Super high-rise building deformation monitoring system and method

Abstract

The invention discloses a super high-rise building deformation monitoring system and method. The super high-rise building deformation monitoring system comprises a data acquisition module, a data processing module and a deformation monitoring module, relates to the technical field of building monitoring, and solves the technical problems of high cost and low efficiency of deformation monitoring of a super high-rise building in the prior art. The method comprises the following steps: acquiring a building image and environment data of a to-be-detected building, and obtaining drawing information of the to-be-detected building; based on the drawing information, the deformation feature information and the environment data, constructing a data twinborn model of the building to be tested; dividing the to-be-monitored building into a plurality of monitoring areas based on the data twinborn model; and determining the acquisition frequency of each monitoring area based on the simulated building deformation data. According to the scheme, accurate monitoring and management of the building are realized, the accuracy of building deformation monitoring is effectively improved, the layout of monitoring equipment is optimized, and the acquisition frequency of each region is reasonably set according to a simulation result, so that the overall monitoring efficiency is improved, and the building deformation monitoring cost is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of building monitoring, and in particular relates to a super high-rise building deformation monitoring system and method. Background Art

[0002] With the development of cities, super high-rise buildings are becoming more and more common in modern cities. They not only represent the pinnacle of engineering technology, but also put forward higher requirements for the safety of buildings. During the long-term use of these structures, they will be affected by various factors such as earthquakes, wind loads, temperature changes, etc., which may cause structural deformation and damage. Traditional monitoring methods are often based on the measurement of characteristic points. The data obtained is limited and not continuous enough, and it is difficult to fully reflect the overall condition of the building. In order to ensure the safety and stability of super high-rise buildings, a more accurate and real-time monitoring system is needed.

[0003] The existing technology sets up several measuring instruments in the super high-rise building, such as total station, laser plumb line, etc., and obtains the deformation degree of the super high-rise building by reading the data of the measuring instruments and analyzing them. However, in the actual deformation monitoring of super high-rise buildings, since different areas of the super high-rise building are affected by the external environment to different degrees, the deformation risks of different areas of the super high-rise building are not equal; the existing technology solutions are difficult to carry out targeted monitoring of the deformation of super high-rise buildings, resulting in high cost of deformation monitoring of super high-rise buildings and low monitoring efficiency.

[0004] The present invention provides a super high-rise building deformation monitoring system and method to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a super high-rise building deformation monitoring system and method, which is used to solve the technical problems that the prior art solutions are difficult to carry out targeted monitoring of the deformation of super high-rise buildings, resulting in high costs for deformation monitoring of super high-rise buildings and low monitoring efficiency.

[0006] To achieve the above-mentioned object, a first aspect of the present invention provides a super high-rise building deformation monitoring system, comprising: a data processing module, and a data acquisition module and a deformation monitoring module connected thereto;

[0007] The data acquisition module is used to collect building images and environmental data of the building to be tested, and obtain drawing information of the building to be tested;

[0008] The data processing module is used to extract features from building images based on image processing technology to obtain deformation feature information; construct a data twin model of the building to be tested based on drawing information, deformation feature information and environmental data; perform simulation tests on the building to be tested through the digital twin model to obtain several groups of simulation data; wherein the simulation data includes simulated environmental data and simulated building deformation data; and,

[0009] The building to be tested is divided into several monitoring areas based on the data twin model; the acquisition equipment in the monitoring area is planned based on the simulated environmental data; the acquisition frequency of each monitoring area is determined based on the simulated building deformation data;

[0010] The deformation monitoring module is used to monitor the deformation of the building to be tested based on the acquisition frequency of each monitoring area.

[0011] Preferably, the feature extraction of the building image based on the image processing technology includes:

[0012] Extract the building image; perform grayscale processing on the building image to obtain the building grayscale image; perform contour extraction on the building grayscale image based on the edge detection algorithm to obtain several target contour features of the building to be tested; compare the several target contour features with the standard contour features respectively, and mark the target contour features with different comparison results as abnormal contour features; integrate the extracted several abnormal contour features into deformation feature information; wherein, the standard contour feature is obtained from the normal building image through image processing.

