A prefabricated magnesium slag component construction standardized management method based on BIM

Abstract

The application discloses a prefabricated magnesium slag component construction standardized management method based on BIM, relates to the technical field of digital construction, and comprises the following steps: establishing an industrial data dictionary and a constraint set and version solidification; extracting component design information from a BIM model to generate a work package; collecting magnesium slag batch data, CO2 concentration, relative humidity, temperature and other curing environment data, calculating a batch deviation index, outputting an inspection strength level according to a threshold value, calculating maturity and outputting a release conclusion; generating a delivery window and performing consistency checking; measuring and collecting reference feature points, calculating a measured mean coordinate and a dispersion, introducing a shape consistency constraint and weighting according to the dispersion, solving a rigid body calibration parameter by using a weighted robust objective function, and outputting a reliable coordinate calibration version; obtaining a deviation according to the calibration and correction of the design coordinate, calculating a compensation amount, forming a component digital archive, and realizing double-constraint quantitative release, coordinate drift robust correction and abnormal point influence suppression.

Description

Technical Field

[0001] This invention relates to the field of digital construction technology, and in particular to a standardized management method for the construction of precast magnesium slag components based on BIM. Background Technology

[0002] In digital construction and prefabricated building scenarios, BIM-based management of prefabricated components typically uses the model as an information carrier, linking unique component codes, design parameters, production, transportation, and installation processes, and coordinating with inspection and measurement to achieve process collaboration from factory prefabrication to on-site assembly. In engineering practice, it can also form unified data standards and work packages to support standardized organization and delivery.

[0003] However, existing methods still have two limitations: First, batch inspection and maintenance release are mostly recorded and judged separately, making it difficult to quantify the coupling between batch fluctuations and maintenance environment on the timing of delivery; Second, on-site coordinate alignment mostly relies on fitting a small number of points, which is sensitive to measurement dispersion and outliers, affecting the stability of drift correction and compensation. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a BIM-based standardized management method for the construction of precast magnesium slag components, which solves the problems of difficulty in uniformly quantifying release judgment and the susceptibility of coordinate calibration to anomalies in existing technologies.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides a BIM-based standardized management method for the construction of precast magnesia slag components. The method includes: establishing an industrial data dictionary and constraint set and fixing their versions; extracting component design information from the BIM model based on the industrial data dictionary and constraint set to generate a work package; collecting magnesia slag batch data and curing environment data according to the work package, performing batch deviation judgment, outputting inspection strength levels and performing maturity judgment, and performing state migration; constructing a shipping window and performing consistency verification; measuring and collecting data for each benchmark feature point based on the work package, calculating the measured mean coordinates and dispersion; calculating the morphological consistency deviation through the benchmark feature points and solving for rigid body calibration parameters, generating a new coordinate calibration version through information gain-driven processing; correcting the BIM design coordinates of the benchmark feature points using the new coordinate calibration version, obtaining actual deviation data, calculating suggested compensation based on the actual deviation data, and generating a digital archive of the component.

[0008] As a preferred embodiment of the BIM-based standardized management method for the construction of precast magnesia slag components described in this invention, the steps of establishing an industrial data dictionary and constraint set and performing version fixation are as follows: collecting batch data of magnesia slag, curing environment data, inspection data, logistics data, and installation measurement data to obtain engineering data; fixing missing data in the engineering data and establishing an industrial data dictionary; fixing the entire life cycle of each precast magnesia slag component into a finite state chain, establishing a constraint set, and performing version fixation on the industrial data dictionary and constraint set.

[0009] As a preferred embodiment of the BIM-based standardized management method for the construction of precast magnesium slag components described in this invention, the method for generating a work package includes: traversing the set of precast component objects through the BIM interface, reading the inherent global identifier and design information for each component object; generating a unique component code for each component in the component candidate list and performing consistency verification; extracting a set of candidate feature points from the geometric information and embedded part information of the BIM component, selecting benchmark feature points for the candidate feature points according to the rules of distributed dispersion and avoiding degradation; and assembling the component's design information, benchmark feature point identifier set, and industrial data dictionary into a work package using the component's unique code as an index.

[0010] As a preferred embodiment of the BIM-based standardized management method for the construction of precast magnesium slag components described in this invention, the batch deviation determination includes: binding the magnesium slag batch number and the unique component code; collecting batch measured data based on the work package; calculating the batch deviation index based on the batch measured data; when the batch deviation index does not exceed a first threshold, it is determined to be at the regular inspection intensity level, and a regular inspection task is issued; when the deviation exceeds the first threshold but does not exceed a second threshold, it is determined to be at the tightened inspection intensity level, and a tightened inspection task is issued; when the deviation exceeds the second threshold, it is determined to be at the extremely strict inspection intensity level, and an extremely strict inspection task is issued.

[0011] As a preferred embodiment of the BIM-based standardized management method for the construction of precast magnesium slag components described in this invention, the maturity determination includes: continuously collecting CO2 volume fraction, relative humidity, and temperature sequences, recording the start and end times of curing, calculating the maturity, and obtaining the cumulative maturity amount; when the cumulative maturity amount reaches the maturity threshold, the component is released and moved from the production and curing state to the factory ready state.

