Intelligent Monitoring Method and System for Foundation Stabilization in Real Estate Projects

Abstract

The present application provides an intelligent monitoring method and system for foundation stability in real estate engineering, which relates to the technical field of foundation monitoring, and includes: multi-dimensionally monitoring a target foundation through intelligent monitoring devices, extracting first monitoring information corresponding to a first foundation point, and establishing a target monitoring viewable image; performing stability rendering analysis on the target monitoring viewable image and determining a target point; activating an ultrasonic transducer to perform ultrasonic tomography scanning detection on the target point, comparing and inverting the actual scanning signal travel time and the predetermined scanning signal travel time according to a predetermined travel time deviation inversion strategy to obtain a target image; and analyzing the actual stability index of the target foundation based on the multi-dimensional image features of the target image. The present application solves the technical problem in the prior art that the accuracy and comprehensiveness of foundation stability monitoring are insufficient due to the inability to comprehensively understand the internal structure and state of the foundation, and improves the accuracy and comprehensiveness of foundation stability monitoring.

Description

Technical Field

[0001] This application relates to the technical field of foundation monitoring, and particularly to an intelligent monitoring method and system for foundation stability in real estate projects. Background Art

[0002] With the acceleration of the urbanization process, the number of real estate projects is increasing day by day. Foundation stability monitoring has become a key link in ensuring project quality and construction safety. Currently, common foundation stability monitoring methods include settlement monitoring, strain monitoring, stress monitoring, etc. These methods collect data through ground sensors or measuring devices to monitor the surface or local areas of the foundation. To a certain extent, these monitoring methods can reflect the overall change trend and surface settlement of the foundation. However, since they cannot penetrate deep into the foundation, it is difficult to detect potential structural problems, such as voids between soil layers, cracks, humidity changes, etc., resulting in errors in the monitoring results and the inability to timely discover and handle deep - seated foundation safety hazards. Summary of the Invention

[0003] This application provides an intelligent monitoring method and system for foundation stability in real estate projects, which solves the technical problem that the prior art can only monitor the surface or local area of the foundation and cannot comprehensively understand the internal structure and state of the foundation, resulting in the inability to comprehensively and accurately evaluate the foundation stability, and achieves the technical effect of improving the accuracy and comprehensiveness of foundation stability monitoring.

[0004] In view of the above problems, on the one hand, this application provides an intelligent monitoring method for foundation stability in real estate projects. The method includes: activating an intelligent monitoring device, and performing multi - dimensional monitoring on a target foundation in a target real estate project through the intelligent monitoring device to obtain a target monitoring result; extracting first monitoring information corresponding to a first foundation point in the target monitoring result, and establishing a target monitoring viewable graph according to a first correspondence between the first foundation point and the first monitoring information; introducing a stability evaluation mechanism to perform stability rendering analysis on the target monitoring viewable graph and determine a target point; activating an ultrasonic transducer to perform ultrasonic tomography scanning detection on the target point to obtain the actual scanning signal travel time; reading a predetermined travel - time deviation inversion strategy, and comparing and inverting the actual scanning signal travel time with a predetermined scanning signal travel time according to the predetermined travel - time deviation inversion strategy to obtain a target image; analyzing the actual stability index of the target foundation according to the multi - dimensional image features of the target image, where the actual stability index is used to characterize the foundation stability of the target foundation.

[0005] On the other hand, the present application also provides an intelligent monitoring system for foundation stabilization in real estate projects. The system includes: a multi-dimensional monitoring module for activating intelligent monitoring devices and performing multi-dimensional monitoring on a target foundation in a target real estate project through the intelligent monitoring devices to obtain target monitoring results; a monitoring visualization module for extracting first monitoring information corresponding to a first foundation point in the target monitoring results and establishing a target monitoring visualization diagram according to a first correspondence between the first foundation point and the first monitoring information; a stabilization evaluation module for introducing a stabilization evaluation mechanism to perform stability rendering analysis on the target monitoring visualization diagram and determining a target point; an ultrasonic scanning module for activating an ultrasonic transducer to perform ultrasonic tomography scanning detection on the target point to obtain the actual scanning signal travel time; a target image acquisition module for reading a predetermined travel time deviation inversion strategy and performing comparative inversion on the actual scanning signal travel time and a predetermined scanning signal travel time according to the predetermined travel time deviation inversion strategy to obtain a target image; and a stabilization index determination module for analyzing the multi-dimensional image features of the target image to obtain the actual stabilization index of the target foundation, where the actual stabilization index is used to characterize the foundation stability of the target foundation.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0007] By activating intelligent monitoring devices to perform multi-dimensional monitoring on the target foundation in a target real estate project, surface and local state data of the foundation are comprehensively collected, providing basic information for subsequent analysis and ensuring that the entire monitoring process can cover all dimensions of the foundation. By extracting the monitoring information of specific foundation points from the monitoring results and establishing a target monitoring visualization diagram, complex monitoring data is converted into intuitive visual images, facilitating subsequent analysis and evaluation and improving the readability and usability of the data. By introducing a stabilization evaluation mechanism to perform stability rendering analysis on the target monitoring visualization diagram, a preliminary judgment on the stability of the foundation is made, and potential risk points of the foundation can be discovered in advance, providing a basis for subsequent ultrasonic detection. After confirming the target point that needs further inspection, tomographic scanning detection is performed on the target point through an ultrasonic transducer to obtain more detailed structural information. According to the predetermined travel time deviation inversion strategy, comparative inversion is performed on the actual scanning signal travel time and the predetermined scanning signal travel time, converting the ultrasonic scanning data into a target image, further confirming the internal structural state of the foundation, helping to identify potential foundation problems, and ensuring the accuracy of the evaluation results. By analyzing the multi-dimensional image features of the target image, combining all the collected data and image features, the actual stability of the foundation is comprehensively evaluated, and the actual stabilization index of the target foundation is obtained, providing data support for further decision-making and repair work.

[0008] In summary, the present application combines multi-dimensional intelligent monitoring and ultrasonic tomography to achieve comprehensive monitoring of the foundation structure and status, significantly improving the accuracy, comprehensiveness, and real-time nature of foundation stability monitoring, providing a scientific and efficient solution for the foundation stability monitoring of real estate projects, and also providing a strong basis for engineering decision-making.

[0009] The above description is only an overview of the technical solution of the present application. In order to be able to more clearly understand the technical means of the present application, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the following specifically gives the specific embodiments of the present application. Brief Description of the Drawings

[0010] Figure 1 It is a schematic flow chart of the method for intelligent monitoring of foundation stability in real estate projects provided by an embodiment of the present application.

[0011] Figure 2 It is a schematic flow chart of the method for intelligent monitoring of foundation stability in real estate projects provided by an embodiment of the present application, for performing stability rendering analysis on the target monitoring viewable image and determining the target point.

[0012] Figure 3 It is a schematic structural diagram of the system for intelligent monitoring of foundation stability in real estate projects provided by an embodiment of the present application.

[0013] Description of the reference numerals: multi-dimensional monitoring module 10, monitoring visualization module 20, stability evaluation module 30, ultrasonic scanning module 40, target image acquisition module 50, stability index determination module 60. Detailed Embodiments

[0014] By providing the method and system for intelligent monitoring of foundation stability in real estate projects in the embodiments of the present application, the technical problem in the prior art that only the surface or part of the foundation can be monitored, and the internal structure and status of the foundation cannot be comprehensively understood, resulting in the inability to comprehensively and accurately evaluate the foundation stability, is solved, and the technical effect of improving the accuracy and comprehensiveness of foundation stability monitoring is achieved.

