A method for intelligent collection of BIM data for prefabricated buildings

Abstract

The present invention discloses an intelligent BIM data collection method for prefabricated buildings, and relates to the technical field of data processing. The method comprises: collecting the initial BIM data of the target prefabricated building in real time through a target sensor set on the target prefabricated building, the target sensor comprises a target vibration sensor, and the initial BIM data comprises the vibration amplitude of the target vibration sensor at each moment; determining the directional difference between the vibration direction of the crane and the wind direction at each moment according to the position of the crane and the wind direction at each moment; determining the amplitude influence of the crane on the target vibration sensor at each moment according to the directional differences and the vibration amplitude of the target vibration sensor at each moment; based on the amplitude influence of the crane on the target vibration sensor at each moment, updating the vibration amplitude of the target vibration sensor at each moment in the initial BIM data to obtain updated BIM data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for intelligently collecting BIM data of assembled buildings. Background Art

[0002] The application of Building Information Modeling (BIM) technology in prefabricated buildings has become an important trend in the development of the industry.

[0003] The BIM data of existing prefabricated buildings is collected in real time through multiple sensors installed at the construction site to monitor the construction quality of each prefabricated concrete building module and ensure the safety and stability of the prefabricated buildings.

[0004] However, during the splicing and construction of prefabricated buildings, the crane is close to the building. The crane's hoisting will generate ground vibration, which affects the vibration sensor's ability to detect the stability of the building, resulting in poor accuracy of the collected data, which in turn leads to poor splicing and construction quality of prefabricated buildings. Summary of the invention

[0005] The embodiment of the present invention provides a method for intelligently collecting BIM data of prefabricated buildings, which can improve the accuracy of data collection and further improve the splicing construction quality of prefabricated buildings.

[0006] A first aspect of an embodiment of the present invention provides a method for intelligently collecting BIM data of prefabricated buildings, comprising:

[0007] The initial BIM data of the target prefabricated building is collected in real time by using a target sensor set on the target prefabricated building, wherein the target sensor includes a target vibration sensor, and the initial BIM data includes the vibration amplitude of the target vibration sensor at each moment;

[0008] According to the crane position and the wind direction at each moment, determine the directional difference between the crane vibration direction and the wind direction at each moment;

[0009] According to the differences in each direction and the vibration amplitude of the target vibration sensor at each moment, determine the influence of the crane on the amplitude of the target vibration sensor at each moment;

[0010] Based on the influence of the crane on the amplitude of the target vibration sensor at each moment, the vibration amplitude of the target vibration sensor in the initial BIM data at each moment is updated to obtain updated BIM data.

[0011] In the method for intelligently collecting BIM data of prefabricated buildings provided by the embodiment of the present invention, the directional difference between the vibration direction of the crane and the wind direction at each moment is first determined according to the position of the crane and the wind direction at each moment; then, the amplitude influence of the crane on the target vibration sensor at each moment is determined according to the directional differences and the vibration amplitude of the target vibration sensor at each moment; finally, based on the amplitude influence of the crane on the target vibration sensor at each moment, the vibration amplitude of the target vibration sensor at each moment in the initial BIM data is updated to obtain updated BIM data. In this way, the present invention updates the initial BIM data by considering the influence of the crane and wind direction on the data collection of the vibration sensor to obtain accurate updated BIM data. The accuracy of data collection can be improved, and thus the splicing construction quality of prefabricated buildings can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0013] Figure 1 A schematic diagram of a flow chart of a first method for intelligently collecting BIM data of prefabricated buildings provided by an embodiment of the present invention;

[0014] Figure 2 A schematic diagram of a flow chart of a second method for intelligently collecting BIM data of prefabricated buildings provided by an embodiment of the present invention;

[0015] Figure 3 A schematic diagram of the directional difference of a target prefabricated building provided by an embodiment of the present invention;

[0016] Figure 4 A schematic diagram of a flow chart of a third method for intelligently collecting BIM data of prefabricated buildings provided by an embodiment of the present invention;

[0017] Figure 5 A schematic diagram of a fourth method for intelligently collecting BIM data for prefabricated buildings provided by an embodiment of the present invention;

[0018] Figure 6 A schematic flow chart of a fifth method for intelligent collection of BIM data for prefabricated buildings provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a prefabricated building BIM data intelligent collection method proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0021] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of the present invention are in compliance with the relevant provisions of laws and regulations.

[0022] It should be noted that in the embodiments of the present invention, certain software, components, models and other existing solutions in the industry may be mentioned, which should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present invention, but it does not mean that the applicant has or will necessarily use the solution.