[0012] As a preferred embodiment of the BIM-based standardized management method for the construction of precast magnesia slag components according to the present invention, the following steps are included: The construction of the shipping window includes setting the starting point of the shipping window for precast magnesia slag components as the time when they can be shipped from the factory, and the ending point of the shipping window as the difference between the planned installation time and the transportation buffer time; the consistency verification includes verifying whether the unique code of the precast magnesia slag component is consistent with the BIM component, verifying whether the proposed installation location of the precast magnesia slag component is consistent with the planned installation location given in the work package, verifying whether the embedded parts meet the embedded part constraints given in the work package, and verifying whether the reference feature point identifiers are complete; the calculation of the measured mean coordinates and dispersion includes, after the precast magnesia slag component is installed, measuring and collecting each reference feature point according to the reference feature point identifiers in the work package, obtaining a repeated measured coordinate sequence, and calculating the measured mean coordinates and dispersion of the feature points.

[0013] As a preferred embodiment of the BIM-based standardized management method for the construction of precast magnesium slag components described in this invention, the method for solving the rigid body calibration parameters includes: reading the design coordinates of the same reference feature point in the BIM and calculating the morphological consistency deviation of the reference feature points within the same component; assigning weights to each paired point based on the morphological consistency deviation and measurement dispersion to obtain point weights; and aggregating the paired datasets of design coordinates and measured coordinates to solve the rigid body calibration parameters with the aim of minimizing the deviation.

[0014] As a preferred embodiment of the BIM-based standardized management method for the construction of precast magnesium slag components described in this invention, the generation of a new coordinate calibration version includes: obtaining calibration confidence based on the solved aggregated residual; determining the next measurement point driven by information gain when the calibration confidence does not reach the confidence threshold and the number of measured feature points has not reached the minimum sample size requirement; obtaining the drift amplitude by calculating the Euclidean length through the difference vector; and generating a new coordinate calibration version when the number of aggregated feature points meets the minimum sample size requirement and the calibration confidence is greater than or equal to the confidence threshold and the drift amplitude is greater than or equal to the drift amplitude threshold.

[0015] As a preferred embodiment of the BIM-based standardized management method for the construction of precast magnesium slag components described in this invention, the information gain driving method includes: constructing a linearized sensitivity matrix based on candidate feature points, accumulating the single-point contribution matrix according to point weights to form an information matrix and inverting it to obtain the parameter covariance matrix; determining the estimated weight of unmeasured points by using the equivalent single uncertainty constant of the measuring equipment and the median of the deviation of the morphological consistency of the measured points in this round; adding candidate feature points to the information matrix through the estimated weight of unmeasured points, calculating the information gain score according to the reduction of the trace of the covariance matrix, and selecting the candidate feature point with the largest information gain score as the next measurement point.

[0016] As a preferred embodiment of the BIM-based standardized management method for the construction of precast magnesium slag components described in this invention, the generation of component digital archives includes: for the component to be inspected, correcting the design coordinates of the benchmark feature points according to the effective coordinate calibration version, and subtracting the actual coordinates to obtain the residual vector; obtaining the unit vector of the component's installation direction from the work package, decomposing the residual vector according to the installation direction to obtain the deviation scalar along the installation direction and the deviation vector perpendicular to the installation direction; using the decomposed deviation scalar along the installation direction and the deviation vector perpendicular to the installation direction as the actual deviation data; calculating the suggested compensation amount based on the actual deviation data, writing the suggested compensation amount into the compensation draft, and generating the component digital archive.

[0017] The beneficial effects of this invention are as follows: by automatically classifying and issuing inspection intensity levels according to the deviation index of magnesium slag batches and using the cumulative maturity amount as the release condition, the invention achieves quantitative release under the dual constraints of raw material batch risk and maintenance compliance, and reduces the risk of erroneous release; by determining the next measurement point through information gain scoring, the invention realizes the transformation of coordinate calibration from fixed measurement points to adaptive convergence supplementary measurement, thereby reducing invalid measurement points and improving the stability and traceability of coordinate correction. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Fig. 1 This is a flowchart of a BIM-based standardized management method for the construction of precast magnesium slag components.

[0020] Fig. 2 A flowchart for generating a job package.

[0021] Fig. 3 This is a flowchart for performing batch deviation determination and maturity determination.

[0022] Fig. 4 A flowchart for compensating for archiving. Detailed Implementation

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0025] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0026] Reference Figs. 1-4 This is one embodiment of the present invention, which provides a BIM-based standardized management method for the construction of precast magnesium slag components, including the following steps:

[0027] S1. Establish an industrial data dictionary and constraint set and solidify its version.

[0028] Magnesia slag batch data is collected by online instruments, curing environment data is continuously collected by sensors in the curing chamber, inspection data is collected by measurement and grading, logistics data is collected by scanning events, and installation measurement data is collected by extracting design coordinates through BIM and importing measured coordinates from the measuring equipment. The magnesium slag batch data, curing environment data, inspection data, logistics data and measurement data are combined to obtain engineering data.

