A dynamic simulation detection method, device and medium for groundwater recharge

Abstract

The present invention discloses a method, device and medium for dynamic simulation detection of groundwater recharge, which specifically relates to the field of dynamic simulation detection of groundwater recharge, including a data batch division step, a data collection step, a data analysis step, a comprehensive evaluation step and a final feedback step. Data batch division step: used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence; data collection step: including a hydrogeological data collection unit and a recharge water source data collection unit, which are used to collect the target data in real time and transmit the collected data to the data analysis step; the present invention effectively improves the accuracy and timeliness of detection by dividing the relevant parameters of groundwater recharge into batches; by collecting data from multiple angles, the limitations of traditional data collection are greatly reduced, providing a reliable information basis for subsequent data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater recharge dynamic simulation detection technology, and more specifically, to a groundwater recharge dynamic simulation detection method, device and medium. Background Art

[0002] With the development of water resources management concepts, the research on groundwater recharge has become increasingly in-depth, and dynamic simulation detection methods play an indispensable role in it. In the field of groundwater management, accurately grasping the recharge situation is crucial to maintaining the stability and sustainable use of groundwater resources. The dynamic simulation detection method constructed with advanced technical means has brought new ideas and efficient tools to the monitoring and management of groundwater recharge.

[0003] The existing groundwater recharge dynamic simulation detection methods, devices and media generally include the following main parts: data collection unit, simulation construction unit, recharge dynamic simulation unit, evaluation and early warning unit. The data collection unit is responsible for collecting various types of data closely related to groundwater recharge, such as vegetation coverage in the surrounding area, permeability coefficients of different strata, groundwater extraction, etc. The collected data will be properly preserved to lay the foundation for subsequent simulation work. Based on the collected data, the simulation construction unit uses specific algorithms and model frameworks to construct a groundwater recharge model that conforms to local actual conditions. This model will comprehensively consider various factors such as local topography, geological structure, etc. The recharge dynamic simulation unit simulates the dynamic process of groundwater recharge in detail on the basis of the constructed model, and adjusts different parameters to reflect the recharge status under different conditions. The assessment and early warning unit conducts a comprehensive assessment of the simulation results, determines whether the replenishment has achieved the expected goals based on pre-set standards, and issues early warnings when abnormal situations occur. Through intuitive charts or reports, it is convenient for relevant personnel to grasp the actual status of the groundwater recharge project in a timely manner, thereby providing a basis for decision-making; therefore, the application of this dynamic simulation detection method in groundwater recharge projects will help improve the overall management level of the project and ensure the rational use of groundwater resources.

[0004] However, in actual application, there are also some defects that cannot be ignored. When collecting data, there is a problem that the scope of data collection is not wide enough, and it is often only possible to take into account some influencing factors, and it is difficult to fully cover all the factors related to groundwater recharge, which leads to the possibility that the constructed model deviates from the actual situation; when conducting dynamic simulation of recharge, due to the complexity of the groundwater system and the existence of many uncertain factors, it is difficult to accurately simulate all possible recharge situations, resulting in certain errors in the simulation results, and the interpretation of the simulation results and the sharing of information are not smooth enough, which affects the accurate judgment of the actual effect of groundwater recharge and has a negative impact on the effective implementation of the entire groundwater recharge project.

[0005] Therefore, there is an urgent need for a dynamic simulation detection method, device and medium for groundwater recharge to solve the problems of lack of comprehensiveness in data collection and inaccurate dynamic simulation of recharge in existing dynamic simulation detection methods. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a dynamic simulation detection method, device and medium for groundwater recharge, and solve the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above object, the present invention provides the following technical solution: a dynamic simulation detection method for groundwater recharge, comprising:

[0008] S1, data batch division step: used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence;

[0009] S2, data collection step: including a hydrogeological data collection unit and a replenishment water source data collection unit, which are used to collect target data in real time and transmit the collected data to the data analysis step. The hydrogeological data collection unit is used to collect stratum structure data, aquifer characteristic data and groundwater level data; the replenishment water source data collection unit is used to collect replenishment water volume data, replenishment water quality data and replenishment water source temperature data;

[0010] S3, data analysis step: including a hydrogeological data analysis unit and a replenishment water source data analysis unit, which are used to analyze the data transmitted in the data collection step and transmit the analysis results to the comprehensive evaluation step. The hydrogeological data analysis unit includes a stratum structure data analysis node, an aquifer characteristic data analysis node, and a groundwater level data analysis node; the replenishment water source data analysis unit includes a replenishment water quantity data analysis node, a replenishment water quality analysis node, and a replenishment water source temperature data analysis node;

[0011] S4, comprehensive evaluation step: including a groundwater recharge dynamic simulation detection data analysis unit, which is used to comprehensively analyze the results transmitted by the data analysis step and transmit the analysis results to the final feedback step;

[0012] S5. Final feedback step: used to establish preset values ​​of comprehensive evaluation index of groundwater recharge dynamic simulation detection, judge the value of comprehensive evaluation index of groundwater recharge dynamic simulation detection according to the preset values ​​of comprehensive evaluation index of groundwater recharge dynamic simulation detection, and send corresponding signals according to the judgment results.

[0013] Preferably, the stratum structure data include stratum clay mineral content Cm, stratum sand and gravel ratio Sg and stratum surface roughness Sr; the aquifer characteristic data include aquifer effective pore radius Ar, aquifer elastic modulus Am and aquifer diffusion coefficient Ac; the groundwater level data include groundwater level spatial variation coefficient Sc, groundwater level change rate Gc and groundwater level hysteresis response time Gr; the replenishment water volume data include replenishment water source precipitation infiltration coefficient Wp, replenishment water source surface runoff coefficient Ws and replenishment water source evaporation loss Wl; the replenishment water quality data include replenishment water source redox potential Pr, replenishment water source microbial community diversity index Mc and replenishment water source organic carbon content Wc; the replenishment water source temperature data include replenishment water source temperature daily difference Wt and replenishment water source vertical temperature gradient Gt.