[0013] Preferably, the data twin model of the building to be tested is constructed based on the drawing information, deformation feature information and environmental data, including:

[0014] Extract the drawing information, deformation feature information and environmental data of the building to be tested; input the drawing information into the 3D model software to create a 3D geometric model of the building corresponding to the building to be tested, synchronize the deformation feature information and environmental data into the 3D geometric model, and mark the synchronized 3D geometric model as a digital twin model.

[0015] Preferably, the building to be tested is divided into several monitoring areas based on the data twin model, including:

[0016] The data twin model corresponding to the building to be tested is extracted, and the building to be tested is divided into several monitoring areas according to the total number of floors of the building to be tested, and the number of each monitoring area is marked as i in sequence; where i = 1, 2, ..., n, and n is the total number of monitoring areas.

[0017] Preferably, the planning of the collection equipment in the monitoring area based on the simulated environment data includes:

[0018] Extract several groups of simulated environmental data from each monitoring area; perform linear fitting on several groups of simulated environmental data to obtain an environmental data curve; calculate the first-order derivative function of the environmental data curve to obtain a first environmental change derivative function; take the absolute value of the function value of the first environmental change derivative function to obtain a second environmental change derivative function; extract the maximum function value of the second environmental change derivative function and mark it as an environmental change characteristic value; wherein the environmental change characteristic value includes a sunshine duration change characteristic value, a humidity change characteristic value, and a wind speed change characteristic value;

[0019] Determine whether the environmental change characteristic value is greater than the corresponding preset threshold; if yes, mark the environmental fluctuation label of the corresponding monitoring area as 1; if no, mark the environmental fluctuation label of the corresponding monitoring area as 0;

[0020] Measuring instruments are installed in several monitoring areas where the environmental fluctuation label value is 1; wherein the measuring instruments include inclinometers, levels and total stations.

[0021] Preferably, performing linear fitting on several groups of simulated environment data includes:

[0022] Several groups of simulated environmental data from each monitoring area are extracted, and the time is used as the independent variable and the simulated environmental data as the dependent variable, and the environmental data curve is generated through linear fitting.

[0023] Preferably, the step of determining the acquisition frequency of each monitoring area based on the simulated building deformation data includes:

[0024] T1: Extract the simulated building deformation data of each monitoring area; the simulated building deformation data includes vertical inclination, horizontal displacement and maximum crack width;

[0025] T2: Calculate the corresponding deformation risk coefficient based on the building deformation data of each monitoring area;

[0026] T3: Determine the risk level of each monitoring area based on the deformation risk coefficient; the risk level includes primary risk and secondary risk;

[0027] T4: Determine whether the risk level of each monitoring area is level 1 risk; if yes, calculate the acquisition frequency CPLi of the corresponding monitoring area by the formula CPLi=α×ln(1+AFXi); if no, set the acquisition frequency of the corresponding monitoring area to a fixed acquisition frequency;

[0028] Among them, α is an influence coefficient greater than 0; the fixed collection frequency includes collecting once a day, once a month, and once a quarter.

[0029] It should be noted that the unit of the collection frequency calculated by the formula is the number of collections per month.

[0030] Preferably, the calculation of the corresponding deformation risk coefficient based on the building deformation data of each monitoring area includes:

[0031] Extract the building deformation data of each monitoring area; calculate the deformation risk coefficient AFXi of monitoring area i by the formula AFXi=b1×ln(1+CQJi)+b2×SWYi+b3×exp(LFKi); where CQJi is the vertical inclination of monitoring area i, SWYi is the horizontal displacement of monitoring area i, and LFKi is the maximum crack width of monitoring area i; b1, b2, and b3 are all proportional coefficients greater than 0; ln() is a logarithmic function with a natural constant as the base, and exp() is an exponential function with a natural constant as the base.

[0032] It should be noted that the values ​​of the proportional coefficients b1, b2, and b3 are related to the structural type and age of the super-high-rise building. When the structural type is a brick-concrete structure, the values ​​of b1, b2, and b3 are set to larger values ​​accordingly; when the building is older, the corresponding proportional coefficients b1, b2, and b3 are set to larger values.

