Foundation settlement prediction system and method based on big data analysis

Abstract

The invention relates to the technical field of foundation settlement monitoring, in particular to a foundation settlement prediction system and method based on big data analysis, and the method comprises the steps: analyzing the adjustment mechanism of salinity on the rising height of capillary water based on salinity concentration distribution data, and evaluating the moisture evaporation effect of a foundation surface layer in combination with surface temperature distribution data; based on the plant root system distribution data and the salinity concentration distribution data, root system growth and water absorption rate change rules in the salt stress environment are analyzed, and the local absorption effect of the plant root system on soil moisture is evaluated; the moisture migration uniformity of the foundation soil body is evaluated by integrating the moisture evaporation effect and the local absorption effect, and foundation non-uniform settlement risk early warning is carried out. According to the method, the accuracy and the reliability of foundation settlement prediction are effectively improved by quantitatively evaluating the moisture migration uniformity of the foundation soil body.

Description

Technical Field

[0001] This application relates to the field of foundation settlement monitoring technology, and in particular to a foundation settlement prediction system and method based on big data analysis. Background Technology

[0002] Foundation settlement is a common and critical geomechanical problem in geotechnical engineering, building engineering, and urban infrastructure construction. Predicting foundation settlement is of great significance for ensuring the structural safety of buildings and infrastructure, extending their service life, and reducing operation and maintenance costs.

[0003] In actual engineering environments, the distribution of water fields within soil is often influenced by a combination of factors. For example, regional surface temperature directly affects the evaporation intensity of the soil surface. Changes in soil salt concentration not only regulate capillary rise height and alter the capillary water supply capacity of groundwater, but also have a cumulative impact on the pore structure and permeability of the foundation soil. At the same time, the absorption of soil water by the widely distributed vegetation roots in the region cannot be ignored. Especially under salt stress, the distribution of roots and their water absorption rate will show significant spatial differences, leading to local imbalances in water migration within the soil, thereby causing non-uniform settlement of the foundation.

[0004] Existing methods for predicting foundation settlement fail to fully consider the combined effects of the inhibitory effect of salt on capillary water supply, the enhancing effect of surface temperature on evaporation, and the local extraction of soil moisture by plant roots under different salt stress conditions. This makes it difficult to accurately reveal the spatiotemporal distribution characteristics of the water field under multiple influences and its potential impact on non-uniform foundation settlement, resulting in insufficient accuracy and timeliness in early warning of non-uniform foundation settlement risks. Summary of the Invention

[0005] To overcome the shortcomings and deficiencies of existing technologies, this application provides a foundation settlement prediction system and method based on big data analysis, which effectively improves the accuracy and reliability of foundation settlement prediction by quantitatively evaluating the uniformity of moisture migration in the foundation soil.

[0006] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application provides a method for predicting foundation settlement based on big data analysis, comprising the following steps: Acquire data on the distribution of salt concentration in the foundation soil, the distribution of plant roots, and the distribution of surface temperature within the monitoring area; Based on the analysis of salt concentration distribution data, the mechanism by which salt regulates the height of capillary water rise was analyzed, and the effect of water evaporation on the ground surface was evaluated in combination with surface temperature distribution data. Based on data on plant root distribution and salt concentration distribution, we analyzed the changes in root growth and water absorption rate under salt stress to assess the local water absorption effect of plant roots on soil. The uniformity of water migration in the foundation soil is assessed by combining the effects of water evaporation and local absorption, and the risk of non-uniform settlement of the foundation is given early warning.

[0007] Optionally, the assessment of the moisture evaporation effect on the foundation surface includes: Discrete salinity concentration distribution data and surface temperature distribution data are transformed into continuous salinity concentration distribution matrices and surface temperature distribution matrices using a spatial interpolation algorithm. The inhibitory effect of salt concentration on capillary rise in foundation soil was analyzed, and the capillary supply coefficient was determined by combining the salt concentration distribution matrix. The capillary supply coefficient is the ratio of the capillary rise height under the influence of salt concentration to the baseline capillary rise height. The influence of surface temperature on the evaporation potential of surface moisture in foundation soil was analyzed, and the evaporation coefficient under the influence of surface temperature was determined by combining the surface temperature distribution matrix. The product of the capillary water supply coefficient and the water evaporation coefficient is used as the water evaporation effect coefficient of the foundation soil surface. The water evaporation effect coefficient is used to quantify the inhibitory effect of salt on capillary water rise and the enhancing effect of surface temperature on evaporation rate.