[0014] Preferably, the formation structure data analysis node is used to establish a formation structure data calculation model, and the formation structure data transmitted in the data acquisition step is imported into the formation structure data calculation model to obtain the formation permeability coefficient value. The formation structure data calculation model is specifically expressed as:

[0015] ,

[0016] in, Indicates the formation permeability coefficient value calculated for the i-th time, Cm i represents the clay mineral content of the stratum collected for the i-th time, Sg i represents the ratio of sand and gravel in the formation collected for the i-th time, Sr i Represents the roughness of the stratum surface collected for the i-th time.

[0017] Preferably, the aquifer characteristic data analysis node is used to establish an aquifer characteristic data calculation model, and the aquifer characteristic data transmitted in the data collection step is imported into the aquifer characteristic data calculation model to obtain the aquifer water storage performance coefficient value. The aquifer characteristic data calculation model is specifically expressed as:

[0018] ,

[0019] in, represents the water storage performance coefficient of the aquifer calculated for the i-th time, Ar i represents the effective pore radius of the aquifer collected for the i-th time, Am i represents the elastic modulus of the aquifer collected for the i-th time, σ represents the standard deviation of the elastic modulus of the aquifer, μ represents the mean of the elastic modulus of the aquifer, Ac i represents the aquifer diffusion coefficient of the i-th collection.

[0020] Preferably, the groundwater level data analysis node is used to establish a groundwater level data calculation model, and the groundwater level data transmitted in the data collection step is imported into the groundwater level data calculation model to obtain the groundwater replenishment effect coefficient value. The groundwater level data calculation model is specifically expressed as:

[0021] ,

[0022] in, Sc represents the groundwater recharge effect coefficient calculated for the i-th time. i Gc represents the spatial variation coefficient of groundwater level collected for the i-th time. i represents the rate of change of groundwater level collected for the i-th time, Gr i It represents the delayed response time of groundwater level collected for the i-th time.

[0023] Preferably, the replenishment water volume data analysis node is used to establish a replenishment water volume data calculation model, and the replenishment water volume data transmitted in the data collection step is imported into the replenishment water volume data calculation model to obtain the replenishment water source contribution coefficient value. The replenishment water volume data calculation model is specifically expressed as:

[0024] ,

[0025] in, Indicates the contribution coefficient of the replenishment water source calculated for the i-th time, Wp i represents the precipitation infiltration coefficient of the replenishment water source collected for the i-th time, Ws i represents the surface runoff coefficient of the replenishment water source collected for the i-th time, Wl i It represents the evaporation loss of replenishment water source collected for the i-th time.

[0026] Preferably, the replenishment water quality data analysis node is used to establish a replenishment water quality data calculation model, and the replenishment water quality data transmitted in the data collection step is imported into the replenishment water quality data calculation model to obtain the replenishment water quality risk coefficient value. The replenishment water quality data calculation model is specifically expressed as:

[0027] ,

[0028] in, represents the risk coefficient value of replenishment water quality calculated for the i-th time, Pr i represents the redox potential of the replenishment water source collected for the i-th time, Mc i represents the diversity index of the replenishment water source microbial community collected for the i-th time, Wc i Represents the organic carbon content of the replenishment water source collected for the i-th time.

[0029] Preferably, the replenishment water source temperature data analysis node is used to establish a replenishment water source temperature data calculation model, and the replenishment water source temperature data transmitted in the data acquisition step is imported into the replenishment water source temperature data calculation model to obtain the replenishment water source thermal impact coefficient value. The replenishment water source temperature data calculation model is specifically expressed as:

[0030] ,

[0031] in, Indicates the thermal impact coefficient of the replenishment water source calculated for the i-th time, Wt i Indicates the daily temperature difference of the replenishment water source collected for the i-th time, Gt i Represents the vertical temperature gradient of the replenishment water source collected for the i-th time.

[0032] Preferably, a dynamic simulation detection device for groundwater recharge comprises:

[0033] Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence;

[0034] Data acquisition module: including hydrogeological data acquisition unit and replenishment water source data acquisition unit, used to collect target data in real time and transmit the collected data to the data analysis module. The hydrogeological data acquisition unit is used to collect stratum structure data, aquifer characteristic data and groundwater level data; the replenishment water source data acquisition unit is used to collect replenishment water quantity data, replenishment water quality data and replenishment water source temperature data;

[0035] Data analysis module: including a hydrogeological data analysis unit and a replenishment water source data analysis unit, used to analyze the data transmitted by the data acquisition module and transmit the analysis results to the comprehensive evaluation module. The hydrogeological data analysis unit includes a stratum structure data analysis node, an aquifer characteristic data analysis node, and a groundwater level data analysis node; the replenishment water source data analysis unit includes a replenishment water quantity data analysis node, a replenishment water quality analysis node, and a replenishment water source temperature data analysis node;

[0036] Comprehensive evaluation module: including groundwater recharge dynamic simulation detection data analysis unit, used to comprehensively analyze the results transmitted by the data analysis module and transmit the analysis results to the final feedback module;

[0037] Final feedback module: used to establish preset values ​​of comprehensive evaluation index of groundwater recharge dynamic simulation detection, judge the value of comprehensive evaluation index of groundwater recharge dynamic simulation detection according to the preset values ​​of comprehensive evaluation index of groundwater recharge dynamic simulation detection, and send corresponding signals according to the judgment results.

[0038] Preferably, a computer-readable storage medium is characterized by storing a computer program that can be loaded by a processor and execute the method as claimed in any one of claims 1 and 8.

[0039] Technical effects and advantages of the present invention:

[0040] The present invention effectively improves the accuracy and timeliness of detection by dividing the relevant parameters of groundwater recharge into batches. By collecting stratum structure data, aquifer characteristic data, groundwater level data, recharge water quantity data, recharge water quality data and recharge water source temperature data from multiple angles, this diversified data collection method greatly reduces the limitations of traditional data collection and provides a rich and reliable information basis for subsequent data analysis;

[0041] The present invention conducts in-depth mining of the collected data through data analysis, and then calculates the formation permeability coefficient value, aquifer water storage performance coefficient value, groundwater replenishment effect coefficient value, replenishment water source contribution coefficient value, replenishment water quality risk coefficient value and replenishment water source thermal impact coefficient value, and clearly points out the possible problems. By comprehensively considering and deeply integrating the results of data analysis, the reliability and accuracy of the analysis results are significantly improved;

[0042] The present invention continuously follows up through real-time monitoring and immediately issues a warning signal once an abnormal situation is found. Relevant researchers and managers can quickly understand the dynamic situation and potential problems of groundwater recharge and take timely measures to adjust and optimize the groundwater recharge plan, providing strong support for efficient and accurate dynamic simulation detection of groundwater recharge and scientific and reasonable decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the overall method structure of the present invention.

