Foundation settlement monitoring and early warning method and system based on BIM

Abstract

The invention provides a BIM-based foundation settlement monitoring and early warning method and system, and belongs to the technical field of foundation settlement monitoring. The method comprises the following steps: firstly, obtaining design information of a target building, carrying out feature extraction, constructing a BIM model of the target building, then randomly selecting a certain sensor in the BIM model, and obtaining a node coordinate position of the sensor; monitoring the change condition of the coordinate position of the sensor along with time, and taking the change condition as to-be-monitored foundation settlement displacement information; fitting the to-be-monitored foundation settlement displacement information by using a data fitting method to obtain a foundation settlement fitting curve; and finally, foundation settlement early warning is carried out based on the foundation settlement fitting curve. According to the foundation settlement monitoring and early warning method, the problems that in the prior art, interaction between a high-precision sensor network system, an InSAR system or a GIS system and an early warning system is complex, computing resources are seriously occupied, and the cost problem of a foundation settlement monitoring and early warning system is solved to a certain extent.

Description

Technical Field

[0001] The present invention belongs to the technical field of foundation settlement monitoring, and in particular relates to a BIM-based foundation settlement monitoring and early warning method and system. 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 ground collapse, damage to building structures, and reduce the life of buildings. Therefore, foundation settlement needs to be monitored in many fields such as construction and transportation. BIM-based foundation settlement monitoring technology has been proven to have good 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 construction project planning, survey, design, construction, operation and maintenance, surface monitoring, foundation settlement and other aspects. 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 obtained by comparing and analyzing the displacement information of the target building with the historical monitoring data to generate the displacement deviation rate, and then determining whether to issue a foundation settlement alarm based on the above displacement deviation rate; the patent technology with application number 202410524817 constructs a BIM visual foundation model, and then uses the edge detection algorithm to identify the BIM visual foundation model to output the foundation settlement area, and then predicts the settlement of the foundation settlement area based on the foundation settlement prediction model, outputs the foundation settlement risk index, and finally generates settlement warning information based on the above foundation settlement risk index. However, the foundation settlement monitoring or prediction method in the prior art needs to rely on a large number of high-precision sensor network systems (such as GNSS), InSAR systems or GIS systems, which will result in high computer resource usage and complex interactions between the above systems and the warning system; the use of the above precision sensor network system, InSAR system or GIS system will also result in a high comprehensive cost of the foundation settlement monitoring and early warning system. Summary of the invention

[0003] In order to solve the above problems existing in the prior art, the present invention proposes a foundation settlement monitoring and early warning method and system based on BIM, so as to solve the problem 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 in the prior art, as well as the cost problem of the foundation settlement monitoring and early warning system, 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, extracting features of 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, and obtaining 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: S31: Calculate the mean point P in a two-dimensional data point set mean , where P mean (x mean ,y mean )=( , ), x mean and 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; S32: fitting all data in the two-dimensional data point set by least squares 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; S33: Calculate the difference between each two-dimensional data point in the two-dimensional data point set and the mean point P mean The distance EP(x,y) between the two-dimensional data points and the initial fitting curve, and the distance EF1(x,y) between each two-dimensional data point and the initial fitting curve; ; S34: Divide the two-dimensional data points in all the two-dimensional data point sets that are closer to the initial fitting curve F1(x, y) into a 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 distances are 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 ordinate 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 coordinate The optimal parameters are obtained, and a fitting curve function is obtained based on the optimal parameters as a foundation settlement fitting curve.

[0007] Furthermore, 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 updating mean The horizontal coordinate x mean and the ordinate 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 coordinate , calculate the second function value of the cost function; take the first parameter, the second parameter, the third parameter, the horizontal coordinate and the vertical coordinate of the mean point corresponding to the smaller value of the first function value and the second function value as the intermediate optimal parameter; S362: Return to step S35, update the first parameter, the second parameter, the third parameter, the horizontal coordinate and the 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, the second parameter, the third parameter, the horizontal coordinate and the vertical coordinate of the mean point, take the horizontal coordinate and the vertical coordinate of the first parameter, the second parameter, the third parameter, and the mean point corresponding to the smaller value among the first function value, the second function value and the third function value, and update the intermediate optimal parameter; S363: Set an upper limit of 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.

[0008] Furthermore, 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 ordinate 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 N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0009] Furthermore, 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 ordinate 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 N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0010] Furthermore, 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 ordinate 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 N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0011] Furthermore, in 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 ordinate 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 N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0012] Furthermore, in 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 ordinate 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 sub2N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0013] 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 period TR, and calculating the slope R of the settlement change: ; Where R represents the slope of the settlement change, i3 represents the number of sampling points in the time period TR, i3=1, 2, ..., N3, N3 represents the total number of sampling points in the time period TR, S i3 represents the sampling value corresponding to the i3th sampling point, T i3 represents the time corresponding to the i3th sampling point, T represents the time mean in the time period TR, and S represents the time mean of the sampling points in 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.

