Fabricated isolation ward construction decision optimization method and system based on BIM model

Abstract

The invention belongs to the technical field of building decision optimization, and discloses an assembly type isolation ward building decision optimization method and system based on a BIM model, and the method comprises the steps: collecting building main body construction three-dimensional point cloud data, and collecting construction process data; constructing an actual three-dimensional model based on the point cloud data, generating a standard three-dimensional model in combination with an electronic drawing, and generating a construction precision error and a deviation evaluation value through feature extraction and quality comparison; evaluating the construction progress by using the deviation evaluation value and the construction process data; inputting the construction progress, the deviation evaluation value, the current unit time construction progress difference value, the next unit time planned construction progress data and the weather duration suitable for operation in the next unit time into a pre-trained construction optimization model to obtain next unit time construction plan data; specific posts are directly associated, responsibility division is clear, and execution efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of construction decision-making optimization, and particularly relates to a method and system for optimizing the construction decision-making of prefabricated isolation wards based on a BIM model. Background Art

[0002] There are many problems in traditional building construction methods, such as long construction periods, high resource consumption, difficult-to-guarantee building quality, and complex on-site construction, which are easily affected by external factors such as weather. In contrast, prefabricated buildings, as an innovative construction model with prefabricated components at the core, have significantly shortened the construction time, reduced resource waste, and at the same time improved the controllability and stability of construction quality. Due to its efficient and environmentally friendly characteristics, prefabricated buildings have received extensive attention and application in recent years and have become an important trend in the development of the construction industry.

[0003] As an advanced digital building design and management tool, BIM technology has been widely used in the construction industry. Through BIM technology, building information can be comprehensively digitally managed and visually presented, which not only improves the design efficiency, reduces errors in the design stage, but also provides strong data support for the building construction process, greatly improving the construction efficiency and quality. For the construction of infectious disease emergency medical facilities with strong completion date restrictions, the adjustment of traditional construction schedules highly depends on the subjective experience of managers and lacks a unified quantitative standard. For example, the compensation plan for lagging construction periods is usually based on fuzzy judgments, which are prone to unreasonable resource allocation due to individual experience differences, resulting in problems such as insufficient compensation or excessive investment. This subjectivity not only reduces the execution efficiency but also makes it difficult to trace responsibilities through data, exacerbating management chaos. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for optimizing the construction decision-making of prefabricated isolation wards based on a BIM model to solve the technical problems raised in the background art.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: A method for optimizing the construction decision-making of prefabricated isolation wards based on a BIM model, including: Collecting the three-dimensional point cloud data of the main building construction and collecting the construction process data; Constructing an actual three-dimensional model based on the point cloud data, generating a standard three-dimensional model in combination with electronic drawings, and generating a construction accuracy error and deviation evaluation value through feature extraction and quality comparison; Evaluating the construction progress by using the deviation evaluation value and the construction process data to reflect the real construction progress; Input the construction progress, deviation evaluation value, current unit time construction progress difference, next unit time planned construction progress data, and the weather duration suitable for operation in the next unit time into the pre-trained construction optimization model to obtain the construction plan data for the next unit time; the construction plan data for the next unit time includes the consumption data of each type of inventory material, the attendance data of each work type, and the operation duration of key equipment. The construction accuracy error is the positioning deviation of the movable components in the isolation ward. The calculation method of the positioning deviation of the movable components in the isolation ward includes: According to the characteristic information of the ward movable components extracted from the actual 3D model and the standard 3D model, obtain the center point coordinates of each ward movable component respectively. By comparing the horizontal, vertical, and vertical axis coordinate differences of the corresponding ward movable components in the standard and actual 3D models, use the 3D Euclidean distance formula to calculate the positioning deviation of the movable components in the isolation ward one by one. The deviation evaluation value is obtained by calculating the average value of the positioning deviation of the movable components in the isolation ward respectively, and then weighted summing according to the preset weight.

[0006] Further, the construction process data includes material consumption data, labor data, and the operation duration of key equipment; the material consumption data includes the consumption quantity of each type of inventory material. The labor data includes the attendance data of each work type and the weather duration suitable for operation, and the attendance data includes the number of attendees and the attendance duration.

[0007] Further, input the construction process data into the pre-constructed construction progress quantification model to output the construction progress. The construction progress quantification model is trained based on historical labor data and historical construction progress, and the construction progress quantification model is one of the naive Bayes model and the support vector machine model.

[0008] Further, the method also includes obtaining the comprehensive efficiency evaluation result. The obtaining method of the comprehensive efficiency evaluation result includes: dividing the deviation evaluation value level by preset two-level deviation thresholds, and combining with the construction progress for combined determination.

