Karst collapse susceptibility evaluation and analysis method and system based on analytic hierarchy process

Abstract

The invention belongs to the technical field of karst collapse analysis, and discloses a karst collapse susceptibility evaluation analysis method and system based on analytic hierarchy process, and the method comprises the steps: marking measurement points in a karst area, collecting karst data of the measurement points, carrying out the data standardization of the karst data, and obtaining standard karst data; performing geomagnetic division on the measurement points based on the geomagnetic intensity to obtain a cavity point set; evaluating the biological activity of the cavity point set according to soil microorganism data to obtain a microorganism activity index; performing pressure fluctuation analysis on the cavity point set based on atmospheric pressure to obtain a pressure fluctuation index; constructing a collapse evaluation function based on the microbial activity index and the pressure fluctuation index; and the accuracy of karst collapse analysis is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of karst collapse analysis, and more specifically, to a karst collapse susceptibility assessment and analysis method and system based on hierarchical analysis. Background Art

[0002] The patent with application publication number CN118114007A discloses a karst collapse geological monitoring information analysis system, including an information collection unit, a regional division unit, a normal analysis unit, an abnormal analysis unit, an information storage unit, an analysis and early warning unit and an information output unit, which relates to the technical field of karst collapse information analysis, solves the technical problem that there is no comprehensive analysis of past data, and there are analysis errors in the data obtained from a single aspect. By combining the obtained data with the past data for analysis, the error in the analysis process is further reduced. At the same time, different areas are divided according to the obtained data, and different methods are adopted to analyze the divided regional data. Secondly, data is obtained and compared for abnormal areas to conduct timely early warning analysis of abnormal areas, which can remind relevant staff to respond. At the same time, data from multiple aspects is obtained for analysis, which can ensure the objectivity of the analysis results.

[0003] In the field of karst collapse susceptibility assessment, current karst collapse assessment 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 collapse. Geomagnetic anomalies are one of the important precursors of karst collapse, because karst pipelines and underground cavities will affect the distribution of underground magnetic fields. Due to the lack of research on the correlation between the morphology of underground karst pipeline networks and geomagnetic anomalies, it is impossible to accurately identify the distribution and changes of underground pipelines in the actual karst collapse assessment process, resulting in inaccurate assessment results, and thus unable to effectively warn of the occurrence of karst collapse; karst development is also affected by the structure of soil microbial communities, and microorganisms can The existing assessment methods fail to take into account the influence of microbial communities and ignore the role of this biological factor in karst development, which makes the assessment model incomplete and underestimates the risk of karst collapse in certain areas, especially in areas where microorganisms are active, increasing the error and inaccuracy of the assessment results; the fluctuation of atmospheric pressure affects the stability of underground cavities, especially under seasonal or sudden climate change, pressure fluctuations will have different degrees of impact on underground structures. The current karst collapse assessment method lacks analysis of the coupling mechanism of atmospheric pressure fluctuations, which increases the assessment error and leads to the inability to effectively warn before the disaster occurs.

[0004] In view of this, the present invention proposes a karst collapse susceptibility assessment analysis method and system based on hierarchical analysis to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a karst collapse susceptibility assessment and analysis method based on hierarchical analysis, comprising: 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 includes: 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 evaluation function based on the microbial activity index and the pressure fluctuation index.

[0006] Furthermore, the karst data collection method includes: 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.

[0007] Furthermore, the method of geomagnetically dividing the measuring points 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.

[0008] Furthermore, the method for obtaining the microbial activity index 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.

[0009] Furthermore, the formula for evaluating the geomagnetic anomaly of 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.

[0010] Furthermore, the method of performing pressure fluctuation analysis on the void point set includes: The soil microbial data of all measurement points were statistically analyzed by statistical analysis method to obtain a microbial category table, which contains microbial species and the total number of corresponding species; the number of each microbial species at the void point in the void concentration was divided by the total number of the corresponding microbial species in the microbial category table as the species richness, and the biodiversity of each measurement point was evaluated based on the species richness to obtain the biodiversity index; the maximum value of the biodiversity index was recorded as the regional maximum activity index, and the microbial activity of the void point was evaluated based on the regional maximum activity index. The formula for evaluating the microbial activity of the void 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.

[0011] Furthermore, the formula for biodiversity assessment at each measurement point is:

[0012] ;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.