[0015] Embodiment 1, as Figure 1 shown, the embodiments of the present application provide a method for intelligent monitoring of foundation stability in real estate projects, and the method includes:

[0016] Step S1: Activate the intelligent monitoring device, and perform multi-dimensional monitoring on the target foundation in the target real estate project through the intelligent monitoring device to obtain a target monitoring result.

[0017] Specifically, the intelligent monitoring device is a device integrated with sensor and data processing functions, capable of automatically collecting, transmitting, and preliminarily processing foundation monitoring data. For example, a device containing various sensors such as acceleration sensors, strain sensors, displacement sensors, etc. Activate the intelligent monitoring device, and use the sensors on the intelligent monitoring device to monitor the target foundation in the target real estate project, collecting multi-dimensional data such as settlement (the degree of foundation subsidence), stress (the pressure borne by the foundation), strain (the deformation of the foundation), water level, etc. For example, install sensors at different depths and positions of the foundation, and the sensors transmit data such as settlement amounts and stress values collected to the processor of the device. After being processed by the processor, a target monitoring result containing multi-dimensional information is obtained.

[0018] Through multi-dimensional monitoring, it is possible to comprehensively and real-time obtain various physical parameters of the foundation, more accurately capture changes in the foundation state, and provide basic data support for subsequent analysis.

[0019] Step S2: Extract the first monitoring information corresponding to the first foundation point in the target monitoring result, and establish a target monitoring visualization diagram according to the first correspondence between the first foundation point and the first monitoring information.

[0020] Specifically, the first foundation point can be any selected monitoring point in the target foundation. For example, on a large foundation plane, a certain corner or the center position can be set as the first foundation point. The first monitoring information is various monitoring data collected at the first foundation point through the intelligent monitoring device, such as the settlement rate and stress change information at this point. Select any one point from multiple monitoring points of the target foundation and denote it as the first foundation point, and then screen out the first monitoring information corresponding to the first foundation point from the target monitoring result. Traverse all monitoring points of the target foundation, pay attention to screening the monitoring information corresponding to each monitoring point, and then according to the first correspondence between these points and the monitoring information, use a data visualization tool (such as professional drawing software or a dedicated engineering monitoring data analysis platform) to establish a target monitoring visualization diagram. The target monitoring visualization diagram is a graph that visually presents the correspondence between monitoring information and foundation points. For example, if the monitoring information of the first foundation point shows a large settlement amount, it can be intuitively represented on the visualization diagram by the depth of color or the size of the value, etc.

[0021] By establishing the target monitoring visualization diagram, the discrete monitoring information is associated in an intuitive way, facilitating engineering personnel to observe the monitoring situation of different foundation points as a whole, helping to discover the laws and abnormal points in the monitoring information, and providing a more intuitive basis for subsequent stability analysis.

[0022] Step S3: Introduce a stability evaluation mechanism to conduct stability rendering analysis on the target monitoring visualization and determine the target points.

[0023] Specifically, the stability evaluation mechanism is an evaluation method based on data analysis used to evaluate the stability of the foundation. For example, according to existing engineering experience and theoretical models, weights for different monitoring indicators are set. For instance, the settlement index accounts for 30% and the stress index accounts for 30%, etc. Then, these indicators are integrated to judge the stability of the foundation. Introduce the stability evaluation mechanism and input the data in the target monitoring visualization into this mechanism. For example, if the stability evaluation mechanism is a software program, import the monitoring data of each point in the visualization into the program. Then, according to the algorithm in the program, calculate the stability of each point in the visualization, conduct stability rendering analysis on the visualization according to the calculation results, and display the stability degree of different areas in a visual way (such as color rendering). For example, use red to represent areas with poor stability and green to represent areas with good stability. Finally, determine the foundation points that may have stability problems or need key attention from these areas, and record them as target points. Exemplarily, these target points may be the foundation points corresponding to the red areas.

[0024] By introducing the stability evaluation mechanism for stability rendering analysis, it is possible to quickly identify the target points that may have stability problems, making the subsequent detection work more targeted, reducing unnecessary detection workload, and improving the efficiency of the entire foundation stability monitoring and evaluation.

[0025] Step S4: Activate the ultrasonic transducer to conduct ultrasonic tomography scanning detection on the target points to obtain the actual scanning signal travel time.

[0026] Specifically, the ultrasonic transducer is a device that can convert electrical energy into ultrasonic energy. In foundation detection, it can send ultrasonic waves into the foundation and receive the echo signals, thereby understanding the internal structure of the foundation. Activate the ultrasonic transducer and place it at a suitable position near the target points. Then, send ultrasonic signals into the target points through the ultrasonic transducer. The ultrasonic waves propagate inside the target points and will undergo reflection, refraction, etc. when encountering different medium interfaces. The transducer receives the reflected ultrasonic signals and records the actual scanning signal travel time from the emission to the reception of the ultrasonic waves. This actual scanning signal travel time is the actual propagation time of the ultrasonic waves at the target points. The travel time can be used to infer the medium characteristics in the propagation path and indirectly reflect the internal structure of the foundation.

[0027] By conducting ultrasonic tomography scanning detection on the target points, obtaining the ultrasonic propagation time information of the internal structure of the target points provides key data for subsequent analysis of the internal structure of the target points, which helps to deeply understand the internal conditions of the target points.

[0028] Step S5: Read a predetermined acoustic time deviation inversion strategy, and compare and invert the actual scanning signal acoustic time and the predetermined scanning signal acoustic time according to the predetermined acoustic time deviation inversion strategy to obtain a target image.

[0029] Specifically, the predetermined acoustic time deviation inversion strategy is a preset analysis method that inversely calculates the internal structure image of the target point position based on the deviation between the acoustic time of ultrasonic waves in an ideal state and the actual acoustic time of propagation. Read the preset predetermined acoustic time deviation inversion strategy, which is stored in computer software or a database, and compare the actual signal acoustic time obtained from ultrasonic scanning with the predetermined acoustic time, analyze the deviation between the two, and use iterative algorithms such as the algebraic reconstruction technique (ART) and the conjugate gradient method for inversion calculation to infer which positions inside the foundation may have defects, thereby obtaining a target image that can reflect the internal structure of the target point position of the foundation. Different regions in the image represent the acoustic velocity changes of the foundation at different depths or positions, and the acoustic velocity changes reflect the density, uniformity of the foundation material, and the presence of defects. Among them, the predetermined acoustic time can be determined through experiments or computer simulations based on the theoretical acoustic velocity of the foundation material. For example, if the scanning result shows that the acoustic time deviation in a certain area is large, it reflects that there are materials with a higher density or cracks in this area than expected.

[0030] Through the deviation inversion technique, the difference between the actual scanning signal acoustic time and the predetermined scanning signal acoustic time can be converted into an intuitive target image, so as to more clearly display the actual situation of the internal structure of the target point position, facilitating the analysis of potential problems affecting the stability inside the target point position.

[0031] Step S6: Analyze the multi-dimensional image features of the target image to obtain the actual stability index of the target foundation, where the actual stability index is used to characterize the foundation stability of the target foundation.

