Intelligent monitoring method and system for foundation stability in real estate engineering

Abstract

The invention provides an intelligent monitoring method and system for foundation stability in real estate engineering, and relates to the technical field of foundation monitoring, and the method comprises the steps: carrying out the multi-dimensional monitoring of a target foundation through an intelligent monitoring device, extracting first monitoring information corresponding to a first foundation point location, and building a target monitoring visual graph; performing stability rendering analysis on the target monitoring view, and determining a target point location; activating an ultrasonic transducer to carry out ultrasonic tomography scanning detection on the target point location, and carrying out comparison inversion on the actual scanning signal sound time and the preset scanning signal sound time according to a preset sound time deviation inversion strategy to obtain a target image; and according to the multi-dimensional image feature analysis of the target image, obtaining an actual stability index of the target foundation. The technical problem that in the prior art, due to the fact that the internal structure and state of a foundation cannot be comprehensively known, the accuracy and comprehensiveness of foundation stability monitoring are insufficient is solved, and the accuracy and comprehensiveness of foundation stability monitoring are improved.

Description

Technical Field

[0001] The present application relates to the field of foundation monitoring technology, and in particular to a method and system for intelligently monitoring foundation stability in real estate projects. Background Art

[0002] With the acceleration of urbanization and the increasing number of real estate projects, foundation stability monitoring has become a key link in ensuring project quality and construction safety. At present, common foundation stability monitoring methods include settlement monitoring, strain monitoring, and stress monitoring. These methods collect data through ground sensors or measuring equipment to monitor the surface or local area of ​​the foundation. These monitoring methods can reflect the overall change trend and surface settlement of the foundation to a certain extent, but because they cannot penetrate deep into the foundation, it is difficult to find potential structural problems, such as gaps, cracks, humidity changes, etc. between soil layers, resulting in errors in the monitoring results and failure to timely discover and deal with deep-seated foundation safety hazards. Summary of the invention

[0003] The present 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 part of the foundation and cannot fully understand the internal structure and state of the foundation, resulting in the inability to comprehensively and accurately evaluate the stability of the foundation, thereby achieving the technical effect of improving the accuracy and comprehensiveness of foundation stability monitoring.

[0004] In view of the above problems, on the one hand, the present application provides an intelligent monitoring method for foundation stability in real estate projects, the method comprising: activating an intelligent monitoring device, and performing multi-dimensional monitoring of 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 visual map based on 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 visual map, and determine the target point; activating an ultrasonic transducer to perform ultrasonic tomography scanning and detection on the target point to obtain an actual scanning signal sound time; reading a predetermined sound time deviation inversion strategy, and comparing and inverting the actual scanning signal sound time with the predetermined scanning signal sound time according to the predetermined sound time deviation inversion strategy to obtain a target image; obtaining an actual stability index of the target foundation based on multi-dimensional image feature analysis of the target image, wherein 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 stability in real estate projects, the system comprising: a multi-dimensional monitoring module, used to activate the intelligent monitoring equipment, and perform multi-dimensional monitoring of the target foundation in the target real estate project through the intelligent monitoring equipment to obtain the target monitoring result; a monitoring visualization module, used to extract the first monitoring information corresponding to the first foundation point in the target monitoring result, and establish a target monitoring visual map according to the first correspondence between the first foundation point and the first monitoring information; a stability evaluation module, used to introduce a stability evaluation mechanism to perform stability rendering analysis on the target monitoring visual map, and determine the target point; an ultrasonic scanning module, used to activate the ultrasonic transducer to perform ultrasonic tomography scanning and detection on the target point to obtain the actual scanning signal sound time; a target image acquisition module, used to read a predetermined sound time deviation inversion strategy, and compare and invert the actual scanning signal sound time with the predetermined scanning signal sound time according to the predetermined sound time deviation inversion strategy to obtain a target image; a stability index determination module, used to obtain the actual stability index of the target foundation according to the multi-dimensional image feature analysis of the target image, wherein the actual stability index is used to characterize the foundation stability of the target foundation.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: By activating intelligent monitoring equipment, multi-dimensional monitoring of the target foundation in the target real estate project is carried out, and the surface and local state data of the foundation are comprehensively collected to provide basic information for subsequent analysis, 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 visual map, complex monitoring data is converted into intuitive visual images, which is convenient for subsequent analysis and evaluation, and improves the readability and usability of the data. A stability assessment mechanism is introduced to perform stability rendering analysis on the target monitoring visual map, make a preliminary judgment on the stability of the foundation, discover possible risk points of the foundation in advance, and provide a basis for subsequent ultrasonic testing. After confirming the target points that need further inspection, the target points are subjected to tomographic scanning and detection by ultrasonic transducers to obtain more detailed structural information. According to the predetermined acoustic time deviation inversion strategy, the actual scanning signal acoustic time is compared and inverted with the predetermined scanning signal acoustic time, and the ultrasonic scanning data is converted into a target image to further confirm the structural state inside the foundation, help identify potential foundation problems, and ensure the accuracy of the evaluation results. By analyzing the multi-dimensional image features of the target image and combining all the collected data and image features, the actual stability of the foundation is comprehensively evaluated, and the actual stability index of the target foundation is obtained, providing data support for further decision-making and repair work.

[0007] To sum up, this application combines multi-dimensional intelligent monitoring and ultrasonic tomography scanning to achieve comprehensive monitoring of foundation structure and status, significantly improves the accuracy, comprehensiveness and real-time performance of foundation stability monitoring, and provides a scientific and efficient solution for foundation stability monitoring of real estate projects. It also provides a strong basis for engineering decision-making.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flow chart of a method for intelligently monitoring foundation stability in real estate projects provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of a process for performing stability rendering analysis on a target monitoring visual graph and determining target points in a method for intelligently monitoring foundation stability in real estate projects provided in an embodiment of the present application.

[0011] Figure 3 A schematic diagram of the structure of an intelligent monitoring system for foundation stability in real estate projects provided in an embodiment of the present application.

[0012] Explanation of reference numerals: multi-dimensional monitoring module 10 , monitoring visualization module 20 , stability assessment module 30 , ultrasound scanning module 40 , target image acquisition module 50 , stability index determination module 60 . DETAILED DESCRIPTION

[0013] The embodiments of the present application provide an intelligent foundation stability monitoring method and system for real estate projects, thereby solving the technical problem that the prior art cannot comprehensively and accurately evaluate the stability of the foundation because it can only monitor the surface or part of the foundation and cannot fully understand the internal structure and state of the foundation, thereby achieving the technical effect of improving the accuracy and comprehensiveness of foundation stability monitoring.