[0008] Optionally, the formula for calculating the water evaporation coefficient is: ; In the formula Indicates the basic background evaporation coefficient. Indicates coordinates within the monitoring area The surface temperature at that location. This indicates the threshold temperature at which evaporation begins. This represents the average daily surface temperature difference within the monitoring area throughout the year. Indicates the temperature sensitivity coefficient. Indicates coordinates within the monitoring area The water evaporation coefficient under the influence of surface temperature.

[0009] Optionally, the assessment of the local water uptake effect of plant roots on soil includes: Plant root distribution data are converted into a plant root density distribution matrix and the plant root density coefficient per unit soil volume at different locations is determined. The plant root density coefficient is the ratio of the plant root density per unit soil volume at different locations to the maximum plant root density per unit soil volume in the monitoring area. Analyze the degree to which root growth is inhibited by salt and determine the root growth capacity coefficient under salt stress; The effect of salt concentration on the water absorption rate of plant roots was analyzed and the root water absorption capacity coefficient under salt stress was determined. The root water absorption capacity coefficient is the ratio of the water absorption rate of plant roots at different salt concentrations to the baseline water absorption rate. The product of the plant root density coefficient, root growth capacity coefficient, and root water absorption capacity coefficient is used as the local absorption effect coefficient of the plant root system. The local absorption effect coefficient is used to evaluate the local absorption effect of plant roots on soil water under salt stress.

[0010] Optionally, the formula for calculating the root growth capacity coefficient is: ; In the formula Indicates coordinates within the monitoring area The salt concentration at that location. This represents the salt concentration at which the root growth rate is reduced to 50%. This represents the salt stress sensitivity coefficient. Indicates coordinates within the monitoring area The root growth capacity coefficient at the location.

[0011] Optionally, the assessment of the uniformity of moisture migration in the foundation soil and the early warning of non-uniform foundation settlement risk include: The water evaporation effect coefficient and the local absorption effect coefficient were obtained and weighted summed to obtain the water migration coefficient per unit soil at different locations within the monitoring area. The regional mean and standard deviation of the water migration coefficient within the statistical monitoring area are used to determine the water migration uniformity coefficient, which is 1 minus the ratio of the regional standard deviation to the regional mean. When the uniformity coefficient of moisture migration is less than or equal to the preset uniformity threshold of moisture migration, a risk warning for non-uniform foundation settlement is issued; when the uniformity coefficient of moisture migration is greater than the preset uniformity threshold of moisture migration, a risk warning for non-uniform foundation settlement is not issued.

[0012] Secondly, this application provides a foundation settlement prediction system based on big data analysis, including: The data acquisition module is used to acquire data on the distribution of salt concentration in the foundation soil, the distribution of plant roots, and the distribution of surface temperature within the monitoring area. The evaporation effect assessment module is used to analyze the regulation mechanism of salt on capillary water rise height based on salt concentration distribution data and to assess the water evaporation effect on the ground surface in combination with surface temperature distribution data. The absorption effect assessment module is used to analyze the root growth and water absorption rate changes under salt stress based on plant root distribution data and salt concentration distribution data, and to assess the local absorption effect of plant roots on soil moisture. The risk warning module is used to assess the uniformity of water migration in the foundation soil by comprehensively considering the water evaporation effect and the local absorption effect, and to provide early warning of the risk of non-uniform foundation settlement.

[0013] Thirdly, this application provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a foundation settlement prediction method based on big data analysis by calling the computer program stored in the memory.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a foundation settlement prediction method based on big data analysis.

[0015] Compared with the prior art, this application has the following advantages and beneficial effects: This application effectively reveals the spatiotemporal distribution pattern of the water field under the combined action of multiple environmental factors by coupling analysis of the inhibitory effect of salt on capillary water rise, the enhancing effect of temperature on soil moisture evaporation, and the local absorption effect of plant roots on soil moisture under salt stress. It can effectively assess the uniformity of soil moisture migration and identify non-uniform settlement risks in a timely manner, significantly improving the accuracy and reliability of foundation settlement prediction. Attached Figure Description

