A method and system for evaluating the susceptibility of karst collapse based on analytic hierarchy process

Abstract

The present invention belongs to the technical field of karst collapse analysis. The present invention discloses a method and system for evaluating and analyzing karst collapse susceptibility based on analytic hierarchy process, including: marking measurement points in a karst area, collecting karst data of the measurement points, performing data standardization on the karst data to obtain standard karst data; performing geomagnetic division on the measurement points based on geomagnetic intensity to obtain a set of cavity points; evaluating the biological activity of the set of cavity points with soil microorganism data to obtain a microorganism activity index; performing pressure fluctuation analysis on the set of cavity points based on atmospheric pressure to obtain a pressure fluctuation index; constructing a collapse evaluation function based on the microorganism activity index and the pressure fluctuation index; effectively improving the accuracy of karst collapse analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of karst collapse analysis. More specifically, the present invention relates to a method and system for evaluating the susceptibility of karst collapse based on analytic hierarchy process. Background Art

[0002] A patent with the application publication number CN118114007A discloses a geological monitoring information analysis system for karst collapse, including an information collection unit, a regional division unit, a normal analysis unit, an abnormal analysis unit, an information storage unit, an analysis and warning unit, and an information output unit. It relates to the technical field of karst collapse information analysis, solves the technical problem of lack of objective analysis by integrating past data, and there are analysis errors in the data obtained from a single aspect. By combining the obtained data with past data for analysis, the error in the analysis process is further reduced. At the same time, different regions are divided according to the obtained data, and different methods are used to analyze the data of the divided regions. Secondly, data acquisition and comparison are carried out for abnormal regions to conduct timely warning analysis of abnormal regions, which can remind relevant staff to take countermeasures. At the same time, obtaining multi-faceted data for analysis can ensure the objectivity of the analysis results.

[0003] In the field of evaluating the susceptibility of karst collapse, current karst collapse evaluation methods usually ignore the relationship between the morphological characteristics of the underground karst pipeline network and geomagnetic anomalies. The existence and morphology of karst pipelines have a direct impact on karst development and the occurrence of collapses. Geomagnetic anomaly is one of the important precursors of karst collapse because karst pipelines and underground cavities will affect the distribution of the underground magnetic field. Due to the lack of research on the correlation between the morphology of the underground karst pipeline network and geomagnetic anomalies, in the actual process of karst collapse evaluation, the distribution and changes of underground pipelines cannot be accurately identified, resulting in inaccurate evaluation results, and thus the occurrence of karst collapse cannot be effectively warned; Karst development is also affected by the structure of the soil microbial community. Microorganisms can change the formation environment of karst through chemical reactions, mineral degradation, etc. Existing evaluation methods fail to consider the influence of the microbial community and ignore the role of this biological factor in karst development, thus making the evaluation model less comprehensive and underestimating the risk of karst collapse in some areas, especially in areas where microorganisms are active, increasing the error and inaccuracy of the evaluation results; The fluctuation of atmospheric pressure affects the stability of underground cavities. Especially under seasonal or sudden climate changes, the pressure fluctuation will have different degrees of impact on underground structures. Current karst collapse evaluation methods lack the analysis of the coupling mechanism of atmospheric pressure fluctuation, increasing the evaluation error, and thus unable to effectively warn before disasters occur.

[0004] In view of this, the present invention proposes a method and system for evaluating the susceptibility of karst collapse based on analytic hierarchy process to solve the above problems. Summary of the Invention

[0005] In order to overcome the above defects of the prior art and achieve the above object, the present invention provides the following technical solution: A method for evaluating and analyzing the susceptibility of karst collapse based on analytic hierarchy process, including:

[0006] S1. Mark the measurement points in the karst area, collect the karst data of the measurement points, and standardize the karst data to obtain standard karst data; the karst data includes: geomagnetic intensity, soil microorganism data, karst temperature, soil nitrogen and carbon content, atmospheric pressure, and point coordinates;

[0007] S2. Conduct geomagnetic division on the measurement points based on the geomagnetic intensity to obtain a set of cavity points;

[0008] S3. Evaluate the biological activity of the set of cavity points with the soil microorganism data to obtain a microbial activity index; conduct pressure fluctuation analysis on the set of cavity points based on the atmospheric pressure to obtain a pressure fluctuation index;

[0009] S4. Construct a collapse evaluation function based on the microbial activity index and the pressure fluctuation index.

