An improved method for constructing CFP model of karst aquifer

Abstract

The present invention discloses a method for constructing an improved CFP model of a karst aquifer, comprising the following steps: S1, pre-experiment of a monitoring site; S2, obtaining groundwater data; S3, obtaining a storage coefficient. The present invention deploys monitoring sites in a karst aquifer for a pre-experiment to evaluate the coverage area of ​​data collection and determine the optimal number of monitoring sites, pre-processes the initial hydrological data collected by the monitoring sites to obtain hydrological data, obtains groundwater data based on the hydrological data, and analyzes the groundwater data through the CFP model of the karst aquifer to obtain the storage coefficient of the karst aquifer, thereby achieving the effect of improving the accuracy of the CFP model of the karst aquifer, and solving the problem of insufficient correlation between the CFP model of the karst aquifer and the actual karst aquifer in the prior art.

Description

Technical Field

[0001] The invention relates to the technical field of karst landforms, and in particular to a method for constructing an improved karst aquifer CFP model. Background Art

[0002] Karst aquifers are one of the important areas of groundwater research. Karst aquifers are usually composed of soluble rocks, with a large number of cracks and conduits inside, forming a complex underground flow system. Due to the existence of karst conduits, the permeability of karst aquifers is high, and the flow velocity is much higher than that of general porous medium aquifers. The hydrological dynamics of karst aquifers are affected by many factors such as precipitation, topography, and geological structure, and have significant temporal and spatial variations. The improvement of the coupled flow process model of karst aquifers (MODFLOW-CFP model) can provide a more scientific and effective tool for the research and management of karst aquifers, and promote the rational development and protection of groundwater resources.

[0003] In the prior art, a conceptual model based on hydrogeological survey is established by collecting hydrogeological data, and model parameters are determined according to measured data and laboratory data. The karst aquifer is predicted and evaluated through the calibrated model, thereby improving the prediction ability of the flow dynamics of the karst aquifer.

[0004] For example, the invention patent with announcement number CN110426504B discloses a karst aquifer sand box test system and method based on tracer hydraulic tomography inversion, which includes: a visualization sand box device, a karst pipeline, a flow control device, a horizontal well, a data acquisition device and a data processing device; the visualization sand box device constitutes a sand layer filling space, and the karst pipeline is buried in the sand layer; the flow control device is a constant head water storage tank; a horizontal well mounting hole and a tracer delivery hole are provided on the back panel; a horizontal well is installed in each horizontal well mounting hole, the monitoring well is connected to an osmotic pressure sensor or a conductivity sensor, the injection well is connected to a peristaltic pump, the conductivity sensor, the osmotic pressure sensor, and the peristaltic pump are connected to the data collection device, and the data acquisition device is connected to the data processing device.

[0005] For example, the invention patent with announcement number: CN116818607B announces a prediction and evaluation method for the migration of pollutants in karst landforms, including: generalizing karst fissures; constructing a mathematical model based on the generalization; solving the model; coupling the pollutant flux to the groundwater model; based on the karst hydrogeological characteristics, combined with the migration characteristics of pollutants in the fracture-matrix dual system, using the fluid dynamics migration equation, establishing a set of mathematical model methods for pollutant migration in the fracture-matrix dual system that conforms to the characteristics of karst aquifers, and solving the model using COMSOL software, developing an arsenic contaminated site risk prediction model and method suitable for karst fissure areas, predicting the impact of pollutant slag dumping sites on underlying aquifers, and providing technical methods for the risk control of pollutant pollution in karst areas.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, the construction process of the CFP model of the karst aquifer is limited by the lack of long-term continuity of hydrological data collection and the low accuracy of measurement of key parameters, which makes it difficult for the model to capture the dynamic changes of hydrological data. There is also a problem of insufficient correlation between the CFP model of the karst aquifer and the actual karst aquifer. Summary of the invention

[0008] The embodiment of the present application solves the problem of insufficient correlation between the CFP model of karst aquifer and the actual karst aquifer in the prior art by providing an improved method for constructing the CFP model of karst aquifer, thereby improving the accuracy of the CFP model of karst aquifer.

[0009] The embodiment of the present application provides a method for constructing an improved CFP model of a karst aquifer, comprising the following steps: S1, evaluating the coverage area of ​​data collection obtained by conducting preliminary experiments on monitoring stations to determine the optimal number of monitoring stations, and using the monitoring stations deployed in the karst aquifer to conduct real-time data collection to obtain initial hydrological data; S2, preprocessing the initial hydrological data collected by the deployed monitoring stations to obtain hydrological data, and obtaining groundwater data based on the hydrological data, wherein the hydrological data includes rainfall, evaporation and groundwater level, and the groundwater data includes a permeability coefficient and a flow coefficient, wherein the permeability coefficient indicates the ability of a rock formation to penetrate water, and the flow coefficient indicates the ability of water to flow in a rock formation; S3, obtaining the storage coefficient of the karst aquifer based on the CFP model of the karst aquifer and the groundwater data, wherein the storage coefficient is used to describe the ability of the aquifer to store water.

[0010] Furthermore, the specific process of the preliminary experiment is as follows: A1, the total area of ​​the karst aquifer area and the coverage radius of the monitoring site are obtained from the preset database, and the coverage index of the monitoring site is obtained based on this, and the coverage index is used to measure the degree of conformity between the total coverage area of ​​the monitoring site and the total area of ​​the karst aquifer area; A2, determine whether the coverage index is greater than 0, if it is greater than 0, execute A3, otherwise it indicates that there is an unmonitored area, and feedback is given; A3, determine whether the coverage index is greater than the preset coverage index, if it is greater than the preset coverage index, there are redundant sites, and feedback is given, otherwise it is determined that the current number of monitoring sites is the optimal number; the coverage index is calculated by the following formula:

[0011]

[0012] Where n represents the total number of monitoring stations, FG represents the coverage index of the monitoring stations, r represents the coverage radius of the monitoring stations, and A represents the total area of ​​the karst aquifer region.

[0013] Furthermore, the specific steps of the preprocessing are as follows: B1, performing data cleaning on the initial hydrological data to obtain the first hydrological data, and the data cleaning includes missing value filling, outlier processing, duplicate value removal and noise data smoothing; B2, performing data transformation on the first hydrological data to obtain hydrological data, and the data transformation includes data format conversion and unit standardization; B3, integrating the hydrological data with the corresponding timestamp to obtain a hydrological time series.