[0014] According to another aspect of the present invention, there is also provided 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 perform foundation settlement early warning according to 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.

[0015] The beneficial technical effects of the present invention compared with the prior art are: (1) Only a single sensor data combined with a 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; (2) The sensor combined with the data fitting method overcomes the problems of complex interaction and heavy computer resource occupation between the high-precision sensor network system (such as GNSS), InSAR system or GIS system and the early warning system in the existing technology, thereby reducing the pressure on computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0017] Figure 1 A flow chart of a foundation settlement monitoring and early warning method based on BIM provided by the present invention; Figure 2 A structural diagram of a BIM-based foundation settlement monitoring and early warning system provided by the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0019] The following first describes the concepts involved in the present application in conjunction with the accompanying drawings. It should be noted that the following description of each concept is only to make the content of the present application easier to understand, and does not limit the scope of protection of the present application; at the same time, the embodiments and features in the embodiments of the present application can be combined with each other in the absence of conflict. The present application will be described in detail with reference to the accompanying drawings and in conjunction with the embodiments.

[0020] In conjunction with the instruction manual Figure 1The present invention provides a foundation settlement monitoring and early warning method based on BIM, which specifically includes the following steps: S1: obtaining design information of a target building, extracting features from 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, the coordinate position being the coordinate of the spatial coordinate of each sensor projected onto a two-dimensional xoy plane, where it should be noted that the sensor is fixed on 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, and then a two-dimensional data point set containing multiple moments is obtained, and the two-dimensional data point set is used as the foundation settlement displacement information to be monitored, the above-mentioned certain moment can be in days, when in days, the sensor coordinate position at the same time point in multiple consecutive days is recorded, or the sensor coordinate position at the same time point in a day is recorded after a fixed time period (for example, every week or month), but it should be ensured that the sensor is monitored for a long time, and the long-term observation value is used as an analysis sample.

[0021] S3: Using a data fitting method, fitting the foundation settlement displacement information to be monitored in the above step S2 to obtain a foundation settlement fitting curve; the fitting method specifically includes: S31: Calculate the mean point P in a two-dimensional data point set mean , where P mean (x mean ,y mean )=( , ), x mean and 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; S32: Utilize 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.

[0022] S33: Calculate the difference between each two-dimensional data point in the two-dimensional data point set and the mean point P mean The distance EP(x,y) between the two-dimensional data points 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 all the two-dimensional data point sets that are closer to the initial fitting curve F1(x, y) into a 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 the subsequent parameter update.

[0023] 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 ordinate 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 coordinate The optimal parameters are obtained, and a fitting curve function is obtained based on the optimal parameters as a foundation settlement fitting curve.

[0024] It should be explained here that, according to experience, the building settlement curve can be approximated to a certain section of a conic curve with different coefficients. Therefore, in this application, a multi-time two-dimensional data point set 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, but the use of a single sensor plays a role in reducing the complexity of interaction with the early warning system and the computer resource occupancy rate, and for the early warning indicator of foundation settlement that requires long-term observation, the time dimension of the observation value is long, and the long-term observation of the displacement change of a single sensor is sufficient to more accurately capture the trend and law of foundation settlement, that is, long-term monitoring of the data change of a single sensor is sufficient for foundation settlement early warning. Therefore, the multi-sensor network in the prior art is abandoned in the present invention, and only a single sensor is used in conjunction with the curve fitting method to monitor foundation settlement.

[0025] Furthermore, 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 updating mean The horizontal coordinate x mean and the ordinate 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 coordinate , calculate the second function value of the cost function; take the first parameter, the second parameter, the third parameter, the horizontal coordinate and the vertical coordinate of the mean point corresponding to the smaller value of the first function value and the second function value as the intermediate optimal parameter; S362: Return to step S35, update the first parameter, the second parameter, the third parameter, the horizontal coordinate and the 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, the second parameter, the third parameter, the horizontal coordinate and the vertical coordinate of the mean point, take the horizontal coordinate and the vertical coordinate of the first parameter, the second parameter, the third parameter, and the mean point corresponding to the smaller value among the first function value, the second function value and the third function value, and update the intermediate optimal parameter; S363: Set an upper limit of the number of updates. When the upper limit of the number of updates 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.

[0026] Furthermore, 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 ordinate 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; and calculate 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 coordinate The last term in the calculation formula is also an attenuation solution adjustment term and can play a similar role.