[0016] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process of the foundation settlement prediction method based on big data analysis provided in the embodiments of this application; Figure 2 This is a schematic diagram of the foundation settlement prediction system based on big data analysis provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0018] Please see Figure 1 , Figure 1 This is a schematic diagram of the overall process of the foundation settlement prediction method based on big data analysis provided in the embodiments of this application, which specifically includes the following steps: S110: Acquire data on the distribution of salt concentration in the foundation soil, the distribution of plant roots, and the distribution of surface temperature within the monitoring area. The salt concentration distribution data can be obtained through field sampling (setting up soil pore water sampling points or collecting soil samples through profile drilling) or in-situ soil salt sensors. The plant root distribution data can be obtained through non-destructive detection of shallow root structures using ground-penetrating radar (GPR), geoelectric resistance, or root imaging equipment. The surface temperature distribution data can be obtained through a surface temperature sensor array.

[0019] S120: Based on salt concentration distribution data, analyze the regulation mechanism of salt on capillary water rise height and combine it with surface temperature distribution data to evaluate the water evaporation effect on the foundation surface; By analyzing the mechanism by which salt regulates capillary water rise, the inhibitory effect of salt concentration on the upward transport capacity of water in foundation soil can be quantified, thereby accurately assessing the sustainability of groundwater supply to the surface layer. Simultaneously, by combining surface temperature distribution data to assess the evaporation potential and intensity of water on the foundation surface, it helps to reveal the spatial differences and dynamic processes of water dissipation from the soil interior to the atmosphere. Furthermore, by accurately understanding the dynamic distribution and uniformity of the moisture field, the risk of non-uniform settlement caused by local stress redistribution can be predicted, thus providing support for accurate prediction and risk warning of foundation settlement. The assessment of the water evaporation effect on the foundation surface includes: Discrete salinity concentration distribution data and surface temperature distribution data are transformed into continuous salinity concentration distribution matrices and surface temperature distribution matrices using a spatial interpolation algorithm. The inhibitory effect of salt concentration on capillary rise in foundation soil was analyzed, and the capillary supply coefficient was determined by combining the salt concentration distribution matrix. The capillary supply coefficient is the ratio of the capillary rise height under the influence of salt concentration to the baseline capillary rise height. In the embodiments of this application, the formula for calculating the capillary rise height under the influence of salt concentration can be: ; In the formula This represents the surface tension of pure water, specifically the water-air interfacial tension under saline-free conditions. It determines the adhesion and capillary action of water within the pores of the foundation soil. This represents the moderating coefficient of salt on surface tension, used to characterize the degree of influence of salt concentration on the water-air interfacial tension. This indicates the salt concentration, specifically the salt concentration of the foundation soil at the target location. The higher the salt concentration, the more significant the inhibition of capillary rise. This represents the equivalent surface tension of water under the influence of salt, reflecting the inhibitory effect of salt on the capillary adhesion of water and preventing overestimation of capillary water replenishment capacity. The contact angle between water and the surface of soil particles determines the adhesion state of the water film in the pores of the soil. Used to describe the contact angle effect, the smaller the contact angle, the more attractive water is to soil, and thus the stronger the capillary force. Used to describe capillary driving force This represents the density of pure water, that is, the density of water without salt. This represents the coefficient by which salt affects water density. Salt dissolution increases the solution density, increases the weight of the liquid column, and thus reduces the capillary rise height. This indicates the density of the solution under the influence of salt. This represents the acceleration due to gravity, used to balance the weight of the water column. This represents the equivalent radius of pores, that is, the equivalent radius of typical pores in the foundation soil. The finer the particles and the smaller the pores, the greater the capillary rise. Used to describe the resistance of a liquid column by its own weight. This indicates that the salt concentration is The capillary rise height is quantified by the ratio of the capillary driving force to the resistance of the liquid column's own weight. It is used to describe the maximum height that water in the foundation soil can migrate upwards by capillary action under the influence of salt concentration. The greater the capillary rise height, the more water the foundation soil can provide to the surface layer. The influence of surface temperature on the evaporation potential of surface moisture in foundation soil was analyzed, and the evaporation coefficient under the influence of surface temperature was determined by combining the surface temperature distribution matrix. The formula for calculating the evaporation coefficient is as follows: ; In the formula This represents the baseline background evaporation coefficient, used to calibrate the evaporation potential under reference conditions. It indicates the minimum background evaporation that still exists at the surface when the surface temperature is below the evaporation initiation temperature threshold. Indicates coordinates within the monitoring area The surface temperature at that location. This represents the threshold temperature at which evaporation begins, i.e., the threshold temperature at which evaporation becomes active. It needs to be selected in conjunction with the region's annual average meteorological conditions and can be obtained through experimental observation. This represents the temperature increment exceeding the evaporation initiation temperature threshold. This represents the annual average daily surface temperature difference within the monitoring area, used to make the temperature increment dimensionless. This represents the proportion of the temperature increment to the annual average daily surface temperature difference, used to convert absolute temperature increments into relative temperature increments, making them independent of the absolute temperature magnitude. The temperature sensitivity coefficient is used to characterize the sensitivity of evaporation potential growth to temperature. It can be obtained by fitting historical meteorological and evaporation data. Since soil moisture evaporation potential is strongly correlated with the saturated vapor pressure in the air, and the saturated pressure increases exponentially with temperature, an exponential function is used to describe the nonlinear change in evaporation potential. Indicates coordinates within the monitoring area The evaporation coefficient of water under the influence of surface temperature is used to characterize the relative evaporation potential of a location driven by temperature. The larger the value of the evaporation coefficient, the higher the evaporation potential. The product of the capillary water supply coefficient and the water evaporation coefficient is used as the water evaporation effect coefficient of the foundation soil surface. The water evaporation effect coefficient is used to quantify the inhibitory effect of salt on capillary water rise and the enhancing effect of surface temperature on evaporation rate.

