A foundation settlement monitoring and early warning method and system based on BIM

Abstract

The present invention proposes a BIM-based foundation settlement monitoring and early warning method and system, which belongs to the field of foundation settlement monitoring technology. First, the design information of the target building is obtained and feature extraction is performed to construct a BIM model of the target building. Then, a sensor in the BIM model is randomly selected and the node coordinate position of the sensor is obtained; the change of the sensor coordinate position over time is monitored and used as the foundation settlement displacement information to be monitored; the foundation settlement displacement information to be monitored is fitted using a data fitting method to obtain a foundation settlement fitting curve; finally, a foundation settlement early warning is performed based on the foundation settlement fitting curve. The foundation settlement monitoring and early warning method of the present invention solves to a certain extent the problem in the prior art that the interaction between the high-precision sensor network system, InSAR system or GIS system and the early warning system is complex and occupies serious computing resources, as well as the cost problem of the foundation settlement monitoring and early warning system.

Description

Technical Field

[0001] The present invention belongs to the technical field of foundation settlement monitoring, and in particular relates to a foundation settlement monitoring and early warning method and system based on BIM. Background Art

[0002] Foundation settlement refers to the sinking of the foundation surface caused by the compaction of the foundation soil layer under the action of additional stress. Foundation settlement may cause major accidents such as ground collapse, structural damage to buildings, and shortened building lifespan. Therefore, foundation settlement monitoring is necessary in many fields such as construction and transportation. BIM-based foundation settlement monitoring technology has been proven to have great practical engineering application value. BIM, or Building Information Model, is a model based on various relevant information data of construction projects. It can be applied to many aspects of construction project planning, investigation, design, construction, operation and maintenance, surface monitoring, foundation settlement, etc. In the patent technology with the published application number 202010724135, sensors are used to monitor surface settlement, stratified settlement, groundwater level, pore water pressure and other data, and then a monitoring point model in the BIM model is established based on the above data. The monitoring data in the monitoring point model is compared with the stored early warning line to realize the visualization monitoring of foundation settlement; in the patent technology with the application number 202111310753, the design drawing information and three-dimensional point cloud data of the target building are obtained to establish a three-dimensional model of the target building, and then the coordinate information of the sensor node is obtained and mapped to the target building. The displacement information of the target building is collected and compared with historical monitoring data to generate a displacement deviation rate. The displacement deviation rate is then used to determine whether to issue a foundation settlement alarm. Patent application number 202410524817 constructs a BIM visual foundation model, then uses an edge detection algorithm to identify the BIM visual foundation model to output a foundation settlement area. A settlement prediction model is then used to predict the settlement area, outputting a foundation settlement risk index. Finally, settlement warning information is generated based on the foundation settlement risk index. However, existing foundation settlement monitoring or prediction methods rely on a large number of high-precision sensor network systems (such as GNSS), InSAR systems, or GIS systems. These systems result in high computer resource utilization and complex interactions between these systems and the warning system. The use of these high-precision sensor network systems, InSAR systems, or GIS systems also results in a high overall cost for foundation settlement monitoring and warning systems. Summary of the Invention

[0003] In order to solve the above-mentioned problems existing in the prior art, the present invention proposes a BIM-based foundation settlement monitoring and early warning method and system to solve the problems in the prior art of complex interaction and serious occupation of computing resources between high-precision sensor network systems, InSAR systems or GIS systems and early warning systems, as well as the cost problem of foundation settlement monitoring and early warning systems, thereby reducing the pressure on computing resources.

[0004] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a BIM-based foundation settlement monitoring and early warning method, which specifically includes the following steps: S1: obtaining the design information of the target building, performing feature extraction on the design information, thereby constructing a BIM model of the target building, and then randomly selecting a sensor in the BIM model and obtaining the node coordinate position of the sensor; S2: monitoring the change of the sensor coordinate position over time and using it as the foundation settlement displacement information to be monitored; S3: using a data fitting method to fit the above-mentioned foundation settlement displacement information to be monitored to obtain a foundation settlement fitting curve; S4: based on the foundation settlement fitting curve, performing foundation settlement early warning.