[0033] Preferably, determining the risk level of each monitoring area based on the deformation risk coefficient includes:

[0034] Extract the deformation risk coefficient of each monitoring area in several consecutive periods;

[0035] By linearly fitting the deformation risk coefficients in several consecutive periods, the deformation risk curve of each monitoring area is obtained;

[0036] Calculate the first-order derivative function of the deformation risk curve to obtain the deformation rate function; obtain the maximum function value of the deformation rate function and mark it as the deformation rate characteristic value;

[0037] Determine whether the deformation risk coefficient and the deformation rate characteristic value are both greater than the corresponding threshold value; if yes, the risk level of the corresponding monitoring area is marked as level one risk; if not, the risk level of the corresponding monitoring area is marked as level two risk.

[0038] A second aspect of the present invention provides a method for monitoring deformation of a super high-rise building, comprising:

[0039] S1: Collecting building images and environmental data of the building to be tested, and obtaining drawing information of the building to be tested;

[0040] S2: Extract features from the building image based on image processing technology to obtain deformation feature information;

[0041] S3: construct a data twin model of the building to be tested based on the drawing information, deformation feature information and environmental data; perform simulation tests on the building to be tested through the digital twin model to obtain several sets of simulation data;

[0042] S4: Divide the building to be tested into several monitoring areas based on the data twin model;

[0043] S5: Plan the collection equipment in the monitoring area based on the simulated environmental data;

[0044] S6: Determine the collection frequency of each monitoring area based on the simulated building deformation data;

[0045] S7: Perform deformation monitoring on the building to be tested based on the acquisition frequency of each monitoring area.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The present invention collects building images, environmental data and drawing information, uses image processing technology to extract features and identify deformation features, and combines drawing information to build a data twin model for simulation testing. This solution achieves accurate monitoring and management of buildings: effectively improves the accuracy of building deformation monitoring, optimizes the layout of monitoring equipment, and reasonably sets the collection frequency of each area according to simulation results, thereby improving overall monitoring efficiency and early warning capabilities, reducing maintenance costs, extending the service life of buildings, and providing a scientific basis for safety assessment and disaster prevention.

[0048] 2. The present invention calculates the deformation risk coefficient and determines the risk level by extracting the vertical inclination, horizontal displacement and maximum crack width of each monitoring area; for the first-level risk area, the acquisition frequency is dynamically calculated according to a specific formula; for the second-level risk area, a fixed acquisition frequency is set. This method is conducive to improving the monitoring accuracy and timeliness of high-risk areas, optimizing the allocation of monitoring resources, and reducing maintenance costs.

[0049] 3. The present invention obtains the deformation risk coefficient of each monitoring area by substituting the vertical inclination angle, horizontal displacement and maximum crack width in the building deformation data into a specific formula; comprehensively considering the influence of the vertical inclination angle, horizontal displacement and maximum crack width on the structural safety of super-high-rise buildings, so that the calculated deformation risk coefficient is more accurate; it is beneficial to more accurately determine the risk level of each monitoring area according to the deformation risk coefficient in the subsequent process, so as to select appropriate collection frequencies for monitoring areas of different risk levels in a targeted manner, which is beneficial to reduce the cost of deformation monitoring of super-high-rise buildings and improve the efficiency of deformation monitoring of super-high-rise buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 It is an overall flow chart of a super high-rise building deformation monitoring method of the present invention;

[0052] Figure 2 A schematic diagram of the principle of a super high-rise building deformation monitoring system of the present invention;

[0053] Figure 3 This is a flow chart for determining the risk level of each monitoring area based on the deformation risk coefficient in the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0055] See also Figure 1-Figure 3 , a first aspect of the present invention provides a super high-rise building deformation monitoring system, comprising: a data processing module, and a data acquisition module and a deformation monitoring module connected thereto;

[0056] Data acquisition module: used to collect building images and environmental data of the building to be tested, as well as obtain drawing information of the building to be tested;

[0057] Data processing module: used to extract features from building images based on image processing technology to obtain deformation feature information; construct a data twin model of the building to be tested based on drawing information, deformation feature information and environmental data; simulate the building to be tested through the digital twin model to obtain several sets of simulation data; wherein the simulation data includes simulated environmental data and simulated building deformation data; and,

[0058] The building to be tested is divided into several monitoring areas based on the data twin model; the acquisition equipment in the monitoring area is planned based on the simulated environmental data; the acquisition frequency of each monitoring area is determined based on the simulated building deformation data;

[0059] Deformation monitoring module: used to monitor the deformation of the building to be tested based on the collection frequency of each monitoring area.