[0009] Further, the training method of the construction optimization model includes: Take the historical feature data and the planned label as the sample set, divide the sample set into a training set and a test set, construct a classifier, take the historical feature data in the training set as the input data, take the historical planned label in the training set as the output data, train the classifier to obtain an initial classifier, and use the test set to test the initial classifier, and output the classifier that meets the preset accuracy as the construction optimization model. Set a planned label for the construction plan data of the next unit time for each group; the historical feature data includes construction progress, deviation evaluation value, construction progress difference in the current unit time, construction progress data planned for the next unit time, and the weather duration suitable for operation in the next unit time.

[0010] An optimization system for the construction decision of prefabricated isolation wards based on a BIM model, implementing the optimization method for the construction decision of prefabricated isolation wards based on a BIM model, including: The first acquisition module: acquire the three-dimensional point cloud data of the building main body construction and collect the construction process data; The modeling and analysis module: construct an actual three-dimensional model based on the point cloud data, generate a standard three-dimensional model in combination with the electronic drawings, and generate the construction accuracy error and deviation evaluation value through feature extraction and quality comparison; The evaluation module: evaluate the construction progress by using the deviation evaluation value and the construction process data to reflect the real construction progress; The decision optimization module: input the construction progress, deviation evaluation value, construction progress difference in the current unit time, construction progress data planned for the next unit time, and the weather duration suitable for operation in the next unit time into a pre-trained construction optimization model to obtain the construction plan data for the next unit time; the construction plan data for the next unit time includes the consumption data of each kind of inventory material, the attendance data of each type of work, and the operation duration of key equipment.

[0011] Beneficial effects: The present invention integrates the construction progress, deviation evaluation value, construction progress difference in the current unit time, construction progress data planned for the next unit time, and the weather duration suitable for operation in the next unit time to achieve multi-factor collaborative optimization. The output construction plan data for the next unit time (such as the consumption data of each kind of inventory material, the attendance data of each type of work, the operation duration of key equipment, etc.) is directly associated with specific positions, with clear responsibility division and improved execution efficiency. Through the deep binding of the BIM model and the progress data, the "geometry-resource-progress" visual linkage is realized, the material inventory and equipment scheduling are optimized, and the model is trained with historical feature data to endow the system with self-learning ability, adapt to different project complexities, continuously improve the prediction accuracy, and provide a reusable intelligent management paradigm for high-tempo projects such as prefabricated buildings. Compared with traditional prefabricated construction, it can effectively shorten the construction period of isolation wards, effectively reduce the rework cost, and provide fast, accurate, and reliable technical support for the construction of emergency medical facilities in public health events. Description of the Drawings

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the attached drawings required in the description of the embodiments or the prior art. Obviously, the attached drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other attached drawings can be obtained based on these attached drawings.

[0013] Figure 1 It shows the module structure diagram of the prefabricated isolation ward construction decision-making optimization system based on the BIM model of the present invention; Figure 2 It shows the schematic diagram of the training process of the construction progress quantification model of the present invention; Figure 3 It shows the schematic diagram of the process of the prefabricated isolation ward construction decision-making optimization method based on the BIM model of the present invention. Detailed implementation manners

[0014] Embodiment 1 Please refer to Figure 1 As shown, this embodiment provides a prefabricated isolation ward construction decision-making optimization system based on the BIM model, including a first acquisition module, a modeling and analysis module, an evaluation module, a second acquisition module, and a decision-making optimization module; each module is connected by wired and / or wireless means to achieve data transmission.

[0015] The first acquisition module: Collects the three-dimensional point cloud data of the building main body construction through a drone and collects the construction process data; uses the drone equipment for aerial photography, equipped with sensors such as a high-resolution camera or lidar, and conducts aerial photography on a predetermined flight path to collect the three-dimensional point cloud data of the construction building main body.

[0016] The construction process data is the construction process data within the current unit time, including the used construction period, material consumption data, labor data, and the operation duration of key equipment.

[0017] Among them, the material consumption data includes the consumption of each inventory material, and the inventory materials such as medical devices, enclosure structures, ventilation structures, horizontal load-bearing members, vertical transportation members, and vertical load-bearing structures.

[0018] The labor data includes the attendance data of each work type and the weather duration suitable for operation; Labor data: Covers work types such as steel structure workers, formwork workers, and crane operators. Through the connection between the attendance equipment and the system, the attendance number and attendance duration of each work type are collected in real time.

[0019] The weather duration suitable for operation: Based on the real-time weather information obtained from the meteorological website of the construction site, according to the requirements of the main building projects such as prefabricated buildings (such as mainly sunny and cloudy days), the cumulative value of the weather duration suitable for operation within the unit attendance duration is statistically calculated.