[0044] Figure 2 It is a schematic diagram of the device structure of the present invention. DETAILED DESCRIPTION

[0045] 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.

[0046] As attached Figure 1 A dynamic simulation detection method for groundwater recharge is shown, which specifically includes: a data batch division step, a data collection step, a data analysis step, a comprehensive evaluation step and a final feedback step.

[0047] S1. Data batch division step: used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence.

[0048] In this embodiment, it is specifically necessary to explain that: the method of dividing by equal time is based on the characteristics of each target data. According to the real-time requirements of each target data, the database is run by the method to collect data once at equal intervals and divided into different collection times.

[0049] S2, data collection step: including a hydrogeological data collection unit and a replenishment water source data collection unit, which are used to collect target data in real time and transmit the collected data to the data analysis step. The hydrogeological data collection unit is used to collect stratum structure data, aquifer characteristic data and groundwater level data; the replenishment water source data collection unit is used to collect replenishment water quantity data, replenishment water quality data and replenishment water source temperature data.

[0050] In this embodiment, it is specifically necessary to explain that: the stratum structure data includes stratum clay mineral content Cm, stratum sand and gravel ratio Sg and stratum surface roughness Sr; the aquifer characteristic data includes aquifer effective pore radius Ar, aquifer elastic modulus Am and aquifer diffusion coefficient Ac; the groundwater level data includes groundwater level spatial variation coefficient Sc, groundwater level change rate Gc and groundwater level hysteresis response time Gr; the replenishment water volume data includes replenishment water source precipitation infiltration coefficient Wp, replenishment water source surface runoff coefficient Ws and replenishment water source evaporation loss Wl; the replenishment water quality data includes replenishment water source redox potential Pr, replenishment water source microbial community diversity index Mc and replenishment water source organic carbon content Wc; the replenishment water source temperature data includes replenishment water source temperature daily difference Wt and replenishment water source vertical temperature gradient Gt.

[0051] The clay mineral content of the formation affects the permeability and adsorption of the formation. The formation with high clay mineral content has low permeability, which may hinder the flow of groundwater and the infiltration of replenishment water sources. At the same time, clay minerals have an adsorption effect on certain pollutants, affecting the quality of groundwater. The collection method is as follows: First, carefully select representative formation core samples. The selection of these samples needs to consider the different depths and different areas of the formation to ensure that the characteristics of the formation can be fully reflected. The collected core samples are brought back to the laboratory and ground into extremely fine powder using professional grinding equipment. Then, an X-ray diffractometer is used for analysis. The powder sample is irradiated with X-rays, and the type of clay mineral is accurately determined according to the diffraction characteristics of different clay minerals to X-rays. Then, chemical extraction and analysis methods are used, such as using specific chemical reagents to treat the sample to dissolve certain components in the clay mineral, and then using precision instruments to analyze the element content in the dissolved solution. After complex calculations, the clay mineral content of the formation is finally determined.

[0052] The formation sand and gravel ratio reflects the particle composition structure of the formation, which in turn affects the pore structure and permeability of the formation. A higher sand and gravel ratio usually means better permeability, which is conducive to the flow and recharge of groundwater. The collection method is: the core samples obtained from the formation are brought back to the laboratory, where they are carefully crushed into particles of appropriate size using special crushing equipment. After that, a set of sieves with different apertures are used for screening operations. The sieves are arranged in sequence from large aperture to small aperture, and the crushed samples are poured into the top sieve, and the particles are naturally dropped by mechanical vibration or manual shaking. The sand and gravel remaining on each sieve are collected separately, and weighed using a high-precision balance, and the sand and gravel ratio is obtained through strict weight calculation.

[0053] The roughness of the stratum layer affects the flow resistance of groundwater between layers. Rough layers will increase water flow resistance, slow down the flow rate of groundwater, and affect the infiltration rate and lateral flow of the replenishing water source during the groundwater replenishment process. The collection method is: first carefully clean the layers of the stratum core samples or stratum outcrops to remove surface impurities and loose particles to ensure that the original state of the layers is preserved. Then use a three-dimensional laser scanner to accurately align the scanner's probe with the layer to be scanned to ensure that the scanning range can completely cover the target layer. During the scanning process, the scanner will emit a laser beam and receive the reflected signal to obtain three-dimensional point cloud data of the layer. After acquiring the data, it is imported into software specifically used to analyze three-dimensional data, and the roughness of the stratum layer is calculated through algorithms and models.

[0054] The effective pore radius of the aquifer determines the size characteristics of the pores in the aquifer, affecting the storage and flow capacity of groundwater. A larger effective pore radius is conducive to the rapid flow of groundwater and is closely related to the diffusion of solutes in the replenishment water source. The collection method is: place the collected core sample into the sample chamber of the mercury intrusion instrument to ensure that the sample and the instrument are well sealed. During the operation of the mercury intrusion instrument, the pressure is gradually increased, and mercury will gradually enter the core pores under the action of pressure. As the pressure continues to increase, the volume of mercury entering the pores will also continue to change, and the mercury intrusion instrument will accurately record the relationship curve between pressure and mercury entry volume. According to a specific mathematical model, this curve is deeply analyzed and calculated to obtain the effective pore radius of the aquifer.