[0013] Furthermore, the method of performing pressure fluctuation analysis on the void point set includes:

[0014] The void point in the void point concentration is taken as the center of the region, the distance scale is preset, the distance scale is used as the region radius, the region center is used as the circle center, and the region radius is used as the circle radius to draw a circle to obtain the region circle, the measurement points contained in the region circle are used as pressure evaluation points, the pressure evaluation points and the pressure center are used 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 used as the selection domain, the distance measurement formula is used to calculate the distance from other fluctuation analysis points to the center of the cross line as the evaluation distance, the fluctuation analysis point with the smallest evaluation distance is selected in each selection domain as the pressure boundary point, the pressure boundary points in the diagonal area are used as a group of boundary groups, the fluctuation analysis point at the center of the cross line is used as the difference center, the central difference algorithm is used to calculate the pressure fluctuation gradient of each group of boundary groups, the mean value of the pressure fluctuation gradient is used as the fluctuation factor, and the mean value of the fluctuation factor of all fluctuation analysis points is calculated as the pressure propagation operator; the pressure fluctuation evaluation of the void point is performed based on the pressure propagation operator, and the formula for the pressure fluctuation evaluation of the void 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.

[0015] Furthermore, 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: .

[0016] A karst collapse susceptibility assessment and analysis system based on hierarchical analysis, comprising: 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; perform pressure fluctuation analysis on the void point set based on atmospheric pressure and obtain the pressure fluctuation index; Evaluation building module: Construct collapse evaluation function based on microbial activity index and pressure fluctuation index.

[0017] The technical effects and advantages of the karst collapse susceptibility assessment and analysis method and system based on hierarchical analysis of the present invention are as follows: By analyzing geomagnetic data, the present invention can accurately identify the distribution and morphology of underground karst pipelines, effectively extract information on the spatial structure of underground karst, and avoid the shortcomings that may exist in traditional methods; by evaluating the biological activity of the void point set, the flexibility of karst collapse assessment is enhanced, so that it can maintain efficient and accurate assessment capabilities in complex karst environments, providing a new perspective for the biological action mechanism in the karst development process, and further improving the early warning capability of karst disasters; by performing pressure fluctuation analysis on the void point set, the influence 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a karst collapse susceptibility assessment and analysis method based on hierarchical analysis according to the present invention; Figure 2 The figure is a schematic diagram of a karst collapse susceptibility assessment and analysis system based on hierarchical analysis according to the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1; See also Figure 1 As shown, the karst collapse susceptibility assessment and analysis method based on hierarchical analysis described in this embodiment includes: 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 includes: 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 evaluation function based on the microbial activity index and the pressure fluctuation index.

[0021] Karst data include: geomagnetic intensity, soil microbial data, karst temperature, soil nitrogen and carbon content, atmospheric pressure and point coordinates; rock formation data collection methods include: presetting measurement routes and measurement intervals, which are set by technicians in the field according to the direction 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. The measurement points in the measurement route are marked based on the measurement intervals, and 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, etc. are set for each measurement point. The geomagnetic detector is used to collect geomagnetic intensity, the microbial sensor is used to collect soil microbial data, the soil microbial data includes microbial species and species content (the number of microorganisms of the same microbial species), the soil micro sensor is used to collect soil nitrogen and carbon content, the soil nitrogen and carbon content is the nitrogen content and carbon content data in the soil, the temperature sensor is used to collect karst temperature, the pressure sensor is used to collect atmospheric pressure, and the data collection of each measuring point is collected in the same time series; the point coordinates are determined based on the karst area, and the technicians in this field select a point in the karst area as the coordinate center point to construct a standard coordinate system, and the horizontal axis distance and vertical axis distance of each measuring point from the coordinate center point constitute the point coordinates.

[0022] In karst hazard assessment, data sources are usually very diverse, including geomagnetic field data, soil microbial data, atmospheric pressure data, etc. The data collected by different types of sensors and monitoring equipment have different dimensions, inconsistent time scales, and different precisions. Sensor data may be affected by factors such as changes in the external environment and fluctuations in sensor accuracy, resulting in data with strong time dependence. For example, changes in atmospheric pressure and soil microbial communities may be seasonal or cyclical, while geomagnetic field data may be affected by interference from different external environments. By standardizing karst data, karst data from different sources and dimensions can be uniformly processed, ensuring that karst data can be compared and integrated under the same standard, thereby eliminating the impact of data dimensions and removing noise caused by accidental fluctuations or external environmental factors. Specifically: 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, used to smooth the effects of time changes, represents the historical mean, Represents the inverse hyperbolic sine function, and all standard data constitute standard karst data. The transformed data are standardized by introducing the time decay term to incorporate the time dependence of karst data into the standardization process. This method can smooth the time variation trend in historical data, so as to better capture the pattern of long-term changes and remove the impact of short-term abnormal fluctuations.

