A house building engineering construction optimization system fusing surveying and mapping and dynamic modeling

Abstract

The application provides a kind of fusion surveying and mapping and dynamic modeling housing construction engineering construction optimization system, comprising: dynamic surveying and mapping module: obtain point cloud data and position data, collect the environmental data of each scanning area;Structure analysis module: determine the geometric deviation of completed structure, determine the concrete early hardening risk area in combination with environmental data, and then determine the maintenance priority of each scanning area and the structure acceptance report;Instruction generation module: determine the GPS trajectory of equipment, and then predict the time when concrete tank truck arrives each scanning area, generate sprinkler route, determine vehicle scheduling instruction set;Cloud chart construction module: generate three-dimensional progress deviation cloud chart based on structure acceptance report and preset BIM model;Signal generation module: generate early warning signal based on vehicle scheduling instruction set, three-dimensional progress deviation cloud chart and preset early warning strategy. Effectively improve the accuracy of construction quality, progress and resource scheduling, and optimize the construction management process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction engineering construction optimization, and in particular to a housing construction engineering construction optimization system integrating surveying and mapping and dynamic modeling. BACKGROUND

[0002] In the current construction process of construction engineering, there are many challenges related to construction progress, quality control, equipment scheduling, etc., especially in terms of structural deviation, construction maintenance, and the accuracy of vehicle scheduling. Traditional construction engineering management methods usually rely on manual measurement and manual scheduling, which is low in efficiency and prone to deviation. In addition, as the scale and complexity of construction projects continue to increase, it becomes increasingly difficult to monitor and manage construction based on traditional methods.

[0003] Existing technical solutions mainly focus on monitoring and optimizing the construction process through computer modeling and Internet of Things technology. However, these solutions mostly lack real-time updating of dynamic data and rapid response to changes in the construction environment, and cannot effectively deal with complex and variable actual situations in the construction process, such as equipment failure, construction progress lag, environmental factor changes, etc. Therefore, there is still much room for improvement in the existing technical solutions in terms of construction optimization, structural monitoring, and equipment scheduling.

[0004] Therefore, the present application provides a housing construction engineering construction optimization system integrating surveying and mapping and dynamic modeling. SUMMARY

[0005] The present application provides a housing construction engineering construction optimization system integrating surveying and mapping and dynamic modeling, which realizes real-time monitoring and optimization of the construction site by integrating dynamic surveying and modeling technology. The dynamic surveying module collects point cloud data, equipment location and environmental information, the structural analysis module analyzes structural deviation and environmental risk, the instruction generation module reasonably schedules construction equipment and vehicles, the cloud map construction module visualizes construction progress, the signal generation module timely issues warning signals, effectively improving the accuracy of construction quality, progress and resource scheduling, and optimizing the construction management process.

[0006] According to the housing construction engineering construction optimization system integrating surveying and mapping and dynamic modeling provided by the present application, it comprises:

[0007] Dynamic surveying module: divide the construction area into scanning areas according to the construction stage, generate point cloud data in real time based on laser radar array, at the same time, track the position data of construction equipment based on engineering vehicle measurement equipment, and collect environmental data of each scanning area based on preset acquisition equipment;

[0008] The structure analysis module determines the geometric deviation of the completed structure based on the point cloud data, environmental data, and a standard CAD contour library of a preset steel structure template, determines the concrete early hardening risk area in combination with the environmental data, and further determines the curing priority of each scanning area and a structure acceptance report.

[0009] The instruction generation module determines the equipment GPS trajectory based on the position data of the construction equipment, further predicts the time when the concrete tank truck arrives at each scanning area, generates a water truck route in combination with the curing priority of each scanning area, and determines a vehicle scheduling instruction set.

[0010] The cloud map construction module generates a three-dimensional progress deviation cloud map based on the structure acceptance report and a preset BIM model.

[0011] The signal generation module generates an early warning signal based on the vehicle scheduling instruction set, the three-dimensional progress deviation cloud map, and a preset early warning strategy.

[0012] Preferably, the dynamic mapping module comprises:

[0013] The coordinate construction unit constructs a space-time four-dimensional coordinate system in the preset BIM model.

[0014] The region division unit divides the construction region into two types of topological domains, namely, permanent structure units and temporary construction units, based on the space-time coordinate system.

[0015] The region determination unit determines a plurality of scanning areas based on the two types of topological domains, namely, the permanent structure units and the temporary construction units, according to a preset hierarchical progression division.

[0016] The data acquisition unit deploys a laser radar array to generate point cloud data in real time in each scanning area.

[0017] The data determination unit tracks the position data of the construction equipment based on the engineering vehicle measurement equipment.

[0018] The environment acquisition unit acquires environmental data of each scanning area based on a preset acquisition device.

[0019] Preferably, the laser radar array comprises:

[0020] A high-precision ground fixed scanning base station is deployed at a core control point of a construction floor.

[0021] A mobile scanning terminal carried on a tower crane jib adjusts the scanning angle synchronously with the increase of the construction height.

[0022] An auxiliary scanning unit attached to a concrete pump truck is used to fill in the blind area of the equipment.

[0023] Preferably, the structure analysis module comprises:

[0024] deviation determination unit: determining the geometric deviation of the completed structure based on the point cloud data, the preset allowable deviation threshold and the standard CAD profile library of the preset steel structure template;

[0025] grade determination unit: determining the risk grade of each scanning area based on the geometric deviation of the completed structure and the environmental data;

[0026] area determination unit: determining a plurality of concrete early hardening risk areas based on the risk grade of each scanning area and a preset risk grade;

[0027] maintenance determination unit: determining the corresponding maintenance grade based on the corresponding risk grade of each concrete early hardening risk area;

[0028] report generation unit: generating a structure acceptance report based on the deviation of the completed structure and the risk grade of each scanning area.