[0020] Key equipment includes tower cranes, truck cranes, negative pressure exhaust equipment, purification duct processing equipment and other key construction machinery.

[0021] Modeling and analysis module: Build the actual 3D model based on point cloud data, generate the standard 3D model in combination with electronic drawings, and generate the construction accuracy error and deviation evaluation value through feature extraction and quality comparison. Visually mark the deviation area in the actual 3D model to facilitate timely correction of the main building during the construction process.

[0022] The actual three-dimensional model will present the geometric shape and spatial layout of the main body of the construction building. The three-dimensional modeling software is such as BIM software or other existing three-dimensional modeling software; the deviation assessment value can reflect the rework cost of the construction. The larger the deviation assessment value, the longer the rework period, which also means that the actual completion progress has been "falsely increased".

[0023] It should be noted that the actual three-dimensional model during the construction process is not the completed main building. The reason is that it is convenient to correct the errors that occur in the main building during the construction process in real time, thereby reducing the cost of rework in the later stage.

[0024] The electronic construction drawings of the main body of the construction building include plan drawings, elevation drawings, section drawings, etc. The electronic construction drawings of the main body of the construction building are a reference during the construction process and are used to guide construction work and record design intent.

[0025] The construction accuracy error is the positioning deviation of the movable components of the isolation ward, which specifically covers the positioning deviation of medical equipment installation, the positioning deviation of the enclosing structure (such as walls), the positioning deviation of the ventilation structure (such as doors, window openings, lighting components, etc.), the positioning deviation of the horizontal load-bearing components (such as floor slabs, floor layers), the positioning deviation of the vertical traffic components (such as stair structures) and the positioning deviation of the vertical load-bearing structure (load-bearing columns).

[0026] The calculation method of the positioning deviation of the movable components in the isolation ward includes: According to the characteristic information of the movable components of the ward extracted from the actual three-dimensional model and the standard three-dimensional model, the center point coordinates of each movable component of the ward are obtained respectively; By comparing the differences in the horizontal, vertical and vertical axis coordinates of the corresponding movable components in the standard and actual three-dimensional models, the three-dimensional Euclidean distance formula is used to calculate the positioning deviation of the movable components in the isolation ward one by one, and the spatial deviation between construction and design is quantified. The Euclidean distance integrates the deviations in the three axes of three-dimensional space to avoid the neglect of errors in a single direction and more truly reflect the overall position offset.

[0027] In this embodiment, the central point coordinates are used to calculate the positioning deviation, which can effectively reduce the amount of data calculation and improve the computing efficiency of the computer. The reason is that, for example, the side length error, thickness error, and inclination error of a single wall can be deduced from the wall position deviation. If the wall is a quadrilateral wall, it is necessary to calculate the corresponding length error, width error, and inclination error of the four sides in sequence, resulting in a large computing amount for the computer.

[0028] Since the staircase is different from the shapes of doors, window openings, and floor surfaces, in this embodiment, the central point coordinates of the top surface of the staircase are the central point coordinates of the connection line between the midpoint of the protruding edge of the first staircase and the midpoint of the protruding edge of the last staircase. By calculating the positioning deviation of the vertical traffic component, it is also possible to infer whether there is an error in the number of staircase steps.

[0029] The deviation evaluation value is generated by separately calculating the average value of the positioning deviations of the ward activity components and then weighted summing according to the preset weights, and finally generating an evaluation value that comprehensively reflects the overall deviation between construction and design.

[0030] Input the construction process data into a pre-constructed construction progress quantification model to output the construction progress; the construction progress includes meeting expectations, falling short of expectations, and exceeding expectations.

[0031] Evaluation module: Use the deviation evaluation value and construction process data to evaluate the construction progress. The evaluated construction progress is the real progress after eliminating the "inflated" part. The construction progress is the corrected used construction period, reflecting the real construction progress.

[0032] As Figure 2 shown, specifically, the construction progress quantification model is trained based on historical labor data, deviation evaluation values, and historical construction progress. The construction progress quantification model is one of the naive Bayes model and the support vector machine model, specifically as follows: Take the historical labor data, deviation evaluation values, and historical construction progress as the sample set, divide the sample set into a training set and a test set, construct a classifier, use the historical labor data and deviation evaluation values in the training set as input data, use the historical construction progress in the training set as output data, train the classifier to obtain an initial classifier, and use the test set to test the initial classifier, and output a classifier that meets the preset accuracy as the construction progress quantification model.