[0014] Furthermore, the specific process of the permeability coefficient is as follows: E1, obtaining the rainfall proportional coefficient and the water level proportional coefficient from the preset database, and obtaining the first influencing parameter by comparing the pre-processed rainfall and the water level difference, the rainfall proportional coefficient represents the influence of the unit rainfall on the permeability coefficient, the water level proportional coefficient represents the influence of the unit water level difference on the permeability coefficient, and the first influencing parameter represents the degree of influence of rainfall on permeability; E2, obtaining the evaporation proportional coefficient and the water level proportional coefficient from the preset database, and obtaining the second influencing parameter by comparing the pre-processed evaporation and the water level difference, the evaporation proportional coefficient represents the influence of the unit evaporation on the permeability coefficient, and the second influencing parameter represents the degree of influence of evaporation on permeability; E3, obtaining the aquifer thickness from the preset database, and obtaining the permeability coefficient by combining the first influencing parameter and the second influencing parameter, the aquifer thickness represents the thickness of the karst aquifer.

[0015] Furthermore, the specific method for obtaining the permeability coefficient is as follows: the permeability coefficient is obtained according to the first influencing parameter, the second influencing parameter and the thickness of the aquifer. The permeability coefficient is used to describe the water permeability of the karst aquifer. The permeability coefficient is calculated by the following formula:

[0016]

[0017] Where ST represents the permeability coefficient, J represents the first influencing parameter, Z represents the second influencing parameter, and b represents the thickness of the aquifer.

[0018] Furthermore, the specific method for obtaining the flow coefficient is as follows: the pipe diameter and the pipe roughness are obtained from a preset database, and the flow coefficient is obtained by combining the hydrological data. The flow coefficient is calculated by the following formula:

[0019]

[0020] In the formula, SL represents the flow coefficient, JY represents the rainfall, ZF represents the evaporation, and K c represents the roughness of the pipe, d represents the pipe diameter, and e represents the natural constant.

[0021] Furthermore, the storage coefficient of the karst aquifer is obtained according to the CFP model of the karst aquifer and the groundwater data, and a sensitivity test is also performed before that. The sensitivity test includes a permeability coefficient sensitivity test and a flow coefficient sensitivity test. The specific process of the permeability coefficient sensitivity test is as follows: C1, obtaining initial permeability hydrological data from a preset database, and obtaining a permeability coefficient baseline value based on the data, the initial permeability hydrological data including initial rainfall, initial evaporation, initial water level difference and initial aquifer thickness; C2, sequentially changing the value of a single permeability coefficient variable in the initial permeability hydrological data while keeping the values ​​of other permeability coefficient variables unchanged to obtain test permeability hydrological data, and recalculating the permeability coefficient according to the test permeability hydrological data, and obtaining a permeability coefficient sensitive value of the corresponding permeability coefficient variable in combination with the permeability coefficient baseline value, the permeability coefficient sensitive value indicating the degree of influence of the corresponding permeability coefficient variable on the permeability coefficient, the permeability coefficient variables including rainfall, evaporation, water level difference and aquifer thickness; C3, comparing the permeability coefficient sensitive values ​​corresponding to each permeability coefficient variable to obtain the variable with the greatest permeability influence, the variable with the greatest permeability influence indicating the variable with the greatest influence on the permeability coefficient.

[0022] Furthermore, the specific process of the flow coefficient sensitivity test is as follows: D1, obtaining initial flow hydrological data from a preset database, and obtaining a flow coefficient baseline value based on this, the initial flow hydrological data including initial rainfall, initial evaporation, initial pipeline roughness and initial pipeline diameter; D2, changing the value of a single flow coefficient variable in the initial flow hydrological data in turn while keeping the values ​​of other flow coefficient variables unchanged to obtain test flow hydrological data, and recalculating the flow coefficient based on the test flow hydrological data, and at the same time combining the flow coefficient baseline value to obtain a flow coefficient sensitive value corresponding to the flow coefficient variable, the flow coefficient sensitive value indicating the degree of influence of the corresponding flow coefficient variable on the flow coefficient, the flow coefficient variables including rainfall, evaporation, pipeline roughness and pipeline diameter; D3, comparing the flow coefficient sensitive values ​​corresponding to each flow coefficient variable to obtain the variable with the greatest flow influence, the variable with the greatest flow influence indicating the variable with the greatest influence on the flow coefficient.

[0023] Furthermore, the storage coefficient is specifically obtained in the following manner: a flow coefficient weight and a permeability coefficient weight are obtained from a preset database, and the storage coefficient is obtained by combining the permeability coefficient and the flow coefficient; the storage coefficient is calculated by the karst aquifer CFP model as follows:

[0024] MX=sinh[α*SL+β*ST*(JY-ZF)];

[0025] Where MX represents the storage coefficient, α represents the flow coefficient weight, SL represents the flow coefficient, β represents the permeability coefficient weight, ST represents the permeability coefficient, JY represents the rainfall, and ZF represents the evaporation.

[0026] Furthermore, the storage coefficient of the karst aquifer is obtained according to the karst aquifer CFP model and groundwater data, and the karst aquifer CFP model is verified. The specific process is as follows: historical hydrological data are obtained from the historical database, and the historical hydrological data are input into the constructed karst aquifer CFP model to obtain the storage coefficient, and the obtained storage coefficient is recorded as the verification storage coefficient; the verification storage coefficient is compared with the storage coefficient baseline value to obtain the model conformity, and the model conformity is used to comprehensively quantify the conformity of the model of the framework with the historical data, and the model conformity is calculated by the following formula:

[0027]

[0028] Where FH represents the model conformity, YZ represents the verification storage coefficient, and MX0 represents the storage coefficient baseline value.

[0029] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0030] 1. Preliminary experiments were conducted on the deployed monitoring stations to evaluate the coverage area of ​​data collection and determine the optimal number of monitoring stations. The initial hydrological data collected were then preprocessed to obtain hydrological data. Finally, groundwater data was obtained based on the hydrological data, and the storage coefficient of the karst aquifer was obtained through the karst aquifer CFP model, thereby improving the monitoring effect of the monitoring stations, and further improving the accuracy of the karst aquifer CFP model, effectively solving the problem of insufficient correlation between the karst aquifer CFP model and the actual karst aquifer in the prior art.