[0055] The elastic modulus of the aquifer reflects the deformation characteristics of the aquifer under stress. During the groundwater recharge process, as the groundwater level changes, the aquifer will be subject to stress changes. The elastic modulus affects the compression and expansion of the aquifer, and then affects the amplitude and speed of the groundwater level change. The collection method is: prepare the core sample obtained from the aquifer in the laboratory and install it on a uniaxial compression test device or a triaxial compression test device. In the uniaxial compression test, axial pressure is applied to the core sample, and a high-precision strain measuring instrument is used to accurately measure the strain of the core sample in the axial direction. During the test, the pressure is increased slowly and evenly, and the corresponding strain values ​​under different pressures are recorded to form a stress-strain curve. According to this curve and the calculation formula of the elastic modulus, the elastic modulus of the aquifer is calculated.

[0056] The aquifer diffusion coefficient describes the ability of solutes in the aquifer (such as substances in the recharge water source) to diffuse and spread in groundwater. During the groundwater recharge process, the diffusion coefficient affects the speed and range of mixing of the recharge water source with the original groundwater. The collection method is as follows: first, carefully select appropriate injection points and multiple observation points in the aquifer. The injection points should enable the tracer to enter the aquifer evenly, and the observation points should be reasonably distributed at different positions and depths to comprehensively monitor the diffusion of the tracer. Then, the tracer (such as fluorescent dye or radioactive isotope) is injected into the injection point. During the injection process, the injection amount and injection speed of the tracer should be accurately controlled. After the tracer is injected, a special detection instrument (a fluorescence detector can be used for fluorescent dyes, and a corresponding radioactive detection instrument can be used for radioactive isotopes) is used in the observation wells at different locations set in advance to continuously monitor the change of the tracer concentration over time at fixed time intervals. After collecting enough data, the aquifer diffusion coefficient is obtained by inversion calculation based on the mathematical model convection and diffusion equation.

[0057] The coefficient of spatial variation of groundwater level describes the degree of change of groundwater level in space. During the process of groundwater recharge, the change of groundwater level in different regions may be different. This coefficient helps to determine the spatial heterogeneity of groundwater level change and provide a basis for the reasonable arrangement of recharge points and monitoring points. The collection method is: select representative groundwater level monitoring wells in the study area and ensure that they can accurately reflect the change of groundwater level. At the same time, to accurately record external factors (such as the start time of rainfall or the start time of recharge), accurate timing can be achieved by installing rain gauges or recharge flow monitoring equipment in the study area. When external factors occur (such as the start of rainfall or the start of recharge operation), the water level changes of groundwater level monitoring wells are closely monitored with extremely high time accuracy (such as minute level). When the water level shows a significant upward or downward trend (by setting a reasonable threshold to judge significant changes), the time at this time is recorded. Finally, the difference between the time when the external factor occurs and the time when the water level begins to change significantly is calculated to obtain the groundwater level hysteresis response time.

[0058] The groundwater level change rate can intuitively reflect the degree of influence of groundwater recharge or exploitation on the groundwater level. During the groundwater recharge process, the water level change rate can be monitored to determine whether the recharge effect meets expectations. The collection method is: measure the water level in the groundwater level monitoring well, calculate the difference between two adjacent measured water levels, and then divide it by the time interval to obtain the groundwater level change rate.

[0059] The groundwater level hysteresis response time refers to the delay time for the groundwater level to respond after being affected by external factors (such as rainfall, recharge operation, etc.). Understanding the hysteresis response time is very important for accurately simulating the dynamic changes in the groundwater recharge process and determining the time relationship between recharge measures and groundwater level rise. The collection method is: select representative groundwater level monitoring wells in the study area and ensure that they can accurately reflect the changes in groundwater level. At the same time, to accurately record external factors (such as the start time of rainfall or the start time of recharge), accurate timing can be achieved by installing rain gauges or recharge flow monitoring equipment in the study area. When external factors occur (such as the start of rainfall or the start of recharge operation), closely monitor the water level changes in the groundwater level monitoring wells with extremely high time accuracy (such as minute level). When the water level shows a significant upward or downward trend (by setting a reasonable threshold to judge significant changes), record the time at this time. Finally, calculate the difference between the time when the external factor occurs and the time when the water level begins to change significantly, and obtain the groundwater level hysteresis response time.

[0060] The precipitation infiltration coefficient of the replenishment water source reflects the proportion of precipitation converted into replenishment groundwater. In the case of precipitation as the replenishment water source, this coefficient is of great significance for accurately estimating the replenishment water volume and simulating the rise of groundwater level. The collection method is: select a typical small watershed or a specially set up experimental site. The selection of the site should consider the representativeness of factors such as soil type, vegetation coverage, topography, etc. Install a high-precision rain gauge in the site. The installation location of the rain gauge should be open and not blocked by surrounding objects to ensure accurate measurement of rainfall. At the same time, multiple groundwater level monitoring wells are reasonably arranged in the site, and the depth of the monitoring wells should be able to accurately reflect the changes in the groundwater level. During natural rainfall, the rain gauge records the rainfall in real time, and the groundwater level monitoring well records the rise of the groundwater level at a shorter time interval. After the rainfall ends, according to the water balance principle, the precipitation infiltration coefficient is obtained by calculating the ratio of the infiltration water volume (which can be calculated from the rise of the groundwater level and the characteristics of the aquifer) to the rainfall.

[0061] The surface runoff coefficient of the replenishment water source indicates the proportion of surface runoff formed by rainfall, which indirectly affects the amount of replenishment water source. A larger surface runoff coefficient means that less precipitation can infiltrate and replenish the ground. By understanding this coefficient, the replenishment project can be better planned, such as increasing the amount of replenishment water by adjusting the surface runoff. The collection method is: in the experimental site or the selected basin, according to the terrain and the direction of water flow, select a suitable location to set the runoff measurement section. The runoff measurement section should be perpendicular to the direction of water flow, and necessary control facilities should be set up upstream and downstream of the section to ensure that all surface runoff can pass through the measurement section. During the rainfall period, a flow meter and a depth sounder are used to measure the flow velocity and water depth at different positions of the runoff section, and the runoff volume is obtained by integral calculation. At the same time, a rain gauge is installed in the site to accurately measure the rainfall. After the rainfall is over, the ratio of runoff volume to rainfall is calculated to obtain the surface runoff coefficient.