[0010] Further, the acquisition method of the karst data includes:

[0011] Preset a measurement route and a measurement interval, mark the measurement points in the measurement line based on the measurement interval, use the initial position of the measurement line as the initial measurement point, starting from the initial measurement point, mark the position at every other measurement interval as a measurement point, and set a geomagnetic detector, a microorganism sensor, a soil micro sensor, a temperature sensor, and a pressure sensor for each measurement point; collect the data of each measurement point in the same time series; the point coordinates are determined based on the karst area, select a point in the karst area as the coordinate center point to construct a standard coordinate system, and the horizontal distance and vertical distance of each measurement point from the coordinate center point form the point coordinates.

[0012] Further, the method for conducting geomagnetic division on the measurement points includes:

[0013] For karst data, each type of data in the karst data is used as the data to be transformed. A preset time scale is set, and based on the time scale parameter, the historical mean of each data to be transformed is calculated. Using the recording time point of the data to be transformed as the cut-off time point, the data to be transformed at the same measurement point within the time scale before the cut-off time point is selected to form a scale set, and the mean value of the data in the scale set is calculated as the historical mean; using the recording time point of the data to be transformed as the measurement moment, the data to be transformed at the same measurement moment is selected to form a moment data set, and the mean value of the data in the moment data set is used as the moment mean, and the standard deviation of the data in the moment data set is used as the moment standard deviation; the data to be transformed is standardized based on the historical mean, moment mean, and moment standard deviation. The formula for standardizing the data to be transformed is:

[0014] ; where represents the standard data, represents the data to be transformed, represents the moment mean, represents the moment standard deviation, represents the time scale, represents the historical mean, represents the inverse hyperbolic sine function, and all standard data constitutes the standard karst data.

[0015] Furthermore, the method for obtaining the microbial activity index includes:

[0016] Based on the measurement route, the previous measurement point of the current measurement point is selected as the differential analysis point. The finite difference method is used at the differential analysis point to perform a horizontal forward difference analysis on the geomagnetic intensity of the current measurement point to obtain a horizontal change factor. The finite difference method is used at the differential analysis point to perform a vertical forward difference analysis on the geomagnetic intensity of the current measurement point to obtain a vertical change factor; a preset depth scale set is traversed, and the traversed element is used as the scale factor to perform a magnetic field tensor analysis on each scale factor to obtain the geomagnetic depth tensor; a preset geomagnetic span is set, and the geomagnetic span includes a horizontal span and a vertical span. Based on the geomagnetic depth tensor, a geomagnetic anomaly assessment is performed on the measurement point to obtain a magnetic anomaly intensity index; the spline analysis method is used to construct a magnetic anomaly fluctuation curve with the scale factor and the corresponding magnetic anomaly intensity index, and the extreme maximum point and extreme minimum point of the magnetic anomaly fluctuation curve are recorded. The difference between the extreme maximum point and the extreme minimum point is used as the cavity evaluation index. A preset cavity evaluation threshold is set, and the measurement points with the cavity evaluation index greater than or equal to the cavity evaluation threshold are marked as cavity points. The scale factor with the smaller value of the scale factors corresponding to the extreme maximum point and the extreme minimum point is used as the cavity starting point, and the other is used as the cavity ending point. The data of the cavity starting point and the cavity ending point are incorporated into the cavity points, and all cavity points constitute the cavity point set.

[0017] Further, the formula for evaluating the geomagnetic anomaly at the measurement points is:

[0018] ; where represents the magnetic anomaly intensity index, represents the horizontal axis span, represents the vertical axis span, represents the horizontal axis value of the point coordinate, represents the vertical axis value of the point coordinate, and represent the integration operation, represents the period adjustment coefficient, represents the angular frequency parameter, represents the time scale.

[0019] Further, the method for analyzing the pressure fluctuation of the cavity point set includes:

[0020] Using statistical analysis method to statistically analyze the soil microorganism data of all measurement points to obtain a microorganism category table, which includes microorganism species and the total quantity of corresponding species; taking the quantity of each microorganism species in the cavity points of the cavity set divided by the total quantity of the corresponding microorganism species in the microorganism category table as the species richness, and evaluating the biodiversity of each measurement point based on the species richness to obtain a biodiversity index; recording the maximum value of the biodiversity index as the regional maximum activity index, and evaluating the microorganism activity of the cavity points based on the regional maximum activity index. The formula for evaluating the microorganism activity of the cavity points is: ; where represents the microorganism activity index, represents the maximum activity index, represents the environmental adjustment coefficient, represents the soil nitrogen content, represents the soil carbon content.

[0021] Further, the formula for evaluating the biodiversity of each measurement point is:

[0022] ; where represents the biodiversity index, represents pi, represents the microorganism species, represents the total number of microorganism species in the microorganism category table, represents the th microorganism species richness in the cavity points, represents the temperature adjustment coefficient, represents the karst temperature.