[0005] Furthermore, in S2, the sensor coordinate position at a certain moment and the moment are recorded as a two-dimensional data point, and then a two-dimensional data point set including multiple moments is obtained, and the two-dimensional data point set is used as the foundation settlement displacement information to be monitored.

[0006] Furthermore, in S3, the data fitting method is used to fit the above-mentioned foundation settlement displacement information to be monitored, specifically:

[0007] S31: Calculate the mean point P in a two-dimensional data point set mean , where P mean (x mean ,y mean )=( , ), x mean and y mean are the mean points P mean The horizontal and vertical coordinates, x i Represents the horizontal coordinate of the i-th two-dimensional data point, y i represents the ordinate of the i-th two-dimensional data point, and N represents the total number of points in the two-dimensional data point set;

[0008] S32: Fit all data in the two-dimensional data point set using least squares to obtain an initial fitting curve F1(x,y), F1(x,y)=ax+by+c; where a represents a first parameter of the initial fitting curve, b represents a second parameter of the initial fitting curve, c represents a third parameter of the initial fitting curve, and x and y are function variables of the initial fitting curve respectively;

[0009] S33: Calculate the value of each two-dimensional data point in the two-dimensional data point set and the mean point P mean The distance EP(x, y) between each two-dimensional data point and the initial fitting curve, and the distance EF1(x, y) between each two-dimensional data point;

[0010] ;

[0011] S34: Divide the two-dimensional data points in the set of all two-dimensional data points that are closer to the initial fitting curve F1(x, y) into the first subset R sub1 , all the two-dimensional data points closer to the mean point P mean The two-dimensional data points are divided into the second subset R sub2 ; If there is a two-dimensional data point that is consistent with the initial fitting curve F1(x,y) and the mean point P mean If the distance is the same, delete the two-dimensional data point;

[0012] S35: Update the first parameter a, the second parameter b, the third parameter c, and the mean point P of the initial fitting curve mean The horizontal coordinate x mean and the vertical coordinate y mean ;

[0013] S36: Based on the updated first parameter a upd , the second parameter b upd 、The third parameter c upd , mean point The horizontal axis and the vertical axis The optimal parameters are obtained, and a fitting curve function is obtained based on the optimal parameters as a foundation settlement fitting curve.

[0014] Furthermore, obtaining the optimal parameters in step S36 specifically includes the following steps:

[0015] S361: Based on the first parameter a, the second parameter b, the third parameter c, and the mean point P before the update mean The horizontal coordinate x mean and the vertical coordinate y mean , calculate the first function value of the cost function; based on the updated first parameter a upd , the second parameter b upd 、The third parameter c upd , mean point The horizontal axis and the vertical axis , calculate the second function value of the cost function; take the first parameter, second parameter, third parameter, and the horizontal and vertical coordinates of the mean point corresponding to the smaller value of the first and second function values ​​as the intermediate optimal parameters;

[0016] S362: Return to step S35, update the first parameter, second parameter, third parameter, and horizontal coordinate and vertical coordinate of the mean point of the initial fitting curve again, calculate the third function value of the cost function based on the updated first parameter, second parameter, third parameter, and horizontal coordinate and vertical coordinate of the mean point, take the first parameter, second parameter, third parameter, and horizontal coordinate and vertical coordinate of the mean point corresponding to the smaller value of the first function value, second function value, and third function value, and update the intermediate optimal parameter;

[0017] S363: Set an upper limit for the number of updates. When the upper limit is reached, stop updating the horizontal and vertical coordinates of the first parameter, the second parameter, the third parameter, and the mean point, and use the current intermediate optimal parameter as the optimal parameter.

