BIM-based building construction data acquisition method and system

Abstract

The invention relates to the technical field of sensor data denoising, in particular to a BIM (Building Information Modeling)-based building construction data acquisition method and system.The method comprises the following steps: firstly, according to the characteristic that noise influence generally does not influence the overall trend and in combination with the characteristics that noise signals generally have relatively high signal-to-noise ratio and noise is generally randomly distributed, performing denoising effect significance calculation; determining a de-noised signal and a noise signal which are decomposed; secondly, calculating the decomposition accuracy of each data type by considering the characteristic that the environment noise generally synchronously affects the data of each data type, so that the noise signals of different data types generally simultaneously present similar noise influences; and finally, combining the denoising effect significance and the decomposition accuracy of the denoised signal to comprehensively determine the iteration stop possibility, so that the denoising effect of the final denoised signal obtained after iteration stop is better, the accuracy of building the BIM model is improved, and the accuracy of building construction data acquisition is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor data denoising, and in particular to a BIM-based building construction data acquisition method and system. Background Art

[0002] BIM is a building information model, which is a digital tool applied to engineering design, construction, and management. The construction of a BIM building model depends on the monitoring of real-time data by sensors. By collecting real-time data and seamlessly docking with the BIM model, intelligent management of the building can be achieved. Through the integration of digital and information models of the building, sharing and transmission are carried out throughout the life cycle of project planning, operation, and maintenance, enabling engineering technicians to correctly understand and efficiently respond to building information. BIM technology can more real-time observe the usage progress, predict and adjust the construction sequence, and make reasonable allocation of resources. Therefore, applying BIM technology in the field of building construction is an inevitable trend of digital transformation and development.

[0003] Due to the complex factors of the construction site and the influence of construction weather, there are many noises in the sensor monitoring data used for BIM model construction during monitoring. It is necessary to separate the monitoring signals to obtain structural information data that accurately reflects real building construction-related information. Existing technologies usually adopt time-domain denoising methods such as the moving average method to reduce the noise impact by smoothing the signals; however, since traditional time-domain denoising methods usually only focus on the monitoring data of a single sensor, and the denoising of data mainly relies on the changes in data in time series, it is impossible to accurately judge whether the data anomaly is an abnormal situation of the real construction building or the noise caused by the influence of the construction environment, resulting in generally poor denoising effects, thereby reducing the accuracy of building construction data acquisition. Summary of the Invention

[0004] In order to solve the technical problem that traditional time-domain denoising methods cannot accurately judge whether the data anomaly is an abnormal situation of the real construction building or the noise caused by the influence of the construction environment, resulting in generally low denoising accuracy, the purpose of the present invention is to provide a BIM-based building construction data acquisition method and system, and the specific technical solutions adopted are as follows:

[0005] The first aspect of this application provides a BIM-based building construction data acquisition method, including:

[0006] Collect the construction data time series signals of each data type during the building construction process; divide the construction data time series signals into at least two local signal segments;

[0007] Iteratively decompose the time series signal of the construction data through ICA decomposition. Determine the significance of the denoising effect of the iterative decomposition signal based on the similarity between the iterative decomposition signal after each iterative decomposition and the time series signal of the construction data, as well as the signal-to-noise ratio fluctuation and data fluctuation of each local signal segment in the iterative decomposition signal. Screen out the denoised signal and noise signal of each data type after each iterative decomposition according to the significance of the denoising effect.

[0008] Under each iterative decomposition, determine the decomposition accuracy of each data type according to the amplitude trend similarity and the overall amplitude difference distribution between the noise signal of each data type and the noise signals of other data types. Under all data types, determine the corresponding iterative stop possibility after each iterative decomposition according to the significance of the denoising effect of the denoised signal and the decomposition accuracy.

[0009] Determine the final denoised signal corresponding to all data types after the iteration stops through ICA decomposition combined with the iterative stop possibility. Construct a BIM model using BIM software based on all the final denoised signals.

[0010] Furthermore, the process of obtaining the significance of the denoising effect includes:

[0011] Obtain two iterative decomposition signals obtained after each iterative decomposition of each data type. Determine the denoising possibility according to the similarity of the signal value distribution between the iterative decomposition signal and the time series signal of the construction data.

[0012] On the iterative decomposition signal, calculate the signal-to-noise ratio of each local signal segment and the amplitude standard deviation of all data points therein. Determine the noise performance value of each local signal segment according to the product of the signal-to-noise ratio and the amplitude standard deviation. Determine the noise possibility of the iterative decomposition signal according to the stability of the noise performance values of each local signal segment and the overall magnitude of the noise performance values.

[0013] Perform a positive correlation mapping on the product of the negative correlation mapping value of the noise possibility and the denoising possibility to determine the significance of the denoising effect of the iterative decomposition signal.

[0014] Furthermore, the process of obtaining the denoising possibility includes:

[0015] Calculate the DTW distance between the iterative decomposition signal and the time series signal of the construction data through the dynamic time warping algorithm. Normalize the negative correlation mapping value of the DTW distance to determine the denoising possibility.