[0023] The BIM data of existing prefabricated buildings is collected in real time through multiple sensors installed at the construction site to monitor the construction quality of each prefabricated concrete building module and ensure the safety and stability of the prefabricated building. However, during the splicing and construction of prefabricated buildings, the crane is close to the building. The hoisting of the crane will generate ground vibration, which affects the stability of the vibration sensor in detecting the building, and the accuracy of the collected data is poor, resulting in poor splicing construction quality of prefabricated buildings.

[0024] The purpose of the present invention is to provide an intelligent BIM data collection method for prefabricated buildings. In the intelligent BIM data collection method for prefabricated buildings provided by the embodiment of the present invention, the directional difference between the vibration direction of the crane and the wind direction at each moment is first determined according to the position of the crane and the wind direction at each moment; then, according to the directional differences and the vibration amplitude of the target vibration sensor at each moment, the amplitude influence of the crane on the target vibration sensor at each moment is determined; finally, based on the amplitude influence of the crane on the target vibration sensor at each moment, the vibration amplitude of the target vibration sensor at each moment in the initial BIM data is updated to obtain updated BIM data. In this way, the present invention updates the initial BIM data by considering the influence of the crane and wind direction on the data collection of the vibration sensor to obtain accurate updated BIM data. It can improve the accuracy of data collection, and thus improve the splicing construction quality of prefabricated buildings.

[0025] The following describes a specific embodiment of a method for intelligently collecting BIM data of prefabricated buildings provided by an embodiment of the present invention.

[0026] like Figure 1 As shown, a flow chart of a method for intelligent collection of BIM data for prefabricated buildings is provided. The method for intelligent collection of BIM data for prefabricated buildings can be applied to a server, and the method for intelligent collection of BIM data for prefabricated buildings can include the following S101 to S104.

[0027] S101, collecting initial BIM data of a target prefabricated building in real time through a target sensor set on the target prefabricated building, the target sensor includes a target vibration sensor, and the initial BIM data includes the vibration amplitude of the target vibration sensor at each moment.

[0028] In this embodiment, the initial BIM data of the target prefabricated building is collected by installing target sensors at key positions of the target prefabricated building.

[0029] The target sensors may include target vibration sensors, target wind direction sensors, etc. The initial BIM data may include the vibration amplitude of the target prefabricated building at each time monitored by the target vibration sensor, the wind direction corresponding to the target prefabricated building at each time monitored by the target wind direction sensor, and the setting elevation data of each target sensor.

[0030] As an example, target sensors are set at various key positions of the target prefabricated building, and then the server collects the initial BIM data of the target prefabricated building at various moments in real time through the target sensors set on the target prefabricated building.

[0031] S102, determining the directional difference between the vibration direction of the crane and the wind direction at each moment according to the position of the crane and the wind direction at each moment.

[0032] In this embodiment, the directional difference is used to characterize the deviation between the vibration direction to which the target prefabricated building is subjected and the wind direction to which the target prefabricated building is subjected.

[0033] As an example, the server uses GPS technology or other positioning technology to track the position of the crane corresponding to the target prefabricated building to obtain the position of the crane; and establishes communication with the weather station or the target wind direction sensor installed on the target prefabricated building to obtain the wind direction at each moment in real time.

[0034] Then, according to the position of the crane, the vibration direction vector of the target prefabricated building is constructed; and according to the wind direction at each moment, the wind direction vector of the target prefabricated building at each moment is constructed. And according to the vibration direction vector of the target prefabricated building and the wind direction vector of the target prefabricated building at each moment, the angle between the vibration direction vector of the target prefabricated building at each moment and the wind direction vector is calculated, and the angle is determined as the directional difference between the vibration direction of the crane and the wind direction.

[0035] S103, determining the influence of the crane on the amplitude of the target vibration sensor at each moment according to the differences in each direction and the vibration amplitude of the target vibration sensor at each moment.

[0036] In this embodiment, the amplitude influence degree is used to characterize the influence degree of the vibration of the crane on the vibration amplitude of the target prefabricated building collected by the target vibration sensor.

[0037] As an example, the server establishes a model based on physical principles or empirical data to estimate the impact of crane vibration on the vibration amplitude collected by the target vibration sensor. Specifically, the model needs to consider factors such as crane weight, crane operating status, distance from the target vibration sensor, directional differences, and vibration amplitude collected by the target vibration sensor.

[0038] Then, the server inputs the directional difference at each moment and the vibration amplitude of the target vibration sensor at the corresponding moment into the model to obtain the amplitude influence of the crane on the target vibration sensor at each moment.