[0023] The current karst collapse assessment methods usually ignore the relationship between the morphological characteristics of the underground karst pipeline network and the geomagnetic anomaly, resulting in the inability to accurately identify the distribution and changes of underground pipelines in the actual karst collapse assessment process, leading to inaccurate assessment results and thus unable to effectively warn of the occurrence of karst collapse. By dividing the measurement points by geomagnetic field, it is possible to more accurately reflect the development of karst at different times and depths, thereby providing a more accurate prediction of karst collapse risk. Specifically: 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 axis forward differential analysis on the geomagnetic intensity of the current measurement point to obtain the lateral change factor, and the finite difference method is used to perform a vertical axis 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. The magnetic field tensor analysis is performed on each scale factor. The formula for performing a magnetic field tensor analysis on each scale factor is: ;in, represents the geomagnetic depth tensor, represents the horizontal axis basis vector, represents the vertical axis basis vector, represents the lateral variation factor, represents the longitudinal variation factor, represents the scale factor, Represents the attenuation coefficient, which is used to control the attenuation rate of the magnetic field with the underground depth; the geomagnetic span is preset, and the geomagnetic span includes the horizontal axis span and the vertical axis span. The geomagnetic anomaly evaluation of the measuring point is performed based on the geomagnetic depth tensor. The formula for evaluating the geomagnetic anomaly of 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 periodic adjustment coefficient, which is used to control the amplitude of periodic changes. It represents the periodic changes of the geomagnetic field over time. Represents the angular frequency parameter, which is 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, the extreme maximum point and the 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 void assessment index, the void assessment threshold is preset, and the measurement points whose void assessment index is greater than or equal to the void assessment threshold are marked as void points, the scale factor with a smaller value of the scale factor corresponding to the extreme maximum point and the extreme minimum point is used as the void starting point, and the other is used as the void end point, the void starting point and void end point data are merged into the void point, and all void points constitute a void point set.

[0024] Karst development is also affected by the structure of soil microbial communities. 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 minerals in rocks to promote dissolution, thereby accelerating the formation and expansion of underground karst. Especially in limestone areas, the generation of acidic substances will lead to the dissolution of calcium carbonate and the formation of karst cavities and pipes. Existing assessment methods fail to take into account the impact of microbial communities and ignore the role of this biological factor in karst development, which makes the assessment incomplete and underestimates the risk of karst collapse in some areas. By evaluating the biological activity of the cavity point set, the contribution of microbial activity to karst development can be better revealed, thereby improving the accuracy of risk assessment. Specifically: The soil microbial data of all measurement points were statistically analyzed by statistical analysis method to obtain a microbial category table, which contains microbial species and the corresponding total number of species. The species richness was calculated by dividing the number of each microbial species at the void concentration point by the total number of the corresponding microbial species in the microbial category table. The biodiversity assessment was performed on each measurement point based on the species richness. 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 adjustment coefficient, which is used to control the effect of temperature on microorganisms and is set based on actual conditions by technicians in the field. represents the karst temperature; the maximum value of the biodiversity index is recorded as the regional maximum activity index, and the microbial activity of the cavity point is evaluated based on the regional maximum activity index. 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 regulation coefficient, which is used to control the effect of environmental nutrients on microbial activity. Represents the soil nitrogen content, Represents soil carbon content.

[0025] Fluctuations in atmospheric pressure affect the stability of underground cavities, especially under seasonal or sudden climate change. Pressure fluctuations will have different degrees of impact on underground structures. The current karst collapse assessment method lacks the analysis of the coupling mechanism of atmospheric pressure fluctuations, which increases the assessment error and leads to the inability to effectively warn before the disaster occurs. By analyzing the pressure fluctuations of the cavity point set, the specific impact of atmospheric pressure fluctuations on the risk of karst collapse can be quantitatively described, providing a unified quantitative standard for cavities in different regions and at different depths, thereby providing strong data support for the prediction of karst disasters. Specifically: Take the hole point in the hole point concentration as the area center, preset the distance scale, use the distance scale as the area radius, use the area center as the circle center, and use the area radius as the circle radius to draw a circle to obtain the area circle. Take the measurement point contained in the area circle as the pressure evaluation point, and use the pressure evaluation point and the pressure center as the fluctuation analysis point. For each fluctuation analysis point, draw a cross line with the fluctuation analysis point as the pressure center, and use the four areas divided by the cross line as the selection domain. Use the distance measurement formula to calculate the distance from other fluctuation analysis points to the cross line center as the evaluation distance. In each selection domain, select the fluctuation analysis point with the smallest evaluation distance as the pressure Boundary points, 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 crosshairs is taken as the difference 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 diagonal area is determined based on the quadrant division, the four areas of the two-dimensional coordinate system represent the four quadrants, and the diagonal area is the first and third quadrants, the second and fourth quadrants; the pressure fluctuation evaluation of the void point is performed based on the pressure propagation operator, and the formula for the pressure fluctuation evaluation of the void point is: ;in, represents the pressure fluctuation index, represents the pressure fluctuation factor, which is set by technicians in the field based on the actual situation. represents standard atmospheric pressure, Represents the difference between atmospheric pressure and standard atmospheric pressure. Represents the fluctuation attenuation coefficient, which is used to simulate the attenuation of pressure fluctuations and is set by technicians in the field based on actual conditions. Represents the starting point of the hole, Represents the end point of the hole, Represents the cavity depth.