[0014] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a method for intelligent monitoring of foundation stability in real estate engineering, the method comprising: Step S1: activating the intelligent monitoring device, and performing multi-dimensional monitoring of a target foundation in a target real estate project through the intelligent monitoring device to obtain a target monitoring result.

[0015] Specifically, intelligent monitoring equipment is a device that integrates sensors and data processing functions, and can automatically collect, transmit and preliminarily process foundation monitoring data. For example, it includes equipment that contains multiple sensors such as acceleration sensors, strain sensors, and displacement sensors. Activate the intelligent monitoring equipment, use the sensors on the intelligent monitoring equipment to monitor the target foundation in the target real estate project, and collect multi-dimensional data, such as settlement (the degree of foundation sinking), stress (the pressure on the foundation), strain (deformation of the foundation), water level, etc. For example, sensors are installed at different depths and positions of the foundation. The sensors transmit the collected data such as settlement and stress values ​​to the processor of the device. After processing by the processor, the target monitoring results containing information in multiple dimensions are obtained.

[0016] Through multi-dimensional monitoring, we can obtain comprehensive and real-time physical parameters of the foundation, more accurately capture changes in the foundation state, and provide basic data support for subsequent analysis.

[0017] Step S2: extracting first monitoring information corresponding to a first ground-based point in the target monitoring result, and establishing a target monitoring visual graph according to a first corresponding relationship between the first ground-based point and the first monitoring information.

[0018] Specifically, the first foundation point can be any monitoring point selected for the target foundation. For example, on a large foundation plane, a corner or a central position can be set as the first foundation point. The first monitoring information is various monitoring data collected by the intelligent monitoring equipment at the first foundation point, such as the settlement rate, stress change and other information of the point. From the multiple monitoring points of the target foundation, any one point is selected and recorded as the first foundation point, and then the first monitoring information corresponding to the first foundation point is filtered out from the target monitoring results. Traverse all the monitoring points of the target foundation, pay attention to filtering the monitoring information corresponding to each monitoring point, and then use data visualization tools (such as professional drawing software or a special engineering monitoring data analysis platform) to establish a target monitoring visual map based on the first correspondence between these points and the monitoring information. The target monitoring visual map is a graphic that presents the correspondence between the monitoring information and the foundation point in a visual way. For example, if the monitoring information of the first foundation point shows that the settlement is large, it can be intuitively represented in the visual map by color depth or numerical value.

[0019] By establishing a target monitoring visualization graph, discrete monitoring information can be linked in an intuitive way, which makes it convenient for engineering personnel to observe the monitoring conditions of different points of the foundation as a whole, helps to discover patterns and anomalies in the monitoring information, and provides a more intuitive basis for subsequent stability analysis.

[0020] Step S3: introducing a stability evaluation mechanism to perform stability rendering analysis on the target monitoring visual graph and determine the target point position.

[0021] Specifically, the stability assessment mechanism is an assessment method based on data analysis, which is used to assess the stability of the foundation. For example, according to existing engineering experience and theoretical models, the weights of different monitoring indicators are set, such as 30% for settlement indicators and 30% for stress indicators, and then these indicators are combined to judge the stability of the foundation. The stability assessment mechanism is introduced, and the data in the target monitoring visual map is input into the mechanism. For example, if the stability assessment mechanism is a software program, the monitoring data of each point in the visual map is imported into the program. Then, according to the algorithm in the program, the stability calculation is performed for each point in the visual map, and the stability rendering analysis of the visual map is performed according to the calculation results, and the stability degree of different areas is displayed in a visual way (such as color rendering). For example, red is used to represent areas with poor stability, and green is used to represent areas with good stability. Finally, the foundation points that may have stability problems or need to be focused on are determined from these areas and recorded as target points. For example, these target points may be foundation points corresponding to the red areas.

[0022] By introducing a stability assessment mechanism to conduct stability rendering analysis, target points that may have stability problems can be quickly identified, making subsequent inspection work more targeted, reducing unnecessary inspection workload, and improving the efficiency of the entire foundation stability monitoring and assessment.

[0023] Step S4: activating the ultrasonic transducer to perform ultrasonic tomography scanning and detection on the target point to obtain actual scanning signal sound time.

[0024] Specifically, an 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 echo signals, thereby understanding the internal structure of the foundation. Activate the ultrasonic transducer and place it in a suitable position near the target point. Then, the ultrasonic transducer transmits an ultrasonic signal to the target point. The ultrasonic wave propagates inside the target point and will reflect and refract when encountering different medium interfaces. The transducer receives the reflected ultrasonic signal and records the actual scanning signal sound time from the transmission to the reception of the ultrasonic wave. The actual scanning signal sound time is the actual propagation time of the ultrasonic wave at the target point. The sound time can be used to infer the medium characteristics in the propagation path and indirectly reflect the internal structure of the foundation.

[0025] By performing ultrasonic tomography scanning on the target point, the ultrasonic propagation time information of the internal structure of the target point is obtained, which provides key data for the subsequent analysis of the internal structure of the target point and helps to gain a deeper understanding of the internal condition of the target point.

[0026] Step S5: reading a predetermined acoustic time deviation inversion strategy, and comparing and inverting the actual scanning signal acoustic time with the predetermined scanning signal acoustic time according to the predetermined acoustic time deviation inversion strategy to obtain a target image.

[0027] Specifically, the predetermined acoustic time deviation inversion strategy is a pre-set analysis method, which inverts the internal structure image of the target point according to the deviation between the propagation acoustic time of ultrasound under ideal conditions and the actual propagation acoustic time. Read the pre-set predetermined acoustic time deviation inversion strategy, which is stored in the computer software or database, to compare the actual signal acoustic time obtained by ultrasonic scanning with the predetermined acoustic time, analyze the deviation between the two, and use iterative algorithms such as algebraic reconstruction technology (ART) and conjugate gradient method to perform inversion calculations 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 of the foundation. Different areas in the image represent the changes in the sound velocity of the foundation at different depths or positions. The changes in sound velocity reflect the density, uniformity and whether there are defects in the foundation material. Among them, the predetermined acoustic time can be determined by experiment or computer simulation based on the theoretical sound velocity of the foundation material. For example, if the scanning results show that the acoustic time deviation in a certain area is large, it reflects that there are materials or cracks with higher density than expected in this area.