[0062] The evaporation loss of the replenishment water source is the amount of water lost due to evaporation in the replenishment process of the replenishment water source. Accurately measuring the evaporation loss helps to accurately calculate the amount of water actually replenished to the ground, which is very important for evaluating the efficiency of the replenishment project. The collection method is as follows: first, an open, flat and well-ventilated location should be selected near the replenishment water source to place a standard evaporating dish. The evaporating dish should be kept horizontal, and its surrounding environment should be as far as possible from being blocked and disturbed by surrounding objects. Inject an appropriate amount of replenishment water source water sample into the evaporating dish so that it can evaporate normally and will not overflow. Use a high-precision balance to weigh the evaporating dish at a fixed time every day, record the total weight of the evaporating dish and the remaining water sample, and measure the ambient temperature, humidity, wind speed and other meteorological conditions at the same time. According to the data measured multiple times, the daily evaporation amount is calculated and converted into the actual evaporation loss amount through a specific conversion factor.

[0063] The redox potential of the replenishment water source reflects the redox state of the replenishment water source, affecting the existence form and reaction direction of chemical substances in the water. Different redox states will affect the chemical properties of groundwater, such as the solubility of elements such as iron and manganese, and thus affect the quality of groundwater. The collection method is: use a special redox potential meter to operate at the sampling point of the replenishment water source. The sampling point should be selected at a location that can represent the overall water quality of the replenishment water source, and avoid sampling in places with turbulent water flow or obvious local pollution sources. Before sampling, the sampling container must be strictly cleaned and pretreated to ensure that there is no residual substance affecting the authenticity of the water sample. Use a clean sampling device to collect water samples, and quickly insert the electrodes of the redox potential meter into the water sample. The electrodes must be completely immersed in the water sample and cannot touch the container wall. After waiting for the potentiometer reading to stabilize, record the redox potential value.

[0064] The diversity index of the replenishment water source microbial community reflects the types and richness of microorganisms in the replenishment water source. Microorganisms play an important role in the biogeochemical cycle of groundwater. Different microbial community structures may affect the changes in water quality during the replenishment process, such as the decomposition and transformation of organic matter. The collection method is: collect multiple water samples at different locations of the replenishment water source, and use sterile sampling equipment when sampling to avoid contamination by external microorganisms. The collected water samples are quickly brought back to the laboratory, where special filtering devices are used to filter the water samples and collect microbial cells in the water samples. Then, high-throughput sequencing technology in molecular biological methods is used to first extract DNA from microbial cells, and specific primers are used to amplify the 16S rRNA gene of the microorganism. The amplified DNA fragments are sequenced, and a large amount of microbial gene sequence data will be obtained during the sequencing process. Complex bioinformatics analysis is performed on these data, including sequence alignment, classification identification and other operations to determine the composition of the microbial community in the water sample. According to the types and relative abundance data of the microbial community, the microbial community diversity index is calculated using a specific diversity index calculation formula.

[0065] The organic carbon content of the replenishment water source is an important indicator to measure the organic matter content in the replenishment water source. High organic carbon content may lead to enhanced microbial activity, consume dissolved oxygen in the water, produce gases such as carbon dioxide, and affect the chemical properties of groundwater. At the same time, organic carbon may also be a potential source of pollutants. The collection method is: after collecting water samples, add a known amount of potassium dichromate solution and sulfuric acid and other reagents to the water samples. Under heating and acidic conditions, potassium dichromate oxidizes the organic carbon in the water sample into carbon dioxide. Then the remaining amount of potassium dichromate is determined by titration, and the organic carbon content is calculated based on the consumption of potassium dichromate and the chemical reaction equation.

[0066] The daily temperature difference of the replenishment water source reflects the fluctuation range of the temperature of the replenishment water source within one day. A larger daily temperature difference may have a more obvious impact on the temperature field of groundwater, change the physical and chemical processes and biological activity laws of groundwater, and the collection method is: select a suitable location at the replenishment water source to install a high-precision temperature sensor. The installation of the sensor must ensure that it can accurately measure the temperature of the replenishment water source, and avoid being affected by factors such as direct sunlight and water flow interference. The temperature sensor must have a high-frequency data acquisition function and be set to record temperature data at intervals of 15 minutes. Continuously collect temperature data throughout the day, and ensure the accuracy and continuity of the data during the collection process. At the end of the day, find the highest and lowest temperatures from the collected data, calculate the difference between the two, and get the daily temperature difference of the replenishment water source.

[0067] The vertical temperature gradient of the replenishment water source describes the rate of change of temperature of the replenishment water source in the vertical direction. In the process of groundwater replenishment, the vertical temperature gradient affects the vertical transfer of heat in the groundwater, and then affects the density stratification and vertical flow of the groundwater. The collection method is: high-precision temperature sensors are set at different depths of the replenishment water source, and the installation of the sensors must use special equipment to ensure their accuracy in the vertical direction. During the measurement process, it is necessary to ensure that the sensors are in full contact with the surrounding water body to accurately measure the temperature at different depths. The data acquisition system is used to simultaneously collect temperature data from sensors at different depths, and the vertical temperature gradient of the replenishment water source is calculated based on the ratio of the temperature difference between adjacent depths to the depth difference.

[0068] S3, data analysis step: including a hydrogeological data analysis unit and a replenishment water source data analysis unit, which are used to analyze the data transmitted in the data collection step and transmit the analysis results to the comprehensive evaluation step. The hydrogeological data analysis unit includes a stratum structure data analysis node, an aquifer characteristic data analysis node, and a groundwater level data analysis node; the replenishment water source data analysis unit includes a replenishment water quantity data analysis node, a replenishment water quality analysis node, and a replenishment water source temperature data analysis node.

[0069] In this embodiment, it is specifically required to be explained that: the stratum structure data analysis node is used to establish a stratum structure data calculation model, and the stratum structure data transmitted in the data acquisition step is imported into the stratum structure data calculation model to obtain the stratum permeability coefficient value. The stratum structure data calculation model is specifically expressed as:

[0070] ,

[0071] in, Indicates the formation permeability coefficient value calculated for the i-th time, Cm i represents the clay mineral content of the stratum collected for the i-th time, Sg irepresents the ratio of sand and gravel in the formation collected for the i-th time, Sr i Represents the roughness of the stratum surface collected for the i-th time.

