Civil construction intelligent management and control method and system based on Internet of Things

Abstract

The invention discloses a civil construction intelligent management and control method and system based on the Internet of Things, and relates to the technical field of buildings, through selecting a to-be-tracked reference point set on site and constructing a drift record sequence and a static credible domain, the time continuity stability of reference points in the construction process can be quantified in real time, and the construction efficiency is improved. And the omission of early drift symptom in single measurement is avoided. The validity of the reference point is judged in combination with the static credible domain, and an error propagation command is triggered through a drift trend, so that the system can intervene and dynamically correct an effective point set and the credible domain when the error is still in an early stage, deviation accumulation and upward propagation are prevented, and the risk of staggered layers of the whole structure is reduced. And constructing a full-field error vector field by utilizing clustering analysis, identifying a high-risk area and a high-risk offset component, and uploading the high-risk area and the high-risk offset component to a construction main control system to generate a retest and re-setting-out task, so that automatic identification and priority correction of the risk component are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of building technology, in particular to a civil construction intelligent management and control method and system based on Internet of Things. BACKGROUND

[0002] With the development of Internet of Things, sensor network and building information modeling (BIM) technology, the civil construction site can realize real-time state perception and data collection of construction reference points through the sensor terminals, laser measuring equipment, total station instruments and the like arranged on each construction floor. By continuously monitoring the spatial position of the construction layout component, dynamic management of the structure layout precision can be realized.

[0003] In the existing civil construction management, the layout operation usually relies on manual total station measurement or local laser line projection for positioning. Although measurement and marking can be completed in a local range, there is a lack of continuous monitoring of the stability changes of the reference points within the construction period. For example, during the construction process of a high-rise building, the floor reference point may drift slightly due to concrete shrinkage, formwork deformation or environmental temperature changes, and such drift is difficult to detect in a single measurement. However, as the floor height increases, the drift error will accumulate layer by layer in the vertical direction, eventually resulting in a few centimeters of misalignment at the top floor. In addition, the existing method often only corrects the structure after discovering the misalignment, lacks a mechanism for early prediction, hierarchical judgment and active correction of errors, and is prone to high rework cost, time delay and precision loss.

[0004] The above-mentioned shortcomings are mainly due to the fact that the existing construction layout process is based on static assumptions, i.e. assuming that the construction reference point remains unchanged during the entire construction period, without considering its stability degradation in the dynamic construction environment. When the reference point is continuously affected by micro-settlement, vibration impact, formwork stress deformation and the like, its spatial position will gradually drift. If the drift trend is not identified and managed, the error will be transmitted to the upper structure through the layout component and form a cumulative effect in the vertical direction, which not only may cause obvious misalignment of the overall structure, but also may cause abnormal stress distribution of the load-bearing component, increasing the safety risk of the structure. More seriously, once such deviation is magnified in the subsequent construction stage, large-scale rework or structural reinforcement is often required, which not only delays the construction period, but also significantly increases the construction cost. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a civil construction intelligent management and control method and system based on Internet of Things, which solves the problems in the above background art.

[0006] To achieve the above purpose, the present application is implemented by the following technical scheme: a civil construction intelligent management and control method based on Internet of Things, comprising the following steps,

[0007] A set of reference points to be tracked is selected in advance at the civil construction site to construct a drift record sequence and a static confidence region. The drift record sequence is used to quantify the temporal continuity stability of each reference point during the construction process.

[0008] The validity of each reference point is determined by the static trust domain, and a set of valid points is generated. The set of valid points includes valid spare points and valid reference points. The error propagation command is triggered based on the drift trend of each point in the set of valid points.

[0009] Receive error propagation commands, analyze the error accumulation and propagation degree of each layer of construction layout components by the drift trend of each point, so as to obtain the misalignment risk index of the entire construction site, and realize dynamic correction of the effective point set and static confidence domain based on its value.

[0010] Based on the corrected set of valid points, cluster analysis is performed on the error vector field of all construction layout components to identify high-risk areas in the error set and the set of high-risk offset components within them.

[0011] The high-risk offset component set is uploaded to the construction main control system and a re-measurement and re-layout task is generated.

[0012] Preferably, a set of reference points to be tracked is selected, and a drift record sequence is constructed to quantify the temporal continuity stability of each reference point during construction, including:

[0013] During the civil construction process, structural surface control points in each construction floor are selected as reference points for axis layout. Microseismic sensors and laser plumb bobs are installed at each reference point to monitor the floor status characteristics in real time.

[0014] Set a continuous observation window and substitute the floor status characteristics. To construct a drift record sequence for each reference benchmark, the drift record sequence is used to quantify the temporal continuity stability of each reference benchmark during construction; wherein, For the i-th reference point at The drift amplitude index at any given time. and For the i-th reference point at and Vertical elevation at any moment For unit conversion factor, For the i-th reference point at The acceleration of minute movements at any given moment;

[0015] S101: The drift record sequence contains a plurality of drift amplitude indicators of the corresponding floor within the continuous observation window, and the sliding mean and variance calculation is performed on the drift amplitude indicators in the drift record sequence to extract the drift expectation value and noise range in the static stable interval;

[0016] S102: Based on the drift expectation value and noise range in the static stable interval, the static trust domain of each reference datum point is set;

[0017] The set of to-be-tracked datum points is composed of a plurality of reference datum points;

[0018] The floor state features include micro-kinetic acceleration and vertical elevation.

[0019] Preferably, S103: The effectiveness of each reference datum point is judged by the static trust domain to generate an effective point set, which includes effective backup points and effective reference datum points. According to the drift trend of each point in the effective point set, an error propagation command is triggered, including:

[0020] Before each construction period, the effectiveness of each reference datum point is judged. If the drift amplitude indicator at the current time falls within the static trust domain, it means that the reference datum point is still effective, otherwise it is not effective;

[0021] If not, record the drift as invalid and automatically find the nearest backup point in its structural correlation range to judge whether the backup point is effective. If the backup point is not effective, a first static trust domain correction command is triggered. If the backup point is effective, the drift record sequence of the backup point is recorded;

[0022] When receiving the first static trust domain correction command, S101 to S102 are repeatedly executed to dynamically correct the static trust domain of each reference datum point;

[0023] The effective backup points and effective reference datum points are combined to generate an effective point set;

[0024] During the construction process, each construction period is set as an observation interval, and the central drift value of each point in the effective point set in each observation interval is recorded to obtain the cross-period sequence of each point;

[0025] The cross-period sequence of the same point is subjected to first-order difference operation to define the periodic drift increment;

[0026] If the periodic drift increment exceeds the standard deviation of the corresponding point and the continuous number of times exceeds the preset period, it is determined that the corresponding point has a trend drift sign, at which time the error propagation command is triggered.

[0027] Preferably, S104: receiving an error propagation command, analyzing the error accumulation and propagation degree of the construction layout components of each floor to obtain the risk index of the whole construction site, and based on the value, dynamically modifying the effective point set and the static trust region, including:

[0028] statistically obtaining the periodic drift increment of the corresponding points to obtain the total value of the vertical drift;

[0029] receiving an error propagation command, analyzing the error distribution of the construction layout components of each floor according to the dependence relationship between the construction layout components and the reference datum points, to obtain the position offset of each floor, which is used to reflect the error accumulation and propagation degree of the construction layout components of each floor;

[0030] synthesizing the position offset of the construction layout components of each floor to obtain the risk index of the whole construction site, which is used to reflect the overall structural stability of the building;

[0031] if the risk index of the wrong floor exceeds the allowable cumulative deviation threshold of the structure, a second static trust region correction command is triggered;

[0032] When receiving the second static trust region correction command, the historical drift record sequence of each point in the effective point set is traced back, and S101-S104 are repeatedly executed to dynamically modify the effective point set and the static trust region of each point in it.

[0033] Preferably, during the layout process of each floor, the actual position data of each construction layout component is collected by a measuring device, and compared with the theoretical position coordinates in the design BIM model to calculate the error deviation, and an error vector field is constructed to describe the error of each construction layout component;

[0034] cluster analysis is performed on the error vector fields of all construction layout components, and the K-means algorithm is used to identify high-risk areas in the error set;

[0035] state traversal is performed on all construction layout components in the high-risk area to identify high-risk offset components, including:

[0036] According to the size and direction of the error vector field of each construction layout component, if the error vector field satisfies exceeds the maximum acceptable layout error of the component and does not exceed the allowable direction angle range condition, it is determined that the offset of the corresponding construction layout component is consistent with the structure wrong floor risk direction, and is recorded as a high-risk offset component, and a high-risk offset component set is constructed;

[0037] represents the included angle between two vectors, that is, the error vector field of the component and the overall building wrong floor risk direction vector The angle between them;

[0038] The high-risk offset component set is uploaded to the construction main control system and a re-measurement and re-layout task is generated.