[0060] In this embodiment, feature extraction is performed on the building image based on image processing technology, including:

[0061] Extract the building image; perform grayscale processing on the building image to obtain the building grayscale image; perform contour extraction on the building grayscale image based on the edge detection algorithm to obtain several target contour features of the building to be tested; compare the several target contour features with the standard contour features respectively, and mark the target contour features with different comparison results as abnormal contour features; integrate the extracted several abnormal contour features into deformation feature information; wherein, the standard contour feature is obtained from the normal building image through image processing.

[0062] In this embodiment, a data twin model of the building to be tested is constructed based on drawing information, deformation feature information and environmental data, including:

[0063] Extract the drawing information, deformation feature information and environmental data of the building to be tested; input the drawing information into the 3D model software to create a 3D geometric model of the building corresponding to the building to be tested, synchronize the deformation feature information and environmental data into the 3D geometric model, and mark the synchronized 3D geometric model as a digital twin model; wherein the drawing information is the CAD design drawing of the building to be tested.

[0064] In this embodiment, the building to be tested is divided into several monitoring areas based on the data twin model, including:

[0065] The data twin model corresponding to the building to be tested is extracted, and the building to be tested is divided into several monitoring areas according to the total number of floors of the building to be tested, and the number of each monitoring area is marked as i in sequence; where i = 1, 2, ..., n, and n is the total number of monitoring areas.

[0066] In this embodiment, the collection equipment in the monitoring area is planned based on the simulated environment data, including:

[0067] Extract several groups of simulated environmental data from each monitoring area; perform linear fitting on several groups of simulated environmental data to obtain an environmental data curve; calculate the first-order derivative function of the environmental data curve to obtain a first environmental change derivative function; take the absolute value of the function value of the first environmental change derivative function to obtain a second environmental change derivative function; extract the maximum function value of the second environmental change derivative function and mark it as an environmental change characteristic value; wherein the environmental change characteristic value includes a sunshine duration change characteristic value, a humidity change characteristic value, and a wind speed change characteristic value;

[0068] Determine whether the environmental change characteristic value is greater than the corresponding preset threshold; if yes, mark the environmental fluctuation label of the corresponding monitoring area as 1; if no, mark the environmental fluctuation label of the corresponding monitoring area as 0;

[0069] Measuring instruments are installed in several monitoring areas where the environmental fluctuation label value is 1; wherein the measuring instruments include inclinometers, levels and total stations.

[0070] In this embodiment, linear fitting is performed on several sets of simulated environment data, including:

[0071] Several groups of simulated environmental data from each monitoring area are extracted, and the time is used as the independent variable and the simulated environmental data as the dependent variable, and the environmental data curve is generated through linear fitting.

[0072] In this embodiment, the acquisition frequency of each monitoring area is determined based on the simulated building deformation data, including:

[0073] T1: Extract the simulated building deformation data of each monitoring area; the simulated building deformation data includes vertical inclination, horizontal displacement and maximum crack width;

[0074] T2: Calculate the corresponding deformation risk coefficient based on the building deformation data of each monitoring area;

[0075] T3: Determine the risk level of each monitoring area based on the deformation risk coefficient; the risk level includes primary risk and secondary risk;

[0076] T4: Determine whether the risk level of each monitoring area is level 1 risk; if yes, calculate the acquisition frequency CPLi of the corresponding monitoring area by the formula CPLi=α×ln(1+AFXi); if no, set the acquisition frequency of the corresponding monitoring area to a fixed acquisition frequency;

[0077] Among them, α is an influence coefficient greater than 0; when the calculated collection frequency is not an integer, the collection frequency is rounded up; fixed collection frequencies include collecting once a day, once a month, and once a quarter.