[0029] Preferably, the deviation determination unit comprises:

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

[0031] theoretical determination subunit: matching the point cloud data with the preset standard CAD profile library to determine a CAD theoretical profile point set C = {c , i = 1, 2,..., n}, wherein the CAD theoretical profile point is a theoretical profile point at a corresponding position in the standard steel structure template CAD model, containing n feature points;

[0032] deviation determination subunit: determining a tolerance threshold matrix based on the preset allowable deviation threshold;

[0033] δ = [δ 梁 , δ 柱 , δ 楼板 ]

[0034] wherein δ 梁 , δ 柱 , δ 楼板 represent the allowable geometric deviation threshold of different component types;

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

[0036] pairing subunit: aligning the CAD profile point set C to the fitted surface S(P) by rigid transformation, and pairing the corresponding points according to the local curvature and normal features

[0037] Distance calculation subunit: for each pair of matched points (p i ,c j ), calculate the distance deviation of point p i to the corresponding theoretical point c j in the normal direction:

[0038] d ij =(p i -c j )·n i

[0039] where d ij is the distance deviation of point p i to the corresponding theoretical point c j in the normal direction, p i is the completed structure surface point cloud coordinate, c j is the CAD model theoretical contour point coordinate, · represents the vector dot product, and the result is the projection length in the normal vector direction, n i is the surface normal vector of point p i after local surface fitting of point cloud data P;

[0040] Overall determination subunit: based on the distance deviation of point p i to the corresponding theoretical point c j in the normal direction and the preset weight of each construction type, determine the geometric deviation index:

[0041]

[0042] where D is the geometric deviation index, k represents the component type, N k is the number of matched points of the kth component, max(0, |d ij |-δ k ) represents the geometric deviation beyond the specified tolerance, only the exceeding part is involved in the evaluation, δ k is the allowable deviation threshold of the kth component, and w k is the preset weight coefficient of the kth component.

[0043] Preferably, the instruction generation module comprises:

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

[0045] Time determination unit: predict the time when the construction equipment reaches each scanning area based on the GPS trajectory data of the construction equipment and the curing grade of each concrete early hardening risk area;

[0046] The matrix generating unit constructs a planning model based on the maintenance level of each concrete early hardening risk area, the time of the construction equipment reaching each scanning area, and the capacity constraint of the construction equipment, and solves the planning model, and outputs a vehicle-task matching matrix.

[0047] The path determining unit determines a navigation path set based on the vehicle-task matching matrix and a preset digital map of the construction site.

[0048] The matrix constructing unit determines the maintenance task of each concrete early hardening risk area based on the maintenance level of each concrete early hardening risk area and a preset level-task database, and further constructs a maintenance task matrix.

[0049] The instruction generating unit encapsulates instructions according to the ID of the construction equipment based on the navigation path set and the maintenance task matching matrix, and generates a vehicle scheduling instruction set.

[0050] Preferably, the cloud map constructing module comprises:

[0051] The progress deviation determining unit compares the actual construction completion time in the structure acceptance report with the preset planned time of the BIM model, determines the time lag and the advanced days of each scanning area, and determines the time deviation.

[0052] The space deviation determining unit performs grid matching on the actual structure surface fitted by the point cloud data and the theoretical surface of the BIM model, and determines the normal distance deviation of the feature points.

[0053] The comprehensive deviation quantifying unit combines the time deviation and the normal distance deviation of the feature points, and generates a comprehensive deviation value of each scanning area according to a preset weight.

[0054] The cloud map generating unit generates a three-dimensional progress deviation cloud map based on the three-dimensional space framework of the BIM model, takes the scanning area as a basic unit, maps the comprehensive deviation value through color gradient and transparency, and further generates a three-dimensional progress deviation cloud map.

[0055] Preferably, the signal generating module comprises:

[0056] The strategy analyzing unit analyzes a preset early warning strategy library, and determines the early warning type and the corresponding trigger condition.

[0057] The risk matching unit performs risk matching based on the vehicle scheduling instruction set, the three-dimensional progress deviation cloud map, and the early warning type and the corresponding trigger condition, and determines whether the early warning is triggered and the early warning level.

[0058] The signal generating unit generates a specific early warning signal according to the risk matching result.

[0059] Compared with the prior art, the application has the following beneficial effects:

[0060] By fusing dynamic mapping and modeling technologies, real-time monitoring and optimization of the construction site are realized. The dynamic mapping module collects point cloud data, equipment position and environment information, the structure analysis module analyzes structure deviation and environment risk, the instruction generation module reasonably schedules construction equipment and vehicles, the cloud map construction module visualizes construction progress, the signal generation module timely issues warning signals, effectively improving the accuracy of construction quality, progress and resource scheduling, and optimizing the construction management process. BRIEF DESCRIPTION OF DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0062] Figure 1 It is a structural schematic diagram of a housing construction engineering construction optimization system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below in combination with the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0064] Embodiment 1:

[0065] An embodiment of the present application provides a housing construction engineering construction optimization system fusing mapping and dynamic modeling, as shown in Figure 1 , comprising:

[0066] Dynamic mapping module: divide the construction area into scanning areas according to the construction stage, generate point cloud data in real time based on the laser radar array, at the same time, track the position data of the construction equipment based on the engineering vehicle measurement equipment, and collect the environment data of each scanning area based on the preset acquisition equipment;

[0067] Structure analysis module: determine the geometric deviation of the completed structure based on the point cloud data, environment data and standard CAD contour library of the preset steel structure template, determine the concrete early hardening risk area combined with the environment data, and further determine the maintenance priority of each scanning area and the structure acceptance report;

[0068] The instruction generation module determines the GPS trajectory of the construction equipment based on the position data of the equipment, and further predicts the time when the concrete truck arrives at each scanning area, generates the sprinkler route based on the maintenance priority of each scanning area, and determines the vehicle scheduling instruction set;

[0069] The cloud map construction module generates a three-dimensional progress deviation cloud map based on the structure acceptance report and the preset BIM model;

[0070] The signal generation module generates an early warning signal based on the vehicle scheduling instruction set, the three-dimensional progress deviation cloud map, and the preset early warning strategy.