[0032] Specifically, the multi-dimensional image features refer to the feature information in multiple dimensions included in the target image, such as the color distribution, texture features, and contrast of different regions in the image. These features can reflect the internal structure status of the target point position from different angles. Use image analysis software or specialized engineering analysis algorithms to perform multi-dimensional feature analysis on the target image to obtain the acoustic velocity differences in different regions, and then determine the internal structure, material uniformity, and potential defects of the foundation. Then, combined with existing engineering experience and mathematical models, calculate the actual stability index of the target foundation. This actual stability index is a quantitative value used to represent the degree of stability of the target foundation.

[0033] Ultrasonic tomography generates a target image of the interior of the foundation by the change in ultrasonic wave velocity. Generally, in areas where the material is dense, the sound velocity is higher, while when there are defects (such as cracks, cavities, or loose soil), the sound wave propagates more slowly in these areas. For example, the propagation velocity of ultrasonic waves slows down in cracks, cavities, or loose areas, thus showing as low-velocity areas, while in areas where the density of certain materials is higher than the standard density of the foundation material, the propagation velocity of ultrasonic waves increases, thus showing as high-velocity areas. Exemplarily, in the target image of the foundation, the low-velocity areas (areas in the image where the sound velocity is lower than that in the ideal situation) usually appear as darker areas, and the high-velocity areas (areas in the image where the sound velocity is higher than that in the ideal situation) usually appear as lighter areas. Using image processing techniques, such as gray value analysis and threshold segmentation, identify the low-velocity areas and high-velocity areas, and calculate the actual stability index of the target foundation based on features such as the area ratio of the low-velocity areas and high-velocity areas in the image and the uniformity of the texture. In addition, feature analysis can also be performed on the entire target image of the foundation to judge the uniformity of the sound velocity distribution. If the sound velocity distribution is relatively uniform, it means that the physical properties of the foundation material are relatively consistent throughout the detection area. On the contrary, if the sound velocity distribution is very uneven, it indicates that there are areas with uneven quality inside the foundation, such as loose soil layers, cracks, or cavities and other quality problems. Through image processing techniques, such as gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP), analyze the texture features of the image to judge the uniformity of the foundation material.

[0034] The actual stability index obtained through multi-dimensional image feature analysis can quantitatively describe the stability of the target foundation and provide a clear basis for engineering decisions.

[0035] Furthermore, the intelligent monitoring device described in step S1 includes at least a displacement sensor, a static level, an inclinometer, an axial force sensor, and a water level sensor. Activate the intelligent monitoring device and perform multi-dimensional monitoring on the target foundation in the target real estate project through the intelligent monitoring device to obtain target monitoring results, including:

[0036] Step S11: Monitor the horizontal displacement of the foundation pit of the target foundation through the displacement sensor.

[0037] Step S12: Monitor the vertical displacement of the foundation pit of the target foundation through the static level.

[0038] Step S13: Monitor the horizontal displacement of the retaining structure of the target foundation through the inclinometer.

[0039] Step S14: Monitor the support axial force of the target foundation through the axial force sensor.

[0040] Step S15: Monitor the groundwater level of the target foundation through the water level sensor.

[0041] Step S16: The horizontal displacement of the foundation pit, the vertical displacement of the foundation pit, the horizontal displacement of the retaining structure, the support axial force, and the groundwater level constitute the target monitoring result.

[0042] Specifically, the intelligent monitoring equipment at least includes displacement sensors, static level gauges, inclinometers, axial force sensors, and water level sensors. The displacement sensors include potentiometer displacement sensors, laser displacement sensors, etc.; the static level gauges include electronic level gauges, laser level gauges, etc.; the inclinometers include electronic inclinometers, fiber optic inclinometers, etc.; the axial force sensors include strain gauge axial force sensors, piezoelectric axial force sensors; the water level sensors include pressure water level sensors, float water level sensors, etc.

[0043] The displacement sensors are installed at the edge or inside of the foundation pit to monitor the horizontal displacement of the soil or structure of the foundation pit in real time. These displacement sensors record the position changes of each monitoring point relative to the reference point, so as to evaluate whether abnormal horizontal displacement occurs in the foundation pit. The static level gauges are installed at different points around the foundation pit to form a level gauge network, and the vertical displacement of the foundation pit is measured by comparing the height differences of multiple monitoring points, judging whether the foundation pit sinks evenly and whether there are potential risks caused by uneven settlement. The inclinometers are installed on the retaining structure of the foundation pit to monitor the horizontal displacement or deformation of the retaining structure caused by soil pressure, settlement or external load changes in real time, judging whether the retaining structure is inclined and whether there is structural instability caused by uneven foundation settlement. The axial force sensors are installed on the support structure to monitor the force condition of the support structure in the vertical direction, that is, the support axial force. Monitoring the change of the axial force can judge whether the foundation or the support structure is overloaded or underloaded. The water level sensors are installed around the foundation to monitor the change of the groundwater level in real time, avoiding soil loosening or settlement problems caused by the change of the groundwater level.

[0044] Integrate the data such as the horizontal displacement of the foundation pit, the vertical displacement of the foundation pit, the horizontal displacement of the retaining structure, the support axial force, and the groundwater level monitored by different sensors to form the target monitoring result. These data can be collected through a unified data acquisition system and then stored in a database or saved in a specific file format. For example, adopt a monitoring system based on Internet of Things technology to centralize the data of each sensor on a platform for subsequent analysis and processing.

[0045] Combined with intelligent monitoring devices such as displacement sensors, hydrostatic level gauges, inclinometers, axial force sensors, and water level sensors, multi-directional monitoring of the target foundation is carried out. Each sensor is responsible for monitoring different aspects of the foundation, from foundation pit displacement to support axial force to groundwater level, forming multi-dimensional monitoring results. Through comprehensive analysis of these data, the stability of the foundation can be evaluated in real time, potential structural risks can be detected in a timely manner, and foundation reinforcement or adjustment of the engineering plan can be effectively guided to ensure building safety and foundation stability.

[0046] Further, as Figure 2 shown, step S3 includes:

[0047] Step S31: Obtain a predetermined stability weight allocation according to the stability evaluation mechanism.

[0048] Step S32: Perform weighted analysis on the first monitoring information in combination with the predetermined stability weight allocation to obtain the first stability value corresponding to the first foundation point.

[0049] Step S33: Mark the first stability value at the first foundation point in the target monitoring viewable graph to obtain a target stability value viewable graph.

[0050] Step S34: Determine whether the first stability value is within a predetermined stability threshold according to the target stability value viewable graph.

[0051] Step S35: If it is not within, add the first foundation point to the set of repeated monitoring points.

[0052] Step S36: Randomly extract any foundation point from the set of repeated monitoring points and denote it as the target point.

[0053] Specifically, read the pre-set predetermined stability weight allocation information from the stability evaluation mechanism. Among them, the stability weight allocation is the weight assigned to each monitoring parameter according to the importance of different monitoring parameters or the degree of influence on the foundation stability. Different monitoring data (such as horizontal displacement, vertical displacement, support axial force, etc.) have different influences on the foundation stability, and the stability weight allocation can reflect the contribution of these data to the final stability evaluation. These weights are obtained based on engineering experience, theoretical analysis, and data statistics of previous similar projects. For example, the weight of the foundation pit horizontal displacement may be 0.3, the weight of the foundation pit vertical displacement may be 0.2, the weight of the retaining structure horizontal displacement may be 0.2, the weight of the support axial force may be 0.2, and the weight of the groundwater level may be 0.1.