[0018] Furthermore, the first parameter a of the initial fitting curve is updated in S35, specifically:

[0019] ;

[0020] Among them, a upd represents the first parameter after update, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0021] Furthermore, the second parameter b of the initial fitting curve is updated in S35, specifically:

[0022] ;

[0023] Among them, b upd represents the updated second parameter, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0024] Furthermore, the third parameter c of the initial fitting curve is updated in S35, specifically:

[0025] ;

[0026] Among them, c upd represents the updated third parameter, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0027] Furthermore, in S35, the mean value point P is updated mean The horizontal coordinate x mean , specifically:

[0028] ;

[0029] in, Represents the updated mean point The horizontal axis, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0030] Furthermore, in S35, the mean value point P is updated mean The vertical coordinate y mean , specifically:

[0031] ;

[0032] in, Represents the updated mean point The vertical coordinate, γ and μ are adjustment coefficients, xi1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0033] Furthermore, the foundation settlement early warning is performed based on the foundation settlement fitting curve in S4, which specifically includes the following steps: S41: sampling the foundation settlement fitting curve in a future time period TR, and calculating the slope R of the settlement change:

[0034] ;

[0035] Among them, R represents the slope of the settlement change, i3 represents the sampling point number in the time period TR, i3=1, 2, ..., N3, N3 represents the total number of sampling points in the time period TR, S i3 Indicates the sampling value corresponding to the i3th sampling point, T i3 Indicates the time corresponding to the i3th sampling point, It represents the time mean within the period TR, Indicates the average value of the sampling points within the time period TR; S42: Determine whether the slope R of the settlement change is greater than the threshold R TH When the judgment result is greater than, a foundation settlement warning is issued.

[0036] According to another aspect of the present invention, a BIM-based foundation settlement monitoring and early warning system is also provided, including a BIM model construction module, a sensor monitoring module, a data processing module and an early warning module. The BIM model construction module, the sensor monitoring module, the data processing module and the early warning module are connected in sequence. The BIM model construction module is used to obtain the design information of the target building, perform feature extraction on the design information, and thus construct a BIM model of the target building; the sensor monitoring module is used to randomly select a sensor in the BIM model, obtain the node coordinate position of the sensor, and monitor the change of the sensor coordinate position over time; the data processing module is used to obtain a foundation settlement fitting curve; the early warning module is used to perform foundation settlement early warning based on the foundation settlement fitting curve; the foundation settlement monitoring and early warning system is configured to be able to execute the foundation settlement monitoring and early warning method described above.

[0037] The beneficial technical effects of the present invention compared with the prior art are:

[0038] (1) Only a single sensor data combined with data fitting method is needed to realize foundation settlement monitoring and early warning, thus reducing the overall cost of the monitoring and early warning system;

[0039] (2) The sensor-based data fitting method overcomes the problems of complex interaction and serious computer resource occupation between high-precision sensor network systems (such as GNSS), InSAR systems or GIS systems and early warning systems in existing technologies, thereby alleviating the pressure on computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0041] Figure 1 A flow chart of a BIM-based foundation settlement monitoring and early warning method provided by the present invention;

[0042] Figure 2 This is a simplified structural diagram of a BIM-based foundation settlement monitoring and early warning system provided by the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] The following first describes the concepts involved in this application with reference to the accompanying drawings. It should be noted that the following description of each concept is intended only to make the content of this application easier to understand and does not limit the scope of protection of this application. At the same time, the embodiments and features in the embodiments of this application can be combined with each other unless there is a conflict. The following detailed description of this application will be made with reference to the accompanying drawings and in conjunction with the embodiments.

[0045] In conjunction with the instructions Figure 1The present invention provides a BIM-based foundation settlement monitoring and early warning method, which specifically includes the following steps: S1: obtaining design information of a target building, performing feature extraction on the design information, thereby constructing a BIM model of the target building, then randomly selecting a sensor in the BIM model and obtaining the node coordinate position of the sensor, which is the coordinate position of the spatial coordinate of each sensor projected onto the two-dimensional xoy plane. It should be noted that the sensor is fixed to a certain part of the building and is in a stationary state relative to the building; S2: monitoring the change of the sensor coordinate position over time and using it as the foundation settlement displacement information to be monitored; specifically, the sensor coordinate position at a certain moment and the moment are recorded as a two-dimensional data point, thereby obtaining a set of two-dimensional data points including multiple moments, and using the set of two-dimensional data points as the foundation settlement displacement information to be monitored. The aforementioned certain moment can be measured in days. When measured in days, the sensor coordinate position at the same time point on multiple consecutive days is recorded, or the sensor coordinate position at the same time point on a day is recorded after a fixed time period (e.g., every week or every month). However, it is necessary to ensure that the sensor is monitored over a long period of time, and use the long-term observation value as the analysis sample.