[0020] S130: Based on plant root distribution data and salt concentration distribution data, analyze the changes in root growth and water absorption rate under salt stress, and evaluate the local absorption effect of plant roots on soil moisture. The localized water uptake effect of plant roots on foundation soil refers to the non-uniform extraction of water from the surrounding soil by plant roots at different spatial locations, influenced by factors such as root density, growth capacity, and water absorption rate. This is particularly true under salt stress, where root growth is inhibited by salt, water absorption capacity is weakened, or distribution changes occur, directly leading to differences in the horizontal and vertical migration of water within the soil. By analyzing the changes in root growth and water absorption rate under salt stress, the actual impact of roots on soil moisture in different regions can be quantified, revealing the spatiotemporal uneven distribution of water due to localized uptake. The localized water uptake effect of plant roots on soil moisture can be assessed, including: Plant root distribution data are converted into a plant root density distribution matrix and the plant root density coefficient per unit soil volume at different locations is determined. The plant root density coefficient is the ratio of the plant root density per unit soil volume at different locations to the maximum plant root density per unit soil volume in the monitoring area. The degree of root growth inhibition by salt was analyzed, and the root growth capacity coefficient under salt stress was determined. The formula for calculating the root growth capacity coefficient is as follows: ; In the formula Indicates coordinates within the monitoring area The salt concentration at that location. This represents the salt concentration at which root growth is partially inhibited, i.e., the salt concentration at which the root growth rate drops to 50%. It can be obtained by querying a vegetation salt tolerance database. This represents the relative magnitude of salt concentration with respect to the root growth half-inhibition salt threshold, and is used to standardize the intensity of salt stress. The salt stress sensitivity coefficient reflects the degree to which plants are sensitive to salt suppression and can be obtained through greenhouse experiments. Indicates coordinates within the monitoring area The root growth capacity coefficient at a given location represents the proportion of root growth capacity retained at that location under the required salt concentration relative to the normal state. hour, This characterizes the root system growing at a normal rate under salt-free stress conditions. The effect of salt concentration on the water absorption rate of plant roots was analyzed and the root water absorption capacity coefficient under salt stress was determined. The root water absorption capacity coefficient is the ratio of the water absorption rate of plant roots at different salt concentrations to the baseline water absorption rate. The product of the plant root density coefficient, root growth capacity coefficient, and root water absorption capacity coefficient is used as the local absorption effect coefficient of the plant root system. The local absorption effect coefficient is used to evaluate the local absorption effect of plant roots on soil water under salt stress.

[0021] S140: Evaluate the uniformity of water migration in foundation soil by combining the effects of water evaporation and local absorption, and provide early warning of the risk of non-uniform foundation settlement.