[0071] In this embodiment, the construction phase refers to each specific implementation phase divided according to different work contents and time schedules of a project in a construction project. For example, a project can be divided into a foundation construction phase, a main body construction phase, a roof construction phase, a decoration phase, etc. Each phase usually has clear goals, tasks, and work contents. For example, in a housing construction project, the construction phase can be divided in the following order: the foundation construction phase includes foundation excavation, foundation reinforcement, etc. The main body construction phase includes structural steel reinforced concrete frame construction, wall construction, etc. The decoration phase includes interior wall painting, floor laying, installation of electrical appliances, etc.

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

[0073] In this embodiment, collecting environmental data of each scanning area includes: arranging a temperature and humidity sensor network on the work surface, monitoring the concrete curing environment parameters, installing an anemograph on the tower crane to collect high-altitude work area wind load data, and outputting: environmental parameter matrix of each construction subarea + dangerous weather early warning signal.

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

[0075] In this embodiment, the structure acceptance report is a structure acceptance report generally includes engineering basic information (project name, construction unit, structure type, etc.), acceptance basis (related building specifications, design drawings, etc.), acceptance content (various parameters of the completed structure), etc. block, fill in the acceptance content: geometric deviation situation: detailed record the statistical results of the geometric deviation of the completed structure, including the deviation value, deviation range, the number and position of the components exceeding the allowed range, etc. For example, the report states that among the 100 columns in this acceptance, the verticality deviation of 5 columns exceeds the allowed range, the specific position is the column in the X axis 5-8 axis, Y axis 3-5 axis area, and the concrete early hardening risk assessment result: record the division of the concrete early hardening risk area, the area, position and corresponding maintenance priority of each risk area. For example, the report points out that there is a 50 square meter high-risk area in the east wing area of the building, which needs emergency maintenance; there is a 100 square meter medium-risk area in the west wing, which should be arranged for maintenance as soon as possible, and the comprehensive acceptance conclusion: according to the geometric deviation and early hardening risk assessment, etc. Give the comprehensive conclusion of whether the structure passes the acceptance. If the geometric deviation is within the allowed range and the early hardening risk is controllable, the conclusion is that the acceptance is passed; if there are many deviations exceeding the allowed range or the high-risk area is too large, etc. The conclusion is that the acceptance is not passed, and the rectification suggestions are put forward, such as reworking the components exceeding the deviation, strengthening the maintenance measures of the high-risk area, etc.

[0076] In this embodiment, the position data of the construction equipment is tracked and recorded in real time by positioning technology (such as GPS) for the position of the construction equipment (such as concrete tank truck, crane, excavator, etc.). The GPS trajectory of the equipment refers to the path or motion trajectory drawn according to the equipment position data, reflecting the moving track and activity range of the equipment in the construction site. For example: construction equipment position data: such as the specific position of a concrete tank truck in the construction site, which can be obtained in real time through the GPS positioning system. Through the GPS trajectory, it can be known that the route, speed and estimated arrival time of the concrete tank truck from construction area A to construction area B. This helps to predict the work progress of the equipment and reasonably arrange the next construction task.

[0077] The beneficial effects of the above technical solutions are: by fusing dynamic mapping and modeling technology, real-time monitoring and optimization of the construction site are realized. The dynamic mapping module collects point cloud data, equipment position and environment information, the structure analysis module analyzes structure deviation and environmental risk, the instruction generation module reasonably schedules construction equipment and vehicles, the cloud map construction module visualizes construction progress, the signal generation module issues warning signals in time, effectively improving the accuracy of construction quality, progress and resource scheduling, and optimizing the construction management process.

[0078] Embodiment 2:

[0079] The embodiment of the present application provides a housing construction engineering construction optimization system integrating mapping and dynamic modeling, a dynamic mapping module, comprising:

[0080] A coordinate construction unit constructs a space-time four-dimensional coordinate system in a preset BIM model.

[0081] A region division unit divides a construction region into two types of topological domains, permanent structure units and temporary construction units, based on the space-time coordinate system.

[0082] A region determination unit determines a plurality of scanning regions based on the two types of topological domains, permanent structure units and temporary construction units, according to a preset hierarchical progression division.

[0083] A data acquisition unit deploys a laser radar array in each scanning region to generate point cloud data in real time.

[0084] A data determination unit tracks position data of construction equipment based on engineering vehicle measurement equipment.

[0085] An environment acquisition unit acquires environmental data of each scanning region based on a preset acquisition device.

[0086] In this embodiment, the space-time four-dimensional coordinate system is XYZ coordinate + stage milestone time axis, in which the three-dimensional geographic coordinate system is superimposed with the fourth dimension time axis (construction stage milestone node); and the output includes the following attributes of the topological network: the space constraint envelope of the permanent structure unit (core tube, etc.), and the life cycle timestamp of the temporary construction unit (scaffolding erection / demolition time).

[0087] In this embodiment, the permanent structure unit refers to the part that will eventually remain and become part of the building's main structure in house construction. These parts have long-term use functions and load-bearing functions, such as the building's foundation, columns, beams, load-bearing walls, floor panels, and roof panels. For example, in a residential building, the reinforced concrete foundation, concrete columns and beams supporting the floor, and load-bearing brick walls separating spaces all belong to the permanent structure unit. They form the basic framework and stable structure of the building, bearing the weight of the building and various loads during use.

[0088] In this embodiment, the temporary construction unit refers to some temporary structures or areas set up during the construction process of house construction to assist construction, which are usually demolished or no longer have actual functions after construction is completed. For example, scaffolding, tower crane foundation, construction access, and material storage platform set up during construction. For example, in high-rise building construction, the external scaffolding for worker operation and material transportation is demolished after the main building is completed; the temporary tower crane foundation for placing construction equipment also has no function after the tower crane is removed, which belongs to the temporary construction unit.

[0089] In this embodiment, the preset hierarchical progressive division is based on the topological network and is divided in the following hierarchical progressive manner: structural dependency layering: according to load transfer analysis, rigid boundaries (such as main beam cantilever roots) of key force transmission areas are marked; process timing constraint layering: associated with the construction progress plan, the time and space occupation body (such as the inviolable domain of the concrete curing period) of each process is calculated; resource flow dynamic layering: real-time acquisition of device GPS data generates a device influence Thiessen polygon.