[0039] S104, based on the influence of the crane on the amplitude of the target vibration sensor at each moment, the vibration amplitude of the target vibration sensor at each moment in the initial BIM data is updated to obtain updated BIM data.

[0040] In this embodiment, the server pre-sets a data update rule. For example, the data update rule may be that when the amplitude influence is less than a preset influence threshold, the corresponding vibration amplitude data remains unchanged; when the amplitude influence is greater than or equal to the preset influence threshold, the corresponding vibration amplitude data is adjusted according to the adjustment ratio corresponding to the influence level of the amplitude influence.

[0041] Then, the amplitude influence of the crane on the target vibration sensor at each moment is compared with the corresponding preset influence threshold. According to the size relationship between the amplitude influence at each moment and the corresponding preset influence threshold, the vibration amplitude of the target vibration sensor in the initial BIM data at each moment is updated to obtain updated BIM data.

[0042] In the method for intelligent collection of BIM data of prefabricated buildings provided in this embodiment, the directional difference between the vibration direction of the crane and the wind direction at each moment is first determined according to the position of the crane and the wind direction at each moment; then, the amplitude influence of the crane on the target vibration sensor at each moment is determined according to the directional differences and the vibration amplitude of the target vibration sensor at each moment; finally, based on the amplitude influence of the crane on the target vibration sensor at each moment, the vibration amplitude of the target vibration sensor at each moment in the initial BIM data is updated to obtain updated BIM data. In this way, the present invention updates the initial BIM data by considering the influence of the crane and wind direction on the data collection of the vibration sensor to obtain accurate updated BIM data. The accuracy of data collection can be improved, and thus the splicing construction quality of prefabricated buildings can be improved.

[0043] As an optional embodiment, Figure 2 As shown, S102 may specifically include the following S201 to S203:

[0044] S201, obtaining the wind direction vector at each moment through a target wind direction sensor;

[0045] S202, connecting the crane position with the center point position of the target prefabricated building to construct a vibration direction vector of the crane;

[0046] S203, based on the wind direction vector at each moment and the vibration direction vector of the crane, respectively calculate the direction difference at each moment.

[0047] In this embodiment, if Figure 3 As shown, a schematic diagram of the directional difference of a target prefabricated building is provided. The target prefabricated building is a house building, and the house building includes two target vibration sensors, namely, a sensor 1 on the windward side and a sensor 2 on the leeward side.

[0048] When the wind blows, the force exerted by the wind on the windward side of the building is greater than that on the leeward side. Therefore, the vibration of sensor 1 on the windward side must be greater than the vibration of sensor 2 on the leeward side. That is, the wind direction will affect the monitoring accuracy of each target vibration sensor. Therefore, it is necessary to evaluate the directional difference between the vibration direction of the crane and the wind direction at each moment.

[0049] Specifically, the server first uses GPS technology or other positioning technology to track the position of the crane corresponding to the building to obtain the position of the crane. Then, the direction in which the crane position points to the center point of the building is determined as the vibration direction of the crane, and the crane and the center point of the building are connected to form the vibration direction vector of the building.

[0050] Then, by establishing communication with the weather station or the target wind direction sensor installed on the building, the wind direction received by the building at each moment is obtained and the corresponding wind direction vector is constructed.

[0051] Finally, the angle between the vibration direction vector of the building at each moment and the wind direction vector is calculated, and the angle is determined as the directional difference between the vibration direction of the crane and the wind direction.

[0052] Through this embodiment, the wind direction at each moment is determined according to the target wind direction sensor; at the same time, the vibration direction of the crane is determined according to the position of the crane and the center point position of the target prefabricated building. In this way, by accurately obtaining the wind direction and the vibration direction of the crane, the directional difference between the vibration direction of the crane and the wind direction at each moment is calculated. The influence of wind direction on the monitoring accuracy of each target vibration sensor can be accurately evaluated, and the accuracy of the collected BIM data can be accurately judged, thereby improving the splicing construction quality of prefabricated buildings.

[0053] As an optional embodiment, S203 may specifically include:

[0054] The cosine similarity calculation is performed on the wind direction vector at the target time and the vibration direction vector of the crane to obtain the similarity between the wind direction at the target time and the vibration direction of the crane, and the target time is any time;

[0055] Perform an exponential operation on the opposite number of the similarity to obtain the directional difference at the target moment.