[0033] Import the electronic construction drawings of the construction building main body into 3D modeling software, extract the feature information of the standard 3D model and the actual 3D model, and the comprehensive analysis of the construction quality can be realized. By comparing and analyzing the feature information of the ward movable components extracted from the actual 3D model and the standard 3D model, the construction precision error can be generated and visually marked in the actual 3D model, so as to correct the building main body in the construction process in a timely manner. In the process of comparing and analyzing the feature information of the ward movable components, the central point coordinates are used to calculate the ward movable components, which can not only effectively reduce the data calculation amount and improve the computer operation efficiency, but also deduce the error of the specific overall feature information.

[0034] Real-time collect labor data, and the construction process data such as material consumption data, labor data, and key equipment operation duration can be dynamically obtained, which helps to grasp the construction progress in real time and carry out progress analysis and evaluation; input the labor data into the construction progress quantification model, and output the construction progress, which is convenient to timely discover progress problems and make targeted adjustments and optimizations.

[0035] The second acquisition module is used to acquire the construction progress difference of the current unit time, the planned construction progress data of the next unit time, the weather duration suitable for operation in the next unit time, and the deviation evaluation value.

[0036] The construction progress difference is the difference between the construction height of the building main body in the current unit time and the predetermined construction height of the building main body in the current unit time; the planned construction progress data of the next unit time is the predetermined construction height of the building main body in the next unit time.

[0037] The decision-making optimization module inputs the construction progress, deviation evaluation value, construction progress difference of the current unit time, planned construction progress data of the next unit time, and weather duration suitable for operation in the next unit time into the pre-trained construction optimization model to obtain the construction plan data of the next unit time; the construction plan data of the next unit time includes the consumption data of each inventory material, the attendance data of each type of work, and the operation duration of key equipment.

[0038] The deviation evaluation value determines the amount of work to be compensated in the next stage, which in turn affects the input intensity of the construction plan data of the next unit time. For example, a 10% lag requires an additional 20% of labor or equipment duration in the next stage to catch up with the progress; the construction progress difference of the previous unit time is the absolute value of the difference between the construction progress and the total construction period, which reflects the immediate problem and requires targeted adjustment of the strategy in the next stage; the planned construction progress data of the next unit time is used as the initial reference data, and the adjustment needs to superimpose the compensation amount on it; the weather duration suitable for operation in the next unit time reflects the available operation weather duration (such as "the effective construction duration in the next 7 days is 40 hours"), which limits the actual available time, and the consumption data of inventory materials, the attendance data of each type of work, and the operation duration of key equipment are dynamically adjusted and determined by the above relevant data.

[0039] The training method of the construction optimization model includes: Using historical feature data and planned labels as a sample set, dividing the sample set into a training set and a test set, constructing a classifier, using the historical feature data in the training set as input data, using the historical planned labels in the training set as output data, training the classifier to obtain an initial classifier, and using the test set to test the initial classifier, and outputting a classifier that meets the preset accuracy as the construction optimization model; the construction optimization model is one of the Naive Bayes model and the Support Vector Machine model.

[0040] Setting planned labels for each group of construction plan data for the next unit time; the historical feature data includes construction progress, deviation evaluation value, construction progress difference in the current unit time, construction plan progress data for the next unit time, and the weather duration suitable for operation in the next unit time; converting the historical feature data into a corresponding set of feature vectors. The historical feature data and planned labels are pre-collected training data that meet the construction quality and construction period limitations.

[0041] By collecting construction progress, deviation evaluation value, construction progress difference in the current unit time, construction plan progress data for the next unit time, and the weather duration suitable for operation in the next unit time, more comprehensive factors are considered to predict the construction plan data for the next unit time. The construction plan data for the next unit time includes the consumption data of each type of inventory material, the attendance data of each type of work, and the operation duration of key equipment. The arrangement data is detailed, and the responsibilities can be assigned according to the construction plan data for the next unit time, improving the execution effect. Secondly, through the prediction of the construction optimization model, the standards are unified, avoiding the differences caused by relying on the experience of construction personnel, and laying a solid foundation for the successful delivery of the project.

[0042] Embodiment 2 Refer to Figure 3 As shown, this embodiment provides an optimization method for the construction decision of an assembled isolation ward based on a BIM model, including: Collecting the three-dimensional point cloud data of the building main body construction and collecting the construction process data; Constructing an actual three-dimensional model based on the point cloud data, generating a standard three-dimensional model in combination with the electronic drawings, and generating the construction accuracy error and deviation evaluation value through feature extraction and quality comparison; Evaluating the construction progress using the deviation evaluation value and the construction process data; Inputting the construction progress, deviation evaluation value, construction progress difference in the current unit time, construction plan progress data for the next unit time, and the weather duration suitable for operation in the next unit time into the pre-trained construction optimization model to obtain the construction plan data for the next unit time; the construction plan data for the next unit time includes the consumption data of each type of inventory material, the attendance data of each type of work, and the operation duration of key equipment.