[0039] Preferably, cluster analysis is performed on the error vector fields of all construction layout components, and the K-means algorithm is used to identify high-risk areas in the error set, including:

[0040] The error vector field is used as input, and the number of clusters P is set.

[0041] Run the K-means algorithm to cluster all error vectors, resulting in P error clusters, where each cluster has its own center point;

[0042] Calculate the average error magnitude for each cluster to measure the severity of the overall error across all clusters;

[0043] Clusters with the largest average error magnitude and whose directionality is consistent with the overall risk of misalignment are marked as high-risk areas.

[0044] Preferably, the set of high-risk offset components is uploaded to the construction main control system and a re-measurement and re-layout task is generated, including:

[0045] The number, spatial coordinates, error vector field, and offset attributes of each high-risk offset component in the high-risk offset component set are packaged into a data packet, and uploaded to the construction main control system through the local area network interface;

[0046] After receiving the data packet, the construction control system automatically generates a retesting task sheet; the retesting task sheet includes the number, spatial coordinates, and retesting priority of the component to be retested.

[0047] Preferably, according to the retest task sheet, path calculation is performed on high-risk offset components. The path calculation steps include:

[0048] Extract the theoretical location coordinates of the target component from the design BIM model;

[0049] By combining the positions of each point in the effective point set with the coordinate system of the construction site, the optimal measurement and projection path from the reference benchmark to the high-risk offset component is calculated.

[0050] Based on the optimal measurement and projection path, and combined with the re-measurement priority, the path point set and construction equipment instructions are output to perform projection and measurement operations on the corresponding high-risk offset components through the on-site layout equipment, so as to achieve accurate repositioning.

[0051] An intelligent management and control system for civil engineering construction based on the Internet of Things, the system includes:

[0052] The monitoring module selects a set of to-be-tracked reference points in advance at a civil construction site to construct a drift record sequence and a static credible domain, to quantify the time-continuous stability of each reference point in the construction process;

[0053] The first correction and analysis module judges the effectiveness of each reference point through the static credible domain, generates an effective point set, the effective point set includes effective backup points and effective reference points, and triggers an error propagation command according to the drift trend of each point in the effective point set;

[0054] The second correction and analysis module receives the error propagation command, analyzes the error accumulation and propagation degree of each layer of construction layout components through the drift trend of each point, to obtain a risk index of the whole construction site, and based on the value, dynamically corrects the effective point set and the static credible domain;

[0055] The control target identification module performs cluster analysis on the error vector field of all construction layout components based on the corrected effective point set, to identify a high-risk area and a high-risk offset component set in the high-risk area;

[0056] The dynamic resetting module uploads the high-risk offset component set to the construction master control system and forms a re-measurement and re-layout task.

[0057] The present application provides a civil construction intelligent control method and system based on Internet of Things, which has the following beneficial effects:

[0058] (1) By selecting a set of to-be-tracked reference points on site and constructing a drift record sequence and a static credible domain, the time-continuous stability of the reference points in the construction process can be quantified in real time, early micro-settlement, vibration or environmental changes caused by small displacement can be found in advance, and early drift signs missed by single measurement can be avoided. The reference point effectiveness is determined in combination with the static credible domain, and the error propagation command is triggered through the drift trend, so that the system can intervene when the error is still in the early stage, dynamically correct the effective point set and the credible domain, prevent deviation accumulation and propagation, and reduce the overall structure risk. The whole field error vector field is constructed by cluster analysis, and the high-risk area and the high-risk offset component are identified, which are uploaded to the construction master control system to generate a re-measurement and re-layout task, realizing the automatic identification and priority correction of the risk component, greatly improving the construction layout precision and correction efficiency, and reducing the manual inspection error.

[0059] (2) The present application calculates the position offset of each floor after receiving the error propagation command, and synthesizes the whole field of the wrong floor risk index based on the position offset, realizes the quantitative evaluation of the stability of the whole structure of the building, this method not only considers the influence of periodic drift increment on vertical cumulative offset, but also avoids the amplification of irrelevant interference on the result by limiting the analysis range of error upward propagation, when the wrong floor risk index exceeds the structure allowable cumulative deviation threshold, the system can automatically trigger the second static trusted domain correction command, backtrack and dynamically correct the effective point set and static trusted domain, so as to intervene in time when the structure risk has not developed into an irreversible state, reduce the rework, shorten the construction period, and improve the precision of construction line and the safety of the whole structure.

[0060] (3) By calculating the error between the measured position coordinates and the theoretical coordinates of the BIM model, a three-dimensional error vector field is constructed, the system can accurately depict the offset amplitude and direction of each component in space, avoiding the ambiguity of traditional two-dimensional deviation judgment, providing a data basis for subsequent offset direction consistency analysis; Secondly, the K-means algorithm is used to cluster the error vector field, which can effectively identify the high-risk areas with high error concentration and consistent direction, avoiding the inefficiency and subjective misjudgment of manual screening. By calculating the average error amplitude of each cluster and combining with the direction angle threshold screening, the clusters consistent with the overall wrong floor risk trend are quantitatively extracted, ensuring that the identified risk areas have actual harmfulness of structural offset; Finally, the high-risk offset component set meeting the conditions is automatically pushed to the construction master control system, and the re-measurement and re-line tasks are automatically generated, so that the line correction has closed-loop control capability, avoiding the late structural hidden dangers caused by missing risk components, and improving the automation and intelligent level of line quality control. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 It is a civil construction intelligent management and control method process schematic diagram based on Internet of Things;

[0062] Figure 2 It is a part of logic diagram of the civil construction intelligent management and control method based on Internet of Things; DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. Embodiment 1

[0064] Please refer to Figure 1 and Figure 2The application provides a civil construction intelligent management and control method based on the Internet of Things, comprising the following steps,

[0065] Step one: a set of to-be-tracked reference points are selected in advance at the civil construction site to construct a drift record sequence and a static trusted domain, and the drift record sequence is used to quantify the time continuous stability of each reference point in the construction process;

[0066] Step two: the effectiveness of each reference point is judged by the static trusted domain to generate an effective point set, the effective point set comprises effective standby points and effective reference points, and an error propagation command is triggered according to the drift trend of each point in the effective point set;

[0067] Step three: the error accumulation and propagation degree of each layer construction layout component are analyzed by the drift trend of each point to obtain the risk index of the whole construction site, and the effective point set and the static trusted domain are dynamically corrected based on the value;

[0068] Step four: on the basis of the corrected effective point set, the error vector field of all construction layout components is analyzed to identify the high-risk area and the high-risk offset component set in the high-risk area;

[0069] Step five: the high-risk offset component set is uploaded to the construction master control system and forms a re-measurement and re-layout task.

[0070] Specifically, step one: a group of representative and stable reference points (used for layout reference) are selected in advance at the construction site, and sensors (such as microseismic sensors and laser plumbs) are deployed on the reference points to collect state data in real time. Through continuous observation for a period of time, a drift record sequence is formed to quantify the position stability of each reference point in the construction process; based on the statistical characteristics (such as mean and variance) of the drift record sequence, a static trusted domain is established to determine whether the reference point is in a normal stable state.

[0071] The reference point is a fixed reference position used for layout positioning in construction.

[0072] The drift record sequence is a sequence of changes in the position of the reference point over time, which is used to reflect long-term stability.

[0073] The static trusted domain is an acceptable range of position changes set by statistical methods, which is used to screen effective reference points.

[0074] By establishing the drift record sequence and static trust domain in advance, the benchmark point with potential drift trend can be identified in the early construction, avoiding the error propagation caused by the use of unstable benchmark point in subsequent construction. For example, in high-rise building construction, if the benchmark point of a certain floor has a displacement of 0.3mm per day due to slight settlement of the foundation, the trend can be found within a week through the drift record sequence and the benchmark point can be replaced in time to prevent the cumulative deviation from affecting the positioning of subsequent floors.

[0075] Step two: Check whether the drift value of the benchmark point is within the range of its static trust domain in each construction period (such as daily or after completing a floor structure): if it is within the range, it is determined to be an effective point; if it exceeds the range, it is determined to be invalid, and attempts to switch to a backup point; drift trend analysis is performed on the effective benchmark points (including backup points), and if the trend continues to exceed the standard, an error propagation command is triggered to inform the system of the potential risk of structural deviation.

[0076] The effective point set is all reference benchmark points and backup points that meet the stability requirements in the current construction period.

[0077] The error propagation command is a system instruction that prompts the need to enter the error analysis and correction process. By real-time screening of the effective point set and monitoring the drift trend, the error propagation risk can be detected in advance, and proactive warning can be achieved instead of taking remedial measures after the structure is misaligned. For example, if the drift amplitude of a certain core tube benchmark point exceeds the upper limit of the static trust domain for three consecutive days, the system will issue an error propagation command on the construction control platform, requiring immediate verification of the alignment accuracy of the associated floor.