[0023] Further, the method for pressure fluctuation analysis of the cavity point set includes:

[0024] Taking the cavity points in the cavity point set as the regional centers, presetting a distance scale, using the distance scale as the regional radius, taking the regional center as the center of the circle, and using the regional radius as the radius of the circle to draw a circle to obtain a regional circle. Taking the measurement points included in the regional circle as pressure evaluation points, and taking the pressure evaluation points and the pressure center as fluctuation analysis points. For each fluctuation analysis point, draw a cross line with the fluctuation analysis point as the pressure center, and take the four regions divided by the cross line as the selection domains. Use the distance measurement formula to calculate the distance from other fluctuation analysis points to the center of the cross line as the evaluation distance. Select the fluctuation analysis point with the minimum evaluation distance in each selection domain as the pressure boundary point. Take the pressure boundary points in the diagonal regions as a group of boundary groups. Taking the fluctuation analysis point at the center of the cross line as the differential center, use the central difference algorithm to calculate the pressure fluctuation gradient of each group of boundary groups. Taking the average value of the pressure fluctuation gradients as the fluctuation factor, and calculate the average value of the fluctuation factors of all fluctuation analysis points as the pressure propagation operator; Based on the pressure propagation operator, perform pressure fluctuation evaluation on the cavity points. The formula for pressure fluctuation evaluation of the cavity points is:

[0025] ; where represents the pressure fluctuation index, represents the pressure fluctuation factor, represents the standard atmospheric pressure, represents the difference between the atmospheric pressure and the standard atmospheric pressure, represents the fluctuation attenuation coefficient, represents the cavity starting point, represents the cavity ending point, represents the cavity depth.

[0026] Further, the formula for the collapse evaluation function is:

[0027] ; where represents the collapse index, represents the microbial activity weight, represents the pressure fluctuation weight; The microbial activity weight and the pressure fluctuation weight satisfy the weight constraint condition: .

[0028] A karst collapse susceptibility assessment and analysis system based on analytic hierarchy process includes:

[0029] Data acquisition module: Mark the measurement points in the karst area, collect the karst data of the measurement points, and perform data standardization on the karst data to obtain standard karst data;

[0030] Point position division module: Perform geomagnetic division on the measurement points based on the geomagnetic intensity to obtain a cavity point set;

[0031] Index evaluation module: Evaluate the biological activity of the cavity point set with soil microorganism data to obtain the microbial activity index; conduct pressure fluctuation analysis on the cavity point set based on atmospheric pressure to obtain the pressure fluctuation index.

[0032] Evaluation construction module: Construct a collapse evaluation function based on the microbial activity index and the pressure fluctuation index.

[0033] The technical effects and advantages of the method and system for evaluating the susceptibility of karst collapse based on analytic hierarchy process of the present invention:

[0034] Through the analysis of geomagnetic data, the present invention can accurately identify the distribution and morphology of underground karst pipelines, effectively extract the information of the underground karst spatial structure, and avoid the possible deficiencies of traditional methods; by evaluating the biological activity of the cavity point set, the flexibility of karst collapse evaluation is enhanced, enabling it to maintain high efficiency and accurate evaluation ability in complex karst environments, providing a new perspective for the biological mechanism in the karst development process, and further improving the early warning ability of karst disasters; by conducting pressure fluctuation analysis on the cavity point set, the influence of atmospheric pressure fluctuation on underground cavities can be quantified, providing a new physical parameter for karst collapse risk assessment and improving the comprehensiveness and accuracy of the assessment. Brief description of the drawings

[0035] Figure 1 It is a schematic diagram of a method for evaluating the susceptibility of karst collapse based on analytic hierarchy process of the present invention;

[0036] Figure 2 It is a schematic diagram of a system for evaluating the susceptibility of karst collapse based on analytic hierarchy process of the present invention. Detailed implementation manners

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] Embodiment 1;

[0039] Please refer to Figure 1 As shown, the method for evaluating the susceptibility of karst collapse based on analytic hierarchy process in this embodiment includes:

[0040] S1. Mark the measurement points in the karst area, collect the karst data of the measurement points, and standardize the karst data to obtain the standard karst data. The karst data includes: geomagnetic intensity, soil microorganism data, karst temperature, soil nitrogen and carbon content, atmospheric pressure, and point coordinates.

[0041] S2. Conduct geomagnetic division on the measurement points based on the geomagnetic intensity to obtain a set of cavity points.

[0042] S3. Evaluate the biological activity of the set of cavity points with the soil microorganism data to obtain the microbial activity index. Conduct pressure fluctuation analysis on the set of cavity points based on the atmospheric pressure to obtain the pressure fluctuation index.

[0043] S4. Construct a collapse evaluation function based on the microbial activity index and the pressure fluctuation index.