[0016] Furthermore, the process of obtaining the noise possibility includes:

[0017] Normalize the product of the mean and standard deviation of the noise performance values of all local signal segments on the iterative decomposition signal to determine the noise possibility of the iterative decomposition signal.

[0018] Further, the process of obtaining the denoised signal and the noise signal after each iterative decomposition for each data type according to the significance of the denoising effect includes:

[0019] After each iterative decomposition of each data type, use the iterative decomposition signal corresponding to the maximum denoising effect significance as the denoised signal; use the iterative decomposition signal corresponding to the maximum denoising effect significance as the noise signal.

[0020] Further, the process of obtaining the decomposition accuracy includes:

[0021] Take each data type in turn as the target type; take the other data types outside the target type as the corresponding comparison types; among them, the construction data time series signals of all data types are iteratively decomposed synchronously;

[0022] At each iterative decomposition, calculate the signal value difference at each sampling moment between the noise signal of each comparison type and the noise signal of the target type; perform a negative correlation mapping on the standard deviation of the signal value differences at all sampling moments corresponding to each comparison type to determine the signal difference consistency between each comparison type and the target type; perform a negative correlation mapping on the mean of the signal value differences at all sampling moments to determine the interference degree consistency between each comparison type and the target type; determine the noise similarity between each comparison type and the target type according to the product between the signal difference consistency and the interference degree consistency;

[0023] Determine the decomposition accuracy of the target type at each iterative decomposition according to the mean of the noise similarities between all comparison types and the target type.

[0024] Further, the process of obtaining the iterative stop possibility includes:

[0025] At each iterative decomposition, determine the iterative stop weight of each data type according to the denoising effect significance of the denoised signal of each data type and the decomposition accuracy; normalize the accumulated value of the iterative stop weights of all data types to determine the iterative stop possibility corresponding to each iterative decomposition.

[0026] Further, the process of obtaining the final denoised signal includes:

[0027] If there is an iteration stop possibility greater than the preset stop threshold within the preset number of iterations, stop the iterative decomposition when the iteration stop possibility greater than the preset stop threshold first appears, and use the denoised signals of all data types obtained when stopping the iterative decomposition as the final denoised signals;

[0028] If there is no iteration stop possibility greater than the preset stop threshold within the preset number of iterations, use the denoised signals of all data types corresponding to the iterative decomposition with the maximum iteration stop possibility as the final denoised signals.

[0029] Further, the process of constructing a BIM model through BIM software based on all the final denoised signals includes:

[0030] After uploading the final denoised signals of all data types to the cloud, establish the coordinates of the project requirement model in the BIM software, import the building model, and form a BIM model.

[0031] In a second aspect, the present application provides a BIM-based building construction data acquisition system, and the system includes:

[0032] A data acquisition and preprocessing module, configured to acquire the construction data time series signals of each data type during the building construction process; divide the construction data time series signals into at least two local signal segments;

[0033] A first determination module, configured to perform iterative decomposition on the construction data time series signals through ICA decomposition, and determine the significance of the denoising effect of the iterative decomposition signals according to the similarity between the iterative decomposition signals after each iterative decomposition and the construction data time series signals, as well as the signal-to-noise ratio fluctuation and data fluctuation of each local signal segment in the iterative decomposition signals; screen out the denoised signals and noise signals of each data type after each iterative decomposition according to the significance of the denoising effect;

[0034] A second determination module, configured to determine the decomposition accuracy of each data type according to the amplitude trend similarity and the overall amplitude difference distribution between the noise signals of each data type and the noise signals of other data types during each iterative decomposition; determine the corresponding iteration stop possibility after each iterative decomposition according to the significance of the denoising effect of the denoised signals and the decomposition accuracy under all data types;

[0035] A BIM model construction module, configured to determine the final denoised signals corresponding to all data types after the iteration stops through ICA decomposition in combination with the iteration stop possibility; construct a BIM model through BIM software according to all the final denoised signals.

[0036] In a third aspect, the present application provides a computer device, including a memory and a processor. The memory is used to store computer program code, and the processor is used to call and run the computer program code from the memory to execute the method according to the first aspect or any embodiment of the first aspect of the present application.

[0037] In a fourth aspect, the present application provides a computer program product, which includes computer program code. When the computer program code is executed, it is used to execute the method according to the first aspect or any embodiment of the first aspect of the present application.

[0038] In a fifth aspect, the present application provides a computer-readable storage medium, which stores computer program code. When the computer program code is executed, it is used to execute the method according to the first aspect or any embodiment of the first aspect of the present application.