[0078] Step three: After receiving the error propagation command, the system will analyze the error distribution and transmission relationship of each floor construction alignment components based on the data of the effective point set, calculate the overall misalignment risk index DRI, and if the risk index exceeds the set structural cumulative deviation threshold, the effective point set and the static trust domain need to be dynamically corrected to ensure the reliability of the subsequent alignment reference benchmark.

[0079] The misalignment risk index is used to quantify the overall misalignment risk of the building in the vertical direction, and the larger the value, the higher the risk; dynamic correction is to re-evaluate and adjust the effectiveness of the benchmark point and the range of the trust domain based on the latest drift and risk data.

[0080] By quantifying the overall error level through risk indicators, local adjustments can be made before the error has developed to an irreversible stage, reducing the probability of large-scale rework.

[0081] Step four: Obtain the error vector field (three-dimensional offset vector of each component) of all construction layout components using the corrected active point set; use clustering analysis (such as K-means) to group the error vector field and identify areas with large error magnitude and consistent direction with the overall risk of layering errors; in these areas, further filter out the high-risk offset component set as the focus of re-measurement and correction.

[0082] The error vector field is the spatial offset vector of each construction component, used to analyze error distribution and directionality. The high-risk area is the area with the most concentrated error after clustering and the greatest impact on structural accuracy.

[0083] Clustering analysis can quickly lock in key risk areas in the full error data, allowing subsequent re-measurement and correction resources to focus on the positions that most affect structural accuracy. For example, in a certain high-rise floor layout, by clustering to identify a consistent directional offset trend in the 3-8 layers of the south facade, a measurement team can be dispatched to perform full point re-measurement in this area.

[0084] Step five: Package the component number, location coordinates, offset vector, and risk level of the high-risk offset component set into a data packet and upload it to the construction control system through the Internet of Things interface; the control system generates re-measurement and re-layout tasks based on the risk level and combines path planning and equipment instructions to achieve precise repositioning on site.

[0085] The re-measurement and re-layout task refers to re-measuring and positioning the high-risk offset components and re-calibrating the layout according to the design coordinates to ensure that the component positions meet the design requirements. Through systematic task generation and delivery, the re-measurement and correction process is highly automated, reducing manual screening errors and improving response speed. For example, if the system detects that two columns on the 12th floor of the east facade have an offset of more than 6mm, it automatically generates a re-measurement task and pushes it to the measurement employee's handheld terminal, while providing a relatively optimal projection path from the core tube reference point to the column, which can be directly executed on site to complete the precise repositioning.

[0086] In this embodiment, by setting up a set of reference points to be tracked at the construction site, constructing a drift record sequence in real time, and establishing a static credibility domain, a quantitative assessment of the temporal continuity and stability of the reference points is achieved. Based on the static credibility domain, effective reference points and backup points are screened, and error propagation commands are triggered by drift trends, allowing for identification and intervention at an early stage of error. By analyzing the accumulation and propagation of errors in the construction layout components of each layer, the overall misalignment risk index is calculated, and the effective point set and static credibility domain are dynamically corrected accordingly to ensure the continuous reliability of the reference benchmark. Based on the corrected effective points, the error vector field is clustered to accurately locate high-risk areas and components in the error concentration, achieving automatic identification of risky components. Finally, the set of high-risk offset components is uploaded to the construction main control system to generate re-measurement and re-layout tasks, forming a closed-loop control of monitoring, judgment, correction, and verification, which significantly reduces the risk of misalignment and improves the accuracy of construction layout and structural safety. Example 2

[0087] Please refer to Figure 1 Specifically, based on the stability reference domains of each benchmark point, the drift trend that gradually accumulates over multiple cycles during actual construction is identified, and a risk assessment model for vertical misalignment across floors is established to form a traceable risk index sequence, providing a basis for decision-making for subsequent correction and alarm strategies.

[0088] A set of reference points to be tracked is selected, and a drift record sequence is constructed to quantify the temporal continuity stability of each reference point during construction, including:

[0089] During the civil construction process, structural surface control points in each construction floor are selected as reference points for axis layout. Microseismic sensors and laser plumb bobs are installed at each reference point to monitor the floor status characteristics in real time.

[0090] Set a continuous observation window and substitute the floor status characteristics. To construct a drift record sequence for each reference benchmark, the drift record sequence is used to quantify the temporal continuity stability of each reference benchmark during the construction process, that is, whether the benchmark maintains its spatial position unchanged over a period of time, and whether it is affected by factors such as micro-settlement, micro-vibration, and changes in ambient temperature.

[0091] in, For the i-th reference point at The drift amplitude index at any given time. and For the i-th reference point at and Vertical elevation at any given time (unit: mm). For the i-th reference point at The micro-motion acceleration (unit: mm / s²) at the moment; is the unit conversion coefficient, which is to make the unit of the second term consistent with the first term (unit: s 4 ), and ensure that the height drift and acceleration are of the same order of magnitude, which has a range that will be divided into different working conditions: in the case of light formwork, short-time excitation, The value range is recommended to be 10³ to 10 4 , in the case of medium shear wall formwork, The value range is recommended to be 10 5 to , in the case of large volume structure, steel pipe column support, The value range is recommended to be to ;

[0092] The first term is the absolute offset on the elevation, reflecting the static drift of the point in the vertical direction, mainly identifying long-term effects such as settlement;

[0093] The second term is the dynamic excitation response degree of the structure disturbed, representing the external disturbance that the point is currently experiencing, such as short-term dynamic factors such as pumping, ramming, equipment resonance, etc.; The square root of the superposition of the two, constitutes a unified spatial drift measure. This makes the drift amplitude index take into account: structure static deformation (such as settlement), construction disturbance excitation (such as vibration, impact), and unstable trend (such as the coexistence of settlement / vibration).

[0094] Because the traditional system only considers identifying obvious position changes, such as foundation settlement of 3mm, formwork overall subsidence of 5mm, etc., but other unstable states in construction will not cause significant elevation changes, such as: structure vibration causes laser ranging value to fluctuate within ±0.5mm, periodic vibration caused by temporary work of workers on the formwork, and although the point itself does not deform during the release, the surrounding structure is unstable, These are difficult to be captured by simple elevation difference, so acceleration is introduced as a response sensitivity index to identify whether the corresponding point is disturbed. Among them, the formwork refers to the construction formwork (formwork), which is a temporary structure or assembly component used to support, shape and constrain concrete components during pouring and molding in civil construction.

[0095] When it is desired to determine whether a reference point is stable, the drift behavior of the point within a period of time needs to be statistically modeled, so the drift sequence of each point is executed:

[0096] S101: The drift record sequence contains multiple drift amplitude indicators of the corresponding floor within the continuous observation window, and the sliding mean and variance of the drift amplitude indicators in the drift record sequence are calculated to extract the drift expectation value and noise range under the static stable interval;

[0097] The drift expectation value refers to the average drift amplitude of the corresponding reference point, and the noise range is the fluctuation intensity of the drift value under the static stable state, usually represented by the standard deviation;

[0098] S102: Based on the drift expectation value and noise range under the static stable interval, the static confidence domain of each reference benchmark is set, specifically: , wherein is the static confidence domain of the i-th reference benchmark, is the average offset value of the i-th reference benchmark, is the offset standard deviation of the i-th reference benchmark, representing the volatility or uncertainty of the point offset behavior.

[0099] This is a statistical interval constructed based on the 3σ principle, which means that under normal stable state, the drift value D of the reference point should fall within this interval with a high probability, if it falls outside the interval, it means that the point has an abnormal offset or trend drift.

[0100] Since the reference point is the source of the setting-out positioning, once it drifts, the entire measurement system will be distorted; a single offset value has no judgment significance and must be combined with historical data to assess the trend; using the confidence domain as the "judgment boundary" can avoid worker misjudgment (human measurement error is mistaken for point drift) and fluctuation misjudgment (such as short-term abnormalities caused by high-frequency construction vibration); after establishing such a domain, the domain can be passed to the subsequent trend identification and point replacement judgment as an "input condition".

[0101] The vertical elevation at the moment is the height reference, serving as a reference;

[0102] The set of to-be-tracked reference points is composed of multiple reference points;

[0103] The floor state features include micro-motion acceleration and vertical elevation;

[0104] In multi-story buildings, to ensure the consistency of the axis positioning of each floor, the construction team usually sets fixed measurement reference points on the structural surfaces (such as beam ends, column feet, shear wall surfaces). These points are repeatedly used for: setting-out positioning (such as wall, column center); construction measurement review; vertical precision projection between floors.