[0056] In this embodiment, the directional difference at the target time can be specifically determined by the following formula 1:

[0057] Formula 1

[0058] In formula 1, Used to characterize the directional difference at the i-th moment, Used to represent the wind direction vector at the i-th moment, Used to represent the vibration direction vector of the crane at the i-th moment. Used to characterize the calculation of cosine similarity, Used to represent exponential operations with natural constants as the base.

[0059] Among them, when the wind direction vector at the i-th moment and the vibration direction vector of the crane at the i-th moment The greater the cosine similarity between them, the greater the wind direction vector at the i-th moment. and the vibration direction vector of the crane at the i-th moment The closer the directions are, the smaller the difference in directions at the i-th moment is.

[0060] Directional difference at the i-th moment The larger the value is, the greater the influence of the crane on the amplitude of the target vibration sensor at the i-th moment.

[0061] Through this embodiment, according to the cosine similarity between the wind direction vector at the target time and the vibration direction vector of the crane, the directional difference between the vibration direction of the crane and the wind direction at the target time can be accurately determined. Therefore, the influence of wind direction on the monitoring accuracy of each target vibration sensor can be accurately evaluated, and the accuracy of the collected BIM data can be accurately determined, thereby improving the splicing construction quality of prefabricated buildings.

[0062] As an optional embodiment, Figure 4 As shown, S103 may specifically include the following S401 to S402:

[0063] S401, determining a first vibration regularity of the target vibration sensor according to the vibration amplitude of the target vibration sensor at each moment;

[0064] S402, determining the amplitude influence of the crane on the target vibration sensor at each moment according to the differences in each direction and the first vibration regularity of the target vibration sensor.

[0065] In this embodiment, the server first calculates the statistical characteristics of the vibration amplitude, such as the mean, standard deviation, maximum value, and minimum value of the vibration amplitude, based on the vibration amplitude of the target vibration sensor at each moment. Then, a machine learning algorithm (such as cluster analysis, time series analysis, and neural network) is used to perform pattern recognition on the statistical characteristics of the vibration amplitude, thereby identifying the inherent regularity of the vibration data, such as periodicity and trend. Then, based on the result of pattern recognition, the first vibration regularity of the target vibration sensor is determined.

[0066] Then, according to the directional difference at each moment and the first vibration regularity of the target vibration sensor, the amplitude influence of the crane on the target vibration sensor at each moment is calculated. Specifically, the server establishes a model based on physical principles or empirical data to estimate the influence of the crane vibration on the vibration amplitude collected by the target vibration sensor. Then, the server inputs the directional difference at each moment and the first vibration regularity of the target vibration sensor into the model to obtain the amplitude influence of the crane on the target vibration sensor at each moment.

[0067] Through this embodiment, the first vibration regularity of the target vibration sensor is determined according to the vibration amplitude of the target vibration sensor at each moment. Thus, according to the directional difference at each moment and the first vibration regularity of the target vibration sensor, the amplitude influence of the crane on the target vibration sensor at each moment is determined. In this way, according to the directional difference at each moment and the vibration amplitude of the target vibration sensor at each moment, the amplitude influence of the crane on the vibration sensor at each moment can be accurately determined. Thus, it is possible to accurately judge whether the collected BIM data is accurate, thereby improving the splicing construction quality of prefabricated buildings.

[0068] As an optional embodiment, S401 may specifically include:

[0069] Based on the vibration amplitude of the target vibration sensor at each moment, construct an amplitude curve graph corresponding to the target vibration sensor;

[0070] According to the amplitude curve graph, determine each maximum point in the amplitude curve graph and the maximum time corresponding to the maximum point;

[0071] Based on each maximum point in the amplitude curve diagram and the maximum time corresponding to the maximum point, the first vibration regularity of the target vibration sensor is calculated.

[0072] In this embodiment, the server uses a data visualization tool (such as Python's Matplotlib, Plotly, etc.) or spreadsheet software to draw a scatter plot or a line graph with the timestamp as the horizontal axis and the vibration amplitude as the vertical axis to form an amplitude curve graph.

[0073] Then, use numerical analysis methods (such as differential method) or special peak detection algorithms to identify the maximum points in the amplitude curve graph. Among them, the maximum point is the local highest point, that is, the vibration amplitude on both sides is smaller than it. At the same time, for each identified maximum point, record its corresponding maximum moment.