[0090] The beneficial effects of the above technical solutions are: by fusing surveying and mapping and dynamic modeling technologies, the real-time monitoring and optimization capability of building construction is improved. By constructing a space-time four-dimensional coordinate system, the construction area is accurately divided, and the construction progress and structural deviation are monitored in real time in combination with laser radar and environmental data. Dynamic acquisition and analysis of construction equipment and environmental data effectively optimize equipment scheduling, maintenance priority and construction quality, solve the problems of information lag and inaccurate scheduling in traditional construction management, and improve construction efficiency and quality control capability.

[0091] Embodiment 3:

[0092] The embodiment of the application provides a housing construction engineering construction optimization system fusing surveying and mapping and dynamic modeling, a laser radar array, comprising:

[0093] A high-precision ground fixed scanning base station is deployed at a core control point of a construction floor.

[0094] A mobile scanning terminal mounted on a tower crane jib adjusts the scanning angle synchronously with the construction height;

[0095] An auxiliary scanning unit attached to a concrete pump truck is used to fill in the equipment blind area.

[0096] In this embodiment, the high-precision ground fixed scanning base station: the laser radar equipment fixedly installed at the core control point of the construction floor can stably acquire point cloud data of the surrounding area, for example, in the construction of a high-rise residential building, a base station is arranged near the elevator shaft of each floor, and the structures such as columns, beams and walls of the floor are scanned with high precision to provide basic data for structural analysis.

[0097] In this embodiment, the mobile scanning terminal mounted on the tower crane jib: the equipment installed on the tower crane jib can change the scanning angle and range with the movement of the tower crane and the progress of construction, for example, as the building construction continuously advances, the terminal adjusts the angle synchronously during the material hoisting process of the tower crane, scans the construction area at different heights, and makes up for the height limitation of the ground base station scanning.

[0098] The auxiliary scanning unit attached to the concrete pump truck in the embodiment: the laser radar device installed on the concrete pump truck is used for scanning the blind area of large equipment operation, for example: when the pump truck is pouring large area floor concrete, the periphery of the truck body and some corners are easy to form scanning blind area, the unit can scan these areas to perfect the point cloud data collection of the construction area.

[0099] The beneficial effects of the above technical solutions are: by fusing the ground fixed scanning base station, the tower crane arm mobile scanning terminal and the concrete pump truck auxiliary scanning unit, an efficient laser radar array is constructed, and the view angle limitation of traditional surveying and mapping equipment is overcome. The ground base station provides stable core control, the tower crane terminal automatically adjusts the view angle according to the construction height, and the concrete pump truck auxiliary scanning unit effectively fills the equipment blind area, so that omnidirectional and accurate construction site monitoring and data collection are realized, and the precision and efficiency of construction progress monitoring and quality control are improved.

[0100] Embodiment 4:

[0101] The embodiment of the present application provides a kind of fusion surveying and mapping and dynamic modeling's housing construction engineering construction optimization system, structural analysis module includes:

[0102] Deviation determination unit: based on point cloud data, preset allowable deviation threshold and the standard CAD contour library of preset steel structure template, the geometric deviation of completed structure is determined;

[0103] Grade determination unit: based on the geometric deviation of completed structure and environmental data, the risk level of each scanning area is determined;

[0104] Region determination unit: based on the risk level of each scanning area and the preset risk level, a plurality of concrete early hardening risk areas are determined;

[0105] Maintenance determination unit: based on the risk level corresponding to each concrete early hardening risk area, the corresponding maintenance level is determined;

[0106] Report generation unit: based on the deviation of completed structure and the risk level of all scanning areas, structure acceptance report is generated.

[0107] In this embodiment, determining the risk level of each scanning area based on the geometric deviation of the completed structure and the environmental data includes: Step 1: Data integration: Collect the geometric deviation information of the completed structure, including the distribution of each component's out-of-tolerance points and the overall geometric deviation index, obtain the environmental data of each scanning area from the dynamic mapping module, such as real-time temperature and humidity collected by temperature and humidity sensors, environmental wind speed collected by anemometers, and concrete surface temperature field distribution collected by infrared cameras; Step 2: Establish a risk level assessment model that considers the influence of geometric deviation and environmental data on the concrete structure. For example, when the environmental temperature T > 30 degrees Celsius, the relative humidity RH < 40%, the concrete pouring time t > 2 hours, and the geometric deviation index D exceeds a certain threshold, it is considered to be at a high risk level. According to different combinations of influencing factors, the risk level is divided into three levels: high, medium, and low. For example: high risk: high environmental temperature, low humidity, long pouring time, and severe geometric deviation. Medium risk: some unfavorable environmental conditions and certain geometric deviation. Low risk: good environmental conditions and small geometric deviation. Step 3: Risk level determination: input the geometric deviation information and environmental data of each scanning area into the risk level assessment model to determine the risk level of the scanning area. The risk level assessment model establishment process: factor determination: clearly define the geometric deviation index D, environmental temperature T, relative humidity RH, and concrete pouring time t as factors affecting the risk level; level division: divide into three risk levels: high, medium, and low according to different factor combinations. High risk: high environmental temperature, low humidity, long pouring time, and severe geometric deviation; medium risk: some unfavorable environmental conditions and certain geometric deviation; low risk: good environmental conditions and small geometric deviation; rule making: according to the characteristics of each level, formulate corresponding judgment rules, such as T > 30℃, RH < 40%, t > 2 hours, and D exceeding the threshold for high risk.

[0108] In this embodiment, reading the preset risk level standard: determining the preset risk level division standard, 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; screening the concrete early hardening risk area: according to the risk level of each scanning area, screening out the scanning areas whose risk level reaches or exceeds the preset high risk level standard, and determining these areas as the concrete early hardening risk area.

[0109] In this embodiment, determining the corresponding maintenance level based on the risk level corresponding to each concrete early hardening risk area comprises: 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 starting maintenance within 1 hour.A medium risk level corresponds to a priority maintenance level, which requires starting maintenance within 4 hours.A low risk level corresponds to a regular maintenance level, which requires starting maintenance within 8 hours.Determining the maintenance level: according to the risk level corresponding to each concrete early hardening risk area, the corresponding maintenance level is found and determined from the maintenance level correspondence relationship.