[0105] S103: judging the validity of each reference datum by the static trust domain, generating the valid point set, the valid point set including the valid backup point and the valid reference datum, triggering the error propagation command according to the drift trend of each point in the valid point set, including:

[0106] Before each construction cycle, the validity of each reference datum is judged, if the drift amplitude index at the current time falls within the static trust domain, it means that the reference datum is still valid, otherwise it is invalid;

[0107] If it is invalid, the drift record is recorded as invalid, that is, the drift record sequence of the invalid reference datum is invalidated, and the nearest backup point in the structural association range (i.e. the corresponding floor range) is automatically searched, and the validity of the backup point is judged, if the backup point is invalid, a first static trust domain correction command is triggered, if the backup point is valid, the drift record sequence of the backup point is recorded; the first static trust domain correction command is triggered in order to adapt to the current structural state and reestablish the reliable judgment boundary, rather than continue to use the old distorted expectation value.

[0108] When receiving the first static trust domain correction command, S101 to S102 are repeatedly executed to dynamically correct the static trust domain of each reference datum until the backup point is valid, and the repeated operation is stopped;

[0109] The valid backup point and the valid reference datum are combined to generate the valid point set;

[0110] The valid backup point and the valid reference datum refer to the state of a reference datum in the current construction cycle, which meets the preset stability condition and can be trusted as a reference point for axis projection, line positioning, component arrangement, error evaluation and other operations.

[0111] In the construction process, each construction cycle (such as daily or after completing one floor structure) is set as an observation interval, and the center drift value of each point (i.e. the valid backup point and the valid reference datum) in the valid point set in each observation interval is recorded to obtain the cross-cycle sequence of each point;

[0112] The center drift value refers to the average value of the drift amplitude index in the corresponding observation interval;

[0113] The cross-cycle sequence of the same point is subjected to first-order difference operation to define the periodic drift increment;

[0114] If the periodic drift increment exceeds the standard deviation (i.e. the drift amplitude index in the static trust domain ) of the corresponding point, and the continuous number of times exceeds the preset period, it is determined that the corresponding point has a trend drift sign, at which time the error propagation command is triggered; otherwise, it is not triggered;

[0115] The preset period is set by experience, such as 3 cycles.

[0116] The center drift value of each point is the mean value of a plurality of drift amplitude indicators of the corresponding effective reference datum point or effective standby point in the observation interval;

[0117] The cross-cycle sequence is composed of center drift values corresponding to a plurality of observation intervals;

[0118] In the embodiment, in the process of civil construction, each floor has a structural surface control point as an axis line layout reference, which is defined as a reference datum point. Microseismic sensors and laser plumbs are installed on the reference datum points to monitor two types of state characteristics in real time: micro-motion acceleration (unit: mm / s², used to reflect the movement trend of the datum point under microseismic and construction disturbance), and vertical elevation (unit: mm, used to reflect the change of the height position of the datum point, such as settlement). A continuous observation window (for example, 24 hours or one construction shift) is set, and the drift state of each reference datum point is recorded continuously in the window to form a drift record sequence.

[0119] The drift amplitude indicators in the sequence are subjected to sliding mean value and variance calculation to extract drift expectation values and noise ranges in the static stable interval, and prepare for subsequent static confidence domain setting. The characteristic data of the reference datum point under normal fluctuation and abnormal drift can be quickly distinguished through real-time acquisition and statistical analysis, and reliable basis is provided for subsequent stability determination. For example, in high-rise construction, if the drift amplitude of a reference datum point in the continuous observation window is less than 0.5 mm and changes smoothly, it can be determined that the point is stable; otherwise, if the fluctuation exceeds 2 mm and is irregular, it can be marked as a potential risk point in advance. Based on the drift expectation values and noise ranges (standard deviations) extracted in S101, a static confidence domain is established for each reference datum point. If the drift amplitude monitored in the future still falls within this interval, it is considered that the point is stable at the current stage. The static confidence domain can convert complex continuous monitoring data into an interval that can be directly used for judgment, greatly reducing the complexity of manual analysis and improving the efficiency of system automatic judgment. For example, if the mean value of a reference datum point is 1.2 mm and the standard deviation is 0.3 mm, the confidence domain can be 0.9-1.5 mm. As long as the subsequent drift value falls within this range, no intervention is needed; if it exceeds the range, the next step can be triggered immediately;

[0120] Before each construction cycle (e.g. every morning shift or after completing a layer of structure), it is determined whether the current drift amplitude of each reference point falls within its static trust domain. When a reference point is invalid, its drift data is recorded as invalid, and the nearest spare point in the same floor range is searched. If the spare point is valid, the original reference point is replaced by the spare point; if the spare point is invalid, a static trust domain correction command is triggered, and the process returns to S101-S102 to dynamically correct the trust domain until a valid point is found. During the construction process, each construction cycle is taken as an observation interval, and the center drift value (the average value in the cycle) of each valid point is calculated to form a cross-cycle sequence. The cross-cycle sequence of the same point is first-order differentiated to obtain the periodic drift increment: if the increment exceeds the standard deviation and the number of consecutive times exceeds the preset period (e.g. 3 cycles), it is determined that the point has a trend drift, and an error propagation command is triggered; otherwise, no command is triggered.

[0121] Through dynamic validity judgment and spare point switching, even if some reference points fail during construction, the system can still maintain a stable reference network, avoiding a significant decrease in the line release accuracy due to single-point failure. The trend judgment of the cross-cycle drift increment can intervene in advance before the error has accumulated, reducing the risk of subsequent large-scale rework. For example, if a reference point has drift increments of 0.4mm, 0.5mm and 0.6mm in three consecutive days, which exceed the standard deviation of 0.3mm, it is determined to have a trend drift and an error propagation command is triggered to correct the entire line release system.

[0122] In summary, the present application realizes real-time collection of floor state characteristics by selecting structural surface control points as reference points at each construction floor and configuring microseismic sensors and laser plumb instruments; by constructing a drift record sequence with a continuous observation window, the time-continuous stability of the reference points during construction can be accurately quantified, and subtle displacements caused by micro-settlement, micro-seismic vibration and temperature changes can be identified. By calculating the drift expectation value and noise range of the static stable interval through sliding mean and variance, a static trust domain is established to realize dynamic judgment of the validity of the reference points and automatic replacement of spare points. By introducing cross-cycle sequence and periodic drift increment calculation, the error propagation command can be triggered at an early stage of drift trend to realize pre-intervention of the reference point state. This method forms a full-link closed-loop control from real-time monitoring, validity judgment, abnormal replacement to trend warning, effectively avoiding the accumulation and amplification of reference point drift, reducing the risk of structure misalignment and cumulative deviation during construction, and further improving the line release accuracy and construction quality stability. Embodiment 3

[0123] Please refer to Figure 1, specifically: S104: receiving error propagation commands, by analyzing the error accumulation and propagation degree of each layer construction layout component, to obtain the risk index of the whole construction site, based on the value, the effective point set and static trust region are dynamically corrected, including:

[0124] The total value of the vertical drift is obtained by adding the periodic drift increment of the corresponding point (i.e. the cumulative offset in the vertical direction, unit: mm);

[0125] Receiving error propagation commands, according to the dependence relationship between construction layout components and reference datum points, analyzing the error distribution of construction layout components of each floor, to obtain the position offset, the position offset is used to reflect the error accumulation and propagation degree of each layer construction layout component, specifically: The formula is used to estimate the error accumulation degree (i.e. position offset) of the construction layout component of the

[0126] Wherein, is the position offset of the construction layout component of the is the floor number, here the floor number only refers to the The floor number of the construction layout component of the

[0127] is the influence factor of the th point in the effective point set on the th construction layout component, the influence factor is used to quantify the contribution degree of the datum point to the position of the upper layer component, and the construction layout correlation matrix generated by the BIM model is given;

[0128] BIM model is a digital three-dimensional information expression method widely used in civil engineering, building construction, structural design and other fields. It is not only a three-dimensional modeling tool, but also a visualized + dataized building full life cycle management model integrating structure, material, process, time, cost and other information.

[0129] is the total vertical drift value of the th point in the effective point set, is the number of floors, is the point number in the effective point set, it should be noted that since each point is relative to each floor, the points in the effective point set and the construction layout components of each layer are in a one-to-one correspondence, and ​​, which means error propagates upward, only considering the lower layer points;

[0130] Dependency relationship between construction alignment components and reference datum points: Each layer of construction alignment components (such as walls, columns, floors, etc.) needs to be positioned and accurately arranged based on the lower layer datum points or plumb projection points. These datum points provide a stable reference system for the current floor, which is used to ensure the structural alignment of multi-story buildings. The stability of the datum points directly affects the positional accuracy of subsequent construction components, and further affects the verticality and overall structural stability of the entire building. In the technical solution, reference points (such as the first to the mth layer of reference points) are usually used to provide accurate positioning support for the upper layers. The projection relationship between the construction alignment components of each layer (such as walls, columns) and the lower layer (such as the first, second, etc.) datum points is managed by the BIM alignment matrix (also known as the "alignment influence matrix"). The value range of each influence factor in the BIM alignment matrix is between 0 and 1, representing the dependency strength or participation degree;

[0131] For example, assume that the alignment positioning of the 6th floor wall uses the 1st, 2nd, 4th, and 5th floor datum points as plumb projections: the 1st floor datum point has drifted by 3mm; the 2nd floor datum point has drifted by 1.2mm; the 4th and 5th floor datum points have not drifted. This means that the alignment positioning of the 6th floor wall will be affected by these four datum points. If there is an alignment matrix to express these relationships, which is managed by the BIM alignment matrix (also known as the "alignment influence matrix"), the final positioning of the 6th floor wall relative to these datum points can be calculated by weighted average.