[0029] For missing data in the project data, if key fields are missing, such as batch number, sampling start and end points of CO2 / temperature and humidity time series, factory inspection conclusion, installation measurement record, etc., they are directly marked as unusable; if non-key fields are missing, the data enters a pending data entry state, but subsequent operations are prohibited until the data entry is completed.

[0030] A three-segment storage strategy is adopted for continuous process quantities. An industrial data dictionary is established. Specifically, the key points that must be stored include the start time, end time, the time when the maturity condition is reached, and the time when abnormal boundaries are crossed. The abnormal segments that must be stored include when the sampled value crosses the boundary or the growth rate is lower than the growth rate threshold, in which the full data of the interval is stored. The remaining segments are thinned and stored at a fixed step size.

[0031] It should be noted that the growth rate threshold is calculated using the maturity curve formed by the first batch of calibrated specimens under the target maintenance conditions, and the value range is greater than 0.

[0032] The entire lifecycle of each precast magnesium slag component is solidified into a finite state chain that cannot be skipped, namely, from left to right: Modeled, Work Package Issued, Production and Maintenance in Progress, Ready for Shipment, Shipped, Arrived on Site, Installation Allowed, Installed, Archived. Unique migration events are defined for adjacent states (e.g., Issued, Released, Shipment Confirmation, On-site Acceptance, Installation Permitted, Installation Completed, Archived Completed). Based on the industrial data dictionary, each migration event is written into the constraint set of "Entry Condition / Prohibition Condition / Abnormal Handling Action". The entry condition is used to determine whether the state is allowed to advance, and the prohibition condition is used to determine whether the migration must be blocked and generate a reason code and trigger a unique handling action. If any key field is missing or exceeds the boundary, release is prohibited and a deterministic action of extended maintenance, re-inspection, or rework is triggered according to the reason code.

[0033] Versioning of the industrial data dictionary and constraint set is implemented.

[0034] S2. Extract component design information from the BIM model based on the industrial data dictionary and constraint set to generate a work package.

[0035] By traversing the collection of prefabricated component objects through the BIM interface, the inherent global identifier and design information of each component object are read. The design information includes the component type identifier, the building, floor and grid information to which the component belongs, the component installation location (coordinates and direction), the planned installation time, the list of embedded parts and the parameters of the embedded parts (specifications, quantity, relative position and direction), and written into the component candidate list.

[0036] For each component in the component candidate list, a unique component code is generated. This code is formed by concatenating the project code, building, floor, component type, component serial number, and BIM global identifier verification segment. It must be unique within the same project. After generation, the unique component code is written into the component's BIM custom attributes. At the same time, the industrial data dictionary and constraint set are written into the component's BIM custom attributes, and consistency verification is performed. If the writing fails or the read-back value is inconsistent with the component's unique code, an exception is recorded and the component is prohibited from entering the "work package issued" state.

[0037] A set of candidate feature points is extracted from the geometric and embedded part information of the BIM components. These candidate feature points include, but are not limited to, component geometric corner points, lifting point centers, positioning hole centers, and key embedded part centers. The candidate feature points are then screened, and benchmark feature points are selected based on a distributed and degenerate rule. Specifically, points located in different boundary areas of the component are prioritized to ensure coverage of differentiated locations in both plane and elevation directions. Combinations where any three points are approximately collinear or too concentrated, leading to unstable positioning, are eliminated. When the component type requires at least three benchmark feature points, a set of three points with the largest coverage area and the most balanced distance distribution between points is selected as the benchmark feature points. When at least four benchmark feature points are required, the candidate point with the greatest spatial distance from the selected point set is added to the three points to enhance the constraint. Each benchmark feature point is assigned a unique identifier, and the identifier and its design coordinate reference in the BIM are written into the component work package to ensure that subsequent installation measurements can stably reproduce the same set of positioning benchmarks and be used for coordinate calibration.

[0038] Using the unique component code as an index, the component's design information, reference feature point identifier set, and industrial data dictionary are assembled into a work package.

[0039] S3. Based on the batch data of magnesium slag and the curing environment data collected from the work package, perform batch deviation judgment, output the inspection intensity level and perform maturity judgment, and carry out state migration.

[0040] The batch number of magnesium slag is obtained through the entry slip and the QR code of the silo and bound to the unique code of the component. The batch measured data is collected according to the batch field list specified in the work package, including moisture content, fineness and particle size characteristics, and key chemical indicators. The moisture content is obtained by drying and weighing, the fineness and particle size characteristics are obtained by particle size analyzer, and the key chemical indicators are imported by laboratory testing.

[0041] The batch deviation index is calculated based on the batch measured data, and the expression is as follows:

[0042] ;

[0043] in, This indicates that the batch deviates from the index. This indicates the measured moisture content of the current batch of magnesium slag. This represents the reference value for the baseline moisture content. This represents the measured value of the particle size characteristics of the current batch of magnesium slag. Indicates a reference value for particle size characteristics. This indicates the key chemical indicators for the current batch. This indicates the reference value for the benchmark key indicator.