[0110] The beneficial effects of the above technical solutions are: the structure analysis module accurately analyzes the structure deviation and risk in the construction process.The deviation determination unit combines point cloud data and a standard CAD contour library to accurately detect the geometric deviation of the completed structure.The level determination unit evaluates the risk level in combination with environmental data.The area determination unit identifies the concrete early hardening risk area.The maintenance determination unit determines the maintenance priority according to the risk level and generates a structure acceptance report.The system effectively improves the accuracy of structure quality monitoring and maintenance management, and ensures construction progress and quality control.

[0111] Embodiment 5:

[0112] The embodiment of the application provides a housing construction engineering construction optimization system integrating surveying and mapping and dynamic modeling, a deviation determination unit, comprising:

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

[0114] Theoretical determination subunit: matching the point cloud data with a preset standard CAD contour library to determine a CAD theoretical contour point set: wherein the CAD theoretical contour point is a theoretical contour point at a corresponding position in a standard steel structure template CAD model, containing n feature points.

[0115] Deviation determination subunit: determining a tolerance threshold matrix based on a preset allowable deviation threshold:

[0116] δ=[δ 梁 ,δ 柱 ,δ 楼板 ]

[0117] wherein δ 梁 , δ 柱 , δ 楼板 represent the allowable geometric deviation threshold values of different component types.

[0118] Curved surface fitting subunit: locally fitting the point cloud data P using the moving least squares method to generate a continuous curved surface S(P);

[0119] Matching subunit: aligning the CAD contour point set C to the fitted curved surface S(P) through rigid transformation, and pairing corresponding points according to local curvature and normal characteristics

[0120] Distance calculation subunit: for each pair of matched points (p i ,c j ), the distance deviation of the point p i to the corresponding theoretical point c j in the normal direction is calculated:

[0121] d ij =(p i -c j )·n i

[0122] where d ij is the distance deviation of the point p i to the corresponding theoretical point c j in the normal direction, p i is the completed structure surface point cloud coordinate, c j is the CAD model theoretical contour point coordinate, · represents the vector dot product, and the result is the projection length in the normal vector direction, n i is the curved surface normal vector at the point p i after local curved surface fitting of the point cloud data P;

[0123] Overall determination subunit: based on the distance deviation of the point p i to the corresponding theoretical point c j in the normal direction and the preset weight of each construction type, the geometric deviation index is determined:

[0124]

[0125] where D is the geometric deviation index, k represents the component type, N k is the number of matched points of the kth component, max(0, |d ij |-δ k ) represents the geometric deviation of the part exceeding the specified tolerance, only the exceeding part is involved in the evaluation, δ k is the allowable deviation threshold of the kth component, and w k is the preset weight coefficient of the kth 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: clearly define the component type and function classification, divide the component type: according to the "Concrete Structure Engineering Construction Quality Acceptance Specification" (GB50204) and "Uniform Standard for Construction Quality Acceptance" (GB50300), the main structure components are divided into three categories: A: key load-bearing components (columns, shear walls), B: secondary load-bearing components (beams, load-bearing floors), C: non-load-bearing components (non-load-bearing floors, enclosure structures), (Note: this system focuses on the main structure, so columns, beams and floors are the core objects); function importance classification: A: directly affects the overall load-bearing capacity and stability of the structure (safety level I), B: bears load transmission and affects the stiffness and deformation of the structure (safety level II), C: affects the use function and has low impact on the structure safety (safety level III); Step 2: extract the deviation limit value and acceptance requirement in the specification, extract the deviation limit value and acceptance requirement in the specification to determine the tolerance threshold; obtain the corresponding section size tolerance (mm) and perpendicularity tolerance (mm) of the component type, and then determine the structure safety impact degree, determine the preset weight coefficient based on the function importance classification and the structure safety impact degree.

[0128] The technical scheme has the beneficial effects that: the deviation determination unit accurately analyzes the geometric deviation of the completed structure. By matching point cloud data with a standard CAD contour library, local surface fitting is performed using the moving least squares method to ensure accurate alignment of the point cloud with the theoretical contour points. Based on the preset tolerance threshold, the geometric deviation of each component is calculated, and a geometric deviation index is generated by combining the weight coefficient, effectively identifying the over-difference parts in the structure. This method improves the accuracy and precision of structure deviation detection, ensuring that the construction quality meets the design requirements.

[0129] Embodiment 6:

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

[0131] The path recognition unit recognizes the resident path of the construction equipment based on the GPS trajectory data of the construction equipment.

[0132] The time determination unit predicts the time when the construction equipment reaches each scanning area based on the GPS trajectory data of the construction equipment and the maintenance level of each concrete early hardening risk area.

[0133] The matrix generation unit constructs a planning model based on the maintenance level of each concrete early hardening risk area, the time when the construction equipment reaches each scanning area, and the capacity constraint of the construction equipment, and solves it to output a vehicle-task matching matrix.

[0134] The path determination unit determines a set of navigation paths based on the vehicle-task matching matrix and a preset digital map of the construction site.

[0135] The matrix construction unit determines the maintenance tasks for each concrete early hardening risk area based on the maintenance level of each concrete early hardening risk area and a preset level-task database, and further constructs a maintenance task matrix.

[0136] The instruction generation unit encapsulates the instructions according to the ID of the construction equipment based on the set of navigation paths and the maintenance task matching matrix, and generates a set of vehicle scheduling instructions.

[0137] In this embodiment, the resident path of the construction equipment is identified based on the GPS trajectory data of the construction equipment, including: trajectory clustering analysis: clustering the preprocessed GPS trajectory according to similarity, and identifying the resident 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 (such as Euclidean distance) between the trajectory points and the density of the points; path feature extraction: function: extract the features of the resident path from the clustering results, such as path starting point, ending point, average speed, driving frequency, etc., implementation: traverse each cluster, 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 ending point is the coordinate of the latest time point, and the average speed is the total distance divided by the total time.