[0132] The alignment matrix in the BIM (Building Information Modeling) system is used to manage the relationship between the datum points of each layer and the construction components. It can be defined as a weighted matrix reflecting the dependency of each layer datum point on the current layer alignment.

[0133] Construction alignment components refer to specific structural components that require precise alignment to determine their spatial position, size, centroid, or boundary contour during the construction phase. In other words, the size and position of these components cannot rely on "visual inspection or drawings". They must be positioned by using total station, laser alignment instrument, level, robot alignment, etc. to output control lines. Specifically, common construction alignment component types include shear walls, frame columns, infill walls, floor openings, beams, window frames / door openings, fire shafts, and stair sections, etc. Among them, shear walls are positioned by wall axis, frame columns are positioned by column centroid, and infill walls are positioned by masonry starting point, etc.

[0134] The positional offset of each floor's construction alignment component is synthesized to obtain the staggered floor risk index of the entire construction site, which is used to reflect the overall structural stability of the building. The specific expression of the staggered floor risk index is: where, is the error risk index of the current construction period, is the total number of floors, is the position offset of the construction alignment component of the i-th floor; the meaning of the formula is: the root mean square amplitude of the staggered layer distribution, reflecting the overall vertical error trend of the structure. If the error risk index exceeds the allowable cumulative deviation threshold of the structure (such as 20 mm), a second static confidence domain correction command is triggered, otherwise not. The allowable cumulative deviation threshold of the structure is the maximum cumulative error value allowed in the design and construction specifications.

[0135] When the second static confidence domain correction command is received, the historical drift record sequence of each point in the effective point set is traced back, and S101 to S104 are repeatedly executed to dynamically correct the static confidence domain of the effective point set and each point therein, until the second static confidence domain correction command is not triggered, and the repetition is stopped. The purpose of correction is to ensure that the system's judgment of the state of the reference point always matches the current structure response and the actual construction condition, to avoid prematurely eliminating effective points or ignoring invalid points, and to achieve the time adaptability and self-evolution ability of the monitoring model.

[0136] In this embodiment, the periodic drift increments of each reference point are calculated in the identified effective point set (effective reference reference points and standby points). The purpose is to accumulate these periodic drift increments to obtain the vertical drift total value (unit: mm) to reflect the cumulative offset of each effective point in the vertical direction. This is the basic data for error risk assessment, because the staggered layer of high-rise buildings mainly accumulates along the vertical direction. By calculating the vertical drift total value, the scattered periodic drift trend can be converted into a quantifiable cumulative displacement index, thereby providing accurate input for subsequent error risk calculation. This processing method can effectively avoid the risk of ignoring single drift values, so that potential structural misalignment can be discovered at an early stage.

[0137] The periodic drift increment is the drift change between two adjacent construction periods, which is used to judge the trend of displacement. The vertical drift total value is the vertical displacement accumulated by multiple periods.

[0138] The periodic drift increment is the drift change between two adjacent construction periods, which is used to judge the trend of displacement. The vertical drift total value is the vertical displacement accumulated by multiple periods.

[0139] ​According to the dependency relationship between the construction line member and the reference datum point, the total vertical drift value is converted to the position offset amount of each layer of construction line member. The influence factor in the position offset amount formula represents the geometric influence degree of a certain datum point on the position of a certain layer of member, for example, the closer the datum point is to the member, the greater the influence factor. Only the error of the lower layer point is considered to be propagated upwards, in order to conform to the rule that the error is transmitted from bottom to top in actual construction. The conversion of the datum point drift into the position offset amount of the specific member can accurately judge the propagation path and accumulation amplitude of the error between the floors. It avoids the situation of simply relying on the datum point data and ignoring the actual position error of the member, and improves the pertinence of risk assessment.

[0140] The position offset amount is the spatial difference value of the construction line member relative to the design theoretical position.

[0141] The position offset amount of the member of each floor is synthesized to obtain the unified staggered floor risk index DRI of the whole field, which is used to reflect the vertical stability of the overall structure, and is the key index for the monitoring system to judge whether the building has overall staggered floor risk. A global risk evaluation value is provided, which is convenient for the construction party, the supervision party and the quality control system to make unified decision. Through the staggered floor risk index, it can be quickly identified whether large-scale correction is needed to be started, so as to prevent the staggered floor risk from spreading to more floors. For example: if the staggered floor risk index of the whole construction site reaches 21 mm, and the structure allows the cumulative deviation threshold to be 20 mm, it indicates that the risk has exceeded the limit, and correction measures must be taken.

[0142] When the staggered floor risk index exceeds the threshold value, the second static trusted domain correction command is triggered, the historical drift record of the effective point set is traced back, and its static trusted domain is recalculated. S101 to S104 are re-executed until the staggered floor risk index falls within the allowable range. A closed-loop correction mechanism is formed, which can automatically intervene when the structure staggered floor risk reaches the critical point, and avoid the risk from further expanding.

[0143] Using historical data to trace back and correct the static trusted domain can enhance the accuracy of the datum point determination and prevent similar problems from occurring again in the future. For example, if the staggered floor risk index has exceeded the limit, and it is found through tracing back that the main reason for the member offset amount contribution is the third layer datum point, the reference point can be recalculated and replaced after correction, reducing the cumulative error of the upper structure. The second static trusted domain correction command: a global reference point correction program started when the staggered floor risk index exceeds the limit.

[0144] The application realizes quantitative evaluation of the stability of the overall structure of the building by receiving error propagation commands, combining the dependency relationship between the construction layout component and the reference datum point, quantitatively analyzing the error accumulation and propagation degree of the construction layout component of each floor, calculating the position offset of each floor, and synthesizing the whole field staggered floor risk index. The total value of the vertical drift is obtained by using the periodic drift increment, only the upward propagation of the error is considered, and the influence of the lower layer offset on the upper layer structure is effectively reflected. When the staggered floor risk index exceeds the allowable cumulative deviation threshold of the structure, the second static trusted domain correction command is automatically triggered, the historical drift record is traced back, and the effective point set and the static trusted domain are dynamically corrected. This method can intervene in time before the structure risk exceeds the limit, avoid the further expansion of the staggered floor risk, and improve the precision control and structural safety in the construction process. In summary, a trend error accumulation judgment mechanism is constructed by using periodic sampling, which gets rid of the isolation caused by the traditional "single cycle error check", and the defined staggered floor risk index has spatial projection characteristics, supporting the overall structure judgment. Embodiment 4

[0145] Please refer to Figure 1 , specifically: comparing the staggered floor risk index with the layout trajectory completed or to be executed in the construction, identifying the components or areas that may deviate from the layout, and completing the early correction strategy before irreversible errors are formed in the local component precision, and constructing the layout correction prompt mechanism.

[0146] In the layout process of each floor, the actual position data (i.e. position coordinates) of each floor construction layout component is collected by measuring equipment (such as a total station, a laser scanner, etc.), and compared with the theoretical position coordinates in the design BIM model to calculate the error deviation of each construction layout component, and an error vector field is constructed to describe the error of each construction layout component.

[0147] The error deviation of each component layout point is calculated as follows: , , wherein, , and are the error deviation amounts in the x-axis, y-axis and z-axis, x, y and z are the coordinates of the actual position data, , and are the theoretical position coordinates.

[0148] The component error vector field is formed as follows: wherein, is the error vector field.

[0149] When a certain reference point is identified as a high-risk source point in trend drift or wire deviation, the system should automatically complete its replacement and reconstruction in the construction measurement system, and migrate the structure measurement domain affected by it to the standby reference point, ensuring the continuity of the wire accuracy and the control of the structure perpendicularity throughout the construction cycle.

[0150] Perform cluster analysis on the error vector field of all construction wire components, and use the K-means algorithm to identify high-risk areas in the error set.