[0039] The present application has the following beneficial effects:

[0040] First, according to the characteristics that the noise influence usually does not affect the overall trend, the denoised construction building data should be similar to the construction data collected by the sensor in terms of the change trend, and combined with the characteristics that the noise signal usually has a high signal-to-noise ratio and the noise is usually randomly distributed, the significance of the denoising effect is calculated, and based on this, the denoised signal and the noise signal decomposed are determined. Then, considering that the environmental noise usually affects the data of each data type synchronously, so that the noise signals of different data types usually show similar noise influences at the same time, the decomposition accuracy of each data type is calculated. Finally, the comprehensive determination of the possibility of iteration stop is combined with the denoising effect significance and the decomposition accuracy of the denoised signal, so that the denoising effect of the final denoised signal obtained after the iteration stop is better, improving the accuracy of BIM model construction, that is, making the accuracy of building construction data collection higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0042] Figure 1 It is a flowchart of a BIM-based building construction data collection method provided by an embodiment of the present invention;

[0043] Figure 2 It is a structural diagram of a BIM-based building construction data collection system provided by an embodiment of the present invention;

[0044] Figure 3 A schematic structural diagram of a computer device provided by an embodiment of the present invention. Specific embodiments

[0045] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific embodiments, structures, features and effects of a BIM-based building construction data collection method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment, and specific features, structures or characteristics in one or more embodiments can be combined in any suitable form. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as implying or indicating relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0047] The following specifically describes the specific solution of a BIM-based building construction data collection method and system provided by the present invention with reference to the accompanying drawings.

[0048] An embodiment of the present application provides a BIM-based building construction data collection method. Please refer to Figure 1 , which shows a flowchart of a BIM-based building construction data collection method provided by an embodiment of the present invention. The method includes:

[0049] Step S101: Collect the construction data time series signals of each data type during the building construction process; divide the construction data time series signals into at least two local signal segments.

[0050] Since construction data in multiple dimensions of a construction building need to be obtained during the construction of a BIM model, the embodiments of the present invention obtain construction data time series signals of multiple data types; in a specific implementation manner of the embodiments of the present invention, the data types include structural stress changes, displacement changes, vibration changes, and inclination changes. Construction data is collected by placing corresponding sensors at the same plane position at the construction site, which can be adjusted according to the construction data monitoring requirements in the specific implementation environment and will not be elaborated further here; in addition, it should be noted that all construction data time series signals of all data types in the embodiments of the present invention are collected synchronously, the sampling frequency is set to be collected once per second, and the sampling duration is set to 10 minutes. That is, this application analyzes the construction data time series signals of 10 minutes, determines the corresponding construction data time series signals by curve fitting the collected all construction time series data, and the implementer can adjust the duration according to the specific implementation environment. In addition, for the convenience of subsequent analysis, this application divides the construction data time series signals of each data type into at least two local signal segments so that the local characteristics of the signals can be combined during subsequent analysis; in a specific implementation manner of the embodiments of the present invention, the division of local signal segments is performed every 30 seconds and can be adjusted by itself. It should be noted that curve fitting is a well-known technical means for those skilled in the art and will not be further defined and elaborated here. In addition, it should be noted that in order to avoid the influence of different dimensions, the signal values of all construction data time series signals in the embodiments of the present invention are all values after normalization and will not be elaborated further later.

[0051] Step S102: Perform iterative decomposition on the construction data time series signal through ICA decomposition, and determine the significance of the denoising effect of the iterative decomposition signal according to the similarity between the iterative decomposition signal and the construction data time series signal after each iterative decomposition, as well as the signal-to-noise ratio fluctuation and data fluctuation of each local signal segment in the iterative decomposition signal; screen out the denoised signal and noise signal of each data type after each iterative decomposition according to the significance of the denoising effect.

[0052] Blind source separation BBS is an effective signal separation technology. Its main purpose is to perform ICA decomposition on signals to achieve signal denoising, that is, to separate the noise signal from the original signal through ICA decomposition to obtain the required denoised signal. The data collected during building construction is mainly affected by the building itself, and the interference signals in the construction environment may come from multiple noise signal sources. However, the number of signal sources is difficult to determine and may not be independent of the signals to be collected. In a certain construction environment, there may be a certain correlation between the construction signals obtained and the noise signals of environmental interference, which makes the ICA decomposition algorithm inaccurate for the collection of real building construction signal data. Therefore, it is necessary to correct the stopping condition of ICA decomposition and not only consider the independence of two signals to achieve signal decomposition. Therefore, the main purpose of this application is to determine the iteration stopping condition based on ICA decomposition. During each iterative decomposition, the signal is decomposed into two iteratively decomposed signals. Among them, ICA decomposition is a well-known technical means for those skilled in the art, and its specific decomposition process and iterative decomposition process will not be further defined and described herein.