[0031] 2. By obtaining the baseline value of the permeability coefficient, the values ​​of single variables in the initial permeability hydrological data are changed in turn while the values ​​of other variables remain unchanged, and the permeability coefficient is recalculated. At the same time, the permeability coefficient sensitivity value of the corresponding variable is obtained in combination with the baseline value of the permeability coefficient. The permeability coefficient sensitivity values ​​corresponding to each variable are compared to obtain the variable with the greatest permeability impact, thereby quantifying the degree of influence of each variable in the permeability coefficient on the permeability coefficient, and thus achieving a more accurate evaluation of the permeability coefficient.

[0032] 3. By obtaining the rainfall proportional coefficient, evaporation proportional coefficient, water level proportional coefficient and aquifer thickness from the preset database, and then comparing the rainfall and water level difference to obtain the first influencing parameter, and comparing the evaporation and water level difference to obtain the second influencing parameter, and finally combining the first influencing parameter and the second influencing parameter to obtain the permeability coefficient, the influence of rainfall and evaporation on permeability is quantified, thereby realizing a systematic evaluation of the influence of rainfall and evaporation on the permeability coefficient of karst aquifers. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flow chart of a method for constructing an improved karst aquifer CFP model provided in an embodiment of the present application;

[0034] Figure 2 A flowchart of data preprocessing provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] The embodiment of the present application solves the problem of insufficient correlation between the CFP model of the karst aquifer and the actual karst aquifer in the prior art by providing a method for constructing an improved CFP model of the karst aquifer. Preliminary experiments are conducted on the deployed monitoring stations to evaluate the coverage area of ​​data collection and determine the optimal number of monitoring stations. The collected initial hydrological data are then preprocessed to obtain hydrological data. Finally, groundwater data is obtained based on the hydrological data, and the storage capacity of the karst aquifer is obtained through the CFP model of the karst aquifer, thereby improving the accuracy of the CFP model of the karst aquifer.

[0036] The technical solution in the embodiment of the present application is to solve the problem of low accuracy of the above-mentioned karst aquifer CFP model, and the overall idea is as follows:

[0037] The hydrological data are obtained by preprocessing the collected initial hydrological data, and then the groundwater data are obtained based on the hydrological data. Finally, the storage capacity of the karst aquifer is obtained through the karst aquifer CFP model, thereby achieving the effect of improving the accuracy of the karst aquifer CFP model.

[0038] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0039] like Figure 1 As shown, it is a flow chart of a method for constructing an improved karst aquifer CFP model provided in an embodiment of the present application, the method comprising the following steps: S1, evaluating the coverage area of ​​data collection obtained by conducting preliminary experiments on monitoring stations to determine the optimal number of monitoring stations, and using the monitoring stations deployed in the karst aquifer to conduct real-time data collection to obtain initial hydrological data, the monitoring stations comprising rainfall sensors, automatic water level gauges and evaporation sensors; S2, pre-processing the initial hydrological data collected by the deployed monitoring stations to obtain hydrological data, and obtaining groundwater data based on the hydrological data, the hydrological data comprising rainfall, evaporation and groundwater level, the groundwater data comprising permeability coefficient and flow coefficient, the permeability coefficient indicating the ability of rock formations to penetrate water, the flow coefficient indicating the ability of water to flow in rock formations; S3, obtaining the storage coefficient of the karst aquifer based on the karst aquifer CFP model and the groundwater data, the karst aquifer CFP model is a mathematical model for simulating the impact of climate on groundwater in karst aquifers, and the storage coefficient is used to describe the ability of the aquifer to store water.

[0040] In this embodiment, a preliminary experiment is conducted on the monitoring sites to evaluate the coverage area of ​​data collection and determine the optimal number of monitoring sites to ensure the comprehensiveness and accuracy of data collection; the sensor data can reflect the state of groundwater in real time; the rainfall sensor is used to measure rainfall; the automatic water level meter is used to measure the groundwater level; the evaporation sensor is used to measure evaporation; the rainfall represents the rainfall per unit time; the evaporation represents the evaporation per unit time; the groundwater level represents the height of the groundwater level; the karst aquifer represents the area where groundwater flows and is stored through cracks and holes in the rock; and the accuracy of the CFP model of the karst aquifer is improved.

[0041] Furthermore, the specific process of the preliminary experiment is as follows: A1, the total area of ​​the karst aquifer area and the coverage radius of the monitoring station are obtained from the preset database, and the coverage index of the monitoring station is obtained based on this. The coverage index is used to measure the degree of conformity between the total coverage area of ​​the monitoring station and the total area of ​​the karst aquifer area; A2, determine whether the coverage index is greater than 0. If it is greater than 0, execute A3, otherwise it indicates that there is an unmonitored area and feedback is given; A3, determine whether the coverage index is greater than the preset coverage index. If it is greater than the preset coverage index, there are redundant stations and feedback is given, otherwise it is determined that the current number of monitoring stations is the optimal number. The preset coverage index represents the minimum area required to be covered during the deployment of the karst aquifer monitoring station; the coverage index is calculated by the following formula:

[0042]

[0043] Where n represents the total number of monitoring stations, FG represents the coverage index of the monitoring stations, r represents the coverage radius of the monitoring stations, and A represents the total area of ​​the karst aquifer region.

[0044] In this embodiment, the coverage radius of the monitoring site indicates the area that each site can cover; if the coverage index is less than 0, it means that there are areas that have not been monitored, and the number of monitoring sites needs to be increased to cover more areas; if the coverage index is greater than the preset coverage index, it means that there are redundant sites, and the number of monitoring sites can be reduced to save costs and resources; the preset coverage index is usually a set standard, which indicates the minimum area required to be covered during the deployment of karst aquifer monitoring sites; the preset coverage index is obtained from a preset database, and is generally 110% of the total area of ​​the karst aquifer area covered by the monitoring site; redundant sites indicate monitoring sites that exceed the necessary coverage range; the optimal number indicates the minimum number of monitoring sites required while ensuring full coverage; and full coverage of the monitoring sites is achieved.

