 3D progress deviation cloud map based on the structural acceptance report and the preset BIM model; Signal generation module: Generates warning signals based on the vehicle scheduling instruction set, the three-dimensional progress deviation cloud map, and the preset warning strategy.

2. A housing construction engineering optimization system integrating surveying and mapping with dynamic modeling according to claim 1, characterized in that: Dynamic mapping module, including: Coordinate construction unit: construct a spatiotemporal four-dimensional coordinate system in the preset BIM model; Regional division unit: Based on the space-time coordinate system, the construction area is decomposed into two types of topological domains: permanent structure units and temporary construction units; Area determination unit: Based on the two types of topological domains, permanent structure units and temporary construction units, the system divides them into preset levels and determines several scanning areas; Data acquisition unit: deploys a lidar array in each scanning area to generate point cloud data in real time; Data determination unit: tracking location data of construction equipment based on engineering vehicle measurement equipment; Environmental collection unit: collects environmental data of each scanning area based on preset collection equipment.

3. A housing construction engineering optimization system integrating surveying and mapping with dynamic modeling according to claim 2, characterized in that: LiDAR array, including: High-precision ground-mounted scanning base stations are deployed at core control points on construction floors; The mobile scanning terminal mounted on the tower crane boom adjusts the scanning angle synchronously with the increase of construction height; The auxiliary scanning unit attached to the concrete pump truck is used to fill the blind spots of the equipment.

4. The housing construction engineering optimization system integrating surveying and mapping with dynamic modeling according to claim 1 is characterized in that: Structural analysis module, including: Deviation determination unit: Determines the geometric deviation of the completed structure based on point cloud data, preset allowable deviation thresholds, and a standard CAD contour library of preset steel structure templates; Level determination unit: determines the risk level of each scanned area based on geometric deviations of the completed structure and environmental data; Area determination unit: determines several concrete early setting risk areas based on the risk level of each scanned area and the preset risk level; Maintenance determination unit: determines the corresponding maintenance level based on the risk level of each concrete early setting risk area; Report Generation Unit: Generates a structural acceptance report based on the deviations of the completed structure and the risk level of all scanned areas.

5. A housing construction engineering optimization system integrating surveying and mapping with dynamic modeling according to claim 4, characterized in that: A deviation determination unit, comprising: Point cloud determination subunit: Determine the point cloud data set based on the point cloud data: P = {p i ∣p i =(x i ,y i ,z i ),i=1,2,...,m}, where the point cloud data set contains m discrete points; Theoretical determination subunit: Match the point cloud data with the preset standard CAD contour library to determine the CAD theoretical contour point set: j = 1, 2, ..., n}, where the CAD theoretical contour point is the theoretical contour point at the corresponding position in the standard steel structure formwork CAD model, including n feature points; Deviation determination subunit: determines the tolerance threshold matrix based on the preset allowable deviation threshold: δ=[δ 梁 ,d 柱 ,d 楼板 ] Among them, δ 梁 ,δ 柱 ,δ 楼板 Indicates the allowable geometric deviation thresholds for different component types; Surface fitting subunit: Use the moving least squares method to perform local surface fitting on the point cloud data P to generate a continuous surface S(P); Pairing subunit: Align the CAD contour point set C to the fitting surface S(P) through rigid transformation, and pair corresponding points based on local curvature and normal features Distance calculation subunit: For each pair of matching points (p i ,c j ), calculate point p i To the corresponding theoretical point c j Distance deviation along the normal direction: d ij =(p i -c j )·n i Among them, d ij For point p i To the corresponding theoretical point c j Distance deviation along the normal direction, p i is the point cloud coordinate of the completed structure surface, c j is the coordinate of the theoretical contour point of the CAD model, represents the vector dot product, and the result is the projection length along the normal vector direction, n i Point p is the point after local surface fitting of point cloud data P i The surface normal vector at ; Overall determination of subunits: based on point p i To the corresponding theoretical point c j The distance deviation along the normal direction and the preset weights for each build type determine the geometric deviation index: Where D is the geometric deviation index, k represents the component type, N k is the number of matching points of the k-th component, max(0,|d ij |-δ k ) represents the geometric deviation of the part that exceeds the specification tolerance. Only the part that exceeds the tolerance is evaluated. k is the allowable deviation threshold of the k-th type component, w k is the preset weight coefficient of the kth type component.

6. The housing construction engineering optimization system integrating surveying and mapping with dynamic modeling according to claim 1 is characterized in that: Instruction generation module, including: Path identification unit: identifies the permanent path of the construction equipment based on the GPS trajectory data of the construction equipment; Time determination unit: predicts the time for construction equipment to arrive at each scanning area based on the GPS trajectory data of the construction equipment and the maintenance level of each concrete early setting risk area; Matrix generation unit: This unit constructs and solves a planning model based on the maintenance level of each concrete early setting risk area, the time it takes for construction equipment to arrive at each scanning area, and the capacity constraints of the construction equipment, and outputs a vehicle-task matching matrix. Path determination unit: determines the navigation path set based on the vehicle-task matching matrix and the preset digital map of the construction site; Matrix construction unit: Instruction generation unit: Determines the maintenance task for each concrete early setting risk zone based on the maintenance level of each concrete early setting risk zone and a preset level-task database, and then constructs a maintenance task matrix; Instruction generation unit: Based on the navigation path set and the maintenance task matching matrix, the instructions are packaged according to the ID of the construction equipment to generate a vehicle scheduling instruction set.

7. The housing construction engineering optimization system integrating surveying and mapping with dynamic modeling according to claim 1 is characterized in that: Cloud graph building blocks, including: Schedule Deviation Determination Unit: Compares the actual construction completion time in the structural acceptance report with the planned time preset in the BIM model, determines the number of days overdue and ahead of schedule in each scanned area, and determines this as schedule deviation; Spatial deviation determination unit: performs grid matching between the actual structural surface fitted by point cloud data and the theoretical surface of the BIM model to determine the normal distance deviation of the feature points; Comprehensive deviation quantification unit: combines the construction period deviation and the normal distance deviation of the feature point to generate the comprehensive deviation value of each scanning area according to the preset weight; Cloud map generation unit: Based on the three-dimensional spatial framework of the BIM model, with the scanning area as the basic unit, the comprehensive deviation value is mapped through color gradient and transparency to generate a three-dimensional progress deviation cloud map.

8. The housing construction engineering optimization system integrating surveying and mapping with dynamic modeling according to claim 1 is characterized in that: Signal generation module, including: Strategy parsing unit: parses the preset warning strategy library to determine the warning type and corresponding triggering conditions; Risk matching unit: performs risk matching based on the vehicle dispatch instruction set, the three-dimensional progress deviation cloud map, the warning type, and the corresponding trigger conditions to determine whether a warning is triggered and the warning level; Signal generation unit: Generates specific warning signals based on risk matching results.

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