[0053] The influence of the construction environmental noise signal on the detection data of the sensor is relatively small compared to the signal corresponding to the real data. Therefore, the denoised data should be similar to the changing trend of the construction data collected by the sensor. That is, in terms of the changing trend, compared with the noise signal, the similarity between the denoised signal and the construction data time-series signal is higher. In addition, environmental noise will cause the stress data signal to fluctuate irregularly up and down on the original stable basis. For example, when there is no other interference, the stress change of a certain part of the structure due to the stable load should be a relatively smooth curve that can be expected according to the mechanical principle. However, if there is environmental noise, the signal curve will sometimes show some abrupt small peaks or valleys, deviating from the normal stress change trend. The time intervals and amplitudes of these fluctuations are often random. If the stability of the construction signal obtained due to the abnormality of the building construction is damaged, the building still undergoes corresponding changes according to the mechanical principle without obvious randomness. For example, if the monitored building shows abnormal characteristics such as tilting and collapsing, the monitored stress data, inclination data, etc. have the changing trend characteristics of building collapse. For example, the inclination data increases and the change amplitude increases; the stress data drops rapidly until it returns to zero after collapse. Therefore, the signal-to-noise ratio is relatively high in each local signal segment of the noise signal, and the difference in the signal-to-noise ratio between different local signal segments changes significantly; the denoised signal, that is, the normal signal, usually remains stable, and the signal-to-noise ratio performance is usually not obvious; and due to the randomness of the noise, the noise signal usually shows a chaotic and disordered distribution in the signal value distribution, and its signal value size is random, that is, the signal value fluctuation characteristics of the noise signal are relatively more significant. Therefore, in this application, first, according to the similarity between the iterative decomposition signal obtained after each iterative decomposition and the construction data time-series signal, as well as the signal-to-noise ratio fluctuation and data fluctuation of each local signal segment in the iterative decomposition signal, the significance of the denoising effect of the iterative decomposition signal is determined. The greater the significance of the denoising effect, the more it conforms to the characteristics of the denoised signal after denoising, and the smaller the possibility of belonging to the noise signal.

[0054] Preferably, in some possible implementation manners of the embodiments of the present invention, the process of obtaining the significance of the denoising effect includes:

[0055] Obtain two iterative decomposition signals obtained after each iterative decomposition for each data type; determine the denoising possibility according to the similarity of the signal value distribution between the iterative decomposition signal and the construction data time-series signal. Among them, the process of obtaining the denoising possibility includes: calculating the DTW distance between the iterative decomposition signal and the construction data time-series signal through the dynamic time warping algorithm; normalizing the negative correlation mapping value of the DTW distance to determine the denoising possibility. It should be noted that the dynamic time warping algorithm is a well-known technical means to those skilled in the art and will not be further defined and described here.

[0056] According to the definition of the dynamic time warping algorithm, the smaller the DTW distance between the iterative decomposition signal and the construction data time series signal, the more similar the two signals are as a whole. Therefore, a negative correlation mapping is performed on DTW, so that when the obtained denoising possibility is greater, it more conforms to the characteristic that the similarity between the denoised signal and the construction data time series signal is higher, and the corresponding iterative decomposition signal is more likely to belong to the denoised signal, and its denoising effect is more significant. In other possible implementation manners of the embodiments of the present invention, the normalized value of the Pearson correlation coefficient between the iterative decomposition signal and the construction data time series signal is used as the denoising possibility; that is, the Pearson correlation coefficient is used to replace the dynamic time warping for calculating the signal similarity, which will not be elaborated further herein.

[0057] Further, on the iterative decomposition signal, calculate the signal-to-noise ratio of each local signal segment and the amplitude standard deviation of all data points therein; determine the noise performance value of each local signal segment according to the product of the signal-to-noise ratio and the amplitude standard deviation; determine the noise possibility of the iterative decomposition signal according to the stability of the noise performance values of each local signal segment and the overall magnitude of the noise performance values; wherein, the process of obtaining the noise possibility includes: normalizing the product of the mean value of the noise performance values of all local signal segments on the iterative decomposition signal and the standard deviation of the noise performance values to determine the noise possibility of the iterative decomposition signal. It should be noted that the calculation method of the signal-to-noise ratio is a well-known technical means to those skilled in the art, and the amplitude standard deviation is the standard deviation of all signal values on the local signal segment, which will not be elaborated further herein.

[0058] The larger the noise performance value, the more significant the signal-to-noise ratio and the data fluctuation characteristics representing the noise characteristics; and due to the randomness of the noise, different local signal segments usually show different noise performances; therefore, the greater the mean value of the noise performance values of all local signal segments on the iterative decomposition signal and the greater the standard deviation of the noise performance values, the more significant the corresponding noise characteristics, that is, the greater the noise possibility of the corresponding iterative decomposition signal, and the smaller the significance of the denoising effect.

[0059] Finally, perform a positive correlation mapping on the product of the negative correlation mapping value of the noise possibility and the denoising possibility to determine the significance of the denoising effect of the iterative decomposition signal; the smaller the noise possibility and the greater the denoising possibility, the greater the possibility that the iterative decomposition signal belongs to the denoised signal, and the better the significance of its denoising effect; therefore, among the two iterative decomposition signals obtained by the iterative decomposition, the one with a greater significance of the denoising effect is usually the denoised signal, and the smaller one is usually the noise signal; therefore, further, the process of obtaining the denoised signal and the noise signal for each data type after each iterative decomposition according to the significance of the denoising effect includes: after each iterative decomposition of each data type, taking the iterative decomposition signal corresponding to the maximum significance of the denoising effect as the denoised signal; taking the iterative decomposition signal corresponding to the maximum significance of the denoising effect as the noise signal.

[0060] In a specific implementation manner of the embodiment of the present invention, the acquisition process of the noise performance value is expressed by the formula: Z g = X g ×σ g ; where, Z g is the noise performance value of the local signal segment g; X g is the signal-to-noise ratio of the local signal segment g.