[0028] Through the deviation inversion technology, the difference between the actual scanning signal sound time and the predetermined scanning signal sound time can be converted into an intuitive target image, thereby more clearly displaying the actual situation of the internal structure of the target point, so as to facilitate the analysis of possible problems that may affect the stability of the target point.

[0029] Step S6: obtaining an actual stability index of the target foundation according to the multi-dimensional image feature analysis of the target image, wherein the actual stability index is used to characterize the foundation stability of the target foundation.

[0030] Specifically, multi-dimensional image features refer to the feature information of multiple dimensions contained in the target image, such as the color distribution, texture features, contrast of different areas, etc. These features can reflect the internal structure of the target point from different angles. Use image analysis software or special engineering analysis algorithms to perform multi-dimensional feature analysis on the target image to obtain the difference in sound speed in different areas, and then determine the internal structure, material uniformity and potential defects of the foundation. Then, combined with existing engineering experience and mathematical models, the actual stability index of the target foundation is calculated. This actual stability index is a quantitative value used to indicate the degree of stability of the target foundation.

[0031] Ultrasonic tomography technology generates a target image inside the foundation through the change of ultrasonic wave velocity. Generally speaking, the sound velocity is higher in dense material areas, and when there are defects (such as cracks, voids or loose soil), the sound waves propagate slowly in these areas. For example, the propagation speed of ultrasound will slow down in cracks, voids or loose areas, thus appearing as low-speed areas, while in some areas where the material density is higher than the standard density of the foundation material, the propagation speed of ultrasound will increase, thus appearing as high-speed areas. For example, in the target image of the foundation, the low-speed area (the area in the image where the sound velocity is lower than the ideal sound velocity) is usually displayed as a darker area, and the high-speed area (the area in the image where the sound velocity is higher than the ideal sound velocity) is usually displayed as a lighter area. Image processing techniques such as gray value analysis and threshold segmentation are used to identify low-speed areas and high-speed areas, and the actual stability index of the target foundation is calculated based on the area ratio of the low-speed area and the high-speed area in the image, the uniformity of the texture and other characteristics. In addition, the entire foundation target image can be feature analyzed to determine 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 means that there are uneven quality areas inside the foundation, such as loose soil, cracks or voids. Image processing techniques such as gray level co-occurrence matrix (GLCM) and local binary pattern (LBP) are used to analyze the texture features of the image to determine the uniformity of the foundation material.

[0032] 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 decision-making.

[0033] Furthermore, in step S1, the intelligent monitoring device at least includes a displacement sensor, a static level, an inclinometer, an axial force sensor and a water level sensor, the intelligent monitoring device is activated, and the target foundation in the target real estate project is multi-dimensionally monitored by the intelligent monitoring device to obtain target monitoring results, including: Step S11: monitoring the horizontal displacement of the foundation pit of the target foundation through a displacement sensor.

[0034] Step S12: The vertical displacement of the foundation pit of the target foundation is obtained by monitoring with a static level.

[0035] Step S13: obtaining the horizontal displacement of the retaining structure of the target foundation through monitoring with an inclinometer.

[0036] Step S14: obtaining the supporting axial force of the target foundation through monitoring by an axial force sensor.

[0037] Step S15: obtaining the underground water level of the target foundation through monitoring by a water level sensor.

[0038] Step S16: The horizontal displacement of the foundation pit, the vertical displacement of the foundation pit, the horizontal displacement of the retaining structure, the supporting axial force and the groundwater level constitute the target monitoring result.

[0039] Specifically, the intelligent monitoring equipment includes at least displacement sensors, static levels, inclinometers, axial force sensors and water level sensors. Displacement sensors include potentiometer displacement sensors, laser displacement sensors, etc.; static levels include electronic levels, laser levels, etc.; inclinometers include electronic inclinometers, fiber optic inclinometers, etc.; axial force sensors include strain gauge axial force sensors, piezoelectric axial force sensors; water level sensors include pressure water level sensors, float water level sensors, etc.

[0040] 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 change of each monitoring point relative to the reference point to evaluate whether the foundation pit has abnormal horizontal displacement. Static levels are installed at different points around the foundation pit to form a level network. By comparing the height difference of multiple monitoring points, the vertical displacement of the foundation pit is measured to determine whether the foundation pit is sinking evenly and whether there are potential risks caused by uneven settlement. 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, to determine whether the retaining structure is tilted, and whether there is structural instability caused by uneven settlement of the foundation. Axial force sensors are installed on the supporting structure to monitor the force of the supporting structure in the vertical direction, that is, the supporting axial force. Monitoring the change in axial force can determine whether the foundation or supporting structure is overloaded or underloaded. Water level sensors are installed around the foundation to monitor the changes in groundwater level in real time to avoid soil loosening or settlement problems caused by changes in groundwater level.

[0041] The data of foundation pit horizontal displacement, foundation pit vertical displacement, retaining structure horizontal displacement, support axial force and groundwater level monitored by different sensors are integrated to form the target monitoring results. 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, a monitoring system based on the Internet of Things technology can be used to centralize the data of various sensors on a single platform to facilitate subsequent analysis and processing.

[0042] Combined with intelligent monitoring equipment such as displacement sensors, static levels, inclinometers, axial force sensors and water level sensors, the target foundation is monitored in all directions. Each sensor is responsible for monitoring different aspects of the foundation, from foundation pit displacement to supporting axial force to groundwater level, forming a multi-dimensional monitoring result. Through the comprehensive analysis of these data, the stability of the foundation can be evaluated in real time, potential structural risks can be discovered in time, and the foundation reinforcement or adjustment engineering plan can be effectively guided to ensure building safety and foundation stability.

[0043] Further, such as Figure 2 As shown, step S3 includes: Step S31: obtaining a predetermined stabilization weight distribution according to the stabilization evaluation mechanism.

[0044] Step S32: 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.

[0045] Step S33: Mark the first stability value to the first foundation point in the target monitoring visual graph to obtain a target stability value visual graph.

[0046] Step S34: judging whether the first stability value is within a predetermined stability threshold value according to the target stability value visual graph.

[0047] Step S35: If not, add the first ground-based point to the repeated monitoring point set.

[0048] Step S36: randomly extracting any ground point in the repeated monitoring point set and recording it as the target point.

[0049] Specifically, the pre-set predetermined stability weight allocation information is read from the stability assessment mechanism, where 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 effects on foundation stability, and the stability weight allocation can reflect the contribution of these data to the final stability assessment. These weights are derived based on engineering experience, theoretical analysis, and data statistics of previous similar projects. For example, the weight of the horizontal displacement of the foundation pit may be 0.3, the weight of the vertical displacement of the foundation pit may be 0.2, the weight of the horizontal displacement of the retaining structure 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.