[0078] It should be noted that the value of the influence coefficient α is related to .

[0079] Exemplarily, the risk level of monitoring area 1 is set to level one risk, the deformation risk coefficient AFX1=55.97, and the influence coefficient α=1.2; since the risk level of monitoring area 1 is level one risk, the collection frequency CPL1≈4.85 is calculated by the formula, and the collection frequency of monitoring area 1 is obtained by rounding up to 5 times per month.

[0080] The present invention calculates the deformation risk coefficient and determines the risk level by extracting the vertical inclination, horizontal displacement and maximum crack width of each monitoring area; for the first-level risk area, the acquisition frequency is dynamically calculated according to a specific formula; for the second-level risk area, a fixed acquisition frequency is set. This method is conducive to improving the monitoring accuracy and timeliness of high-risk areas, optimizing the allocation of monitoring resources, and reducing maintenance costs.

[0081] In this embodiment, the corresponding deformation risk coefficient is calculated based on the building deformation data of each monitoring area, including:

[0082] Extract the building deformation data of each monitoring area; calculate the deformation risk coefficient AFXi of monitoring area i by the formula AFXi=b1×ln(1+CQJi)+b2×SWYi+b3×exp(LFKi); where CQJi is the vertical inclination of monitoring area i, SWYi is the horizontal displacement of monitoring area i, and LFKi is the maximum crack width of monitoring area i; b1, b2, and b3 are all proportional coefficients greater than 0, and the specific values ​​of b1, b2, and b3 are set by relevant experts based on experience; ln() is a logarithmic function with natural constants as the base, and exp() is an exponential function with natural constants as the base.

[0083] Exemplarily, the proportional coefficients b1=10, b2=1.5, and b3=1.2 are set; the vertical inclination angle CQJ1=3° of the monitoring area 1, the horizontal displacement SWY1=12 cm of the monitoring area 1, and the maximum crack width LFK1=3 cm of the monitoring area 1 are set; the deformation risk coefficient AFX1≈55.97 of the monitoring area 1 is calculated by the formula.

[0084] The present invention obtains the deformation risk coefficient of each monitoring area by substituting the vertical inclination angle, horizontal displacement and maximum crack width in the building deformation data into a specific formula; comprehensively considering the influence of the vertical inclination angle, horizontal displacement and maximum crack width on the structural safety of the super-high-rise building, so that the calculated deformation risk coefficient is more accurate; it is beneficial to more accurately determine the risk level of each monitoring area according to the deformation risk coefficient in the subsequent process, so as to select appropriate collection frequencies for monitoring areas of different risk levels in a targeted manner, thereby helping to reduce the cost of deformation monitoring of super-high-rise buildings and improve the efficiency of deformation monitoring of super-high-rise buildings.

[0085] In this embodiment, the risk level of each monitoring area is determined based on the deformation risk coefficient, including:

[0086] Extract the deformation risk coefficient of each monitoring area in several consecutive periods;

[0087] By linearly fitting the deformation risk coefficients in several consecutive periods, the deformation risk curve of each monitoring area is obtained;

[0088] Calculate the first-order derivative function of the deformation risk curve to obtain the deformation rate function; obtain the maximum function value of the deformation rate function and mark it as the deformation rate characteristic value;

[0089] Determine whether the deformation risk coefficient and the deformation rate characteristic value are both greater than the corresponding threshold value; if yes, the risk level of the corresponding monitoring area is marked as level one risk; if not, the risk level of the corresponding monitoring area is marked as level two risk.

[0090] For example, the deformation risk coefficient of monitoring area 1 is set to 55.97, the deformation rate characteristic value is set to 3, the deformation risk threshold is set to 50, and the deformation rate threshold is set to 2; since the deformation risk coefficient and the deformation rate characteristic value are both greater than the corresponding thresholds, the risk level of monitoring area 1 is marked as level one risk.