[0138] In this embodiment, the time determination unit: subunit 1: speed model establishment, function: according to the historical driving data of the construction equipment in different road sections (determined by the path identification unit), establish a speed prediction model, considering road conditions, equipment type and other factors. Algorithm: a multiple linear regression model is used, which sets the speed v to be related to the road condition factor r (such as congestion, taking value 0 / 1), the equipment type factor t (such as tower crane, concrete tank truck, using one-hot encoding) and other environmental factors e (such as weather conditions), and the model formula is: v = β0 + β1r + β2t + β3e + ∈ (the model is trained by historical data to determine the coefficients βi) subunit 2: distance calculation: function: based on the digital map of the construction site (GIS map data can be used), calculate the distance from the current position of the construction equipment to the center of each scanning area. Algorithm: A* algorithm is used to search the shortest path in the road network of the digital map, and the distance calculation formula is the sum of the lengths of each road section in the road network; subunit 3: time prediction: function: combine the speed model and the distance calculation result to predict the time for the construction equipment to reach each scanning area. Implementation: time T = vd, where d is the distance and v is the speed predicted according to the speed model. Considering the uncertainty of road conditions, a time buffer coefficient k = 1.2 can be set (i.e. the predicted time is T' = kT).

[0139] In this embodiment, the optimization objectives of the planning model include: Objective 1: maximize the maintenance coverage of high-priority areas; Objective 2: minimize the empty mileage of vehicles.

[0140] In this embodiment, determining the navigation path set based on the vehicle-task matching matrix and the preset construction site digital map includes: Subunit 1: map data loading, function: load the high-precision digital map of the construction site, containing road, obstacle, construction area and other information; implementation: support common map formats such as GeoJSON, Shapefile, read map data through map parsing library (such as GDAL); Subunit 2: path planning algorithm selection: function: according to the vehicle-task matching matrix, select appropriate path planning algorithm for each vehicle to calculate the shortest reachable route; algorithm: for areas with relatively simple road conditions, use Dijkstra algorithm; for areas with complex road conditions and high real-time requirements, use A algorithm. Dijkstra algorithm maintains the shortest distance set from the source point to gradually expand to get the global shortest path; A algorithm introduces a heuristic function to preferentially search paths that are more likely to reach the target point; Subunit 3: path optimization and adjustment: function: consider real-time road conditions (such as temporary construction leading to road closure), dynamically optimize and adjust the planned path; implementation: through the interface with the real-time road condition monitoring system (such as the road condition recognition system based on camera), when obstacles or congestion are detected on the path, the path is re-planned to ensure that the vehicle can reach the maintenance area in time and safely.

[0141] In this embodiment, the matrix encapsulates instructions according to the ID of the construction equipment: navigation instructions: sequence of path coordinate points; work instructions: maintenance area coordinates, water volume (increase water volume by 20% in early-curing high-risk areas); time window instructions: time interval allowed for work (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 the construction equipment scheduling instruction, including instruction header (device ID, instruction type), instruction content (maintenance task, navigation path), instruction tail (check code) and the like; implementation: the instruction header uses fixed-length coding, such as 8-bit digits for the device ID and 2-bit digits for the instruction type (such as 01 indicating a maintenance instruction). The instruction content is encapsulated in JSON format for easy data parsing. The instruction tail uses CRC check code to ensure the accuracy of instruction transmission; Subunit 2: instruction encapsulation: function: encapsulate the maintenance task matrix and real-time navigation path set into scheduling instructions according to the ID of the construction equipment in accordance with the instruction format; implementation: traverse the construction equipment list, for each device, extract its corresponding maintenance task and navigation path, fill in the instruction format, and generate a complete scheduling instruction. For example, the instruction is as follows: "device id": "12345678", "instruction t ype": "01", "task": ["Spray maintenance for 30 minutes", "Cover the moisture cloth"], "ro child unit 3: instruction set generation and transmission: function: all construction equipment scheduling instructions are summarized into instruction set, and are transmitted to the control system of construction equipment through wireless communication module (such as 4G / 5G module), realizing: the encapsulated instructions are sorted according to device ID, and instruction set file is formed.

[0144] The beneficial effects of the above technical scheme are: the construction equipment scheduling and maintenance task allocation are optimized through the instruction generation module. The path recognition unit accurately identifies the permanent path of the construction equipment. The time determination unit predicts the time when the equipment arrives at the scanning area. The matrix generation unit combines the risk area maintenance level, equipment capacity and arrival time to construct and solve the vehicle-task matching matrix, and then determines the optimal navigation path. Through the maintenance task matrix and task matching, the system generates efficient vehicle scheduling instruction set, and improves the construction efficiency and concrete maintenance accuracy.

[0145] Embodiment 7:

[0146] The embodiment of the application provides a kind of fusion surveying and mapping and dynamic modeling's housing construction engineering construction optimization system, cloud chart construction module, comprising:

[0147] Progress deviation determination unit: compare the actual construction completion time in structure acceptance report with the planned time preset in BIM model, determine the construction period lag and the number of days in advance of each scanning area, and determine as construction period deviation;

[0148] Space deviation determination unit: grid matching is carried out between the actual structure surface fitted by point cloud data and the theoretical surface of BIM model, to determine the normal distance deviation of feature point;

[0149] Comprehensive deviation quantification unit: combined with construction period deviation and the normal distance deviation of feature point, the comprehensive deviation value of each scanning area is generated according to preset weight;

[0150] Cloud chart generation unit: based on the three-dimensional space framework of BIM model, taking scanning area as basic unit, the comprehensive deviation value is mapped through color gradient and transparency, and then three-dimensional progress deviation cloud chart is generated.

[0151] In this embodiment, the actual structure surface fitted by the point cloud data is matched with the theoretical surface of the BIM model: based on the space-time four-dimensional coordinate system constructed by the dynamic mapping module, the theoretical coordinates of the BIM model are unified with the mapping coordinates of the actual point cloud data through rigid transformation (translation, rotation, scaling); for the BIM model of different construction stages (such as foundation, main body, decoration), an index is established according to floor and component type, which corresponds to the scanning area division rule (such as the permanent / temporary topological domain described in claim 2) one by one.