[0045] The algorithm of this embodiment combines the "total number of monitoring sites", "coverage radius of monitoring sites" and "total area of ​​karst aquifer region" to obtain the "coverage index of monitoring sites" through comprehensive analysis. Moreover, each parameter is related to each other and does not exist independently. It is a whole and the "coverage index of monitoring sites" cannot be obtained by simple combination and addition. For example, the increase of the "total number of monitoring sites" in this formula will lead to an increase of the "coverage index of monitoring sites", which does not necessarily mean that the "coverage index of monitoring sites" is large. At this time, it is also necessary to consider that the "coverage radius of monitoring sites" and "total area of ​​karst aquifer region" can also determine the coverage degree of monitoring sites at the next moment. The correlation and mutual influence between various factors are considered in a quantitative way. The algorithm itself is obtained through comprehensive analysis, which can accurately and quantitatively analyze the coverage degree of monitoring sites, indicating the effect of accurately evaluating the coverage index of monitoring sites. Therefore, accurate analysis is performed by establishing a mathematical form.

[0046] Specifically, the sensors deployed at each monitoring site are the same, so the coverage radius of each monitoring site is the same; the coverage index increases with the increase of the total number of monitoring sites and the coverage radius of the monitoring sites, and the coverage index decreases with the increase of the total area of ​​the karst aquifer region. Therefore, the coverage index is proportional to the total number of monitoring sites and the coverage radius of the monitoring sites, and the coverage index is inversely proportional to the total area of ​​the karst aquifer region.

[0047] Furthermore, the specific steps of preprocessing are as follows: B1, the initial hydrological data is cleaned to obtain the first hydrological data, and the data cleaning includes missing value filling, outlier processing, duplicate value removal and noise data smoothing. Missing value filling means identifying and processing the missing values ​​in the initial hydrological data set, outlier processing means identifying and processing the outliers in the initial hydrological data set, duplicate value removal means identifying and processing the duplicate data in the initial hydrological data set, and noise data smoothing means processing the errors appearing in the data; B2, the first hydrological data is transformed to obtain the hydrological data, and the data transformation includes data format conversion and unit standardization. The data format conversion means identifying and processing the data with inconsistent formats in the first hydrological data set, and the unit standardization means identifying the first hydrological data and judging whether there are inconsistent units in the first hydrological data set. If so, the units of all numerical values ​​are unified into the same unit; B3, the hydrological data are integrated with the corresponding timestamps to obtain the hydrological time series.

[0048] In this embodiment, if Figure 2As shown, it is a flowchart of data preprocessing provided in an embodiment of the present application; missing value filling means filling with a suitable method, for example, filling with the mean or median; outlier means data that deviates significantly from the overall trend of the initial hydrological data. Generally, if a value exceeds the mean plus or minus 2 times the standard deviation, it is considered an outlier; duplicate value deletion means removing duplicate data records and retaining only one record; data format conversion means converting all data to the same format, for example, unifying all dates and converting all temperature units to degrees Celsius; unit standardization means unifying all values ​​into the same unit for subsequent processing, for example, unifying all radii into meters (m); integrating timestamps means making one-to-one correspondence between hydrological data and corresponding timestamps to ensure that each piece of data has a corresponding timestamp to form a complete hydrological time series; the quality of data acquisition is improved.

[0049] Furthermore, the specific process of the permeability coefficient is as follows: E1, obtain the rainfall proportional coefficient and the water level proportional coefficient from the preset database, and obtain the first influencing parameter by comparing the pre-processed rainfall and the water level difference. The rainfall proportional coefficient represents the influence of unit rainfall on the permeability coefficient, and the water level proportional coefficient represents the influence of unit water level difference on the permeability coefficient. The first influencing parameter represents the degree of influence of rainfall on permeability; E2, obtain the evaporation proportional coefficient and the water level proportional coefficient from the preset database, and obtain the second influencing parameter by comparing the pre-processed evaporation and the water level difference. The evaporation proportional coefficient represents the influence of unit evaporation on the permeability coefficient, and the second influencing parameter represents the degree of influence of evaporation on permeability; E3, obtain the aquifer thickness from the preset database, and obtain the permeability coefficient by combining the first influencing parameter and the second influencing parameter. The aquifer thickness represents the thickness of the karst aquifer.

[0050] In this embodiment, the rainfall proportionality coefficient is used to quantify the influence of the increase or decrease of rainfall on the permeability coefficient of the karst aquifer, the water level proportionality coefficient is used to quantify the influence of the change of water level difference on the permeability coefficient of the karst aquifer; the evaporation proportionality coefficient is used to quantify the influence of the change of evaporation on the permeability coefficient of the karst aquifer; the aquifer thickness represents the vertical distance from the top to the bottom of the aquifer; the permeability coefficient is a key hydrological parameter used to describe the permeability of water in the karst aquifer; the accuracy of karst aquifer monitoring and hydrological modeling is achieved.

[0051] It should be understood that the first influencing parameter represents the comprehensive influence of rainfall on the permeability coefficient; as the rainfall and water level difference increase, the first influencing parameter increases accordingly, so the first influencing parameter is proportional to the rainfall and water level difference; the first influencing parameter is calculated by the following formula:

[0052] J = a*JY+f*ΔSW;

[0053] Where J represents the first influencing parameter, a represents the rainfall proportional coefficient, f represents the water level proportional coefficient, JY represents the rainfall, and ΔSW represents the water level difference.

[0054] It should be understood that the second influencing parameter represents the comprehensive influence of evaporation on the permeability coefficient; as the evaporation and water level difference increase, the second influencing parameter increases accordingly, so the second influencing parameter is proportional to the evaporation and water level difference; the second influencing parameter is calculated by the following formula:

[0055] Z = h*ZF + f*ΔSW;

[0056] Wherein, Z represents the second influencing parameter, h represents the evaporation proportional coefficient, f represents the water level proportional coefficient, ZF represents the evaporation amount, and ΔSW represents the water level difference.

[0057] Specifically, the rainfall proportional coefficient is obtained from a preset database. In a specific embodiment, the relationship between rainfall and permeability coefficient is fitted by fitting the maximum rainfall and minimum rainfall in the historical database with the permeability loss to obtain a fitting curve, and the corresponding rainfall proportional coefficient is determined based on the real-time maximum rainfall and minimum rainfall obtained from the fitting curve, and the permeability loss represents the reduction in the water flow obtained by the water flow sensor during the permeation process in the rock formation.