[0061] In a specific implementation manner of the embodiment of the present invention, the acquisition process of the denoising effect significance is expressed by the formula: where, R k,h,r is the denoising effect significance of the r-th iterative decomposition signal of the h-th data type after the k-th iterative decomposition; D k,h,r is the DTW distance between the r-th iterative decomposition signal of the h-th data type after the k-th iterative decomposition and the corresponding construction data time series signal; exp() is the exponential function with the natural constant as the base; Norm() is the linear normalization function; Norm(exp(-D k,h,r )) is the denoising possibility of the r-th iterative decomposition signal of the h-th data type after the k-th iterative decomposition; is the mean value of the noise performance values of all local signal segments on the r-th iterative decomposition signal of the h-th data type after the k-th iterative decomposition; S h,k,r is the standard deviation of the noise performance values of all local signal segments on the r-th iterative decomposition signal of the h-th data type after the k-th iterative decomposition; is the noise possibility of the r-th iterative decomposition signal of the h-th data type after the k-th iterative decomposition; α is a preset adjustment parameter used to prevent the denominator from being 0, and the empirical value is taken as 0.1; by dividing the denoising possibility by the noise possibility and then subtracting the constant 1, the greater the difference between the obtained denoising effect significance and 0, the stronger the significance of the decomposed iterative decomposition signal, and a negative value indicates a noise signal, while a positive value indicates a denoising signal. Thus, the noise signal and the denoising signal corresponding to each data type after each iterative decomposition are screened out.

[0062] Step S103: In each iterative decomposition, determine the decomposition accuracy of each data type according to the amplitude trend similarity and the overall amplitude difference distribution between the noise signals of each data type and the noise signals of other data types; among all data types, determine the corresponding iterative stop possibility after each iterative decomposition according to the denoising effect significance and the decomposition accuracy of the denoising signal.

[0063] Different monitored construction signals, such as inclination, displacement, stress, etc., represent different physical characteristics of construction, and are associated with the building structure itself and the external environment in different ways. For example, the inclination signal mainly reflects the changes in the spatial angle of the building structure and is more easily affected by factors such as vibration of uneven ground nearby and foundation settlement; while the displacement signal pays more attention to the movement of the building structure in the planar space, and the influence of temperature, wind load, etc. on this signal is more obvious. Therefore, the influence degree of environmental noise on construction monitoring signals of different data types is different. At this time, since the positions of the sensors used to monitor construction data are fixed, this means that each sensor is simultaneously exposed to the same construction environmental noise field. Sensors for monitoring different signals such as inclination, displacement, and stress are installed at the same planar position on a certain floor of a construction site. When a pile driver starts working nearby, the generated vibration noise, air-borne noise, etc. will propagate to the positions where these sensors are located at the same time, causing them to receive the interference of this environmental noise at the same moment. Therefore, for different monitoring signals, the influence of construction environmental noise starts synchronously and changes synchronously, and there is no difference in the time sequence; that is, if there is interference from environmental noise on construction data, it is synchronous at the time of noise generation; so the construction environmental noise has both influence differences due to different physical characteristics and response methods on different construction monitoring signals, and shows the characteristics of synchronous influence and similar change trends due to the fixed positions of the sensors; therefore, similar noise influences usually appear simultaneously among noise signals of different data types. Based on this, in this application, the decomposition accuracy of each data type is determined according to the amplitude trend similarity and the overall amplitude difference distribution between the noise signal of each data type and the noise signals of other data types; the ICA iterative decomposition is further evaluated by calculating the decomposition accuracy.

[0064] Preferably, in some possible implementation manners of the embodiments of the present invention, the process of obtaining the decomposition accuracy includes:

[0065] Each data type is sequentially used as the target type; the other data types except the target type are used as the corresponding comparison types; among them, the time series signals of construction data of all data types are iteratively decomposed synchronously; in each iterative decomposition, the signal value difference between the noise signal of each comparison type and the noise signal of the target type at each sampling moment is calculated; the standard deviation of the signal value differences at all sampling moments corresponding to each comparison type is negatively correlated and mapped to determine the signal difference consistency between each comparison type and the target type; the mean value of the signal value differences at all sampling moments is negatively correlated and mapped to determine the interference degree consistency between each comparison type and the target type; among them, the signal value difference is the absolute value of the signal value difference.