[0046] S3: Using a data fitting method, fitting the foundation settlement displacement information to be monitored in the above step S2 is performed to obtain a foundation settlement fitting curve; the fitting method specifically includes:

[0047] S31: Calculate the mean point P in a two-dimensional data point set mean , where P mean (x mean ,y mean )=( , ), x mean and y mean are the mean points P mean The horizontal and vertical coordinates, x i Represents the horizontal coordinate of the i-th two-dimensional data point, y i represents the ordinate of the i-th two-dimensional data point, and N represents the total number of points in the two-dimensional data point set;

[0048] S32: Use least squares fitting to fit all the data in the two-dimensional data point set to obtain an initial fitting curve F1(x,y), F1(x,y)=ax+by+c; wherein a represents the first parameter of the initial fitting curve, b represents the second parameter of the initial fitting curve, c represents the third parameter of the initial fitting curve, and x and y are function variables of the initial fitting curve respectively; the above least squares fitting method adopts the existing conventional least squares fitting, that is, to find the best function match for all the data in the two-dimensional data point set by minimizing the sum of squares of errors, which will not be repeated here.

[0049] S33: Calculate the value of each two-dimensional data point in the two-dimensional data point set and the mean point P mean The distance EP(x,y) between each two-dimensional data point and the initial fitting curve, and the distance EF1(x,y) between each two-dimensional data point:

[0050] ;

[0051] S34: Divide the two-dimensional data points in the set of all two-dimensional data points that are closer to the initial fitting curve F1(x, y) into the first subset R sub1 , all the two-dimensional data points closer to the mean point P mean The two-dimensional data points are divided into the second subset R sub2 ; If there is a two-dimensional data point that is consistent with the initial fitting curve F1(x,y) and the mean point P mean by dividing the subsets in step S34 and updating the parameters based on different subsets in subsequent steps, it is possible to ensure that the initial fitting curve has a large curvature, which is convenient for adjusting the fitting curve after subsequent parameter updates.

[0052] S35: Update the first parameter a, the second parameter b, the third parameter c, and the mean point P of the initial fitting curve mean The horizontal coordinate x mean and the vertical coordinate y mean ;

[0053] S36: Based on the updated first parameter a upd , the second parameter b upd 、The third parameter c upd , mean point The horizontal axis and the vertical axis The optimal parameters are obtained, and a fitting curve function is obtained based on the optimal parameters as a foundation settlement fitting curve.

[0054] It should be explained here that, according to experience, the building settlement curve can be approximated by a certain section of a conic curve with different coefficients. Therefore, in this application, a set of two-dimensional data points at multiple times of a single sensor fixed on a certain part of the building is detected. The initial position of the single sensor is fixed, and its spatial displacement changing with time can be equivalent to the displacement change of the target building, which can also be equivalent to the displacement change of the foundation settlement of the target building. Then, the conic curve is used as the initial fitting curve for simulating foundation settlement. It must be pointed out that compared with the displacement detection using a sensor network composed of multiple sensors, the displacement detection accuracy of a single sensor is relatively low. However, the use of a single sensor has the effect of reducing the interaction complexity between the early warning system and the computer resource utilization rate. Moreover, for the early warning indicator of foundation settlement that requires long-term observation, the time dimension of the observation value is long. Long-term observation of the displacement change of a single sensor is sufficient to more accurately capture the trend and law of foundation settlement. In other words, long-term monitoring of the data change of a single sensor is sufficient for foundation settlement early warning. Therefore, the present invention abandons the multi-sensor network in the prior art and only uses a single sensor and a curve fitting method to monitor foundation settlement.