[0058] Specifically, the water level proportional coefficient is obtained from a preset database. In a specific embodiment, the relationship between the water level difference and the permeability coefficient is fitted by the maximum water level and the minimum water level in the historical database and the permeability loss to obtain a fitting curve, and the corresponding water level proportional coefficient is determined based on the real-time maximum water level and the minimum water level obtained by the fitting curve, and the water level difference represents the difference in water levels obtained by the automatic water level meter.

[0059] Specifically, the evaporation proportional coefficient is obtained from a preset database. In a specific embodiment, the relationship between the evaporation loss and the permeability coefficient is fitted by fitting the maximum evaporation and the minimum evaporation and the permeability loss in the historical database to obtain a fitting curve, and the real-time maximum evaporation and the minimum evaporation are obtained according to the fitting curve to determine the corresponding evaporation proportional coefficient, and the evaporation loss represents the reduction value of the evaporation obtained by the evaporation sensor.

[0060] Furthermore, the specific method for obtaining the permeability coefficient is as follows: the permeability coefficient is obtained according to the first influencing parameter, the second influencing parameter and the thickness of the aquifer. The permeability coefficient is used to describe the water permeability of the karst aquifer. The permeability coefficient is calculated by the following formula:

[0061]

[0062] Where ST represents the permeability coefficient, J represents the first influencing parameter, Z represents the second influencing parameter, and b represents the thickness of the aquifer.

[0063] In this embodiment, the permeability coefficient is a physical quantity that describes the ability of a rock to allow water to pass through its pores or cracks. In karst geology, it is particularly used to describe the permeability of a karst aquifer. The permeability coefficient is a physical quantity that describes the ability of a rock to allow water to pass through its pores or cracks. In karst geology, it is particularly used to describe the permeability of a karst aquifer. The thickness of the aquifer is one of the important factors affecting the permeability coefficient, because a thicker aquifer usually means more pores and crack space, thereby allowing more water to pass through. The accuracy of the permeability coefficient is improved.

[0064] The algorithm of this embodiment combines the "first influencing parameter", "second influencing parameter" and "rock aquifer thickness" to obtain the "permeability coefficient" through comprehensive analysis. In addition, the various parameters are related to each other and do not exist independently. They are a whole, and the "permeability coefficient" cannot be obtained by simple combination and addition. For example, the increase of the "first influencing parameter" in this formula will lead to an increase in the "permeability coefficient", which does not necessarily mean that the "permeability coefficient" is large. At this time, it is also necessary to consider that the "second influencing parameter" and "rock aquifer thickness" can also determine the permeability of the karst aquifer at the next moment. The correlation and mutual influence between various factors are considered in a quantitative manner. The algorithm itself is obtained through comprehensive analysis, which can accurately and quantitatively analyze the permeability of the karst aquifer, indicating the effect of accurately evaluating the permeability coefficient of the karst aquifer. Therefore, accurate analysis is performed by establishing a mathematical form.

[0065] Specifically, when the first influencing parameter and the thickness of the aquifer increase, the permeability coefficient increases, and when the second influencing parameter increases, the permeability coefficient decreases. Therefore, the permeability coefficient is proportional to the first influencing parameter and the thickness of the aquifer, and the permeability coefficient is inversely proportional to the second influencing parameter.

[0066] Furthermore, the specific method for obtaining the flow coefficient is as follows: the pipeline diameter and pipeline roughness are obtained from the preset database, and the flow coefficient is obtained by combining the hydrological data. The pipeline diameter represents the diameter of the karst pipeline, and the pipeline roughness represents the roughness of the karst pipeline surface. The flow coefficient is calculated by the following formula:

[0067]

[0068] In the formula, SL represents the flow coefficient, JY represents the rainfall, ZF represents the evaporation, and K c represents the roughness of the pipe, d represents the pipe diameter, and e represents the natural constant.

[0069] In this embodiment, the karst aquifer is composed of bedrock fissures and karst pipeline media; the flow coefficient is used to quantify the flow performance of water in the karst pipeline medium; the higher the flow coefficient, the better the flow performance of the liquid in the pipeline; the evaluation of the flow performance of water in the karst pipeline medium is achieved.

[0070] The algorithm of this embodiment combines "rainfall", "evaporation", "pipeline roughness" and "pipeline diameter" to obtain the "flow coefficient" through comprehensive analysis. In addition, the various parameters are related and do not exist independently. They are a whole, and the "flow coefficient" cannot be obtained by simple combination and addition. For example, the increase of "rainfall" in this formula will lead to an increase of "flow coefficient", which does not necessarily mean that the "flow coefficient" is large. At this time, it is also necessary to consider that "evaporation", "pipeline roughness" and "pipeline diameter" can also determine the flow performance of the karst aquifer at the next moment. The correlation and mutual influence between various factors are considered in a quantitative way. The algorithm itself is obtained through comprehensive analysis, which can accurately and quantitatively analyze the flow performance of the karst aquifer, indicating the effect of accurately evaluating the flow coefficient of the karst aquifer. Therefore, accurate analysis is performed by establishing a mathematical form.

[0071] Specifically, the difference between rainfall and evaporation is the effective rainfall. When the effective rainfall and the pipe diameter increase, the flow coefficient increases, and when the pipe roughness increases, the flow coefficient decreases; therefore, the flow coefficient is proportional to the effective rainfall and the pipe diameter, and inversely proportional to the pipe roughness.

[0072] Furthermore, the storage coefficient of the karst aquifer is obtained according to the CFP model of the karst aquifer and the groundwater data, and sensitivity tests are also performed before. The sensitivity tests include permeability coefficient sensitivity tests and flow coefficient sensitivity tests. The specific process of the permeability coefficient sensitivity test is as follows: C1, the initial permeability hydrological data is obtained from the preset database, and the permeability coefficient baseline value is obtained based on this. The initial permeability hydrological data include initial rainfall, initial evaporation, initial water level difference and initial aquifer thickness; C2, the value of a single permeability coefficient variable in the initial permeability hydrological data is changed in turn, and the values ​​of other permeability coefficient variables remain unchanged to obtain the test permeability hydrological data, and the permeability coefficient is recalculated according to the test permeability hydrological data. At the same time, the permeability coefficient sensitivity value of the corresponding permeability coefficient variable is obtained in combination with the permeability coefficient baseline value. The permeability coefficient sensitivity value indicates the degree of influence of the corresponding permeability coefficient variable on the permeability coefficient. The permeability coefficient variables include rainfall, evaporation, water level difference and aquifer thickness; C3, the permeability coefficient sensitivity values ​​corresponding to each permeability coefficient variable are compared to obtain the variable with the greatest permeability influence. The variable with the greatest permeability influence indicates the variable with the greatest influence on the permeability coefficient;

[0073] In this embodiment, the permeability coefficient sensitivity test is an analytical method for evaluating the degree of influence of the permeability coefficient variable on the permeability coefficient; the single value in the permeability coefficient variable is increased by 10% in turn, and the other values ​​remain unchanged to obtain the test permeability hydrological data, and the permeability coefficient is recalculated based on the test permeability hydrological data to evaluate the sensitivity of each variable to the permeability coefficient; a systematic evaluation of the influence of different permeability coefficient variables on the permeability coefficient is achieved.