[0044] The karst data includes: geomagnetic intensity, soil microorganism data, karst temperature, soil nitrogen and carbon content, atmospheric pressure, and point coordinates. The acquisition method of the rock stratum data includes: preset measurement routes and measurement intervals. The measurement routes and measurement intervals are set by those skilled in the art based on the trend of the karst area or the distribution of potential underground cavities (through geological data or remote sensing data analysis). The route can be straight or curved, depending on the geological characteristics of the karst zone. Mark the measurement points on the measurement line based on the measurement interval. Use the initial position of the measurement line as the initial measurement point. Starting from the initial measurement point, mark the positions at every other measurement interval as measurement points. Set a geomagnetic detector, a microorganism sensor, a soil micro-sensor, a temperature sensor, and a pressure sensor for each measurement point. The geomagnetic detector is used to collect the geomagnetic intensity. The microorganism sensor is used to collect the soil microorganism data. The soil microorganism data includes the types of microorganisms and the content of each type (the number of microorganisms of the same type). The soil micro-sensor is used to collect the soil nitrogen and carbon content. The soil nitrogen and carbon content are the data of the nitrogen content and the carbon content in the soil. The temperature sensor is used to collect the karst temperature. The pressure sensor is used to collect the atmospheric pressure. The data collection for each measurement point is carried out in the same time series. The point coordinates are determined based on the karst area. Those skilled in the art select a point in the karst area as the coordinate center point to construct a standard coordinate system. The point coordinates are composed of the horizontal distance and the vertical distance of each measurement point from the coordinate center point.

[0045] In karst disaster assessment, data sources are usually very diverse, including geomagnetic data, soil microorganism data, atmospheric pressure data, etc. There are problems such as different dimensions, inconsistent time scales, and accuracy differences in the data collected by different types of sensors and monitoring devices. Sensor data may be affected by factors such as changes in the external environment and fluctuations in sensor accuracy, resulting in strong time dependence of the data. For example, the changes in atmospheric pressure and soil microbial communities may be seasonal or periodic, while geomagnetic data may be affected by different external environmental interferences. By standardizing karst data, karst data from different sources and with different dimensions can be uniformly processed, ensuring that karst data can be compared and fused under the same standard, thus eliminating the influence of data dimensions and removing noise caused by accidental fluctuations or external environmental factors. Specifically:

[0046] For karst data, each type of data in the karst data is used as the data to be transformed. A preset time scale is set, and the historical mean of each data to be transformed is calculated based on the time scale parameter. Taking the recording time point of the data to be transformed as the cut-off time point, the data to be transformed at the same measurement point within the time scale before the cut-off time point is selected to form a scale set, and the mean value of the data in the scale set is calculated as the historical mean; taking the recording time point of the data to be transformed as the measurement moment, the data to be transformed at the same measurement moment is selected to form a moment data set, the mean value of the data in the moment data set is used as the moment mean, and the standard deviation of the data in the moment data set is used as the moment standard deviation; the data to be transformed is standardized based on the historical mean, moment mean, and moment standard deviation. The formula for standardizing the data to be transformed is:

[0047] ; where represents the standard data, represents the data to be transformed, represents the moment mean, represents the moment standard deviation, represents the time scale, used to smooth the influence of time changes, represents the historical mean, represents the inverse hyperbolic sine function. All standard data constitutes the standard karst data; standardizing the data to be transformed incorporates the time dependence of karst data into the standardization process by introducing a time decay term. This method can smooth the time change trend in historical data, thus better capturing the pattern of long-term changes and further removing the influence of short-term abnormal fluctuations.

[0048] Current karst collapse assessment methods usually ignore the relationship between the morphological characteristics of the underground karst pipeline network and geomagnetic anomalies. As a result, during the actual karst collapse assessment process, the distribution and changes of underground pipelines cannot be accurately identified, leading to inaccurate assessment results and further inability to effectively predict the occurrence of karst collapses. By performing geomagnetic zoning on measurement points, the development status of karst can be more accurately reflected under different time and depth conditions, thereby providing a more accurate prediction of karst collapse risks. Specifically:

[0049] Based on the measurement route, select the previous measurement point of the current measurement point as the differential analysis point. Use the finite difference method at the differential analysis point to perform a forward horizontal axis differential analysis on the geomagnetic intensity of the current measurement point to obtain the horizontal change factor. Use the finite difference method at the differential analysis point to perform a forward vertical axis differential analysis on the geomagnetic intensity of the current measurement point to obtain the vertical change factor; preset a depth scale set, traverse the depth scale set, and use the traversed element as the scale factor to perform magnetic field tensor analysis on each scale factor. The formula for performing magnetic field tensor analysis on each scale factor is:

[0050] ; where represents the geomagnetic depth tensor, represents the horizontal axis basis vector, represents the vertical axis basis vector, represents the horizontal change factor, represents the vertical change factor, represents the scale factor, represents the attenuation coefficient, used to control the attenuation rate of the magnetic field with underground depth; preset the geomagnetic span, the geomagnetic span includes the horizontal axis span and the vertical axis span. Based on the geomagnetic depth tensor, perform a geomagnetic anomaly assessment on the measurement point. The formula for performing a geomagnetic anomaly assessment on the measurement point is: ; where represents the magnetic anomaly intensity index, represents the horizontal axis span, represents the vertical axis span, represents the horizontal axis value of the point coordinate, represents the vertical axis value of the point coordinate, and represent the integration operation, represents the period adjustment coefficient, used to control the amplitude of periodic changes. It represents the periodic change of the geomagnetic field over time, represents the angular frequency parameter, used to simulate the fluctuation of the direction of the geomagnetic intensity over time, Represents the time scale; the spline analysis method is used to construct the magnetic anomaly fluctuation curve with the scale factor and the corresponding magnetic anomaly intensity index, record the maximum and minimum extreme points of the magnetic anomaly fluctuation curve, use the difference between the maximum and minimum extreme points as the cavity evaluation index, preset the cavity evaluation threshold, mark the measurement points where the cavity evaluation index is greater than or equal to the cavity evaluation threshold as cavity points, use the scale factor with the smaller value of the scale factors corresponding to the maximum and minimum extreme points as the cavity starting point, and the other as the cavity ending point, incorporate the cavity starting point and cavity ending point data into the cavity points, and all cavity points form the cavity point set.

[0051] The karst development is also affected by the soil microbial community structure. Microorganisms can change the karst formation environment through chemical reactions, mineral degradation, etc. For example, sulfate-reducing bacteria and iron-reducing bacteria produce acidic substances such as sulfuric acid and hydrogen ions through their metabolic processes. These acidic substances can react with the minerals in the rock, promote the dissolution effect, and thus accelerate the formation and expansion of underground karst. Especially in limestone areas, the generation of acidic substances will cause the dissolution of calcium carbonate, forming karst cavities and pipelines. The existing evaluation methods fail to consider the influence of the microbial community and ignore the role of this biological factor in karst development, thus making the evaluation less comprehensive and underestimating the risk of karst collapse in some areas. By evaluating the biological activity of the cavity point set, the contribution of microbial activities to karst development can be better revealed, and thus the accuracy of risk assessment can be improved. Specifically:

[0052] Use the statistical analysis method to statistically analyze the soil microbial data of all measurement points to obtain the microbial category table. The microbial category table contains the microbial species and the total corresponding species quantity; divide the species quantity of each microbial species in the cavity points of the cavity set by the total species quantity of the corresponding microbial species in the microbial category table as the species richness, and conduct biodiversity assessment for each measurement point based on the species richness. The formula for conducting biodiversity assessment for each measurement point is:

[0053] ; where represents the biodiversity index, represents pi, represents the microbial species, represents the total number of microbial species in the microbial category table, represents the th microbial species in the cavity points, represents the temperature adjustment coefficient, which is used to control the influence of temperature on microorganisms and is set by those skilled in the art according to the actual situation. Represents the karst temperature; record the maximum value of the biodiversity index as the regional maximum activity index, and conduct a microbial activity assessment on the cavity points based on the regional maximum activity index. The formula for the microbial activity assessment of the cavity points is: ; where represents the microbial activity index, represents the maximum activity index, represents the environmental regulation coefficient, which is used to control the influence of environmental nutrients on microbial activity, represents the soil nitrogen content, represents the soil carbon content.

[0054] The fluctuation of atmospheric pressure affects the stability of underground cavities. Especially under seasonal or sudden climate changes, the pressure fluctuation will have varying degrees of impact on underground structures. The current karst collapse assessment methods lack the analysis of the coupling mechanism of atmospheric pressure fluctuation, increasing the assessment error, and thus leading to the inability to effectively give early warnings before disasters occur. By conducting pressure fluctuation analysis on the cavity point set, it is possible to quantitatively describe the specific impact of atmospheric pressure fluctuation on the risk of karst collapse, providing a unified quantification standard for cavities in different regions and at different depths, thereby providing strong data support for the prediction of karst disasters. Specifically:

[0055] Taking the cavity points in the cavity point set as the regional center, presetting the distance scale, using the distance scale as the regional radius, taking the regional center as the center of the circle, and using the regional radius as the radius of the circle to draw a circle to obtain the regional circle. Taking the measurement points included in the regional circle as the pressure assessment points, and taking the pressure assessment points and the pressure center as the fluctuation analysis points. For each fluctuation analysis point, draw a cross line with the fluctuation analysis point as the pressure center, and take the four regions divided by the cross line as the selection domains. Use the distance measurement formula to calculate the distance from other fluctuation analysis points to the center of the cross line as the assessment distance. Select the fluctuation analysis point with the smallest assessment distance in each selection domain as the pressure boundary point. Taking the pressure boundary points in the diagonal regions as a group of boundary groups, taking the fluctuation analysis point at the center of the cross line as the differential center, and using the central difference algorithm to calculate the pressure fluctuation gradient of each group of boundary groups. Taking the mean value of the pressure fluctuation gradients as the fluctuation factor, and calculating the mean value of the fluctuation factors of all fluctuation analysis points as the pressure propagation operator; the diagonal regions are determined based on quadrant division. The four regions of the two-dimensional coordinate system represent four quadrants, and the diagonal regions are the first quadrant and the third quadrant, the second quadrant and the fourth quadrant; conduct a pressure fluctuation assessment on the cavity points based on the pressure propagation operator. The formula for the pressure fluctuation assessment of the cavity points is:

[0056] ; where represents the pressure fluctuation index, represents the pressure fluctuation factor, which is set by those skilled in the art according to the actual situation, represents the standard atmospheric pressure, represents the difference between the atmospheric pressure and the standard atmospheric pressure, represents the fluctuation attenuation coefficient, which is used to simulate the attenuation of pressure fluctuations and is set by those skilled in the art based on the actual situation, represents the starting point of the cavity, represents the ending point of the cavity, represents the depth of the cavity.

[0057] Assume that the atmospheric pressures of a group of boundary groups are respectively and , and the point coordinates are respectively and , the atmospheric pressure at the differential center is , and the point coordinate is ; then the calculation formula of the central difference algorithm is: ; where represents the pressure fluctuation gradient.

[0058] Construct a collapse evaluation function based on the microbial activity index and the pressure fluctuation index. The formula of the collapse evaluation function is: ; where represents the collapse index, represents the microbial activity weight, represents the pressure fluctuation weight; the microbial activity weight and the pressure fluctuation weight satisfy the weight constraint condition: ; the larger the collapse index, the easier the point is to collapse. The microbial activity weight and the pressure fluctuation weight are set by those skilled in the art based on the actual situation. For areas with higher soil nitrogen and carbon content, the value of the microbial activity weight should be increased. For areas with larger atmospheric pressure fluctuations, the value of the pressure fluctuation weight should be increased. Deploy the collapse evaluation function to the monitoring terminal. When the collapse index is greater than or equal to the preset collapse threshold, the monitoring terminal issues an alarm.

[0059] Through the analysis of geomagnetic data in this embodiment, the distribution and morphology of underground karst pipelines can be accurately identified, the information of the underground karst spatial structure can be effectively extracted, and the deficiencies that may exist in traditional methods can be avoided; by evaluating the biological activity of the cavity point set, the flexibility of karst collapse evaluation is enhanced, enabling it to maintain high-efficiency and accurate evaluation capabilities in complex karst environments, providing a new perspective for the biological action mechanism during karst development, and further improving the early warning ability of karst disasters; by conducting pressure fluctuation analysis on the cavity point set, the impact of atmospheric pressure fluctuations on underground cavities can be quantified, providing a new physical parameter for karst collapse risk assessment and improving the comprehensiveness and accuracy of the assessment.

[0060] Embodiment 2;

[0061] Please refer to Figure 2As shown, for the parts not described in detail in this embodiment, refer to the description in Embodiment 1. A karst collapse susceptibility assessment and analysis system based on analytic hierarchy process is provided, including:

[0062] Data acquisition module: Mark the measurement points in the karst area, collect the karst data of the measurement points, and standardize the karst data to obtain the standard karst data;

[0063] Point location division module: Based on the geomagnetic intensity, divide the measurement points to obtain the set of cavity points;

[0064] Index evaluation module: Evaluate the biological activity of the set of cavity points with soil microorganism data to obtain the microbial activity index; Based on the atmospheric pressure, conduct pressure fluctuation analysis on the set of cavity points to obtain the pressure fluctuation index;

[0065] Evaluation construction module: Construct a collapse evaluation function based on the microbial activity index and the pressure fluctuation index;

[0066] Each module is connected by wired and / or wireless means to achieve data transmission between modules.

[0067] Embodiment 3;

[0068] This embodiment publicly provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it realizes the operation mode of the above-provided method for assessing and analyzing the susceptibility of karst collapse based on analytic hierarchy process.