[0044] It should be noted that, , as well as The calibration batches are selected from the first batch of magnesium slag that have arrived and been tested, arranged in chronological order of arrival. Measured values ​​for moisture content, fineness and particle size characteristics, and key chemical indicators are obtained for each batch. The arithmetic mean of these measured values ​​is then calculated. , as well as .

[0045] When the batch deviation index does not exceed the first threshold, it is determined to be at the routine inspection intensity level and a routine inspection task is issued. The task specifies the sampling ratio and the list of mandatory inspection items. When the deviation exceeds the first threshold but does not exceed the second threshold, it is determined to be at the tightened inspection intensity level and a tightened inspection task is issued. The sampling ratio is increased and the number of mandatory inspection items is increased. When the deviation exceeds the second threshold, it is determined to be at the extremely stringent inspection intensity level and an extremely stringent inspection task is issued. The sampling ratio is further increased and the number of mandatory inspection items is further increased. The unique component code is scanned one by one according to the task to perform measurement and judgment. The sampling quantity and mandatory inspection items are verified to meet the level requirements. Only if they meet the requirements are they allowed to be released. If they do not meet the requirements, the status transition is prohibited and supplementary inspection is carried out.

[0046] It should be noted that among the first batches of magnesium slag that have arrived and been tested, the first twenty batches are taken as samples in the order of arrival time. The batch deviation index is calculated for each batch and a sample sequence is formed. The sample sequence is sorted from smallest to largest, and the corresponding percentile value is taken as the first threshold and the second threshold. For example, the deviation value corresponding to the 70th percentile after sorting is taken as the first threshold, and the deviation value corresponding to the 90th percentile after sorting is taken as the second threshold.

[0047] After the components enter the curing process, CO2 volume fraction, relative humidity, and temperature sequences are continuously collected from sensors in the curing chamber. The start and end times of curing are also recorded. Maturity is calculated using the following expression:

[0048] ;

[0049] in, This indicates the cumulative amount of maturity. This indicates the cumulative time since the start of maintenance. Indicates time Measured CO2 volume fraction Represents a timestamp. Indicates the humidity sensitivity coefficient. Indicates time The measured relative humidity value, Indicates the humidity inflection point threshold. This represents the apparent activation energy parameter. Indicates the value of the gas constant. Indicates time The actual measured temperature value.

[0050] It should be noted that the CO2 volume fraction and relative humidity are converted from percentages to dimensionless values ​​of 0 to 1, and the temperature is converted from degrees Celsius to Kelvin for calculation.

[0051] It should be noted that, The process of curing was calibrated by setting different relative humidities in groups under constant carbon dioxide concentration and temperature, recording the time required to reach the specified maturity standard under each humidity condition, and obtaining the humidity sensitivity coefficient by fitting the relative humidity and the time to reach the standard data. The method involves gradually increasing the relative humidity in fixed increments while keeping the carbon dioxide concentration and temperature constant, and recording the time required for each level to reach the specified maturity standard. The level with the smallest rate of change in relative humidity with the time to reach the standard is taken as the inflection point, and the relative humidity value at the inflection point is selected as the humidity inflection point threshold. The process involves setting up at least three different temperature levels for maintenance calibration while keeping carbon dioxide concentration and relative humidity constant. The time required for each temperature level to reach the specified maturity standard is recorded. The reciprocal of the time required to reach the same maturity standard is used as the reaction rate characterization quantity. The linear relationship between the rate characterization quantity and the reciprocal of the absolute temperature is fitted with parameters. The temperature-sensitive parameter obtained from the fitting is converted into an apparent activation energy parameter. Relative humidity represents the relative degree of water vapor content in the air with respect to the maximum water vapor content at the current temperature.

[0052] When the accumulated maturity reaches the maturity threshold, the component is released and moved from the production and maintenance state to the factory ready state.

[0053] It should be noted that the maturity threshold is determined by continuously collecting carbon dioxide concentration, temperature, and relative humidity data for the first batch of calibration parts of the same component type under the target curing conditions, calculating the cumulative maturity at fixed time intervals (e.g., every four hours), and taking samples at the end of each time interval to perform a prescribed factory inspection (fixed as "compressive strength meets design requirements and appearance and dimensions pass inspection"). The earliest time point when the factory inspection is passed is identified, and the cumulative maturity at that time point is used as the maturity threshold.

[0054] S4. Construct a shipping window and perform consistency verification. Based on the work package, measure and collect data for each benchmark feature point, calculate the measured mean coordinates and dispersion, calculate the morphological consistency deviation through the benchmark feature points, solve the rigid body calibration parameters, generate a new coordinate calibration version by executing information gain drive, correct the BIM design coordinates of the benchmark feature points through the new coordinate calibration version, and obtain the true deviation data.