[0022] The evaporation effect and local absorption effect jointly determine the dynamic migration pattern of water within the foundation soil. The evaporation effect reflects the intensity and spatial distribution differences of surface water loss, while the local absorption effect reveals the non-uniform extraction of soil water by plant roots in different areas. Both can affect the spatiotemporal distribution uniformity of water in the soil. Calculating the water migration coefficient can effectively quantify the intensity of water migration at different locations within the soil. Furthermore, calculating the water migration uniformity coefficient characterizes the consistency of the water field distribution throughout the monitoring area. Lower water migration uniformity indicates greater differences in soil moisture distribution, making localized areas prone to non-uniform shrinkage and stress concentration, thus inducing uneven foundation settlement. Assessing the water migration uniformity of foundation soil and providing early warning of uneven foundation settlement risks includes: The water evaporation effect coefficient and the local absorption effect coefficient were obtained and weighted summed to obtain the water migration coefficient per unit soil at different locations within the monitoring area. The regional mean and standard deviation of the water migration coefficient within the statistical monitoring area are used to determine the water migration uniformity coefficient, which is 1 minus the ratio of the regional standard deviation to the regional mean. When the uniformity coefficient of moisture migration is less than or equal to the preset uniformity threshold of moisture migration, a risk warning for non-uniform foundation settlement is issued; when the uniformity coefficient of moisture migration is greater than the preset uniformity threshold of moisture migration, a risk warning for non-uniform foundation settlement is not issued.

[0023] In this embodiment of the application, the determination of parameters such as weighting weights and preset water migration uniformity thresholds can be achieved by: constructing a dataset by acquiring salt concentration distribution data, plant root distribution data, and surface temperature distribution data; substituting these data into the dataset to calculate the water migration coefficient and water migration uniformity coefficient; simultaneously acquiring expert judgments on the water migration effect and water migration uniformity; importing the calculated water migration coefficient, water migration uniformity coefficient, and judgment results into fitting software; and outputting the weighting weights and preset water migration uniformity thresholds that meet the maximum judgment accuracy.

[0024] Please see Figure 2 , Figure 2 This is a schematic diagram of the foundation settlement prediction system based on big data analysis provided in this application embodiment. The foundation settlement prediction system based on big data analysis includes: Data acquisition module 210 is used to acquire data on the distribution of salt concentration in the foundation soil, the distribution of plant roots, and the distribution of surface temperature in the monitoring area. Evaporation effect assessment module 220 is used to analyze the regulation mechanism of salt on capillary water rise height based on salt concentration distribution data and to assess the water evaporation effect of the foundation surface layer in combination with surface temperature distribution data. The absorption effect assessment module 230 is used to analyze the root growth and water absorption rate changes under salt stress based on plant root distribution data and salt concentration distribution data, and to assess the local absorption effect of plant roots on soil water. The risk warning module 240 is used to assess the uniformity of water migration in the foundation soil by comprehensively considering the water evaporation effect and the local absorption effect, and to provide early warning of the risk of non-uniform foundation settlement.

[0025] In this embodiment, the evaporation effect assessment module 220 is used to analyze the regulation mechanism of salt on capillary water rise height based on salt concentration distribution data and to assess the water evaporation effect on the foundation surface in conjunction with surface temperature distribution data, including: Discrete salinity concentration distribution data and surface temperature distribution data are transformed into continuous salinity concentration distribution matrices and surface temperature distribution matrices using a spatial interpolation algorithm. The inhibitory effect of salt concentration on capillary rise in foundation soil was analyzed, and the capillary supply coefficient was determined by combining the salt concentration distribution matrix. The capillary supply coefficient is the ratio of the capillary rise height under the influence of salt concentration to the baseline capillary rise height. The influence of surface temperature on the evaporation potential of surface moisture in foundation soil was analyzed, and the evaporation coefficient under the influence of surface temperature was determined by combining the surface temperature distribution matrix. The product of the capillary water supply coefficient and the water evaporation coefficient is used as the water evaporation effect coefficient of the foundation soil surface. The water evaporation effect coefficient is used to quantify the inhibitory effect of salt on capillary water rise and the enhancing effect of surface temperature on evaporation rate.