[0072] In this embodiment, it is specifically required to be explained that: the aquifer characteristic data analysis node is used to establish an aquifer characteristic data calculation model, and the aquifer characteristic data transmitted in the data collection step is imported into the aquifer characteristic data calculation model to obtain the aquifer water storage performance coefficient value. The aquifer characteristic data calculation model is specifically expressed as:

[0073] ,

[0074] in, represents the water storage performance coefficient of the aquifer calculated for the i-th time, Ar i represents the effective pore radius of the aquifer collected for the i-th time, Am i represents the elastic modulus of the aquifer collected for the i-th time, σ represents the standard deviation of the elastic modulus of the aquifer, μ represents the mean of the elastic modulus of the aquifer, Ac i represents the aquifer diffusion coefficient of the i-th collection.

[0075] In this embodiment, it is specifically required to be explained that: the groundwater level data analysis node is used to establish a groundwater level data calculation model, and the groundwater level data transmitted in the data collection step is imported into the groundwater level data calculation model to obtain the groundwater replenishment effect coefficient value. The groundwater level data calculation model is specifically expressed as:

[0076] ,

[0077] in, Sc represents the groundwater recharge effect coefficient calculated for the i-th time, i Gc represents the spatial variation coefficient of groundwater level collected for the i-th time. i represents the rate of change of groundwater level collected for the i-th time, Gr i It represents the delayed response time of groundwater level collected for the i-th time.

[0078] In this embodiment, it is specifically required to be explained that: the replenishment water volume data analysis node is used to establish a replenishment water volume data calculation model, and the replenishment water volume data transmitted in the data collection step is imported into the replenishment water volume data calculation model to obtain the replenishment water source contribution coefficient value. The replenishment water volume data calculation model is specifically expressed as:

[0079] ,

[0080] in, Indicates the contribution coefficient of the replenishment water source calculated for the i-th time, Wp irepresents the precipitation infiltration coefficient of the replenishment water source collected for the i-th time, Ws i represents the surface runoff coefficient of the replenishment water source collected for the i-th time, Wl i It represents the evaporation loss of replenishment water source collected for the i-th time.

[0081] In this embodiment, it is specifically required to be explained that: the replenishment water quality data analysis node is used to establish a replenishment water quality data calculation model, and the replenishment water quality data transmitted in the data collection step is imported into the replenishment water quality data calculation model to obtain the replenishment water quality risk coefficient value. The replenishment water quality data calculation model is specifically expressed as:

[0082] ,

[0083] in, represents the risk coefficient value of replenishment water quality calculated for the i-th time, Pr i represents the redox potential of the replenishment water source collected for the i-th time, Mc i represents the diversity index of the replenishment water source microbial community collected for the i-th time, Wc i Represents the organic carbon content of the replenishment water source collected for the i-th time.

[0084] In this embodiment, it is specifically required to be explained that: the replenishment water source temperature data analysis node is used to establish a replenishment water source temperature data calculation model, and the replenishment water source temperature data transmitted in the data acquisition step is imported into the replenishment water source temperature data calculation model to obtain the replenishment water source thermal impact coefficient value. The replenishment water source temperature data calculation model is specifically expressed as:

[0085] ,

[0086] in, Indicates the thermal impact coefficient of the replenishment water source calculated for the i-th time, Wt i Indicates the daily temperature difference of the replenishment water source collected for the i-th time, Gt i Represents the vertical temperature gradient of the replenishment water source collected for the i-th time.

[0087] S4. Comprehensive evaluation step: including a groundwater recharge dynamic simulation detection data analysis unit, which is used to comprehensively analyze the results transmitted by the data analysis step and transmit the analysis results to the final feedback step.

[0088] In this embodiment, it should be specifically noted that: the groundwater recharge dynamic simulation detection data analysis unit is used to establish a calculation model for groundwater recharge dynamic simulation detection data, and import the formation permeability coefficient value, aquifer water storage performance coefficient value, groundwater recharge effect coefficient value, recharge water source contribution degree coefficient value, recharge water quality risk degree coefficient value, and recharge water source heat impact degree coefficient value transmitted in the data analysis step into the calculation model for groundwater recharge dynamic simulation detection data to obtain the comprehensive evaluation index value of groundwater recharge dynamic simulation detection. The calculation model for groundwater recharge dynamic simulation detection data is specifically expressed as:

[0089] ,

[0090] where A represents the calculated comprehensive evaluation index value of groundwater recharge dynamic simulation detection, represents the formation permeability coefficient value calculated for the i-th time, represents the aquifer water storage performance coefficient value calculated for the i-th time, represents the groundwater recharge effect coefficient value calculated for the i-th time, represents the recharge water source contribution degree coefficient value calculated for the i-th time, represents the minimum value of the calculated recharge water source contribution degree coefficient, represents the maximum value of the calculated recharge water source contribution degree coefficient, represents the recharge water quality risk degree coefficient value calculated for the i-th time, represents the minimum value of the calculated recharge water quality risk degree coefficient, represents the maximum value of the calculated recharge water quality risk degree coefficient, represents the recharge water source heat impact degree coefficient value calculated for the i-th time, represents the minimum value of the calculated recharge water source heat impact degree coefficient, represents the maximum value of the calculated recharge water source heat impact degree coefficient. i represents starting from the i-th number, and n represents ending at the n-th number.

[0091] S5. Final feedback step: used to establish a preset value of the comprehensive evaluation index for groundwater recharge dynamic simulation detection, judge the comprehensive evaluation index value of groundwater recharge dynamic simulation detection according to the preset value of the comprehensive evaluation index for groundwater recharge dynamic simulation detection, and send out corresponding signals according to the judgment results.