[0091] A second aspect of the present invention provides a method for monitoring deformation of a super high-rise building, comprising:

[0092] S1: Collecting building images and environmental data of the building to be tested, and obtaining drawing information of the building to be tested;

[0093] S2: Extract features from the building image based on image processing technology to obtain deformation feature information;

[0094] S3: construct a data twin model of the building to be tested based on the drawing information, deformation feature information and environmental data; perform simulation tests on the building to be tested through the digital twin model to obtain several sets of simulation data;

[0095] S4: Divide the building to be tested into several monitoring areas based on the data twin model;

[0096] S5: Plan the collection equipment in the monitoring area based on the simulated environmental data;

[0097] S6: Determine the collection frequency of each monitoring area based on the simulated building deformation data;

[0098] S7: Perform deformation monitoring on the building to be tested based on the acquisition frequency of each monitoring area.

[0099] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0100] Working principle of the present invention:

[0101] The present invention collects building images and environmental data of a building to be tested, and obtains drawing information of the building to be tested; extracts features and deformation feature information of the building images based on image processing technology; constructs a data twin model of the building to be tested based on the drawing information, deformation feature information and environmental data; performs simulation tests on the building to be tested through the digital twin model to obtain several groups of simulation data; divides the building to be tested into several monitoring areas based on the data twin model; plans collection equipment in the monitoring area based on the simulated environmental data; determines the collection frequency of each monitoring area based on the simulated building deformation data; and performs deformation monitoring on the building to be tested based on the collection frequency of each monitoring area.

[0102] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A super high-rise building deformation monitoring system, comprising: The data processing module, and the data acquisition module and deformation monitoring module connected thereto are characterized in that: The data acquisition module is used to collect building images and environmental data of the building to be tested, and obtain drawing information of the building to be tested; The data processing module is used to extract features from building images based on image processing technology to obtain deformation feature information; construct a data twin model of the building to be tested based on drawing information, deformation feature information and environmental data; perform simulation tests on the building to be tested through the digital twin model to obtain several groups of simulation data; wherein the simulation data includes simulated environmental data and simulated building deformation data; and, The building to be tested is divided into several monitoring areas based on the data twin model; the acquisition equipment in the monitoring area is planned based on the simulated environmental data; the acquisition frequency of each monitoring area is determined based on the simulated building deformation data; The deformation monitoring module is used to monitor the deformation of the building to be tested based on the acquisition frequency of each monitoring area.

2. A super high-rise building deformation monitoring system according to claim 1, characterized in that: The feature extraction of the building image based on the image processing technology includes: Extract the building image; perform grayscale processing on the building image to obtain the building grayscale image; perform contour extraction on the building grayscale image based on the edge detection algorithm to obtain several target contour features of the building to be tested; compare the several target contour features with the standard contour features respectively, and mark the target contour features with different comparison results as abnormal contour features; integrate the extracted several abnormal contour features into deformation feature information; wherein, the standard contour feature is obtained from the normal building image through image processing.

3. A super high-rise building deformation monitoring system according to claim 1, characterized in that: The data twin model of the building to be tested is constructed based on the drawing information, deformation feature information and environmental data, including: Extract the drawing information, deformation feature information and environmental data of the building to be tested; input the drawing information into the 3D model software to create a 3D geometric model of the building corresponding to the building to be tested, synchronize the deformation feature information and environmental data into the 3D geometric model, and mark the synchronized 3D geometric model as a digital twin model.

4. A super high-rise building deformation monitoring system according to claim 1, characterized in that: The building to be tested is divided into several monitoring areas based on the data twin model, including: The data twin model corresponding to the building to be tested is extracted, and the building to be tested is divided into several monitoring areas according to the total number of floors of the building to be tested, and the number of each monitoring area is marked as i in sequence; where i = 1, 2, ..., n, and n is the total number of monitoring areas.