[0069] Since the electronic device introduced in this embodiment is the electronic device adopted for implementing the method for assessing and analyzing the susceptibility of karst collapse based on analytic hierarchy process in the embodiments of the present application, based on the method for assessing and analyzing the susceptibility of karst collapse based on analytic hierarchy process introduced in the embodiments of the present application, those skilled in the art can understand the specific implementation manners and various forms of change of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device realizes the method in the embodiments of the present application will not be described in detail here. As long as those skilled in the art implement the electronic device adopted for the method for assessing and analyzing the susceptibility of karst collapse based on analytic hierarchy process in the embodiments of the present application, it falls within the scope of protection of the present application.

[0070] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0071] The above are only the preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as within the protection scope of the present invention.

Claims

1. A karst collapse susceptibility assessment and analysis method based on hierarchical analysis, characterized in that: include: S1. Mark the measuring points in the karst area, collect karst data of the measuring points, standardize the karst data, and obtain standard karst data; The karst data include: geomagnetic intensity, soil microbial data, karst temperature, soil nitrogen and carbon content, atmospheric pressure and point coordinates; S2, geomagnetically dividing the measurement points based on the geomagnetic intensity to obtain a void point set; S3. Evaluate the biological activity of the void point set using soil microbial data to obtain a microbial activity index; perform pressure fluctuation analysis on the void point set based on atmospheric pressure to obtain a pressure fluctuation index; S4. Construct a collapse assessment function based on the microbial activity index and the pressure fluctuation index, and deploy it to the monitoring terminal; The method for obtaining the microbial activity index includes: Statistical analysis was used to analyze soil microbial data at all measurement points to obtain a microbial category table, which contained microbial species and the total number of corresponding species. The species richness was calculated by dividing the number of each microbial species at the void point in the void concentration by the total number of the corresponding microbial species in the microbial category table. Based on the species richness, biodiversity was evaluated at each measurement point to obtain a biodiversity index. The maximum value of the biodiversity index was recorded as the regional maximum activity index. Based on the regional maximum activity index, microbial activity was evaluated at the void point to obtain a microbial activity index. The method for obtaining the pressure fluctuation index includes: The void point in the void point concentration is taken as the regional center, the distance scale is preset, the distance scale is taken as the regional radius, the regional center is taken as the circle center, and the regional radius is taken as the circle radius to draw a circle to obtain the regional circle, the measurement points contained in the regional circle are taken as pressure evaluation points, the pressure evaluation points and the pressure center are taken as fluctuation analysis points, for each fluctuation analysis point, a cross line is drawn with the fluctuation analysis point as the pressure center, the four areas divided by the cross line are taken as the selection domain, the distance measurement formula is used to calculate the distance from other fluctuation analysis points to the cross line center as the evaluation distance, the fluctuation analysis point with the smallest evaluation distance is selected as the pressure boundary point in each selection domain, the pressure boundary points in the diagonal area are taken as a group of boundary groups, the fluctuation analysis point at the center of the cross line is taken as the differential center, the central difference algorithm is used to calculate the pressure fluctuation gradient of each boundary group, the mean of the pressure fluctuation gradient is taken as the fluctuation factor, and the mean of the fluctuation factors of all fluctuation analysis points is calculated as the pressure propagation operator; the pressure fluctuation of the void point is evaluated based on the pressure propagation operator to obtain the pressure fluctuation index.

2. The karst collapse susceptibility assessment and analysis method based on hierarchical analysis according to claim 1 is characterized in that: The karst data collection methods include: The measurement route and measurement interval are preset, and the measurement points in the measurement route are marked based on the measurement intervals. The initial position of the measurement route is used as the initial measurement point. Starting from the initial measurement point, the position of every other measurement interval is marked as a measurement point, and a geomagnetic detector, a microbial sensor, a soil microsensor, a temperature sensor and a pressure sensor are set for each measurement point. The data collection for each measurement point is carried out in the same time series. The point coordinates are determined based on the karst area, and a point is selected in the karst area as the coordinate center point to construct a standard coordinate system, and the point coordinates are composed of the horizontal axis distance and the vertical axis distance of each measurement point from the coordinate center point.

3. The karst collapse susceptibility assessment and analysis method based on hierarchical analysis according to claim 2 is characterized in that: The method of standardizing karst data includes: For karst data, each type of data in the karst data is used as the data to be converted, and the time scale is preset. The historical mean of each data to be converted is calculated based on the time scale parameter. The recording time point of the data to be converted is used as the cutoff time point, and the data to be converted at the same measurement point in the time scale before the cutoff time point are selected to form a scale set, and the mean of the data in the scale set is calculated as the historical mean; the recording time point of the data to be converted is used as the measurement moment, and the data to be converted at the same measurement moment is selected to form a moment data set, and the mean of the data in the moment data set is used as the moment mean, and the standard deviation of the data in the moment data set is used as the moment standard deviation; the data to be converted is standardized based on the historical mean, moment mean and moment standard deviation, and the formula for standardizing the data to be converted is: ;in, Represents standard data, Represents the data to be converted. represents the time mean, represents the time standard deviation, represents the time scale, represents the historical mean, represents the inverse hyperbolic sine function, and all standard data constitute the standard karst data.