[0066] For any target type and a comparison type, the closer the signal value differences at each sampling moment are, the more consistent the impact of the environmental noise during building construction on the two data types is, which also means that the decomposed noise signal better conforms to the characteristic of noise impact, and the corresponding decomposition accuracy is higher. Therefore, for the target type, when the signal difference consistency between it and each comparison type is greater, the decomposition accuracy is higher. In addition, when the signal value differences are smaller, it can better indicate that the noise signal generated by the building construction environment has a consistent interference on the signals of the two data types. At this time, the decomposed noise signal better conforms to the characteristic of noise impact, and the corresponding decomposition accuracy is higher. Therefore, for the target type, when the interference degree consistency between it and each comparison type is greater, the decomposition accuracy is higher. Therefore, finally, according to the product of the signal difference consistency and the interference degree consistency, the noise similarity between each comparison type and the target type is determined. According to the mean value of the noise similarities between all comparison types and the target type, the decomposition accuracy of the target type under each iterative decomposition is determined. By combining the noise similarity with the decomposition accuracy characteristics represented by the signal difference consistency and the interference degree consistency, a more accurate decomposition accuracy is determined, making the subsequent evaluation accuracy of the iterative stop condition higher. That is, when the decomposition accuracy is greater, the separated noise signal is more accurate, and the iterative decomposition effect characterized at the noise level is better.

[0067] In a specific implementation manner of the embodiment of the present invention, the process of obtaining the decomposition accuracy is represented by the formula: H k,h is the decomposition accuracy of the h-th data type after the k-th iterative decomposition; N h is the number of comparison types corresponding to the h-th data type; P k,h,i is the standard deviation of the signal value differences at all sampling moments between the h-th data type and the corresponding i-th comparison type after the k-th iterative decomposition; exp() is the exponential function with the natural constant as the base; exp(-P k,h,i ) is the signal difference consistency between the h-th data type and the corresponding i-th comparison type after the k-th iterative decomposition; Q k,h,i is the mean value of the signal value differences at all sampling moments between the h-th data type and the corresponding i-th comparison type after the k-th iterative decomposition; exp(-Q k,h,i ) is the interference degree consistency between the h-th data type and the corresponding i-th comparison type after the k-th iterative decomposition; exp(-P k,h,i ) × exp(-Q k,h,i ) is the noise similarity between the h-th data type and the corresponding i-th comparison type after the k-th iterative decomposition.

[0068] The greater the decomposition accuracy, the better the iterative decomposition effect represented at the noise level; when the denoising effect of the denoised signal obtained after iterative decomposition is more significant, it indicates that the reliability of the obtained denoised signal is higher and the denoising effect is greater; therefore, when the denoising effect of the denoised signal is more significant, the iterative decomposition effect represented at the denoising level is better; and when the iterative decomposition effect is better, it means that the ICA decomposition accuracy corresponding to the number of times is higher, and at this time, it can be more considered that the separation of the construction environment noise signal is achieved and the ICA decomposition should be stopped; therefore, preferably, in some possible implementation manners of the embodiments of the present invention, the process of obtaining the iterative stop possibility includes: in each iterative decomposition, according to the denoising effect significance and decomposition accuracy of the denoised signal of each data type, determine the iterative stop weight of each data type; normalize the accumulated value of the iterative stop weights of all data types to determine the iterative stop possibility corresponding to each iterative decomposition. That is, when the denoising effect significance and decomposition accuracy of all data types are overall greater, the signal separation or decomposition effect at the corresponding number of iterations is better, and it is more necessary to stop the iteration.

[0069] In a specific implementation manner of the embodiments of the present invention, the process of obtaining the iterative stop possibility is expressed by the formula: where, Y k is the iterative stop possibility corresponding to the kth iterative decomposition; Norm() is a linear normalization function; M is the number of data types; H k,m is the decomposition accuracy of the mth data type in the kth iterative decomposition; R ′ k,m is the denoising effect significance of the denoised signal of the mth data type in the kth iterative decomposition; H k,m ×R ′ k,m is the iterative stop weight of the mth data type in the kth iterative decomposition.

[0070] Step S104: Determine the final denoised signals corresponding to all data types after the iteration stops through ICA decomposition combined with the iterative stop possibility; construct a BIM model through BIM software according to all the final denoised signals.

[0071] After obtaining the iterative stop possibility of each iterative decomposition, finally, it is necessary to select the final denoised signal obtained after the iteration stops according to the iterative stop possibility for BIM model construction to collect construction data.

[0072] Preferably, in some possible implementation manners of the embodiments of the present invention, the process of obtaining the final denoised signal includes:

[0073] If there is an iteration stop possibility greater than the preset stop threshold within the preset number of iterations, stop the iterative decomposition when the iteration stop possibility greater than the preset stop threshold first appears, and use the denoised signals of all data types obtained when stopping the iterative decomposition as the final denoised signals; if there is no iteration stop possibility greater than the preset stop threshold within the preset number of iterations, use the denoised signals of all data types obtained under the iterative decomposition corresponding to the maximum iteration stop possibility as the final denoised signals. In a specific implementation manner of the embodiment of the present invention, the preset number of iterations is set to 20. By setting the preset number of iterations, the situation of infinite iterative loop can be prevented; the preset stop threshold is set to 0.9. Here, it is considered that the iteration stop possibility is a normalized value; the purpose of setting the preset stop threshold relatively large is to ensure the decomposition effect of the ICA algorithm.