[0055] Furthermore, obtaining the optimal parameters in step S36 specifically includes the following steps:

[0056] S361: Based on the first parameter a, the second parameter b, the third parameter c, and the mean point P before the update mean The horizontal coordinate x mean and the vertical coordinate y mean , calculate the first function value of the cost function; based on the updated first parameter a upd , the second parameter b upd 、The third parameter c upd , mean point The horizontal axis and the vertical axis , calculate the second function value of the cost function; take the first parameter, second parameter, third parameter, and the horizontal and vertical coordinates of the mean point corresponding to the smaller value of the first and second function values ​​as the intermediate optimal parameters;

[0057] S362: Return to step S35, update the first parameter, second parameter, third parameter, and horizontal coordinate and vertical coordinate of the mean point of the initial fitting curve again, calculate the third function value of the cost function based on the updated first parameter, second parameter, third parameter, and horizontal coordinate and vertical coordinate of the mean point, take the first parameter, second parameter, third parameter, and horizontal coordinate and vertical coordinate of the mean point corresponding to the smaller value of the first function value, second function value, and third function value, and update the intermediate optimal parameter;

[0058] S363: Set an upper limit for the number of updates. When the upper limit is reached, stop updating the horizontal and vertical coordinates of the first parameter, the second parameter, the third parameter, and the mean point, and use the current intermediate optimal parameter as the optimal parameter.

[0059] Furthermore, the first parameter a of the initial fitting curve is updated in S35, specifically:

[0060] ;

[0061] Among them, a upd represents the first parameter after update, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in the set of all two-dimensional data points is N; and the calculation of a upd The last term in the above formula is actually the attenuation solution adjustment term, which ensures that the attenuation solution component in the first parameter a is small after multiple updates, thereby improving the accuracy of the fitting curve. The horizontal axis and the vertical axis The last term in the calculation formula is also an attenuation solution adjustment term and can play a similar role.

[0062] Furthermore, the second parameter b of the initial fitting curve is updated in S35, specifically:

[0063] ;

[0064] Among them, b upd represents the updated second parameter, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0065] Furthermore, the third parameter c of the initial fitting curve is updated in S35, specifically:

[0066] ;

[0067] Among them, c upd represents the updated third parameter, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0068] Furthermore, in S35, the mean value point P is updated mean The horizontal coordinate x mean , specifically:

[0069] ;

[0070] in, Represents the updated mean point The horizontal axis, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0071] Furthermore, in S35, the mean value point P is updated mean The vertical coordinate y mean , specifically:

[0072] ;

[0073] in, Represents the updated mean point The vertical coordinate, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0074] In S4, foundation settlement early warning is performed based on the foundation settlement fitting curve, specifically: S41: sampling the foundation settlement fitting curve in a future time period TR, and calculating the slope R of the settlement change:

[0075] ;

[0076] Among them, R represents the slope of the settlement change, i3 represents the sampling point number in the time period TR, i3=1, 2, ..., N3, N3 represents the total number of sampling points in the time period TR, S i3 Indicates the sampling value corresponding to the i3th sampling point, T i3 Indicates the time corresponding to the i3th sampling point, It represents the time mean within the period TR, Indicates the average value of the sampling points within the time period TR; S42: Determine whether the slope R of the settlement change is greater than the threshold R TH When the judgment result is greater than, a foundation settlement warning is issued. In the present invention, foundation settlement warning is performed based on the slope of the settlement change. This objectively matches the conic curve described above that is close to the actual settlement situation. By calculating the slope of the settlement change, the error between the foundation settlement fitting curve and the actual settlement situation can be reduced, thereby improving the accuracy of foundation settlement monitoring and warning. At the same time, it can at least to some extent compensate for the problem of insufficient monitoring accuracy of a single sensor.

[0077] As the instruction manual Figure 2As shown, the present invention also provides a BIM-based foundation settlement monitoring and early warning system, comprising a BIM model construction module, a sensor monitoring module, a data processing module, and an early warning module, wherein the BIM model construction module, the sensor monitoring module, the data processing module, and the early warning module are connected in sequence. The BIM model construction module is used to obtain the design information of the target building, perform feature extraction on the design information, and thus construct a BIM model of the target building; the sensor monitoring module is used to randomly select a sensor in the BIM model, obtain the node coordinate position of the sensor, and monitor the change of the sensor coordinate position over time; the data processing module is used to obtain a foundation settlement fitting curve; the early warning module is used to provide a foundation settlement early warning based on the foundation settlement fitting curve; the foundation settlement monitoring and early warning system is configured to execute the foundation settlement monitoring and early warning method described above. The above-mentioned data processing module can utilize existing data processing chips such as CPUs and GPUs; the early warning module can include a strobe light early warning unit or a text early warning unit.