[0151] Perform state traversal on all construction wire components in the high-risk area to identify high-risk deviation components, including:

[0152] According to the size and direction of the error vector field of each construction wire component, if the error vector field satisfies exceeds the maximum acceptable wire error of the component (e.g., 5mm) and the included angle does not exceed the allowable directional angle range (e.g., 15°), it is determined that the deviation of the corresponding construction wire component is consistent with the risk direction of the structure layer error, and is recorded as a high-risk deviation component. A high-risk deviation component set is constructed for; otherwise, it is not a high-risk deviation component.

[0153] The included angle represents the included angle between two vectors, i.e., the included angle between the component error vector field and the overall building layer error risk direction vector . This direction is usually determined by the weighted sum of factors such as reference point drift, wire error, etc. Through principal component analysis (PCA) method, the main layer error risk direction vector can be extracted from the overall error, which represents the main layer error direction that may occur during construction. At the same time, the dot product of the two vectors can be calculated, and then the included angle can be calculated,

[0154] The included angle also reflects the relationship between the reference point drift error and the building layer error risk direction. The smaller the included angle, the more likely it is that the error of the reference point will cause the structure to be out of layer, as its deviation direction is consistent with the overall layer error risk direction. Conversely, in the case of a larger included angle, the error of the reference point has less impact on the layer error risk.

[0155] Upload the high-risk deviation component set to the construction master control system and form a re-measurement and re-wire task. The construction master control system refers to the core control platform for unified reception, aggregation, analysis, and dispatch of on-site construction data, usually including data reception interface, task management module, equipment control interface, state monitoring module, and decision assistance unit, etc.

[0156] The re-measurement and correction task refers to re-measuring the position of a high-risk deviation component and re-staking according to the theoretical position coordinates in the design BIM model to realize timely correction of the component.

[0157] The error vector field of all construction staking components is subjected to cluster analysis, and the K-means algorithm is used to identify high-risk areas in the error set, including:

[0158] The error vector field is used as input, and the number of clusters P is set (which can be determined according to the error distribution characteristics or the elbow rule);

[0159] Run the K-means algorithm to cluster all error vectors and obtain P error clusters, each of which has a center point (i.e., cluster mean vector);

[0160] Calculate the average error magnitude of each cluster to measure the severity of the overall error of each cluster. The specific calculation formula is: wherein is the average error magnitude of the i-th cluster, is the total number of construction staking components in the i-th cluster, is the cluster number, is the number of construction staking components in the i-th cluster, is the error vector field length (magnitude) of the construction staking component, which represents the overall size of the component error. Select the cluster with the largest average error magnitude as the candidate high-risk area; Mark the cluster with the largest average error magnitude and the same directionality as the overall risk direction of the cluster as a high-risk area. The directionality is consistent with the overall risk direction of the layering error, and the specific steps are as follows:

[0161] Calculate the average value of each cluster;

[0162] Select the cluster whose average value does not exceed the allowable directional angle range and mark it as a screened cluster. The screened cluster is the quantitative result of the directionality consistent with the overall risk direction of the layering error.

[0163] The reference point of each floor affects the positioning of the construction staking of the lower floor. For example, the wall staking of the 6th floor depends on the reference points of the 5th floor and the 4th floor, and the 5th floor depends on the 4th floor, the 3rd floor, etc. Therefore, it is necessary to determine the effectiveness of the reference point to prevent subsequent position deviation of the staking component.

[0164]

[0165]

[0166] ​​​​​The main direction of the risk of structural misalignment is usually caused by the drift of the reference point in the building, the error of the construction line, and other factors, which will occur along a certain direction. By determining whether the offset is consistent with the direction of the risk of structural misalignment, the potential risk of misalignment can be more accurately judged, so that more effective correction measures can be taken. At the same time, the consistency of the direction of the risk of structural misalignment can help us determine whether the error is the cumulative effect of the overall error of the building. If the direction of the error offset is consistent with the direction of the overall misalignment risk, it may cause the misalignment or imbalance of the building structure. On the contrary, if the direction of the error offset is not consistent with the direction of the misalignment risk, the offset may have less impact on the misalignment risk.

[0167] In this embodiment, during the construction line process, the actual three-dimensional coordinates (x, y, z) of each construction line component are obtained by measuring equipment (such as a total station, a laser scanner, etc.), and then compared with the theoretical coordinates in the design BIM model to calculate the error deviation in three-dimensional direction. These deviation values are used to construct the error vector field.

[0168] The error deviation reflects the difference between the actual position of the component and the design position in the direction of each coordinate axis, and the unit is usually millimeters. The error vector field is a collection of three-dimensional error vectors of all components, which can intuitively describe the error distribution of the whole field. For example, the design coordinates of a column on a certain floor are (1000, 2000, 3000), and the measured coordinates are (1002, 1997, 3004). The deviation is Δx = 2 mm, Δy = -3 mm, and Δz = 4 mm. This step realizes the accurate comparison between the actual position of the component in the construction site and the design position, forming the error vector basic data which can be used for subsequent analysis, and providing data support for identifying high-risk areas and developing correction strategies. To form the error vector field, set the number of clusters (which can be determined by the error distribution characteristics or the elbow rule), and use the K-means algorithm to divide the components into P clusters, each cluster having a cluster mean vector (representing the average error direction and size of the cluster).

[0169] The K-means algorithm is a clustering method based on distance measurement, which is used to divide the data into P clusters, so that the similarity of the data within the cluster is high and the difference between the clusters is large.

[0170] The cluster mean vector is the center point of each cluster after clustering, representing the average error state of the components in the cluster. For example, the 200 construction line components in the whole field are divided into 3 clusters, with the average error vector of cluster A being (5, 1, 2) mm, the average error vector of cluster B being (1, -2, 1) mm, and the average error vector of cluster C being (0.5, 0.2, 0.1) mm. This step classifies and aggregates the complex and dispersed error data, which can quickly lock the area with concentrated error distribution, reduce the calculation amount of subsequent analysis, and improve the accuracy of high-risk area identification. For each cluster, calculate the average error amplitude,

[0171] The average error amplitude reflects the severity of the overall error of a cluster and is an important numerical indicator for screening high-risk areas. The module length is the overall size of the error vector, considering the comprehensive deviation in X, Y, and Z directions. For example, the average error amplitude of cluster A is 5.5 mm, that of cluster B is 2.1 mm, and that of cluster C is 0.9 mm, so the error severity of cluster A is the highest. This step provides a quantitative criterion for error severity, ensuring that the identified high-risk areas are truly significant deviation areas rather than accidental single-point large deviations.

[0172] For each cluster, the angle between the cluster mean vector and the overall building story risk direction vector is calculated. If the angle is less than the allowable directional angle threshold (e.g., 15°), it is considered that the error direction of the cluster is consistent with the story risk direction.

[0173] The overall building story risk direction is the dominant risk direction obtained by analyzing the overall reference point drift and error propagation trend. Meanwhile, directional screening avoids misjudging error-prone but directionally irrelevant areas as high-risk areas, improving the relevance of risk determination. In the high-risk area, each component is traversed to determine whether its error amplitude exceeds the maximum acceptable setting-out error (e.g., 5 mm) and whether the angle with the risk direction is within the allowable range. Components that meet the conditions are marked as high-risk offset components and added to the high-risk component set, which is uploaded to the construction control system to generate re-measurement and re-setting-out tasks.

[0174] High-risk offset components are components that may cause structural story or precision loss of control and need to be prioritized for re-measurement and correction. Re-measurement and re-setting-out tasks refer to re-measuring components and repositioning them based on BIM theoretical coordinates to ensure construction precision. This step directly translates risk analysis results into executable construction tasks, achieving seamless integration from risk identification to deviation correction, ensuring that deviations are within a controllable range.

[0175] The present application accurately calculates the error deviation of each component in the x, y, and z directions by comparing the actual position coordinates of the construction setting-out components with the BIM model theoretical coordinates, and constructs a full-field error vector field. Using K-means clustering analysis, clusters with concentrated error distribution and large amplitude are identified as high-risk areas, and combined with directional determination, high-risk offset components consistent with the overall story risk direction are screened to form a high-risk component set. This method can quickly and quantitatively lock the components that pose the greatest threat to structural stability, enabling accurate re-measurement and timely setting-out correction, significantly improving construction precision and structural safety. Example 5

[0176] Please refer to Figure 1 , in particular: uploading the high-risk offset component set to the construction control system and forming re-measurement and re-setting-out tasks, including:

[0177] Packaging the number, spatial coordinates, error vector field and offset attribute of each high-risk offset component in the high-risk offset component set into a data packet, uploading the data packet to the construction master control system through the local area network interface;

[0178] The offset attribute is a classification label or descriptive information of the offset state of the component. It is a qualitative result obtained after analyzing the error vector. The attribute types include normal, controllable drift, high-risk offset, and unusable reference. Among them, the normal attribute type is that the offset is within the allowable error range (e.g. ≤3mm); the controllable drift attribute type is that the offset exceeds the allowable range but the directionality is inconsistent, and the overall structure influence is controllable; the high-risk offset attribute type is that the offset amplitude is out of limit and the direction is consistent with the overall staggered risk direction, which has a great influence on the structural stability; the unusable reference attribute type is that the offset amplitude and direction are abnormal, and cannot be used as a line laying reference point and must be replaced.