[0092] In this embodiment, it should be specifically noted that: the preset value of the comprehensive evaluation index for groundwater recharge dynamic simulation detection is marked as A def , when A def < A, a normal signal is sent. This signal indicates that the preset value of the comprehensive evaluation index for groundwater recharge dynamic simulation detection is less than the comprehensive evaluation index value of groundwater recharge dynamic simulation detection, indicating that the groundwater recharge state is relatively normal; when A defWhen >=A, an early warning signal is issued to the relevant technical management personnel. This signal indicates that the preset value of the comprehensive evaluation index of the dynamic simulation detection of groundwater recharge is greater than or equal to the value of the comprehensive evaluation index of the dynamic simulation detection of groundwater recharge, indicating that the groundwater recharge status is poor.

[0093] Based on the above scheme and attached Figure 2 The present application also discloses a dynamic simulation detection device for groundwater recharge, including:

[0094] Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence.

[0095] Data acquisition module: including hydrogeological data acquisition unit and replenishment water source data acquisition unit, used to collect target data in real time and transmit the collected data to the data analysis module. The hydrogeological data acquisition unit is used to collect stratum structure data, aquifer characteristic data and groundwater level data; the replenishment water source data acquisition unit is used to collect replenishment water quantity data, replenishment water quality data and replenishment water source temperature data.

[0096] Data analysis module: including hydrogeological data analysis unit and replenishment water source data analysis unit, used to analyze the data transmitted by the data acquisition module and transmit the analysis results to the comprehensive evaluation module. The hydrogeological data analysis unit includes a stratum structure data analysis node, aquifer characteristics data analysis node and groundwater level data analysis node; the replenishment water source data analysis unit includes a replenishment water quantity data analysis node, a replenishment water quality analysis node and a replenishment water source temperature data analysis node.

[0097] Comprehensive evaluation module: including groundwater recharge dynamic simulation detection data analysis unit, which is used to comprehensively analyze the results transmitted by the data analysis module and transmit the analysis results to the final feedback module.

[0098] Final feedback module: used to establish preset values ​​of comprehensive evaluation index of groundwater recharge dynamic simulation detection, judge the value of comprehensive evaluation index of groundwater recharge dynamic simulation detection according to the preset values ​​of comprehensive evaluation index of groundwater recharge dynamic simulation detection, and send corresponding signals according to the judgment results.

[0099] An embodiment of the present application also discloses a computer-readable storage medium, which stores a computer program that can be loaded by a processor and execute a dynamic simulation detection method for groundwater recharge such as the above-mentioned one. The computer-readable storage medium includes, for example: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0100] The present invention effectively improves the accuracy and timeliness of detection by dividing the relevant parameters of groundwater replenishment into batches. By collecting stratum structure data, aquifer characteristic data, groundwater level data, replenishment water volume data, replenishment water quality data and replenishment water source temperature data from multiple angles, this diversified data collection method greatly reduces the limitations of traditional data collection and provides a rich and reliable information basis for subsequent data analysis. Through data analysis, the collected data is deeply mined to calculate the stratum permeability coefficient value, the aquifer water storage performance coefficient value, the groundwater replenishment effect coefficient value, the replenishment water source contribution coefficient value, the replenishment water quality risk coefficient value and the replenishment water source thermal impact coefficient value, and clearly point out the possible problematic aspects. By comprehensively considering and deeply integrating the results of data analysis, the reliability and accuracy of the analysis results are significantly improved. Through real-time monitoring and continuous follow-up, warning signals will be issued immediately once abnormal situations are discovered. Relevant researchers and managers can quickly understand the dynamic situation and potential problems of groundwater recharge, and take timely measures to adjust and optimize groundwater recharge plans, providing strong support for efficient and accurate groundwater recharge dynamic simulation detection and scientific and reasonable decision-making.

[0101] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0102] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dynamic simulation detection method for groundwater recharge, characterized in that: include: S1, data batch division step: used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence; S2, data collection step: including a hydrogeological data collection unit and a replenishment water source data collection unit, which are used to collect target data in real time and transmit the collected data to the data analysis step, wherein the hydrogeological data collection unit is used to collect stratum structure data, aquifer characteristic data and groundwater level data; the replenishment water source data collection unit is used to collect replenishment water quantity data, replenishment water quality data and replenishment water source temperature data; S3, data analysis step: including a hydrogeological data analysis unit and a replenishment water source data analysis unit, which are used to analyze the data transmitted in the data collection step and transmit the analysis results to the comprehensive evaluation step, the hydrogeological data analysis unit includes a stratum structure data analysis node, an aquifer characteristic data analysis node and a groundwater level data analysis node; the replenishment water source data analysis unit includes a replenishment water quantity data analysis node, a replenishment water quality analysis node and a replenishment water source temperature data analysis node; The stratum structure data analysis node is used to import the stratum structure data into the stratum structure data calculation model to obtain the stratum permeability coefficient value; The aquifer characteristic data analysis node is used to import the aquifer characteristic data into the aquifer characteristic data calculation model to obtain the aquifer water storage performance coefficient value; The groundwater level data analysis node is used to import the groundwater level data into the groundwater level data calculation model to obtain the groundwater replenishment effect coefficient value; The replenishment water volume data analysis node is used to import the replenishment water volume data into the replenishment water volume data calculation model to obtain the replenishment water source contribution coefficient value; The replenishment water quality analysis node is used to import the replenishment water quality data into the replenishment water quality data calculation model to obtain the replenishment water quality risk coefficient value; The replenishment water source temperature data analysis node is used to import the replenishment water source temperature data into the replenishment water source temperature data calculation model to obtain the replenishment water source thermal impact coefficient value; S4, comprehensive evaluation step: including a groundwater recharge dynamic simulation detection data analysis unit, which is used to comprehensively analyze the results transmitted by the data analysis step and transmit the analysis results to the final feedback step; S5. Final feedback step: used to establish preset values ​​of comprehensive evaluation index of groundwater recharge dynamic simulation detection, judge the value of comprehensive evaluation index of groundwater recharge dynamic simulation detection according to the preset values ​​of comprehensive evaluation index of groundwater recharge dynamic simulation detection, and send corresponding signals according to the judgment results.