[0074] After obtaining the final denoised signals with relatively high decomposition accuracy and good denoising effect, finally, combine the BIM model construction to collect building construction data. In a specific implementation manner of the embodiment of the present invention, the process of constructing the BIM model according to all the final denoised signals through BIM software includes:

[0075] After uploading the final denoised signals of all data types to the cloud, establish the coordinates of the project requirement model in the BIM software, import the building model, and form the BIM model. Among them, the BIM software can be selected from Revit or Archicad. In this application, Revit is used to establish the BIM model; it should be noted that the process of establishing the BIM model belongs to the technical means well-known to those skilled in the art and will not be further described here.

[0076] In summary, for a building construction data collection method based on BIM proposed in this application, first, according to the characteristics that noise influence usually does not affect the overall trend, so that the denoised construction building data should be similar to the construction data collected by the sensor in terms of the change trend, and combined with the characteristics that the noise signal usually has a high signal-to-noise ratio and the noise is usually randomly distributed, calculate the significance of the denoising effect, and thereby determine the decomposed denoised signals and noise signals; then, it is necessary to consider that environmental noise usually affects the data of each data type synchronously, so that the noise signals of different data types usually show similar noise influence characteristics at the same time, and calculate the decomposition accuracy of each data type; finally, comprehensively determine the iteration stop possibility by combining the denoising effect significance and decomposition accuracy of the denoised signals, so that the denoising effect of the final denoised signals obtained after the iteration stops is better, improve the accuracy of the BIM model construction, that is, make the accuracy of the building construction data collection higher.

[0077] This application also provides a building construction data collection system based on BIM. Please refer to Figure 2, which shows the structural diagram of a BIM-based building construction data acquisition system provided by an embodiment of the present invention. The system includes: a data acquisition and preprocessing module 201, a first determination module 202, a second determination module 203, and a BIM model construction module 204.

[0078] The data acquisition and preprocessing module 201 is used to collect the construction data time series signals of each data type during the building construction process; divide the construction data time series signals into at least two local signal segments;

[0079] The first determination module 202 is used to perform iterative decomposition on the construction data time series signals through ICA decomposition, and determine the significance of the denoising effect of the iterative decomposition signals according to the similarity between the iterative decomposition signals and the construction data time series signals after each iterative decomposition, as well as the signal-to-noise ratio fluctuation and data fluctuation of each local signal segment in the iterative decomposition signals; screen out the denoised signals and noise signals of each data type after each iterative decomposition according to the significance of the denoising effect.

[0080] The second determination module 203 is used to determine the decomposition accuracy of each data type according to the amplitude trend similarity and the overall amplitude difference distribution between the noise signals of each data type and the noise signals of other data types under each iterative decomposition; determine the corresponding iterative stop possibility after each iterative decomposition according to the significance of the denoising effect and the decomposition accuracy of the denoised signals under all data types.

[0081] The BIM model construction module 204 is used to determine the final denoised signals corresponding to all data types after the iteration stops by combining ICA decomposition with the iterative stop possibility; construct a BIM model through BIM software according to all the final denoised signals.

[0082] It should be noted that for the system provided in the above embodiment, only the above division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, a BIM-based building construction data acquisition system provided in the above embodiment and an embodiment of a BIM-based building construction data acquisition method belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0083] An embodiment of the present application also provides a computer device. Please refer to Figure 3, which shows a schematic structural diagram of a computer device provided by an embodiment of the present invention. The computer device includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302. When the processor 302 executes the computer program 303, the computer device can execute any one of the above-described BIM-based building construction data collection methods.

[0084] An embodiment of the present application also provides a computer program product. When the computer program product runs on a computer device, the computer device can execute any one of the above-described BIM-based building construction data collection methods.

[0085] An embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium stores computer program code. When the computer program code runs on a computer device, the computer device can execute any one of the above-described BIM-based building construction data collection methods.

[0086] In the embodiments provided in the present application, it should be understood that the provided computer device, computer program product, and computer-readable storage medium are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the methods provided above, which will not be elaborated here.

[0087] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0088] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. A method for collecting building construction data based on BIM, characterized in that: The method comprises: Collecting construction data time series signals of each data type during the construction process; dividing the construction data time series signals into at least two local signal segments; The construction data time series signal is iteratively decomposed by ICA decomposition, and the significance of the denoising effect of the iterative decomposition signal is determined according to the similarity between the iterative decomposition signal after each iterative decomposition and the construction data time series signal, and the signal-to-noise ratio fluctuation and data fluctuation of each local signal segment in the iterative decomposition signal; the denoised signal and the noise signal of each data type after each iterative decomposition are screened out according to the significance of the denoising effect; In each iterative decomposition, the decomposition accuracy of each data type is determined according to the similarity of the amplitude trend between the noise signal of each data type and the noise signals of other data types and the distribution of the overall amplitude difference; in all data types, the possibility of stopping the corresponding iteration after each iterative decomposition is determined according to the denoising effect significance of the denoised signal and the decomposition accuracy; The final denoised signals corresponding to all data types after the iteration stops are determined by ICA decomposition combined with the iteration stop possibility; and the BIM model is constructed by BIM software according to all the final denoised signals.