[0027] Furthermore, 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 ordinate 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 N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0028] Furthermore, 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 ordinate 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 N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0029] Furthermore, in 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 ordinate 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 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: ; 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 ordinate 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 sub2N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

[0031] 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 period TR, and calculating the slope R of the settlement change: ; Where R represents the slope of the settlement change, i3 represents the number of sampling points in the time period TR, i3=1, 2, ..., N3, N3 represents the total number of sampling points in the time period TR, S i3 represents the sampling value corresponding to the i3th sampling point, T i3 represents the time corresponding to the i3th sampling point, T represents the time mean in the time period TR, and S represents the time mean of the sampling points in 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, the foundation settlement warning is issued based on the settlement change slope, which can objectively match the conic curve close to the actual settlement situation mentioned above, and by calculating the settlement change slope, the error between the foundation settlement fitting curve and the actual settlement situation can be reduced, and the accuracy of foundation settlement monitoring and early warning can be improved, while at least to a certain extent, compensating for the problem of insufficient monitoring accuracy of a single sensor.

[0032] As the instruction manual Figure 2 As shown, the present invention also provides a BIM-based foundation settlement monitoring and early warning system, including 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 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, and 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 according to 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. The above-mentioned data processing module can utilize existing data processing chips such as CPU and GPU; the early warning module can include a strobe light early warning unit or a text early warning unit.

[0033] 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 are not intended to limit the implementation methods of the technology of the present invention in any form. Any technical personnel in this field 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.

[0034] 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 its 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 and the objective existence of infinite specific structures, ordinary technicians in this technical field can make several improvements, modifications or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the concept and technical solution of the invention 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 change of the node coordinate position of the sensor 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 two-dimensional data point set containing multiple moments is obtained, and the two-dimensional data point set is used as the foundation settlement displacement information to be monitored; S3: using a data fitting method, fitting 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, a foundation settlement early warning is performed.

2. A BIM-based foundation settlement monitoring and early warning method according to claim 1, characterized in that: In S3, the data fitting method is used to fit the above-mentioned 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 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: fitting all data in the two-dimensional data point set by least squares 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; S33: Calculate the difference between each two-dimensional data point in the two-dimensional data point set and the mean point P mean The distance EP(x,y) between the two-dimensional data points and the initial fitting curve, and the distance EF1(x,y) between each two-dimensional data point and the initial fitting curve; ； S34: Divide the two-dimensional data points in all the two-dimensional data point sets that are closer to the initial fitting curve F1(x, y) into a 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 distances are 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 ordinate 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 coordinate The optimal parameters are obtained, and a fitting curve function is obtained based on the optimal parameters as a foundation settlement fitting curve.

3. A BIM-based foundation settlement monitoring and early warning method according to claim 2, characterized in that: The optimal parameters are obtained in S36, which 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 updating mean The horizontal coordinate x mean and the ordinate 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 coordinate , calculate the second function value of the cost function; take the first parameter, the second parameter, the third parameter, the horizontal coordinate and the vertical coordinate of the mean point corresponding to the smaller value of the first function value and the second function value as the intermediate optimal parameter; S362: Return to step S35, update the first parameter, the second parameter, the third parameter, the horizontal coordinate and the 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, the second parameter, the third parameter, the horizontal coordinate and the vertical coordinate of the mean point, take the horizontal coordinate and the vertical coordinate of the first parameter, the second parameter, the third parameter, and the mean point corresponding to the smaller value among the first function value, the second function value and the third function value, and update the intermediate optimal parameter; S363: Set an upper limit of the number of updates. When the upper limit of the number of updates 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.

4. The BIM-based foundation settlement monitoring and early warning method according to claim 2 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 ordinate 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 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 2 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 ordinate 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 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 2 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 ordinate 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 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 2 is characterized in that: In 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 ordinate 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 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 2 is characterized in that: In S35, the mean value point P is updated. mean The y-coordinate 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 ordinate 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 N represents the total number of two-dimensional data points in the set of all two-dimensional data points.

9. 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: ； Where R represents the slope of the settlement change, i3 represents the number of sampling points in the time period TR, i3=1, 2, ..., N3, N3 represents the total number of sampling points in the time period TR, S i3 represents the sampling value corresponding to the i3th sampling point, T i3 represents the time corresponding to the i3th sampling point, T represents the mean of the time in the period TR, and S represents the mean of the sampling points in the period TR; S42: Determine whether the slope R of the sedimentation change is greater than the threshold R TH , when the judgment result is greater than, a foundation settlement warning is issued.

10. A foundation settlement monitoring and early warning system based on BIM, comprising a BIM model building module, a sensor monitoring module, a data processing module and an early warning module, wherein the BIM model building 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 perform foundation settlement early warning according to 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 as described in any one of claims 1-9.

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