[0026] In this embodiment, the absorption effect assessment module 230 is used to analyze the root growth and water absorption rate changes under salt stress based on plant root distribution data and salt concentration distribution data, and to assess the local absorption effect of plant roots on soil moisture, including: Plant root distribution data are converted into a plant root density distribution matrix and the plant root density coefficient per unit soil volume at different locations is determined. The plant root density coefficient is the ratio of the plant root density per unit soil volume at different locations to the maximum plant root density per unit soil volume in the monitoring area. Analyze the degree to which root growth is inhibited by salt and determine the root growth capacity coefficient under salt stress; The effect of salt concentration on the water absorption rate of plant roots was analyzed and the root water absorption capacity coefficient under salt stress was determined. The root water absorption capacity coefficient is the ratio of the water absorption rate of plant roots at different salt concentrations to the baseline water absorption rate. The product of the plant root density coefficient, root growth capacity coefficient, and root water absorption capacity coefficient is used as the local absorption effect coefficient of the plant root system. The local absorption effect coefficient is used to evaluate the local absorption effect of plant roots on soil water under salt stress.

[0027] In this embodiment, the risk warning module 240 is used to comprehensively assess the uniformity of water migration in the foundation soil and to provide early warning of non-uniform foundation settlement risk, including: The water evaporation effect coefficient and the local absorption effect coefficient were obtained and weighted summed to obtain the water migration coefficient per unit soil at different locations within the monitoring area. The regional mean and standard deviation of the water migration coefficient within the statistical monitoring area are used to determine the water migration uniformity coefficient, which is 1 minus the ratio of the regional standard deviation to the regional mean. When the uniformity coefficient of moisture migration is less than or equal to the preset uniformity threshold of moisture migration, a risk warning for non-uniform foundation settlement is issued; when the uniformity coefficient of moisture migration is greater than the preset uniformity threshold of moisture migration, a risk warning for non-uniform foundation settlement is not issued.

[0028] The parameters and steps for implementing the corresponding functions of each unit module in the foundation settlement prediction system based on big data analysis described above can be referred to the parameters and steps in the embodiments of the foundation settlement prediction method based on big data analysis above, and will not be repeated here.

[0029] Please refer to Figure 3The present invention also provides an electronic device 300, including a memory 310, a processor 320, and a communication bus 330; the memory 310 and the processor 320 are connected via the communication bus 330. The memory 310 stores a foundation settlement prediction method based on big data analysis that can be loaded and executed by the processor 320 as provided in the above embodiments.

[0030] The memory 310 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the foundation settlement prediction method based on big data analysis provided in the above embodiments, etc. The data storage area may store data involved in the foundation settlement prediction method based on big data analysis provided in the above embodiments, etc.

[0031] Processor 320 may include one or more processing cores. Processor 320 executes instructions, programs, code sets, or instruction sets stored in memory 310, and calls data stored in memory 310 to perform various functions and process data as described in this application. Processor 320 may be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), Controller, Microcontroller, and Microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of processor 320 may also be other types, and this application embodiment does not specifically limit the specific devices used.

[0032] The communication bus 330 may include a path for transmitting information between the aforementioned components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 330 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3The symbol is represented by a single double arrow, but this does not mean that there is only one bus or one type of bus.

[0033] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, which is a foundation settlement prediction method based on big data analysis.

[0034] In this embodiment, a computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, a computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), spoofing random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0035] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0036] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for predicting foundation settlement based on big data analysis, characterized in that, Includes the following steps: Acquire data on the distribution of salt concentration in the foundation soil, the distribution of plant roots, and the distribution of surface temperature within the monitoring area; Based on the analysis of salt concentration distribution data, the mechanism by which salt regulates the height of capillary water rise was analyzed, and the effect of water evaporation on the ground surface was evaluated in combination with surface temperature distribution data. Based on data on plant root distribution and salt concentration distribution, we analyzed the changes in root growth and water absorption rate under salt stress to assess the local water absorption effect of plant roots on soil. The uniformity of water migration in the foundation soil is assessed by combining the effects of water evaporation and local absorption, and the risk of non-uniform settlement of the foundation is given early warning.

2. The foundation settlement prediction method based on big data analysis according to claim 1, characterized in that, The assessment of the surface moisture evaporation effect includes: Discrete salinity concentration distribution data and surface temperature distribution data are transformed into continuous salinity concentration distribution matrices and surface temperature distribution matrices using a spatial interpolation algorithm. The inhibitory effect of salt concentration on capillary rise in foundation soil was analyzed, and the capillary supply coefficient was determined by combining the salt concentration distribution matrix. The capillary supply coefficient is the ratio of the capillary rise height under the influence of salt concentration to the baseline capillary rise height. The influence of surface temperature on the evaporation potential of surface moisture in foundation soil was analyzed, and the evaporation coefficient under the influence of surface temperature was determined by combining the surface temperature distribution matrix. The product of the capillary water supply coefficient and the water evaporation coefficient is used as the water evaporation effect coefficient of the foundation soil surface. The water evaporation effect coefficient is used to quantify the inhibitory effect of salt on capillary water rise and the enhancing effect of surface temperature on evaporation rate.