[0054] After obtaining the predetermined stable weight distribution, multiply each data in the first monitoring information by the corresponding weight, and then sum these products to obtain the first stability value corresponding to the first foundation point. This first stability value is a quantitative value representing the stability degree of the first foundation point. For example, in the first monitoring information, the horizontal displacement of the foundation pit is 10 mm, and the weight is 0.3; the vertical displacement of the foundation pit is 5 mm, and the weight is 0.2; the horizontal displacement of the retaining structure is 8 mm, and the weight is 0.2; the support axial force is 200 kN, and the weight is 0.2; the groundwater level is 3 m, and the weight is 0.1. Then the first stability value = 10×0.3 + 5×0.2 + 8×0.2 + 200×0.2 + 3×0.1 = 45.9.

[0055] Find the corresponding first foundation point in the target monitoring view, and then mark the calculated first stability value on this point in a certain way. For example, the stability value can be directly displayed in digital form on the view, or the size of the stability value can be represented by the depth of color (e.g., the higher the stability value, the lighter the color, indicating better stability), so as to obtain the target stability value view. In the specific implementation process, the annotation function or layer property setting function of the drawing software (such as AutoCAD or GIS software) can be used to mark the stability value, or the text function in the matplotlib library of Python can be used to add text annotations or the size of the stability value can be represented by setting the color map (colormap).

[0056] The predetermined stability threshold is a preset numerical range used to judge whether the stability of the foundation point is qualified. Obtain the upper and lower limit values from the setting of the predetermined stability threshold, and then compare the first stability value in the target stability value view with this threshold range. If the first stability value is within this threshold range, it means that the stability of this point meets the requirements; on the contrary, if the first stability value is not within this threshold range, there may be a stability risk.

[0057] If the first stability value is not within the predetermined stability threshold, add the corresponding first foundation point to the set of points for repeated monitoring for further stability monitoring. Randomly select a foundation point from the set of points for repeated monitoring and mark it as the target point. This can be achieved by using a random number generation function combined with index operations. For example, in Python, the randint function in the random module can be used to generate a random integer within the set index range, and then the corresponding foundation point can be obtained according to this integer.

[0058] Through the above steps, the stability evaluation mechanism can comprehensively analyze the stability of each foundation point based on the monitoring data, present the stability value in a visual manner, and quickly identify potential risk areas. If the stability values of some points are lower than the predetermined threshold, these points will be included in the set of points for repeated monitoring, and key attention and further monitoring will be carried out, thereby effectively avoiding omissions caused by single monitoring data errors or local problems, and improving the accuracy and comprehensiveness of foundation stability monitoring.

[0059] Further, step S31 includes:

[0060] Step S311: Extract the first foundation data set from the real estate project foundation database.

[0061] Step S312: Denote the first historical stability value of the first historical foundation in the first foundation data set as the dependent variable.

[0062] Step S313: Obtain any foundation characteristic index, and match the corresponding first historical characteristic parameter of the any foundation characteristic index in the first foundation data set.

[0063] Step S314: Denote the first historical characteristic parameter as the independent variable, and calculate the maximum information coefficient with the dependent variable to obtain the first maximum information coefficient.

[0064] Step S315: Set the predetermined stability weight distribution based on the first maximum information coefficient.

[0065] Specifically, the real estate project foundation database is a database that stores a large amount of data related to the foundations of real estate projects, including various monitoring data (such as foundation displacement, axial force, groundwater level, etc.) collected from different time points and different monitoring tools, as well as the corresponding stability values. Extract the data set of any foundation (denoted as the first historical foundation) from the real estate project foundation database as the first foundation data set. The first foundation data set covers the monitoring characteristic data (such as foundation displacement, support axial force, groundwater level, etc.) of the first historical foundation in different monitoring periods and the historical stability values of different monitoring periods.

[0066] In the first foundation dataset, determine the first historical stability value of the first historical foundation and define it as the dependent variable. Obtain any foundation feature index, which can be any one of the foundation features that can be monitored by intelligent monitoring devices (i.e., any one of foundation features such as foundation pit horizontal displacement, foundation pit vertical displacement, retaining structure horizontal displacement, support axial force, and groundwater level). Then search for the first historical feature parameter corresponding to this arbitrary foundation feature index in the first foundation dataset. For example, if the arbitrary foundation feature index is "foundation pit horizontal displacement", search for the parameter records related to all foundation pit horizontal displacements of the first historical foundation in the first foundation dataset.

[0067] Use the found first historical feature parameter as the independent variable and perform the maximum information coefficient calculation with the first historical stability value as the dependent variable to obtain the first maximum information coefficient. The maximum information coefficient calculation traverses each data point in the dataset, calculates the mutual information between the two variables, and normalizes it to obtain the maximum information coefficient. A dedicated statistical analysis software can be used to implement this calculation process. For example, in Python, the minepy library can be used to calculate the maximum information coefficient. First, install the minepy library, then import the library in the code, and use relevant functions (such as the MIC function) to pass in the data of the independent variable and the dependent variable to obtain the first maximum information coefficient.

[0068] Set the predetermined stability weight allocation according to the calculated first maximum information coefficient. If the first maximum information coefficient is large, it indicates a strong correlation between the corresponding foundation feature index and the foundation stability value, so a larger weight is given to this feature index in the predetermined stability weight allocation; conversely, if the first maximum information coefficient is small, a smaller weight is given. For example, if the maximum information coefficient of foundation pit horizontal displacement is 0.8, the maximum information coefficient of foundation pit vertical displacement is 0.6, the maximum information coefficient of retaining structure horizontal displacement is 0.7, the maximum information coefficient of support axial force is 0.9, and the maximum information coefficient of groundwater level is 0.5, then the weight of foundation pit horizontal displacement can be set to 0.2, the weight of foundation pit vertical displacement can be set to 0.15, the weight of retaining structure horizontal displacement can be set to 0.2, the weight of support axial force can be set to 0.3, and the weight of groundwater level can be set to 0.15.

[0069] Through the above steps, it is possible to scientifically set the predetermined stability weight allocation based on historical data. Utilize the historical data in the real estate project foundation database, fully consider the relationship between different foundation feature indexes and the foundation stability value, accurately measure the strength of this relationship through the maximum information coefficient calculation, avoid the subjectivity of weight allocation, make the predetermined stability weight allocation more reasonable and accurate, and provide a more reliable basis for the subsequent foundation stability assessment.

[0070] Furthermore, step S4 includes:

[0071] Step S41: Obtain an ultrasonic scanning plan, where the ultrasonic scanning plan includes a first plan and a second plan.

[0072] Step S42: The transmitting transducer in the ultrasonic transducer emits ultrasonic waves to the target point of the target foundation based on the first plan, and receives and collects through the receiving transducer in the ultrasonic transducer to obtain a first actual scanning signal.

[0073] Step S43: The transmitting transducer emits ultrasonic waves to the target point based on the second plan, and receives and collects through the receiving transducer to obtain a second actual scanning signal.

[0074] Step S44: Establish a mapping between the first angle and the first level in the first plan and the first signal travel time obtained by analyzing the first actual scanning signal to obtain a first mapping relationship.

[0075] Step S45: Establish a mapping between the second angle and the second level in the second plan and the second signal travel time obtained by analyzing the second actual scanning signal to obtain a second mapping relationship.

[0076] Step S46: Obtain the travel time of the actual scanning signal according to the first mapping relationship and the second mapping relationship.