[0074] Finally, based on each maximum point in the amplitude curve diagram and the maximum moment corresponding to the maximum point, the first vibration regularity of the target vibration sensor is calculated by the following formula 2:

[0075] Formula 2

[0076] In formula 2, Used to characterize the first vibration regularity of the target vibration sensor z at the i-th moment. It is used to characterize the vibration amplitude corresponding to the vth maximum point before the i-th moment. It is used to represent the vibration amplitude corresponding to the v-1 maximum points before the i-th moment, and V is used to represent that there are a total of V maximum points before the i-th moment in the amplitude curve diagram. It is used to characterize the discrete degree of the time interval between adjacent maximum points before the i-th moment in the amplitude curve diagram. Used to represent exponential operations.

[0077] Among them, when the discrete degree of the time intervals between adjacent maximum points of the target vibration sensor z is smaller and the difference in vibration amplitude between adjacent maximum points is smaller, it indicates that the vibration of the target vibration sensor z is more regular, that is, the first vibration regularity of the target vibration sensor is greater.

[0078] Through this embodiment, based on each maximum point in the amplitude curve diagram and the maximum moment corresponding to the maximum point, the first vibration regularity of the target vibration sensor is accurately calculated. Therefore, the vibration regularity of the target vibration sensor can be accurately evaluated, which helps to accurately judge whether the collected BIM data is accurate, thereby improving the splicing construction quality of prefabricated buildings.

[0079] As an optional embodiment, S402 may specifically include:

[0080] Acquire a reference vibration sensor that is on the same elevation plane as the target vibration sensor;

[0081] Acquire the horizontal distance between the reference vibration sensor and the target vibration sensor, and acquire the vibration amplitude of the reference vibration sensor at each moment and the second vibration regularity of the reference vibration sensor;

[0082] Based on the vibration amplitude of the target vibration sensor at each moment, the vibration amplitude of the reference vibration sensor at each moment, the first vibration regularity, the second vibration regularity, the horizontal distance and the differences in each direction, the amplitude influence of the crane on the target vibration sensor at each moment is calculated.

[0083] In this embodiment, the reference vibration sensor is used to characterize a vibration sensor that is located on the same elevation plane as the target vibration sensor.

[0084] As an example, the server searches for a reference vibration sensor on the same elevation plane as the target vibration sensor among the vibration sensors of the target prefabricated building according to the elevation value of the target vibration sensor.

[0085] Then, the horizontal distance between the reference vibration sensor and the target vibration sensor and the vibration amplitude of the reference vibration sensor at each time are obtained. At the same time, based on the vibration amplitude of the reference vibration sensor at each time, the second vibration regularity of the reference vibration sensor is calculated by the above formula 2.

[0086] Finally, based on the vibration amplitude of the target vibration sensor at each moment, the vibration amplitude of the reference vibration sensor at each moment, the first vibration regularity, the second vibration regularity, the horizontal distance, and the differences in each direction, the amplitude influence of the crane on the target vibration sensor at each moment is calculated by the following formula 3:

[0087] Formula 3

[0088] In formula 3, It is used to characterize the influence of the crane on the amplitude of the target vibration sensor z at the height h at the i-th moment. The first vibration regularity of the target vibration sensor z used to characterize the elevation h at the i-th moment, Used to characterize the second vibration regularity of the reference vibration sensor c at the elevation h at the i-th moment. The vibration amplitude of the target vibration sensor z used to characterize the elevation h at the i-th moment, The vibration amplitude of the reference vibration sensor c used to characterize the elevation h at the i-th moment. It is used to characterize the horizontal distance between the target vibration sensor z and the reference vibration sensor c, and C is used to characterize the total number of reference vibration sensors. It is used to characterize the directional difference at the i-th moment, and sigmoid is used to characterize the activation function operation.

[0089] Among them, at the same elevation plane h at the same time i, the directional difference between the wind direction and the vibration direction of the crane is used as the weight. The greater the difference in vibration regularity between the target vibration sensor z and the reference vibration sensor c, the greater the impact of the crane vibration on the vibration of the target vibration sensor z, that is, the greater the impact of the crane on the amplitude of the target vibration sensor z; the greater the horizontal distance between the target vibration sensor z and the reference vibration sensor c, the smaller the reference significance of the reference vibration sensor c.

[0090] Through this embodiment, based on the vibration amplitude of the target vibration sensor at each moment, the vibration amplitude of the reference vibration sensor at each moment, the first vibration regularity, the second vibration regularity, the horizontal distance and the differences in each direction, the amplitude influence of the crane on the target vibration sensor at each moment is accurately calculated. This helps to accurately judge whether the collected BIM data is accurate based on the amplitude influence, thereby improving the splicing construction quality of prefabricated buildings.