[0074] Specifically, the initial infiltration hydrological data is obtained from the preset database, and the initial infiltration hydrological data is input into the permeability coefficient formula to obtain the baseline value of the permeability coefficient; the permeability coefficient sensitivity value is used to quantify the response degree of the permeability coefficient when each permeability coefficient variable changes; the larger the sensitivity value of the permeability coefficient, the greater the influence of the corresponding permeability coefficient variable on the permeability coefficient; the calculation formula of the permeability coefficient sensitivity value is as follows:

[0075]

[0076] Where i is the number of the permeability coefficient variable, i = 1, 2, 3, 4, i = 1 represents rainfall, i = 2 represents evaporation, i = 3 represents water level difference, i = 4 represents aquifer thickness, MG ST.i represents the sensitive value of the permeability of the ith permeability variable, ST i represents the permeability of the ith permeability variable, and ST0 represents the baseline value of the permeability.

[0077] Furthermore, the specific process of the flow coefficient sensitivity test is as follows: D1, obtaining initial flow hydrological data from a preset database, and obtaining a flow coefficient baseline value based on the data, the initial flow hydrological data including initial rainfall, initial evaporation, initial pipe roughness and initial pipe diameter; D2, changing the value of a single flow coefficient variable in the initial flow hydrological data in turn while keeping the values ​​of other flow coefficient variables unchanged to obtain test flow hydrological data, and recalculating the flow coefficient based on the test flow hydrological data, and combining the flow coefficient baseline value to obtain a flow coefficient sensitivity value of the corresponding flow coefficient variable, the flow coefficient sensitivity value indicates the degree of influence of the corresponding flow coefficient variable on the flow coefficient, the flow coefficient variables including rainfall, evaporation, pipe roughness and pipe diameter; D3, comparing the flow coefficient sensitivity values ​​corresponding to each flow coefficient variable to obtain the variable with the greatest flow influence, the variable with the greatest flow influence indicates the variable with the greatest influence on the flow coefficient;

[0078] In this embodiment, the flow coefficient sensitivity test is an analytical method for evaluating the degree of influence of the flow coefficient variable on the flow coefficient; the single value in the flow coefficient variable is increased by 10% in turn, and the other values ​​remain unchanged to obtain the test flow hydrological data, and the flow coefficient is recalculated based on the test flow hydrological data to evaluate the sensitivity of each variable to the flow coefficient; a systematic evaluation of the influence of different flow coefficient variables on the flow coefficient is achieved.

[0079] Specifically, the initial flow hydrological data is obtained from the preset database, and the initial flow hydrological data is input into the flow coefficient formula to obtain the flow coefficient baseline value. The flow coefficient sensitivity value is used to quantify the response degree of the flow coefficient when each flow coefficient variable changes; the larger the flow coefficient sensitivity value, the greater the influence of the corresponding flow coefficient variable on the flow coefficient; the calculation formula of the flow coefficient sensitivity value is as follows:

[0080]

[0081] Where q is the number of the flow coefficient variable, q = 1, 2, 3, 4, q = 1 represents rainfall, q = 2 represents evaporation, q = 3 represents pipe roughness, q = 4 represents pipe diameter, MG SL.q The sensitivity value of the flow coefficient of the qth flow coefficient variable, SL q represents the flow coefficient of the qth flow coefficient variable, and SL0 represents the baseline value of the flow coefficient.

[0082] Furthermore, the specific method of obtaining the storage coefficient is as follows: the flow coefficient weight and the permeability coefficient weight are obtained from the preset database, and the storage coefficient is obtained by combining the permeability coefficient and the flow coefficient. The flow coefficient weight represents the influence of the flow coefficient on the storage coefficient, and the permeability coefficient weight represents the influence of the permeability coefficient on the storage coefficient. The expression for calculating the storage coefficient through the karst aquifer CFP model is:

[0083] MX=sinh[α*SL+β*ST*(JY-ZF)];

[0084] Where MX represents the storage coefficient, α represents the flow coefficient weight, SL represents the flow coefficient, β represents the permeability coefficient weight, ST represents the permeability coefficient, JY represents the rainfall, and ZF represents the evaporation.

[0085] In this embodiment, the storage coefficient is an indicator to measure the ability of an underground aquifer to store water, reflecting the efficiency of the aquifer in storing and releasing water under rainfall and evaporation; rainfall and evaporation are important factors affecting the groundwater level and the storage coefficient by the external environment; an increase in rainfall usually leads to an increase in the groundwater level, while an increase in evaporation leads to a decrease in the groundwater level; and an assessment of the groundwater resources of the karst aquifer is achieved.

[0086] The algorithm of this embodiment combines the "flow coefficient", "permeability coefficient", "precipitation" and "evaporation" to obtain the "storage coefficient" through comprehensive analysis. In addition, the various parameters are related to each other and do not exist independently. They are a whole, and the "storage coefficient" cannot be obtained by simple combination and addition. For example, the increase of the "flow coefficient" in this formula will lead to an increase in the "storage coefficient", which does not necessarily mean that the "storage coefficient" is large. At this time, it is also necessary to consider that the "permeability coefficient", "precipitation" and "evaporation" can also determine the storage performance of the karst aquifer at the next moment. The correlation and mutual influence between various factors are considered in a quantitative way. The algorithm itself is obtained through comprehensive analysis, which can accurately and quantitatively analyze the storage performance of the karst aquifer, indicating the effect of accurately evaluating the storage coefficient of the karst aquifer. Therefore, accurate analysis is performed by establishing a mathematical form.