[0055] When transitioning from production and maintenance status to a ready-to-ship status, the current time is taken as the ready-to-ship time; the planned installation time is extracted from the BIM construction plan attributes; the fixed transportation buffer time is determined by the latest arrival time given in the BIM construction plan, and the average value is calculated from the transportation time of component types in at least three consecutive actual shipment and arrival records.

[0056] The shipping window for components starts at the time when they are ready to leave the factory and ends at the difference between the planned installation time and the transportation buffer time. When the current timestamp falls within the shipping window, the component is ready to leave the factory, and the loading list and the list to be shipped are consistent, the component status is allowed to be migrated to "shipped".

[0057] Once the components arrive on site, the inspection task is carried out according to the strength level. After the inspection task is completed, the components are allowed to enter the "arrived on site" state.

[0058] Before installation, the unique component code, planned installation location, installation direction, embedded part constraint information, and reference feature point identifiers are read from the work package. After scanning the unique component code, a consistency check is performed, including checking whether the unique component code and the corresponding component in the BIM are consistent, whether the proposed installation location of the component is consistent with the planned installation location given in the work package, whether the specifications, quantity, and direction of the embedded parts meet the embedded part constraints given in the work package, and whether the reference feature point identifiers used for positioning are complete and can be identified by on-site measurement methods. If any check is not met, installation is not allowed; installation is only allowed if all checks are met.

[0059] After the components are installed, the first round of measurement sets is established. Each benchmark feature point is measured at least twice according to the benchmark feature point markings in the work package, yielding the results. The first component A repeated sequence of measured coordinates for feature points is given, and the mean coordinates and dispersion of the measured coordinates for each feature point are calculated. The expression is as follows:

[0060] ; ;

[0061] in, Indicates the first The first component The feature point of the th feature point The three-dimensional coordinate vector acquired in this second acquisition Indicates the first The first component The number of repeated measurements for each feature point Indicates the first The first component Measured mean coordinates of each feature point Indicates the first The first component The measurement dispersion of each feature point.

[0062] Read the design coordinates of the same reference feature point in the BIM, and calculate the deviation in morphological consistency for the reference feature points within the same component. The expression is as follows:

[0063] ;

[0064] in, In BIM, the first The first component Design of three-dimensional coordinates of each reference feature point Indicates the first The first component Deviation in morphological consistency of each feature point Indicates that within the same component, except for the first Number the feature points other than the original feature points.

[0065] Weights are assigned to each paired point based on morphological consistency deviation and measurement dispersion, as expressed in the following expression:

[0066] ;

[0067] in, Indicates the first The first component The point weights of each feature point This represents the morphological consistency weighting coefficient.

[0068] It should be noted that, This process involves selecting historical sample data (each sample contains paired points of BIM design coordinates and on-site measured coordinates), constructing a candidate coefficient set, calculating the paired point weights for each candidate coefficient, performing a coordinate calibration, and statistically analyzing the robustness of the calibrated residuals (e.g., the 90ths of the residual L2 norm). The goal is to minimize this robustness. As a weighting coefficient for morphological consistency.

[0069] Using the regional time window and construction area as the scope, the paired datasets of design coordinates and measured coordinates that have been signed and confirmed within the scope are aggregated. Rigid body calibration parameters used to transform BIM coordinates to on-site measurement coordinates are solved. To avoid bias from a few outliers, a weighted robust objective function is minimized. The calibration reliability of the current version is given based on the solved aggregated residuals, expressed as:

[0070] ;

[0071] ;

[0072] ;

[0073] in, This represents the rotation matrix that rotates the BIM coordinate system to the field measurement coordinate system. This represents the translation vector that moves the BIM coordinate system to the field measurement coordinate system. This represents the translation vector recorded in the most recently implemented coordinate calibration version. This represents the threshold for Huber's inflection point. Indicates the time continuity constraint coefficient. This represents the set of paired points after aggregation. Indicates the number of paired points. Indicates calibration reliability. This indicates an indicator function; the value is 1 if the condition in parentheses is true, and 0 otherwise.

[0074] It should be noted that, This is achieved by solving the least-squares rigid body registration problem for historical pairing point data using two sets of 3D point data, obtaining the residual vector for each pairing point, and calculating the residual L2 norm set. The residual L2 norm set is then sorted in ascending order, and the corresponding quantile is used as the Huber inflection point threshold. For example, the value corresponding to the 90th percentile is used as... ; This method involves using historical paired-point data arranged chronologically, defining a set of candidate coefficients, performing coordinate calibration on each candidate coefficient to obtain the corresponding calibrated residual L2 norm set, calculating the 90ths of the calibrated residual L2 norm set, and then selecting the candidate coefficients that minimizes the 90ths of the calibrated residual L2 norm set. As a time continuity constraint coefficient, the regional time window is determined by reading the daily installation cycle and shift start and end times of the construction area in the BIM construction plan, dividing a shift into equal-length statistical intervals (e.g., one interval every two hours), and triggering an aggregation solution when the number of paired points that have completed signature confirmation within the equal-length statistical interval reaches the minimum sample size; otherwise, merging into the next interval until the condition is met. Since the rigid body calibration parameters to be determined consist of three-dimensional rotation and three-dimensional translation (a total of six degrees of freedom), each design coordinate and measured coordinate paired point can provide three independent constraints. Therefore, the minimum sample size is taken as 3, with at least 3 constraints and non-degenerate spatial distribution (e.g., non-collinearity).