[0179] After the construction master control system receives the data packet, i.e. the high-risk offset component data, it automatically generates a re-measurement task sheet. The re-measurement task sheet contains the number, spatial coordinates and re-measurement priority of the components that need to be re-measured (i.e. high-risk offset components).

[0180] The re-measurement priority is to prioritize the components that have the greatest risk to the overall structure and the most direct impact on subsequent construction in the case of limited measurement equipment or personnel, to avoid waste of resources. High-priority components will be placed at the front of the task sheet to ensure that these components can be re-measured and corrected in a short period of time. That is, the role of the re-measurement priority is to prioritize the components that are most likely to affect the safety, accuracy and progress of the structure to be re-measured and corrected, thereby minimizing error risk and maximizing construction efficiency under limited resources.

[0181] The risk offset components are sorted according to the following logic (i.e. re-measurement priority): first, sort by error amplitude from large to small; if the amplitudes are the same, prioritize by direction consistency (smaller angle first); if the first two are the same, sort by construction timing urgency (the closer to the key construction node, the higher the priority). The sorted list is the execution order of the re-measurement task.

[0182] According to the re-measurement task sheet, the path of the high-risk offset component (the component that needs to be re-measured) is calculated, and the path calculation steps include:

[0183] Extract the theoretical position coordinates of the target component from the design BIM model;

[0184] Combine the positions of the points in the effective point set (i.e. the latest reference point positions) with the construction site coordinate system to calculate the optimal measurement and line projection path from the reference reference point to the high-risk offset component;

[0185] The line-throwing path is the process of determining the positional relationship and layout path of each component (such as walls, beams, columns, etc.) of the building structure according to design requirements. It is usually controlled by reference points and measuring equipment (such as total stations, laser scanners, etc.). Key elements of the line-throwing path: Reference points: the reference point for all line-throwing operations, usually the known position established at the beginning of construction, used to position the coordinate system of the entire building. Component positioning: according to design drawings and BIM models, determine the specific position of each component (such as columns, beams, walls, etc.), and ensure their correct installation position through line-throwing.

[0186] According to the optimal measurement and line-throwing path, and combined with the re-measurement priority, output the path point set and construction equipment instructions (adapt to laser line-throwing instrument, total station, etc.) to perform line-throwing and measurement operations on the corresponding high-risk offset components through the on-site line-throwing equipment, and realize accurate repositioning.

[0187] The path point set is composed of a series of ordered three-dimensional coordinate points, which define the measurement / line-throwing route from the reference point to the target component. It is equivalent to a "navigation map" that tells the measuring instrument (total station, laser line-throwing instrument, etc.) where to start, which path to follow, and which points to pass through, and finally projects the line-throwing information to the target component position. For example, P0 (reference point position) → P1 (measurement transfer point) → P2 (target component line-throwing point);

[0188] The construction equipment instruction is generated according to the path point set, which can be directly issued to the operation instruction of the construction measurement equipment. These instructions will tell the equipment: the coordinates that the equipment needs to aim at or line-throw, the angle, distance, direction of line-throwing, and the projection mark position on the target component (such as marking a vertical line, a horizontal line, or an intersection on the wall surface); Its purpose is: construction personnel do not need to manually measure repeatedly, automatically position the target point after receiving the instruction through the equipment, reduce human error; if the equipment position changes (moves or collides), quickly reposition the target point according to the instruction and ensure that the component line-throwing position is highly consistent with the BIM design coordinates, reducing error accumulation.

[0189] Convert the path point set data into a control command format that the measuring equipment can recognize;

[0190] In this embodiment, the identified high-risk offset component information is standardized into a data packet and transmitted to the construction master control system through a local area network, ensuring accurate, fast, and directly usable data for task scheduling;

[0191] The data content includes component number, spatial coordinates, error vector field, and offset attribute. The structured data packet can be directly parsed by the master control system to generate a task sheet, reducing the delay and errors of manual input. It ensures that high-risk component information is quickly and accurately transmitted to the master control system, reduces the time for manual sorting and secondary input, and improves data interaction efficiency and accuracy. For example, a certain high-rise building has a 8mm offset at the 12th floor of a load-bearing column, which is consistent with the risk of staggered floors. The system packages the column number C12-045, coordinates (12500, 3800, 36.2), error vector field (6, 4, -2), and offset attribute high risk as a data packet, and sends it to the construction master control system through the local area network.

[0192] The error vector field is used to describe the actual offset of the component in the x, y, and z directions.

[0193] The offset attribute is a risk level indicating the offset, such as normal, controllable, and high risk. The master control system parses the data packet and automatically generates a retest task sheet. The task sheet content includes the number of components that need to be retested, spatial coordinates, and retest priority. The retest priority is determined by the offset amplitude, directionality, and construction process urgency, which is used to optimize the allocation of construction resources, further avoid the delay and waste of resources caused by manual determination of task order, ensure that key risk components are retested first, and reduce the risk of error propagation. The retest priority determines the execution order of the retest task, ensuring that components with relatively large structural impact are processed first. The theoretical position coordinates of the target component are extracted from the BIM model, combined with the latest reference point position and the construction site coordinate system, to calculate the relatively optimal measurement and projection path from the reference reference point to the high-risk offset component. The optimal path needs to avoid obstacles on site, ensure clear equipment measurement line of sight, good angle and distance accuracy, and avoid repeated movement of equipment and secondary measurement due to unreasonable measurement path, improving retest efficiency and accuracy.

[0194] The optimal measurement and projection path is the best observation route of the measurement equipment from the reference point to the target point, which ensures the measurement accuracy and operation efficiency. The system converts the optimal path into a path point set (a set of ordered three-dimensional coordinate points) and construction equipment instructions (adapted to laser projectors, total stations, etc.), and issues them to the on-site equipment. The path point set guides the equipment on how to move and aim, and the construction equipment instructions include specific operation information such as rotation angle, ranging parameters, etc. Through the automatic issuance of instructions, manual input and human error are reduced, the equipment is ready for use, and the accuracy and speed of re-measurement and line correction are improved. For example, the system generates the path point set {P0 (reference point), P1 (transfer point), P2 (component position)} and issues the total station instructions with an angle of 95.3°, a vertical angle of 88.7°, and a ranging of 12.5m to the measurer, who can directly execute it. The path point set is a coordinate sequence that the equipment needs to aim or project in sequence on site. The construction equipment instructions are the precise operation parameters required for the equipment to perform measurement or projection tasks. The on-site line equipment performs measurement and projection according to the path point set and instructions, re-corrects the component position to the theoretical coordinates, and realizes accurate repositioning. It ensures the position accuracy of high-risk components during construction, prevents errors from accumulating in subsequent construction, and ensures the overall safety and geometric accuracy of the structure. For example, after the total station projection is completed, the construction personnel adjust the load-bearing column position to within ±2mm of the theoretical position, eliminating the risk of 8mm misalignment. Accurate repositioning is to restore the component position to the state consistent with the design model, meeting the construction accuracy requirements.

[0195] The application realizes centralized management and rapid scheduling of high-risk data by packaging and uploading the number, spatial coordinates, error vector field, and offset attributes of high-risk offset components to the construction master control system. The system automatically generates a task list containing re-measurement priority, and calculates the optimal measurement and projection path based on the BIM model theoretical coordinates, the latest reference point position, and the on-site coordinate system. The path point set and construction equipment instructions are output, which can directly drive the line equipment to accurately position and reposition the target component, significantly improve the re-measurement and correction efficiency, reduce manual errors, and ensure the construction line accuracy and structural safety. Embodiment 6

[0196] Please refer to Figure 2 , in particular: a civil construction intelligent management and control system based on the Internet of Things, the system comprising:

[0197] A monitoring module, a set of reference points to be tracked is selected in advance at the civil construction site to construct a drift record sequence and a static trusted domain, which is used to quantify the time continuous stability of each reference point during construction;

[0198] The first correction and analysis module judges the validity of each reference datum point through the static trusted domain, generates an effective point set including effective backup points and effective reference datum points, and triggers an error propagation command according to the drift trend of each point in the effective point set;

[0199] The second correction and analysis module receives the error propagation command, analyzes the error accumulation and propagation degree of each layer construction layout component through the drift trend of each point, obtains the risk index of the whole construction site, and dynamically corrects the effective point set and the static trusted domain based on the value;

[0200] The control target identification module performs cluster analysis on the error vector field of all construction layout components based on the corrected effective point set, to identify a high-risk area and a high-risk offset component set in the area;

[0201] The dynamic reset module uploads the high-risk offset component set to the construction master control system and forms a re-measurement and re-layout task.