5. The super high-rise building deformation monitoring system according to claim 1, characterized in that: The planning of the collection equipment in the monitoring area based on the simulated environment data includes: Extract several groups of simulated environmental data from each monitoring area; perform linear fitting on several groups of simulated environmental data to obtain an environmental data curve; calculate the first-order derivative function of the environmental data curve to obtain a first environmental change derivative function; take the absolute value of the function value of the first environmental change derivative function to obtain a second environmental change derivative function; extract the maximum function value of the second environmental change derivative function and mark it as an environmental change characteristic value; wherein the environmental change characteristic value includes a sunshine duration change characteristic value, a humidity change characteristic value, and a wind speed change characteristic value; Determine whether the environmental change characteristic value is greater than the corresponding preset threshold; if yes, mark the environmental fluctuation label of the corresponding monitoring area as 1; if no, mark the environmental fluctuation label of the corresponding monitoring area as 0; Measuring instruments are installed in several monitoring areas where the environmental fluctuation label value is 1; wherein the measuring instruments include inclinometers, levels and total stations.

6. A super high-rise building deformation monitoring system according to claim 5, characterized in that: The linear fitting of several groups of simulated environment data comprises: Several groups of simulated environmental data from each monitoring area are extracted, and the time is used as the independent variable and the simulated environmental data as the dependent variable, and the environmental data curve is generated through linear fitting.

7. A super high-rise building deformation monitoring system according to claim 4, characterized in that: The step of determining the acquisition frequency of each monitoring area based on the simulated building deformation data includes: T1: Extract the simulated building deformation data of each monitoring area; the simulated building deformation data includes vertical inclination, horizontal displacement and maximum crack width; T2: Calculate the corresponding deformation risk coefficient based on the building deformation data of each monitoring area; T3: Determine the risk level of each monitoring area based on the deformation risk coefficient; the risk level includes primary risk and secondary risk; T4: Determine whether the risk level of each monitoring area is level 1 risk; if yes, calculate the acquisition frequency CPLi of the corresponding monitoring area by the formula CPLi=α×ln(1+AFXi); if no, set the acquisition frequency of the corresponding monitoring area to a fixed acquisition frequency; Among them, α is an influence coefficient greater than 0; the fixed collection frequency includes collecting once a day, once a month, and once a quarter.

8. A super high-rise building deformation monitoring system according to claim 7, characterized in that: The calculation of the corresponding deformation risk coefficient based on the building deformation data of each monitoring area includes: Extract the building deformation data of each monitoring area; calculate the deformation risk coefficient AFXi of monitoring area i by the formula AFXi=b1×ln(1+CQJi)+b2×SWYi+b3×exp(LFKi); where CQJi is the vertical inclination of monitoring area i, SWYi is the horizontal displacement of monitoring area i, and LFKi is the maximum crack width of monitoring area i; b1, b2, and b3 are all proportional coefficients greater than 0; ln() is a logarithmic function with a natural constant as the base, and exp() is an exponential function with a natural constant as the base.

9. A super high-rise building deformation monitoring system according to claim 7, characterized in that: The risk level of each monitoring area is determined based on the deformation risk coefficient, including: Extract the deformation risk coefficient of each monitoring area in several consecutive periods; By linearly fitting the deformation risk coefficients in several consecutive periods, the deformation risk curve of each monitoring area is obtained; Calculate the first-order derivative function of the deformation risk curve to obtain the deformation rate function; obtain the maximum function value of the deformation rate function and mark it as the deformation rate characteristic value; Determine whether the deformation risk coefficient and the deformation rate characteristic value are both greater than the corresponding threshold value; if yes, the risk level of the corresponding monitoring area is marked as level one risk; if not, the risk level of the corresponding monitoring area is marked as level two risk.

10. A method for monitoring deformation of a super high-rise building, based on the operation of a super high-rise building deformation monitoring system according to any one of claims 1 to 9, characterized in that: include: S1: Collecting building images and environmental data of the building to be tested, and obtaining drawing information of the building to be tested; S2: Extract features from the building image based on image processing technology to obtain deformation feature information; S3: Build a data twin model of the building to be tested based on drawing information, deformation feature information and environmental data; Use the digital twin model to simulate the building to be tested and obtain several sets of simulation data; S4: Divide the building to be tested into several monitoring areas based on the data twin model; S5: Plan the collection equipment in the monitoring area based on the simulated environmental data; S6: Determine the collection frequency of each monitoring area based on the simulated building deformation data; S7: Perform deformation monitoring on the building to be tested based on the acquisition frequency of each monitoring area.

Images