[0077] Specifically, the ultrasonic scanning plan is an ultrasonic scanning scheme for the target point, which defines the scanning parameters, such as scanning angle, scanning level, the ultrasonic frequency used, etc. The ultrasonic scanning plan includes a first plan and a second plan. These two plans represent two different scanning schemes respectively, with different scanning angles, depths or frequencies to ensure obtaining ultrasonic scanning data from multiple directions and different depths. Obtain the ultrasonic scanning plan from a pre-stored database or configuration file. These plans are pre-developed according to factors such as the type of the target foundation, engineering requirements, and past experience.

[0078] According to the settings in the first plan, adjust the parameters of the transmitting transducer in the ultrasonic transducer, such as the transmitting angle, transmitting frequency, etc., and then emit ultrasonic waves to the target point of the target foundation. The emitted ultrasonic waves propagate in the medium at the target point, and the receiving transducer receives the reflected ultrasonic waves at a predetermined position, and processes the received signals, such as collecting, amplifying, filtering, etc., to obtain a first actual scanning signal. This signal contains the reflection information inside the foundation at the target point. For example, in actual operation, the transmitting transducer sets the transmitting angle to 30° and the transmitting frequency to 50 kHz according to the first plan, and the receiving transducer processes the received signal through devices such as a preamplifier and a filter to obtain a first actual scanning signal.

[0079] Similarly, adjust the parameters of the transmitting transducer according to the second plan and transmit ultrasonic waves to the target point, and then receive and collect them by the receiving transducer to obtain the second actual scanning signal. The difference is that the parameters in the second plan (such as the transmitting angle, frequency, or scanning level, etc.) are different from those in the first plan, so as to scan the target point from different angles and conditions. For example, in actual operation, the transmitting transducer sets the transmitting angle to 45° according to the second plan and transmits ultrasonic waves with a frequency of 50 kHz.

[0080] Use signal analysis software to analyze the first actual scanning signal to determine the first signal travel time. Then establish a mapping relationship between the first angle and the first level in the first plan and the obtained first signal travel time. For example, different combinations of the first angle and the first level can be used as indexes, and the corresponding first signal travel time can be used as values and stored in a data structure.

[0081] Similarly, analyze the second actual scanning signal to obtain the second signal travel time, and then establish a mapping relationship between the second angle and the second level in the second plan and the second signal travel time. Combine the first mapping relationship and the second mapping relationship to determine the actual scanning signal travel time. For example, the signal travel times in the two mapping relationships can be weighted and averaged (determine the weights according to factors such as the importance of the angle and the level), or merged and calculated according to a custom fusion rule.

[0082] Through the above steps, perform ultrasonic scanning on the target point from multiple angles and levels to obtain more comprehensive and accurate information about the internal structure of the target point. Different scanning plans can cover more detection conditions. By establishing a mapping relationship and comprehensively calculating the actual scanning signal travel time, the medium characteristics, defect conditions, etc. of the target point can be analyzed more accurately, providing a more detailed and reliable basis for the stability assessment of the foundation.

[0083] Further, step S5 includes:

[0084] Step S51: Obtain the travel time comparison deviation between the actual scanning signal travel time and the predetermined scanning signal travel time.

[0085] Step S52: Establish a linear deviation equation based on the travel time comparison deviation.

[0086] Step S53: According to the predetermined travel time deviation inversion strategy, perform inversion iteration on the linear deviation equation and the predetermined foundation slowness model to obtain the actual foundation slowness model.

[0087] Step S54: Analyze the actual foundation slowness model to obtain the target image.

[0088] Specifically, due to defects (such as cracks, cavities) in the internal structure of the foundation or differences in material uniformity, there is a difference between the actual travel time of the target foundation's scanning signal measured on-site and the predetermined travel time of the scanning signal calculated based on the theoretical sound velocity of the foundation material. The travel time comparison deviation between the two is obtained through subtraction operation, that is , where, t actual is the actual travel time of the scanning signal, t theoretical is the predetermined travel time of the scanning signal, and Δt is the travel time comparison deviation. This difference reflects the degree of deviation in the ultrasonic propagation time between the actual situation and the expected situation, and is an important basis for subsequent establishment of deviation equations and inversion iteration.

[0089] A linear deviation equation is established based on the travel time comparison deviation to describe the mathematical relationship of the deviation between the actual situation and the theoretical or expected situation. For each target point, the predetermined travel time of the scanning signal is determined by the sound velocity of the material and the propagation path length. Under ideal conditions, the predetermined travel time of the scanning signal can be calculated by the formula t theoretical = d / v theoretical , where d is the distance of the propagation path and v theoretical is the theoretical sound velocity. The actual travel time of the scanning signal is affected by the change in the sound velocity of the foundation material. The change in the sound velocity in the foundation will cause a change in the propagation time of the ultrasonic signal. The slower the sound velocity, the longer the propagation time. When the sound velocity changes in some areas, the local sound velocity of the foundation at different positions can be represented by v (x,y,z) . The deviation between the actual propagation time and the theoretical propagation time can be expressed in the following form: . To simplify the calculation process, it is assumed that the change in the sound velocity inside the foundation is small, that is, v (x,y,z) has little change relative to the theoretical sound velocity v theoretical . At this time, the travel time comparison deviation Δt can be expressed by a linearized approximate relationship: where, α is a constant representing the degree of influence of the sound velocity change on the propagation time. For multiple target points, a corresponding travel time comparison deviation equation is established for each. For the convenience of subsequent calculation and optimization, these travel time comparison equations are linearized and represented by matrices. For example, for multiple deviation values of multiple measurement points, the deviation values can be aggregated into a vector Δt', and the sound velocity change is represented as a vector Δt'. Then the linear deviation equation can be written as: Δt' = AΔt'. Where, A is a matrix containing the geometric parameters and propagation path lengths of different measurement points. This equation describes the linear relationship between the travel time deviation and the sound velocity change. Through this model, the sound velocity distribution of the foundation can be further analyzed and deduced, so as to obtain the stability information of the foundation.

[0090] Using the predetermined acoustic time deviation inversion strategy, the linear deviation equation and the predetermined foundation slowness model are iteratively calculated to obtain the slowness model of the actual foundation. The foundation slowness model reflects the propagation speed inside the foundation and indirectly shows the density, material distribution and other characteristics of the foundation. Through iterative optimization, the inversion method continuously adjusts the model parameters until the calculated acoustic time deviation matches the actual scanning results. In the actual operation process, iterative algorithms such as algebraic reconstruction technology (ART) and conjugate gradient method can be used for inversion operations to gradually approach the actual situation.

[0091] The actual foundation slowness model is analyzed, the slowness distribution in the model is analyzed, and the slowness model is converted into a target image according to the conversion relationship between slowness and image features (based on physical principles or experience). For example, if the area with larger slowness is displayed as a darker color on the image, the image processing software can be used to determine the color of the corresponding point on the image according to the slowness value of each point in the actual foundation slowness model, thereby generating a target image. The target image can intuitively show the slowness distribution of different areas in the foundation, such as dense areas, soft areas, cracked areas, etc.

[0092] Through the above steps, we can compare the acoustic time deviation of the actual scanning signal with the theoretical value of the predetermined scanning signal, establish a linear deviation equation and combine it with the inversion technology to deduce the actual slowness model of the foundation. This process accurately reveals the internal physical properties of the foundation, such as density distribution and structural uniformity, by continuously optimizing the foundation slowness model. Ultimately, the generated target image provides a visual and clear structural diagram for foundation stability analysis, providing a scientific basis for foundation reinforcement or other subsequent treatments.