[0091] As an optional embodiment, Figure 5 As shown, S104 may specifically include the following S501 to S503:

[0092] S501, retaining the vibration amplitude at each moment whose amplitude influence is less than a preset influence threshold, to obtain a first vibration amplitude;

[0093] S502, filtering the vibration amplitudes at each moment whose amplitude influence is greater than or equal to a preset influence threshold value, and the vibration amplitudes whose occurrence frequency is less than the target cutoff frequency, to obtain a second vibration amplitude;

[0094] S503: Based on the first vibration amplitude and the second vibration amplitude, the vibration amplitude of the target vibration sensor in the initial BIM data at each moment is updated to obtain updated BIM data.

[0095] In this embodiment, the preset influence threshold is used to distinguish whether the vibration amplitude is abnormal. When the amplitude influence is greater than or equal to the preset influence threshold, it indicates that the corresponding vibration amplitude may be abnormal; when the amplitude influence is less than the preset influence threshold, it indicates that the corresponding vibration amplitude must be normal.

[0096] As an example, the server traverses the vibration amplitude of the target vibration sensor at each moment and calculates the amplitude influence corresponding to each moment. Then, the vibration amplitude at each moment with an amplitude influence less than a preset influence threshold is retained to obtain a first vibration amplitude; for each vibration amplitude with an amplitude influence greater than or equal to the preset influence threshold, its occurrence frequency is calculated, and those vibration amplitudes with an occurrence frequency greater than or equal to the target cutoff frequency are retained, and the vibration amplitudes with an occurrence frequency less than the target cutoff frequency are filtered out to obtain a second vibration amplitude.

[0097] Finally, the first vibration amplitude and the second vibration amplitude are combined as new vibration data and updated into the BIM data.

[0098] Through this embodiment, the vibration amplitude at each moment with an amplitude influence less than a preset influence threshold is retained; among the vibration amplitudes at each moment with an amplitude influence greater than or equal to the preset influence threshold, the vibration amplitudes with an occurrence frequency less than a target cutoff frequency are filtered. In this way, the BIM data is updated according to the amplitude influence, thereby improving the splicing construction quality of prefabricated buildings.

[0099] As an optional embodiment, S502 may specifically include:

[0100] The vibration amplitude at each moment when the amplitude influence is greater than or equal to the preset influence threshold is counted to construct a spectrum diagram;

[0101] The vibration amplitudes in the spectrum graph whose frequencies are less than the target cutoff frequency are filtered through a high-pass filter to obtain a second vibration amplitude.

[0102] In this embodiment, the high-pass filter is a filter that allows signals above a certain frequency to pass through, while signals below the certain frequency are attenuated or blocked. The target cutoff frequency is the dividing line of the high-pass filter.

[0103] As an example, the server selects all vibration amplitudes whose amplitude influence is greater than or equal to a preset influence threshold according to the amplitude influence corresponding to the vibration amplitude at each moment. Then, the vibration amplitudes at each moment selected are counted. Fast Fourier transform (FFT) or other spectrum analysis methods are used to convert the statistically obtained vibration amplitude data into a frequency domain representation, thereby obtaining the corresponding spectrum diagram.

[0104] Finally, a high-pass filter is applied to the vibration amplitudes in the spectrogram. The filtering process retains the vibration amplitudes that occur at frequencies above the target cutoff frequency, while attenuating or removing the vibration amplitudes that occur below the threshold. After the high-pass filter is processed, a second vibration amplitude is obtained that occurs at a frequency above the target cutoff frequency.

[0105] Through this embodiment, the vibration amplitude at each moment when the amplitude influence is greater than or equal to the preset influence threshold is counted to construct a spectrum graph. Then, through a high-pass filter, each vibration amplitude with a frequency less than the target cutoff frequency in the spectrum graph is filtered to obtain a second vibration amplitude. In this way, the abnormal vibration amplitude in the BIM data is removed, thereby improving the accuracy of data collection and the splicing construction quality of prefabricated buildings.

[0106] As an optional embodiment, Figure 6 As shown, before S101, the method for intelligently collecting BIM data of prefabricated buildings may also include the following S601 to S603:

[0107] S601, obtaining at least one preset layout position of a target vibration sensor;

[0108] S602, determining the layout unreasonableness of each preset layout position according to each preset layout position and the crane position;

[0109] S603: Based on the layout unreasonableness of each preset layout position, adjust the preset layout position of the target vibration sensor to obtain a final layout position of the target vibration sensor.

[0110] In this embodiment, the layout unreasonableness is used to indicate whether the preset layout position is susceptible to the influence of the crane and whether the layout is reasonable.