[0050] After obtaining the predetermined stability weight distribution, multiply each data in the first monitoring information by the corresponding weight, and then add these products to obtain the first stability value corresponding to the first foundation point. The first stability value is a quantitative value of the stability degree of the first foundation point. For example, in the first monitoring information, the horizontal displacement of the foundation pit is 10mm, and the weight is 0.3; the vertical displacement of the foundation pit is 5mm, and the weight is 0.2; the horizontal displacement of the enclosure structure is 8mm, and the weight is 0.2; the supporting axial force is 200kN, and the weight is 0.2; the groundwater level is 3m, 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.

[0051] Find the corresponding first foundation point in the target monitoring visual map, and then mark the calculated first stability value on the point in some way. For example, the stability value can be directly displayed in digital form on the visual map, or the size of the stability value can be indicated by the depth of color (such as the higher the stability value, the lighter the color, indicating better stability), so as to obtain the target stability value visual map. In the specific implementation process, the stability value can be marked by the annotation function or layer property setting function of the drawing software (such as AutoCAD or GIS software), or the text function in the Python matplotlib library can be used to add text annotations or the size of the stability value can be indicated by setting the color map (colormap).

[0052] The predetermined stability threshold is a pre-set value range used to determine whether the stability of the foundation point is qualified. The upper and lower limit values ​​are obtained from the setting of the predetermined stability threshold, and then the first stability value in the target stability value visualization diagram is compared with this threshold range. If the first stability value is within this threshold range, it means that the stability of the point meets the requirements; conversely, if the first stability value is not within this threshold range, there may be a stability risk.

[0053] If the first stability value is not within the predetermined stability threshold, the corresponding first foundation point is added to the set of repeated monitoring points for further stability monitoring. A foundation point is randomly selected from the set of repeated monitoring points and marked as a target point. This can be achieved by using a random number generation function combined with an index operation. 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 is obtained based on this integer.

[0054] Through the above steps, the stability assessment mechanism can conduct a comprehensive analysis of the stability of each foundation point based on the monitoring data, and present the stability value in a visual way to quickly identify potential risk areas. If the stability value of some points is lower than the predetermined threshold, these points will be included in the set of repeated monitoring points for key attention and further monitoring, which can effectively avoid omissions caused by single monitoring data errors or local problems, and improve the accuracy and comprehensiveness of foundation stability monitoring.

[0055] Further, step S31 includes: Step S311: extracting a first foundation data set from a real estate project foundation database.

[0056] Step S312: Recording the first historical stability value of the first historical foundation in the first foundation data set as a dependent variable.

[0057] Step S313: 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.

[0058] Step S314: record the first historical characteristic parameter as an independent variable, and calculate the maximum information coefficient with the dependent variable to obtain a first maximum information coefficient.

[0059] Step S315: Setting the predetermined stable weight distribution based on the first maximum information coefficient.

[0060] Specifically, the real estate engineering foundation database is a database that stores a large amount of real estate engineering foundation-related data, including a variety of monitoring data (such as foundation displacement, axial force, groundwater level, etc.) collected from different time points and different monitoring tools, as well as corresponding stability values, etc. The data set of any foundation (recorded as the first historical foundation) is extracted from the real estate engineering foundation database as the first foundation data set. The first foundation data set covers various monitoring characteristic data (such as foundation displacement, supporting axial force, groundwater level, etc.) of the first historical foundation in different monitoring periods and the corresponding historical stability values ​​of different monitoring periods.

[0061] In the first foundation data set, the first historical stability value of the first historical foundation is determined and defined as the dependent variable. An arbitrary foundation characteristic index is obtained, which can be any one of the foundation characteristics that can be monitored in the intelligent monitoring device (i.e., any one of the foundation characteristics such as foundation pit horizontal displacement, foundation pit vertical displacement, retaining structure horizontal displacement, support axial force and groundwater level). Then, the first historical characteristic parameter corresponding to this arbitrary foundation characteristic index is searched in the first foundation data set. For example, if the arbitrary foundation characteristic index is "foundation pit horizontal displacement", the parameter records related to all foundation pit horizontal displacements of the first historical foundation are searched in the first foundation data set.

[0062] The first historical characteristic parameter found is used as the independent variable, and the maximum information coefficient is calculated with the first historical stable value as the dependent variable to obtain the first maximum information coefficient. The maximum information coefficient calculation traverses each data point in the data set, calculates the mutual information between the two variables, and standardizes it to obtain the maximum information coefficient. Specialized 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 related 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.

[0063] The predetermined stability weight distribution is set according to the calculated first maximum information coefficient. If the first maximum information coefficient is large, it means that the correlation between the corresponding foundation characteristic index and the foundation stability value is strong, then this characteristic index is given a larger weight in the predetermined stability weight distribution; conversely, if the first maximum information coefficient is small, a smaller weight is given. For example, the maximum information coefficient of the horizontal displacement of the foundation pit is 0.8, the maximum information coefficient of the vertical displacement of the foundation pit is 0.6, the maximum information coefficient of the horizontal displacement of the retaining structure is 0.7, the maximum information coefficient of the supporting axial force is 0.9, and the maximum information coefficient of the groundwater level is 0.5. Then the weight of the horizontal displacement of the foundation pit can be set to 0.2, the weight of the vertical displacement of the foundation pit is 0.15, the weight of the horizontal displacement of the retaining structure is 0.2, the weight of the supporting axial force is 0.3, and the weight of the groundwater level is 0.15.

[0064] Through the above steps, the predetermined stability weight distribution can be scientifically set based on historical data. By using the historical data in the real estate engineering foundation database, the relationship between different foundation characteristic indicators and foundation stability values ​​is fully considered, and the strength of this relationship is accurately measured through the maximum information coefficient calculation, avoiding the subjectivity of weight distribution, making the predetermined stability weight distribution more reasonable and accurate, and providing a more reliable basis for subsequent foundation stability assessment.

[0065] Further, step S4 includes: Step S41: Acquire an ultrasonic scanning plan, wherein the ultrasonic scanning plan includes a first plan and a second plan.

[0066] Step S42: 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 the ultrasonic waves through the receiving transducer in the ultrasonic transducer to obtain a first actual scanning signal.

[0067] Step S43: the transmitting transducer transmits ultrasonic waves to the target point based on the second plan, and receives and collects ultrasonic waves through the receiving transducer to obtain a second actual scanning signal.