[0093] Further, step S53 includes:

[0094] Step S535: extracting a first inversion strategy from the predetermined acoustic time deviation inversion strategies.

[0095] Step S536: performing inversion iteration on the linear deviation equation and the predetermined foundation slowness model according to the first inversion strategy to obtain a first slowness model.

[0096] Step S537: extracting a second inversion strategy from the predetermined acoustic time deviation inversion strategy.

[0097] Step S538: performing inversion iteration on the linear deviation equation and the predetermined foundation slowness model according to the second inversion strategy to obtain a second slowness model.

[0098] Step S539: Compare and analyze the first slowness model and the second slowness model to determine the actual foundation slowness model.

[0099] Specifically, the predetermined acoustic time deviation inversion strategy is a set of pre-set strategy combinations for inversion based on the linear deviation equation and the predetermined subsurface slowness model, which includes a variety of different inversion strategies, such as the algebraic reconstruction technique (ART), conjugate gradient method, etc. Read and extract the first inversion strategy from the database or configuration file storing the predetermined acoustic time deviation inversion strategy. Use the linear deviation equation and the predetermined subsurface slowness model as input data, and perform inversion iterations according to the algorithm specified by the first inversion strategy (such as ART or conjugate gradient method). Among them, the predetermined subsurface slowness model is an initial subsurface model constructed based on previous experimental data, theoretical calculations, or the basic characteristics of the subsurface, which provides a preliminary reference framework but cannot fully and accurately describe the actual situation of the subsurface. Taking the algebraic reconstruction technique (ART) as an example, in each iteration, compare the deviation between the theoretical acoustic time calculated according to the current model parameters and the actual measured acoustic time, and adjust the slowness parameters in the model to gradually approach the actual subsurface slowness distribution. After multiple iterations, the first slowness model is obtained. The specific process is as follows: Simplify the slowness distribution of the subsurface into a discrete grid according to the linear deviation equation. The entire subsurface area is divided into multiple small units, and each unit corresponds to a "pixel" value representing the subsurface slowness. The propagation time of each ultrasonic signal can be represented by a mathematical model as: , where is the propagation time of the i-th ultrasonic path, is the slowness of the j-th unit (the target of inversion), is the matrix element between the ultrasonic path and the subsurface unit. By comparing the deviation between the theoretical value (calculated from the predetermined subsurface slowness model) and the actual measured value, the error is obtained: , where is the error of the i-th path, is the actual acoustic time of the actual scanned signal measured, is the predetermined acoustic time of the scanned signal predicted according to the current model. The ART algorithm gradually corrects the slowness parameters of each small unit through iteration. In each iteration, the algorithm sequentially updates the slowness parameters of each grid unit, and the update process is carried out by minimizing the error. The formula is as follows: , where α is the step size control factor, which determines the amplitude of each update. The update process will be carried out on all paths until the error converges or the maximum number of iterations is reached. Usually, in actual operation, the update order can be row by row, column by column, randomly, etc. Through multiple rounds of iteration, gradually correct the error in the subsurface slowness model, and finally converge to obtain a slowness model that matches the actual propagation time.

[0100] Next, a second inversion strategy is extracted from the predetermined sound time deviation inversion strategy. Using the linear deviation equation and the predetermined subsurface slowness model as inputs, inversion iteration is performed according to the algorithm specified by the second inversion strategy to obtain a second slowness model. For example, the second inversion strategy is an inversion method based on the conjugate gradient method. First, an objective function (least squares error) is defined to represent the difference between the predicted propagation time and the actual measured time. During the iterative process, the aim is to minimize this error. Next, the gradient of the objective function with respect to the subsurface slowness is calculated, that is, the rate of change of the error, which can be achieved by solving the partial derivative of the objective function with respect to the slowness parameter. By calculating the gradient and updating according to its direction, different from the traditional gradient descent method, the gradient direction is not directly adjusted in the reverse direction, but a "conjugate direction" is selected, that is, more efficient updates are made through the optimization direction in each iteration. Through multiple rounds of iteration, the conjugate gradient method can effectively optimize the subsurface slowness model and finally obtain the second slowness model.

[0101] A comparative analysis is performed on the first slowness model and the second slowness model. The comparison can be carried out from multiple aspects, such as the distribution of slowness values in the model, the stability of the model, and the matching degree with some known subsurface characteristics, etc. For example, the difference in slowness values at corresponding positions of the two models can be calculated, or the slowness performance of the two models in a specific area (such as a suspected defect area) can be compared. According to the comparison results, the final actual subsurface slowness model is determined by comprehensive consideration. If the slowness performance of the first slowness model in a certain key area is more in line with some characteristics in the actual measurement data, while the second slowness model performs better in other areas, then the two models can be weighted and fused or one of them can be selected as the actual subsurface slowness model.

[0102] Through the above steps, different slowness models are obtained using multiple inversion strategies, and then the actual subsurface slowness model is determined through comparative analysis. This multi-strategy approach can make full use of the advantages of different inversion methods, reduce the errors that may be brought by a single method, improve the accuracy and reliability of the model, and thus more accurately reflect the slowness characteristics of the actual subsurface, providing a more reliable basis for subsequent operations such as analyzing the target image.

[0103] Furthermore, step S53 further includes:

[0104] Step S531: Obtain the target structure of the target subsurface, where the target structure includes multiple structural layers.

[0105] Step S532: Extract the first structural layer from the multiple structural layers and obtain the first layer material of the first structural layer.

[0106] Step S533: Match the first ultrasonic slowness coefficient of the first layer material.

[0107] Step S534: Establish the predetermined foundation slowness model according to the first structural layer and the first ultrasonic slowness coefficient.

[0108] Specifically, obtain the target structural information of the target foundation by analyzing the preliminary exploration data, design drawings or other relevant materials of the target foundation. The structure of the foundation generally includes multiple layers, and the physical properties (such as density, elastic modulus, sound velocity, etc.) of each layer are different. The target structural information includes information such as the number of structural layers of the foundation, the thickness of each layer, and the general distribution.

[0109] Extract the first structural layer from the multiple structural layers in the obtained target structure, and then determine the first layer of material in the first structural layer. Then, search for the first ultrasonic slowness coefficient that matches the first layer of material in the material property database or the ultrasonic material property manual. This coefficient is obtained based on a large number of experiments or theoretical studies and reflects the influence of the material on the ultrasonic wave propagation slowness.

[0110] Traverse the multiple structural layers in the target structure, pay attention to extracting the materials corresponding to each structural layer, and then search for the ultrasonic slowness coefficients corresponding to these materials to obtain multiple first structural layers and multiple first ultrasonic slowness coefficients. According to the geometric information (such as thickness, area, etc., factors that may affect the ultrasonic wave propagation path) of these first structural layers and the first ultrasonic slowness coefficient, use mathematical modeling software (such as Matlab) to construct the predetermined foundation slowness model.

[0111] By establishing the predetermined foundation slowness model, the propagation characteristics of ultrasonic waves in the foundation can be predicted, providing a basic model for subsequent inversion iteration and improving the accuracy and reliability of foundation monitoring.