[0111] As an example, the server pre-sets several possible layout positions of the target vibration sensor, wherein these preset layout positions should cover the key areas of the target prefabricated building while taking into account the convenience of installation and maintenance of the target vibration sensor.

[0112] Then, the geometric relationship between each preset layout position and the current operating position of the crane, such as the distance and angle, is calculated to obtain the layout irrationality of each preset layout position. The higher the value of the layout irrationality, the lower the rationality of the preset layout position as the layout position of the target vibration sensor.

[0113] Finally, according to the layout irrationality of each preset layout position, the preset layout position of the target vibration sensor is adjusted to obtain the final layout position of the target vibration sensor. Specifically, the layout irrationality of the target vibration sensor at different positions of the target prefabricated building can be used as a weight to adjust the layout density of the target vibration sensor. The greater the layout irrationality, that is, the lower the accuracy of the collected data, the lower the density of the vibration sensor arranged; conversely, the smaller the layout irrationality, that is, the lower the degree of influence of the crane on the collected data, the greater the density of the vibration sensor arranged.

[0114] Through this embodiment, the layout irrationality of each preset layout position is determined according to the preset layout position and the crane position. Then, based on the layout irrationality of each preset layout position, the preset layout position of the target vibration sensor is adjusted to obtain the final layout position of the target vibration sensor. In this way, by reasonably arranging the position of the target vibration sensor, the accuracy of data collection can be improved, thereby improving the splicing construction quality of the prefabricated building.

[0115] As an optional embodiment, S602 may specifically include:

[0116] Acquire a horizontal distance between a target preset layout position and a crane position, and a vertical distance between the target preset layout position and a horizontal plane where the crane position is located, wherein the target preset layout position is any one of the preset layout positions;

[0117] The sum of the vertical distance and the preset adjustment coefficient is multiplied by the horizontal distance to obtain the target calculation result;

[0118] The opposite number of the target calculation result is subjected to exponential operation to obtain the layout unreasonableness of the target preset layout position.

[0119] In this embodiment, the layout unreasonableness of the target preset layout position can be specifically determined by the following formula 4:

[0120] Formula 4

[0121] In formula 4, It is used to characterize the unreasonableness of the layout of the target preset layout position n. Used to represent the vertical distance between the target preset layout position n and the horizontal plane where the crane position is located. Used to represent the preset adjustment coefficient, and its preset value is 1. It is used to represent the horizontal distance between the target preset layout position n and the crane position. Used to represent exponential operations.

[0122] Among them, when the vertical distance between the target preset layout position and the horizontal plane where the crane position is located is smaller, or when the horizontal distance between the target preset layout position and the crane position is smaller, it means that the closer the target preset layout position is to the crane position, the greater the degree to which the target preset layout position is affected by the crane, that is, the greater the unreasonableness of the layout of the target preset layout position.

[0123] The greater the layout irrationality of the target preset layout position, the greater the degree to which the position is affected by the crane, and at this time, the layout density of the target vibration sensor at this position should be smaller.

[0124] Through this embodiment, the layout irrationality of the target preset layout position is accurately evaluated according to the horizontal distance and vertical distance between the target preset layout position and the crane position. This helps to reasonably arrange the positions of the target vibration sensors according to the layout irrationality of each preset layout position, improve the accuracy of data collection, and thus improve the splicing construction quality of the prefabricated building.

[0125] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0126] It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in a different order from the embodiments, or several steps can be performed simultaneously.

[0127] The above is only a specific implementation of the present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system, module and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be covered within the protection scope of the present invention.