[0087] Specifically, the flow coefficient weight is obtained from a preset database. In a specific embodiment, a fitting curve is obtained by fitting the relationship between the flow coefficient and the storage coefficient through the pipeline roughness, the pipeline diameter and the pipeline water content, and the corresponding flow coefficient weight is determined based on the real-time pipeline roughness and pipeline diameter obtained from the fitting curve, and the pipeline water content represents the amount of water stored in the karst pipeline obtained by the water flow sensor.

[0088] Specifically, the permeability coefficient weight is obtained from a preset database. In a specific embodiment, the relationship between the permeability coefficient and the storage coefficient is fitted by the aquifer thickness and the effective rainfall and the permeability to obtain a fitting curve, and the real-time aquifer thickness and effective rainfall are obtained according to the fitting curve to determine the corresponding flow coefficient weight, the effective rainfall represents the difference between the rainfall and the evaporation, and the permeability represents the amount of water that is obtained by the water flow sensor and penetrates the rock formation.

[0089] Furthermore, the storage coefficient of the karst aquifer is obtained according to the karst aquifer CFP model and groundwater data. Previously, the karst aquifer CFP model was also verified. The specific process is as follows: historical hydrological data are obtained from the historical database, and the historical hydrological data are input into the constructed karst aquifer CFP model to obtain the storage coefficient, and the obtained storage coefficient is recorded as the verification storage coefficient; the verification storage coefficient is compared with the storage coefficient baseline value to obtain the model conformity, and the storage coefficient baseline value represents the storage coefficient corresponding to the hydrological data obtained from the historical database. The model conformity is used to comprehensively quantify the conformity of the model of the framework with the historical data. The model conformity is calculated by the following formula:

[0090]

[0091] Where FH represents the model conformity, YZ represents the verification storage coefficient, and MX0 represents the storage coefficient baseline value.

[0092] In this embodiment, the historical hydrological data include rainfall, evaporation and groundwater level; the verified storage coefficient represents the storage coefficient calculated by the historical hydrological data, which is used to verify the conformity of the model; the model conformity is used to quantify the degree of matching between the CFP model and the hydrological data, and the higher the conformity, the more the model conforms to the actual situation; the CFP model is used to accurately evaluate the water storage capacity of the karst aquifer.

[0093] The algorithm of this embodiment combines the "verification storage coefficient" and the "storage coefficient baseline value" to obtain the "model conformity" through comprehensive analysis. In addition, the various parameters are related and do not exist independently. They are a whole, and the "model conformity" cannot be obtained by simple combination and addition. For example, an increase in the "verification storage coefficient" in this formula may lead to an increase in the "model conformity", which does not necessarily mean that the "model conformity" is large. At this time, it is also necessary to consider that the "storage coefficient baseline value" can also determine the degree of conformity of the model at the next moment. The correlation and mutual influence between various factors are considered in a quantitative way. The algorithm itself is obtained through comprehensive analysis, which can accurately quantify the degree of conformity of the analysis model, indicating the effect of accurately evaluating the model conformity. Therefore, accurate analysis is performed by establishing a mathematical form.

[0094] It should be understood that when the verified storage coefficient is greater than the storage coefficient baseline value, the larger the verified storage coefficient, the smaller the model compliance, and the verified storage coefficient is inversely proportional to the model compliance; when the verified storage coefficient is less than the storage coefficient baseline value, the larger the verified storage coefficient, the greater the model compliance, and the verified storage coefficient is directly proportional to the model compliance. When the verified storage coefficient is equal to the storage coefficient baseline value, the model compliance reaches its maximum value, which is 1. At this time, the constructed karst aquifer CFP model is completely consistent with the historical hydrological data.

[0095] In summary, the embodiment of the present application conducts a preliminary experiment on the deployed monitoring stations to evaluate the coverage area of ​​data collection and determine the optimal number of monitoring stations, then pre-processes the collected initial hydrological data to obtain hydrological data, and finally obtains groundwater data based on the hydrological data, and obtains the storage coefficient of the karst aquifer through the karst aquifer CFP model, thereby improving the monitoring effect of the monitoring station, and further achieving an improvement in the accuracy of the karst aquifer CFP model, effectively solving the problem of insufficient correlation between the karst aquifer CFP model and the actual karst aquifer in the prior art.

[0096] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0100] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0101] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for constructing an improved CFP model of a karst aquifer, characterized in that: The following steps are involved: S1, evaluate the coverage area of ​​data collection obtained from the preliminary experiment of monitoring stations to determine the optimal number of monitoring stations, and use the monitoring stations deployed in the karst aquifer to collect real-time data to obtain initial hydrological data; S2, preprocessing the initial hydrological data collected by the deployed monitoring stations to obtain hydrological data, and obtaining groundwater data based on the hydrological data, wherein the hydrological data includes rainfall, evaporation and groundwater level, and the groundwater data includes a permeability coefficient and a flow coefficient, wherein the permeability coefficient indicates the ability of a rock formation to penetrate water, and the flow coefficient indicates the ability of water to flow in a rock formation; S3, obtaining the storage coefficient of the karst aquifer according to the CFP model of the karst aquifer and groundwater data, wherein the storage coefficient is used to describe the ability of the aquifer to store water; The specific process of the pre-experiment is as follows: A1, obtaining the total area of ​​the karst aquifer region and the coverage radius of the monitoring site from a preset database, and obtaining the coverage index of the monitoring site based on the total area of ​​the karst aquifer region and the coverage index is used to measure the degree of conformity between the total coverage area of ​​the monitoring site and the total area of ​​the karst aquifer region; A2, determine whether the coverage index is greater than 0. If it is greater than 0, execute A3. Otherwise, it indicates that there is an unmonitored area and feedback is given; A3, determining whether the coverage index is greater than a preset coverage index. If so, redundant sites exist and feedback is provided. Otherwise, determining that the current number of monitoring sites is the optimal number. The coverage index is calculated by the following formula: Where n represents the total number of monitoring stations, FG represents the coverage index of the monitoring stations, r represents the coverage radius of the monitoring stations, and A represents the total area of ​​the karst aquifer region.

2. A method for constructing an improved CFP model of a karst aquifer as claimed in claim 1, characterized in that: The specific steps of the pretreatment are as follows: B1, performing data cleaning on the initial hydrological data to obtain first hydrological data, wherein the data cleaning includes missing value filling, outlier processing, duplicate value removal and noise data smoothing; B2, performing data transformation on the first hydrological data to obtain hydrological data, wherein the data transformation includes data format conversion and unit standardization; B3, integrate the hydrological data with the corresponding timestamps to obtain the hydrological time series.