[0075] When the calibration confidence level does not reach the confidence threshold and the number of feature points that have been measured has not yet reached the minimum sample size requirement, the next measurement point is determined by information gain-driven determination. Specifically, the components to be estimated for the rigid body calibration parameters are fixedly defined as two parts: the first part is the three-dimensional rotation increment component, and the second part is the three-dimensional translation increment component. For each candidate feature point that has not yet been measured, a linearized sensitivity matrix with respect to the six-dimensional parameter increment is constructed based on the three-dimensional vector of the candidate feature point under the action of the current rotation matrix. The sensitivity matrix is ​​fixed at three rows and six columns, where the first three columns are obtained by taking the negative of the antisymmetric matrix of the point vector after the current rotation, and are used to describe the influence of the rotation increment on the residual; the last three columns are a third-order identity matrix, used to describe the influence of the translation increment on the residual.

[0076] After each new measurement point is added, the current information matrix is ​​constructed. Specifically, all feature points that have been measured in this round are traversed, and a 3x6 sensitivity matrix is ​​generated for each measured feature point. The point weights of the measured feature points are calculated. The sensitivity matrix is ​​transposed to obtain a 6x3 matrix. The product of the untransposed sensitivity matrix and the transposed sensitivity matrix is ​​used as the single-point contribution matrix. Each element of the single-point contribution matrix is ​​scaled according to the same scaling factor, which is taken as the point weight of the current feature point. The scaled single-point contribution matrix is ​​added element by element to the current information matrix. After traversing all feature points measured in this round, the information matrix of this round is obtained. After inverting the information matrix of this round, the parameter covariance matrix of this round is obtained. The estimated weight of the unmeasured points of this round is calculated by using the equivalent single uncertainty constant of the measuring equipment and the median of the shape consistency deviation of the measured points in this round. Specifically, the median of the shape consistency deviation of the measured points in this round is taken as the representative value of shape deviation, and the equivalent single uncertainty constant of the measuring equipment is taken as the estimated measurement dispersion of the unmeasured points. The representative value of shape deviation and the equivalent single uncertainty constant of the measuring equipment are squared respectively and then weighted and summed through the shape consistency weight coefficient to form the estimated uncertainty. The estimated weight of the unmeasured points of this round is obtained by taking the reciprocal of the estimated uncertainty.

[0077] It should be noted that the equivalent single uncertainty constant of the measuring equipment is obtained by selecting a stable and immovable calibration target point on site, keeping the station position and observation method of the measuring equipment unchanged, continuously performing no less than a fixed number of repeated coordinate acquisitions (e.g., 5 times) on the target point, averaging the position of the multiple acquisition results, calculating the three-dimensional distance of each acquisition result relative to the average position, obtaining the dispersion of the distance sequence, and using it as the equivalent single uncertainty constant of the measuring equipment.

[0078] For each candidate feature point that has not yet been measured, assume that the candidate feature point is added to the information matrix with the estimated weight, calculate the covariance matrix after adding the candidate feature point, and use the reduction in the trace of the covariance matrix before and after addition as the information gain score of the candidate feature point; the larger the information gain score, the greater the contribution of measuring the candidate feature point to reducing parameter uncertainty; determine the candidate feature point with the largest information gain score as the unique next measurement point; when there are multiple candidate points with the same information gain score, select the smallest one according to the lexicographical order of the candidate point identifier; add the candidate feature point to the set of measured points and enter the next round of iteration calculation until the minimum sample size requirement is reached.

[0079] Based on translation vector and The difference vector is obtained by subtracting the two components one by one. The Euclidean length of the difference vector is calculated to obtain the drift amplitude.

[0080] If and only if the number of aggregated feature points satisfies the minimum sample size requirement and simultaneously satisfies When the confidence threshold and the drift amplitude are both greater than or equal to the drift amplitude threshold, a new coordinate calibration version is generated. In addition to the applicable scope (floor, grid segment, time window), effective time, and calibration parameters, the new version also has the calibration confidence level written into the version quality field.

[0081] It should be noted that the credibility threshold is calculated by using historical pairing point data whose installation results are deemed qualified, and calculating the calibration credibility of each batch of data according to the same coordinate calibration solution process to obtain a credibility sample set. The credibility sample set is then sorted from smallest to largest, and the corresponding percentile is taken as the credibility threshold, such as taking the 10th percentile as the credibility threshold. The drift amplitude threshold is calculated by solving the translation vector for two adjacent batches of historical pairing point data, calculating adjacent drift amplitude samples to form a drift amplitude sample set, and then sorting the drift amplitude sample set from smallest to largest and taking the corresponding quantile as the drift amplitude threshold, such as taking the 90th quantile as the drift amplitude threshold.