3. The foundation settlement prediction method based on big data analysis according to claim 2, characterized in that, The formula for calculating the water evaporation coefficient is as follows: ； In the formula Indicates the basic background evaporation coefficient. Indicates coordinates within the monitoring area The surface temperature at that location. This indicates the threshold temperature at which evaporation begins. This represents the average daily surface temperature difference within the monitoring area throughout the year. Indicates the temperature sensitivity coefficient. Indicates coordinates within the monitoring area The water evaporation coefficient under the influence of surface temperature.

4. The foundation settlement prediction method based on big data analysis according to claim 1, characterized in that, The assessment of the localized water uptake effect of plant roots on soil includes: Plant root distribution data are converted into a plant root density distribution matrix and the plant root density coefficient per unit soil volume at different locations is determined. The plant root density coefficient is the ratio of the plant root density per unit soil volume at different locations to the maximum plant root density per unit soil volume in the monitoring area. Analyze the degree to which root growth is inhibited by salt and determine the root growth capacity coefficient under salt stress; The effect of salt concentration on the water absorption rate of plant roots was analyzed and the root water absorption capacity coefficient under salt stress was determined. The root water absorption capacity coefficient is the ratio of the water absorption rate of plant roots at different salt concentrations to the baseline water absorption rate. The product of the plant root density coefficient, root growth capacity coefficient, and root water absorption capacity coefficient is used as the local absorption effect coefficient of the plant root system. The local absorption effect coefficient is used to evaluate the local absorption effect of plant roots on soil water under salt stress.

5. The foundation settlement prediction method based on big data analysis according to claim 4, characterized in that, The formula for calculating the root growth capacity coefficient is as follows: ； In the formula Indicates coordinates within the monitoring area The salt concentration at that location. This represents the salt concentration at which the root growth rate is reduced to 50%. This represents the salt stress sensitivity coefficient. Indicates coordinates within the monitoring area The root growth capacity coefficient at the location.

6. The foundation settlement prediction method based on big data analysis according to claim 1, characterized in that, The assessment of the uniformity of moisture migration in the foundation soil and the early warning of non-uniform foundation settlement risk include: The water evaporation effect coefficient and the local absorption effect coefficient were obtained and weighted summed to obtain the water migration coefficient per unit soil at different locations within the monitoring area. The regional mean and standard deviation of the water migration coefficient within the statistical monitoring area are used to determine the water migration uniformity coefficient, which is 1 minus the ratio of the regional standard deviation to the regional mean. When the uniformity coefficient of moisture migration is less than or equal to the preset uniformity threshold of moisture migration, a risk warning for non-uniform foundation settlement is issued; when the uniformity coefficient of moisture migration is greater than the preset uniformity threshold of moisture migration, a risk warning for non-uniform foundation settlement is not issued.

7. A foundation settlement prediction system based on big data analysis, applied to the foundation settlement prediction method based on big data analysis as described in any one of claims 1-6, characterized in that, The system includes: The data acquisition module is used to acquire data on the distribution of salt concentration in the foundation soil, the distribution of plant roots, and the distribution of surface temperature within the monitoring area. The evaporation effect assessment module is used to analyze the regulation mechanism of salt on capillary water rise height based on salt concentration distribution data and to assess the water evaporation effect on the ground surface in combination with surface temperature distribution data. The absorption effect assessment module is used to analyze the root growth and water absorption rate changes under salt stress based on plant root distribution data and salt concentration distribution data, and to assess the local absorption effect of plant roots on soil moisture. The risk warning module is used to assess the uniformity of water migration in the foundation soil by comprehensively considering the water evaporation effect and the local absorption effect, and to provide early warning of the risk of non-uniform foundation settlement.

8. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the foundation settlement prediction method based on big data analysis as described in any one of claims 1-6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the foundation settlement prediction method based on big data analysis as described in any one of claims 1-6.

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