[0068] Step S44: mapping the first angle and the first level in the first plan with the first signal sound obtained by analyzing the first actual scanning signal to obtain a first mapping relationship.

[0069] Step S45: mapping the second angle and the second level in the second plan with the second signal sound obtained by analyzing the second actual scanning signal to obtain a second mapping relationship.

[0070] Step S46: obtaining the actual scanning signal sound timing according to the first mapping relationship and the second mapping relationship.

[0071] Specifically, the ultrasonic scanning plan is an ultrasonic scanning scheme for the target point, which defines the scanning parameters, such as scanning angle, scanning layer, ultrasonic frequency used, etc. The ultrasonic scanning plan includes the first plan and the second plan, which represent two different scanning schemes with different scanning angles, depths or frequencies to ensure that ultrasonic scanning data is obtained from multiple directions and different depths. The ultrasonic scanning plan is obtained from a pre-stored database or configuration file. These plans are pre-made based on factors such as the type of target foundation, engineering requirements, and previous experience.

[0072] 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 transmit the ultrasonic wave to the target point of the target foundation. The transmitted ultrasonic wave propagates in the medium of the target point, and the receiving transducer receives the reflected ultrasonic wave at a predetermined position, collects, amplifies, filters, and processes the received signal to obtain the first actual scanning signal. This signal contains the reflection information inside the foundation of the target point. For example, in actual operation, the transmitting transducer sets the transmitting angle to 30° and the transmitting frequency to 50kHz according to the first plan. The receiving transducer processes the received signal through a preamplifier, a filter, and other equipment to obtain the first actual scanning signal.

[0073] Similarly, according to the second plan, the parameters of the transmitting transducer are adjusted and ultrasonic waves are transmitted to the target point, and then the receiving transducer receives and collects the ultrasonic waves 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 that the target point is scanned from different angles and conditions. For example, in actual operation, the transmitting transducer sets the transmitting angle to 45° and transmits ultrasonic waves at a frequency of 50kHz according to the second plan.

[0074] The first actual scanning signal is analyzed using signal analysis software to determine the first signal sound time. Then a mapping relationship is established between the first angle and the first level in the first plan and the obtained first signal sound time. For example, different first angle and first level combinations can be used as indexes, and the corresponding first signal sound time can be used as values ​​and stored in a data structure.

[0075] Similarly, the second actual scanning signal is analyzed to obtain the second signal sound time, and then a mapping relationship is established between the second angle and the second level in the second plan and the second signal sound time. The first mapping relationship and the second mapping relationship are combined to determine the actual scanning signal sound time. For example, the signal sound times in the two mapping relationships can be weighted averaged (the weights are determined according to factors such as the importance of the angle and level), or combined and calculated according to a custom fusion rule.

[0076] Through the above steps, ultrasonic scanning is performed on the target points from multiple angles and levels to obtain more comprehensive and accurate internal structure information of the target points. Different scanning plans can cover more detection conditions. By establishing mapping relationships and comprehensively calculating the actual scanning signal sound time, the medium characteristics and defects of the target points can be analyzed more accurately, providing a more detailed and reliable basis for the stability assessment of the foundation.

[0077] Further, step S5 includes: Step S51: obtaining a sound timing comparison deviation between the actual scanning signal sound timing and the predetermined scanning signal sound timing.

[0078] Step S52: establishing a linear deviation equation based on the acoustic-temporal contrast deviation.

[0079] Step S53: According to the predetermined acoustic time deviation inversion strategy, the linear deviation equation and the predetermined foundation slowness model are inverted and iterated to obtain an actual foundation slowness model.

[0080] Step S54: Analyze the actual foundation slowness model to obtain the target image.

[0081] Specifically, due to defects in the internal structure of the foundation (such as cracks, voids), or differences in material uniformity, there is a difference between the actual scanning signal acoustic time of the target foundation measured on site and the predetermined scanning signal acoustic time calculated based on the theoretical sound velocity of the foundation material. The acoustic time comparison deviation between the two is obtained by subtraction, that is, , where t actual is the actual scanning signal sound, t theoretical is the predetermined scanning signal sound time, and Δt is the sound time comparison deviation. This difference reflects the degree of deviation between the actual situation and the expected situation in ultrasonic propagation time, and is an important basis for the subsequent establishment of the deviation equation and inversion iteration.

[0082] The linear deviation equation is established based on the acoustic time contrast deviation to describe the mathematical relationship between the actual situation and the theoretical or expected situation. For each target point, the predetermined scanning signal acoustic time is determined by the sound velocity of the material and the propagation path length. Under ideal conditions, the predetermined scanning signal acoustic time can be calculated by the formula t theoretical =d / v theoretical Calculate, where d is the distance of the propagation path, v theoretical is the theoretical sound speed. The actual scanning signal sound is affected by the change of sound speed of the foundation material. The change of sound speed in the foundation will cause the propagation time of the ultrasonic signal to change. The slower the sound speed, the longer the propagation time. When the sound speed changes in certain areas, it can be measured by v (x,y,z) To represent the local sound velocity at different locations of the foundation. The deviation between the actual propagation time and the theoretical propagation time can be expressed in the following form: In order to simplify the calculation process, it is assumed that the sound velocity inside the foundation changes little, that is, v (x,y,z) Relative to the theoretical speed of sound v theoretical At this time, the acoustic time contrast deviation Δt can be expressed by the linearized approximate relationship: Among them, α is a constant, which indicates the influence of the change of sound speed on the propagation time. For multiple target points, a corresponding sound time contrast deviation equation is established. In order to facilitate subsequent calculations and optimizations, these sound time contrast 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 speed change is represented as a vector Δt', then the linear deviation equation can be written as: Δt'=AΔt'. Among them, A is a matrix containing the geometric parameters and propagation path length of different measurement points. This equation describes the linear relationship between the sound time deviation and the sound speed change. Through this model, the sound speed distribution of the foundation can be further analyzed and calculated, so as to obtain the stability information of the foundation.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] Further, step S53 includes: Step S535: extracting a first inversion strategy from the predetermined acoustic time deviation inversion strategies.

[0087] 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.

[0088] Step S537: extracting a second inversion strategy from the predetermined acoustic time deviation inversion strategy.

[0089] 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.

[0090] Step S539: Compare and analyze the first slowness model and the second slowness model to determine the actual foundation slowness model.