Claims

1. A method for intelligent collection of BIM data for prefabricated buildings, characterized in that: include: The initial BIM data of the target prefabricated building is collected in real time by using a target sensor set on the target prefabricated building, wherein the target sensor includes a target vibration sensor, and the initial BIM data includes the vibration amplitude of the target vibration sensor at each moment; According to the crane position and the wind direction at each moment, determine the directional difference between the crane vibration direction and the wind direction at each moment; Determining the amplitude influence of the crane on the target vibration sensor at each moment according to the differences in each direction and the vibration amplitude of the target vibration sensor at each moment, including: determining the first vibration regularity of the target vibration sensor according to the vibration amplitude of the target vibration sensor at each moment; determining the amplitude influence of the crane on the target vibration sensor at each moment according to the differences in each direction and the first vibration regularity of the target vibration sensor; According to the differences in each direction and the first vibration regularity of the target vibration sensor, the influence of the crane on the amplitude of the target vibration sensor at each moment is determined, including: Acquire a reference vibration sensor that is on the same elevation plane as the target vibration sensor; Acquire the horizontal distance between the reference vibration sensor and the target vibration sensor, and acquire the vibration amplitude of the reference vibration sensor at each moment and the second vibration regularity of the reference vibration sensor; Based on the vibration amplitude of the target vibration sensor at each moment, the vibration amplitude of the reference vibration sensor at each moment, the first vibration regularity, the second vibration regularity, the horizontal distance and the differences in each direction, the influence of the crane on the amplitude of the target vibration sensor at each moment is calculated; Based on the influence of the crane on the amplitude of the target vibration sensor at each moment, the vibration amplitude of the target vibration sensor in the initial BIM data at each moment is updated to obtain updated BIM data; according to the position of the crane and the wind direction at each moment, the directional difference between the vibration direction of the crane and the wind direction at each moment is determined, including: Obtain the wind direction vector at each moment through the target wind direction sensor; Connect the crane position with the center point of the target prefabricated building to construct the vibration direction vector of the crane; Based on the wind direction vector at each moment and the vibration direction vector of the crane, the direction difference at each moment is calculated respectively; Based on the wind direction vector at each moment and the vibration direction vector of the crane, the direction difference at each moment is calculated respectively, including: The cosine similarity calculation is performed on the wind direction vector at the target time and the vibration direction vector of the crane to obtain the similarity between the wind direction at the target time and the vibration direction of the crane, and the target time is any time; Perform an exponential operation on the opposite number of the similarity to obtain the directional difference at the target moment.

2. The method for intelligent collection of BIM data of prefabricated buildings according to claim 1 is characterized in that: Determining a first vibration regularity of the target vibration sensor according to the vibration amplitude of the target vibration sensor at each moment includes: Based on the vibration amplitude of the target vibration sensor at each moment, construct an amplitude curve graph corresponding to the target vibration sensor; According to the amplitude curve graph, determine each maximum point in the amplitude curve graph and the maximum time corresponding to the maximum point; Based on each maximum point in the amplitude curve diagram and the maximum time corresponding to the maximum point, the first vibration regularity of the target vibration sensor is calculated.

3. The method for intelligent collection of BIM data of prefabricated buildings according to claim 1 is characterized in that: Based on the influence of the crane on the amplitude of the target vibration sensor at each moment, the vibration amplitude of the target vibration sensor at each moment in the initial BIM data is updated to obtain updated BIM data, including: The vibration amplitude at each moment whose amplitude influence is less than a preset influence threshold is retained to obtain a first vibration amplitude; Among the vibration amplitudes at each moment whose amplitude influence is greater than or equal to a preset influence threshold, each vibration amplitude whose occurrence frequency is less than a target cutoff frequency is filtered to obtain a second vibration amplitude; Based on the first vibration amplitude and the second vibration amplitude, the vibration amplitude of the target vibration sensor in the initial BIM data at each moment is updated to obtain updated BIM data.

4. The method for intelligent collection of BIM data of prefabricated buildings according to claim 3 is characterized in that: Among the vibration amplitudes at each moment when the amplitude influence is greater than or equal to the preset influence threshold, each vibration amplitude whose occurrence frequency is less than the target cutoff frequency is filtered to obtain a second vibration amplitude, including: The vibration amplitude at each moment when the amplitude influence is greater than or equal to the preset influence threshold is counted to construct a spectrum diagram; The vibration amplitudes in the spectrum graph whose frequencies are less than the target cutoff frequency are filtered through a high-pass filter to obtain a second vibration amplitude.

5. The method for intelligently collecting BIM data of prefabricated buildings according to any one of claims 1 to 4, characterized in that: Before collecting the initial BIM data of the target prefabricated building in real time through the target sensor set on the target prefabricated building, the method further includes: Acquiring at least one preset layout position of a target vibration sensor; Determine the layout irrationality of each preset layout position according to each preset layout position and the crane position; Based on the layout irrationality of each preset layout position, the preset layout position of the target vibration sensor is adjusted to obtain a final layout position of the target vibration sensor.

6. The method for intelligent collection of BIM data of prefabricated buildings according to claim 5 is characterized in that: According to each preset layout position and the crane position, the layout irrationality of each preset layout position is determined, including: Obtaining a horizontal distance between a target preset layout position and a crane position, and a vertical distance between the target preset layout position and a horizontal plane where the crane position is located, wherein the target preset layout position is any one of the preset layout positions; The sum of the vertical distance and the preset adjustment coefficient is multiplied by the horizontal distance to obtain the target calculation result; The opposite number of the target calculation result is subjected to exponential operation to obtain the layout unreasonableness of the target preset layout position.

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