[0026] Assume that the atmospheric pressure of a set of boundary groups is and The point coordinates are and , the atmospheric pressure at the differential center is , the point coordinates are ; Then the calculation formula of the central difference algorithm is: ;in, Represents the pressure fluctuation gradient.

[0027] The collapse evaluation function is constructed based on the microbial activity index and the pressure fluctuation index. 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: The larger the collapse index is, the easier it is for the point to collapse. The microbial activity weight and the pressure fluctuation weight are set by technical personnel in this field based on actual conditions. The value of the microbial activity weight should be increased for areas with higher soil nitrogen and carbon content, and the value of the pressure fluctuation weight should be increased for areas with larger atmospheric pressure fluctuations. The collapse assessment function is deployed to the monitoring terminal. When the collapse index is greater than or equal to the preset collapse threshold, the monitoring terminal issues an alarm.

[0028] By analyzing geomagnetic data, this embodiment can accurately identify the distribution and morphology of underground karst pipelines, effectively extract information on the spatial structure of underground karst, and avoid the shortcomings that may exist in traditional methods; by evaluating the biological activity of the void point set, the flexibility of karst collapse assessment is enhanced, so that it can maintain efficient and accurate assessment capabilities even in complex karst environments, providing a new perspective for the biological action mechanism in the karst development process and further improving the early warning capability of karst disasters; by performing pressure fluctuation analysis on the void 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.

[0029] Embodiment 2; See also Figure 2 As shown, the part not described in detail in this embodiment is described in Example 1, and a karst collapse susceptibility assessment and analysis system based on hierarchical analysis is provided, including: 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; perform pressure fluctuation analysis on the void point set based on atmospheric pressure and obtain the pressure fluctuation index; Evaluation building module: Construct collapse evaluation function based on microbial activity index and pressure fluctuation index; The modules are connected via wired and / or wireless means to achieve data transmission between the modules.

[0030] Embodiment 3; This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the karst collapse susceptibility assessment and analysis method based on hierarchical analysis provided above is implemented.

[0031] Since the electronic device introduced in this embodiment is an electronic device used to implement a karst collapse susceptibility assessment and analysis method based on hierarchical analysis in the embodiment of the present application, based on the karst collapse susceptibility assessment and analysis method based on hierarchical analysis introduced in the embodiment of the present application, the technical personnel of the field can understand the specific implementation of the electronic device of the present embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present application is not described in detail here. As long as the technical personnel of the field implement the electronic device used in the karst collapse susceptibility assessment and analysis method based on hierarchical analysis in the embodiment of the present application, it belongs to the scope of protection of the present application.

[0032] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0033] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical users in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as 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 pressure fluctuation index, and deploy it to the monitoring terminal.

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 method for obtaining the microbial activity index includes: The soil microbial data of all measurement points were statistically analyzed by statistical analysis method to obtain a microbial category table, which contains microbial species and the total number of corresponding species; the number of each microbial species at the void point in the void concentration was divided by the total number of the corresponding microbial species in the microbial category table as the species richness, and the biodiversity of each measurement point was evaluated based on the species richness to obtain the biodiversity index; the maximum value of the biodiversity index was recorded as the regional maximum activity index, and the microbial activity of the void point was evaluated based on the regional maximum activity index. The formula for evaluating the microbial activity of the void 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 method of performing pressure fluctuation analysis on the void point set includes: The void point in the void point concentration is taken as the center of the region, the distance scale is preset, the distance scale is taken as the region radius, the region center is taken as the circle center, and the region radius is taken as the circle radius to draw a circle, and the region circle is obtained. The measurement points contained in the region circle are taken as pressure evaluation points, and 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, and 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 center of the cross line as the evaluation distance. In each selection domain, the fluctuation analysis point with the smallest evaluation distance is selected as the pressure boundary point, and 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 difference center, and the central difference algorithm is used to calculate the pressure fluctuation gradient of each group of boundary groups. The mean value of the pressure fluctuation gradient is taken as the fluctuation factor, and the mean value of the fluctuation factor of all fluctuation analysis points is calculated as the pressure propagation operator; the pressure fluctuation evaluation of the void point is performed based on the pressure propagation operator, and the formula for the pressure fluctuation evaluation of the void 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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