[0091] 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 foundation slowness model, which includes a variety of different inversion strategies, such as algebraic reconstruction technology (ART), conjugate gradient method, etc. The first inversion strategy is read and extracted from the database or configuration file storing the predetermined acoustic time deviation inversion strategy. The linear deviation equation and the predetermined foundation slowness model are used as input data, and the inversion iteration is performed according to the algorithm specified by the first inversion strategy (such as ART or conjugate gradient method). Among them, the predetermined foundation slowness model is an initial foundation model constructed based on previous experimental data, theoretical calculations or basic characteristics of the foundation, which provides a preliminary reference framework, but cannot fully and accurately describe the actual situation of the foundation. Taking the algebraic reconstruction technology (ART) as an example, in each iteration, the slowness parameters in the model are adjusted according to the deviation between the theoretical acoustic time calculated by the current model parameters and the actual measured acoustic time, and the actual foundation slowness distribution is gradually approached. After multiple iterations, the first slowness model is obtained. The specific process is as follows: According to the linear deviation equation, the slowness distribution of the foundation is simplified into a discrete grid, and the entire foundation area is divided into multiple small units, each unit corresponds to a "pixel" value, representing the slowness of the foundation. The propagation time of each ultrasonic signal can be expressed by a mathematical model as follows: ,in, is the propagation time of the ith ultrasonic path, is the slowness of the jth unit (the target of inversion), is the matrix element between the ultrasonic path and the foundation unit. By comparing the deviation between the theoretical value (calculated by the predetermined foundation slowness model) and the actual measured value, the error is obtained: ,in, is the error of the ith path, is the actual scan signal sound actually measured, It is the sound time of the predetermined scanning signal predicted by the current model. The ART algorithm gradually corrects the slowness parameter of each small unit through iteration. In each iteration, the algorithm updates the slowness parameter of each grid unit in turn. The updating process is carried out by minimizing the error. The formula is as follows: , where α is the step size control factor, which determines the magnitude of each update. The update process is performed 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, random, etc. Through multiple rounds of iterations, the errors in the foundation slowness model are gradually corrected, and finally a slowness model that matches the actual propagation time is converged.

[0092] Next, the second inversion strategy is extracted from the predetermined acoustic time deviation inversion strategy, and the linear deviation equation and the predetermined foundation slowness model are used as inputs. The inversion iteration is performed according to the algorithm specified by the second inversion strategy to obtain the second slowness model. For example, the second inversion strategy is an inversion method based on the conjugate gradient method. First, an objective function (least square error) is defined to represent the difference between the predicted propagation time and the actual measurement time, and then given in the iterative process, the purpose is to minimize the error. Next, the gradient of the objective function relative to the foundation slowness, that is, the rate of change of the error, is calculated, 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 it according to its direction, unlike the traditional gradient descent method, the gradient direction is not directly adjusted in reverse, but a "conjugate direction" is selected, that is, a more efficient update is performed by optimizing the direction in each iteration. Through multiple rounds of iterations, the conjugate gradient method can effectively optimize the slowness model of the foundation and finally obtain the second slowness model.

[0093] Compare and analyze the first slowness model and the second slowness model. The comparison can be made from multiple aspects, such as the distribution of slowness values ​​in the model, the stability of the model, and the degree of matching with some known foundation characteristics. 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 specific areas (such as suspected defect areas) can be compared. Based on the comparison results, the final actual foundation slowness model is determined by comprehensive consideration. If the slowness performance of the first slowness model in a certain key area is more consistent with some characteristics in the actual measurement data, and the second slowness model performs better in other areas, then the two models can be weighted fused or one of them can be selected as the actual foundation slowness model.

[0094] Through the above steps, different slowness models are obtained by using multiple inversion strategies, and then the actual foundation slowness model is determined by comparative analysis. This multi-strategy approach can make full use of the advantages of different inversion methods, reduce the errors that may be caused by a single method, and improve the accuracy and reliability of the model, thereby more accurately reflecting the slowness characteristics of the actual foundation and providing a more reliable basis for subsequent analysis of target images and other operations.

[0095] Furthermore, step S53 further includes: Step S531: Acquire a target structure of the target foundation, wherein the target structure includes multiple structural layers.

[0096] Step S532: extracting a first structural layer from the plurality of structural layers, and obtaining a first layer material of the first structural layer.

[0097] Step S533: matching the first ultrasonic slowness coefficient of the first layer of material.

[0098] Step S534: establishing the predetermined foundation slowness model according to the first structural layer and the first ultrasonic slowness coefficient.

[0099] Specifically, the target structural information of the target foundation is obtained by analyzing the preliminary exploration data, design drawings or other relevant information of the target foundation. The structure of the foundation generally consists of multiple layers, and the physical properties of each layer (such as density, elastic modulus, sound speed, etc.) are different. The target structural information includes information about the number of foundation structural layers, the thickness of each layer, and the approximate distribution.

[0100] Extract the first structural layer from the multiple structural layers in the acquired target structure, and then determine the first layer material in the first structural layer. Then, find the first ultrasonic slowness coefficient that matches the first layer material in the material property database or ultrasonic material property manual. This coefficient is based on a large number of experiments or theoretical studies, and reflects the influence of the material on the ultrasonic propagation slowness.

[0101] Traverse multiple structural layers in the target structure, pay attention to extracting the materials corresponding to each structural layer, and then find 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 of these first structural layers (such as thickness, area, and other factors that may affect the ultrasonic propagation path) and the first ultrasonic slowness coefficient, use mathematical modeling software (such as Matlab) to build a predetermined foundation slowness model.

[0102] By establishing a predetermined foundation slowness model, the propagation characteristics of ultrasonic waves in the foundation can be predicted, providing a basic model for subsequent inversion iterations and improving the accuracy and reliability of foundation monitoring.

[0103] In summary, the intelligent monitoring method for foundation stability in real estate projects provided by the embodiments of the present application has the following technical effects: The embodiment of the present application first comprehensively collects foundation status information through multi-dimensional monitoring, and converts the data into intuitive monitoring images by establishing visual graphics, thereby enhancing the ease of use of the data. Secondly, through the stability assessment mechanism and ultrasonic tomography scanning technology, a deep-level detection of the interior of the foundation is carried out, and potential problems inside the foundation can be accurately identified. Finally, the target image is generated using inversion technology, and combined with multi-dimensional image feature analysis, the stability index of the foundation is obtained to fully reflect the stability of the foundation. Overall, the embodiment of the present application overcomes the limitation that the prior art can only monitor the surface or part of the foundation, improves the accuracy and comprehensiveness of foundation stability monitoring, and provides a more accurate and reliable foundation stability assessment method for the foundation safety of real estate projects.