[0112] In summary, the method for intelligent monitoring of foundation stability in real estate projects provided by the embodiments of the present application has the following technical effects:

[0113] The embodiments of the present application first comprehensively collect foundation state information through multi-dimensional monitoring, and convert the data into an intuitive monitoring image by establishing a visualization graph, enhancing the usability of the data. Secondly, through the stability evaluation mechanism and ultrasonic tomography scanning technology, deep exploration of the inside of the foundation is carried out, and potential problems inside the foundation can be accurately identified. Finally, the inversion technology is used to generate the target image, and combined with multi-dimensional image feature analysis, the stability index of the foundation is obtained, comprehensively reflecting the stability status of the foundation. Overall, the embodiments of the present application overcome the limitations of the prior art that can only monitor the surface or local area of the foundation, improve the accuracy and comprehensiveness of foundation stability monitoring, and provide a more accurate and reliable foundation stability evaluation method for the foundation safety of real estate projects.

[0114] Embodiment 2, as Figure 3As shown, based on the same inventive concept as in the foregoing Embodiment 1, an embodiment of the present application provides an intelligent monitoring system for foundation stability in real estate engineering. The system includes:

[0115] A multi-dimensional monitoring module 10, configured to activate intelligent monitoring devices and perform multi-dimensional monitoring on a target foundation in a target real estate project through the intelligent monitoring devices to obtain a target monitoring result.

[0116] A monitoring visualization module 20, configured to extract first monitoring information corresponding to a first foundation point in the target monitoring result and establish a target monitoring visualization diagram according to a first correspondence between the first foundation point and the first monitoring information.

[0117] A stability evaluation module 30, configured to introduce a stability evaluation mechanism to perform stability rendering analysis on the target monitoring visualization diagram and determine a target point.

[0118] An ultrasonic scanning module 40, configured to activate an ultrasonic transducer to perform ultrasonic tomography scanning detection on the target point to obtain the actual scanning signal travel time.

[0119] A target image acquisition module 50, configured to read a predetermined travel time deviation inversion strategy and perform comparative inversion on the actual scanning signal travel time and a predetermined scanning signal travel time according to the predetermined travel time deviation inversion strategy to obtain a target image.

[0120] A stability index determination module 60, configured to analyze the multi-dimensional image features of the target image to obtain the actual stability index of the target foundation, where the actual stability index is used to characterize the foundation stability of the target foundation.

[0121] Furthermore, the intelligent monitoring devices at least include a displacement sensor, a static level, an inclinometer, an axial force sensor, and a water level sensor. The multi-dimensional monitoring module 10 of the embodiment of the present application is further configured to perform the following steps:

[0122] Monitor the horizontal displacement of the foundation pit of the target foundation through the displacement sensor; monitor the vertical displacement of the foundation pit of the target foundation through the static level; monitor the horizontal displacement of the retaining structure of the target foundation through the inclinometer; monitor the support axial force of the target foundation through the axial force sensor; monitor the underground water level of the target foundation through the water level sensor; the horizontal displacement of the foundation pit, the vertical displacement of the foundation pit, the horizontal displacement of the retaining structure, the support axial force, and the underground water level constitute the target monitoring result.

[0123] Furthermore, the stability evaluation module 30 of the embodiment of the present application is further configured to perform the following steps:

[0124] Obtain a predetermined stability weight distribution according to the described stability evaluation mechanism; perform weighted analysis on the first monitoring information in combination with the predetermined stability weight distribution to obtain the first stability value corresponding to the first foundation point; mark the first stability value at the first foundation point in the target monitoring viewable image to obtain the target stability value viewable image; according to the target stability value viewable image, judge whether the first stability value is within a predetermined stability threshold; if not, add the first foundation point to the repeated monitoring point set; randomly extract any foundation point in the repeated monitoring point set and denote it as the target point.

[0125] Further, the stability evaluation module 30 of the embodiment of the present application is further configured to perform the following steps:

[0126] Extract the first foundation data set from the real estate project foundation database; denote the first historical stability value of the first historical foundation in the first foundation data set as the dependent variable; obtain any foundation feature index, and match the first historical feature parameter corresponding to the any foundation feature index in the first foundation data set; denote the first historical feature parameter as the independent variable, and perform the maximum information coefficient calculation with the dependent variable to obtain the first maximum information coefficient; set the predetermined stability weight distribution based on the first maximum information coefficient.

[0127] Further, the ultrasonic scanning module 40 of the embodiment of the present application is further configured to perform the following steps:

[0128] Obtain an ultrasonic scanning plan, where the ultrasonic scanning plan includes a first plan and a second plan; the transmitting transducer in the ultrasonic transducer transmits ultrasonic waves to the target point of the target foundation based on the first plan, and receives and collects through the receiving transducer in the ultrasonic transducer to obtain the first actual scanning signal; the transmitting transducer transmits ultrasonic waves to the target point based on the second plan, and receives and collects through the receiving transducer to obtain the second actual scanning signal; establish a mapping between the first angle and the first layer in the first plan and the first signal time obtained by analyzing the first actual scanning signal to obtain a first mapping relationship; establish a mapping between the second angle and the second layer in the second plan and the second signal time obtained by analyzing the second actual scanning signal to obtain a second mapping relationship; obtain the actual scanning signal time according to the first mapping relationship and the second mapping relationship.

[0129] Further, the target image acquisition module 50 of the embodiment of the present application is further configured to perform the following steps:

[0130] Obtain the acoustic time comparison deviation between the actual scanning signal sound time and the predetermined scanning signal sound time; establish a linear deviation equation based on the acoustic time comparison deviation; according to the predetermined acoustic time deviation inversion strategy, perform inversion iteration on the linear deviation equation and the predetermined foundation slowness model to obtain the actual foundation slowness model; analyze the actual foundation slowness model to obtain the target image.

[0131] Further, the target image acquisition module 50 in the embodiment of the present application is further configured to perform the following steps:

[0132] Extract the first inversion strategy in the predetermined acoustic time deviation inversion strategy; perform inversion iteration on the linear deviation equation and the predetermined foundation slowness model according to the first inversion strategy to obtain the first slowness model; extract the second inversion strategy in the predetermined acoustic time deviation inversion strategy; perform inversion iteration on the linear deviation equation and the predetermined foundation slowness model according to the second inversion strategy to obtain the second slowness model; compare and analyze the first slowness model and the second slowness model to determine the actual foundation slowness model.

[0133] Further, the target image acquisition module 50 in the embodiment of the present application is further configured to perform the following steps:

[0134] Obtain the target structure of the target foundation, where the target structure includes multiple structural layers; extract the first structural layer in the multiple structural layers, and obtain the first layer material of the first structural layer; match the first ultrasonic slowness coefficient of the first layer material; establish the predetermined foundation slowness model according to the first structural layer and the first ultrasonic slowness coefficient.

[0135] Through the foregoing detailed description of the method for intelligent monitoring of foundation stability in real estate engineering in this specification, those skilled in the art can clearly know the system for intelligent monitoring of foundation stability in real estate engineering in this embodiment. For the system disclosed in the second embodiment, since it corresponds to the method disclosed in the first embodiment, it has corresponding functional modules and beneficial effects. For the relevant parts, refer to the description in the method part.