[0202] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent management and control of civil construction based on the Internet of Things, characterized in that: Includes the following steps, A set of reference points to be tracked is selected in advance at the civil construction site to construct a drift record sequence and a static confidence region. The drift record sequence is used to quantify the temporal continuity stability of each reference point during the construction process. The validity of each reference point is determined by the static trust domain, and a set of valid points is generated. The set of valid points includes valid spare points and valid reference points. The error propagation command is triggered based on the drift trend of each point in the set of valid points. Receive error propagation commands, analyze the error accumulation and propagation degree of each layer of construction layout components by the drift trend of each point, so as to obtain the misalignment risk index of the entire construction site, and realize dynamic correction of the effective point set and static confidence domain based on its value. Based on the corrected set of valid points, cluster analysis is performed on the error vector field of all construction layout components to identify high-risk areas in the error set and the set of high-risk offset components within them. The high-risk offset component set is uploaded to the construction main control system and a re-measurement and re-layout task is generated.

2. The intelligent management and control method for civil construction based on the Internet of Things according to claim 1, characterized in that: A set of reference points to be tracked is selected, and a drift record sequence is constructed to quantify the temporal continuity stability of each reference point during construction, including: During the civil construction process, structural surface control points in each construction floor are selected as reference points for axis layout. Microseismic sensors and laser plumb bobs are installed at each reference point to monitor the floor status characteristics in real time. Set a continuous observation window and substitute the floor status characteristics. To construct a drift record sequence for each reference benchmark, the drift record sequence is used to quantify the temporal continuity stability of each reference benchmark during construction; wherein, For the i-th reference point at The drift amplitude index at any given time. and For the i-th reference point at and Vertical elevation at any moment For unit conversion factor, For the i-th reference point at The acceleration of minute movements at any given moment; S101: The drift record sequence contains multiple drift amplitude indices for the corresponding floors within a continuous observation window. The moving mean and variance of the drift amplitude indices in the drift record sequence are calculated to extract the expected drift value and noise range in the static and stable interval. S102: Based on the expected drift value and noise range in the static steady interval, set the static credibility region of each reference point; The set of reference points to be tracked consists of multiple reference reference points; Floor condition characteristics include micro-motion acceleration and vertical elevation.

3. The intelligent management and control method for civil construction based on the Internet of Things according to claim 2, characterized in that: S103: Determine the validity of each reference point through the static trust domain, generate a set of valid points, including valid spare points and valid reference points. Based on the drift trend of each point in the set of valid points, trigger error propagation commands, including: Before each construction cycle, the validity of each reference point is determined. If the drift amplitude index at the current moment falls within the static confidence region, it means that the reference point is still valid; otherwise, it is invalid. If invalid, the drift record is invalidated, and the nearest backup point within its structural association range is automatically searched to determine whether the backup point is valid. If the backup point is invalid, the first static trusted domain correction command is triggered. If the backup point is valid, the drift record sequence of the backup point is recorded. When the first static trust domain correction command is received, S101 to S102 are executed repeatedly to dynamically correct the static trust domain of each reference point. Combine valid backup points and valid reference points to generate a set of valid points; During the construction process, each construction cycle is set as an observation interval, and the center drift value of each point in the set of effective points within each observation interval is recorded to obtain the cross-cycle sequence of each point. Perform first-order difference operations on cross-period sequences at the same point to define the periodic drift increment; If the periodic drift increment exceeds the standard deviation of the corresponding point and the number of consecutive occurrences exceeds the preset time limit, it is determined that the corresponding point has signs of trend drift, and an error propagation command will be triggered.

4. The intelligent management and control method for civil construction based on the Internet of Things according to claim 3, characterized in that: S104: Receives the error propagation command, analyzes the error accumulation and propagation degree of each layer of construction layout components to obtain the misalignment risk index of the entire construction site, and dynamically corrects the effective point set and static confidence domain based on its value, including: Calculate the periodic drift increments at the corresponding points to obtain the total vertical drift value; Upon receiving the error propagation command, the system analyzes the error distribution of the construction layout components on each floor based on their dependence on the reference benchmark, in order to obtain their position offset. The position offset is used to reflect the degree of error accumulation and propagation of the construction layout components on each floor. The positional offset of the construction layout components on each floor is contributed to the synthesis to obtain the misalignment risk index of the entire construction site, which is used to reflect the overall structural stability of the building. If the misalignment risk index exceeds the structural allowable cumulative deviation threshold, then the second static trusted domain correction command is triggered. When the second static trust domain correction command is received, the historical drift record sequence of each point in the set of valid points is traced back, and S101 to S104 are executed repeatedly to dynamically correct the static trust domain of the set of valid points and each point in it.

5. The intelligent management and control method for civil construction based on the Internet of Things according to claim 4, characterized in that: During the layout process on each floor, the actual position data of the construction layout components on each floor are collected by measuring equipment and compared with the theoretical position coordinates in the design BIM model to calculate the error deviation and construct an error vector field to describe the error of each construction layout component. Cluster analysis was performed on the error vector fields of all construction layout components, and the K-means algorithm was used to identify high-risk areas in the error set. A state traversal is performed on all construction layout components within the high-risk area to identify high-risk offset components, including: Based on the magnitude and directionality of the error vector field of each construction layout component, if the error vector field satisfies Exceeding the maximum acceptable layout error of the component and the included angle If the deviation does not exceed the allowable directional angle range, it is determined that the deviation of the corresponding construction layout component is consistent with the structural misalignment risk direction, and it is recorded as a high-risk deviation component, and a set of high-risk deviation components is constructed. included angle The vector denoted by represents the angle between two vectors, i.e., the component error vector field. and the overall building's staggered risk direction vector The angle between them; The high-risk offset component set is uploaded to the construction main control system and a re-measurement and re-layout task is generated.

6. The intelligent management and control method for civil construction based on the Internet of Things according to claim 5, characterized in that: Cluster analysis was performed on the error vector fields of all construction layout components, and the K-means algorithm was used to identify high-risk areas in the error set, including: The error vector field is used as input, and the number of clusters P is set. Run the K-means algorithm to cluster all error vectors, resulting in P error clusters, where each cluster has its own center point; Calculate the average error magnitude for each cluster to measure the severity of the overall error across all clusters; Clusters with the largest average error magnitude and whose directionality is consistent with the overall risk of misalignment are marked as high-risk areas.

7. The intelligent management and control method for civil construction based on the Internet of Things according to claim 6, characterized in that: The high-risk offset component set is uploaded to the construction main control system and a re-measurement and re-layout task is generated, including: The number, spatial coordinates, error vector field, and offset attributes of each high-risk offset component in the high-risk offset component set are packaged into a data packet, and uploaded to the construction main control system through the local area network interface; After receiving the data packet, the construction control system automatically generates a retesting task sheet; the retesting task sheet includes the number, spatial coordinates, and retesting priority of the component to be retested.

8. The intelligent management and control method for civil construction based on the Internet of Things according to claim 7, characterized in that: According to the retest task sheet, path calculations are performed on high-risk offset components. The path calculation steps include: Extract the theoretical location coordinates of the target component from the design BIM model; By combining the positions of each point in the effective point set with the coordinate system of the construction site, the optimal measurement and projection path from the reference benchmark to the high-risk offset component is calculated. Based on the optimal measurement and projection path, and combined with the re-measurement priority, the path point set and construction equipment instructions are output to perform projection and measurement operations on the corresponding high-risk offset components through the on-site layout equipment, so as to achieve the resetting effect.

9. An Internet of Things (IoT)-based intelligent management and control system for civil construction, used to implement the IoT-based intelligent management and control method for civil construction as described in any one of claims 1 to 8, characterized in that: The system includes: The monitoring module pre-selects a set of reference points to be tracked at the civil construction site to construct a drift record sequence and a static confidence domain, which is used to quantify the temporal continuity stability of each reference point during the construction process. The first correction and analysis module determines the validity of each reference benchmark point through the static confidence domain and generates a set of valid points. The set of valid points includes valid spare points and valid reference benchmark points. Based on the drift trend of each point in the set of valid points, the error propagation command is triggered. The second correction and analysis module receives the error propagation command and analyzes the error accumulation and propagation degree of each layer of construction layout components by the drift trend of each point, so as to obtain the misalignment risk index of the entire construction site. Based on its value, it realizes dynamic correction of the effective point set and static confidence domain. The target identification module performs cluster analysis on the error vector field of all construction layout components based on the corrected set of valid points to identify high-risk areas in the error concentration and the set of high-risk offset components within them. The dynamic reset module uploads the set of high-risk offset components to the construction main control system and generates a re-measurement and re-layout task.

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