[0082] For the component to be inspected, the effective coordinate calibration version is called to correct the BIM design coordinates of the reference feature points to the calibration target coordinates in the field coordinate system, and the difference between the BIM design coordinates of the reference feature points and the actual measured coordinates of the corresponding feature points collected on site is used to obtain the residual vector; the unit vector of the installation direction of the component is read from the work package, and the residual vector is decomposed according to the installation direction to obtain the deviation scalar along the installation direction and the deviation vector perpendicular to the installation direction. The decomposed deviation scalar along the installation direction and the deviation vector perpendicular to the installation direction are used as the true deviation data.

[0083] S5. Calculate the suggested compensation amount based on the actual deviation data and generate a digital file of the component.

[0084] To avoid a few outliers causing deviations in the compensation direction, a suggested compensation amount is calculated based on the actual deviation data, expressed as:

[0085] ;

[0086] in, This indicates the suggested compensation amount. Indicates the recharge gain coefficient. Indicates the true deviation data. This indicates that the median of the true deviation data is taken. This indicates the historical compensation benchmark.

[0087] It should be noted that, The method involves selecting two consecutive batches of components of the same component type. The first batch is compensated according to the existing compensation and the median of the actual deviation data is summarized. The second batch is compensated with an additional compensation and the median of the actual deviation data is summarized. The ratio of the compensation increment to the change in the median of the actual deviation data is used as the recharge gain coefficient. The compensation increment is taken from the minimum adjustable resolution of the adjustment mechanism. The compensation amount is read from the most recently signed and effective compensation order. If there is no effective compensation order for the current type, then 0 is taken.

[0088] Write the suggested compensation amount into the draft compensation form, and fill in the applicable component type, applicable scope, effective batch number, and execution process; after the draft compensation form is generated, the quality responsible person signs to confirm the data source and statistical scope, and then the process responsible person signs to confirm the feasibility of the compensation and the effective batch. After both levels of signatures are completed, the compensation form status is set to effective. If either signature is not completed, the compensation form remains ineffective.

[0089] Once the compensation order enters the effective state, the batch number of the effective compensation order, the scope of application, and the compensation consistency check items in the allowed state transition conditions are written into the new version of the constraint set, while the previous version is retained as a traceable historical version. The industrial data dictionary is iterated, and the compensation execution record fields that must be collected in subsequent batches (e.g., execution time, executor signature, reviewer signature, and key dimension remeasurement values ​​before and after execution) are written into the new version of the data dictionary. A digital archive of the component is generated using the unique component code as an index. The archive includes the component work package, inspection strength level, maturity judgment result, shipping window, on-site inspection record, coordinate calibration version number, actual deviation data, and compensation order. The index identifier of the archive is written back to the BIM component attributes, and a one-to-one correspondence is established between the BIM component and the archive index, so that the archive content can be located by selecting the component in BIM.

[0090] In summary, this invention achieves quantitative release of raw material batches under dual constraints of risk and maintenance compliance by automatically classifying and issuing inspection intensity levels according to the magnesium slag batch deviation index and using the cumulative maturity amount as the release condition, thereby reducing the risk of erroneous release. By determining the next measurement point through information gain scoring, the coordinate calibration is transformed from fixed measurement points to adaptive convergence supplementary measurement, thereby reducing invalid measurement points and improving the stability and traceability of coordinate correction.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A standardized management method for the construction of precast magnesium slag components based on BIM, characterized in that: include, Establish an industrial data dictionary and constraint set, and solidify its version. Based on the industrial data dictionary and constraint set, component design information is extracted from the BIM model to generate a work package; Based on the batch data of magnesium slag and the curing environment data collected from the work package, batch deviation judgment is performed, the inspection intensity level is output and maturity judgment is performed, and state migration is carried out. Construct a shipping window and perform consistency verification. Measure and collect data for each benchmark feature point based on the work package, and calculate the measured mean coordinates and dispersion. The deviation in morphological consistency is calculated using reference feature points, and the rigid body calibration parameters are solved. A new coordinate calibration version is then generated by performing information gain-driven operation. The BIM design coordinates of the benchmark feature points are corrected by using the new coordinate calibration version, the actual deviation data is obtained, the suggested compensation amount is calculated based on the actual deviation data, and the digital archive of the component is generated.

2. The BIM-based standardized management method for the construction of precast magnesium slag components as described in claim 1, characterized in that: The specific steps for establishing the industrial data dictionary and constraint set and fixing its version are as follows: Collect batch data of magnesium slag, curing environment data, inspection data, logistics data, and installation measurement data to obtain engineering data; Solidify missing data in engineering data and establish an industrial data dictionary; The entire life cycle of each precast magnesium slag component is solidified into a finite state chain, a constraint set is established, and the industrial data dictionary and constraint set are versioned and solidified.