2. A method for collecting construction data based on BIM according to claim 1, characterized in that: The process of obtaining the significance of the denoising effect includes: Obtain two iterative decomposition signals obtained after each iterative decomposition of each data type; determine the possibility of denoising based on the similarity of signal value distribution between the iterative decomposition signal and the construction data time series signal; On the iterative decomposition signal, the signal-to-noise ratio of each local signal segment and the amplitude standard deviation of all data points therein are calculated; the noise performance value of each local signal segment is determined according to the product of the signal-to-noise ratio and the amplitude standard deviation; the noise possibility of the iterative decomposition signal is determined according to the stability of the noise performance value of each local signal segment and the overall size of the noise performance value; A positive correlation mapping is performed on the product of the negative correlation mapping value of the noise possibility and the denoising possibility to determine the significance of the denoising effect of the iterative decomposition signal.

3. The method for collecting construction data based on BIM according to claim 2, characterized in that: The process of obtaining the denoising possibility includes: The DTW distance between the iterative decomposition signal and the construction data timing signal is calculated by a dynamic time warping algorithm; the negative correlation mapping value of the DTW distance is normalized to determine the denoising possibility.

4. The method for collecting construction data based on BIM according to claim 2, characterized in that: The process of obtaining the noise possibility includes: The product of the noise performance value mean and the noise performance value standard deviation of all local signal segments on the iterative decomposition signal is normalized to determine the noise possibility of the iterative decomposition signal.

5. The method for collecting construction data based on BIM according to claim 1, characterized in that: The process of obtaining the denoised signal and the noise signal of each data type after each iterative decomposition according to the denoising effect significance includes: After each iterative decomposition of each data type, the iterative decomposition signal corresponding to the maximum denoising effect significance is used as the denoising signal; the iterative decomposition signal corresponding to the maximum denoising effect significance is used as the noise signal.

6. The method for collecting construction data based on BIM according to claim 1, characterized in that: The process of obtaining the decomposition accuracy includes: Each data type is taken as the target type in turn; other data types other than the target type are taken as the corresponding comparison types; wherein the construction data timing signals of all data types are synchronously iteratively decomposed; Under each iterative decomposition, the signal value difference between the noise signal of each comparison type and the noise signal of the target type at each sampling time is calculated; the standard deviation of the signal value difference at all sampling times corresponding to each comparison type is negatively correlated to determine the signal difference consistency between each comparison type and the target type; the mean of the signal value difference at all sampling times is negatively correlated to determine the interference degree consistency between each comparison type and the target type; the noise similarity between each comparison type and the target type is determined based on the product of the signal difference consistency and the interference degree consistency; The decomposition accuracy of the target type at each iterative decomposition is determined based on the mean of the noise similarities between all comparison types and the target type.

7. The method for collecting construction data based on BIM according to claim 1, characterized in that: The process of obtaining the possibility of iterative stop includes: In each iterative decomposition, the iterative stop weight of each data type is determined according to the denoising effect significance of the denoised signal of each data type and the decomposition accuracy; the accumulated values ​​of the iterative stop weights of all data types are normalized to determine the corresponding iterative stop possibility after each iterative decomposition.

8. The method for collecting construction data based on BIM according to claim 1, characterized in that: The process of obtaining the final denoised signal includes: If there is an iteration stop possibility greater than a preset stop threshold within the preset number of iterations, the iterative decomposition is stopped when the iteration stop possibility greater than the preset stop threshold first appears, and the denoised signals of all data types obtained when the iterative decomposition is stopped are used as the final denoised signals; If there is no iteration stop possibility greater than the preset stop threshold within the preset number of iterations, the denoised signals of all data types obtained under the iterative decomposition corresponding to the largest iteration stop possibility are taken as the final denoised signals.

9. The method for collecting construction data based on BIM according to claim 1, characterized in that: The process of constructing a BIM model through BIM software according to all the final denoised signals includes: After uploading the final denoised signals of all data types to the cloud, the project requirement model coordinates are established in the BIM software and imported into the building model to form a BIM model.

10. A BIM-based construction data acquisition system, characterized in that: The system comprises: A data acquisition preprocessing module is used to acquire the construction data time series signal of each data type during the construction process; and divide the construction data time series signal into at least two local signal segments; The first determination module is used to iteratively decompose the construction data time series signal through ICA decomposition, and determine the significance of the denoising effect of the iterative decomposition signal according to the similarity between the iterative decomposition signal after each iterative decomposition and the construction data time series signal, and the signal-to-noise ratio fluctuation and data fluctuation of each local signal segment in the iterative decomposition signal; and screen out the denoised signal and the noise signal of each data type after each iterative decomposition according to the significance of the denoising effect; The second determination module is used to determine the decomposition accuracy of each data type according to the similarity of the amplitude trend between the noise signal of each data type and the noise signals of other data types and the distribution of the overall amplitude difference under each iterative decomposition; under all data types, determine the possibility of stopping the iteration corresponding to each iterative decomposition according to the denoising effect significance of the denoised signal and the decomposition accuracy; The BIM model construction module is used to determine the final denoised signals corresponding to all data types after the iteration stops by combining the possibility of iterative stop through ICA decomposition; and construct the BIM model through BIM software according to all the final denoised signals.

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