[0152] In this embodiment, the preset weight is a numerical proportion artificially set to comprehensively consider the influence degree of different types of deviations on the overall construction condition. When generating the comprehensive deviation value of each scanning area, the weight is used to weigh the relative importance of the schedule deviation and the space deviation. Taking the construction of a certain commercial complex as an example, the progress deviation determination unit obtains that the schedule of a certain scanning area is delayed by 5 days, and according to certain conversion rules, it is assumed that the schedule deviation value is quantized as 80 (the value is only for example calculation). The space deviation determination unit calculates that the average normal distance deviation of the feature points in this area is 10 mm, which is also converted into a space deviation value of 90. If the preset schedule accounts for 40% of the weight and the space deviation accounts for 60% of the weight, then the comprehensive deviation value of this scanning area is calculated as: (80 x 40%) + (90 x 60%) = 32 + 54 = 86. By calculating the comprehensive deviation value through the preset weight, the construction progress and quality can be comprehensively reflected, which helps managers to judge the construction state of each area and take targeted optimization measures.

[0153] In this embodiment, based on the three-dimensional space framework of the BIM model, the scanning area is taken as the basic unit, and the color gradient and transparency mapping are used to comprehensively reflect the deviation value L: the red area: the comprehensive deviation value > 15% and there is a high risk level (refer to claim 4), indicating that the progress is seriously delayed and the structure deviation is out of standard; the yellow area: 5% ≤ comprehensive deviation value ≤ 15% or medium risk level, indicating that the progress delay / deviation needs attention; the green area: comprehensive deviation value < 5% and low risk level, indicating that the progress and quality meet the expectations.

[0154] The beneficial effects of the above technical solutions are: through the cloud map construction module, the visual monitoring of the construction progress and the structure deviation is realized. The progress deviation determination unit compares the actual construction progress with the planned time to identify the schedule lag or advance; the space deviation determination unit detects the structure deviation through the matching of the point cloud data and the BIM model. The comprehensive deviation quantization unit combines the schedule and space deviation to generate the comprehensive deviation value of each scanning area, and visually displays the construction state through the three-dimensional cloud map, which helps to optimize the construction progress and quality control, and improves the management efficiency.

[0155] Embodiment 8:

[0156] The embodiment of the application provides a housing construction engineering construction optimization system fusing surveying and mapping and dynamic modeling, a signal generation module based block, comprising:

[0157] A strategy analysis unit: analyzing a preset early warning strategy library, determining an early warning type and a corresponding trigger condition;

[0158] A risk matching unit: matching a vehicle scheduling instruction set, a three-dimensional progress deviation cloud picture and the early warning type and the corresponding trigger condition, judging whether early warning and an early warning level are triggered;

[0159] A signal generation unit: generating a specific early warning signal according to a risk matching result.

[0160] In the embodiment, the early warning type and the corresponding trigger condition are strategy classification:

[0161] Progress class early warning: if a red area proportion in the three-dimensional progress deviation cloud picture is greater than 5% or a construction period lag is greater than 7 days, a progress delay early warning is triggered; quality class early warning: if a geometric deviation index D in a structure acceptance report is greater than 10 mm and a high-risk area number is greater than 3, a structure deviation early warning is triggered; equipment class early warning: if a construction equipment scheduling instruction execution time is greater than 15 minutes or a vehicle track deviates from a navigation path by greater than 20 m, an equipment abnormality early warning is triggered.

[0162] In the embodiment, the risk matching is: extracting a comprehensive deviation value and a risk level of each scanning area from the three-dimensional progress deviation cloud picture, comparing with progress / quality class early warning conditions; analyzing a construction equipment scheduling instruction set, tracking a device real-time position (from a GPS track of a dynamic surveying and mapping module) and a task completion state, judging whether the device class early warning trigger condition is met; adopting a logic gate combination (such as an 'and' or 'or' relationship) to process multi-condition triggering, for example, judging that the progress lag is greater than 7 days and the red area proportion is greater than 5% as a high-priority early warning.

[0163] In the embodiment, the specific early warning signal is generated according to the risk matching result: signal elements: basic information: early warning time, project name, scanning area number; risk details: deviation parameters (such as a construction period lag of 10 days, a space deviation of 12 mm), involved component types (such as a column / beam), equipment ID and abnormality state; processing suggestions: maintenance level adjustment (such as upgrading to emergency maintenance), construction process suspension instruction, deviation rectification technical scheme (refer to a preset BIM model correction parameter). A grading mechanism: generating early warning signals of different colors / types according to risk levels, for example: high risk: red sound and light alarm (on site) + vibration pop-up window (mobile terminal) + voice broadcast rectification requirement; medium risk: yellow flashing prompt (on site) + message push (mobile terminal) + highlight annotation deviation cloud picture area; low risk: blue state reminder (on site board) + log record (management platform).

[0164] The above technical scheme has the beneficial effects that the signal generation module realizes real-time early warning of construction risks. The strategy analysis unit analyzes the preset early warning strategy to determine a trigger condition; the risk matching unit matches the trigger condition with a vehicle dispatch instruction set and a progress deviation cloud map to determine whether to trigger early warning and a level thereof. The signal generation unit generates a specific early warning signal based on the risk matching result to timely remind the construction personnel to take measures, effectively prevent potential risks in the construction process, and improve construction safety and efficiency.