[0104] Embodiment 2, as Figure 3As shown, based on the same inventive concept as the above-mentioned embodiment 1, the embodiment of the present application provides an intelligent monitoring system for foundation stability in real estate engineering, the system comprising: The multi-dimensional monitoring module 10 is used 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 the target monitoring result.

[0105] The monitoring visualization module 20 is used to extract the first monitoring information corresponding to the first ground-based point in the target monitoring result, and establish a target monitoring visualization diagram according to a first corresponding relationship between the first ground-based point and the first monitoring information.

[0106] The stability evaluation module 30 is used to introduce a stability evaluation mechanism to perform stability rendering analysis on the target monitoring visual graph and determine the target point position.

[0107] The ultrasonic scanning module 40 is used to activate the ultrasonic transducer to perform ultrasonic tomography scanning detection on the target point to obtain actual scanning signal sound time.

[0108] The target image acquisition module 50 is used to read the predetermined acoustic time deviation inversion strategy, and compare and invert the actual scanning signal acoustic time with the predetermined scanning signal acoustic time according to the predetermined acoustic time deviation inversion strategy to obtain a target image.

[0109] The stability index determination module 60 is used to obtain the actual stability index of the target foundation according to the multi-dimensional image feature analysis of the target image, wherein the actual stability index is used to characterize the foundation stability of the target foundation.

[0110] Furthermore, the intelligent monitoring device includes at least 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 also used to perform the following steps: The horizontal displacement of the foundation pit of the target foundation is obtained by monitoring with a displacement sensor; the vertical displacement of the foundation pit of the target foundation is obtained by monitoring with a static level; the horizontal displacement of the retaining structure of the target foundation is obtained by monitoring with an inclinometer; the supporting axial force of the target foundation is obtained by monitoring with an axial force sensor; the groundwater level of the target foundation is obtained by monitoring with 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 supporting axial force and the groundwater level constitute the target monitoring result.

[0111] Furthermore, the stability assessment module 30 of the embodiment of the present application is also used to perform the following steps: According to the stability assessment mechanism, a predetermined stability weight distribution is obtained; in combination with the predetermined stability weight distribution, a weighted analysis is performed on the first monitoring information to obtain a first stability value corresponding to the first foundation point; the first stability value is marked to the first foundation point in the target monitoring visual map to obtain a target stability value visual map; according to the target stability value visual map, it is determined whether the first stability value is within a predetermined stability threshold; if not, the first foundation point is added to a set of repeated monitoring points; any foundation point in the set of repeated monitoring points is randomly extracted and recorded as the target point.

[0112] Furthermore, the stability assessment module 30 of the embodiment of the present application is also used to perform the following steps: A first foundation data set is extracted from a real estate engineering foundation database; a first historical stability value of a first historical foundation in the first foundation data set is recorded as a dependent variable; an arbitrary foundation characteristic index is obtained, and a first historical characteristic parameter corresponding to the arbitrary foundation characteristic index is matched in the first foundation data set; the first historical characteristic parameter is recorded as an independent variable, and a maximum information coefficient is calculated with the dependent variable to obtain a first maximum information coefficient; and the predetermined stability weight distribution is set based on the first maximum information coefficient.

[0113] Furthermore, the ultrasonic scanning module 40 in the embodiment of the present application is also used to perform the following steps: Acquire an ultrasonic scanning plan, the ultrasonic scanning plan including 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 the ultrasonic waves through the receiving transducer in the ultrasonic transducer to obtain a first actual scanning signal; the transmitting transducer transmits ultrasonic waves to the target point based on the second plan, and receives and collects the ultrasonic waves through the receiving transducer to obtain a second actual scanning signal; establish a mapping between the first angle and the first level in the first plan and the first signal sound 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 level in the second plan and the second signal sound time obtained by analyzing the second actual scanning signal to obtain a second mapping relationship; obtain the actual scanning signal sound time according to the first mapping relationship and the second mapping relationship.

[0114] Furthermore, the target image acquisition module 50 of the embodiment of the present application is also used to perform the following steps: Acquire the acoustic time contrast deviation between the actual scanning signal acoustic time and the predetermined scanning signal acoustic time; establish a linear deviation equation based on the acoustic time contrast deviation; perform inversion iteration on the linear deviation equation and the predetermined foundation slowness model according to the predetermined acoustic time deviation inversion strategy to obtain the actual foundation slowness model; analyze the actual foundation slowness model to obtain the target image.

[0115] Furthermore, the target image acquisition module 50 of the embodiment of the present application is also used to perform the following steps: Extract the first inversion strategy from 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 a first slowness model; extract the second inversion strategy from 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 a second slowness model; compare and analyze the first slowness model and the second slowness model to determine the actual foundation slowness model.

[0116] Furthermore, the target image acquisition module 50 of the embodiment of the present application is also used to perform the following steps: Acquire a target structure of the target foundation, wherein the target structure includes multiple structural layers; extract a first structural layer from the multiple structural layers, and acquire a first layer material of the first structural layer; match a first ultrasonic slowness coefficient of the first layer material; and establish the predetermined foundation slowness model according to the first structural layer and the first ultrasonic slowness coefficient.

[0117] Through the above-mentioned detailed description of the intelligent monitoring method for foundation stability in real estate projects, those skilled in the art can clearly understand that the intelligent monitoring system for foundation stability in real estate projects in this embodiment is used. For the system disclosed in Example 2, since it corresponds to the method disclosed in Example 1 and has corresponding functional modules and beneficial effects, the relevant parts can be referred to the description of the method part.

[0118] 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 apparent to those skilled in the art, and the general principles defined herein may 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 conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent monitoring method for foundation stability in real estate engineering, characterized in that: include: Activate the intelligent monitoring device, and use the intelligent monitoring device to perform multi-dimensional monitoring of the target foundation in the target real estate project to obtain target monitoring results; Extracting first monitoring information corresponding to a first ground-based point in the target monitoring result, and establishing a target monitoring visual graph according to a first corresponding relationship between the first ground-based point and the first monitoring information; A stability evaluation mechanism is introduced to perform stability rendering analysis on the target monitoring visual graph and determine the target point position; Activate the ultrasonic transducer to perform ultrasonic tomography scanning and detection on the target point to obtain an actual scanning signal sound; Reading a predetermined acoustic time deviation inversion strategy, and comparing and inverting the actual scanning signal acoustic time with the predetermined scanning signal acoustic time according to the predetermined acoustic time deviation inversion strategy to obtain a target image; An actual stability index of the target foundation is obtained according to multi-dimensional image feature analysis of the target image, wherein the actual stability index is used to characterize the foundation stability of the target foundation.