[0078] The embodiments and / or implementation methods described above are only used to illustrate the preferred embodiments and / or implementation methods for realizing the technology of the present invention, and do not impose any form of limitation on the implementation methods of the technology of the present invention. Any person skilled in the art may make slight changes or modifications to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as technologies or embodiments that are essentially the same as the present invention.

[0079] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of this application, they can also make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A foundation settlement monitoring and early warning method based on BIM, characterized in that: The specific steps include: S1: Obtain design information of a target building, perform feature extraction on the design information, thereby constructing a BIM model of the target building, and then randomly select one of the sensors in the BIM model and obtain the node coordinate position of the sensor; S2: Monitor the changes in the sensor node coordinate position over time and use it as the foundation settlement displacement information to be monitored. Specifically, the sensor coordinate position at a certain moment and the moment are recorded as a two-dimensional data point, and then a set of two-dimensional data points containing multiple moments is obtained, and the set of two-dimensional data points is used as the foundation settlement displacement information to be monitored. S3: Using a data fitting method, fitting the foundation settlement displacement information to be monitored to obtain a foundation settlement fitting curve; S4: performing foundation settlement early warning based on the foundation settlement fitting curve; In S3, the data fitting method is used to fit the foundation settlement displacement information to be monitored, specifically: S31: Calculate the mean point P in a two-dimensional data point set mean , where P mean (x mean , y mean )=( , ), x mean and y mean are the mean points P mean The horizontal and vertical coordinates of x i Represents the horizontal coordinate of the i-th two-dimensional data point, y i represents the ordinate of the i-th two-dimensional data point, and N represents the total number of points in the two-dimensional data point set; S32: Fit all data in the two-dimensional data point set using least squares to obtain an initial fitting curve F1(x,y), F1(x,y)=ax+by+c; where a represents a first parameter of the initial fitting curve, b represents a second parameter of the initial fitting curve, c represents a third parameter of the initial fitting curve, and x and y are function variables of the initial fitting curve respectively; S33: Calculate the value of each two-dimensional data point in the two-dimensional data point set and the mean point P mean The distance EP(x, y) between each two-dimensional data point and the initial fitting curve, and the distance EF1(x, y) between each two-dimensional data point; ； S34: Divide the two-dimensional data points in the set of all two-dimensional data points that are closer to the initial fitting curve F1(x, y) into the first subset R sub1 , all the two-dimensional data points closer to the mean point P mean The two-dimensional data points are divided into the second subset R sub2 ; If there is a two-dimensional data point that is consistent with the initial fitting curve F1(x,y) and the mean point P mean If the distance is the same, delete the two-dimensional data point; S35: Update the first parameter a, the second parameter b, the third parameter c, and the mean point P of the initial fitting curve mean The horizontal coordinate x mean and the vertical coordinate y mean ; S36: Based on the updated first parameter a upd , the second parameter b upd 、The third parameter c upd , mean point The horizontal axis and the vertical axis The optimal parameters are obtained, and a fitting curve function is obtained based on the optimal parameters as a foundation settlement fitting curve.