[0043] What is not described in this application can be realized by adopting or referring to the prior art.

[0044] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0045] The above are only the embodiments of this application and are not intended to limit this application. For those skilled in the art, various modifications and changes can be made to this application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the scope of the claims of this application.

Claims

1. A construction decision optimization method for prefabricated isolation wards based on BIM model, characterized in that: include: Collect 3D point cloud data of the main building construction and collect construction process data; Build the actual 3D model based on the point cloud data, generate the standard 3D model in combination with the electronic drawings, and generate the construction accuracy error and deviation evaluation value through feature extraction and quality comparison; Use deviation assessment values ​​and construction process data to evaluate construction progress and reflect the actual construction progress; Based on the construction progress, deviation evaluation value, current unit time construction progress difference, next unit time planned construction progress data and next unit time suitable weather duration for operation input into the pre-trained construction optimization model, the next unit time construction plan data is obtained; the next unit time construction plan data includes each inventory material consumption data, each type of work attendance data, and key equipment operation duration; The construction accuracy error is the positioning deviation of the movable components of the isolation ward; The calculation method of the positioning deviation of the movable component of the isolation ward includes: According to the characteristic information of the movable components of the ward extracted from the actual three-dimensional model and the standard three-dimensional model, the center point coordinates of each movable component of the ward are respectively obtained; By comparing the horizontal, vertical and vertical axis coordinate differences of the corresponding ward movable components in the standard and actual three-dimensional models, the positioning deviations of the isolation ward movable components are calculated one by one using the three-dimensional Euclidean distance formula; The deviation evaluation value is obtained by respectively calculating the average positioning deviation of the movable components in the isolation ward and then weighted summing them according to preset weights.

2. The method for optimizing the construction decision of prefabricated isolation wards based on the BIM model as claimed in claim 1, characterized in that: The construction process data includes material consumption data, labor data and key equipment operation time; the material consumption data includes the consumption of each inventory material; Employment data includes attendance data for each type of work and the duration of weather suitable for work; attendance data includes the number of employees present and attendance duration.

3. The method for optimizing the construction decision of prefabricated isolation wards based on the BIM model as claimed in claim 2, characterized in that: The construction process data is input into a pre-built construction progress quantification model, and the construction progress is output. The construction progress quantification model is trained based on historical employment data and historical construction progress. The construction progress quantification model is one of a naive Bayes model and a support vector machine model.

4. The method for optimizing the construction decision of prefabricated isolation wards based on the BIM model as claimed in claim 3, characterized in that: The method also includes obtaining a comprehensive performance evaluation result, and the method for obtaining the comprehensive performance evaluation result includes: dividing the deviation evaluation value level by presetting a two-level deviation threshold, and performing a combined judgment in combination with the construction progress.

5. The method for optimizing the construction decision of prefabricated isolation wards based on the BIM model as claimed in claim 4, characterized in that: The training method of the construction optimization model includes: Take historical feature data and plan labels as sample sets, divide the sample sets into training sets and test sets, build a classifier, take historical feature data in the training set as input data, take historical plan labels in the training set as output data, train the classifier to obtain an initial classifier, test the initial classifier using the test set, and output a classifier that meets the preset accuracy as a construction optimization model; A planning label is set for each set of next unit time construction plan data; historical feature data include construction progress, deviation assessment value, current unit time construction progress difference, next unit time planned construction progress data and next unit time suitable weather duration for operation.

6. The construction decision optimization system of prefabricated isolation wards based on BIM model is characterized by: The method for optimizing the construction decision of a prefabricated isolation ward based on a BIM model according to any one of claims 1 to 5 comprises: The first acquisition module: collects the three-dimensional point cloud data of the main building construction and collects the construction process data; Modeling and analysis module: builds actual 3D models based on point cloud data, generates standard 3D models in combination with electronic drawings, and generates construction accuracy error and deviation assessment values ​​through feature extraction and quality comparison; Evaluation module: Use deviation evaluation value and construction process data to evaluate construction progress and reflect the actual construction progress; Decision-making optimization module: Based on the construction progress, deviation evaluation value, current unit time construction progress difference, next unit time planned construction progress data and next unit time suitable weather duration for operation, the pre-trained construction optimization model is input to obtain the next unit time construction plan data; the next unit time construction plan data includes each inventory material consumption data, each type of work attendance data, and key equipment operation duration.

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