2. The intelligent monitoring method for foundation stability in real estate engineering according to claim 1 is characterized in that: The intelligent monitoring device includes at least a displacement sensor, a static level, an inclinometer, an axial force sensor and a water level sensor. The intelligent monitoring device is activated, and the target foundation in the target real estate project is multi-dimensionally monitored by the intelligent monitoring device to obtain target monitoring results, including: The horizontal displacement of the foundation pit of the target foundation is obtained by monitoring the displacement sensor; The vertical displacement of the foundation pit of the target foundation is obtained by monitoring with a static level; The horizontal displacement of the retaining structure of the target foundation is obtained by monitoring with an inclinometer; The supporting axial force of the target foundation is obtained by monitoring the axial force sensor; Monitoring the underground water level of the target foundation by 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 supporting axial force and the groundwater level constitute the target monitoring result.

3. The intelligent monitoring method for foundation stability in real estate engineering according to claim 1 is characterized in that: A stability evaluation mechanism is introduced to perform stability rendering analysis on the target monitoring visual graph and determine the target point position, including: Obtaining a predetermined stabilization weight allocation according to the stabilization assessment mechanism; Performing a 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 visual graph to obtain a target stability value visual graph; Determining, according to the target stability value visual graph, whether the first stability value is within a predetermined stability threshold; If not, adding the first ground-based point to the repeated monitoring point set; Randomly extract any ground point in the repeated monitoring point set and record it as the target point.

4. The method for intelligent monitoring of foundation stability in real estate engineering according to claim 3 is characterized in that: Obtaining a predetermined stabilization weight distribution according to the stabilization evaluation mechanism includes: Extracting a first foundation data set from a real estate engineering foundation database; recording a first historical stability value of a first historical foundation in the first foundation data set as a dependent variable; Obtaining any foundation characteristic index, and matching a first historical characteristic parameter corresponding to the any foundation characteristic index in the first foundation data set; Record the first historical characteristic parameter as an independent variable, and calculate the maximum information coefficient with the dependent variable to obtain a first maximum information coefficient; The predetermined stable weight assignment is set based on the first maximum information coefficient.

5. The method for intelligent monitoring of foundation stability in real estate engineering according to claim 1 is characterized in that: Activating the ultrasonic transducer to perform ultrasonic tomography scanning and detection on the target point to obtain an actual scanning signal sound includes: Acquire an ultrasonic scanning plan, wherein 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 the ultrasonic waves through the receiving transducer in the ultrasonic transducer to obtain a first actual scanning signal; The transmitting transducer transmits ultrasonic waves to the target point based on the second plan, and receives and collects ultrasonic waves through the receiving transducer to obtain a second actual scanning signal; Establishing a mapping between the first angle and the first level in the first plan and the first signal sound obtained by analyzing the first actual scanning signal to obtain a first mapping relationship; Establishing a mapping between the second angle and the second level in the second plan and the second signal sound obtained by analyzing the second actual scanning signal to obtain a second mapping relationship; The actual scanning signal sound timing is obtained according to the first mapping relationship and the second mapping relationship.

6. The intelligent monitoring method for foundation stability in real estate engineering according to claim 1 is characterized in that: Reading a predetermined acoustic time deviation inversion strategy, and comparing and inverting the actual scanning signal acoustic time with the predetermined scanning signal acoustic time according to the predetermined acoustic time deviation inversion strategy to obtain a target image, including: Obtaining a sound time comparison deviation between the actual scanning signal sound time and the predetermined scanning signal sound time; Establishing a linear deviation equation based on the acoustic-temporal contrast deviation; According to the predetermined acoustic time deviation inversion strategy, the linear deviation equation and the predetermined foundation slowness model are inverted and iterated to obtain an actual foundation slowness model; The target image is obtained by analyzing the actual foundation slowness model.

7. The method for intelligent monitoring of foundation stability in real estate engineering according to claim 6 is characterized in that: According to the predetermined acoustic time deviation inversion strategy, the linear deviation equation and the predetermined foundation slowness model are inverted and iterated to obtain an actual foundation slowness model, including: extracting a first inversion strategy from among the predetermined acoustic time deviation inversion strategies; Inverting and iterating the linear deviation equation and the predetermined foundation slowness model according to the first inversion strategy to obtain a first slowness model; extracting a second inversion strategy from the predetermined acoustic time deviation inversion strategy; Inverting and iterating the linear deviation equation and the predetermined foundation slowness model according to the second inversion strategy to obtain a second slowness model; The first slowness model and the second slowness model are compared and analyzed to determine the actual foundation slowness model.

8. The intelligent monitoring method for foundation stability in real estate engineering according to claim 7 is characterized in that: According to the predetermined acoustic time deviation inversion strategy, the linear deviation equation and the predetermined foundation slowness model are inverted and iterated to obtain an actual foundation slowness model, including: Acquire a target structure of the target foundation, wherein the target structure includes a plurality of structural layers; Extracting a first structural layer from the plurality of structural layers, and acquiring a first layer material of the first structural layer; a first ultrasonic slowness coefficient matching the first layer of material; The predetermined foundation slowness model is established according to the first structural layer and the first ultrasonic slowness coefficient.

9. Intelligent monitoring system for foundation stability in real estate projects, characterized by: The system is used to execute the intelligent monitoring method for foundation stability in real estate engineering according to any one of claims 1 to 8, comprising: A multi-dimensional monitoring module is used to activate the intelligent monitoring device and perform multi-dimensional monitoring of the target foundation in the target real estate project through the intelligent monitoring device to obtain the target monitoring result; A monitoring visualization module, used for extracting first monitoring information corresponding to a first ground-based point in the target monitoring result, and establishing a target monitoring visualization graph according to a first corresponding relationship between the first ground-based point and the first monitoring information; A stability evaluation module, used to introduce a stability evaluation mechanism to perform stability rendering analysis on the target monitoring visual graph and determine the target point position; An ultrasonic scanning module is used to activate the ultrasonic transducer to perform ultrasonic tomographic scanning detection on the target point to obtain an actual scanning signal sound; A target image acquisition module, used for reading a predetermined acoustic time deviation inversion strategy, and comparing and inverting the actual scanning signal acoustic time with the predetermined scanning signal acoustic time according to the predetermined acoustic time deviation inversion strategy to obtain a target image; The stability index determination module is used to obtain the actual stability index of the target foundation according to the multi-dimensional image feature analysis of the target image, wherein the actual stability index is used to characterize the foundation stability of the target foundation.

Images