2. A dynamic simulation detection method for groundwater recharge according to claim 1, characterized in that: The stratum structure data include stratum clay mineral content Cm, stratum sand and gravel ratio Sg and stratum surface roughness Sr; the aquifer characteristic data include stratum effective pore radius Ar, aquifer elastic modulus Am and aquifer diffusion coefficient Ac; the groundwater level data include groundwater level spatial variation coefficient Sc, groundwater level change rate Gc and groundwater level hysteresis response time Gr; the replenishment water volume data include replenishment water source precipitation infiltration coefficient Wp, replenishment water source surface runoff coefficient Ws and replenishment water source evaporation loss Wl; the replenishment water quality data include replenishment water source redox potential Pr, replenishment water source microbial community diversity index Mc and replenishment water source organic carbon content Wc; the replenishment water source temperature data include replenishment water source temperature daily difference Wt and replenishment water source vertical temperature gradient Gt.

3. A dynamic simulation detection method for groundwater recharge according to claim 1, characterized in that: The stratigraphic structure data calculation model is specifically expressed as: ， in, Indicates the formation permeability coefficient value calculated for the i-th time, Cm i represents the clay mineral content of the stratum collected for the i-th time, Sg i represents the ratio of sand and gravel in the formation collected for the i-th time, Sr i Represents the roughness of the stratum surface collected for the i-th time.

4. A dynamic simulation detection method for groundwater recharge according to claim 1, characterized in that: The aquifer characteristic data calculation model is specifically expressed as follows: ， in, represents the water storage performance coefficient of the aquifer calculated for the i-th time, Ar i represents the effective pore radius of the aquifer collected for the i-th time, Am i represents the elastic modulus of the aquifer collected for the i-th time, σ represents the standard deviation of the elastic modulus of the aquifer, μ represents the mean of the elastic modulus of the aquifer, Ac i represents the aquifer diffusion coefficient of the i-th collection.

5. A dynamic simulation detection method for groundwater recharge according to claim 1, characterized in that: The groundwater level data calculation model is specifically expressed as: ， in, Sc represents the groundwater recharge effect coefficient calculated for the i-th time, i Gc represents the spatial variation coefficient of groundwater level collected for the i-th time. i represents the rate of change of groundwater level collected for the i-th time, Gr i It represents the delayed response time of groundwater level collected for the i-th time.

6. A dynamic simulation detection method for groundwater recharge according to claim 1, characterized in that: The replenishment water volume data calculation model is specifically expressed as: ， in, Indicates the contribution coefficient of the replenishment water source calculated for the i-th time, Wp i Ws represents the precipitation infiltration coefficient of the replenishment water source collected for the i-th time. i represents the surface runoff coefficient of the replenishment water source collected for the i-th time, Wl i It represents the evaporation loss of replenishment water source collected for the i-th time.

7. A dynamic simulation detection method for groundwater recharge according to claim 1, characterized in that: The replenishment water quality data calculation model is specifically expressed as: ， in, represents the risk coefficient value of replenishment water quality calculated for the i-th time, Pr i represents the redox potential of the replenishment water source collected for the i-th time, Mc i represents the diversity index of the replenishment water source microbial community collected for the i-th time, Wc i Represents the organic carbon content of the replenishment water source collected for the i-th time.

8. A dynamic simulation detection method for groundwater recharge according to claim 1, characterized in that: The replenishment water source temperature data calculation model is specifically expressed as: ， in, Indicates the thermal impact coefficient of the replenishment water source calculated for the i-th time, Wt i Indicates the daily temperature difference of the replenishment water source collected for the i-th time, Gt i Represents the vertical temperature gradient of the replenishment water source collected for the i-th time.

9. A dynamic simulation detection device for groundwater recharge, used to implement a dynamic simulation detection method for groundwater recharge as described in any one of claims 1 and 8, characterized in that: include: Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence; Data acquisition module: including a hydrogeological data acquisition unit and a replenishment water source data acquisition unit, which are used to acquire target data in real time and transmit the acquired data to the data analysis module. The hydrogeological data acquisition unit is used to acquire stratum structure data, aquifer characteristic data and groundwater level data; the replenishment water source data acquisition unit is used to acquire replenishment water quantity data, replenishment water quality data and replenishment water source temperature data; Data analysis module: including a hydrogeological data analysis unit and a replenishment water source data analysis unit, which are used to analyze the data transmitted by the data acquisition module and transmit the analysis results to the comprehensive evaluation module. The hydrogeological data analysis unit includes a stratum structure data analysis node, an aquifer characteristic data analysis node, and a groundwater level data analysis node; the replenishment water source data analysis unit includes a replenishment water quantity data analysis node, a replenishment water quality analysis node, and a replenishment water source temperature data analysis node; The stratum structure data analysis node is used to import the stratum structure data into the stratum structure data calculation model to obtain the stratum permeability coefficient value; The aquifer characteristic data analysis node is used to import the aquifer characteristic data into the aquifer characteristic data calculation model to obtain the aquifer water storage performance coefficient value; The groundwater level data analysis node is used to import the groundwater level data into the groundwater level data calculation model to obtain the groundwater replenishment effect coefficient value; The replenishment water volume data analysis node is used to import the replenishment water volume data into the replenishment water volume data calculation model to obtain the replenishment water source contribution coefficient value; The replenishment water quality analysis node is used to import the replenishment water quality data into the replenishment water quality data calculation model to obtain the replenishment water quality risk coefficient value; The replenishment water source temperature data analysis node is used to import the replenishment water source temperature data into the replenishment water source temperature data calculation model to obtain the replenishment water source thermal impact coefficient value; Comprehensive evaluation module: including groundwater recharge dynamic simulation detection data analysis unit, used to comprehensively analyze the results transmitted by the data analysis module and transmit the analysis results to the final feedback module; Final feedback module: used to establish preset values ​​of comprehensive evaluation index of groundwater recharge dynamic simulation detection, judge the value of comprehensive evaluation index of groundwater recharge dynamic simulation detection according to the preset values ​​of comprehensive evaluation index of groundwater recharge dynamic simulation detection, and send corresponding signals according to the judgment results.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method as claimed in any one of claims 1 and 8.

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