2. A BIM-based foundation settlement monitoring and early warning method according to claim 1, characterized in that: The step S36 of obtaining the optimal parameters specifically includes the following steps: S361: Based on the first parameter a, the second parameter b, the third parameter c, and the mean point P before the update mean The horizontal coordinate x mean and the vertical coordinate y mean , calculate the first function value of the cost function; based on the updated first parameter a upd , the second parameter b upd 、The third parameter c upd , mean point The horizontal axis and the vertical axis , calculate the second function value of the cost function; take the first parameter, second parameter, third parameter, and the horizontal and vertical coordinates of the mean point corresponding to the smaller value of the first and second function values ​​as the intermediate optimal parameters; S362: Return to step S35, update the first parameter, second parameter, third parameter, and horizontal coordinate and vertical coordinate of the mean point of the initial fitting curve again, calculate the third function value of the cost function based on the updated first parameter, second parameter, third parameter, and horizontal coordinate and vertical coordinate of the mean point, take the first parameter, second parameter, third parameter, and horizontal coordinate and vertical coordinate of the mean point corresponding to the smaller value of the first function value, second function value, and third function value, and update the intermediate optimal parameter; S363: Set an upper limit for the number of updates. When the upper limit is reached, stop updating the horizontal and vertical coordinates of the first parameter, the second parameter, the third parameter, and the mean point, and use the current intermediate optimal parameter as the optimal parameter.

3. The BIM-based foundation settlement monitoring and early warning method according to claim 1 is characterized in that: The first parameter a of the initial fitting curve is updated in S35, specifically: ； Among them, a upd represents the first parameter after update, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

4. The BIM-based foundation settlement monitoring and early warning method according to claim 1 is characterized in that: The second parameter b of the initial fitting curve is updated in S35, specifically: ； Among them, b upd represents the updated second parameter, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

5. The BIM-based foundation settlement monitoring and early warning method according to claim 1 is characterized in that: The third parameter c of the initial fitting curve is updated in S35, specifically: ； Among them, c upd represents the updated third parameter, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

6. The BIM-based foundation settlement monitoring and early warning method according to claim 1 is characterized in that: In step S35, the mean value point P is updated. mean The horizontal coordinate x mean , specifically: ； in, Represents the updated mean point The horizontal axis, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

7. The BIM-based foundation settlement monitoring and early warning method according to claim 1 is characterized in that: In step S35, the mean value point P is updated. mean The vertical coordinate y mean , specifically: ； in, Represents the updated mean point The vertical coordinate, γ and μ are adjustment coefficients, x i1 Indicates the horizontal coordinate of the i1th two-dimensional data point, y i1 Represents the vertical coordinate of the i1th two-dimensional data point, i1 represents the first subset R sub1 The two-dimensional data point number in, N1 represents the first subset R sub1 The total number of two-dimensional data points in , i2 represents the second subset R sub2 The two-dimensional data point number in, N2 represents the second subset R sub2 The total number of two-dimensional data points in , N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

8. The BIM-based foundation settlement monitoring and early warning method according to claim 1 is characterized in that: In S4, a foundation settlement early warning is performed based on the foundation settlement fitting curve, specifically: S41: Sample the foundation settlement fitting curve within a future period TR and calculate the slope R of the settlement change: ； Among them, R represents the slope of the settlement change, i3 represents the sampling point number in the time period TR, i3=1, 2, ..., N3, N3 represents the total number of sampling points in the time period TR, S i3 Indicates the sampling value corresponding to the i3th sampling point, T i3 Indicates the time corresponding to the i3th sampling point, It represents the time mean within the period TR, Indicates the mean value of the sampling points within the time period TR; S42: Determine whether the slope R of the settlement change is greater than the threshold R TH When the judgment result is greater than, a foundation settlement warning is issued.

9. A BIM-based foundation settlement monitoring and early warning system, comprising a BIM model construction module, a sensor monitoring module, a data processing module, and an early warning module, wherein the BIM model construction module, the sensor monitoring module, the data processing module, and the early warning module are connected in sequence, and characterized in that: The BIM model construction module is used to obtain the design information of the target building, perform feature extraction on the design information, and thus construct the BIM model of the target building; the sensor monitoring module is used to randomly select a sensor in the BIM model, obtain the node coordinate position of the sensor, and monitor the change of the sensor coordinate position over time; the data processing module is used to obtain the foundation settlement fitting curve; the early warning module is used to issue a foundation settlement early warning based on the foundation settlement fitting curve; The foundation settlement monitoring and early warning system is configured to be able to execute the foundation settlement monitoring and early warning method according to any one of claims 1 to 8.

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