3. The BIM-based standardized management method for the construction of precast magnesium slag components as described in claim 2, characterized in that: The generated task package includes traversing the collection of prefabricated component objects through the BIM interface and reading the inherent global identifier and design information for each component object; Generate a unique component code for each component in the component candidate list and perform a consistency check; Extract a set of candidate feature points from the geometric information and embedded part information of BIM components, and select benchmark feature points based on the rules of dispersed distribution and avoiding degradation of the candidate feature points. Using the unique component code as an index, the component's design information, reference feature point identifier set, and industrial data dictionary are assembled into a work package.

4. The BIM-based standardized management method for the construction of precast magnesium slag components as described in claim 3, characterized in that: The batch deviation determination includes binding the magnesium slag batch number and the unique component code, and collecting batch measured data based on the work package; The batch deviation index is calculated based on the actual batch measurement data. When the batch deviation index does not exceed the first threshold, it is determined to be the routine inspection intensity level, and routine inspection tasks are issued. When the deviation exceeds the first threshold but does not exceed the second threshold, it is determined to be at the tightened inspection intensity level, and a tightened inspection task is issued. When the deviation exceeds the second threshold, it is determined to be at the extremely stringent inspection intensity level, and an extremely stringent inspection task is issued.

5. The BIM-based standardized management method for the construction of precast magnesium slag components as described in claim 4, characterized in that: The maturity determination process includes continuously collecting CO2 volume fraction, relative humidity, and temperature sequences, recording the start and end times of maintenance, calculating maturity, and obtaining the cumulative maturity amount. When the accumulated maturity reaches the maturity threshold, the component is released and moved from the production and maintenance state to the factory ready state.

6. The BIM-based standardized management method for the construction of precast magnesium slag components as described in claim 5, characterized in that: The construction of the shipping window includes setting the starting point of the shipping window for precast magnesium slag components as the time when they can be shipped from the factory, and the ending point of the shipping window as the difference between the planned installation time and the transportation buffer time. The consistency check includes checking whether the unique code of the precast magnesium slag component is consistent with the BIM component, checking whether the proposed installation position of the precast magnesium slag component is consistent with the planned installation position given by the work package, checking whether the embedded parts meet the embedded part constraints given by the work package, and checking whether the benchmark feature point markings are complete. The calculation of the measured mean coordinates and dispersion includes, after the precast magnesium slag components are installed, measuring and collecting data for each benchmark feature point according to the benchmark feature point identification in the work package, obtaining a repeating measured coordinate sequence, and calculating the measured mean coordinates and dispersion of the feature points.

7. The BIM-based standardized management method for the construction of precast magnesium slag components as described in claim 6, characterized in that: The solution of rigid body calibration parameters includes reading the design coordinates of the same reference feature point in the BIM and calculating the deviation of morphological consistency for the reference feature points within the same component. Weights are assigned to each paired point based on the deviation from morphological consistency and the measurement dispersion to obtain the point weights. The design coordinates and measured coordinates are paired and aggregated to solve for the rigid body calibration parameters with the aim of minimizing them.

8. The BIM-based standardized management method for the construction of precast magnesium slag components as described in claim 7, characterized in that: The process of generating a new coordinate calibration version includes obtaining calibration confidence based on the solved aggregated residual; When the calibration confidence level does not reach the confidence level threshold and the number of feature points that have been measured has not reached the minimum sample size requirement, determine the next measurement point driven by information gain. The drift amplitude is obtained by calculating the Euclidean length using the difference vector. A new coordinate calibration version is generated when the number of aggregated feature points meets the minimum sample size requirement and the calibration confidence is greater than or equal to the confidence threshold and the drift amplitude is greater than or equal to the drift amplitude threshold.

9. The BIM-based standardized management method for the construction of precast magnesium slag components as described in claim 8, characterized in that: The information gain driving method includes constructing a linearized sensitivity matrix based on candidate feature points, accumulating the single-point contribution matrix according to the point weights to form an information matrix and inverting it to obtain the parameter covariance matrix. The estimated weight of unmeasured points is determined by the equivalent single uncertainty constant of the measuring equipment and the median of the deviation of the consistency of the shape of the measured points in this round. Candidate feature points are added to the information matrix by pre-estimating the weights of unmeasured points, and the information gain score is calculated based on the reduction in the trace of the covariance matrix. The candidate feature point with the largest information gain score is selected as the next measurement point.

10. The BIM-based standardized management method for the construction of precast magnesium slag components as described in claim 9, characterized in that: The generated component digital file includes: for the component to be inspected, the design coordinates of the reference feature points are corrected according to the effective coordinate calibration version, and the residual vector is obtained by subtracting the actual coordinates from the design coordinates. Obtain the unit vector of the component's installation direction from the work package, decompose the residual vector according to the installation direction, and obtain the deviation scalar along the installation direction and the deviation vector perpendicular to the installation direction. The decomposed deviation scalar along the installation direction and the deviation vector perpendicular to the installation direction are used as the true deviation data. The recommended compensation amount is calculated based on the actual deviation data, and the recommended compensation amount is written into the draft compensation form to generate a digital file of the component.

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