[0136] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent monitoring method for foundation stability in real estate projects, characterized in that, Including: Activating the intelligent monitoring device, and performing multi-dimensional monitoring on the target foundation in the target real estate project through the intelligent monitoring device to obtain a target monitoring result; Extracting the first monitoring information corresponding to the first foundation point in the target monitoring result, and establishing a target monitoring viewable diagram according to the first corresponding relationship between the first foundation point and the first monitoring information; Introducing a stability evaluation mechanism to perform stability rendering analysis on the target monitoring viewable diagram, and determining a target point, including: Obtaining a predetermined stability weight distribution according to the stability evaluation mechanism; Performing weighted analysis on the first monitoring information in combination with the predetermined stability weight distribution to obtain a first stability value corresponding to the first foundation point; Marking the first stability value to the first foundation point in the target monitoring viewable diagram to obtain a target stability value viewable diagram; Judging whether the first stability value is within a predetermined stability threshold according to the target stability value viewable diagram; If it is not within, adding the first foundation point to the repeated monitoring point set; Randomly extracting any foundation point in the repeated monitoring point set and denoting it as the target point; The stability evaluation mechanism is an evaluation method based on data analysis for evaluating the stability of the foundation; Showing the stability degree of different regions in a visual way through the stability rendering analysis; Activating an ultrasonic transducer to perform ultrasonic tomography scanning detection on the target point to obtain the actual scanning signal travel time; Reading a predetermined travel time deviation inversion strategy, and performing comparative inversion on the actual scanning signal travel time and the predetermined scanning signal travel time according to the predetermined travel time deviation inversion strategy to obtain a target image; Analyzing the multi-dimensional image features of the target image to obtain the actual stability index of the target foundation, where the actual stability index is used to characterize the foundation stability of the target foundation.

2. The intelligent monitoring method for foundation stabilization in real estate projects according to claim 1, characterized in that, The intelligent monitoring device at least includes a displacement sensor, a static level, an inclinometer, an axial force sensor, and a water level sensor. Activating the intelligent monitoring device, and performing multi-dimensional monitoring on the target foundation in the target real estate project through the intelligent monitoring device to obtain a target monitoring result, including: Monitoring the horizontal displacement of the foundation pit of the target foundation through a displacement sensor; Monitoring the vertical displacement of the foundation pit of the target foundation through a static level; Monitoring the horizontal displacement of the retaining structure of the target foundation through an inclinometer; Monitoring the support axial force of the target foundation through an axial force sensor; Monitoring the groundwater level of the target foundation through a water level sensor; The horizontal displacement of the foundation pit, the vertical displacement of the foundation pit, the horizontal displacement of the retaining structure, the support axial force, and the groundwater level constitute the target monitoring result.

3. The intelligent monitoring method for foundation stabilization in real estate projects according to claim 1, characterized in that, Obtaining a predetermined stability weight distribution according to the stability evaluation mechanism, including: Extracting a first foundation data set from the real estate project foundation database; Denoting the first historical stability value of the first historical foundation in the first foundation data set as the dependent variable; Obtaining any foundation characteristic index, and matching the first historical characteristic parameter corresponding to the any foundation characteristic index in the first foundation data set; Denote the first historical feature parameter as the independent variable, and calculate the maximum information coefficient with the dependent variable to obtain the first maximum information coefficient; Set the predetermined stable weight distribution based on the first maximum information coefficient.

4. The intelligent monitoring method for foundation stabilization in real estate projects according to claim 1, characterized in that, Activate the ultrasonic transducer to perform ultrasonic tomography scanning detection on the target point, and obtain the actual scanning signal travel time, including: Obtain an ultrasonic scanning plan, where the ultrasonic scanning plan includes a first plan and a second plan; The transmitting transducer in the ultrasonic transducer emits ultrasonic waves to the target point of the target foundation based on the first plan, and receives and collects through the receiving transducer in the ultrasonic transducer to obtain a first actual scanning signal; The transmitting transducer emits ultrasonic waves to the target point based on the second plan, and receives and collects through the receiving transducer to obtain a second actual scanning signal; Establish a mapping between the first angle and the first layer in the first plan and the first signal travel time obtained by analyzing the first actual scanning signal to obtain a first mapping relationship; Establish a mapping between the second angle and the second layer in the second plan and the second signal travel time obtained by analyzing the second actual scanning signal to obtain a second mapping relationship; Obtain the actual scanning signal travel time according to the first mapping relationship and the second mapping relationship.

5. The intelligent monitoring method for foundation stabilization in real estate projects according to claim 1, wherein Read a predetermined travel time deviation inversion strategy, and perform a comparison inversion on the actual scanning signal travel time and the predetermined scanning signal travel time according to the predetermined travel time deviation inversion strategy to obtain a target image, including: Obtain the travel time comparison deviation between the actual scanning signal travel time and the predetermined scanning signal travel time; Establish a linear deviation equation based on the travel time comparison deviation; According to the predetermined travel time deviation inversion strategy, perform inversion iteration on the linear deviation equation and the predetermined foundation slowness model to obtain an actual foundation slowness model; Analyze the actual foundation slowness model to obtain the target image.

6. The intelligent monitoring method for foundation stabilization in real estate projects according to claim 5, characterized in that According to the predetermined travel time deviation inversion strategy, perform inversion iteration on the linear deviation equation and the predetermined foundation slowness model to obtain an actual foundation slowness model, including: Extract the first inversion strategy in the predetermined travel time deviation inversion strategy; Perform inversion iteration on the linear deviation equation and the predetermined foundation slowness model according to the first inversion strategy to obtain a first slowness model; Extract the second inversion strategy in the predetermined travel time deviation inversion strategy; Perform inversion iteration on the linear deviation equation and the predetermined foundation slowness model according to the second inversion strategy to obtain a second slowness model; Compare and analyze the first slowness model and the second slowness model to determine the actual foundation slowness model.

7. The intelligent monitoring method for foundation stabilization in real estate projects according to claim 6, characterized in that, According to the predetermined travel time deviation inversion strategy, perform inversion iteration on the linear deviation equation and the predetermined foundation slowness model to obtain an actual foundation slowness model, including: Obtain the target structure of the target foundation, where the target structure includes multiple structural layers; Extract the first structural layer from the multiple structural layers, and obtain the first layer material of the first structural layer; Match the first ultrasonic slowness coefficient of the first layer material; Based on the first structural layer and the first ultrasonic slowness coefficient, establish the predetermined foundation slowness model.

8. An intelligent monitoring system for foundation stability in real estate projects, characterized in that, The system is used to execute the intelligent monitoring method for foundation stabilization in real estate projects according to any one of claims 1-7, and includes: A multi-dimensional monitoring module, configured to activate the intelligent monitoring device, and perform multi-dimensional monitoring on the target foundation in the target real estate project through the intelligent monitoring device to obtain a target monitoring result; A monitoring visualization module, configured to extract the first monitoring information corresponding to the first foundation point in the target monitoring result, and establish a target monitoring visual diagram according to the first correspondence between the first foundation point and the first monitoring information; A stability evaluation module, configured to introduce a stability evaluation mechanism to perform stability rendering analysis on the target monitoring visual diagram and determine the target point; An ultrasonic scanning module, configured to activate an ultrasonic transducer to perform ultrasonic tomography scanning detection on the target point to obtain the actual scanning signal travel time; A target image acquisition module, configured to read a predetermined travel time deviation inversion strategy, and perform comparative inversion on the actual scanning signal travel time and the predetermined scanning signal travel time according to the predetermined travel time deviation inversion strategy to obtain a target image; A stability index determination module, configured to analyze the multi-dimensional image features of the target image to obtain the actual stability index of the target foundation, where the actual stability index is used to characterize the foundation stability of the target foundation.

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