4. The karst collapse susceptibility assessment and analysis method based on hierarchical analysis according to claim 3 is characterized in that: The method of geomagnetically dividing the measuring points includes: Based on the measurement route, the previous measurement point of the current measurement point is selected as the differential analysis point, and the finite difference method is used to perform a horizontal forward differential analysis on the geomagnetic intensity of the current measurement point to obtain the lateral change factor. The finite difference method is used to perform a vertical forward differential analysis on the geomagnetic intensity of the current measurement point to obtain the longitudinal change factor; a depth scale set is preset, the depth scale set is traversed, and the traversed elements are used as scale factors. A magnetic field tensor analysis is performed on each scale factor to obtain the geomagnetic depth tensor; a geomagnetic span is preset, and the geomagnetic span includes a horizontal span and a vertical span. The measurement point is analyzed based on the geomagnetic depth tensor. Geomagnetic anomaly assessment, obtain the magnetic anomaly intensity index; use the spline analysis method to construct the magnetic anomaly fluctuation curve with the scale factor and the corresponding magnetic anomaly intensity index, record the extreme maximum point and the extreme minimum point of the magnetic anomaly fluctuation curve, use the difference between the extreme maximum point and the extreme minimum point as the void assessment index, preset the void assessment threshold, mark the measurement points whose void assessment index is greater than or equal to the void assessment threshold as void points, use the smaller scale factor corresponding to the extreme maximum point and the extreme minimum point as the void starting point, and the other as the void end point, merge the void starting point and void end point data into the void point, and all void points constitute a void point set.

5. The karst collapse susceptibility assessment and analysis method based on hierarchical analysis according to claim 4 is characterized in that: The formula for evaluating the geomagnetic anomaly at the measuring point is: ;in, represents the magnetic anomaly intensity index, represents the horizontal axis span, represents the vertical axis span, Represents the horizontal axis value of the point coordinates, Represents the vertical axis value of the point coordinates, and represents the integral operation, represents the period adjustment coefficient, represents the angular frequency parameter, Represents a time scale.

6. The karst collapse susceptibility assessment and analysis method based on hierarchical analysis according to claim 5 is characterized in that: The formula for evaluating the microbial activity of the cavity point is: ;in, represents the microbial activity index, represents the maximum activity index, represents the environmental adjustment factor, Represents the soil nitrogen content, Represents soil carbon content.

7. The karst collapse susceptibility assessment and analysis method based on hierarchical analysis according to claim 6 is characterized in that: The formula for biodiversity assessment at each measurement point is: ;in, stands for Biodiversity Index, represents pi, Representative microbial species, Represents the total number of microbial species in the microbial category table. Represents the empty point The species richness of microbial species, represents the temperature regulation coefficient, Represents the karst temperature.

8. The karst collapse susceptibility assessment and analysis method based on hierarchical analysis according to claim 7 is characterized in that: The formula for evaluating the pressure fluctuation at the cavity point is: ;in, represents the pressure fluctuation index, represents the pressure fluctuation factor, represents standard atmospheric pressure, Represents the difference between atmospheric pressure and standard atmospheric pressure. represents the fluctuation attenuation coefficient, Represents the starting point of the hole, Represents the end point of the hole, Represents the cavity depth.

9. The karst collapse susceptibility assessment and analysis method based on hierarchical analysis according to claim 8, characterized in that: The formula of the collapse evaluation function is: ;in, represents the collapse index, represents the weight of microbial activity, Represents the pressure fluctuation weight; the microbial activity weight and the pressure fluctuation weight satisfy the weight constraint: .

10. A karst collapse susceptibility assessment and analysis system based on hierarchical analysis, which is used to implement a karst collapse susceptibility assessment and analysis method based on hierarchical analysis as claimed in any one of claims 1 to 9, characterized in that: include: Data acquisition module: marking the measuring points in the karst area, collecting the karst data of the measuring points, standardizing the karst data, and obtaining standard karst data; Point division module: geomagnetically divides the measurement points based on the geomagnetic intensity to obtain the void point set; Index evaluation module: Use soil microbial data to evaluate the biological activity of the void point set and obtain the microbial activity index; Based on the atmospheric pressure, the pressure fluctuation analysis of the cavity point set is performed to obtain the pressure fluctuation index; Evaluation building module: Construct collapse evaluation function based on microbial activity index and pressure fluctuation index.

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