[0165] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A system for construction optimization of a building project that integrates surveying and dynamic modeling, characterized in that, Comprising: A dynamic mapping module: dividing the construction area into scanning areas according to the construction stage, generating point cloud data in real time based on the laser radar array, tracking the position data of the construction equipment based on the engineering vehicle measurement equipment, and collecting environmental data of each scanning area based on the preset acquisition equipment; A structure analysis module: determining the geometric deviation of the completed structure based on the point cloud data, environmental data, and standard CAD profile library of the preset steel structure template, determining the concrete early hardening risk area combined with the environmental data, and further determining the maintenance priority of each scanning area and the structure acceptance report; The structure analysis module comprises a deviation determination unit: determining the geometric deviation of the completed structure based on the point cloud data, the preset allowable deviation threshold, and the standard CAD profile library of the preset steel structure template; The deviation determination unit comprises: The point cloud determining subunit determines a point cloud data set based on the point cloud data: wherein 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 a CAD theoretical contour point set: CAD theoretical contour point is a theoretical contour point at a corresponding position in a standard steel structure template CAD model, containing n feature points; A deviation determination subunit: determining a tolerance threshold matrix based on the preset allowable deviation threshold: ； wherein denote the allowed geometric deviation thresholds for different component types; Surface fitting sub-units: using moving least squares method for point cloud data Perform local surface fitting to generate a continuous surface. ; Pairing subunit: CAD profile point set by rigid transformation Align to fitted surface and corresponding point pairing according to local curvature and normal features ; Distance calculation sub-unit: for each pair of matched points , calculate the distance deviation of point to the corresponding theoretical point along the normal direction: ； wherein, is the point to the corresponding theoretical point distance deviation along the normal direction, is the completed structure surface point cloud coordinate, is the CAD model theoretical profile point coordinate, denotes the vector dot product, and the result is the projection length along the normal vector direction, is the point after local surface fitting on the point cloud data is the surface normal vector at the point Overall determination sub-unit: based on points to the corresponding theoretical point Distance deviation along the normal direction and preset weight of each construction type determine the geometric deviation index: ； wherein, is a geometric deviation index, denotes a component type, is a deviation threshold value of the kth component, is a number of matching points of the kth component, denotes a geometric deviation exceeding a part of a specification tolerance, only the exceeding part is involved in the evaluation, is a deviation threshold value of the kth component, is a preset weight coefficient of the kth component; An instruction generation module: determining the equipment GPS trajectory based on the position data of the construction equipment, further predicting the time of the concrete tank truck arriving at each scanning area, generating the water truck route combined with the maintenance priority of each scanning area, and determining the vehicle scheduling instruction set; A cloud map construction module: generating a three-dimensional progress deviation cloud map based on the structure acceptance report and the preset BIM model; A signal generation module: generating an early warning signal based on the vehicle scheduling instruction set, the three-dimensional progress deviation cloud map, and the preset early warning strategy.

2. The housing construction engineering construction optimization system of claim 1, wherein The dynamic mapping module comprises: A coordinate construction unit: constructing a spatiotemporal four-dimensional coordinate system in the preset BIM model; A region division unit: dividing the construction area into two types of topological domains, permanent structure units and temporary construction units, based on the spatiotemporal four-dimensional coordinate system; A region determination unit: determining a plurality of scanning areas by progressively dividing the two types of topological domains, permanent structure units and temporary construction units, according to a preset level; A data acquisition unit: deploying a laser radar array to generate point cloud data in real time in each scanning area; A data determination unit: tracking the position data of the construction equipment based on the engineering vehicle measurement equipment; An environmental acquisition unit: collecting environmental data of each scanning area based on the preset acquisition equipment.

3. The housing construction engineering construction optimization system of claim 2, wherein The laser radar array comprises: A high-precision ground fixed scanning base station deployed at the core control point of the construction floor; A mobile scanning terminal mounted on the tower crane jib, which adjusts the scanning angle synchronously with the construction height; An auxiliary scanning unit attached to the concrete pump truck, which is used to fill in the equipment blind area.

4. The housing construction engineering construction optimization system of claim 1, wherein, The structure analysis module further comprises: A grade determination unit: determining the risk grade of each scanning area based on the geometric deviation of the completed structure and the environmental data; A region determination unit: determining a plurality of concrete early hardening risk areas based on the risk grade of each scanning area and the preset risk grade; A maintenance determination unit: determining the corresponding maintenance grade based on the corresponding risk grade of each concrete early hardening risk area; A report generation unit: generating a structure acceptance report based on the deviation of the completed structure and the risk grade of all scanning areas.

5. The system of claim 1, wherein, The instruction generation module comprises: A path identification unit: identifying the resident path of the construction equipment based on the GPS trajectory data of the construction equipment; The time determination unit predicts the time when the construction equipment reaches each scanning area based on the GPS trajectory data of the construction equipment and the curing level of each concrete early hardening risk area. The matrix generation unit constructs a planning model based on the curing level of each concrete early hardening risk area, the time when the construction equipment reaches each scanning area, and the capacity constraint of the construction equipment, and solves the planning model to output a vehicle-task matching matrix. The path determination unit determines a set of navigation paths based on the vehicle-task matching matrix and a preset digital map of the construction site. The matrix construction unit determines the curing task of each concrete early hardening risk area based on the curing level of each concrete early hardening risk area and a preset level-task database, and further constructs a curing task matrix. The instruction generation unit encapsulates instructions according to the ID of the construction equipment based on the set of navigation paths and the curing task matching matrix, and generates a set of vehicle scheduling instructions.

6. The system of claim 1, wherein, The cloud map construction module includes: The progress deviation determination unit compares the actual construction completion time in the structure acceptance report with the preset planned time of the BIM model to determine the time lag and the advanced days of each scanning area, and determines the time deviation. The spatial deviation determination unit performs grid matching between the actual structure surface fitted by the point cloud data and the theoretical surface of the BIM model to determine the normal distance deviation of the feature points. The comprehensive deviation quantification unit combines the time deviation and the normal distance deviation of the feature points to generate a comprehensive deviation value of each scanning area according to a preset weight. The cloud map generation unit generates a three-dimensional progress deviation cloud map based on the three-dimensional space framework of the BIM model, takes the scanning area as the basic unit, maps the comprehensive deviation value through color gradient and transparency, and further generates a three-dimensional progress deviation cloud map.

7. The system of claim 1, wherein, The signal generation module includes: The strategy analysis unit analyzes the preset early warning strategy library to determine the warning type and the corresponding trigger condition. The risk matching unit matches the risk based on the set of vehicle scheduling instructions, the three-dimensional progress deviation cloud map, and the warning type and the corresponding trigger condition, judges whether the warning is triggered and the warning level, and determines the specific warning signal according to the risk matching result. ​

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