3. A method for constructing an improved CFP model of a karst aquifer as claimed in claim 2, characterized in that: The specific method for obtaining the permeability coefficient is as follows: E1, obtaining a rainfall proportional coefficient and a water level proportional coefficient from a preset database, and obtaining a first influencing parameter by comparing the pre-processed rainfall and water level difference, wherein the rainfall proportional coefficient represents the influence of unit rainfall on the permeability coefficient, the water level proportional coefficient represents the influence of unit water level difference on the permeability coefficient, and the first influencing parameter represents the degree of influence of rainfall on permeability; E2, obtaining an evaporation proportional coefficient and a water level proportional coefficient from a preset database, and obtaining a second influencing parameter by comparing the pre-processed evaporation amount and the water level difference, wherein the evaporation proportional coefficient represents the influence of the unit evaporation amount on the permeability coefficient, and the second influencing parameter represents the degree of influence of the evaporation amount on the permeability; E3, obtaining the thickness of the aquifer from a preset database, and obtaining the permeability coefficient by combining the first influencing parameter and the second influencing parameter, wherein the thickness of the aquifer represents the thickness of the karst aquifer.

4. A method for constructing an improved CFP model of a karst aquifer as claimed in claim 3, characterized in that: The specific method for obtaining the permeability coefficient is as follows: The permeability coefficient is obtained according to the first influencing parameter, the second influencing parameter and the thickness of the aquifer. The permeability coefficient is used to describe the water permeability of the karst aquifer. The permeability coefficient is calculated by the following formula: Where ST represents the permeability coefficient, J represents the first influencing parameter, Z represents the second influencing parameter, and b represents the thickness of the aquifer.

5. A method for constructing an improved CFP model of a karst aquifer as claimed in claim 3, characterized in that: The specific method for obtaining the flow coefficient is as follows: The pipe diameter and pipe roughness are obtained from the preset database, and the flow coefficient is obtained by combining the hydrological data. The flow coefficient is calculated by the following formula: In the formula, SL represents the flow coefficient, JY represents the rainfall, ZF represents the evaporation, and K c represents the roughness of the pipe, d represents the pipe diameter, and e represents the natural constant.

6. A method for constructing an improved CFP model of a karst aquifer as claimed in claim 3, characterized in that: The storage coefficient of the karst aquifer is obtained according to the CFP model of the karst aquifer and the groundwater data, and sensitivity tests are performed before that, and the sensitivity tests include permeability coefficient sensitivity tests and flow coefficient sensitivity tests; The specific process of the permeability coefficient sensitivity test is as follows: C1, obtaining initial infiltration hydrological data from a preset database, and obtaining a baseline value of the permeability coefficient based on the initial infiltration hydrological data, wherein the initial infiltration hydrological data includes initial rainfall, initial evaporation, initial water level difference and initial aquifer thickness; C2, sequentially changing the value of a single permeability coefficient variable in the initial permeability hydrological data while keeping the values ​​of other permeability coefficient variables unchanged to obtain the test permeability hydrological data, and recalculating the permeability coefficient according to the test permeability hydrological data, and combining the permeability coefficient baseline value to obtain the permeability coefficient sensitivity value of the corresponding permeability coefficient variable, the permeability coefficient sensitivity value indicating the influence of the corresponding permeability coefficient variable on the permeability coefficient, the permeability coefficient variable including rainfall, evaporation, water level difference and aquifer thickness; C3, comparing the permeability coefficient sensitivity values ​​corresponding to the permeability coefficient variables to obtain the variable with the greatest permeability influence, wherein the variable with the greatest permeability influence represents the variable with the greatest influence on the permeability coefficient.

7. A method for constructing an improved CFP model of a karst aquifer as claimed in claim 6, characterized in that: The specific process of the flow coefficient sensitivity test is as follows: D1, obtaining initial flow hydrological data from a preset database, and obtaining a flow coefficient baseline value based on the data, wherein the initial flow hydrological data includes initial rainfall, initial evaporation, initial pipeline roughness and initial pipeline diameter; D2, sequentially changing the value of a single flow coefficient variable in the initial flow hydrological data while keeping the values ​​of other flow coefficient variables unchanged to obtain test flow hydrological data, and recalculating the flow coefficient according to the test flow hydrological data, and combining the flow coefficient baseline value to obtain a flow coefficient sensitive value of the corresponding flow coefficient variable, wherein the flow coefficient sensitive value indicates the influence of the corresponding flow coefficient variable on the flow coefficient, and the flow coefficient variable includes rainfall, evaporation, pipeline roughness and pipeline diameter; D3, comparing the flow coefficient sensitivity values ​​corresponding to the flow coefficient variables to obtain the variable with the greatest flow influence, wherein the variable with the greatest flow influence represents the variable with the greatest influence on the flow coefficient.

8. The method for constructing an improved CFP model of a karst aquifer as claimed in claim 5, characterized in that: The specific method of obtaining the storage coefficient is as follows: Obtaining a flow coefficient weight and a permeability coefficient weight from a preset database, and obtaining a storage coefficient by combining the permeability coefficient and the flow coefficient; The storage coefficient is calculated by the karst aquifer CFP model as follows: MX=sinh[α*SL+β*ST*(JY-ZF)]; Where MX represents the storage coefficient, α represents the flow coefficient weight, SL represents the flow coefficient, β represents the permeability coefficient weight, ST represents the permeability coefficient, JY represents the rainfall, and ZF represents the evaporation.

9. A method for constructing an improved CFP model of a karst aquifer as claimed in claim 8, characterized in that: The storage coefficient of the karst aquifer is obtained according to the karst aquifer CFP model and groundwater data, and the karst aquifer CFP model is previously verified. The specific process is as follows: Obtain historical hydrological data from a historical database, and input the historical hydrological data into the constructed karst aquifer CFP model to obtain a storage coefficient, and record the obtained storage coefficient as a verification storage coefficient; The verification storage coefficient is compared with the storage coefficient baseline value to obtain the model conformity, which is used to comprehensively quantify the conformity of the model of the framework with historical data. The model conformity is calculated by the following formula: Where FH represents the model conformity, YZ represents the verification storage coefficient, and MX0 represents the storage coefficient baseline value.

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